Emissions Inventory for Air Quality
Modeling Technical Support Document:
Proposed Tier 3 Emissions Standards
&EPA
United States
Environmental Protection
Agency
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Emissions Inventory for Air Quality
Modeling Technical Support Document:
Proposed Tier 3 Emissions Standards
Rich Mason
Alexis Zubrow
Alison Eyth
Air Quality Assessment Division
Office of Planning and Standards
U.S. Environmental Protection Agency
Research Triangle Park, NC 27711
&EPA
United States
Environmental Protection
Agency
EPA-454/R-13-002
March 2013
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TABLE OF CONTENTS
LIST OF TABLES ii
LIST OF APPENDICES Hi
ACRONYMS iv
1 Introduction to the Modeling Platform 1
2 2005 Emission Inventories and Their Preparation 2
2.1 Custom configuration for emissions modeling for Tier 3 NPRM 4
2.2 Onroad mobile sources (onroad) 6
2.2.1 MOVES 6
2.2.2 Representing counties 7
2.2.3 SMOKE-MOVES inputs 11
2.2.4 Generating emission factors for SMOKE 12
2.2.5 Running SMOKE for onroad mobile 14
2.3 Nonroad mobile sources (nonroad, alm_no_c3, seca_c3) 15
2.3.1 Emissions generated with the NONROAD model (nonroad) 15
2.3.2 Locomotives and commercial marine vessels (alm_no_c3, seca_c3) 18
2.4 2005 point sources (ptipm and ptnonipm sectors) 19
2.4.1 Ethanol plants (ptnonipm) 19
2.5 2005 nonpoint sources (afdust, ag, avefire, nonpt) 20
2.5.1 Portable fuel containers 20
2.5.2 Onroad refueling 20
2.6 Other sources (biogenics, othpt, othar, and othon) 21
3 VOC Speciation Changes that Represent Fuel Changes 21
4 2017 and 2030 Reference Cases 25
4.1 Stationary source projections: EGU sector (ptipm) 31
4.2 Stationary source proj ections: non-EGU sectors (ptnonipm, nonpt, ag, afdust) 32
4.2.1 Ethanol plants (ptnonipm) 33
4.2.2 Biodiesel plants (ptnonipm) 34
4.2.3 Portable fuel containers (nonpt) 34
4.2.4 Cellulosic fuel production (nonpt) 35
4.2.5 Ethanol transport and distribution (nonpt) 36
4.2.6 Onroad refueling (nonpt) 36
4.2.7 Refinery adjustments (ptnonipm) 36
4.2.8 Ethanol transport gasoline and blends (ptnonipm, nonpt) 37
4.2.9 Upstream agricultural adjustments (afdust, ag, nonpt, ptnonipm) 37
4.2.10 Livestock emissions growth (ag, afdust) 37
4.2.11 Residential wood combustion growth (nonpt) 38
4.2.12 Aircraft growth (ptnonipm) 39
4.2.13 Stationary source control programs, consent decrees & settlements, and plant closures
(ptnonipm, nonpt) 40
4.2.14 Oil and gas projections in TX, OK, and non-California WRAP states (nonpt) 45
4.3 Onroad mobile source projections (onroad) 45
4.3.1 California LEV 46
4.4 Nonroad mobile source projections (nonroad, alm_no_c3, seca_c3) 46
4.4.1 Emissions generated with the NONROAD model (nonroad) 47
4.4.2 Locomotives and Class 1 & 2 commercial marine vessels (alm_no_c3) 48
4.4.3 Class 3 commercial marine vessels (seca_c3) 50
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4.5 Canada, Mexico, and offshore sources (othar, othon, and othpt) 50
5 2017 and 2030 Tier 3 Control Cases 51
5.1 Non-EGU stationary source projections (nonpt) 51
5.2 Onroad mobile (onroad) 52
5.3 Nonroad mobile (nonroad) 52
5.3.1 Emissions generated with the NONROAD model (nonroad) 52
6 Tier 3 emissions summaries 53
7 References 56
LIST OF TABLES
Table 1-1. List of cases run in support of the Proposed Tier 3 air quality modeling 2
Table 2-1. Sectors used in emissions modeling for the Tier 3 NPRM 2005v4.3 platform 3
Table 2-2. Model species produced by SMOKE for CB05 with SOA for the Tier 3 NPRM platform 5
Table 2-3. Description of differences in 2005 case ancillary data (unrelated to SMOKE to MOVES) between
the 2005v4.3 and 2005v4.2 platforms 6
Table 2-4. Allocation of states to the Petroleum Administration for Defense Districts 8
Table 2-5. Gasoline parameter categories 10
Table 2-6. States adopting California emission standards 10
Table 2-7. Summary of county grouping characteristics for representative counties 11
Table 2-8. Updated 1,3-butadiene to VOC ratio for 2-stroke snowmobiles for NMIM's gasoline categorieslS
Table 2-9. Criteria for grouping representative counties for nonroad mobile analysis 16
Table 2-10. NONROAD model temperature (F) categories 17
Table 2-11. NONROAD NMIM runs 18
Table 2-12. Summary of NONROAD modeling components 18
Table 2-13. 2005 ethanol plant emissions 20
Table 3-1. Summary of VOC speciation profile approaches by sector across cases 23
Table 4-1. Control strategies and growth assumptions for creating the Tier 3 NPRM 2017 and 2030
reference case emissions inventories from the 2005 base case 28
Table 4-2. Tier 3 NPRM reference case stationary non-EGU source-related projection methods 33
Table 4-3. 2017 and 2030 corn ethanol plant emissions [tons] 34
Table 4-4. 2017 and 2030 biodiesel plant emissions [tons] 34
Table 4-5. PFC emissions for 2017 and 2030 [tons] 35
Table 4-6. 2017 and 2030 cellulosic plant emissions [tons] 35
Table 4-7. 2017 and 2030 VOC losses (Emissions) due to ethanol transport and distribution [tons] 36
Table 4-8. Tier 3 NPRM Reference case onroad gasoline and diesel refueling emissions [tons] 36
Table 4-9. Impact of refinery adjustments on 2017 and 2030 emissions [tons] 37
Table 4-10. Upstream agricultural emission increases due to RFS2 fuels in 2017 and 2030 [tons] 37
Table 4-11. Growth factors from year 2005 to 2017 and 2030 for animal operations 38
Table 4-12. Projection factors for growing year 2005 residential wood combustion sources 39
Table 4-13. Impact of year 2017 projection factor error on residential wood combustion estimates 39
Table 4-14. Factors used to project 2005 base-case aircraft emissions to 2017 and 2030 40
Table 4-15. Summary of non-EGU emission reductions applied to the 2005 inventory due to unit and plant
closures 41
Table 4-16. Future-year ISIS-based cement industry annual reductions [tons/yr] for the non-EGU
(ptnonipm) sector 43
Table 4-17. State-level non-MACT boiler reductions from ICR data gathering [tons] 43
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Table 4-18. National impact of RICE controls on non-EGU projections 44
Table 4-19. Impact of fuel sulfur (SO2) controls on 2017 and 2030 non-EGU projections [tons] 44
Table 4-20. Oil and gas emissions for 2005, 2017 and 2030 including additional reductions due to the RICE
NESHAP 45
Table 4-21. Factors applied to year 2005 emissions to project locomotives and class 1 and class 2
commercial marine vessel emissions to 2017 and 2030 48
Table 4-22. Additional class 1 railroad and C1/C2 CMV emissions from RFS2 fuel volume changes 49
Table 4-23. NOX, SO2, PM2.5 and VOC factors to project class 3 CMV emissions for 2017 and 2030 50
Table 6-1. National (49-state) 2005 U.S. emissions (tons/year) by sector 53
Table 6-2. National (49-state) 2017 Reference Case U.S. emissions (tons/year) by sector 54
Table 6-3. National (49-state) 2030 Reference Case U.S. emissions (tons/year) by sector 54
Table 6-4. National (49-state) 2017 Tier 3 Control Case U.S. emissions (tons/year) by sector 54
Table 6-5. National (49-state) 2030 Tier 3 Control Case U.S. emissions (tons/year) by sector 55
LIST OF APPENDICES
APPENDIX A: Ancillary Datasets and Parameters Used for Each Tier 3 NPRM Modeling Case
APPENDIX B: Inventory Data Files Used for Each Tier 3 NPRM Modeling Case - SMOKE Input
Inventory Datasets
in
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ACRONYMS
AEO Annual Energy Outlook
BEIS Biogenic Emission Inventory System
bps Bulk plant storage
btp Bulk plant terminal-to-pump
C3 Category 3 (commercial marine vessels)
CAP Criteria Air Pollutant
CMAQ Community Multiscale Air Quality
CSAPR Cross-State Air Pollution (formerly Transport) Rule
EO 0% Ethanol gasoline (by volume)
E10 10% Ethanol gasoline
E15 15% Ethanol gasoline
EGU Electric Generating Utility
EISA Energy Independence and Security Act of 2007
EPAct Energy Policy Act of 2005
FAA Federal Aviation Administration
FIPS Federal Information Processing Standard
FRM Final Rulemaking
HAP Hazardous Air Pollutant
HDGHG Heavy Duty Greenhouse Gas
HONO HNO2, nitrous acid
IPM Integrated Planning Model
LDGHG Light Duty Greenhouse Gas
LEV (California) Low-Emission Vehicle Program
MOBILE6 Mobile Source Emission Factor Model, version 6
MOVES Motor Vehicle Emissions Simulator
MY Model Year
NEEDS National Electric Energy Database System
NEI National Emission Inventory
NMIM National Mobile Inventory Model
NPRM Notice for Proposed Rulemaking
OAQPS EPA's Office of Air Quality Planning and Standards
ORL One Record per Line (a SMOKE input format)
MP Multipollutant
NO Nitric oxide
NO2 Nitrogen dioxide
NOX Nitrogen oxides
PFC Portable Fuel Container
PEC Elemental carbon component of PM2.5
PMFINE Leftover "Other", or "crustal" component of PM2.5
PNO3 Particulate nitrate component of PM2.5
PSO4 Particulate sulfate component of PM2.5
POC Organic carbon component of PM2.5
rbt Refinery-to-bulk terminal
RFS2 Revised annual renewable fuel standard (mandate)
SMOKE Sparse Matrix Operator Kernel Emissions
SCC Source Category Code
TAF Terminal Area Forecast
IV
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TSD Technical Support Document
VOC Volatile Organic Compound
WRAP Western Regional Air Partnership
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1 Introduction to the Modeling Platform
This Technical Support Document (TSD) describes the development of the emissions inventories used as
inputs to the air quality modeling that the U.S. Environmental Protection Agency (EPA) performed to assess
the impact of the proposed Tier 3 vehicle and fuel emission standards for cars and trucks. This document
provides the details of emissions modeling done to support the development of the Regulatory Impact
Assessment (RIA) for the Tier 3 Notice of Proposed Rulemaking (NPRM), hereafter referred to as the "Tier
3 NPRM". The emissions inventories were generated using the Sparse Matrix Operator Kernel Emissions
(SMOKE) modeling system (http://www.smoke-model.org/index.cfm) version 2.7 and processed into the
form required by the Community Multi-scale Air Quality (CMAQ) model. CMAQ simulates the numerous
physical and chemical processes involved in the formation, transport, and destruction of ozone, particulate
matter and air toxics.
As part of the analysis for this rulemaking, the modeling system was used to calculate daily and annual PM2.5
concentrations, 8-hr maximum ozone, visibility impairment and seasonal and annual concentrations for the
following HAPs: acetaldehyde, acrolein, benzene, 1,3-butadiene, ethanol and formaldehyde. Model
predictions of PM2.5 and ozone are used in a relative sense to estimate scenario-specific, future-year design
values of PM2.5 and ozone. These are combined with monitoring data to estimate population-level exposures
to changes in ambient concentrations for use in estimating health and welfare effects. In this document, we
provide an overview of (1) the emissions components of the modeling platform, (2) the development of the
2005 base year emissions, (3) the development of the future year 2017 and 2030 baseline emissions, and (4)
the development of the future year 2017 and 2030 control case emissions.
A modeling platform is the collection of the inputs to an air quality model, including emissions data,
meteorology, initial conditions, and boundary conditions. The 2005-based air quality modeling platform
includes 2005 base year emissions and 2005 meteorology for modeling ozone, PM2.5 and other model species
with CMAQ. In support of this rule, EPA modeled the air quality in the Eastern and the Western United
States using two separate model runs, each with a horizontal grid resolution of 12 km x 12 km. These 12 km
modeling domains were "nested" within a modeling domain covering the remainder of the lower 48 states
and surrounding areas using a grid resolution of 36 x 36 km. The results from the 36-km modeling were
used to provide incoming "boundary" for the 12km grids. Additional details on the non-emissions portion of
this 2005v4.3 modeling platform are described in the air quality modeling TSD.
Version 4.3 of the 2005-based air quality modeling platform was used for the Tier3 NPRM and is referred to
as the 2005v4.3 platform. The 2005v4.3 platform builds upon the 2005-based platform, version 4.2, which
was the version of the platform used for the final Cross-State Air Pollution Rule (CSAPR) and incorporated
changes made in response to public comments on the proposed version of that rule. The Technical Support
Document "Preparation of Emissions Inventories for the Version 4.2, 2005-based Platform" (see
http://www.epa.gov/ttn/chief/emch/index.html#final) provides information on the platform used for the
proposed version of this rule.
Table 1-1 provides a high-level summary of the five emissions cases that were modeled in support of the
Tier 3 NPRM. The form of the fuel used for mobile sources is a key discriminator between the cases.
Therefore, the mobile source emissions are described with respect to the impacts of the Energy Independence
and Security Act of 2007 (EISA) and the Energy Policy Act of 2005 (EPAct) on mobile source fuels.
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Table 1-1. List of cases run in support of the Proposed Tier 3 air quality modeling
Case Name
2005 base
case
2017
reference case
20 17 Tier 3
control case
2030
reference case
2030 Tier 3
control case
Internal EPA
Abbreviation
2005ct
2017ct_ref
2017ct_ctla
203 Oct ref csapr
2030ct_ctl_csapr
Description
2005 calendar year case / scenarios that use an average year
temporal allocation approach for Electrical Generating Units
(EGUs), a pre-EISA/EPAct fuel supply for mobile sources, and
average year fires data. Air quality outputs from this case are used
to compute relative response factors with the 2017 future year
reference case scenarios.
2017 future year reference scenario with EGU emissions that
represent the implementation of the Cross-State Air Pollution Rule
(CSAPR) and upstream stationary and mobile sources representing
the implementation of the EISA/EPAct fuel supply (RFS2 Rule).
2017 Tier 3 control case scenario representing national Tier 3
vehicle and fuels emissions standards.
2030 future year reference scenario with EGU emissions that
represent the implementation of the Cross-State Air Pollution Rule
(CSAPR) and upstream stationary and mobile sources representing
the implementation of the EISA/EPAct fuel supply (RFS2 Rule).
2030 Tier 3 control case scenario representing national Tier 3
vehicle and fuels emissions standards.
In the remainder of this document, we provide a description of the approach taken to generate the emissions
in support of air quality modeling for the Tier 3 NPRM. In Section 2, we describe the 2005v4.3 platform
custom configurations, ancillary data and 2005 inventory differences from the v4.2 platform. In Section 3,
we describe the speciation differences among each of the cases run. In Section 4, we describe the 2017 and
2030 Reference (i.e., future year baseline) cases as compared to the 2005 base case. In Section 5, we
describe the 2017 and 2030 Tier 3 Control cases as compared to the 2017 and 2030 Reference cases.
Emission summaries for all Tier 3 NPRM scenarios are provided in Section 6. Appendix A provides a
comparison of the ancillary datasets and parameters used for the various Tier 3 NPRM emissions cases, and
Appendix B compares the emissions inventory and other input data files used for each of the Tier 3 NPRM
cases.
2 2005 Emission Inventories and Their Preparation
As mentioned previously, the 2005 emissions modeling approach for the Tier 3 NPRM used much of the
same data and approaches as the 2005v4.2 platform. In this section, we identify the differences between the
data used for the Tier 3 NPRM 2005v4.3 platform and that used for the 2005v4.2 platform. Section 2.1
provides ancillary data differences that impact multiple sectors. Section 2.2 discusses the new approach used
for emissions preprocessing and processing for all onroad mobile sources. Section 2.3 discusses the updated
nonroad mobile components. Sections 2.4 and 2.5 provide differences for the point and nonpoint (area)
inventories, respectively. Section 2.6 discusses other emissions categories such as biogenic and non-U.S.
sources.
The data used in the 2005 emissions case is often the same as those described in the Final Cross-State Air
Pollution Rule TSD (http://www.epa.gov/ttn/chief/emch/index.html#2005X also known as the CAP-BAFM
2005-based Version 4.2 Platform (i.e., 2005v4.2). However, some different emissions data are used for this
rulemaking. All of the documentation provided here describes what was done differently and specifically for
the Tier 3 NPRM in contrast to what was done for the 2005v4.2 platform.
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For the Tier 3 NPRM, a 2005 base case approach was used for the year 2005 emissions scenario. This
approach is very similar to that taken for the CSAPR Final Rule (formerly known as the "Transport Rule").
A base case approach uses average year fires and EGU temporal profiles developed from three years of EGU
data. We use a base case approach to reduce year-specific variability in some components of the inventory.
For example, large fires vary in location and day of the year each year, and EGU shutdowns and high use on
high energy demand days also vary by year. By using a base case approach, these two aspects of the
inventory are maintained into the future year modeling but do not introduce potentially spurious year-
specific artifacts into the air quality modeling estimates. The biogenic emissions data were the same as those
used for the 2005v4.2 platform and were also the same for the 2005 case and for both future-year cases. The
only significant data changes between the 2005 and the 2017 and 2030 future-year Tier 3 reference and
control cases are the emission inventories and speciation approaches.
Table 2-1 below lists the platform sectors used for the Tier 3 NPRM modeling platform. It also indicates
which platform sectors include HAP emissions and the associated sectors from the National Emission
Inventory (NEI). Subsequent subsections refer to these platform sectors to identify the emissions differences
between the 2005v4.2 platform and the Tier 3 NPRM 2005v4.3-based platform.
Table 2-1. Sectors used in emissions modeling for the Tier 3 NPRM 2005v4.3 platform
Platform Sector
IPM sector: ptipm
Non-IPM sector:
ptnonipm
Average-fire sector:
avefire
Agricultural sector:
ag
Area fugitive dust
sector: afdust
Remaining nonpoint
sector: nonpt
Nonroad sector:
nonroad
Cl & C2 CMV and
locomotives:
aim no c3
C3 commercial
marine: seca c3
2005 NEI
Sector
Point
Point+
N/A
Nonpoint
Nonpoint
Nonpoint+
Mobile:
Nonroad
Mobile:
Nonroad
Mobile:
nonroad
Description
NEI EGU units at facilities mapped to the IPM model using the
National Electric Energy Database System (NEEDS) database.
All NEI point source units not matched to the ptipm sector,
including airports.
Average-year wildfire and prescribed fire emissions, county and
annual resolution.
Ammonia (NH3) emissions from NEI nonpoint livestock and
fertilizer application.
PM10 and PM25 emissions from fugitive dust sources in the NEI
nonpoint inventory.
All U.S. nonpoint (i.e. inventoried at the county-level) sources not
otherwise included in other emissions modeling sectors.
Monthly nonroad emissions from the NONROAD model version
NROSb and National Mobile Inventory Model (NMIM) software
version NMIM20090504b and NMIM and Meteorology database
version NCD20101201Tier3. Nonroad version is equivalent to
NONROAD2008a used in 2005v4.2 for future year 2017;
however, E15 fuels are allowed for the 2030 cases.
Primarily 2002 NEI non-rail maintenance locomotives, and
category 1 and category 2 commercial marine vessel (CMV)
emissions sources, county and annual resolution. Aircraft
emissions are no longer in this sector and are now included in the
Non-EGU sector (as point sources); also, category 3 CMV
emissions are no longer in this sector and are now contained in the
seca c3 sector.
Annual point source-formatted, year 2005 category 3 (C3) CMV
emissions, 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 (EGA) study (http://www.epa.gov/otaq/oceanvessels.htm).
Utilized final projections from 2002, developed for the C3 EGA
Proposal to the International Maritime Organization (EPA-420-F-
10-041, August 20 10).
Contains HAP
emissions?
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
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Platform Sector
2005 NEI
Sector
Description
Contains HAP
emissions?
Onroad Mobile:
onroad
Mobile:
onroad+
Motor Vehicle Emissions Simulator (MOVES) emission factors
created using the Tier 3 NPRM version to account for hourly-
based meteorology dependencies at a select number of
representative counties. Includes local input information such as
fuels, temperatures, vehicle fleet, speed distributions and controls.
Emission factors are combined with activity data and gridded
temperature via SMOKE to produce gridded emissions. These
emissions are discussed extensively in Section 2.2.
Yes
Biogenic: biog
N/A
Hour-specific, grid cell-specific emissions generated from the
BEIS3.14 model, including emissions in Canada and Mexico.
Unchanged from the 2005v4 platform, and the same data are used
for all future year scenarios.
No
Other point sources
not from the NEI:
othpt
N/A
Point sources from Canada's 2006 inventory and Mexico's Phase
III 1999 inventory, annual resolution. Also includes annual U.S.
offshore oil 2005v2 NEI point source emissions. Unchanged from
the 2005v4 platform, and the same data are used for all future year
scenarios.
No
Other nonpoint and
nonroad not from
the NEI: othar
N/A
Annual year 2006 Canada (province resolution) and year 1999
Mexico Phase III (municipio resolution) nonpoint and nonroad
mobile inventories. Unchanged from the 2005v4 platform, and
the same data is used for all future year scenarios.
No
Other onroad
sources not from the
NEI: othon
N/A
Year 2006 Canada (province resolution) and year 1999 Mexico
Phase III (municipio resolution) onroad mobile inventories,
annual resolution. Unchanged from the 2005v4 platform, and the
same data are used for all future year scenarios.
No
Some data included in modeling sector has been revised beyond what is included in the 2005 NEI vl or v2.
2.1 Custom configuration for emissions modeling for Tier 3 NPRM
Unlike the 2005v4.2 platform, the configuration for Tier 3 NPRM modeling included additional hazardous
air pollutants (HAPs) and used slightly revised ancillary speciation data. Both of these differences are
described in this section.
Table 2-2 lists the additional HAP pollutants processed for the Tier 3 NPRM 2005v4.3 platform, which were
not included in the 2005v4.2 platform. A "lite" version of the multi-pollutant CMAQ (Version 4.7) was used
that required emissions only for the species listed in the footnote of Table 2-2. In addition to the model
species differences, the Tier 3 NPRM platform had a few additional custom aspects in the 2005 cases. Table
2-3 lists the datasets used by the (Tier 3 NPRM) 2005v4.3 platform that are different from the 2005v4.2
platform.
Another consideration is the speciation across the Tier 3 NPRM future-year cases as compared to 2005.
Section 3 provides a detailed account of these differences. The future-year ancillary data were largely the
same as those in 2005, with no substantial differences for most modeling sectors. The exception to this is
onroad mobile, which required several new ancillary input files to support the SMOKE to MOVES modules;
these are discussed in detail in Section 2.4. All other ancillary data files not required for SMOKE to
MOVES processing can otherwise be found at the 2005-based platform website
(http ://www. epa. gov/ttn/chief/emch/index. html#2005).
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Table 2-2. Model species produced by SMOKE for CB05 with SOA for the Tier 3 NPRM platform
Inventory Pollutant
CL2
HC1
CO
NOX
SO2
NH3
VOC
Various additional
VOC species from the
biogenics model which
do not map to the
above model species
PM10
PM25
Sea-salt species (non -
anthropogenic
emissions)
Model Species
CL2
HCL
CO
NO
NO2
HONO
SO2
SULF
NH3
ACROLEIN'
ALD2
ALD PRIMARY'
ALDX
BENZENE
BUTADIENE 13'
ETH
ETHA
ETOH
FORM
FORM PRIMARY'
IOLE
ISOP
MEOH
OLE
PAR
TOL
XYL
SESQ
TERP
PMC
PEC
PNO3
POC
PSO4
PMFINE
PCL
PNA
Model species description
Atomic gas-phase chlorine
Hydrogen Chloride (hydrochloric acid) gas
Carbon monoxide
Nitrogen oxide
Nitrogen dioxide
Nitrous acid
Sulfur dioxide
Sulfuric acid vapor
Ammonia
Acrolein from the HAP inventory
Acetaldehyde from VOC speciation
Acetaldehyde from the HAP inventory
Propionaldehyde and higher aldehydes
Benzene (not part of CB05)
1,3 -butadiene from the HAP inventory
Ethene
Ethane
Ethanol, from select inventories provided by OTAQ
Formaldehyde
Formaldehyde from the HAP inventory
Internal olefm carbon bond (R-C=C-R)
Isoprene
Methanol
Terminal olefm carbon bond (R-C=C)
Paraffin carbon bond
Toluene and other monoalkyl aromatics
Xylene and other polyalkyl aromatics
Sesquiterpenes
Terpenes
Coarse PM > 2.5 microns and < 10 microns
Particulate elemental carbon < 2.5 microns
Particulate nitrate < 2.5 microns
Particulate organic carbon (carbon only) < 2.5 microns
Particulate Sulfate < 2.5 microns
Other particulate matter < 2.5 microns
Particulate chloride
Particulate sodium
• - ACROLEIN, ALD2_PRIMARY, BUTADIENE13, ETHANOL and FORM_PRIMARY are the extra
"CMAQ-lite" HAPs that are not in the v4.2 platform.
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Table 2-3. Description of differences in 2005 case ancillary data (unrelated to SMOKE to MOVES)
between the 2005v4.3 and 2005v4.2 platforms
Ancillary Data Type
Speciation cross-
references and
Speciation profiles
Speciation VOC to TOG
conversion profiles
SCC Descriptions
Inventory tables
Difference between 2005v4.2 platform and 2005v4.3 platform
The Tier 3 NPRM 2005v4.3 data files are configured to support the multi-
pollutant (MP) version of CMAQ, whereas the 2005v4.2 platform data files are
configured to support only the non-MP version. Therefore, the Tier 3 NPRM
data files include profiles for additional VOC HAP species.
Added Tier 3 -specific VOC to TOG and nonHAP VOC to nonHAP TOG
assignments
Added onroad diesel SCCs representing start and idle modes (223007X000)
The Tier 3 NPRM data file was updated to support SMOKE to MOVES
pollutants and modes, the MP "lite" version of CMAQ, and, to accept inventory
Ethanol (ETOH). The 2005v4.2 platform data file is configured to support only
the non-MP version.
2.2 Onroad mobile sources (onroad)
For each scenario, emissions from cars, trucks and motorcycles were estimated by using the EPA's Motor
Vehicle Emission Simulator (MOVES) to create emission factors that were then input to the Sparse Matrix
Operator Kernel Emissions system (SMOKE). The SMOKE-MOVES integration tools combined the county
and temperature-specific emission factors with the activity data to compute the actual emissions. In brief,
our approach was to use the met4moves program to identify a set of temperatures that needed emission rates.
For each scenario, we ran MOVES repeatedly to produce emission rates by temperature, Source
Classification Code (SCC), speed bin, and representing county. The moves2smk tool then reformatted the
MOVES rates and selected the appropriate rates for each county and month. Movesmrg multiplies the
emission rates by county VMT or vehicle population, applies Speciation profiles to develop inventories for
pollutants not included in MOVES and temporally, and spatially allocates emissions to individual grid cells
for CMAQ input.
2.2.1 MOVES
For the Tier 3 NPRM, EPA used a version of the MOVES 2010a model that was enhanced for the rule. This
version o fthe model included updated information on how fuel parameters impact vehicle emissions and
updates on our understanding of evaporative emissions. It also included some minor updates to emission
rates and some changes designed to make the model run more efficiently. All updates are described in detail
in a memorandum to the docket (U.S. EPA 2013, Memorandum to Docket: Updates to MOVES for the Tier
3 NPRM). The following sections describe inputs to the MOVES model that were specific to this analysis.
The gridded meteorological input data for the entire year of 2005 were derived from simulations of the
Pennsylvania State University / National Center for Atmospheric Research Mesoscale Model (MM5), a
limited-area, nonhydrostatic, terrain-following system that solves for the full set of physical and
thermodynamic equations which govern atmospheric motions. The Meteorology-Chemistry Interface
Processor (MCIP) version 3.4 was used as the software for maintaining dynamic consistency between the
meteorological model and chemistry mechanisms. The hourly gridded meteorological data was post-
processed by met4moves to determine the maximum temperature ranges, average relative humidity, and a
series of diurnal temperature profiles. MOVES was then run for each temperature bin and diurnal profile to
compute the required emission factors.
Vehicle population data is a required input for MOVES when modeling on a county basis. Using the
technical guidance provided to states by EPA, estimates for vehicle populations were generated for use in the
MOVES databases. The populations were computed using the county-specific VMT and national average
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ratios of vehicle population to vehicle VMT from the MOVES application. This method is described in
Section 3.3 of the document, "Technical Guidance on the Use of MOVES2010 for Emission Inventory
Preparation in State Implementation Plans and Transportation Conformity" (EPA-420-B-10-023, April
2010), which is available on the EPA web site at: http://www.epa.gov/otaq/models/moves/index.htm.
The county inputs used for the rule were derived from the inputs used for the 2005 National Emissions
Inventory (NEI). This inventory covers the 50 United States (U.S.), Washington DC, Puerto Rico and the
U.S. Virgin Islands. The NEI was created by the EPA's Emission Inventory and Analysis Group (EIAG) in
Research Triangle Park, North Carolina, in cooperation with the Office of Transportation and Air Quality
(OTAQ) in Ann Arbor, Michigan.
OTAQ has developed a consolidated modeling system known as the National Mobile Inventory Model
(NMIM) to calculate emissions from onroad highway mobile source and nonroad mobile sources. NMIM
documentation is available at http://www.epa.gov/otaq/models/nmim/420r05024.pdf NMIM includes a
county-level database with the important input parameters specific to each county. The data in the NMIM
county database (NCD) are used to develop NONROAD model input files within NMIM. The basis for the
2005 default vehicle miles traveled (VMT) is data supplied by the Federal Highway Administration
(FHWA), as well as publicly available data from FHWA's Highway Statistics series. Details of how the
NCD was developed are documented for the NEI "Documentation for the 2005 Mobile National Emissions
Inventory, Version 2 (December 2008)", which can be obtained on EPA web site:
http://www.epa.gov/ttn/chief/net/2005inventory.html.
For the onroad portion of the inventory estimates for the rule, including all base and control scenarios, the
current EPA highway mobile source emission model (MOVES) was used. This required conversion of the
NCD parameters to a format consistent with MOVES. A contractor was given the assignment (TranSystems,
Contract No. EP-D-06-001, WA 4-65) to convert the NCD to MOVES formatted input databases. This was
accomplished with the assistance of converters designed for this purpose. These converters are available on
the EPA web site at: http://www.epa.gov/otaq/models/moves/tools.htm.
All of the county-specific onroad data available in the NCD was converted to MOVES format for use at the
county scale consistent with the databases created using the MOVES County Data Manager (CDM), except
for the information regarding fuel properties. The fuel properties were updated using more recent
information and methods specifically for this rule and described elsewhere in this document. Any table
entries in the NCD that contained national average default information from the MOBILE6 model were
replaced with the more recent national average default information used by MOVES.
2.2.2 Representing counties
Although EPA compiles county-specific databases for all counties in the nation, many of the states can
provide little or no county-specific information for most counties. Rather than explicitly model every county
in the nation (there are over 3,000 counties), we have performed detailed modeling for some counties and
less detailed estimates for the other counties. In this rule, this has been accomplished using a concept called
"representing counties".
In this approach, we group counties that have similar properties and therefore would have similar emission
rates. Then, we explicitly compute emission factors for only one county in the group (the "representing"
county). These representative emission factors are then used, in combination with county specific activity
and meteorology data to generate emissions estimates for all of the counties in the group. This approach
dramatically reduces the number of modeling runs required to generate inventories and still takes into
account differences between counties.
7
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As described in Section 2.2.4, to generate onroad mobile emissions, MOVES was run in conjunction with the
EPA SMOKE model to generate the gridded inventories used in air quality modeling. SMOKE uses
emission rates (not inventories) to generate inventory estimates within each grid. Since SMOKE handles the
differences in the fleet mix, temperatures, speeds and VMT versus location and time, MOVES can be run in
the "emission rate" (i.e. emissions factor) mode. As a result, when counties are grouped, they can be
grouped independently of fleet mix, speeds and temperature. This greatly increases the number of counties
that can be in each grouping, since temperature is a factor that varies among the counties1. For this analysis,
we grouped counties with similar fuel, emission standards, altitude, and inspection and maintenance (I/M)
programs.
The information used to group the counties was derived from the NMIM inputs used for the 2005 NEI
onroad and nonroad mobile sectors. For the onroad portion of the inventory estimates for the rule, including
all base and control scenarios, the current EPA highway mobile source emission model (MOVES) was used.
This required conversion of the NCD parameters to a format consistent with MOVES.
The NCD also does not contain county-specific information regarding vehicle populations and there are no
default values. Vehicle population data is a required input for MOVES when modeling on a county basis.
Using the technical guidance provided to states by EPA, the contractor generated appropriate estimates for
vehicle populations for use in the MOVES databases using the county specific VMT and national average
ratios of vehicle populations versus vehicle VMT from the MOVES application. This method is described in
Section 3.3 of the document, "Technical Guidance on the Use of MOVES2010 for Emission Inventory
Preparation in State Implementation Plans and Transportation Conformity" (EPA-420-B-10-023, April
2010), which is available at: http://www.epa.gov/otaq/models/moves/index.htm.
The grouping of counties uses a tree algorithm, which is conceptually simple. In the tree algorithm, all
counties are assigned to various categories. Then, by grouping counties within the same categories, you get
groups of counties that have the similar parameters. Counties were sorted into their Petroleum
Administration for Defense Districts (PADDs). PADD 1 is divided into three sub-PADD groupings and
each sub-group is treated as a separate PADD (la, Ib and Ic). Each state belongs to a PADD and all
counties in any state are within the same PADD. Table 2-4 below shows the PADDs and the states within
each PADD.
Table 2-4. Allocation of states to the Petroleum Administration for Defense Districts
PADD
la
la
la
la
la
la
Ib
Ib
Ib
Ib
Ib
Ib
State FIPS
09
23
25
33
44
50
10
11
24
34
36
42
State Name
CONNECTICUT
MAINE
MASSACHUSETTS
NEW HAMPSHIRE
RHODE ISLAND
VERMONT
DELAWARE
DISTRICT OF COLUMBIA
MARYLAND
NEW JERSEY
NEW YORK
PENNSYLVANIA
Abbreviation
CT
ME
MA
NH
RI
VT
DE
DC
MD
NJ
NY
PA
This differs from the calculation of nonroad inventories where temperature was considered in the choice of representing county.
-------
PADD
Ic
Ic
Ic
Ic
Ic
Ic
Ic
Ic
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
O
O
O
O
3
3
4
4
4
4
4
5
5
5
5
5
5
5
State FIPS
12
13
37
45
51
54
72
78
17
18
19
20
21
26
27
29
31
38
39
40
46
47
55
01
05
22
28
35
48
08
16
30
49
56
02
04
06
15
32
41
53
State Name
FLORIDA
GEORGIA
NORTH CAROLINA
SOUTH CAROLINA
VIRGINIA
WEST VIRGINIA
PUERTO RICO
VIRGIN ISLANDS
ILLINOIS
INDIANA
IOWA
KANSAS
KENTUCKY
MICHIGAN
MINNESOTA
MISSOURI
NEBRASKA
NORTH DAKOTA
OHIO
OKLAHOMA
SOUTH DAKOTA
TENNESSEE
WISCONSIN
ALABAMA
ARKANSAS
LOUISIANA
MISSISSIPPI
NEW MEXICO
TEXAS
COLORADO
IDAHO
MONTANA
UTAH
WYOMING
ALASKA
ARIZONA
CALIFORNIA
HAWAII
NEVADA
OREGON
WASHINGTON
Abbreviation
FL
GA
NC
sc
VA
wv
PR
VI
IL
IN
IA
KS
KY
MI
MN
MO
NE
ND
OH
OK
SD
TN
WI
AL
AR
LA
MS
NM
TX
CO
ID
MT
UT
WY
AK
AZ
CA
HI
NV
OR
WA
The counties in each PADD were sorted into fuel groups using the January fuel properties and the July fuel
properties. The fuel supply and fuel formulation data were taken from the 2005 fuels developed for the rule.
The fuel parameters used for grouping and the ranges of values used for the bins are described in Table 2-5;
the parameter categories in Table 2-5 are used in all calendar years for Tier 3 NPRM.
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Table 2-5. Gasoline parameter categories
Gasoline Parameter
Reid Vapor Pressure (psi)
Sulfur (ppm)
Ethanol (volume percent)
Benzene (volume percent)
Category ID
1
2
O
4
1
2
3
4
1
2
3
1
2
O
4
Minimum Value (>=)
0
7.3
8.2
9.2
0
50
100
110
0
3
8
0
1
1.5
2
Maximum Value (<)
7.3
8.2
9.2
100
50
100
110
1000
O
8
100
1
1.5
2
10
Some states have adopted California Low-Emissions Vehicle (LEV) highway vehicle emission standards or
plan to adopt them. Since the emission rates in these states will be different than in neighboring states, they
must be modeled separately. Also, because the implementation of California standards varies between these
states, each state with California standards must be modeled independently from the other states with
California standards as well. Each state with California standards will be treated separately when choosing
representing counties. Table 2-6 shows the states with California emission standards.
Table 2-6. States adopting California emission standards
State ID
06
25
36
50
23
09
42
44
41
53
34
24
10
35
State Name
California
Massachusetts
New York
Vermont
Maine
Connecticut
Pennsylvania
Rhode Island
Oregon
Washington
New Jersey
Maryland
Delaware
New Mexico
Abbreviation
CA
MA
NY
VT
ME
CT
PA
RI
OR
WA
NJ
MD
DE
NM
CA Program Begins
1994
1995
1996
2000
2001
2008
2008
2008
2009
2009
2009
2011
2014
2016
The counties in each PADD-fuel group were sorted into groups with and without I/M vehicle inspection
programs. I/M programs were determined using the 2005 calendar year entries in the IMCoverage table of
the MOVES database. The I/M category is the state in which the county resides. All I/M programs within a
state were considered as a single program, even though each county may be administered separately and
have a different program design.
10
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Altitude was also added as its own category. Altitude is a field in the County table of the
MOVESDB20101006 database. Counties are either high (H) or low (L) altitude based on the criteria set
forth by EPA certification procedures (4,000 feet above sea level). The result is a set of county groups with
similar fuel, emission standards, altitude and I/M program. Then the county with the highest VMT in each
group is chosen as the representing county. The categories are summarized below in Table 2-7.
Table 2-7. Summary of county grouping characteristics for representative counties
County Grouping Characteristic
PADD
Fuel Parameters
Emission Standards.
Inspection/Maintenance Programs
Altitude
Description
Petroleum Administration for Defense Districts (PADDs).
PADD 1 is divided into three sub-PADD groupings and
each sub-group is treated as a separate PADD (la, Ib and
Ic). Each state belongs to a PADD and all counties in any
state are within the same PADD.
Average gasoline fuel properties for January and July
2005, including RVP, sulfur level, ethanol fraction and
percent benzene.
Some states have adopted California LEV highway
vehicle emission standards or plan to adopt them. Since
implementation of the standards varies, each state with
California standards is treated separately.
Counties were grouped within a state according to whether
or not they had an I/M program. All I/M programs within
a state were considered as a single program, even though
each county may be administered separately and have a
different program design.
Counties are either high or low altitude based on the
criteria set forth by EPA certification procedures (4,000
feet above sea level).
Using these criteria, a set of 106 counties were selected to represent the nation. Of these, only 103 were
needed to model the 48 states included in the modeling domain for the air quality analysis inventory. If
MOVES runs were performed for all U.S. counties and months, there would be 3,141 counties (excluding
AK and HI) times 12 months = 37,692 county-months rather than the 1,236 needed with representative
counties. The MOVES runs for each representative county and fuel month were performed independently of
one another on different computer processors, with each accessing a MySQL database specific to that run.
2.2.3 SMOKE-MOVES inputs
Both MOVES and SMOKE require meteorological data. The program met4moves takes gridded hourly
meteorological data, the representative counties, and the representative fuel months as inputs and produces
separate meteorological products for MOVES and SMOKE. Met4moves uses the representative counties
and fuel months to determine the full range of meteorology in that county group. For each representative
county and fuel month, it determines all the grid cells that fall within the corresponding counties in that
county group for the number of months that correspond to the fuel month2. The temperature range is then
determined by looking at the minimum and maximum temperature across all these grid cells for all hours in
that time period. Relative humidity is calculated by taking an average over these same grid cells.
2 Spatial surrogates are used in determining which grid cells to pull in calculating the various meteorological statistics. These
spatial surrogates both map counties to grid cells. The spatial surrogates further limit the grid cells by determining whether some
of the grid cells should not be included in the calculation of temperature range. For example, if some of the county has no roads or
population, e.g. high mountains, then there is no reason to include it in the temperature range for onroad emissions.
11
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For rate-per-profile (RPP) process emissions, SMOKE-MOVES uses the change in temperature over the day
(i.e., the diurnal profile), instead of the temperature at the hour of processing. Met4moves create a series of
diurnal profiles based on the extent of the temperature range and the size of the temperature bins. For
MOVES, these diurnal profiles span the full range of temperatures for that representative county and fuel
month. To satisfy SMOKE's needs in computing RPP emissions, met4moves creates a minimum and
maximum temperature range for each county in the domain. Note that these temperature ranges are county-
specific, and are not based on the representative county or county group.
Met4moves can be run in daily or monthly mode for producing SMOKE input. In monthly mode, the
temperature range is determined by looking at the range of temperatures over the whole month for that
specific county. Therefore, there is one temperature range per county per month. While in daily mode, the
temperature range is determined by evaluating the range of temperatures in that county for that day. The
output for the daily mode is one temperature range per county per day. Typically, the SMOKE input
produced in monthly mode will have larger temperature ranges for each county than when it is run in daily
mode. For the Tier 3 NPRM runs, met4moves was run in daily mode.
In addition to the lookup tables of emission rates produced by MOVES, SMOKE requires county-specific
VMT, population, and average speed by road type to calculate the necessary emissions for air quality
modeling. VMT by county and Source Classification Code (SCC) was developed using MOVES2010a and
the National County Database. The National County Database (NCD20101201) contained the most recent
estimates of 2005 VMT and the best available estimates of allocation of VMT from national to the county
level at the time the modeling was performed. Accordingly, for the 2005 base year, the estimates of VMT by
county and SCC were taken directly from the NCD.
The average speeds provided to SMOKE for each county were derived from the default national average
speed distributions found in the default MOVES2010a database AvgSpeedDistribution table. These average
speeds are the average speeds developed for the previous EPA highway vehicle emission factor model,
MOBILE6. The same speed data was used for the base and future year cases.
In MOVES, there is a distribution of average speeds for each hour of the day for each road type. The
average speeds in these distributions were used to calculate an overall average speed for each hour of the
day. These hourly average speeds were weighted together using the default national average hourly vehicle
miles traveled (VMT) distribution found in the MOVES default database HourlyVMTFraction table, to
calculate an average speed for each road type. This average speed by road type was provided to SMOKE for
each county.
2.2.4 Generating emission factors for SMOKE
After representative counties and fuel months were chosen, the met4moves script was executed to produce
the set of MOVES RunSpecs and meteorology tables that would ultimately generate a set of SMOKE lookup
tables encompassing the full range of temperatures for all the counties and months in each group. OTAQ
also provided VMT, population, and average speed tables for every county.
The onroad model-ready emissions were produced by running SMOKE-MOVES using 103 representative
counties and two fuel months. SMOKE-MOVES is a series of scripts and programs that 1) produce
meteorological data for MOVES (Met4Moves), 2) construct a set of MOVES RunSpecs that produce lookup
tables by temperature and average speed (runspec_generator), 3) process the MOVES lookup tables into a
SMOKE-ready format (moves2smkEF), and 4) runs SMOKE. The way that OTAQ used SMOKE-MOVES
12
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differs somewhat from the way that SMOKE-MOVES was initially designed to be run. The full sequence of
events was the following:
1) OAQPS ran met4moves for a nation-wide 12 km grid. This generated the temperatures needed for the
emission factor lookup tables and an average humidity for each county and month. The inputs to
met4moves are the hourly gridded temperature and humidity generated by the meteorological model
used for CMAQ along with the list of representative counties and fuel months. For each
representative county and fuel-month, met4moves queries all the grid cells in all the represented
county-months to find the full range of temperatures and profiles needed and averages the relative
humidity.
2) OTAQ ran the runSpec_generator Perl script, (runspec_generator_v0.3_04Nov2010.plx). The inputs
to this process include the representative county list, fuel month list, temperature bin size (=10
degrees here), and the outputs from met4moves. The runspec_generator script produced MOVES
run-specifications that control how the MOVES run is configured, along with zonemonthhour tables
in CSV format. Specifications were generated for the three types of MOVES processes: rate-per-
distance (RPD), rate-per-profile (RPP), and rate-per-vehicle (RPV). Run specifications were
generated as needed to simulate the range of conditions reflected in the meteorological inputs. For
RPD and RPV, a series of run specifications were created for each representative county, one for
each temperature bin covering the temperature ranges provided by the met4moves output. For RPP, a
second series of run specifications were created for each representative county, one for each diurnal
profile provided by the met4moves output. The input data specific to each county were loaded into
databases called "scaleinputdatabases", and the zonemonthhour tables were also loaded into
databases.
3) OTAQ ran a tool to read the county databases, the zonemonthhour databases, other user-supplied
databases, and the run specifications. The tool implemented LEV programs into the specifications as
appropriate and also modified the pollutant-process associations in the run specifications to meet the
needs of the Tier 3 NPRM. The tool then packaged the information into a form that could be used by
the compute server.
4) OTAQ issued the command to start the required MOVES runs for each county and fuel month on the
compute servers.
5) Once the MOVES runs were complete, OTAQ ran the moves2smkEF postprocessor to reformat the
MySQL tables into the emission factor tables in CSV format that are readable by the SMOKE
movesmrg program. The postprocessor also performed additional calculations to support SMOKE
processing of CMAQ ready model emissions: speciating HONO from NO and NO2, speciating the
AE5 PM species (PEC, POC, PNO3, PSO4, PMFINE, and PMC for break and tire wear), and
aggregating the detailed MOVES modes into 5 broader modes (exhaust, evaporative, permeation,
break wear, and tire wear)3.
6) OAQPS downloaded the emission factors from the server and executed SMOKE programs to produce
gridded, hourly, speciated emissions for CMAQ. See the next section for details.
3 The moves2smk postprocessor also corrects the extended idle emissions for RPV by merging in data from a separate national
extended idle run and replaces missing EF from RPD due to missing SCCroadtypes in some reference counties.
13
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2.2.5 Running SMOKE for onroad mobile
Running SMOKE using emission factors (EF) from MOVES required the development of a new set of
functionality. The central SMOKE program that performs this new analysis is movesmrg which takes
activity data, meteorological data, and the EF to produce gridded emissions. SMOKE is run independently
for each of the three processes: rate-per-distance (RPD), rate-per-vehicle (RPV) and rate-per-profile (RPP).
The emissions process RPD is for modeling the on-network emissions. This includes the following modes:
vehicle exhaust, evaporation, permeation, break wear, and tire wear. For RPD, the activity data is monthly
VMT, monthly speed (SPEED), and hourly speed profiles for weekday versus weekend (SPDPRO)4. The
SMOKE program temporal takes vehicle and roadtype specific temporal profiles and distributes the monthly
VMT to day of the week and hour. Movesmrg reads the speed data for that county and SCC and the
temperature from the gridded hourly data and uses these values to look-up the appropriate EF from the
representative county's EF table. It then multiplies this EF by hourly temporalized VMT to calculate the
emissions for that grid cell and hour. This is repeated for each pollutant and SCC in that grid cell.
The emissions process RPV is for modeling the off-network emissions. This includes the following modes:
vehicle exhaust, evaporative, and permeation For RPV, the activity data is vehicle population (VPOP).
Movesmrg reads the temperature from the gridded hourly data and uses the temperature plus SCC and the
hour of the day to look up the appropriate EF from the representative county's EF table. It then multiplies
this EF by the VPOP for that SCC and FIPS to calculate the emissions for that grid cell and hour. This
repeats for each pollutant and SCC in that grid cell.
The emissions process RPP is for modeling the off-network emissions for parked vehicles. This includes the
mode vehicle evaporative (fuel vapor venting). For RPP, the activity data is VPOP. Movesmrg reads the
county based diurnal temperature range from met4moves output for SMOKE. It uses this temperature range
to determine the most similar idealized diurnal profile from the EF table using the temperature min and max,
SCC, and hour of the day. It then multiplies this EF by the VPOP for that SCC and FIPS to calculate the
emissions for that grid cell and hour. This repeats for each pollutant and SCC within the county. For more
details on processing RPD, RPV, and RPP in SMOKE, see:
http://www.smoke-model.Org/version3.l/html/ch02s08s04.html.
MOVES was run for a series of representative counties and fuel months. For each representative county and
fuel month, three EF tables were created: RPD, RPV, and RPP. SMOKE was run so that for each model day
it would read in a single EF table (based on the appropriate fuel month), process all the counties that are part
of the county group (i.e. are represented by that representative county), then read the next representative
county EF table, etc. After all days in the model year were looped over, SMOKE has generated a separate
set of daily intermediate files for each of the emissions processes (RPD, RPV, and RPP). Post-processing
scripts were developed to integrate the process specific intermediate files into model-ready intermediate files
for the onroad sector. These files were generated for a national 12km domain. To support the CMAQ runs
they were further processed to create an aggregated 36km sector specific model-ready file and two 12km
domains (12EUS1 and 12WUS1).
4 If the SPDPRO is available, the hourly speed takes precedence over the average speed in the SPEED inventory. Due to an
oversight, SPDPRO was not used in the base and future-years modeling. A later sensitivity was run including the SPDPRO input
for the base year which found that the use of hourly speed slightly increased the emissions for most pollutants (e.g. nationally NOX
showed a 0.8% increase, VOC showed a 1% increase, and PM25 showed a 3% increase).
14
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2.3 Nonroad mobile sources (nonroad, alm_no_c3, seca_c3)
The nonroad sectors include a wide-range of mobile emission sources ranging from locomotives, marine
vessels, construction and farming equipment to hand-held lawn tools. As discussed in Section 4, nonroad
upstream impacts also impact the (post-EPAct/EISA/RFS2) Tier 3 NPRM future year cases, reflecting
increased ethanol production resulting in fuel volume increases for locomotives and C1/C2 CMV emissions.
2.3.1 Emissions generated with the NONROAD model (nonroad)
Most nonroad emissions were estimated using the NONROAD model, as run by EPA's NMIM.
NONROAD is EPA's model for calculating emissions from nonroad equipment, except for aircraft,
locomotives, and commercial marine vessels. The NONROAD model and extensive documentation can be
found at http://www.epa.gov/otaq/nonrdmdl.htm. NMEVI is a program that references a national database of
county-month data, writes county-month input files for NONROAD based on that data, runs NONROAD
once for every county and month requested by the user and collects the results in an output database.
Information and downloads for NMIM can be found at http://www.epa.gov/otaq/nmim.htm. Rather than
running every county, NMIM is designed to run NONROAD for "representative counties" and to use
individual county activity data to develop national inventories.
Inputs for NMIM runs were stored in the NMIM County Database (NCD). The NCD version used for this
modeling was NCD20101201Tier3. This NCD is based on NCD20101201, which is the version that
includes all updates from the 2008 National Emission Inventory process described at
http://www.epa.gov/ttn/chief/net/2008inventory.html. For this analysis, updates were made to the underlying
fuel supply for the Tier 3 NPRM future year reference cases. Thus, the NCD20101201Tier3 contained
special versions of countyyearmonth, gasoline, and diesel. The fuels in the NCD2010201Tier3 were
developed from the fuels used for onroad vehicles, as described in Section 2.2.1.
A special countymonthhour table that contained 2005 meteorology was copied into the standard
countymonthhour table. The use of the countymonthhour table for meteorology was selected by the RunSpec
setting use Yearly WeatherDataSelected="false." A minor change was also made for snowmobiles: the SCC
toxics table in the NMIM County Database (NCD) was updated to correct 1,3-butadiene exhaust emissions
for 2-stroke snowmobiles (SCC 2260001020), as shown in Table 2-8 below. This correction addressed an
issue identified in air quality modeling for the RFS2 rule, where unexpected increases in ambient
concentrations were observed in rural areas during winter due to snowmobile emissions, available at:
http://www.epa.gov/otaq/renewablefuels/420rl0006.pdf The increases were based on data from only three
engines, which showed unusually high 1,3-butadiene emissions with 10% ethanol (Eth oxygenate). Other
data suggests that this increase is highly unlikely to be representative of the in-use fleet as a whole; thus
results were corrected to those in the "NCD20101201Tier3" column. In Table 2-8, Base Gasoline represents
cases where the fuel type is not Eth, MTBE or RFG. Eth gas is used where the fuel contains ethanol which is
greater than or equal to 5% by volume or Ethyl Tertiary Butyl Ether (ETBE) is greater than or equal to 5%
by volume. MTBE gas is used where the fuel contains MTBE which is greater than or equal to 12% by
volume or Tertiary Amine Methyl Ether (TAME) is greater than or equal to 13% by volume. Finally, RFG
gas is used where the fuel is RFG and where the fuel contains oxygenate greater than 5% by volume and
where the fuel contains MTBE which is less than 12% by volume or TAME is less than 13% by volume.
Table 2-8
. Updated 1,3-butadiene to VOC ratio for 2-stroke snowmobiles for NMIM's gasoline categories
Fuel Type
Base Gasoline
Ethyl Tertiary Butyl Ether (Eth) Gas
Methyl Tertiary Butyl Ether (MTBE) Gas
Reformulated Gasoline (RFG) Gas
NCD20101201
0.0012
0.00732
0.0012
0.0012
NCD20101201Tier3
0.0012
0.0012
0.0012
0.0012
15
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A special county map table was developed to use representing counties in the NMIM runs. The algorithm
for producing representing counties for NMIM was identical to that used for MOVES except that ten degree
temperature bins were added to the criteria. The result was 293 representing counties. Finally, NMIM does
not estimate ethanol emissions, so the inventory for this pollutant was from the chemical speciation that is
obtained by post-processing through SMOKE.
2.3.1.1 Representing counties for NONROAD
"Representing counties" is a way of saving NMIM run-time by grouping together similar counties and
generating emission factors by running the NONROAD model for only one of those counties and then using
those emission factors for all the counties in the group. For this analysis, 293 county groups were developed.
The counties in each group were in the same state, had similar fuels in both summer and winter, and had
similar I/M programs. Since there are winter fuels and summer fuels, January was chosen as the fuel-month
to represent the seven months October through April, and July was chosen to represent the five months May-
September. The total number of county-months for which NMIM runs needed to be performed was thus 293
times 12 months = 3,516 county-months for each scenario-year. If NMIM runs were performed for all U.S.
counties and months, there would be 3141 counties (excluding AK and HI) times 12 months = 37,692
county-months. Representing counties were chosen for NONROAD model NMIM runs by grouping
counties based on the characteristics listed in Table 2-9.
Table 2-9. Criteria for grouping representative counties for nonroad mobile analysis
Characteristic
Petroleum Administration for Defense
District (PADD)
Gasoline parameters
Inspection/Maintenance Programs
Altitude
Temperatures
Grouping Criteria
All counties in a group must be in the same PADD.
Fuel bins were created for RVP, sulfur, benzene, and
ethanol. All counties in each group had all of these fuel
properties in the same bins for all twelve months.
Counties with I/M programs were grouped with other
counties with I/M programs in the same state.
All counties in the group must be in the same altitude
category (high or low).
All counties in the group must have similar temperatures, as
detailed below.
Nonroad inventories are not calculated on a grid basis, as the highway mobile sources were. So, when
running NMIM for nonroad emissions, the representing counties must also account for temperatures. The
temperatures are taken from the 2005 calendar year values in the County YearMonthHour table of the
NCD20100602 NMIM database. As shown in
16
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Table 2-10, ten-degree Fahrenheit (F) bins were created for min and max temperatures for each month. All
counties in each group had all min and max temperatures for all twelve months in the same bins. The lowest
interval includes all temperatures below -10 degrees F. The highest interval includes all temperatures above
100 degrees F.
17
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Table 2-10. NONROAD model temperature (F) categories
Temperature
Bin
1
2
O
4
5
6
7
8
9
10
11
12
13
Minimum
Temperature (>=)
-20
-10
0
10
20
30
40
50
60
70
80
90
100
Maximum
Temperature (<)
-10
0
10
20
30
40
50
60
70
80
90
100
200
Once counties were grouped, the representing county was chosen as the one with the highest VMT. The
same set of 293 county groups and representing counties was used for all years and scenarios.
2.3.1.2 Fuel inputs for NONROAD runs
For the nonroad mobile portion of the inventory estimates for the rule, the NMIM county database (NCD)
developed for the 2005 NEI, with the one exception of the county-specific fuel properties, was used to
calculate nonroad emissions. The fuels that were developed for use with MOVES to compute onroad mobile
emissions for the Tier 3 NPRM (see Section 2.2) were converted to NMIM fuels. Practically, this means the
fuelsupply and fuelformulation tables from MOVES were converted into the countyyearmonth, gasoline, and
diesel tables in the NCD. For the year 2005 modeling, onroad and nonroad gasoline formulations are
assumed to be identical.
MOVES allows for multiple gasoline fuels, each with a market-share, for a single county-month, the market
shares always sum to one. The NCD allows only one fuel per county month, but, for each of the four
oxygenates (ETOH, MTBE, TAME, and ETBE), the NCD has columns for both volume percent and market
share. The sum of these market shares is less than or equal to one. If the market-share is less than one, the
remainder of the market is non-oxygenated (conventional) gasoline. When there are multiple MOVES
gasoline fuels for a single county-month, non-oxygenate MOVES fuel properties are multiplied by market
share and summed to produce the fuel property in the gasoline table. Individual non-ethanol oxygenates and
California ethanol occur in only one fuel per county-month in MOVES, so the volume and market share are
transferred to the appropriate columns for that oxygenate in NMIM. In states other than California, for
multiple ethanol volumes with volume percents less than 10, the product of market-share and volume percent
is averaged and then divided by 10, resulting in a market share for E10.
2.3.1.3 NMIM runs
Table 2-11 shows the NMIM runs that were performed to generate the NONROAD model county-month
results for both national inventories and air quality modeling inventories.
18
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Table 2-11. NONROAD NMIM runs
Case
Base
Reference
Reference
Control
Control
Year
2005
2017
2030
2017
2030
Run Name
Tier3Base2005Nr
Tier3Ref2017elONr
Tier3Ref2030NrAdj2
Tier3 Ctla2017elONr
Tier3Ctl2030NrAdj2
MOVES fuels were used for future year cases and converted to NMIM format. However, for 2017, EPA
assumed that nonroad equipment would use only E10. For 2030, EPA assumed that nonroad equipment
would use only El 5. The details of the fuels conversion from MOVES to NMIM were discussed above.
Table 2-12 describes the components in the NONROAD/NMEVI system common to all Tier 3 NPRM
modeling scenarios.
Table 2-12. Summary of NONROAD modeling components
Model
NONROAD
NMIM Code
NMIM Database
Meteorology
Version
NROSb
NMIM20090504b
NCD20 10 1201 Tier3
NCD20 10 1201 Tier3
Description
This is identical to the official NONROAD2008, (see:
http ://www. epa. sov/otaq/nonrdmdl . htm#model) except
it was modified to allow modeling of emissions on El 5
fuels in 2030. The existing fuel effects algorithm was
retained.
This is the same as the official NMIM2008a software,
(available at http://www.epa.sov/otaq/nmim.htm)
except the NONROAD model was updated to NROSb.
This is based on NCD20101201, which was developed
for the 2008 NEI. It was adapted to model the desired
scenarios.
Historical data for calendar year 2005 from the
National Climatic Data Center. County temperatures
were determined by weighting nearby temperature
stations by their distance from the population-based
centroid of each county.
2.3.2 Locomotives and commercial marine vessels (alm_no_c3, seca_c3)
The year 2005 emissions for locomotive and commercial marine vessel sources used for this rule are the
same as they were for the Final Rulemaking: Greenhouse Gas Emissions Standards and Fuel Efficiency
Standards for Medium- and Heavy-Duty Engines and Vehicles signed on August 9, 2011 and available at
http://www.epa.gov/oms/climate/regulations.htm# 1 -2. and the Final Cross-State Air Pollution (CSAPR)
Rule: ftp://ftp.epa.gov/EmisInventory/2005v4_2/transportrulefinal_eitsd_28jun2011.pdf The procedures for
calculating emissions from locomotives and C1/C2 commercial marine were developed for the Locomotive
Marine Rule (2008) and are detailed in the RIA "Final Rule: Control of Emissions of Air Pollution from
Locomotives and Marine Compression-Ignition Engines Less Than 30 Liters per Cylinder", published May
6, 2008 and republished June 30, 2008, and available at:
http://www.epa.gov/oms/locomotives.htmtf2008final. The procedures used for calculating C3 commercial
marine emissions are those developed in the recent C3 "Final Rule: Control of Emissions from New Marine
Compression-Ignition Engines at or Above 30 Liters per Cylinder", published April 30, 2010 and available
at: http://www.epa.gov/oms/oceanvessels.htmtfcar-ems.
19
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2.4 2005 point sources (ptipm and ptnonipm sectors)
Point sources are sources of emissions for which geographic coordinates (e.g., latitude/longitude) are
specified, as in the case of an individual facility. A facility may have multiple emission points that 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). Note that this section
describes only NEI point sources within the contiguous United States. The offshore oil platform (othpt
sector) and category 3 CMV emissions (seca_c3 sector) are also point source formatted inventories but are
unchanged for the Tier 3 NPRM modeling from the modeling of other recent rules. Discussion of the
seca_c3 and othpt sector emissions can be found in the Final CSAPR TSD referenced in Section 2.3.2.
After removing offshore oil platforms (othpt sector), two platform sectors were created from the remaining
2005v2 NEI point sources to provide inputs into SMOKE: the EGU sector - also called the Integrated
Planning Model (IPM) sector (i.e., ptipm) and the non-EGU sector - also called the non-IPM sector (i.e.,
ptnonipm). This split facilitates the use of different SMOKE temporal processing and future-year projection
techniques for each of these sectors, along with the replacement of ptipm emissions with outputs from IPM
in emissions cases for future years. The inventory pollutants processed through SMOKE for both ptipm and
ptnonipm sectors were: CO, NOX, VOC, SO2, NH3, PMio, and PM2.5 and the following HAPs: HC1
(pollutant code = 7647010), and C12 (code = 7782505). We did not utilize BAFM from these sectors as we
chose to speciate VOC without any use (i.e., integration) of VOC HAP pollutants from the inventory.
Integration is discussed in detail in Section 3.
The ptnonipm emissions were provided to SMOKE as annual emissions. The ptipm emissions for the base
case were input to SMOKE as daily emissions. The ptipm emissions are unchanged from those in the
2005v4.2 -the basis for the Final CSAPR and Heavy Duty Greenhouse Gas (HDGHG) FRM- emission
modeling platform. However, for the ptnonipm sector for all Tier 3 NPRM scenarios, including year 2005
emissions, additional known ethanol plants were included that were not in 2005v4.2. We also removed all
onroad refueling emissions as these were replaced with MOVES-based onroad refueling emissions
(discussed in Section 2.5.2).
2.4.1 Ethanol plants (ptnonipm)
The ethanol plant information originally used in the RFS2 rule was updated. All ethanol plants were assigned
coordinates based on analysis using searches of company web sites and Google Earth verification for many
sites. Emissions were calculated based on plant design capacity and emission factors based on production
process and energy source (e.g. dry mill natural gas, wet mill coal, etc.). Finally, because benzene,
acetaldehyde and formaldehyde (BAF) emissions were directly computed for these sources, we treated these
ethanol plants as VOC integrate sources, unlike the rest of the ptnonipm sector. A summary of the ethanol
plant emissions used in the 2005 scenario is provided in
20
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Table 2-13. More details are provided in a memorandum to the docket (Cook, 2012).
21
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Table 2-13. 2005 ethanol plant emissions
Pollutant
1,3 -Butadiene
Acrolein
Formaldehyde
Benzene
Acetaldehyde
CO
NOX
PMio
PM2.5
SO2
VOC
Emissions
0.0003
10.5
13.3
5.7
314.4
7,023
8,204
10,107
3,691
9,001
10,754
2.5 2005 nonpoint sources (afdust, ag, avefire, nonpt)
The year 2005 area-source fugitive dust (afdust), agricultural animal and fertilizer NHa (ag), and average
(typical)-year fires (avefire) emissions are the same as those used in the CSAPR Final (2005v4.2) emission
modeling platform. Nonpoint sources that were not subdivided into the afdust, ag, or avefire sectors were
assigned to the "nonpt" sector, and most of these sources are also unchanged for Tier 3 NPRM modeling.
The 2005 nonpoint sources that change in this study are limited to portable fuel containers (PFCs) and
onroad refueling.
2.5.1 Portable fuel containers
Year 2005 PFC emissions are unchanged from the CSAPR Final inventory except for the addition of ethanol.
Ethanol emissions were not provided for 2005, but were supplied for future year scenarios. Therefore, we
scaled year 2017 pre-RFS2 (not Tier 3 NPRM reference case) ethanol emissions by the ratio of 2005 to 2017
pre-RFS2 VOC emissions to compute year 2005 ethanol emissions as follows:
Ethanol_2005 = Ethanol_2017(pre-RFS2) * [VOC_2005 / VOC_2017(pre-RFS2)]
2.5.2 Onroad refueling
As mentioned in Section 2.2, NEI-based onroad refueling emissions were replaced with estimates from the
revised version of EPA's Motor Vehicle Emissions Simulator (MOVES2010a) at the county level for all
twelve months. This section describes how the emission inventories for refueling from on-road vehicles in
calendar years 2005, 2017 and 2030 for Tier 3 NPRM reference and control cases were generated for air
quality modeling. The refueling inventory includes emissions from spillage loss and displacement vapor
loss. For this analysis, the refueling emissions were estimated using the revised Tier 3 version of EPA's
Motor Vehicle Emissions Simulator (MOVES2010a) at the county level for all twelve months.
In an effort to reduce MOVES runtime, the "representing counties" approach was used instead of running
every single county in the lower 48 states. As described in Section 2.2 for onroad counties, we selected
representing counties by grouping counties based on Petroleum Administration for Defense Districts
(PADD), fuel parameters, usage of California emission standards, I/M programs, and altitude. One
additional parameter included in developing the representing counties for refueling was temperature.
Temperature bins with increments often degrees F were created for the minimum and maximum
temperatures for each month using the temperatures from the 2005 calendar year values in the
22
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County YearMonthHour table of the NMIM County Database (NCD) NCD20100602 NMIM database. All
counties in each group had min and max temperatures for all twelve months in the same temperature bins.
Once counties were grouped, the representing county was chosen as the one with the highest VMT, resulting
in total of 238 counties. The same set of county groups and representing counties was used for all years and
scenarios.
MOVES was run in inventory mode for only the representing counties using the county-specific on-road
data, such as vehicle miles travelled, fleet age distribution, speed distribution, and meteorology, available
from the NCD. The customized fuel inputs, discussed in Section 2.2.2.1, were used for each of the
representing counties.
The resulting refueling emission inventories for 238 representing counties in U.S. short tons were converted
to emission factors by dividing the inventory by the corresponding activity in each representing county.
Then, the calculated emission factors from the representing counties were applied to the represented counties
and multiplied by the county-specific activity to generate the inventories for all counties.
2.6 Other sources (biogenics, othpt, othar, and othon)
All emissions from Canada, Mexico, and Offshore Drilling platforms (othpt, othar, and othon), and all non-
anthropogenic inventories (biogenics and ocean chlorine) are unchanged from the 2005v4.2 (used for the
Final CSAPR and HDGHG FRM) emissions modeling platform. The same emissions are used for all Tier 3
NPRM scenarios and years.
3 VOC Speciation Changes that Represent Fuel Changes
A significant detail that is different in each of the Tier 3 NPRM modeling cases and different from the
2005v4.2 emissions modeling is the VOC speciation profiles used to split total VOC emissions into the VOC
model species needed for CMAQ. In this section, we summarize the various speciation profile information
used in configuring the various cases.
A major change between the 2005v4.2 platform and the Tier 3 NPRM base and future modeling is the
integration of ethanol for key sectors and specific inventories. In the previous platform, the inventories for
specific sources had benzene, acetaldehyde, formaldehyde and/or methanol (BAFM). These emissions
would be integrated, namely their emissions would come from the inventory not from speciating VOC. To
prevent double counting, BAFM would be removed from VOC, leaving the remainder (NONHAPVOC) to
be speciated to other components (i.e. non-BAFM species). See section 3.1.2.1 of the 2005v4 platform for
more details ftp://ftp.epa.gov/EmisInventory/2005v4/2005_emissions_tsd_07jul2010.pdf
In the Tier 3 NPRM modeling, if ethanol was present in the inventories, it would also be integrated. To
differentiate when a source was integrating BAFM versus EBAFM (ethanol in addition to BAFM), the
speciation profiles which do not include ethanol are referred to as an "E-profile", for example pre-Tier 2
vehicles E10 gasoline exhaust speciation profile 8751 where ethanol is speciated from VOC, versus 875 IE
where ethanol is obtained directly from the inventory. For the onroad sector, ethanol is integrated for all
emissions from gasoline vehicles. For the nonpt sector, ethanol is integrated for refueling and portable fuel
containers (PFCs). In the future-year case, the nonpt sector includes a cellulosic corn ethanol and biodiesel
inventory (SCC 30125010) in which ethanol is integrated. For fuel distribution operations associated with
the bulk-plant-to-pump (btp) distribution, ethanol is speciated from VOC -thus a "BAFM" profile- because
the fuel distribution operations in the nonpoint inventories are NEI-based and therefore do not include
ethanol specifically because the NEI does not provide ethanol as a pollutant.
23
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The onroad sector has some additional changes to VOC speciation. Instead of speciating VOC directly,
SMOKE-MOVES uses TOG instead of VOC. Therefore, SMOKE does not need to convert VOC to TOG
before creating NONHAPTOG and performing additional speciation. A second change in VOC speciation is
the differentiation of a new mode. In previous platforms, onroad mobile emissions were divided into
exhaust, evaporative and refueling modes. For the Tier 3 NPRM base and future years, gasoline vehicle's
evaporative mode is further divided into permeation-specific emissions and evaporative. Similar to
evaporative and exhaust profiles, these profiles change between the base and future year cases. Additional
updates include headspace vapor speciation utilizes a combination of the E10 headspace vapor profile and EO
headspace vapor profile as opposed to using solely EO for 20055, and a new Heavy Duty Diesel vehicle
exhaust mode profile for pre-2007 model year (MY) vehicles that replaces an older 2004-vintage medium-
duty diesel profile. See Table 3-1 for more details.
The VOC speciation approach is customized to account for the impact of fuel changes in the future year
cases. These changes affect the onroad sector, the nonroad sector, and parts of the nonpt and ptnonipm
sectors. These fuel changes and vehicle changes are implemented by using different VOC profiles and
combinations of profiles between the base and future cases. The speciation changes from fuels in the nonpt
sector are for refueling, cellulosic ethanol and cellulosic diesel, portable fuel containers (PFCs), and fuel
distribution operations associated with the bulk-plant-to-pump (btp) distribution. The speciation changes
from fuels in the ptnonipm sector include btp distribution operations inventoried as point sources. Refinery
to bulk terminal (rbt) fuel distribution and bulk plant storage (bps) speciation does not change across the
modeling cases because this is considered upstream from the introduction of ethanol into the fuel. Mapping
of fuel distribution SCCs to btp and rbt emissions categories can be found in Appendix B of the Technical
Support Document (TSD) Preparation of Emissions Inventories for the Version 5.0, 2007 Emissions
Modeling Platform, http://epa.gov/ttn/chief/emch/2007v5/2007v5_TSD_Appendices_14decl2.pdf.
Note that VOC speciation is customized by using different speciation profiles in the base versus future year
cases. For some sources related to the mobile sector and fuel distribution, a combination of profiles are
specified by county, month and mode (e.g. exhaust, evaporative, permeation). SMOKE calculates a resultant
profile by calculating the fraction of each profile by month, county, and mode. The GSPRO_COMBO
ancillary file controls this feature in SMOKE. The GSPRO_COMBO file represents the county specific
mixture of fuels, for example the mixture of E10 and EO. For the nonpt sector, a further complication in
developing the GSPRO_COMBO is differentiating the sources that integrate ethanol (i.e. use E-profiles) and
those that do not integrate ethanol. By using the mode for refueling (RFL VOC) and PFC (EVP VOC),
these ethanol integrated sectors can be differentiated from btp (VOC).
Table 3-1 summarizes the different profiles utilized for the fuel-related sources in each of the sectors for
2005 and the future year cases. A comparison of the 2005v4.2 platform with the Tier 3 NPRM 2005 case is
also included. Appendix A lists ancillary input data set names used for Tier 3 NPRM emissions modeling.
1 This was an oversight in the 2005v4.2 platform corrected for this modeling effort.
24
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Table 3-1. Summary of VOC speciation profile approaches by sector across cases
Category
Onroad Gasoline
Exhaust
Evaporative
Refueling
Onroad Diesel
Exhaust
Evaporative
Refuel
Nonroad Gasoline
Exhaust
Evaporative
2005v4.2
2005 Tier 3 NPRM
2017 Tier 3 reference & control
COMBO
8750
8751
Pre-Tier 2 EO Exhaust
Pre-Tier 2 E10 Exhaust
COMBO
8753
8754
8762
EO Evap
E10 Evap
EO Headspace composite
COMBO
8750E
875 IE
Pre-Tier 2 EO Exhaust
Pre-Tier 2 E10 Exhaust
COMBO (All evap except permeation)
8753E
8754E
EO Evap
E10 Evap
COMBO (Permeation evap)
8766E
8769E
EO Evap perm
E 10 Evap perm
COMBO
8869E
8870E
EO Headspace
E10 Headspace
COMBO
875 IE
8757E
8758E
Pre-Tier 2 E10 Exhaust
Tier 2 E 10 Exhaust
Tier 2 E15 Exhaust
COMBO (All evap except permeation)
8754E
8872E
E10 Evap
E15 Evap
COMBO (Permeation evap)
8769E
8770E
E 10 Evap perm
E15 Evap perm
COMBO
8870E
8871E
E10 Headspace
E15 Headspace
4674
4547
4547
2004 MOD exhaust
Diesel Headspace
Diesel Headspace
8774
4547
4547
Pre-2007 MY HDD exhaust
Diesel Headspace
Diesel Headspace
877T3
Pre& Post 2007 MY HDD
exhaust
Weighted 8774 and 8775 profiles
4547
4547
Diesel Headspace
Diesel Headspace
COMBO
8750
8751
Pre-Tier 2 EO exhaust
Pre-Tier 2 E 10 exhaust
COMBO
8753
8754
EO evap
E10 evap
COMBO
8750
8751
Pre-Tier 2 EO exhaust
Pre-Tier 2 E 10 exhaust
COMBO
8753
8754
EO evap
E10 evap
8751
8754
Pre-Tier 2 E 10 exhaust
E10 Evap
2030 reference & control
8758E
8872E
8770E
8871E
Tier2 E15 Exhaust
E15 Evap
E15 Evap permeation
E15 Headspace
8775
877RM
877RH
2007+ MY HDD Exh
Weighted HDD Exha
Weighted HDD Exhb
a Class 6&7 HDDVs
b Class 8a&8b HDDVs
4547
4547
Diesel Headspace
Diesel Headspace
8758
8872
Tier 2 E15 exhaust
E15 Evap
25
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Category
Refueling
Nonroad Diesel
Exhaust
Evaporative
Refueling
PFC
Aircraft
Locomotives
Marine
BTP
RBT/BPS
Ethanol Plants
8762
2005v4.2
EO Headspace composite
2005 Tier 3 NPRM
COMBO
8869
8870
EO Headspace
E10 Headspace
201'
8870
1 Tier 3 reference & control
E10 Headspace
4674
4547
4547
8762
5565
4674
2480
8762
8762
1188
2004 MOD exhaust
Diesel Headspace
Diesel Headspace
EO Headspace composite
Aircraft Exhaust
2004 MOD exhaust
Ship Channel
Downwind
EO Headspace composite
EO Headspace composite
fermentation process
8774
4547
4547
Pre-2007 MY HDD exhaust
Diesel Headspace
Diesel Headspace
COMBO
8869E
8870E
5565*
EO Headspace
E10 Headspace
Aircraft Exhaust
* Updated version in SPECIATE 4.3
8774
2480
Pre-2007 MY HDD exhaust
Ship Channel Downwind
COMBO
8869
8870
8869
8776
EO Headspace
E10 Headspace
EO Headspace
Ethanol Fuel Prod
8774
4547
4547
8870E
5565*
Pre-2007 MY HDD exhaust
Diesel Headspace
Diesel Headspace
E10 Headspace
Aircraft Exhaust
* Updated version in SPECIATE 4.3
8774
2480
Pre-2007 MY HDD exhaust
Ship Channel Downwind
COMBO
8870
8871
8869
8776
8776E
E10 Headspace
E15 Headspace
EO Headspace
Ethanol Fuel Prod3
Ethanol Fuel Prodb
a corn ethanol and biodiesel ptnonipm
b cellulosic ethanol & cellulosic diesel
nonpt
2030
8871
reference & control
E15 Headspace
8774
4547
4547
8871E
5565*
Pre-2007 MY HDD
exhaust
Diesel Headspace
Diesel Headspace
E15 Headspace
Aircraft Exhaust
* Updated version in SPECIATE
4.3
8774
2480
8871
8869
8776
8776E
Pre-2007 MY HDD
exhaust
Ship Channel
Downwind
E15 Headspace
EO Headspace
Ethanol Fuel Prod3
Ethanol Fuel Prodb
a corn ethanol and biodiesel
ptnonipm
b cellulosic ethanol & cellulosic
diesel nonpt
26
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4 2017 and 2030 Reference Cases
The 2017 and 2030 Tier 3 NPRM reference cases represent emissions in the future, including emissions
impacts of the fuel volumes mandated by the 2005 EPAct and 2007 EISA and finalized in the RFS2
program. The reference cases include MSAT2 and LDGHG but do not include HDGHG impacts. The 2017
reference case assumes 21.6 billion gallons of renewable fuels (24 billion ethanol-equivalent gallons due to
volume increases of ethanol), with 17.8 billion gallons of E10 and E15, 1.5 billion gallons of biodiesel, 0.2
billion gallons of renewable diesel, and 2.2 billion gallons of cellulosic diesel. By the year 2030, the
reference case assumes 30.5 billion gallons of renewable fuels (36 billion ethanol-equivalent gallons due to
volume increases of ethanol), with 22.2 billion gallons of E15 (no E10), 1.7 billion gallons of biodiesel, 0.2
billion gallons of renewable diesel, and 6.5 billion gallons of cellulosic diesel. The fuel changes required
upstream emissions estimates and adjustments in addition to the downstream changes to onroad and nonroad
mobile source emissions. For nonroad mobile sources, and for onroad mobile including refueling sources,
OTAQ-generated emissions were provided to reflect the reference case fuels.
The 2017 and 2030 reference cases use many of the same growth and control assumptions as those for the
Final Cross-State Air Pollution Rule (CSAPR), because other than onroad mobile, nonroad mobile, onroad
refueling, PFC, and ethanol plant sources, both Tier 3 NPRM and CSAPR use the same 2005v4.2-based
emissions inventories. There are some differences between the shared projection inputs from the 2012 and
2014 base case projections in CSAPR and the 2017 and 2030 reference cases for Tier 3 NPRM:
1) 2017 includes some additional controls that were promulgated after 2014, (e.g., post-2014 consent
decrees and fuel sulfur rules in a couple of states). Likewise, 2030 includes some additional controls
promulgated after 2017.
2) Growth factors for several sources are year-specific; so while the methodology is the same as
CSAPR, the future year emissions estimates differ (e.g., oil and gas in a couple states, residential
wood combustion).
3) Onroad refueling uses year and scenario-specific (i.e., reference) MOVES emissions for all Tier 3
NPRM modeling, rather than NEI emissions.
4) There is a new dataset of ethanol plants that replaces a limited set of NEI ethanol plants in 2005v4.2-
based CSAPR 2012 and 2014 projections. These Tier 3 reference case emissions are different in
2017 and 2030 and also in the 2005 base case.
5) Minor errors identified after CSAPR modeling was complete were fixed (e.g., we include agricultural
dust projections for the couple of states that provided point source farms).
There are other new inputs unique to the Tier 3 NPRM reference cases that were not part of the CSAPR
projections. Examples of these are RFS2 upstream inputs such as biodiesel and cellulosic ethanol plants.
These new inputs and projections for Tier 3 NPRM reference cases are discussed later in this section. The
remainder of Section 4 is very similar to Section 4 in the CSAPR emissions modeling TSD, available from
ftp://ftp.epa.gov/EmisInventory/2005v4_2/transportrulefinal_eitsd_28jun2011.pdf. but with the updates just
discussed.
The future case projection methodologies vary by sector. For EGU emissions (ptipm sector), the emissions
reflect state rules and federal consent decrees through December 1, 2010. For onroad mobile sources, all
national measures for which data were available at the time of modeling have been included. The future case
scenarios reflect projected economic changes and fuel usage for EGU and mobile sectors. For nonEGU
point (ptnonipm sector) and nonpoint stationary sources (nonpt, ag, and afdust sectors), local control
27
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programs that might have been necessary for areas to attain the 1997 PM2.5 NAAQS annual standard, 2006
PM NAAQS (24-hour) standard, and the 1997 ozone NAAQS are generally not included in the future base-
case projections for most states. Exceptions are some NOx and VOC reductions associated with the New
York, Virginia, and Connecticut State Implementation Plans (SIP), which were added as part of the
comments received from the CSAPR and a larger effort to start including more local control information on
stationary non-EGU sources; this is described further in Section 4.2. The following bullets summarize the
projection methods used for sources in the various sectors, while additional details and data sources are given
in Table 4-1.
• IPM sector (ptipm): Unit-specific estimates from IPM, version 4.10.
• Non-IPM sector (ptnonipm): Projection factors and percent reductions reflect CSAPR (Transport
Rule) comments and emission reductions due to control programs, plant closures, consent decrees
and settlements, and 1997 and 2001 ozone State Implementation Plans in NY, CT, and VA. We also
used projection approaches for point-source livestock and aircraft that are consistent with projections
used for the sectors that contain the bulk of these emissions. Terminal area forecast (TAP) data
aggregated to the national level were used for aircraft to account for projected changes in
landing/takeoff activity. Year-specific speciation was applied to some portions of this sector and was
discussed in Section 3.
• Average fires sector (avefire): No growth or control.
• Agricultural sector (ag): Projection factors for livestock estimates based on expected changes in
animal population from 2005 Department of Agriculture data; no growth or control for NHs
emissions from fertilizer application.
• Area fugitive dust sector (afdust): Projection factors for dust categories related to livestock estimates
based on expected changes in animal population; no growth or control for other categories in this
sector.
• Remaining Nonpoint sector (nonpt): Projection factors that implement CSAPR Proposal comments
and reflect emission reductions due to control programs. Residential wood combustion projections
based on growth in lower-emitting stoves and a reduction in higher emitting stoves. PFC projection
factors reflecting impact of the final Mobile Source Air Toxics (MSAT2) rule and include ethanol
emissions. Gasoline stage II onroad refueling emissions obtained directly from MOVES. Oil and gas
projection estimates are provided for the non-California Western Regional Air Partnership (WRAP)
states as well as Oklahoma and Texas. Year-specific speciation was applied to some portions of this
sector and was discussed in Section 3.
• Nonroad mobile sector (nonroad): Same version of the NONROAD2008a, including same set of 293
county groups and representing counties as the 2005 base case. Future-year equipment population
estimates and control programs (final locomotive-marine and small spark ignition) to years 2017 and
2030 are included. The only differences between the Tier 3 NPRM future case runs are the fuels
used, specifically, the ratio of E10 and El5 fuels. Year-specific speciation was applied to some
portions of this sector and is discussed in Section 3.
• Locomotive, and non-Class 3 commercial marine sector (alm_no_c3): Projection factors for Class 1
and Class 2 commercial marine and locomotives which reflect CSAPR Proposal comments and
activity growth and final locomotive-marine controls.
• Class 3 commercial marine vessel sector (seca_c3): Base-year 2005 emissions grown and controlled
to 2017 and 2030, incorporating CSAPR Proposal comments and controls based on Emissions
Control Area (EGA) and International Marine Organization (IMO) global NOx and SO2 controls.
• Onroad mobile: uses a version of MOVES developed for the Tier 3 NPRM that incorporates new car
and light truck greenhouse gas emissions standards (LDGHG) affecting model years 2012 and later
28
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(published May 7, 2010). These emissions also include RFS2 fuels. VOC speciation uses different
future-year values to take into account both the increase in ethanol use, and the existence of Tier 2
vehicles that use a different speciation profile. This sector includes all non-refueling onroad mobile
emissions (exhaust, evaporative, brake wear and tire wear modes). SMOKE-MOVES was used in a
similar configuration as the 2005 base case to apportion MOVES emissions factors into hourly
gridded temperature-adjusted emissions.
• Other nonroad/nonpoint (othar): No growth or control.
• Other onroad sector (othon): No growth or control.
• Other nonroad/nonpoint (othar): No growth or control.
• Other point (othpt): No growth or control.
• Biogenic: 2005 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 2017 and 2030 reference-case emissions from the 2005v4.3 base-case inventories. EGU projections are
discussed separately in the next section. These future year reference case projections and controls are also
included in the Tier 3 NPRM control cases. All Mexico, Canada, and offshore oil emissions are unchanged
in all future cases from those in the 2005 base case.
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
Tier 3 NPRM. 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.
29
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Table 4-1. Control strategies and growth assumptions for creating the Tier 3 NPRM 2017 and 2030
reference case emissions inventories from the 2005 base case
Control Strategies and/or growth assumptions
(grouped by affected pollutants or standard and approach used to
apply to the inventory)
Pollutants
affected
Approach/
Reference
Non-EGU Point (ptnonipm sector) projection approaches
MACT rules, national, VOC: national applied by SCC, MACT
Boat Manufacturing
Wood Building Products Surface Coating
Generic MACT II: Spandex Production, Ethylene manufacture
Large Appliances
Miscellaneous Organic NESHAP (MON): Alkyd Resins, Chelating Agents, Explosives,
Phthalate Plasticizers, Polyester Resins, Polymerized Vinylidene Chloride
Reinforced Plastics
Asphalt Processing & Roofing
Iron & Steel Foundries
Metal: Can, Coil
Metal Furniture
Miscellaneous Metal Parts & Products
Municipal Solid Waste Landfills
Paper and Other Web
Plastic Parts
Plywood and Composite Wood Products
Carbon Black Production
Cyanide Chemical Manufacturing
Friction Products Manufacturing
Leather Finishing Operations
Miscellaneous Coating Manufacturing
Organic Liquids Distribution (Non-Gasoline)
Refractory Products Manufacturing
Sites Remediation
Consent decrees on companies (based on information from the Office of Enforcement
and Compliance Assurance - OECA) apportioned to plants owned/operated by the
companies
DOJ Settlements: plant SCC controls for:
Alcoa, TX
Premcor (formerly Motiva), DE
Refinery Consent Decrees: plant/SCC controls
Hazardous Waste Combustion
Municipal Waste Combustor Reductions -plant level
Hospital/Medical/Infectious Waste Incinerator Regulations
Large Municipal Waste Combustors - growth applied to specific plants
MACT rules, plant-level, VOC: Auto Plants
MACT rules, plant-level, PM & SO2: Lime Manufacturing
MACT rules, plant-level, PM: Taconite Ore
Livestock Emissions Growth from year 2002 to years 2017 and 2030 (some farms in the
point inventory)
NESHAP: Portland Cement (09/09/10) - plant level based on Industrial Sector
Integrated Solutions (ISIS) policy emissions in 2013. The ISIS results are from the
ISIS-Cement model runs for the NESHAP and NSPS analysis of July 28, 2010 and
include closures.
New York ozone SIP controls
Additional plant and unit closures provided by state, regional, and the EPA agencies and
additional consent decrees. Includes updates from CSAPR comments.
VOC
VOC, CO, NOx,
PM, S02
All
NOx, PM, SO2
PM
PM
NOX, PM, SO2
All (including Hg)
VOC
PM, S02
PM
NH3, PM
Hg, NOX, SO2,
PM, HC1
VOC, NOX,
HAP VOC
All
EPA, 2007a
1
2
o
J
4
5
EPA, 2005
5
6
7
8
9
10; EPA,
2010
11
12
30
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Control Strategies and/or growth assumptions
(grouped by affected pollutants or standard and approach used to
apply to the inventory)
Emission reductions resulting from controls put on specific boiler units (not due to
MACT) after 2005, identified through analysis of the control data gathered from the
Information Collection Request (ICR) from the Industtial/Commercial/Institutional
Boiler NESHAP.
Reciprocating Internal Combustion Engines (RICE) NESHAP
Ethanol plants that account for increased ethanol production due to RFS2 mandate
State fuel sulfur content rules for fuel oil -effective only in Maine, New Jersey, and New
York
Pollutants
affected
NOX, SO2, HC1
NOX, CO, PM,
SO2
All
SO2
Approach/
Reference
Section
4.2.13.2
13
14
15
Nonpoint (nonpt sector) projection approaches
Municipal Waste Landfills: projection factor of 0.25 applied
Livestock Emissions Growth from year 2002 to years 2017 and 2030
New York, Connecticut, and Virginia ozone SIP controls
RICE NESHAP
State fuel sulfur content rules for fuel oil -effective only in Maine, New Jersey, and New
York
Residential Wood Combustion Growth and Change-outs from year 2005 to years 2017
and 2030
Gasoline and diesel fuel Stage II refueling via MOVES2010 Tier 3 month-specific
inventories for 2017 and 2030 with assumed RFS2 and LDGHG fuels
Portable Fuel Container Mobile Source Air Toxics Rule 2 (MSAT2) inventory growth
and control from year 2005 to years 2017 and 2030
Use Phase II WRAP 2018 Oil and Gas for both 2017 and 2030
Use 2008 Oklahoma and Texas Oil and Gas, and apply year 2017 and 2021 projections
for TX (last year available used as surrogate for 2030), and RICE NESHAP controls to
Oklahoma emissions.
All
NH3, PM
voc
NOX, CO, VOC,
PM, SO2
SO2
All
VOC, Benzene,
Ethanol
VOC
VOC, SO2, NOX,
CO
VOC, SO2, NOX,
CO, PM
EPA, 2007a
9
11, 16
13
15
17
18
19
Section
4.2.14
Section
4.2.14
APPROACHES/REFERENCES- Non-EGU Stationary Sources:
1. Appendix B in the MATS Proposal TSD:
http://www.epa.gov/ttn/chief/emch/toxics/proposed toxics rule appendices.pdf
2. For Alcoa consent decree, used http:// cfpub.epa.gov/compliance/cases/index.cfm; for Motiva: used information sent by
State of Delaware
3. Used data provided by the EPA, OAQPS, Sector Policies and Programs Division (SPPD).
4. Obtained from Anne Pope, the US EPA - Hazardous Waste Incinerators criteria and hazardous air pollutant controls
carried over from 2002 Platform, v3.1.
5. Used data provided by the EPA, OAQPS SPPD expert.
6. Percent reductions and plants to receive reductions based on recommendations by rule lead engineer, and are consistent
with the reference: EPA, 2007a
7. Percent reductions recommended are determined from the existing plant estimated baselines and estimated reductions as
shown in the Federal Register Notice for the rule. SO2 percent reduction are computed by 6,147/30,783 = 20% and
PM10 and PM25 reductions are computed by 3,786/13,588 = 28%
8. Same approach as used in the 2006 Clean Air Interstate Rule (CAIR), which estimated reductions of "PM emissions by
10,538 tpy, a reduction of about 62%." Used same list of plants as were identified based on tonnage and SCC from
CAIR: http://www.envinfo.com/caain/June04updates/tiopfr2.pdf
9. Except for dairy cows and turkeys (no growth), based on animal population growth estimates from the US Department
of Agriculture (USDA) and the Food and Agriculture Policy and Research Institute. See Section 4.2.10.
10. Data files for the cement sector provided by Elineth Torres, the EPA-SPPD, from the analysis done for the Cement
NESHAP: The ISIS documentation and analysis for the cement NESHAP/NSPS is in the docket of that rulemaking-
docket # EPA-HQ-OAR-2002-005. The Cement NESHAP is in the Federal Register: September 9, 2010 (Volume 75,
31
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Number 174, Page 54969-55066
11. New York NOX and VOC reductions obtained from Appendix J in NY Department of Environmental Conservation
Implementation Plan for Ozone (February 2008): http://www.dec.ny.gov/docs/air pdf/NYMASIP7fmal.pdf.
12. Appendix D of Cross-State Air Pollution Rule:
ftp://ftp.epa.gov/EmisInventory/2005v4 2/transportrulefmal eitsd appendices 28jun2011.pdf
13. Appendix F in the Proposed (Mercury and Air) Toxics Rule TSD:
http://www.epa.gov/ttn/chief/emch/toxics/proposed toxics rule appendices.pdf
14. The 2008 data used came from Illinois' submittal of 2008 emissions to the NEI.
15. Based on available, enforceable state sulfur rules as of November, 2010:
http://www.ilta.org/LegislativeandRegulatorvMVNRLM/NEUSASulfur%20Rules 09.2010.pdf,
http://www.mainelegislature.org/legis/bills/bills 124th/biHpdfs/SP062701 .pdf,
http://switchboard.nrdc.org/blogs/rkassel/governor paterson signs new la.html,
http://green.blogs.nvtimes.com/2010/07/20/new-vork-mandates-cleaner-heating-oil/
16. VOC reductions in Connecticut and Virginia obtained from CSAPR comments.
17. Growth and Decline in woodstove types based on industry trade group data, See Section 4.2.11.
18. MOVES (2010a) results for onroad refueling including activity growth from VMT, Stage II control programs at gasoline
stations, and phase in of newer vehicles with onboard Stage II vehicle controls.
http://www.epa.gov/otaq/models/moves/index.htm
19. VOC, benzene, and ethanol emissions for 2017 and 2030 based on MSAT2 rule and ethanol fuel assumptions (EPA,
2007b)
Control Strategies and/or growth assumptions
(grouped by affected pollutants or standard and approach used to
apply to the inventory)
Pollutants
affected
Approach/
Reference
Onroad mobile and nonroad mobile controls
(list includes all key mobile control strategies but is not exhaustive)
National Onroad Rules:
Tier 2 Rule: Signature date February, 2000
2007 Onroad Heavy-Duty Rule: February, 2009
Final Mobile Source Air Toxics Rule (MSAT2): February, 2007
Renewable Fuel Standard: March, 2010
Light Duty Greenhouse Gas Rule: May, 2010
Corporate Average Fuel Economy standards for 2008-201 1
Local Onroad Programs:
National Low Emission Vehicle Program (NLEV): March, 1998
Ozone Transport Commission (OTC) LEV Program: January, 1995
National Nonroad Controls:
Clean Air Nonroad Diesel Final Rule - Tier 4: June, 2004
Control of Emissions from Nonroad Large-Spark Ignition Engines and Recreational
Engines (Marine and Land Based): "Pentathalon Rule": November, 2002
Clean Bus USA Program: October, 2007
Control of Emissions of Air Pollution from Locomotives and Marine Compression-Ignition
Engines Less than 30 Liters per Cylinder: October, 2008
Locomotive and marine rule (May 6, 2008)
Marine SI rule (October 4, 1996)
Nonroad large SI and recreational engine rule (November 8, 2002)
Nonroad SI rule (October 8, 2008)
Phase 1 nonroad SI rule (July 3, 1995)
Tier 1 nonroad diesel rule (June 17, 2004)
Aircraft (emissions are in the nonEGU point inventory):
Itinerant (ITN) operations at airports to years 2017 and 2030
all
VOC
all
all
1
2
3,4,5
6
32
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Control Strategies and/or growth assumptions
(grouped by affected pollutants or standard and approach used to
apply to the inventory)
Pollutants
affected
Approach/
Reference
Locomotives:
Energy Information Administration (EIA) fuel consumption projections for freight rail
Clean Air Nonroad Diesel Final Rule - Tier 4: June 2004
Locomotive Emissions Final Rulemaking, December 17, 1997
Locomotive rule: April 16, 2008
Control of Emissions of Air Pollution from Locomotives and Marine: May 2008
all
EPA, 2008;
3; 4; 5
Commercial Marine:
Category 3 marine diesel engines Clean Air Act and International Maritime Organization
standards (April, 30, 2010) -also includes CSAPR comments.
EIA fuel consumption projections for diesel-fueled vessels
Clean Air Nonroad Diesel Final Rule - Tier 4
Emissions Standards for Commercial Marine Diesel Engines, December 29, 1999
Locomotive and marine rule (May 6, 2008)
Tier 1 Marine Diesel Engines, February 28, 2003
all
7, 3; EPA,
2008
APPROACHES/REFERENCES - Mobile Sources
1. http://epa.gov/otaq/hwy.htm
2. Only for states submitting these inputs: http://www.epa.gov/otaq/lev-nlev.htm
3. http://www.epa.gov/otaq/nonroad-diesel.htm
4. http://www.epa.gov/cleanschoolbus/
5. http://www.epa.gov/otaq/marinesi.htm
6. Federal Aviation Administration (FAA) Terminal Area Forecast (TAP) System, January 2010:
http://www.apo.data.faa.gov/main/taf.asp
7. http://www.epa.gov/otaq/oceanvessels.htm
4.1 Stationary source projections: EGU sector (ptipm)
The future-year data for the ptipm sector used in the air quality modeling were created using version 4.10
Final of the Integrated Planning Model (IPM) (http://www.epa.gov/airmarkt/progsregs/epa-ipm/index.htmn.
The IPM is a multiregional, dynamic, deterministic linear programming model of the U.S. electric power
sector. Version 4.10 Final reflects federal and state rules and binding, enforceable consent decrees through
December of 2010. The 2017 IPM emissions reflect the CSAPR as finalized in July 2011; the 2030 IPM
emissions reflected the CSAPR proposal6. Both the reference and control case emissions do not reflect the
final Mercury and Air Toxics (MATS) rule or the Boiler MACT regulatory assumptions. Therefore, the
same year-specific IPM emissions are used in the reference and control cases for Tier 3 NPRM.
Version 4.10 Final reflects state rules and consent decrees through December 1, 2010, information obtained
from the 2010 Information Collection Request (ICR), and information from comments received on the IPM-
related Notice of Data Availability (NOD A) published on September 1, 2010. Notably, IPM 4.10 Final
included the addition of over 20 GW of existing Activated Carbon Injection (ACI) for coal-fired EGUs
reported to EPA via the ICR. Additional unit-level updates that identified existing pollution controls (such
as scrubbers) were also made based on the ICR and on comments from the IPM NOD A. Units with SO2 or
NOx advanced controls (e.g., scrubber, SCR) that were not required to run for compliance with Title IV,
New Source Review (NSR), state settlements, or state-specific rules were modeled by IPM to either operate
those controls or not based on economic efficiency parameters. The IPM run for the reference (and control)
cases modeled with CMAQ assumed that 100% of the HC1 found in the coal was emitted into the
6 The intention was to use the final CSAPR in the 2030 scenarios. Although this was an oversight, subsequent analysis showed
that the differences in NOx between proposal and final would have a minimal impact on AQ results.
33
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atmosphere. However, in the final IPM results for the rule, neutralization of 75% of the available HC1 was
included based on recent findings.
Further details on the reference case EGU emissions inventory used for this rule can be found in the IPM
v.4.10 Documentation, available at http://www.epa.gov/airmarkets/progsregs/epa-ipm/transport.html. The
reference cases modeled in IPM for this rule includes estimates of emissions reductions that will result from
the Cross-State Air Pollution Rule (CSAPR). However, reductions from the Boiler MACT rule were not
represented this modeling because the rule was stayed at the time the modeling was performed. A complete
list of state regulations, NSR settlements, and state settlements included in the IPM modeling is given in
Appendices 3-2, 3-3, and 3-4 beginning on p. 68 of http://www.epa.gov/airmarkets/progsregs/epa-
ipm/CSAPR/docs/DocSuppv410_FTransport.pdf. For the 2017 reference (and control) case EGU emissions,
the IPM outputs for 2020, which are also representative of the year 2017, were used. These emissions were
very similar to the year 2015 emissions output from the same IPM modeling case. For the 2030 cases, IPM
outputs were year 2030.
Directly emitted PM emissions (i.e., PM2.5 and PMio) from the EGU sector are computed via a post
processing routine which applies emission factors to the IPM-estimated fuel throughput based on fuel,
configuration and controls to compute the filterable and condensable components of PM. This methodology
is documented in the IPM CSAPR TSD.
4.2 Stationary source projections: non-EGU sectors (ptnonipm, nonpt, ag,
afdust)
To project U.S. stationary sources other than the ptipm sector, we applied growth factors and/or controls to
certain categories within the ptnonipm, nonpt, ag and afdust platform sectors. This subsection provides
details on the data and projection methods used for these sectors. The Tier 3 NPRM future year scenarios
also required obtaining and preprocessing numerous other inputs that we received directly from OTAQ.
In estimating future-year emissions, we 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. 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, 2006).
The starting point for projecting the 2005 Tier 3 NPRM emissions was to use similar emission projection
methodologies as used for the 2005v4.2 platform for the Final CSAPR, which incorporated responses to
public comments on the modeling inventories. The 2012 and 2014 projection factors developed for the
CSAPR (see http://www.epa.gov/ttn/chief/emch/index.html#finan were updated to reflect years 2017 and
2030.
Year-specific projection factors for years 2017 and 2030 were created. Growth factors (and control factors)
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., gasoline
distribution varies by state and pollutant and has hundreds of records).
Table 4-2 lists the stationary non-EGU inputs and projection factors that were applied to account for year
2017 and 2030 RFS2 mandate impacts on emissions to the reference and control cases. These inputs are
discussed in more detail in Section 4.2.1 through Section 4.2.9. All other stationary non-EGU projections,
controls and plant closure information not related to the RFS2 impacts are discussed in Section 4.2.10
through Section 4.2.14. With the exception of onroad refueling emissions, all other stationary non-EGU
emissions in the 2017 and 2030 reference cases are unchanged in the 2017 and 2030 control cases (see
34
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Section 5); therefore, with the exception of onroad refueling, we will simply note that these emissions are
"year 2017" or "year 2030" rather than the more cumbersome "year 2017 reference case" and "year 2030
reference case", respectively.
Table 4-2. Tier 3 NPRM reference case stationary non-EGU source-related projection methods
Input
Corn ethanol plants
Biodiesel plants
Cellulosic fuel
production
Ethanol transport
and distribution
Portable Fuel
Containers (PFCs)
Onroad refueling
Refinery
adjustments
Ethanol transport
gasoline & ethanol
blends
Upstream
agricultural
adjustments
Type
SMOKE ORL file that
replaces 2005 base case
ORL file
SMOKE ORL file
SMOKE ORL file
SMOKE ORL file
SMOKE ORL
SMOKE ORL file
Projection factors
Projection factors
Projection factors
Sector(s)
Ptnonipm
Ptnonipm
Nonpt
Nonpt
Nonpt
nonpt
Ptnonipm
nonpt,
ptnonipm
afdust, ag,
nonpt,
ptnonipm
Description
Based on RFS2 analysis and production
volumes. Point source format.
Accounts for facilities with current production
capacities, to support RFS2 biodiesel
production. Point source format.
Accounts for cellulosic ethanol and cellulosic
diesel to support RFS2 cellulosic production.
County-level (nonpoint) format.
Accounts for ethanol vapor losses and
spillage at any point in the transport and
distribution chain. County-level (nonpoint)
format.
NONROAD-model based emissions from
PFCs, including vapor displacement, tank
permeation, and diurnal evaporation. County-
level (nonpoint) format.
MOVES-based gasoline and diesel fuel
spillage and displacement vapor losses.
County-level (nonpoint) format, monthly
resolution. This is the only non-EGU
component that has different emissions in the
Tier 3 control cases compared to the
reference cases.
Accounts for changes in various refinery
processes due to incorporation of RFS2 fuels.
Accounts for RFS impacts on emissions from
bulk plant storage, refinery to bulk terminal,
and bulk terminal to pump.
Accounts for changes in ag burning/dust,
fertilizer application/production, livestock
dust/waste and pesticide
application/production.
4.2.1 Ethanol plants (ptnonipm)
As discussed in Section 2.4.1, we replaced all corn ethanol plants that OTAQ had supplied from the RFS2
rule -see Section 2.1.2 in the CSAPR Final TSD- with those more recently compiled for the 2005 Tier 3
NPRM. Additional ethanol plants cited for development in support of increased ethanol production for
RFS2 are the cause for the increased number of facilities and emissions in years 2017 and 2030. Table 4-3
provides the summaries for the corn ethanol plants in the 2017 and 2030 cases.
35
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Table 4-3. 2017 and 2030 corn ethanol plant emissions [tons]
Pollutant
1,3 -Butadiene
Acrolein
Formaldehyde
Benzene
Acetaldehyde
CO
NOX
PMio
PM2.5
S02
VOC
2017
0.0011
41.7
45.2
20.3
643.2
14,847
20,035
21,639
6,825
11,299
35,459
2030
0.0003
10.5
13.3
5.7
314.4
7,023
7,396
10,107
3,691
9,001
10,754
4.2.2 Biodiesel plants (ptnonipm)
OTAQ developed an inventory of biodiesel plants for 2017 and 2030 that were sited at existing plant
locations in support of producing biodiesel fuels for the RFS2 mandate. The RFS2 rule estimated 1.45
billion gallons per year (Bgal) of biodiesel fuel production by year 2017 and 1.67 Bgal by year 2030. Only
plants with current production capacities were assumed to be operating in 2017 and 2030. Total plant
capacity at these existing facilities is limited to just over 1 Bgal. There was no attempt to site future year
plants to account for the need to match biodiesel production needed for RFS2. Therefore, OTAQ applied
scalar adjustments to each individual biodiesel plant to match the 2017 and 2030 production targets of 1.45
Bgal and 1.67 Bgal respectively: 1.41 for 2017 and 1.63 for 2030. Once facility-level production capacities
were scaled, emission factors were applied based on soybean oil feedstock. Inventories were modeled as
point sources with Google Earth and web searching validating facility coordinates and correcting state-
county FIPS. Table 4-4 provides the 2017 and 2030 biodiesel plant emissions estimates.
Table 4-4. 2017 and 2030 biodiesel plant emissions [tons]
Pollutant
Acrolein
Formaldehyde
Benzene
Acetaldehyde
CO
NOX
PMio
PM2.5
SO2
VOC
2017
3.09E-04
2.23E-03
4.71E-05
3.59E-04
726
1,171
99
99
9
64
2030
3.56E-04
2.56E-03
5.42E-05
4.14E-04
836
1,349
114
114
10
73
4.2.3 Portable fuel containers (nonpt)
OTAQ provided year 2017 and 2030 PFC emissions that include estimated Reid Vapor Pressure (RVP) and
oxygenate impacts on VOC emissions, and more importantly, large increases in ethanol emissions from
RFS2. Existing inventories were adjusted to account for impacts of RVP and ethanol from RFS2, using
36
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adjustment factors derived from modeling done with the NONROAD2008b model. NONROAD was run at
the national level using average nationwide fuel parameters for an all EO case, the low ethanol base case, and
the reference case. The percent change in refueling emissions from gasoline equipment was used to adjust
the vapor displacement emissions, the percent change in tank permeation was used for PFC permeation, and
the percent change in diurnal emissions was used for evaporation. Because these PFC inventories contain
ethanol, we developed a VOC E-profile that integrated ethanol, see Section 3 for more details. Emissions for
2017 and 2030 are provided in Table 4-5.
Table 4-5. PFC emissions for 2017 and 2030 [tons]
Pollutant
VOC
Benzene
Ethanol
2017
123,186
1,368
11,565
2030
146,593
1,622
31,632
4.2.4 Cellulosic fuel production (nonpt)
OTAQ developed county-level inventories for cellulosic diesel and cellulosic ethanol production for 2017
and 2030 to satisfy RFS2 production. The methodology for building cellulosic plant emissions inventories is
fairly similar conceptually to that for building the biodiesel plant inventories. First, we assume that
cellulosic diesel and cellulosic ethanol are produced in the same counties where current production capacity
exists, based on RFS2 FRM inventories. Design capacities for 2022 used in the RFS2 rule air quality
modeling were adjusted to account for differences with estimated volumes of cellulosic fuels produced for
2017 and 2030, using final RFS2 rule data. Since the final RFS2 rule assumed about 57 percent of cellulosic
fuel nationwide was cellulosic diesel, with the remainder cellulosic ethanol, we assumed this split would
apply to every plant. In reality, however, depending on available feedstocks, plants are likely to produce one
fuel or the other. Emission factors were applied based on an assumed natural gas combustion process.
Table 4-6 provides the year 2017 and 2030 cellulosic plant emissions estimates.
Table 4-6. 2017 and 2030 cellulosic plant emissions [tons]
Pollutant
Acrolein
Formaldehyde
Benzene
Acetaldehyde
CO
Ethanol
NH3
NOX
PMio
PM2.5
S02
VOC
2017
21
58
27
786
42,839
1,875
0.5
64,062
7,533
3,796
4,973
5,336
2030
61
168
79
2,286
124,336
5,530
1.6
185,745
21,862
10,986
14,475
15,489
We had no refined information on potential VOC speciation differences between cellulosic diesel and
cellulosic ethanol sources. Therefore, we summed up cellulosic diesel and cellulosic ethanol sources and
used the same SCC (30125010: Industrial Chemical Manufacturing, Ethanol by Fermentation production) for
VOC speciation as was used for corn ethanol plants. However, these cellulosic inventories contain ethanol;
therefore we developed a VOC E-profile that integrated ethanol, see Section 3 for more details.
37
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4.2.5 Ethanol transport and distribution (nonpt)
OTAQ developed county-level inventories for vapor losses from ethanol transport and distribution for 2017
and 2030 to account for losses for the processes such as truck, rail and waterways loading/unloading and
intermodal transfers such as highway-to-rail, highways-to-waterways, and all other possible combinations of
transfers. Emission rates were applied based on June 2008 AP-42 factors and ethanol versus gasoline vapor
mass equations. These emissions are entirely evaporative and therefore limited to VOC and are summarized
in Table 4-7. The leading descriptions are "Industrial Processes; Food and Agriculture; Ethanol Production"
for each SCC.
Table 4-7. 2017 and 2030 VOC losses (Emissions) due to ethanol transport and distribution [tons]
SCC
30205031
30205052
30205053
Description
Denatured Ethanol Storage Working Loss
Ethanol Loadout to Truck
Ethanol Loadout to Railcar
2017
27,763
19,069
9,610
2030
34,642
23,794
11,991
4.2.6 Onroad refueling (nonpt)
As discussed in Section 2.5.2, the refueling inventory includes gasoline and diesel fuel emissions from
spillage loss and displacement vapor loss. For this analysis, the refueling emissions were estimated using the
revised version of EPA's Motor Vehicle Emissions Simulator (MOVES2010a) at the county level for all
twelve months. The same set of representative counties and temperatures were used for all Tier 3 NPRM
scenarios. VMT, fleet age distribution and speed distribution were developed for 2017 and 2030. Because
these refueling inventories contain ethanol, we developed a VOC E-profile that integrated ethanol, see
Section 3 for more details. A summary of the 2017 and 2030 onroad mobile refueling emissions is provided
in Table 4-8. As discussed earlier in Section 4.2 and later in Section 5, these are the only stationary
emissions components of the Tier 3 NPRM inventories that are (very slightly) changed in the Tier 3 control
cases.
Table 4-8. Tier 3 NPRM Reference case onroad gasoline and diesel refueling emissions [tons]
Fuel Type
Gasoline
Diesel
Gasoline
Gasoline
Pollutant
VOC
VOC
Benzene
Ethanol
2017
63,759
12,962
161
8,735
2030
40,781
16,449
91
7,253
4.2.7 Refinery adjustments (ptnonipm)
Refinery emissions were adjusted for changes in fuels due to the RFS2 mandate. These adjustments were
provided by OTAQ and impact processes such as process heaters, catalytic cracking units, blowdown
systems, wastewater treatment, condensers, cooling towers, flares and fugitive emissions. The impact of the
RFS2-based reductions is shown in
38
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Table 4-9.
39
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Table 4-9. Impact of refinery adjustments on 2017 and 2030 emissions [tons]
Pollutant
CO
NOX
PMio
PM2.5
S02
VOC
Reductions 2017
12,674
20,183
4,367
2,525
13,846
3,693
Reductions 2030
13,602
34,850
7,550
4,365
24,014
6,428
4.2.8 Ethanol transport gasoline and blends (ptnonipm, nonpt)
Emissions changes in the transport of changing fuels from the RFS2 mandate impact several processes
including bulk plant storage (BPS), refinery to bulk terminal (RBT) and bulk terminal to pump (BTP). These
impacts, provided by OTAQ, result in approximately 15,000 tons of VOC reductions in 2017 and 46,000
tons in 2030 for these processes.
4.2.9 Upstream agricultural adjustments (afdust, ag, nonpt, ptnonipm)
Changes in domestic biofuel volumes, resulting from the RFS2 fuels mandate, impact upstream agricultural-
related source categories in several emissions modeling sectors. These source categories include fertilizer
application, pesticide application and livestock waste (NHs only), agricultural tilling, unloading and livestock
dust (PM only) and fertilizer production mixing and blending, pesticide production and agricultural burning
(all pollutants). As seen in Table 4-10, the cumulative impact of these source-specific changes is a net
increase in emissions for upstream agricultural sources.
Table 4-10. Upstream agricultural emission increases due to RFS2 fuels in 2017 and 2030 [tons]
Pollutant
CO
NH3
NOX
PMio
PM2.5
SO2
VOC
Increases 2017
302
45,272
363
42,934
6,500
69
16
Increases 2030
416
61,793
500
59,004
8,972
95
23
4.2.10 Livestock emissions growth (ag, afdust)
Growth in ammonia (NHs) and dust (PMio and PM2.5) emissions from livestock in the ag, afdust and
ptnonipm sectors was based on projections of growth in animal population.
40
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Table 4-11 provides the growth factors from the 2005 base-case emissions to year 2017 and 2030 scenarios
for animal categories applied to the ag, afdust, and ptnonipm sectors for livestock-related SCCs.
41
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Table 4-11. Growth factors from year 2005 to 2017 and 2030 for animal operations
Animal Category
Dairy Cow
Beef
Pork
Broilers
Turkeys
Layers
Poultry Average
Overall Average
2017
1.0000
1.0206
1.0893
1.3442
1.0000
1.2406
1.2674
1.0935
2030
1.0000
1.0385
1.1666
1.6426
1.0000
1.4491
1.4991
1.1745
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 (USD A) and the Food and
Agriculture Policy and Research Institute (FAPRI). For dairy cows and turkeys, we assumed that there
would be no growth in emissions. 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. Thus, we do not believe that production forecasts provide representative
estimates of the future number of cows and turkeys; therefore, we did not use these forecasts for estimating
future-year emissions from these animals. In particular, the dairy cow population is projected to decrease in
the future as it has for the past few decades; however, milk production will be increasing over the same
period. Note that the ammonia emissions from dairies are not directly related to animal population but also
nitrogen excretion. With the cow numbers going down and the production going up we suspect the excretion
value will be changing, but we assumed no change because we did not have a quantitative estimate.
The inventory for livestock emissions used 2002 emissions values therefore, our projection method projected
from 2002 rather than from 2005.
Appendix E in the 2002v3 platform documentation provides the animal population data and regression
curves used to derive the growth factors:
http://www.epa.gov/scramOOl/reports/Emissions%20TSD%20Vol2_Appendices_01 -15-08.pdf Appendix F
in the same document provides the cross references of livestock sources in the ag, afdust and ptnonipm
sectors to the animal categories in
42
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Table 4-11
43
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Table 4-11.
4.2.11 Residential wood combustion growth (nonpt)
We projected residential wood combustion (RWC) emissions based on the expected increase in the number
of low-emitting wood stoves and the corresponding decrease in other types of wood stoves. As newer,
cleaner woodstoves replace older, higher-polluting wood stoves, there will be an overall reduction of the
emissions from these sources. The approach cited here was developed as part of a modeling exercise to
estimate the expected benefits of the woodstoves change-out program (http ://www. epa. gov/burnwise).
Details of this approach can be found in Section 2.3.3 of the PM NAAQS Regulatory Impact Analysis (EPA,
2006).
The specific assumptions we made were:
• Fireplaces, source category code (SCC)=2104008001: increase 1%/year
• Old woodstoves, SCC=2104008002, 2104008010, or 2104008051: decrease 2%/year
• New woodstoves, SCC=2104008003, 2104008004, 2104008030, 2104008050, 2104008052 or
2104008053: increase 2%/year
For the general woodstoves and fireplaces category (SCC 2104008000) we computed a weighted average
distribution based on 19.4% fireplaces, 71.6% old woodstoves, 9.1% new woodstoves using 2002v3
Platform missions for PM2.5. These fractions are based on the fraction of emissions from these processes in
the states that did not have the "general woodstoves and fireplaces" SCC in the 2002v3 NEI. This approach
results in an overall decrease of 1.056% per year for this source category.
We discovered an interpolation error in the year 2017 projection factors for RWC after air quality modeling.
Table 4-12 presents the projection factors used to project the 2005 base case (2002 emissions) for RWC,
including these 2017 errors. Table 4-13 shows the national impact (tons) of the 2017 projection factor error.
Table 4-12. Projection factors for growing year 2005 residential wood combustion sources
SCC
2104008000
2104008001
2104008070
2104008002
2104008010
2104008051
2104008003
2104008004
2104008030
2104008050
2104008052
2104008053
SCC Description
Total: Woodstoves and Fireplaces
Fireplaces: General
Outdoor Wood Burning Equipment
Fireplaces: Insert; non-EPA certified
Woodstoves: General
Non-catalytic Woodstoves: Non-EPA certified
Fireplaces: Insert; EPA certified; non-catalytic
Fireplaces: Insert; EPA certified; catalytic
Catalytic Woodstoves: General
Non-catalytic Woodstoves: EPA certified
Non-catalytic Woodstoves: Low Emitting
Non-catalytic Woodstoves: Pellet Fired
Erroneous
2017
0.45
0.65
0.36
0.74
Correct
2017
0.84
1.15
0.70
1.30
2030
0.70
1.28
0.44
1.56
Table 4-13. Impact of year 2017 projection factor error on residential wood combustion estimates
Pollutant
NOX
2005
Emissions
38,292
Erroneous 2017
Emissions
18,023
Erroneous 2017
Reductions
20,270
Correct 2017
Emissions
33,545
Correct 2017
Reductions
4,747
44
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PM2.5
S02
VOC
381,362
5,302
569,950
174,769
2,529
242,126
206,593
2,773
327,824
326,706
4,697
450,990
54,656
605
118,959
4.2.12 Aircraft growth (ptnonipm)
The 2005 point-source emissions for aircraft are projected to future years by applying activity growth using
data on itinerant (ITN) operations at airports. The ITN operations are defined as aircraft take-offs whereby
the aircraft leaves the airport vicinity and lands at another airport, or aircraft landings whereby the aircraft
has arrived from outside the airport vicinity. We used projected ITN information available from the Federal
Aviation Administration's (FAA) Terminal Area Forecast (TAP) System:
http://www.apo.data.faa.gov/main/taf.asp (publication date January 2010). This information is available for
approximately 3,300 individual airports, for all years up to 2030. We aggregated and applied this
information at the national level by summing the airport-specific (U.S. airports only) ITN operations to
national totals by year and by aircraft operation, for each of the four available operation types: commercial,
general, air taxi and military. We computed growth factors for each operation type by dividing future-year
ITN by 2005-year ITN. We assigned factors to inventory SCCs based on the operation type.
The methods that the FAA used for developing the ITN data in the TAP are documented in:
http://www.faa.gov/data_research/aviation/aerospace_forecasts/2009-
2025/media/2009%20Forecast%20Doc.pdf
Table 4-14 provides the national growth factors for aircraft; all factors are applied to year 2005 emissions.
For example, year 2017 commercial aircraft emissions are 12.88% higher than year 2005 emissions. The
same aircraft factors were used for each of the year-specific scenarios: reference and control.
Table 4-14. Factors used to project 2005 base-case aircraft emissions to 2017 and 2030
sec
2275001000
2275020000
2275050000
2275060000
27501015
27502001
27502011
27505001
27505011
27601014
27601015
SCC Description
Military aircraft
Commercial aircraft
General aviation
Air taxi
Internal Combustion Engines;Fixed Wing Aircraft L &
TO Exhaust;Military;Jet Engine: JP-5
Internal Combustion Engines;Fixed Wing Aircraft L &
TO Exhaust;Commercial;Piston Engine: Aviation Gas
Internal Combustion Engines;Fixed Wing Aircraft L &
TO Exhaust;Commercial;Jet Engine: Jet A
Internal Combustion Engines;Fixed Wing Aircraft L &
TO Exhaust;Civil;Piston Engine: Aviation Gas
Internal Combustion Engines;Fixed Wing Aircraft L &
TO Exhaust;Civil;Jet Engine: Jet A
Internal Combustion Engines;Rotary Wing Aircraft L &
TO Exhaust;Military;Jet Engine: JP-4
Internal Combustion Engines;Rotary Wing Aircraft L &
TO Exhaust;Military;Jet Engine: JP-5
2017
1.0229
1.1288
0.8918
0.8620
1.0229
1.1288
1.1288
0.8918
0.8918
1.0229
1.0229
2030
1.0275
1.5059
0.9916
1.0259
1.0275
1.5059
1.5059
0.9916
0.9916
1.0275
1.0275
We did not apply growth factors to any point sources with SCC 27602011 (Internal Combustion Engines;
Rotary Wing Aircraft L & TO Exhaust; Commercial; Jet Engine: Jet A) because the facility names
associated with these point sources appeared to represent industrial facilities rather than airports. This SCC
is only in one county, Santa Barbara, California (State/County FIPS 06083).
45
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None of our aircraft emission projections account for any control programs. We considered the NOx
standard adopted by the International Civil Aviation Organization's (ICAO) Committee on Aviation
Environmental Protection (CAEP) in February 2004, which is expected to reduce NOx by approximately 2%
in 2015 and 3% in 2020. However, this rule, signed July 2011 (see http://www.epa.gov/otaq/aviation.htm),
was not adopted as an EPA (or U.S.) rule prior to Tier 3 NPRM modeling; therefore, the effects of this rule
were not included in the future-year emissions projections.
4.2.13 Stationary source control programs, consent decrees & settlements, and
plant closures (ptnonipm, nonpt)
We applied emissions reduction factors to the 2005 emissions for particular sources in the ptnonipm and
nonpt sectors to reflect the impact of stationary-source control programs including consent decrees,
settlements, and plant closures. Some of the controls described in this section were obtained from comments
on the CSAPR proposal. Detailed summaries of the impacts of the control programs are provided in
Appendix D of the CSAPR TSD:
ftp://ftp.epa.gov/EmisInventory/2005v4_2/transportrulefinal_eitsd_appendices_28jun2011.pdf.
Controls from the NOx SIP call were assumed to have been implemented by 2005 and captured in the 2005
base case (2005v2 point inventory). This assumption was confirmed by review of the 2005 NEI that showed
reductions from Large Boiler/Turbines and Large Internal Combustion Engines in the Northeast states
covered by the NOx SIP call. The future-year base controls consist of the following:
• We did not include MACT rules where compliance dates were prior to 2005, because we assumed
these were already reflected in the 2005 inventory. The EPA OAQPS Sector Policies and Programs
Division (SPPD) provided all controls information related to the MACT rules, and this information is
as consistent as possible with the preamble emissions reduction percentages for these rules.
• Various emissions reductions from the CSAPR comments, including but not limited to: fuel
switching at units, shutdowns, future-year emission limits, ozone SIP VOC controls for some sources
in Virginia and Connecticut, and state and local control programs were included.
• Evolutionary information regarding plant closures (i.e., emissions were zeroed out for future years)
was also included where information indicated that the plant was actually closed after the 2005 base
year and prior to CSAPR and Tier 3 NPRM modeling that began in the spring of 2011. We also
applied unit and plant closures received from the CSAPR comments. However, plants projected to
close in the future (post-2010) were not removed in the future years because these projections can be
inaccurate due to economic improvements. We also applied cement kiln (unit) and cement plant
closures discussed later in Section 4.2.6.1. More detailed information on the overall state-level
impacts of all control programs and projection datasets, including units and plants closed in the 2017
and 2030 reference (same in the control) case ptnonipm inventories are provided in Appendix D of
the Final CSAPR TSD:
ftp://ftp.epa.gov/EmisInventory/2005v4_2/transportrulefinal_eitsd_appendices_28jun2011.pdf The
magnitude of all unit and plant closures on the non-EGU point (ptnonipm) sector 2005 base-case
emissions is shown in Table 4-15 below. These same reductions are seen in all Tier 3 NPRM future
year scenarios.
Table 4-15. Summary of non-EGU emission reductions applied to the 2005 inventory due to unit and plant
closures
Reductions
CO
125,162
NH3
636
NOX
109,237
PM10
21,143
PM25
12,600
SO2
190,734
VOC
26,750
46
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• In addition to plant closures, we included the effects of the Department of Justice Settlements and
Consent Decrees on the non-EGU (ptnonipm) sector emissions. We also included estimated impacts
of HAP standards per Section 112, 129 of the Clean Air Act on the non-EGU (ptnonipm) and
nonpoint (nonpt) sector emissions, based on expected CAP co-benefits to sources in these sectors.
• Numerous controls have compliance dates beyond 2008; these include refinery and the Office of
Compliance and Enforcement (OECA) consent decrees, Department of Justice (DOJ) settlements, as
well as most national VOC MACT controls. Additional OECA consent decree information is
provided in Appendix B of the Proposed Toxics Rule TSD:
http://www.epa.gov/ttn/chief/emch/toxics/proposed_toxics_rule_appendices.pdf. The detailed data
used are available at the website listed in Section 1.
• Refinery consent decree controls at the facility and SCC level (collected through internal
coordination on refineries by the EPA) were included.
• Fuel sulfur fuel limits were enforceable for Maine, New Jersey and New York.
• Criteria air pollutant (cap) reductions which are a cobenefit to RICE NESHAP controls, including
SO2 RICE cobenefit controls, were included.
• We applied New York State Implementation Plan available controls for the 1997 8-hour Ozone
standard for non-EGU point and nonpoint NOx and VOC sources based on NY State Department of
Environmental Conservation February 2008 guidance. These reductions are found in Appendix J in:
http://www.dec.ny.gov/docs/airj)df/NYMASIP7fmal.pdf (see Section 3.2.6 in the CSAPR TSD:
ftp://ftp.epa.gov/EmisInventory/2005v4_2/transportrulefinal_eitsd_28jun2011.pdf.)
Most of the control programs were applied as replacement controls, which means that any existing percent
reductions ("baseline control efficiency") reported in the NEI were removed prior to the addition of the
percent reductions due to these control programs. Exceptions to replacement controls are "additional"
controls, which ensure that the controlled emissions match desired reductions regardless of the baseline
control efficiencies in the NEI. We used the "additional controls" approach for many permit limits,
settlements and consent decrees where specific plant and multiple-plant-level reductions/targets were desired
and at municipal waste landfills where VOC was reduced 75% via a MACT control using projection factors
ofO.25.
4.2.13.1 Reductions from the Portland Cement NESHAP (ptnonipm)
As indicated in Table 4-1, the Industrial Sectors Integrated Solutions (ISIS) model (EPA, 2010) was used to
project the cement industry component of the ptnonipm emissions modeling sector to 2013. There were no
future year estimates for 2017 or 2030, so 2013 estimates were used for all future year Tier 3 NPRM
modeling scenarios. This approach provided reductions of criteria and hazardous air pollutants, including
mercury. The ISIS cement emissions were developed in support for the Portland Cement NESHAPs and the
NSPS for the Portland cement manufacturing industry.
The ISIS model produced a Portland Cement NESHAP policy case of multi-pollutant emissions for
individual cement kilns (emission inventory units) that were relevant for years 2013 through 2017. These
ISIS-based emissions included information on new cement kilns, facility and unit-level closures, and updated
policy case emissions at existing cement kilns. The units that opened or closed before 2010 were included in
the projections as were the ISIS-based policy case predictions of emissions reductions and activity growth.
The ISIS model results for the future show a continuation of the recent trend in the cement sector of the
replacement of lower capacity, inefficient wet and long dry kilns with bigger and more efficient preheater
and precalciner kilns. Multiple regulatory requirements such as the NESHAP and NSPS currently apply to
47
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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.
48
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Table 4-16 shows the magnitude of the ISIS-based cement industry reductions in the future-year emissions,
and the impact that these reductions have on total stationary non-EGU point source (ptnonipm) emissions.
49
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Table 4-16. Future-year ISIS-based cement industry annual reductions [tons/yr] for the non-EGU
(ptnonipm) sector
Pollutant
NOX
PM2.5
SO2
VOC
HC1
Cement Industry
emissions in 2005
193,000
14,400
128,400
6,900
2,900
Reductions in
2017 & 2030
56,740
7,840
106,000
5,570
2,220
Percent
Reduction
2.4%
1.8%
5.0%
0.4%
4.5%
4.2.13.2 Boiler reductions not associated with the MACT rule (ptnonipm)
The Boiler MACT ICR collected data on existing controls. We used an early version of a database
developed for that rulemaking entitled "survey_database_2008_results2.mdb" (EPA-HQ-OAR-2002-0058-
0788) which is posted under the Technical Information for the Boiler MACT major source rule
(http://www.epa.gov/ttn/atw/boiler/boilerpg.html). We extracted all non-EGU stationary (ptnonipm)
controls that were installed after 2005, determined a percent reduction, and verified with source owners that
these controls were actively in use. In many situations we learned that the controls were on site but were not
in use. A summary of the plant-unit specific reductions that were verified to be actively in use are
summarized in Table 4-17. All reductions are promulgated by the present day, and therefore these
reductions are the same for all Tier 3 NPRM future year scenarios.
Table 4-17. State-level non-MACT boiler reductions from ICR data gathering [tons]
State
Michigan
North Carolina
Virginia
Washington
North Carolina
Pollutant
NOX
SO2
S02
S02
HC1
Pre-controlled
Emissions
907
652
3,379
639
31
Controlled
Emissions
544
65
338
383
3
Reductions in
2017 & 2030
363
587
3,041
256
28
Percent
Reduction
40
90
90
40
90
4.2.13.3 RICE NESHAP (ptnonipm and nonpt)
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 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 2017 and 2030 emissions projections.
The rules can be found at http://www.epa.gov/ttn/atw/rice/ricepg.html and are listed below:
• National Emission Standards for Hazardous Air Pollutants for Reciprocating Internal Combustion
Engines; Final Rule (69 FR 33473) published 06/15/04
• National Emission Standards for Hazardous Air Pollutants for Reciprocating Internal Combustion
Engines; Final Rule (FR 9648 ) published 03/03/10
• National Emission Standards for Hazardous Air Pollutants for Reciprocating Internal Combustion
Engines; Final Rule (75 FR 51570) published 08/20/2010
50
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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 (HP). In addition,
they have different compliance dates. We project CAPs from the 2005 NEI RICE sources, based on the
requirements of the rule for existing sources only because the inventory includes only existing sources and
the current projection approach does not estimate emissions from new sources.
A complete discussion on the methodology to estimate RICE controls is provided in Appendix F in the
Proposed MATS Rule TSD:
http://www.epa.gov/ttn/chief/emch/toxics/proposed_toxics_rule_appendices.pdf. Impacts of the RICE
controls on stationary non-EGU emissions (nonpt and ptnonipm sectors), excluding WRAP, Texas, and
Oklahoma oil and gas emissions (see Section 4.2.7) are provided in Table 4-18. These reductions are
promulgated before year 2017, and therefore these reductions are the same for all Tier 3 NPRM future year
scenarios.
Table 4-18. National impact of RICE controls on non-EGU projections
Reductions
CO
116,434
NOX
111,749
PM10
1,595
PM25
1,368
SO2
21,957
voc
14,669
4.2.13.4 Fuel sulfur rules (ptnonipm and nonpt)
Fuel sulfur rules that were signed (enforceable) at the time of the Tier 3 NPRM emissions processing are
limited to Maine, New Jersey and New York. Several other states have fuel sulfur rules that were in
development but not finalized prior to the final CSAPR and proposed Tier 3 emissions processing:
http://www.ilta.org/LegislativeandRegulatory/MVNRLM/NEUSASulfur%20Rules_09.2010.pdf
The fuel sulfur content for all home heating oil SCCs in 2005 is assumed to by 3000 part per million (ppm).
Effective July 1, 2012, New York requires all heating oil sold in New York to contain no more than 15 ppm
of sulfur, thus reducing SC>2 emissions by 99.5% for post-2012 (2017 and 2030) projections. These New
York sulfur content reductions are further discussed here:
http ://switchboard.nrdc. org/blogs/rkassel/governor_paterson_signs_new_la. html.
The New Jersey year 2017 standard of 15ppm (assuming SOOppm baseline for Kerosene) sulfur content
yields a 96.25% SC>2 emissions reduction for kerosene (fuel #1). The New Jersey sulfur content reductions
are discussed here: http://njtoday.net/2010/09/01/nj-adopts-rule-limiting-sulfur-content-in-fuel-oil/.
For Tier 3 year 2017 projections, the Maine fuel sulfur rule, effective in year 2016, reduces sulfur to50 ppm
from 3,000 ppm in 2005, resulting in a 98.3% reduction for all Tier 3 year 2017 scenarios. A more stringent
Maine fuel sulfur rule effective January 1, 2018 reduces sulfur to 15ppm from 3,000 ppm in 2005, resulting
in a 99.5% reduction for all Tier 3 year 2030 scenarios. These Maine sulfur content reductions are discussed
here: http://www.mainelegislature.org/legis/bills/bills_124th/billpdfs/SP062701.pdf. The impact of these
fuel sulfur content reductions on SO2 is shown in Table 4-19. These year-specific reductions are the same
for all Tier 3 scenarios: low-ethanol, reference and control.
Table 4-19. Impact of fuel sulfur (802) controls on 2017 and 2030 non-EGU projections [tons]
State
Maine
New Jersey
New York
Total
2017 Reductions
8,323
998
54,431
63,751
2030 Reductions
18,470
998
54,431
73,898
51
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4.2.14 Oil and gas projections in TX, OK, and non-California WRAP states
(nonpt)
For the 2005v4.2 platform, we incorporated updated 2005 oil and gas emissions from Texas and Oklahoma.
For Texas oil and gas production we used year 2017 estimates and for year 2030 we used the last available
future year, year 2021, estimates from the Texas Commission of Environmental Quality (TCEQ) and used
them as described in:
http://www.tceq.state.tx.us/assets/public/implementation/air/am/contracts/reports/ei/5820783985FY0901-
20090715-ergi-Drilling Rig_EI.pdf.
We also received 2008 data for Oklahoma that we used as the best available data to represent 2017 and 2030.
We utilized the latest available future year, year 2018, Phase II WRAP oil and gas emissions data for the
non-California Western Regional Air Partnership (WRAP) states to represent both 2017 and 2030. RICE
NESHAP reductions, discussed earlier in this section, which are effective by year 2014, were applied to the
year 2008 Oklahoma oil and gas inventory but not applied to the 2017 and 2021 TCEQ oil and gas estimates
or 2018 WRAP Phase II oil and gas inventory.
For Oklahoma, we applied CO, NOx, SO2 and VOC emissions reductions from the RICE NESHAP, which
we assumed has some applicability to this industry (see Appendix F in the Proposed Toxics Rule TSD:
http://www.epa.gov/ttn/chief/emch/toxics/proposed_toxics_rule_appendices.pdf). All these year-specific oil
and gas projection estimates are the same for the reference and control Tier 3 NPRM scenarios. Table 4-20
shows the 2005, 2017 and 2030 emissions including RICE reductions for Oklahoma.
Table 4-20. Oil and gas emissions for 2005, 2017 and 2030 including additional reductions due to the RICE
NESHAP
Alaska
Arizona
Colorado
Montana
Nevada
New Mexico
North Dakota
Oklahoma
Oregon
South Dakota
Texas
Utah
Wyoming
Total
NOX
2005
836
13
32,188
10,617
71
61,674
6,040
39,668
61
566
42,854
6,896
36,172
237,656
2017
453
15
33,517
13,880
63
74,648
20,869
42,402
44
557
34,772
6,297
34,142
261,659
2030
453
15
33,517
13,880
63
74,648
20,869
42,402
44
557
26,061
6,297
34,142
252,948
PM2.5
2005
1,918
2,945
4,862
2017
2,231
1,085
3,316
2030
2,231
435
2,666
SO2
2005
62
350
640
1
369
688
1,014
43
5,977
149
541
9,834
2017
1
11
6
0
12
4
2
0
36
1
3
76
2030
1
11
6
0
12
4
2
0
33
1
3
73
VOC
2005
68
37
35,500
9,187
105
215,636
8,988
155,908
19
370
4,337
43,403
166,939
640,498
2017
12
49
43,639
14,110
163
267,846
17,968
163,598
14
562
2,800
81,890
304,748
897,400
2030
12
49
43,639
14,110
163
267,846
17,968
163,598
14
562
1,504
81,890
304,748
896,104
4.3 Onroad mobile source projections (onroad)
The same versions of MOVES and SMOKE-MOVES were used to create all Tier 3 NPRM onroad emission
scenarios. Section 2.2 describes these components in support of year 2005 processing. This section will
only address the differences related to creating and processing year 2017 and 2030 Tier 3 NPRM reference
case emissions. Section 5 will discuss the differences related to creating and processing year 2017 and 2030
Tier 3 NPRM control case emissions. Speciation changes for all scenarios are discussed in Section 3.
52
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Inputs for temperatures (Section 2.2.1), the representative counties and fuel months (Section 2.2.2), the
overall parallel processing procedures (Section 2.2.3), speed data (Section 2.2.4), and SMOKE-MOVES
configurations (Section 2.2.4) were previously discussed and were the same for all Tier 3 NPRM scenarios.
However, year-specific MOVES inputs were obtained for fuels and California LEV standards, and SMOKE
inputs of VMT and vehicle populations were year-specific and are described below.
For the 2017 and 2030 VMT inventories, MOVES2010a was run with default inputs to generate total
national VMT by SCC. But, because MOVES uses a static (1999) default allocation of VMT to county,
MOVES was not used for these allocations. Instead, the 2017 county VMT was created by interpolating
between the NCD VMT values for 2015 and those for 2020 and computing the NCD fraction for each
county, then multiplying these fractions by the MOVES VMT. (Unlike MOVES, the NCD accounts for
geographical shifts in activity over time.) For 2030, NCD values for 2030 were used directly. The VMT
was also adjusted to account for increased onroad transportation of ethanol fuels and the resulting increase in
travel by large tanker trucks.
Vehicle populations by county and SCC were developed similarly to the VMT, using MOVES to generate
national totals for each year and using the NCD to allocate to county. However, the NCD does not include
population estimates, so we used MOVES to generate the 2005 national population and we assumed that, for
each calendar year (2005, 2017 and 2030) and for each SCC, the allocation of national vehicle population to
county was proportional to the allocation of VMT (summed across road types).
The MOVES 2017 and 2030 emissions used for Tier 3 NPRM reference cases reflect onroad mobile control
programs that were final at the time the modeling was done. This included the Light-Duty Vehicle Tier 2
Rule and the Mobile Source Air Toxics (MSAT2) final rule. MOVES used fuel sulfur levels of 30ppm in all
states in the Tier 3 reference scenarios (except California which was modeled with MOVES2010a default
fuels). In terms of fleet composition, both reference scenarios assumed 100% Tier 2 and older vehicles.
4.3.1 California LEV
The list of states which have implemented programs to require the sale of vehicles in their state certified for
sale in California began with the information stored in the modeling inputs used for the 2008 National
Emission Inventory (NEI) stored in the National Mobile Inventory Model (NMIM) County database. This
information was reviewed and updated by states during the process of developing the national inventory for
calendar year 2008. This information was supplemented with information from a "Dear Manufacturer"
letter, "Sales of California-certified 2008-2010 Model Year Vehicles (Cross-Border Sales Policy)" (October
29,2007) produced by the Compliance and Innovative Strategies Division of the US Environmental
Protection Agency which describes the areas that have recently implemented a California standards program.
This information was used to generate emission rate table inputs for the MOVES model for each of these
areas using the guidance provided in the document, "Instructions for Using LEV and NLEV Inputs for
MOVES" (EPA-420-B-10-003, January 2010) provided to States with the MOVES model. For calendar year
2017, areas that had implemented California standards would still have these programs in place in calendar
years 2017 and 2030. See Table 2-6 for a list of these states and dates of implementation. More information
on the states that have implemented California LEV standards can be found at:
http://www.dieselnet.com/standards/us/tfcal.
4.4 Nonroad mobile source projections (nonroad, alm_no_c3, seca_c3)
The components of the nonroad mobile sectors are discussed in Section 2.3. Nonroad mobile emissions
reductions for the Tier 3 NPRM reference cases include year-specific regulations affecting locomotives,
various nonroad engines including diesel engines and various marine engine types, fuel sulfur content, and
53
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evaporative emissions. This section discusses the changes due to the NONROAD/NMIM system (nonroad
sector) and additional C1/C2 CMV and locomotive emissions from volume increases resulting from
incorporation of larger amounts of renewable fuels in the 2017 and 2030 reference cases.
4.4.1 Emissions generated with the NONROAD model (nonroad)
As discussed in Section 2.3.1, most nonroad emissions were estimated using the EPA's NONROAD model,
run via EPA's consolidated modeling system known as the National Mobile Inventory Model (NMIM).
NONROAD is EPA's model for calculating emissions from nonroad equipment, except for aircraft,
locomotives, and commercial marine vessels. Like the onroad emissions, the NONROAD/NMIM system
provides nonroad emissions for VOC by three emission modes: exhaust, evaporative and refueling. Unlike
the onroad sector, nonroad refueling emissions for nonroad sources are not included in the nonpoint (nonpt)
sector and so are retained in the nonroad sector.
The same temperatures and representative counties were used for all NONROAD model-generated Tier 3
NPRM scenarios for all years as were used for the 2005 base case, describe in section 2.3.1. For 2017, E10
and El5 are available in every county, but nonroad equipment is assumed to burn only E10. For 2030, EPA
assumed that nonroad equipment would use only El5. To generate the NMIM fuels, the E10 fuel was copied
from MOVES to NMIM, and the E10 oxygenate was assigned a market share of 1. Nonroad diesel fuel
sulfur levels are retained from NMIM.
Table 2-11 in Section 2.3.1.3 lists the NMIM emission scenarios used in the Tier 3 NPRM modeling. The
only difference between the reference scenarios are the increases in activity between calendar years (based
on NONROAD model default growth estimates of future-year equipment population) and changes in fuels
and engines that reflect implementation of national regulations and local control programs that impact each
year differently due to engine turnover. For year 2017, EPA assumed that nonroad equipment would use
only E10. The 2030 modeling, EPA assumed use of E15 fuels for both the reference and control scenarios.
For all scenarios, the NONROAD sulfur levels are taken from the NCD. Although the NONROAD Model
estimates changes in VOC emissions from E15, NMIM calculates toxics as if the fuel were E10. EPA
calculated adjustment factors based on highway effects to apply to certain toxic emissions to correct for the
use of E15 in 2030. Emission estimates for ethanol come from speciation of VOC in the SMOKE model.
These ethanol adjustments for nonroad engines running on El5 came from the EPAct Phase 1 data.
We have not included voluntary nonroad programs in our projections such as programs encouraging either
no refueling or evening refueling on Ozone Action Days and diesel retrofit programs. The national nonroad
regulations incorporated in all Tier 3 NPRM future year scenarios are those promulgated prior to December
2009, and beginning about 1990. Recent rules include:
• "Clean Air Nonroad Diesel Final Rule - Tier 4": (http://www.epa.gov/nonroaddiesel/2004fr.htm ),
published June 29, 2004, and,
• Control of Emissions from Nonroad Large Spark-Ignition Engines, and Recreational Engines (Marine
and Land-Based), November 8, 2002 ("Pentathalon Rule").
• OTAQ's Locomotive Marine Rule, March 2008: (http ://www.epa.gov/otaq/nonrdmdl.htm)
• OTAQ' s Small Engine Spark Ignition ("Bond") Rule, November 2008:
(http ://www. epa. gov/otaq/smallsi. htm)
All future year nonroad emissions used NMIM data that are based on AEO2009 fuels and the same NMIM
county database NCD20101201Tier3. We converted emissions from monthly totals to monthly average-day
values based the on number of days in each month. Only criteria and select HAPs (benzene, acetaldehyde,
54
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butadiene, acrolein, and formaldehyde) were retained when creating SMOKE one record per line (ORL)
files.
4.4.2 Locomotives and Class 1 & 2 commercial marine vessels (alm_no_c3)
Aircraft emissions reside in the nonEGU point inventory (ptnonipm), and the projection factors used to
create year 2017 and year 2030 estimates, are discussed in Section 4.2. The remaining 2005 NEI emissions
for locomotives and Class 1 and Class 2 commercial marine vessel (C1/C2 CMV) use year-specific
projection estimates. Base future year locomotive and C1/C2 CMV emissions were calculated using
projection factors that were computed based on national, annual summaries of emissions in 2002, 2017 and
2030. Some additional emissions were then factored in due to changes in fuels. These national summaries
were used to create national by-pollutant, by-SCC projection factors; these factors include final locomotive-
marine controls and are provided in Table 4-21. Modest additive Class I railroad and C1/C2 CMV emissions
that account for RFS2 volume increases in the Tier 3 future year reference scenarios were then added into the
reference case due to the volume differences in corn, cellulosic and imported ethanol and cellulosic diesel
fuels. These additional emissions are summarized in
Table 4-22.
Table 4-21. Factors applied to year 2005 emissions to project locomotives and class 1 and class 2
commercial marine vessel emissions to 2017 and 2030
sec
2280002X00
2280002X00
2280002X00
2280002X00
2280002X00
2280002X00
2280002X00
2285002006
2285002006
2285002006
2285002006
2285002006
2285002006
2285002006
2285002007
2285002007
2285002007
2285002007
2285002007
2285002007
2285002007
2285002008
2285002008
2285002008
2285002008
2285002008
2285002008
2285002008
2285002009
2285002009
2285002009
2285002009
2285002009
SCC Description
Marine Vessels, Commercial;Diesel;Underway & port emissions
Marine Vessels, Commercial;Diesel;Underway & port emissions
Marine Vessels, Commercial;Diesel;Underway & port emissions
Marine Vessels, Commercial;Diesel;Underway & port emissions
Marine Vessels, Commercial;Diesel;Underway & port emissions
Marine Vessels, Commercial;Diesel;Underway & port emissions
Marine Vessels, Commercial;Diesel;Underway & port emissions
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak)
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
Pollutant
CO
NH3
NOX
PM10
PM25
S02
voc
CO
NH3
NOX
PM10
PM25
SO2
VOC
CO
NH3
NOX
PM10
PM25
SO2
VOC
CO
NH3
NOX
PM10
PM25
SO2
VOC
CO
NH3
NOX
PM10
PM25
2017
0.938
1.144
0.700
0.642
0.653
0.087
0.786
1.334
1.325
0.627
0.578
0.586
0.005
0.589
0.328
1.325
0.352
0.286
0.288
0.001
0.315
1.071
1.325
0.496
0.461
0.463
0.005
0.475
1.057
1.325
0.489
0.455
0.455
2030
0.956
1.285
0.372
0.350
0.356
0.045
0.402
1.640
1.627
0.357
0.260
0.263
0.006
0.293
0.403
1.627
0.350
0.272
0.275
0.001
0.387
1.188
1.627
0.241
0.148
0.149
0.005
0.136
1.172
1.627
0.237
0.146
0.146
55
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sec
2285002009
2285002009
2285002010
2285002010
2285002010
2285002010
2285002010
2285002010
2285002010
SCC Description
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines
Railroad Equipment;Diesel;Yard Locomotives
Railroad Equipment;Diesel;Yard Locomotives
Railroad Equipment;Diesel;Yard Locomotives
Railroad Equipment;Diesel;Yard Locomotives
Railroad Equipment;Diesel;Yard Locomotives
Railroad Equipment;Diesel;Yard Locomotives
Railroad Equipment;Diesel;Yard Locomotives
Pollutant
SO2
voc
CO
NH3
NOX
PM10
PM25
SO2
VOC
2017
0.005
0.469
1.341
1.325
1.128
0.914
0.934
0.006
1.509
2030
0.005
0.134
1.649
1.627
0.851
0.690
0.704
0.007
1.074
Table 4-22. Additional class 1 railroad and C1/C2 CMV emissions from RFS2 fuel volume changes
Pollutant
1,3 -Butadiene
Acrolein
Formaldehyde
Benzene
Acetaldehyde
CO
NH3
NOX
PMio
PM2.5
S02
VOC
2017 Class 1
Rail (tons)
0.83
0.80
11.12
0.66
4.83
1,250
3.93
5,731
141
136
2.96
257
2017 C1/C2
CMV (tons)
0.01
0.08
3.21
0.44
1.59
197
0.62
890
29
27
3.96
21
2030 Class 1
Rail (tons)
0.56
0.54
7.50
0.45
3.26
1,906
5.99
4,298
86
83
4.51
173
2030 C1/C2
CMV (tons)
0.01
0.05
2.23
0.30
1.11
272
0.95
642
21
20
5.99
15
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, 2008). 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: http://www.epa.gov/otaq/nonrdmdl.htm.
Voluntary retrofits under the National Clean Diesel Campaign (http://www.epa.gov/otaq/diesel/index.htm)
are not included in our projections.
We applied HAP factors for VOC HAPs by using the VOC projection factors to obtain 1,3-butadiene,
acetaldehyde, acrolein, benzene, and formaldehyde.
Class 1 and 2 CMV gasoline emissions (SCC = 2280004000) were not changed for future-year processing.
C1/C2 diesel emissions (SCC = 2280002100 and 2280002200) were projected based on the Final
Locomotive Marine rule national-level factors provided in Table 4-21. Similar to locomotives, VOC HAPs
were projected based on the VOC factor.
Delaware provided updated future-year NOx, SO2, and PM emission estimates for C1/C2 CMV as part of the
Transport Rule comments. These updated emissions were applied to the 2017 and 2030 inventories and
override the C1/C2 projection factors in Table 4-21.
56
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4.4.3 Class 3 commercial marine vessels (seca_c3)
The seca_c3 sector emissions data were provided by OTAQ in an ASCII raster format used since the
Emissions Control Area-International Marine Organization (ECA-IMO) project began in 2005. The (S)ECA
Category 3 (C3) commercial marine vessel 2002 base-case emissions were projected to year 2005 for the
2005 base case and to 2017 and 2030, which includes ECA-IMO controls. The resulting 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 is at:
http://www.epa.gov/oms/oceanvessels.htm
These projection factors vary depending on geographic region and pollutant; where VOC HAPs are assigned
the same growth rates as VOC. The projection factors used to create the 2017 and 2030 seca_c3 sector
emissions are provided in Table 4-23. Note that these factors are relative to 2002. Factors relative to 2005
can be computed from the 2002-2005 factors.
The geographic regions are described in the EGA Proposal technical support document:
http://www.epa.gov/oms/regs/nonroad/marine/ci/420r09007-chap2.pdf These regions extend up to 200
nautical miles offshore, though less at international boundaries. North and South Pacific regions are divided
by the Oregon-Washington border, and East Coast and Gulf Coast regions are divided east-west by roughly
the upper Florida Keys just southwest of Miami.
The factors to compute HAP emission are based on emissions ratios discussed in the 2005v4 documentation
fflp://ftp.epa.gov/EmisInventory/2005v4/2005_emissions_tsd_07jul2010.pdf). As with the 2005 base case,
this sector uses CAP-HAP VOC integration.
Table 4-23. NOX, SO2, PM2.5 and VOC factors to project class 3 CMV emissions for 2017 and 2030
Region
Alaska East
Alaska West
East Coast
Gulf Coast
Hawaii East
Hawaii West
North Pacific
South Pacific
Great Lakes
Outside ECA
NOx
2017
1.409
1.469
1.435
1.120
1.539
1.725
1.240
1.573
1.106
1.585
2030
1.702
2.052
1.072
0.688
1.416
2.783
0.874
1.232
1.090
2.427
SO2
2017
0.062
1.571
0.070
0.055
0.078
2.037
0.064
0.084
0.046
1.891
2030
0.095
0.456
0.123
0.079
0.147
0.733
0.098
0.166
0.057
0.623
PM2*
2017
0.203
1.571
0.264
0.207
0.268
2.035
0.222
0.293
0.171
1.891
2030
0.312
0.571
0.470
0.303
0.506
0.871
0.348
0.589
0.214
0.745
VOC
2017
1.631
1.571
1.955
1.529
2.036
2.037
1.644
2.114
1.302
1.891
2030
2.487
2.396
3.464
2.217
3.839
3.842
2.528
4.225
1.621
3.417
4.5 Canada, Mexico, and offshore sources (othar, othon, and othpt)
Emissions for Canada, Mexico, and offshore sources were not projected to future years, and are therefore the
same as those used in the 2005 base case for all Tier 3 future year scenarios. Therefore, the Mexico
emissions are based on year 1999, offshore oil is based on year 2005, and Canada is based on year 2006. For
both Mexico and Canada, their responsible agencies did not provide future-year emissions that were
consistent with the base year emissions.
57
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5 2017 and 2030 Tier 3 Control Cases
The 2017 and 2030 Tier 3 NPRM control (hereafter simply referred to as the "control") cases represent the
future with implementation of all RFS2 impacts discussed in Section 4 plus the inclusion of Tier 3 fuel sulfur
reductions and phasing in of Tier 3 vehicle controls. Similar to the 2017 and 2030 base cases discussed in
Section 4, the control cases also include MS AT2 and LDGHG but do not include HDGHG impacts.
Similar to the 2017 reference case, the 2017 control case assumes 21.6 billion gallons of renewable fuels (24
billion ethanol-equivalent gallons due to volume increases of ethanol), with 17.8 billion gallons of E10 and
E15, 1.5 billion gallons of biodiesel, 0.2 billion gallons of renewable diesel, and 2.2 billion gallons of
cellulosic diesel. By the year 2030, the control (and reference) case assumes 30.5 billion gallons of
renewable fuels (36 billion ethanol-equivalent gallons due to volume increases of ethanol), with 22.2 billion
gallons of E15 (no E10), 1.7 billion gallons of biodiesel, 0.2 billion gallons of renewable diesel, and 6.5
billion gallons of cellulosic diesel.
The only notable differences between the reference and control scenarios for both 2017 and 2030 are the fuel
sulfur levels and fleet composition. For both 2017 and 2030, fuel sulfur levels are 30ppm (though lOppm in
California) in the reference case and lOppm in all states in the Tier 3 control scenarios. For both the 2017
and 2030 reference scenarios, fleet composition is composed entirely (100%) of Tier 2 and older vehicles.
For the Tier 3 control scenarios, new emission standards for model year 2014 and later light-duty motor
vehicles result in assumed 93% Tier 2 and older vehicles (fraction of vehicle population) and 7% Tier 3
vehicles in 2017 and 20% Tier 2 and older vehicles and 80% Tier 3 vehicles by 2030.
Tier 3 standards are expected to impact onroad mobile, and to a much smaller extent, nonroad mobile in
2017 and 2030, and onroad refueling emissions in 2030. However, all other upstream sources, including
portable fuel containers, are not expected to be affected. Therefore, the year 2017 and 2030 Tier 3 control
and reference case emissions are the same for several components of the modeling inventory. This section
will address only those components that are different between the reference and control scenarios in years
2017 and 2030.
VOC speciation changes between these control cases and the reference cases are discussed in Section 3.
5.1 Non-EGU stationary source projections (nonpt)
The 2017 Tier 3 control case non-EGU emissions are unchanged from the 2017 reference case. For the 2030
Tier 3 control case emissions, the only update is to use different onroad refueling emissions. These monthly-
resolution county-level (nonpoint sector) refueling emissions are MOVES-based gasoline and diesel fuel
spillage and include displacement vapor losses. A summary of the onroad refueling emissions in the 2030
control and reference cases is provided in Table 6-1; note that there is a negligible difference in refueling
emissions between the 2030 reference and 2030 control cases.
Table 6-1. Onroad Gasoline and Diesel Refueling Emissions for 2017 and 2030 Reference Cases
Fuel Type
Gasoline
Diesel
Gasoline
Gasoline
Pollutant
VOC
VOC
Benzene
Ethanol
2017 Reference
and Control
63,759
12,962
161
8,735
2030
Reference
40,781
16,449
91
7,253
2030
Control
40,777
16,450
91
7,252
58
-------
5.2 Onroad mobile (onroad)
The same version of MOVES and SMOKE-MOVES Integration Tool was used to create all Tier 3 NPRM
onroad emission scenarios. Section 2.2 describes these components in support of year 2005 processing. This
section will only address the differences related to creating and processing year 2017 and 2030 Tier 3 NPRM
control case emissions. Section 4 discussed the differences related to creating and processing year 2017 and
2030 Tier 3 NPRM reference case emissions. Speciation changes for all scenarios are discussed in Section 3.
Inputs for temperatures (Section 2.2.1), the representative counties and fuel months (Section 2.2.2), the
overall parallel processing procedures (Section 2.2.3), speed data (Section 2.2.4), and SMOKE-MOVES
configurations (Section 2.2.4) were previously discussed and were the same for all Tier 3 NPRM scenarios.
However, SMOKE inputs of VMT and vehicle populations were year-specific but were consistent between
reference and control (Section 4.3).
MOVES used fuel sulfur levels of lOppm in all states in the Tier 3 control scenarios, including California.
New emission standards for model year 2014 and later light-duty motor vehicles result in assumed 93% Tier
2 and older vehicles (fraction of vehicle population) and 7% Tier 3 vehicles in the 2017 control scenario and
20% Tier 2 and older vehicles and 80% Tier 3 vehicles in the 2030 control scenario. Other than fuels and the
application of Tier 3 vehicle emission standards, (described in the RIA for the rule) the MOVES runs were
identical between the reference and control scenarios of the same year.
5.3 Nonroad mobile (nonroad)
The components of the nonroad mobile sectors are discussed in Section 2.3. Nonroad mobile emissions
reductions for the Tier 3 NPRM control cases are restricted to various nonroad engines including diesel
engines, fuel sulfur content, and evaporative emissions. This section discusses the changes due to the
NONROAD/NMIM system (nonroad sector). The C1/C2 CMV and locomotive emissions (alm_no_c3) and
C3 CMV (seca_c3 sector) are unchanged in the control cases and use the same emissions as the 2017 and
2030 reference cases.
5.3.1 Emissions generated with the NONROAD model (nonroad)
As discussed in Section 2.3.1, most nonroad emissions are were estimated using the EPA's NONROAD
model, as run by the EPA's consolidated modeling system known as the National Mobile Inventory Model
(NMIM). NONROAD is EPA's model for calculating emissions from nonroad equipment, except for
aircraft, locomotives, and commercial marine vessels. Like the onroad emissions, the NONROAD/NMIM
system provides nonroad emissions for VOC by three emission modes: exhaust, evaporative and refueling.
Unlike the onroad sector, nonroad refueling emissions for nonroad sources are not included in the nonpoint
(nonpt) sector and so are retained in this sector.
The same temperatures and representative counties were used for all NONROAD model-generated Tier 3
NPRM scenarios. For 2017, El0 and El5 are available in every county, but nonroad equipment is assumed
to burn only E10. For 2030, nonroad equipment is assumed to burn only E15. To generate the NMIM fuels
in 2017 the E10 fuels were converted from MOVES to NMIM as described for the 2005 base case, assigned
a market share of 1. In 2030, the El5 fuels were used. For both calendar years, the controlled sulfur levels
were carried over to the nonroad case where they affected the sulfate emissions. Highway diesel fuel sulfur
levels (unchanged from the reference case) are converted directly from MOVES to NMIM. Nonroad diesel
fuel sulfur levels are retained from NMIM.
59
-------
Section 2.3.1.3 provides a cross-walk of the nonroad mobile NMIM emission scenarios used in the Tier 3
NPRM modeling; the only difference between the calendar year scenarios are the increases in activity (based
on NONROAD model default growth estimates of future-year equipment population) and changes in fuels
and engines that reflect implementation of national regulations and local control programs that impact each
year differently due to engine turnover. For year 2017, EPA assumed that nonroad equipment would use
only E10. The 2030 modeling, EPA assumed use of E15 fuels. For both control scenario years, the
NONROAD gasoline sulfur level was set to equal the level in the onroad fuel. Although the NONROAD
model estimates changes in VOC production from E15, NMIM calculates toxics as if the fuel were E10.
EPA calculated adjustment factors based on highway effects to apply to certain toxic emissions to correct for
the use of E15 in 2030. Emission estimates for ethanol come from speciation of VOC in the SMOKE model.
These ethanol adjustments for nonroad engines running on El5 came from the EPAct Phase 1 data for
highway gasoline vehicles.
6 Tier 3 emissions summaries
Once developed, the emissions inventories were processed to provide the hourly, gridded emissions for the
model-species needed by CMAQ. Table 6-1 provides national-level summaries of the 2005 U.S. CAP
emissions inventories modeled for this rule by sector for the lower 48-states and D.C.
60
-------
Table 6-2 and Table 6-3 provide these national summaries of the 2017 and 2030 Reference case U.S. CAP
inventories by sector. Table 6-4 and
61
-------
Table 6-5 provide national summaries of the 2017 and 2030 Tier 3 Control case U.S. CAP inventories by
sector. Alaska and Hawaii emissions summaries are not included in this TSD because they are outside of the
air quality modeling domain and modeling sectors that utilize domain-specific meteorology, such as onroad
mobile, are needed to compute model-ready emissions.
Table 6-1. National (49-state) 2005 U.S. emissions (tons/year) by sector
Emissions Sector
Agriculture
Area fugitive Dust
Average fires
Commercial marine
Category 3 (US)
ECU
Locomotive/ marine
Non-EGU Point
Nonpoint
Nonroad
Onroad
US TOTAL
NOX
189,428
130,164
3,729,161
1,922,723
2,213,471
1,696,902
2,031,527
8,235,002
20,148,378
SO2
49,094
97,485
10,380,883
153,068
2,030,759
1,216,362
196,277
168,480
14,292,410
PM25
1,030,391
684,035
10,673
496,877
56,666
433,346
1,079,906
201,406
301,073
4,294,373
PM10
8,858,992
796,229
11,628
602,236
59,342
647,873
1,349,639
210,767
369,911
12,906,616
NH3
3,251,990
36,777
21,995
773
158,342
133,962
1,971
144,409
3,750,218
CO
8,554,551
11,862
603,788
270,007
3,201,418
7,410,946
20,742,873
41,117,658
81,913,104
voc
1,958,992
4,570
41,089
67,690
1,279,308
7,560,061
2,806,422
3,267,931
16,986,064
62
-------
Table 6-2. National (49-state) 2017 Reference Case U.S. emissions (tons/year) by sector
Emissions Sector
Agriculture
Area fugitive Dust
Average fires
Commercial marine
Category 3 (US)
ECU
Locomotive/ marine
Non-EGU Point
Nonpoint
Nonroad
Onroad
US TOTAL
NOX
189,428
155,281
1,930,769
1,302,445
2,003,736
1,713,238
1,140,942
3,204,871
11,640,709
SO2
49,094
5,880
3,281,364
7,143
1,534,991
1,125,985
2,736
29,288
6,036,480
PM25
1,037,079
684,035
2,201
276,430
35,648
411,437
875,678
112,372
129,416
3,564,297
PM10
8,904,386
796,229
2,417
371,101
36,770
618,157
1,145,768
118,463
194,597
12,187,889
NH3
3,505,410
36,777
40,259
957
159,867
130,258
2,403
85,378
3,961,309
CO
8,554,551
18,274
873,344
299,265
2,995,095
5,854,632
13,551,846
18,690,890
50,837,897
voc
1,958,992
7,028
46,050
46,664
1,169,826
7,167,620
1,509,698
1,397,668
13,303,546
Table 6-3. National (49-state) 2030 Reference Case U.S. emissions (tons/year) by sector
Emissions Sector
Agriculture
Area fugitive Dust
Average fires
Commercial marine
Category 3 (US)
ECU
Locomotive/ marine
Non-EGU Point
Nonpoint
Nonroad
Onroad
US TOTAL
NOX
189,428
115,719
2,028,844
741,611
2,013,274
1,837,589
765,026
1,846,571
9,538,062
SO2
49,094
9,740
3,746,573
3,951
1,519,548
1,137,029
3,154
30,526
6,499,615
PM25
1,039,699
684,035
3,619
296,789
18,467
411,693
986,447
68,308
88,516
3,597,572
PM10
8,922,577
796,229
3,933
394,471
19,049
617,377
1,267,217
72,989
166,158
12,260,000
NH3
3,702,527
36,777
48,773
1,129
161,546
132,659
2,902
90,104
4,176,416
CO
8,554,551
29,906
1,091,438
338,728
3,065,975
6,728,522
12,921,772
17,021,674
49,752,567
VOC
1,958,992
11,479
53,468
25,006
1,171,769
7,290,413
1,209,534
911,513
12,632,174
Table 6-4. National (49-state) 2017 Tier 3 Control Case U.S. emissions (tons/year) by sector
Emissions Sector
Agriculture
Area fugitive Dust
Average fires
Commercial marine
Category 3 (US)
ECU
Locomotive/ marine
Non-EGU Point
Nonpoint
Nonroad
Onroad
US TOTAL
NOX
189,428
155,281
1,930,769
1,302,445
2,003,736
1,713,238
1,140,942
2,966,589
11,402,428
SO2
49,094
5,880
3,281,364
7,143
1,534,991
1,125,985
1,969
14,621
6,021,046
PM25
1,037,079
684,035
2,201
276,430
35,648
411,437
875,678
112,372
130,778
3,565,659
PM10
8,904,386
796,229
2,417
371,101
36,770
618,157
1,145,768
118,463
196,150
12,189,442
NH3
3,505,410
36,777
40,259
957
159,867
130,258
2,403
85,378
3,961,309
CO
8,554,551
18,274
873,344
299,265
2,995,095
5,854,632
13,551,846
18,055,099
50,202,106
VOC
1,958,992
7,028
46,050
46,664
1,169,213
7,145,569
1,498,122
1,353,170
13,224,807
63
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Table 6-5. National (49-state) 2030 Tier 3 Control Case U.S. emissions (tons/year) by sector
Emissions Sector
Agriculture
Area fugitive Dust
Average fires
Commercial marine
Category 3 (US)
ECU
Locomotive/ marine
Non-EGU Point
Nonpoint
Nonroad
Onroad
US TOTAL
NOX
189,428
115,719
2,028,844
741,611
2,013,274
1,837,589
765,026
1,371,925
9,063,416
SO2
49,094
9,740
3,746,573
3,951
1,519,548
1,137,029
2,257
15,068
6,483,260
PM25
1,039,699
684,035
3,619
296,789
18,467
411,693
986,447
68,308
83,842
3,592,898
PM10
8,922,577
796,229
3,933
394,471
19,049
617,377
1,267,217
72,989
161,173
12,255,016
NH3
3,702,527
36,777
48,773
1,129
161,546
132,659
2,902
90,104
4,176,416
CO
8,554,551
29,906
1,091,438
338,728
3,065,975
6,728,522
12921772.4
11984061.3
44,714,954
voc
1,958,992
11,479
53,468
25,006
1,171,769
7,290,410
1,209,452
699,592
12,420,167
64
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7 References
Cook, R, 2012. Development of Air Quality Reference Case Upstream and Portable Fuel Container
Inventories for Tier 3 Proposal. Memorandum to the Docket, December 12, 2012. Docket EPA-HQ-
OAR-2011-0135.
EPA, 2005. Clean Air Interstate Rule Emissions Inventory Technical Support Document, U.S.
Environmental Protection Agency, Office of Air Quality Planning and Standards, March 2005.
Available at http://www.epa.gov/cair/pdfs/finaltechO 1 .pdf.
EPA, 2006. Regulatory Impact Analyses, 2006 National Ambient Air Quality Standards for Particle
Pollution. U.S. Environmental Protection Agency, Office of Air Quality Planning and Standards,
October, 2006. Docket # EPA-HQ-OAR-2001-0017, # EPAHQ-OAR-2006-0834. Available at
http ://www. epa. gov/ttn/ecas/ria. html.
EPA, 2007a. Guidance for Estimating VOC and NOx Emission Changes from MACT Rules, U.S.
Environmental Protection Agency Office of Air Quality Planning and Standards, Air Quality Policy
Division, Research Triangle Park, NC 27711, EPA-457/B-07-001, May 2007. Available at
http://www.epa.gov/ttn/naaqs/ozone/o3imp8hr/documents/guidance/200705_epa457_b-07-
001_emission_changes_mact_rules.pdf
EPA. 2007b. National Scale Modeling for the Final Mobile Source Air Toxics Rule, Office of Air Quality
Planning and Standards, Emissions Analysis and Monitoring Division, Research Triangle Park, NC
27711, EPA 454/R-07-002, February 2007. Available at
http://www.epa.gov/otaq/regs/toxics/420f07017.pdf
EPA, 2008. Final Regulatory Impact Analysis: Control of Emissions of Air Pollution from Locomotive
Engines and Marine Compression Ignition Engines Less than 30 Liters Per Cylinder. U.S.
Environmental Protection Agency Office of Transportation and Air Quality, Assessment and
Standards Division, Ann Arbor, MI 48105, EPA420-R-08-001a, May 2008. Available at:
http ://www. epa. gov/otaq/nonrdmdl. htm
EPA, 2010. Technical Support Document: The Industrial Sectors Integrated Solutions (ISIS) Model and the
Analysis for the National Emission Standards for Hazardous Air Pollutants and New Source
Performance Standards for the Portland Cement Manufacturing Industry, U.S. Environmental
Protection Agency, Sectors Policies and Program Division and Air Pollution Prevention and Control
Division, Research Triangle Park, NC 27711, August 2010.
65
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Emissions Inventory for Air Quality Modeling Technical Support
Document: Proposed Tier 3 Emissions Standards
Appendix A
Ancillary Datasets and Parameters Used for Each Tier 3 Modeling Case
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC 27711
March 2013
A-l
-------
The ancillary data files used for the Tier 3 NPRM cases are shown in Table A-l. The Input name column gives a brief designator for the dataset.
The Environment Variable column gives the name of the environment variable that is used by SMOKE to specify the input. The Sector column
specifies the modeling sector for the dataset. The remaining columns show the data set name and version used in the 2005 base case and 2017 and
2030 reference and control cases. Most ancillary data sets are the same for all future year scenarios; we indicate where data sets differ.
To match the Datasets and Versions listed in this table to actual data files, combine the Dataset name and the version number in the following
pattern: __.txt, where is the last date of change for that version and will have a unique value for
the combination of Dataset Name and Version number.
Table A-2 shows the parameters used for the Tier 3 NPRM modeling cases; these parameters are the same for all future year scenarios. The
columns are the same as in Table A-l except that the Program is not shown. Many of the parameters apply to all programs, or all programs for the
specified processing sector.
Table A-l. List of ancillary data sets associated with the Tier 3 NPRM modeling cases.
Input Name
Area-to-point data
BEIS3 emission factors
Biogenic gridding surrogate for reports
12EUS1
Biogenic gridding surrogate for reports
36US1
Biogenic land use, file A, 12EUS1
Biogenic land use, file A, 36US1
Biogenic land use, file B, 12EUS1
Biogenic land use, file B, 36US1
Biogenic land use, totals, 12EUS1
Biogenic land use, totals, 36US1
Bioseasons file 12EUS1
Bioseasons file 36US1 mcip v3.4 beta4 b
Environment
Variable
ARTOPNT
B3FAC
BGPRO
BGPRO
BELD3 A
BELD3 A
BELD3 B
BELD3 B
BELD3 TOT
BELD3 TOT
BIOSEASON
BIOSEASON
Program
smkinven
TmpbeisS
Smkmerge
Smkmerge
NormbeisS
NormbeisS
NormbeisS
NormbeisS
NormbeisS
NormbeisS
TmpbeisS
TmpbeisS
Sector
beis
beis
beis
beis
beis
beis
beis
beis
beis
beis
beis
2005 Base Case
artopnt 2002detroit [vO]
beisS efac v3.14 [vO]
bgpro 12EUS1 (/garnet/oaqps)
[vO]
bgpro 36US1 (/garnet/oaqps) [vO]
LANDA_EUS 12_279X240
(/garnet/oaqps) [vO]
LANDA_US36_148X1 12
(/garnet/oaqps) [vO]
LANDB_EUS 12_279X240
(/garnet/oaqps) [vO]
LANDB_US36_148X1 12
(/garnet/oaqps) [vO]
LAND_TOTALS_EUS 12_279X24
0 (/garnet/oaqps) [vO]
LAND_TOTALS_US36_148X1 12
(/garnet/oaqps) [vO]
bioseason. cmaq. 2005b_ 1 2km
(/garnet/oaqps) [vO]
bioseason. cmaq. 2005b_3 6km
(/garnet/oaqps) [vO]
2017 & 2030 Reference/Control
Cases
artopnt 2002detroit [vO]
beis3 efac v3.14 [vO]
bgpro_12EUSl (/garnet/oaqps) [vO]
bgpro 36US1 (/garnet/oaqps) [vO]
LANDA_EUS 12_279X240
(/garnet/oaqps) [vO]
LANDA_US36_148X1 12
(/garnet/oaqps) [vO]
LANDB_EUS 12_279X240
(/garnet/oaqps) [vO]
LANDB_US36_148X1 12
(/garnet/oaqps) [vO]
LAND_TOTALS_EUS 12_279X240
(/garnet/oaqps) [vO]
LAND_TOTALS_US36_148X1 12
(/garnet/oaqps) [vO]
bioseason. cmaq.2005b_ 1 2km
(/garnet/oaqps) [vO]
bioseason.cmaq.2005b_36km
(/garnet/oaqps) [vO]
A-2
-------
Input Name
CEM annually summed data
Combination profiles
Combination profiles - nonpt
Combination profiles - onroad
Combination profiles - ptnonipm (same as
nonpt)
Country, State, County Information
Elevation Configuration File for Point
Sources
Elevation Configuration File for seca_c3
sector
Grid Description List
Gridding surrogates CAN-MEX 12km
Gridding surrogates CAN-MEX 12km
Gridding surrogates CAN-MEX 36km
Gridding surrogates CAN-MEX 36km
Gridding surrogates USA 12km
Gridding surrogates USA 36km
GSCNV - pollutant to pollutant conversions
GSPRO speciated MOVES PM
GSREF speciated PM
Environment
Variable
CEMSUM
GSPRO COM
BO
GSPRO COM
BO
GSPRO COM
BO
GSPRO COM
BO
COSTCY
PELVCONFIG
PELVCONFIG
GRIDDESC
SRGPRO
SRGPRO
SRGPRO
SRGPRO
SRGPRO
SRGPRO
GSCNV
GSPROTMP
L
GSREFTMP
L
Program
smkinven
Spcmat
Spcmat
Spcmat
Spcmat
smkinven
Laypoint
Laypoint
Grdmat
Grdmat
Grdmat
Grdmat
Grdmat
Grdmat
Grdmat
Spcmat
Spcmat
Spcmat
Sector
ptipm
nonpt
onroad
ptnonipm
seca c3
othon
othar
othar
othon
2005 Base Case
cemsum_ptipm 2005
(/orchid/share) [vO]
gspro combo 2005 [v6]
gspro_combo_tier3_2005_base_no
npt v2 [v2]
gspro_combo_tier3_2005_base_on
road v2 [vO]
gspro_combo_tier3_2005_base_no
npt v2 [v2]
costcy for 2002 [v5]
pelvconfig inline allpts [vl]
pelvconfig seca c3 [vl]
griddesc lambertonly [v39]
Canada_ 12km_revised
(/garnet/oaqps) [vO]
Canada_ 12km_revised
(/garnet/oaqps) [vO]
Canada_36km_revised
(/garnet/oaqps) [vO]
Canada_36km_revised
(/garnet/oaqps) [vO]
USA-CAN-MEX_12km
(/garnet/oaqps) [vO]
USA-CAN-MEX_36km
(/garnet/oaqps) [vO]
gscnv cb05 soa [v2]
gspro speciated_pm [v3]
gsref speciated_pm [v2]
2017 & 2030 Reference/Control
Cases
cemsum_ptipm 2005 (/garnet/oaqps)
[vO]
gspro combo 2005 [v6]
2017 Reference and Control:
gspro combo tier3 2017 ref nonpt
[vl]
2017 Reference and Control:
gspro combo tier3 2017 ref onroad
[v2]
2017 Reference and Control:
gspro combo tier3 2005 base nonpt
v2 [v2]
costcy for 2002 [v5]
pelvconfig inline allpts [vl]
pelvconfig seca c3 [vl]
griddesc lambertonly [v39]
Canada 12km revised (/garnet/oaqps)
[vO]
Canada 12km revised (/garnet/oaqps)
[vO]
Canada 36km revised (/garnet/oaqps)
[vO]
Canada 36km revised (/garnet/oaqps)
[vO]
USA-CAN-MEX_12km
(/garnet/oaqps) [vO]
USA-CAN-MEX_36km
(/garnet/oaqps) [vO]
2017 Reference & Control:
gscnv_cb05_soa [v3]
2030 Reference & Control:
gscnv cb05 soa [v4]
gspro speciated_pm [v3]
gsref speciated_pm [v2]
A-2
-------
Input Name
Holidays table
Inventory Table - HAPCAP EBAFM
integration CMAQ-lite v4.7 Nle HDGHG
Inventory Table - HAPCAP EBAFM
integration CMAQ-lite v4.7 Nle HDGHG
Inventory Table - HAPCAP integration
CMAQ-lite v4.7 Nle HDGHG
Inventory Table -no-BAFM CMAQ-lite
v4.7 Nle HDGHG
Inventory Table -no-BAFM CMAQ-lite
v4.7 Nle HDGHG
List of sectors for mrggrid
MACT Description
Meteorology temperature profiles
Mobile codes file default
MOVES county cross-reference
MOVES Emission Factor Table list
Environment
Variable
HOLIDAYS
INVTABLE
INVTABLE
INVTABLE
INVTABLE
INVTABLE
SECTORLIST
MACTDESC
METMOVES
MCODES
MCXREF
MRCLIST
Program
Temporal
smkinven
smkinven
smkinven
smkinven
smkinven
Mrggrid
Smkreport
movesmrg
smkinven
movesmrg
movesmrg
Sector
onroad
nonpt
avefire
ptipm
onroad
onroad
onroad
2005 Base Case
holidays [vO]
invtable hapcap cbOSsoa [v!3]
invtable hapcap cbOSsoa [v!3]
invtable hapcap cbOSsoa [v!2]
invtable hapcap cb05 no bafm
[v3]
invtable hapcap cb05 no bafm
[v3]
sectorlist 2005ct 05b [v3]
mactdesc 2002v3 [vl]
SMOKE DAILY 12MERGEUS1
2005 [vO]
mcodes [vl]
MCXREF tier3 [vO]
mrclist RPV 05jul2011 2005ct 0
5b [vO]
2017 & 2030 Reference/Control
Cases
holidays [vO]
invtable hapcap cbOSsoa [v!3]
invtable hapcap cbOSsoa [v!3]
invtable hapcap cbOSsoa [v!2]
invtable hapcap cb05 no bafm [v3]
invtable hapcap cb05 no bafm [v3]
20 17 Reference:
sectorlist 2017ct ref 05b [vl]
2017 Control:
sectorlist 2017ct ctla 05b [vl]
2030 Reference:
sectorlist 2030ct ref csapr 05b [vl]
2030 Control:
sectorlist 2030ct ctl csapr 05b [vl]
mactdesc 2002v3 [vl]
SMOKE DAILY 12MERGEUS1 20
05 [vO]
mcodes [vl]
MCXREF tier3 [vO]
20 17 Reference:
mrclist RPV Oljul2011 2017ct ref 0
5b [vO]
2017 Control:
mrclist RPV 08aug2011 2017ct ctla
05b [vO]
2030 Reference:
mrclist RPV 06jul2011 2030ct ref 0
5b [vO]
2030 Control:
mrclist RPV OSaugll 2030ct ctl 05
b[vO]
A-4
-------
Input Name
Environment
Variable
Program
Sector
2005 Base Case
2017 & 2030 Reference/Control
Cases
MOVES Emission Factor Table list
MRCLIST
movesmrg
onroad
mrclist_RPD_20may201 l_2005ct_
05b [vO]
2017 Reference:
mrclist_RPD_10jun2011_2017ct_ref_
05b [vO]
2017 Control:
mrclist_RPD_08aug2011_2017ct_ctla
_05b [vO]
2030 Reference:
mrclist_RPD_06jul201 l_2030ct_ref_0
5b [vO]
2030 Control:
mrclist_RPD_03augl l_2030ct_ctl_05
b[vO]
MOVES Emission Factor Table list
MRCLIST
movesmrg
onroad
mrclist_RPP_20may201 l_2005ct_
05b [vO]
2017 Reference:
mrclist_RPP_10jun2011_2017ct_ref_0
5b [vO]
2017 Control:
mrclist_RPP_08aug2011_2017ct_ctla_
05b [vO]
2030 Reference:
mrclist_RPP_06jul201 l_2030ct_ref_0
5b [vO]
2030 Control:
mrclist_RPP_03augl l_2030ct_ctl_05
b[vl]
A-5
-------
Input Name
MOVES Emission Factor Tables
MOVES processes and pollutants
MOVES processes and pollutants
MOVES processes and pollutants
MOVES reference county fuel month
NAICS descriptions
NHAPEXCLUDE aim no c3
NHAPEXCLUDE avefire
NHAPEXCLUDE nonpt
NHAPEXCLUDE NONROAD
NHAPEXCLUDE ptnonipm
NHAPEXCLUDE seca c3
nonpoint & nonroad surrogate xref
onroad surrogate xref default
OPJS Description
Environment
Variable
EFTABLES
MEPROC
MEPROC
MEPROC
MFMREF
NAICSDESC
NHAPEXCLU
DE
NHAPEXCLU
DE
NHAPEXCLU
DE
NHAPEXCLU
DE
NHAPEXCLU
DE
NHAPEXCLU
DE
AGREF
MGREF
ORISDESC
Program
movesmrg
movesmrg
movesmrg
movesmrg
movesmrg
Smkreport
smkinven
smkinven
smkinven
smkinven
smkinven
smkinven
Grdmat
Grdmat
smkinven
Sector
onroad
onroad
onroad
onroad
onroad
aim no c3
avefire
nonpt
nonroad
ptnonipm
seca c3
2005 Base Case
EFtables 20110520 Tier3Base200
5[vO]
EFtables 20110705 Tier3Base200
5 RPVfix [vO]
meproc RPP mplite [vO]
meproc RPV mplite [vl]
meproc RPD mplite [v2]
MFMREF tier3 [vO]
naicsdesc [vO]
nhapexclude aim no c3_pf4 [vl]
nhapexclude everything [vO]
nhapexclude nonpt_pf4 addpestici
des [v3]
nhapexclude nonroad_pf4 [vO]
nhapexclude_ptnonipm include 30
125010 [vO]
nhapexclude nothing [vO]
amgref us can mex revised [vll]
amgref us can mex revised [vll]
orisdesc [vO]
2017 & 2030 Reference/Control
Cases
20 17 Reference:
EFtables 20110610 Tier3Ref2017
[vO]
EFtables 20110701 Tier3Ref2017 R
PVfix [vO]
2017 Control:
EFtables 20110830 Tier3Ctla2017
[vO]
2030 Reference:
EFtables 20110706 Tier3Ref2030 R
PP [vO]
EFtables 20110706 Tier3Ref2030 R
PD [vO]
EFtables 20110706 Tier3Ref2030 R
PV [vO]
2030 Control:
EFtables 20110803 Tier3Ctl2030
[vO]
meproc RPP mplite [vO]
meproc RPV mplite [vl]
meproc RPD mplite [v2]
MFMREF tier3 [vO]
naicsdesc [vO]
nhapexclude aim no c3_pf4 [vl]
nhapexclude everything [vO]
nhapexclude nonpt_pf4 addpesticides
[v3]
nhapexclude nonroad_pf4 [vO]
nhapexclude_ptnonipm include 3012
5010 [vO]
nhapexclude nothing [vO]
amgref us can mex revised [v!5]
amgref us can mex revised [v!5]
orisdesc [vO]
A-6
-------
Input Name
SCC descriptions
SIC descriptions
Smkmerge representative dates files
Speciation profiles additional for SMOKE-
MOVES
Speciation profiles Canada PM
Speciation profiles for biogenics
Speciation profiles for HG
Speciation profiles for INTEGRATE HAPS
Speciation profiles for NONHAPTOG
Speciation profiles for NONHAPTOG
w/ETOH integration
Speciation profiles for NONHAPTOG
w/ETOH integration
Speciation profiles for NOX
Speciation profiles for PM2.5
Speciation profiles for SO2-SULF
Speciation profiles for TOG
Speciation profiles Other VOC HAP
Speciation profiles speciated VOC
Speciation profiles static
Speciation xref CAP static
Speciation xref for Canada PM
Speciation xref for Integrate-HAPs static
Environment
Variable
SCCDESC
SICDESC
MRGDATE F
ILES
GSPROTMP
0
GSPROTMP J
GSPROTMP
K
GSPROTMP
H
GSPROTMP
F
GSPROTMP
E
GSPROTMP
E
GSPROTMP
E
GSPROTMP
G
GSPROTMP
C
GSPROTMP
B
GSPROTMP
D
GSPROTMP
M
GSPROTMP I
GSPROTMP
A
GSREFTMP
A
GSREFTMP
N
GSREFTMP J
Program
smkinven
Smkreport
Run script
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Sector
onroad
othpt
beis
onroad
nonpt
othpt
2005 Base Case
sccdesc_pf31 [v!2]
sic desc [vO]
merge dates 2005 (/garnet/oaqps)
[vO]
gspro new for smoke-moves [vO]
gspro_pm25 Canada 2006_point
[vO]
gspro biogenics [vl]
gspro hg [v2]
gspro integratehaps cb05 tx_pf4
[vl]
gspro nonhaptog cb05 [v3]
gspro nonhaptog cb05 eprofiles
[vO]
gspro nonhaptog cb05 eprofiles
[vO]
gspro nox hono_pf4 [vO]
gspro_pm25 [v2]
gspro sulf[vl]
gspro tog cb05 soa [v3]
gspro other hapvoc no benz-benz
[vO]
gspro speciated voc [vO]
gspro static cmaq [v!2]
gsref static cap_pf4 [vl]
gsref_pm25 Canada 2006_point
[v3]
gsref static integratehap emv4
[v2]
2017 & 2030 Reference/Control
Cases
sccdesc_pf31 [v!2]
sic desc [vO]
merge dates 2005 (/garnet/oaqps)
[vO]
gspro new for smoke-moves [vO]
gspro_pm25 Canada 2006_point [vO]
gspro biogenics [vl]
gspro hg [v2]
gspro integratehaps cb05 tx_pf4 [vl]
gspro nonhaptog cb05 [v3]
gspro nonhaptog cb05 eprofiles [vO]
gspro nonhaptog cb05 eprofiles [vO]
gspro nox hono_pf4 [vO]
gspro_pm25 [v2]
gspro sulf [vl]
gspro tog cb05 soa [v3]
gspro other hapvoc no benz-benz
[vO]
gspro speciated voc [vO]
gspro static cmaq [v!2]
gsref static cap_pf4 [vl]
gsref_pm25 Canada 2006_point [v3]
gsref_static_integratehap_emv4 [v2]
A-7
-------
Input Name
Speciation xref for NONHAPVOC, not
year-specific
Speciation xref for NONHAPVOC, not
year-specific
Speciation xref for NONHAPVOC, year-
specific
Speciation xref for NONHAPVOC, year-
specific
Speciation xref for PM2.5 diesel SCCs but
do not produce diesel
Speciation xref for PM2.5 non-diesel SCCs
Speciation xref for SMOKE-MOVES not
TOG
Speciation xref for SMOKE-MOVES TOG
Speciation xref for SO2-SULF
Speciation xref for speciated VOC
Speciation xref for speciated VOC
Speciation xref for VOC, not year-specific
Speciation xref for VOC, year-specific
Environment
Variable
GSREFTMP
H
GSREFTMP
H
GSREFTMP I
GSREFTMP I
GSREFTMP
D
GSREFTMP
E
GSREFTMP P
GSREFTMP
0
GSREFTMP
B
GSREFTMP
M
GSREFTMP
M
GSREFTMP F
GSREFTMP
G
Program
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Sector
nonpt
nonpt
onroad
onroad
onroad
othpt
2005 Base Case
gsref nonhapvoc general hdghg
[v2]
gsref nonhapvoc 2005 hdghg [v2]
gsref no dieselpm [v3]
gsref_pm25_pf4 nondiesel [v!4]
gsref new for smoke -
moves otherthantog [vO]
gsref new for smoke-moves tog
[vl]
gsref sulf [vO]
gsref speciated voc [v2]
gsref speciated voc [v2]
gsref voc general hdghg [v3]
gsref_voc_2005_hdghg [v4]
2017 & 2030 Reference/Control
Cases
2017 Reference & Control:
gsref nonhapvoc general hdghg [v3]
2017 Reference & Control:
gsref nonhapvoc general hdghg [v2]
2030 Reference & Control:
gsref nonhapvoc general tier3 2030
ref [vl]
2017 Reference & Control:
gsref_nonhapvoc_20 1 7_ref_tier3 [v 1 ]
2030 Reference & Control:
gsref nonhapvoc 2030 ref tier3 [v3]
2017 Reference & Control:
gsref nonhapvoc 2017 ref tier3 [v2]
gsref no dieselpm [v3]
gsref_pm25_pf4 nondiesel [v!4]
gsref new for smoke-
moves otherthantog [vO]
2017 Reference and Control:
gsref 2017 for smoke moves tog
[vl]
2030 Reference and Control:
gsref 2030 ref for smoke moves to
g[vl]
gsref sulf [vO]
gsref speciated voc [v2]
gsref speciated voc [v2]
2017 Reference and Control:
gsref_voc_general_hdghg [v3]
2030 Reference & Control:
gsref voc general tier 3 2030 ref
[vl]
2017 Reference & Control:
gsref_voc_20 1 7_ref_tier3 [v3 ]
2030 Reference & Control:
gsref_voc_2030_ref_tier3 [vl]
A-8
-------
Input Name
Speciation xref HG
Speciation xref static NOX - HONO for
mobile sources
Stack replacement
surrogate descriptions (works for all grids)
surrogate descriptions (works for all grids)
surrogate descriptions (works for all grids)
Temporal profiles, all nonpoint and nonroad
Temporal profiles, all point
Temporal profiles, onroad default
Temporal xref, all nonpoint and nonroad
Temporal xref, onroad mobile default
Temporal xref, othpt
Temporal xref, point default
Temporal xref, ptipm only
Environment
Variable
GSREFTMP
K
GSREFTMP
C
PSTK
SRGDESC
SRGDESC
SRGDESC
ATPRO
PTPRO
MTPRO
ATREF
MTREF
PTREF
PTREF
PTREF
Program
Spcmat
Spcmat
smkinven
Grdmat
Grdmat
Grdmat
Temporal
Temporal
Temporal
Temporal
Temporal
Temporal
Temporal
Temporal
Sector
othon
othar
othpt
Ptipm
2005 Base Case
gsrefjig [v8]
gsref static nox hono_pf4 [v6]
pstk [vO]
srgdesc 36km revised [vl]
srgdesc 12km [v2]
srgdesc 36km revised [vl]
amptpro 2005 us can revised
[v2]
amptpro 2005 us can revised
[v2]
amptpro 2005 us can revised
[v2]
amptref v3 3 revised [v!2]
amptref v3 3 revised [v!2]
ptref_othpt [v4]
amptref v3 3 revised [v!2]
ptref_ptipm us [vO]
2017 & 2030 Reference/Control
Cases
gsrefjig [v8]
gsref static nox hono_pf4 [v6]
pstk [vO]
srgdesc 36km revised [vl]
srgdesc 12km [v2]
srgdesc 36km revised [vl]
amptpro 2005 us can revised [v2]
amptpro 2005 us can revised [v2]
amptpro 2005 us can revised [v2]
amptref v3 3 revised [v!2]
amptref v3 3 revised [v!2]
ptref_othpt [v4]
amptref v3 3 revised [v!2]
ptref_ptipm us [vO]
Table A-2. Parameters used in the Tier 3 NPRM cases
Parameter Name
All months across all sectors
BEIS3 version
Biogenics land area surrogate
Biogenics speciation profile code
Check for duplicate sources
Check for duplicate sources
Check for duplicate sources
Check for duplicate sources
Check for duplicate sources
Check for duplicate sources
Environment Variable
ALL MONTHS
BEIS3 VERSION
AREA SURROGATE NU
M
BIOG SPRO
RAW DUP CHECK
RAW DUP CHECK
RAW DUP CHECK
RAW DUP CHECK
RAW DUP CHECK
RAW DUP CHECK
Sector
beis
beis
beis
ptfire
ptnonipm
ptipm
aim no c3
nonpt
2005 Base Case
123456789 10 11 12
3.14
340
B10C5
N
N
N
Y
2017/2030 Reference and Control
Cases
123456789 10 11 12
3.14
340
B10C5
N
N
N
Y
N
N
A-9
-------
Parameter Name
Check for duplicate sources
Check for duplicate sources
Check for duplicate sources
Check stack parameters for missing
Convective rainfall variable for Pleim-Xiu
Count of underscores for Daily data prefix
Custom merge output
Custom merge output - MOVES
Default surrogate code
Default surrogate code
Don't need spinup for most sectors
Don't need spinup for most sectors
Don't need spinup for most sectors
Don't need spinup for most sectors
Don't need spinup for most sectors
Don't need spinup for most sectors
Don't need spinup for most sectors
Don't need spinup for most sectors
Don't speciate zero emission SCCs
Don't speciate zero emission SCCs
Don't use day-specific emission
Don't use pollutant conversion
ECU daily type
EMF queue options
Emission rate model
Fill annual values
Fill annual values
Fill annual values
Environment Variable
RAW DUP CHECK
RAW DUP CHECK
RAW DUP CHECK
CHECK STACKS YN
RC VAR
NAMEBREAK DAILY
SMKMERGE CUSTOM O
UTPUT
MOVESMRG CUSTOM O
UTPUT
SMK DEFAULT SRGID
SMK DEFAULT SRGID
SPINUP DURATION
SPINUP DURATION
SPINUP DURATION
SPINUP DURATION
SPINUP DURATION
SPINUP DURATION
SPINUP DURATION
SPINUP DURATION
NO SPC ZERO EMIS
NO SPC ZERO EMIS
DAY SPECIFIC YN
POLLUTANT CONVERSI
ON
ECU TYPE
EMF QUEUE OPTIONS
SMK EF MODEL
FILL ANNUAL
FILL ANNUAL
FILL ANNUAL
Sector
othon
othpt
othar
ptfire
beis
ptipm
onroad
afdust
ptipm
nonpt
ptipm
ptnonipm
nonroad
othpt
seca c3
seca c3
ptnonipm
nonpt
ptipm
onroad
onroad
nonroad
nonpt
2005 Base Case
N
N
N
N
RC
9
Y
Y
100
340
0
0
0
0
Y
Y
N
N
model_performance
#NAME?
MOVES
Y
N
Y
2017/2030 Reference and Control
Cases
N
N
N
N
RC
10
Y
Y
100
340
0
0
0
0
0
0
Y
Y
N
N
model_performance
#NAME?
MOVES
Y
N
Y
A-10
-------
Parameter Name
Fire-specific plume rise calculations
Formula for Smkinven
Formula for Smkinven
Formula for Smkinven
Formula for Smkinven
Include market penetration
I/O API Sphere type
Laypoint uses Elevpoint to set sources for plume
rise calc
Match Ml SCCs
Maximum errors printed
Maximum warnings printed
MCIP name abbreviation
Merge by day
Merge by day
Merge by day
Merge type
Merge type
Merge type
Merge type
Merge type
Merge type
Merge type
Merge type
Merge type
Merge type
Merge type
Merge type
Merge type
Model output format
Environment Variable
FIRE PLUME YN
SMKINVEN FORMULA
SMKINVEN FORMULA
SMKINVEN FORMULA
SMKINVEN FORMULA
MRG MARKETPEN YN
IOAPI ISPH
SMK SPECELEV YN
FULLSCC ONLY
SMK MAXERROR
SMK MAXWARNING
MCIPNAME
MRG BYDAY
MRG BYDAY
MRG BYDAY
M TYPE
M TYPE
M TYPE
M TYPE
M TYPE
M TYPE
M TYPE
M TYPE
M TYPE
M TYPE
M TYPE
M TYPE
M TYPE
OUTPUT FORMAT
Sector
ptfire
ag
nonroad
onroad
ptnonipm
seca c3
othpt
ptipm
ptnonipm
ptfire
avefire
ag
afdust
onroad
nonptfire
othpt
othon
seca c3
beis
2005 Base Case
Y
PMC=PM10-PM2 5
EXH PMC=EXH PM10-
EXH PM2 5
N
19
Y
Y
10000
10
MCIP v3.4beta4
P
P
P
Mwdss
All
Mwdss
All
Aveday
Aveday
Week
All
Aveday
Mwdss
Week
Aveday
All
$EMF_AQM
2017/2030 Reference and Control
Cases
Y
PMC=PM10-PM2 5
EXH PMC=EXH PM10-
EXH PM2 5
N
19
Y
Y
10000
10
MCIP v3.4beta4
P
P
P
Mwdss
All
Mwdss
All
Aveday
Aveday
Week
All
Aveday
Mwdss
Week
Aveday
All
$EMF_AQM
A-ll
-------
Parameter Name
Nonhap Type
Nonhap Type
Nonhap Type
Nonhap Type
Nonhap Type
Nonhap Type
Nonhap Type
Number of emissions layers
Ocean Chlorine filename extension
Output county biogenic totals
Output county/SCC totals
Output county totals
Output county totals
Output county totals
Output SCC totals
Output state biogenic totals
Output state totals
Output state totals
Output state totals
Output time zone
Platform name
Pleim-Xiu land surface used?
Plume-in-grid method
Pressure variable name
PTDAY file name case
Radiation/cloud variable name
Renormalize temporal profiles
Report default profiles used
Run holidays
Run holidays
Environment Variable
NONHAP TYPE
NONHAP TYPE
NONHAP TYPE
NONHAP TYPE
NONHAP TYPE
NONHAP TYPE
NONHAP TYPE
SMK EMLAYS
EXT
BIO COUNTY SUMS
MRG REPSRC YN
MRG REPCNY YN
MRG REPCNY YN
MRG REPCNY YN
MRG REPSCC YN
BIO STATE SUMS
MRG REPSTA YN
MRG REPSTA YN
MRG REPSTA YN
OUTZONE
PLATFORM
PX VERSION
SMK PING METHOD
PRES VAR
DAILY CASE
RAD VAR
RENORM TPROF
REPORT DEFAULTS
RUN HOLIDAYS
RUN HOLIDAYS
Sector
nonpt
ptnonipm
avefire
nonroad
onroad
aim no c3
seca c3
mrggrid
beis
onroad
onroad
onroad
beis
onroad
beis
beis
ptipm
beis
ag
avefire
2005 Base Case
voc
voc
voc
voc
TOG
VOC
VOC
10
.ncf
Y
Y
N
Y
Y
Y
Y
Y
N
N
0
v4.3
Y
0
PRSFC
2005ck
RGRND
Y
Y
N
N
2017/2030 Reference and Control
Cases
VOC
voc
voc
voc
TOG
VOC
VOC
10
.ncf
Y
Y
N
Y
Y
Y
Y
N
0
v4.3
Y
0
PRSFC
2005ck
RGRND
Y
Y
N
N
A-12
-------
Parameter Name
Run holidays
Run holidays
Run holidays
Run holidays
Run holidays
Run holidays
Run holidays
Run holidays
Run in inline mode
Run in inline mode SECA C3
Run script for Smkmerge annual totals
Smkmerge reports units
SMOKE-MOVES processing mode
SMOKE-MOVES processing mode
SMOKE-MOVES processing mode
Soil moisture variable for Pleim-Xiu
Soil temperature variable for Pleim-Xiu
Soil type variable for Pleim-Xiu
Sort inventory EVs by letter
Sort inventory EVs by letter
Sort inventory EVs by letter
Speciation type name
Spinup Duration
Spinup Duration
Temperature variable name
Temperature variable name - MOVES
Temporal type
Temporal type
Temporal type
Temporal type
Environment Variable
RUN HOLIDAYS
RUN HOLIDAYS
RUN HOLIDAYS
RUN HOLIDAYS
RUN HOLIDAYS
RUN HOLIDAYS
RUN HOLIDAYS
RUN HOLIDAYS
INLINE MODE
INLINE MODE
RUN PYTHON ANNUAL
MRG TOTOUT UNIT
MOVES TYPE
MOVES TYPE
MOVES TYPE
SOIM1 VAR
SOILT VAR
ISLTYP VAR
SORT LIST EVS
SORT LIST EVS
SORT LIST EVS
SPC
SPINUP DURATION
SPINUP DURATION
TMPR VAR
TVARNAME
L TYPE
L TYPE
L TYPE
L TYPE
Sector
seca c3
aim no c3
othon
othpt
othar
nonptfire
afdust
seca c3
onroad
onroad
onroad
beis
beis
beis
othpt
avefire
ptipm
beis
onroad
ptipm
ptfire
avefire
2005 Base Case
Y
N
N
N
N
N
N
Y
Both
Only
Y
tons/dy
RPD
RPP
RPV
SOIM1
SOIT1
SLTYP
Y
Y
Y
$EMF SPC
10
3
TEMP2
TEMP2
Mwdss
All
All
Aveday
2017/2030 Reference and Control
Cases
Y
N
N
N
N
N
N
Y
Both
Only
Y
RPD
RPP
RPV
SOIM1
SOIT1
SLTYP
Y
Y
Y
$EMF SPC
10
3
TEMP2
TEMP2
Mwdss
All
All
Aveday
A-13
-------
Parameter Name
Temporal type
Temporal type
Temporal type
Temporal type
Temporal type
Temporal type
Temporal type
Use area-to-point
Use area-to-point
Use area-to-point
Use average day emissions
Use day-specific emission
Use day-specific emission
Use hourly plume rise data
Use NHAPEXCLUDE file
Use NHAPEXCLUDE file
Use NHAPEXCLUDE file
Use NHAPEXCLUDE file
Use NHAPEXCLUDE file
Use NHAPEXCLUDE file
Use NHAPEXCLUDE file
Use pollutant conversion
Western hemisphere?
Write zero emissions
Write zero emissions
Zip merged model-ready files
Base Year
Downstream Model
End Date & Time
Environment Variable
L TYPE
L TYPE
L TYPE
L TYPE
L TYPE
L TYPE
L TYPE
SMK ARTOPNT YN
SMK ARTOPNT YN
SMK ARTOPNT YN
SMK AVEDAY YN
DAY SPECIFIC YN
DAY SPECIFIC YN
HOURLY FIRE YN
SMK PROCESS HAPS
SMK PROCESS HAPS
SMK PROCESS HAPS
SMK PROCESS HAPS
SMK PROCESS HAPS
SMK PROCESS HAPS
SMK PROCESS HAPS
POLLUTANT CONVERSI
ON
WEST HSPHERE
WRITE ANN ZERO
WRITE ANN ZERO
GZIP OUTPUTS
Sector
ag
afdust
onroad
nonptfire
othon
seca c3
beis
aim no c3
nonpt
nonroad
ptipm
ptfire
ptfire
aim no c3
seca c3
onroad
nonroad
avefire
ptnonipm
nonpt
ptfire
ptipm
mrggrid
2005 Base Case
Aveday
Week
All
Aveday
Week
Aveday
All
Y
Y
Y
N
Y
Y
Y
PARTIAL
ALL
ALL
PARTIAL
NONE
PARTIAL
PARTIAL
Y
Y
Y
Y
Y
2005
CMAQ v4.7 N5c
12/31/200523:59
2017/2030 Reference and Control
Cases
Aveday
Week
All
Aveday
Week
Aveday
All
Y
Y
Y
N
Y
Y
Y
PARTIAL
ALL
ALL
PARTIAL
NONE
PARTIAL
PARTIAL
Y
Y
Y
Y
Y
2005
CMAQ v4.7 N5c
12/31/200523:59
A-14
-------
Parameter Name
Future Year
Last Modified Date
Meteorological Year
Model
Modeling Region
# of emission layers
# of met layers
Speciation
Start Date
Version
Environment Variable
Sector
2005 Base Case
0
13:57.7
2005
SMOKE
National
14
14
cmaq_cb05 tx
1/1/2005 0:00
2.7
2017/2030 Reference and Control
Cases
2017
22:39.1
2005
SMOKE
National
14
14
cmaq_cb05 tx
1/1/2005 0:00
2.7
A-15
-------
Emissions Inventory for Air Quality Modeling Technical Support
Document: Proposed Tier 3 Emissions Standards
Appendix B
Inventory Data Files Used for Each Tier 3 Modeling Case - SMOKE Input
Inventory Datasets
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC 27711
March 2013
B-l
-------
The emissions inventory data files used for the Tier 3 cases are shown in Table B-l. The Input name column gives a brief designator for the
inventory. The Sector column specifies the modeling sector for the inventory. The remaining columns show the data set name and version used
in the 2005 base case and 2017 reference cases. The datasets used for the 2017 and 2030 control cases are identical to the 2017 and 2030
reference cases, respectively, except for the following replacements:
• Onroad mobile inventories (onroad sector)
• Onroad refueling inventories (nonpt sector)
• Nonroad mobile inventories (nonroad sector)
To match the Datasets and Versions listed in this table to actual data files, combine the Dataset name and the version number in the following
pattern: __.txt, where is the last date of change for that version and will have a unique value for
the combination of Dataset Name and Version number.
Table B-l. List of inventory data sets associated with the Tier 3 NPRM modeling cases.
Input name
Inventory afdust CAP
Inventory ag CAP
Inventory aim no c3 CAP
Inventory aim no c3 HAP
Inventory avefire CAP
Inventory avefire HAP
Inventory C1/C2 additional CAP/HAP
Inventory nonpt CAP and HAP (PFC
only)
Inventory nonpt CAP/HAP Cellulosic
Biodiesel plants for TierS
Inventory nonpt CAP/HAP Ethanol
Transport for TierS
Inventory nonpt CAP (no PFC, no
refueling)
Inventory nonpt CAP: TX and OK
Oil and Gas
Inventory nonpt CAP: WRAP Oil
and Gas
Sector
afdust
ag
aim no
c3
aim no
c3
avefire
avefire
aim no
c3
nonpt
nonpt
nonpt
nonpt
nonpt
nonpt
2005 Base Case
afdust 2002ad xportfrac [vO]
ag cap2002nei [vO]
1m no c3 cap2002v3 [vl]
1m no c3 hap2002v4 [vO]
avefire 2002ce [vO]
avefire 2002 hap [vO]
pfc 2002 caphap wETOH [vl]
nonpt_pf4 cap nopfc [v6]
nonpt_cap_2005_TCEQ_Oklahoma
OilGas [vO]
nonpt cap 2005 WRAP OilGas
[vO]
2017 Reference/Control Cases
afdust 2017ct ref [vO]
ag cap2017ct ref [vO]
1m no c3 cap2017ct lowE [vO]
1m no c3 hap2017ct lowE [vO]
avefire 2002ce [vO]
avefire 2002 hap [vO]
clc2 additional 2017ct ref caphap 25
ju!2011 [vO]
pfc 2017 ref caphap 23aug2011 [vO]
cellulosic ETOH Biodiesel 2017ct re
f caphap 29jul2011 [vO]
Ethanol transport vapor 2017ct ref c
aphap 25jul2011 [vO]
nonpt_pf4 cap nopfc 2017ct ref [vO]
nonpt_cap_20 17ct_lowE_TCEQ_Oklah
oma OilGas [vO]
nonpt cap 2018PhaseII WRAP OilGa
s[vO]
2030 Reference/Control Cases
afdust 2030ct ref [vO]
ag cap2030ct ref [vO]
1m no c3 cap2030cs [vO]
1m no c3 hap2030cs [vO]
avefire 2002ce [vO]
avefire 2002 hap [vO]
clc2 additional 2030ct ref caphap 25
ju!2011 [vO]
pfc_2030_ref_caphap_23aug201 1 [vO]
cellulosic_ETOH_Biodiesel_2030ct_re
f caphap 29jul2011 [vO]
Ethanol transport vapor 2030ct ref c
aphap 25jul2011 [vO]
nonpt_pf4 cap nopfc 2030ct ref [vO]
nonpt_cap_2030ct_lowE_TCEQ_Oklah
oma OilGas [vO]
nonpt cap 2018PhaseII WRAP OilGa
s[vO]
B-2
-------
Input name
Sector
2005 Base Case
2017 Reference/Control Cases
2030 Reference/Control Cases
Inventory nonpt HAP (no PFC, no
refueling)
nonpt
nonpt_pf4_hap_nopfc_nobafmpestic
idesplus [v4]
nonpt_pf4_hap_nopfc_nobafmpesticide
splus_2017ct_ref [vO]
nonpt_pf4_hap_nopfc_nobafmpesticide
splus_2030ct_ref [vO]
Inventory nonpt Refueling from
MOVES, April
nonpt
rfl_moves_wETOH_2005ct_apr_18
may2011 [vO]
rfl_moves_wETOH_2017ct_ref_apr_27
jul2011 [vO]
2030 Reference:
rfl_moves_wETOH_2030ct_ref_apr_27
ju!2011 [vO]
2030 Control:
rfl_moves_wETOH_2030ct_ctl_apr_28
jul2011 [vO]
Inventory nonpt Refueling from
MOVES, August
nonpt
rfl_moves_wETOH_2005ct_aug_18
may2011 [vO]
rfl_moves_wETOH_2017ct_ref_aug_2
7jul2011 [vO]
2030 Reference:
rfl_moves_wETOH
7jul2011 [vO]
2030 Control:
rfl_moves_wETOH
8jul2011 [vO]
2030ct_ref_aug_2
2030ct_ctl_aug_2
Inventory nonpt Refueling from
MOVES, December
nonpt
rfl_moves_wETOH_2005ct_dec_18
may2011 [vO]
rfl_moves_wETOH_2017ct_ref_dec_2
7jul2011 [vO]
2030 Reference:
rfl_moves_wETOH
7jul2011 [vO]
2030 Control:
rfl_moves_wETOH
jul2011 [vO]
2030ct ref dec 2
2030ct ctl dec 28
Inventory nonpt Refueling from
MOVES, February
nonpt
rfl_moves_wETOH_2005ct_feb_18
may2011 [vO]
rfl_moves_wETOH_2017ct_ref_feb_27
jul2011 [vO]
2030 Reference:
rfl_moves_wETOH
jul2011 [vO]
2030 Control:
rfl_moves_wETOH
jul2011 [vO]
2030ct ref feb 27
2030ct ctl feb 28
Inventory nonpt Refueling from
MOVES, January
nonpt
rfl_moves_wETOH_2005ctJan_18
may2011 [vO]
rfl_moves_wETOH_2017ct_refjan_27
jul2011 [vO]
2030 Reference:
rfl_moves_wETOH
jul2011 [vO]
2030 Control:
rfl_moves_wETOH
jul2011 [vO]
2030ct_refjan_27
2030ct_ctljan_28
Inventory nonpt Refueling from
MOVES, July
nonpt
rfl_moves_wETOH_2005ctJul_18
may2011 [vO]
rfl_moves_wETOH_2017ct_refjul_27
jul2011 [vO]
2030 Reference:
rfl_moves_wETOH
jul2011 [vO]
2030 Control:
rfl_moves_wETOH
ul2011 [vO] ~
2030ct_refjul_27
2030ct_ctljul_28j
Inventory nonpt Refueling from
MOVES, June
nonpt
rfl_moves_wETOH_2005ctJun_18
may2011 [vO]
rfl_moves_wETOH_2017ct_refjun_27
jul2011 [vO]
2030 Reference:
rfl_moves_wETOH_2030ct_refjun_27
jul2011 [vO]
B-3
-------
Input name
Inventory nonpt Refueling from
MOVES, March
Inventory nonpt Refueling from
MOVES, May
Inventory nonpt Refueling from
MOVES, November
Inventory nonpt Refueling from
MOVES, October
Inventory nonpt Refueling from
MOVES, September
Inventory nonroad cap+CMAQ-lite
HAPs US, incl Calif April
Inventory nonroad cap+CMAQ-lite
HAPs US, incl Calif August
Sector
nonpt
nonpt
nonpt
nonpt
nonpt
nonroad
nonroad
2005 Base Case
rfl moves wETOH 2005ct mar 18
may2011 [vO]
rfl moves wETOH 2005ct may 1
8may2011 [vO]
rfl moves wETOH 2005ct nov 18
may2011 [vO]
rfl moves wETOH 2005ct oct 18
may2011 [vO]
rfl moves wETOH 2005ct sep 18
may2011 [vO]
nonroad cmaq_lite 2005ct apr 19
may2011 [vO]
nonroad cmaq_lite 2005ct aug 19
may2011 [vO]
2017 Reference/Control Cases
rfl moves wETOH 2017ct ref mar 2
7jul2011 [vO]
rfl moves wETOH 2017ct ref may 2
7jul2011 [vO]
rfl moves wETOH 2017ct ref nov 2
7jul2011 [vO]
rfl moves wETOH 2017ct ref oct 27
jul2011 [vO]
rfl moves wETOH 2017ct ref sep 2
7jul2011 [vO]
20 17 Reference:
nonroad cmaq_lite 2017ct ref apr 20
jul2011 [vO]
2017 Control:
nonroad cmaq_lite 2017ct ctla apr 0
8sep2011 [vO]
20 17 Reference:
nonroad cmaq_lite 2017ct ref aug 20
2030 Reference/Control Cases
2030 Control:
rfl moves wETOH 2030ct ctljun 28
jul2011 [vO]
2030 Reference:
rfl moves wETOH 2030ct ref mar 2
7jul2011 [vO]
2030 Control:
rfl moves wETOH 2030ct ctl mar 2
8jul2011 [vO]
2030 Reference:
rfl moves wETOH 2030ct ref may 2
7jul2011 [vO]
2030 Control:
rfl moves wETOH 2030ct ctl may 2
8jul2011 [vO]
2030 Reference:
rfl moves wETOH 2030ct ref nov 2
7jul2011 [vO]
2030 Control:
rfl moves wETOH 2030ct ctl nov 2
8jul2011 [vO]
2030 Reference:
rfl moves wETOH 2030ct ref oct 27
jul2011 [vO]
2030 Control:
rfl moves wETOH 2030ct ctl oct 28
jul2011 [vO]
2030 Reference:
rfl moves wETOH 2030ct ref sep 2
7jul2011 [vO]
2030 Control:
rfl moves wETOH 2030ct ctl sep 28
jul2011 [vO]
2030 Reference:
nonroad cmaq_lite 2030ct ref apr 03
aug2011 [vO]
2030 Control:
nonroad cmaq_lite 2030ct ctl apr 03
aug2011 [vO]
2030 Reference:
nonroad cmaq_lite 2030ct ref aug 03
B-4
-------
Input name
Inventory nonroad cap+CMAQ-lite
HAPs US, incl Calif December
Inventory nonroad cap+CMAQ-lite
HAPs US, incl Calif February
Inventory nonroad cap+CMAQ-lite
HAPs US, incl Calif January
Inventory nonroad cap+CMAQ-lite
HAPs US, incl Calif July
Inventory nonroad cap+CMAQ-lite
HAPs US, incl Calif June
Inventory nonroad cap+CMAQ-lite
HAPs US, incl Calif March
Inventory nonroad cap+CMAQ-lite
Sector
nonroad
nonroad
nonroad
nonroad
nonroad
nonroad
nonroad
2005 Base Case
nonroad cmaq_lite 2005ct dec 19
may2011 [vO]
nonroad cmaq_lite 2005ct feb 19
may2011 [vO]
nonroad cmaq_lite 2005ctjan 19
may2011 [vO]
nonroad cmaq_lite 2005ctjul 19m
ay2011 [vO]
nonroad cmaq_lite 2005ctjun 19
may2011 [vO]
nonroad cmaq_lite 2005ct mar 19
may2011 [vO]
nonroad cmaq_lite 2005ct may 19
2017 Reference/Control Cases
ju!2011 [vO]
2017 Control:
nonroad cmaq_lite 2017ct ctla aug 0
8sep2011 [vO]
20 17 Reference:
nonroad cmaq_lite 2017ct ref dec 20
ju!2011 [vO]
2017 Control:
nonroad cmaq_lite 2017ct ctla dec 0
8sep2011 [vO]
20 17 Reference:
nonroad cmaq_lite 2017ct ref feb 20
ju!2011 [vO]
2017 Control:
nonroad cmaq_lite 2017ct ctla feb 0
8sep2011 [vO]
20 17 Reference:
nonroad cmaq_lite 2017ct ref may 2
Ojul2011 [vO]
2017 Control:
nonroad cmaq_lite 2017ct ctla may
08sep2011 [vO]
20 17 Reference:
nonroad cmaq_lite 2017ct refjul 20j
u!2011 [vO]
2017 Control:
nonroad cmaq_lite 2017ct ctlajul 08
sep2011 [vO]
20 17 Reference:
nonroad cmaq_lite 2017ct refjun 20
ju!2011 [vO]
2017 Control:
nonroad cmaq_lite 2017ct ctlajun 0
8sep2011 [vO]
20 17 Reference:
nonroad cmaq_lite 2017ct ref mar 2
Ojul2011 [vO]
2017 Control:
nonroad cmaq_lite 2017ct ctla mar 0
8sep2011 [vO]
20 17 Reference:
2030 Reference/Control Cases
aug2011 [vO]
2030 Control:
nonroad cmaq_lite 2030ct ctl aug 03
aug2011 [vO]
2030 Reference:
nonroad cmaq_lite 2030ct ref dec 03
aug2011 [vO]
2030 Control:
nonroad cmaq_lite 2030ct ctl dec 03
aug2011 [vO]
2030 Reference:
nonroad cmaq_lite 2030ct ref feb 03
aug2011 [vO]
2030 Control:
nonroad cmaq_lite 2030ct ctl feb 03
aug2011 [vO]
2030 Reference:
nonroad cmaq_lite 2030ct ref may 0
3aug2011 [vO]
2030 Control:
nonroad cmaq_lite 2030ct ctl may 0
3aug2011 [vO]
2030 Reference:
nonroad cmaq_lite 2030ct refjul 03a
ug2011 [vO]
2030 Control:
nonroad cmaq_lite 2030ct ctljul 03a
ug2011 [vO]
2030 Reference:
nonroad cmaq_lite 2030ct refjun 03
aug2011 [vO]
2030 Control:
nonroad cmaq_lite 2030ct ctljun 03
aug2011 [vO]
2030 Reference:
nonroad cmaq_lite 2030ct ref mar 0
3aug2011 [vO]
2030 Control:
nonroad cmaq_lite 2030ct ctl mar 03
aug2011 [vO]
2030 Reference:
B-5
-------
Input name
HAPs US, incl Calif May
Inventory nonroad cap+CMAQ-lite
HAPs US, incl Calif November
Inventory nonroad cap+CMAQ-lite
HAPs US, incl Calif October
Inventory nonroad cap+CMAQ-lite
HAPs US, incl Calif September
Inventory onroad RPD
Inventory onroad RPD
Inventory onroad RPP
Inventory onroad RPV
Inventory othar nonpoint CAP
Mexico border states
Inventory othar nonpoint CAP
Mexico interior states
Inventory othar nonroad CAP Mexico
border states
Inventory othar nonroad CAP Mexico
interior states
Inventory othon CAP Mexico border
states
Inventory othon CAP Mexico interior
states
Sector
nonroad
nonroad
nonroad
onroad
onroad
onroad
onroad
othar
othar
othar
othar
othon
othon
2005 Base Case
may2011 [vO]
nonroad cmaq_lite 2005ct nov 19
may2011 [vO]
nonroad cmaq_lite 2005ct oct 19
may2011 [vO]
nonroad cmaq_lite 2005ct sep 19
may2011 [vO]
VMT tierS 2005 [vO]
SPEED tierS [vO]
VPOP tierS 2005 [vO]
VPOP tierS 2005 [vO]
nonptmexicoborder!999 [vO]
nonpt mexico interior!999 [vO]
nonroad mexico border 1999 [vO]
nonroad mexico interior!999 [vO]
onroad mexico border!999 [vO]
onroad mexico interior!999 [vO]
2017 Reference/Control Cases
nonroad cmaq_lite 2017ct ref may 2
Ojul2011 [vO]
2017 Control:
nonroad cmaq_lite 2017ct ctla may
08sep2011 [vO]
20 17 Reference:
nonroad cmaq_lite 2017ct ref nov 20
ju!2011 [vO]
2017 Control:
nonroad cmaq_lite 2017ct ctla nov 0
8sep2011 [vO]
20 17 Reference:
nonroad cmaq_lite 2017ct ref oct 20j
ul2011 [vO]
2017 Control:
nonroad cmaq_lite 2017ct ctla oct 0
8sep2011 [vO]
20 17 Reference:
nonroad cmaq_lite 2017ct ref sep 20
ju!2011 [vO]
2017 Control:
nonroad cmaq_lite 2017ct ctla sep 0
8sep2011 [vO]
VMT tierS 2017 ref cntl [vS]
SPEED tierS [vO]
WOP tierS 2017 [vO]
WOP tierS 2017 [vO]
nonpt mexico border!999 [vO]
nonpt mexico interior!999 [vO]
nonroad mexico border!999 [vO]
nonroad mexico interior!999 [vO]
onroad mexico border!999 [vO]
onroad mexico interior!999 [vO]
2030 Reference/Control Cases
nonroad cmaq_lite 2030ct ref may 0
3aug2011 [vO]
2030 Control:
nonroad cmaq_lite 2030ct ctl may 0
3aug2011 [vO]
2030 Reference:
nonroad cmaq_lite 2030ct ref nov 03
aug2011 [vO]
2030 Control:
nonroad cmaq_lite 2030ct ctl nov 03
aug2011 [vO]
2030 Reference:
nonroad cmaq_lite 2030ct ref oct 03
aug2011 [vO]
2030 Control:
nonroad cmaq_lite 2030ct ctl oct 03
aug2011 [vO]
2030 Reference:
nonroad cmaq_lite 2030ct ref sep 03
aug2011 [vO]
2030 Control:
nonroad cmaq_lite 2030ct ctl sep 03
aug2011 [vO]
VMT_tier3_2030_ref_cntl [vl]
SPEED_tier3 [vO]
VPOP_tier3_2030 [vO]
VPOP_tier3_2030 [vO]
nonpt mexico border!999 [vO]
nonpt mexico interior!999 [vO]
nonroad mexico border!999 [vO]
nonroad mexico interior!999 [vO]
onroad mexico border!999 [vO]
onroad mexico interior!999 [vO]
B-f
-------
Input name
Inventory othon CAP onroad Canada
Inventory othpt CAP Mexico border
states
Inventory othpt CAP Mexico interior
states
Inventory othpt CAP offshore
Inventory ptipm CAP
Inventory ptipm daily data (CEM
sources)
Inventory ptipm daily data (nonCEM
sources)
Inventory ptipm HAP
Inventory ptnonipm CAP
Inventory ptnonipm CAPHAP
biodiesel plant additions forTierS
Inventory ptnonipm CAPHAP ethanol
plant additions forTierS
Inventory ptnonipm cement capHg
Inventory ptnonipm HAP
Inventory rail additional CAP/HAP
for TierS ref/ctl
Inventory seca_c3 BAF HAPs Canada
Sector
othon
othpt
othpt
othpt
ptipm
ptipm
ptipm
ptipm
ptnonip
m
ptnonip
m
ptnonip
m
ptnonip
m
ptnonip
m
aim no
c3
seca c3
2005 Base Case
Canada onroad cap 2006 [vO]
mexico border99 [vl]
mexico interior99 [vO]
ptnonipm offshore oil cap2005v2
20nov2008 [vO]
ptipm 2005cs cap 27dec2010.txt
[vl]
ptday_ptipm_caphap_cem_2005cs_
05b (/garnet/oaqps) [vO]
ptday_ptipm_caphap_noncem_2005
cs 05b (/garnet/oaqps) [vO]
ptipm 2005cs hap 27dec2010.txt
[vO]
ptnonipm_xportfrac_cap2005v2_20
OSes orl [v7]
ethanol_plants_2005ct_2017ct_low
E caphap [vO]
ptnonipm hap2005v2 2005cs orl
[v6]
eca imo CANADA SCC fix voch
2017 Reference/Control Cases
Canada onroad cap 2006 [vO]
mexico border99 [vl]
mexico interior99 [vO]
ptnonipm offshore oil cap2005v2 20
nov2008 [vO]
PTINV EPA410FINAL BC 58 summ
er 2020 21MAY2011 ORL [vO]
ptday_ptipm caphap cem 2017ct 05b
[vO]
ptday_ptipm caphap noncem 2017ct
05b [vO]
ptnonipm xportfrac cap2017ct ref
[vO]
biodiesel_plants 2017ct ref caphap 2
9jul2011 [vO]
ethanol_plants 2017ct ref caphap 19j
ul2011 [vO]
ptnonipm capHG cementlSIS 2016cr
16AUG2010 [vO]
ptnonipm hap2017ct ref [vO]
rail additional 2017ct ref caphap 26j
ul2011 [vO]
eca_imo_CANADA_SCC_fix_vochaps
2030 Reference/Control Cases
Canada onroad cap 2006 [vO]
mexico border99 [vl]
mexico interior99 [vO]
ptnonipm offshore oil cap2005v2 20
nov2008 [vO]
2030 Reference Case:
PTINV EPA410FINAL BC 58 summ
er_2030_27MAY2011_ORL [vO]
2030 Control Case:
PTINV EPA410 BC 15b summer 20
30 02FEB2011 ORL [vO]
2030 Reference Case:
ptday_ptipm caphap cem 2030ct low
E[vO]
2030 Control:
ptday_ptipm caphap cem 2030cs hdg
hg ref 05b [vO]
2030 Reference:
ptday_ptipm caphap noncem 203 Oct 1
owE [vO]
2030 Control:
ptday_ptipm caphap noncem 2030cs
hdghg ref 05b [vO]
ptnonipm xportfrac cap2030ct ref
[vO]
biodiesel_plants 2030ct ref caphap 2
9jul2011 [vO]
ethanol_plants 2030ct ref caphap 19j
ul2011 [vO]
ptnonipm capHG cementlSIS 2016cr
16AUG2010 [vO]
ptnonipm hap2030ct ref [vO]
rail additional 2030ct ref caphap 26j
ul2011 [vO]
eca_imo_CANADA_SCC_fix_vochaps
B-7
-------
Input name
Inventory seca_c3 BAF HAPs US
includes EEZ and offshore FIPS
Inventory seca c3 CAP Canada
Inventory seca_c3 CAP US + EEZ +
Offshore non-Canada
ORL Nonpoint Inventory - Afdust
Canada 2006
ORL Nonpoint Inventory - Ag Canada
2006
ORL Nonpoint Inventory - Aircraft
Canada 2006
ORL Nonpoint Inventory -
Commercial Marine Canada 2006
ORL Nonpoint Inventory - Nonroad
Canada 2006
ORL Nonpoint Inventory - Oarea
Canada 2006
ORL Nonpoint Inventory - Rail
Canada 2006
ORL Point Inventory - Point 2006
ORL Point Inventory - Point CBS
2006
ORL Point Inventory - Upstream Oil
& Gas 2006
Sector
seca c3
seca c3
seca c3
othar
othar
othar
othar
othar
othar
othar
othpt
othpt
othpt
2005 Base Case
aps_2005_09DEC2010 [vO]
eca imo fixFIPS US andSCC fix
vochaps 2005 09DEC2010 [vO]
eca imo CANADA SCC fix caps
2005 09DEC2010 [vO]
eca imo fixFIPS US wDE andSC
C fix caps 2005 09DEC2010 [vO]
Canada afdust xportfrac cap 2006
[vO]
Canada ag cap 2006 [vO]
Canada aircraft cap 2006 [vO]
Canada marine cap 2006 [vO]
Canada offroad cap 2006 [vO]
Canada oarea cap 2006 [v3]
Canada rail cap 2006 [vO]
canada_point 2006 orl [v2]
canada_point cb5 2006 orl [vO]
canada_point uog 2006 orl [vO]
2017 Reference/Control Cases
_2017 [vO]
eca_imo_fixFIPS_US_andSCC_fix_vo
chaps 2017 [vO]
eca imo CANADA SCC fix caps 20
17 [vO]
eca_imo_fixFIPS_US_wDE_andSCC_f
ix caps 2017 [vO]
Canada afdust xportfrac cap 2006
[vO]
Canada ag cap 2006 [vO]
Canada aircraft cap 2006 [vO]
Canada marine cap 2006 [vO]
Canada offroad cap 2006 [vO]
Canada oarea cap 2006 [v3]
Canada rail cap 2006 [vO]
canada_point 2006 orl [v2]
canada_point cb5 2006 orl [vO]
canada_point uog 2006 orl [vO]
2030 Reference/Control Cases
_2030_08FEB2011 [vO]
eca imo fixFIPS US andSCC fix vo
chaps 2030 08FEB2011 [vO]
eca imo CANADA SCC fix caps 20
30 08FEB2011 [vO]
eca imo fixFIPS US wDE andSCC f
ix caps 2030 08FEB2011 [vO]
Canada afdust xportfrac cap 2006
[vO]
Canada ag cap 2006 [vO]
Canada aircraft cap 2006 [vO]
Canada marine cap 2006 [vO]
Canada offroad cap 2006 [vO]
Canada oarea cap 2006 [v3]
Canada rail cap 2006 [vO]
canada_point 2006 orl [v2]
canada_point cb5 2006 orl [vO]
canada_point uog 2006 orl [vO]
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United States Office of Air Quality Planning and Standards Publication No. EPA-454/R-13-002
Environmental Protection Air Quality Assessment Division March, 2013
Agency Research Triangle Park, NC
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