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Emissions Modeling for the Final Mercury and Air Toxics Standards
Technical Support Document
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EPA-454/R-11-011
December 2011
Emissions Modeling for the Final Mercury and Air Toxics Standards
Technical Support Document
By:
Alison Eyth
Rich Mason
Alexis Zubrow
U.S. Environmental Protection Agency
Office of Air and Radiation
Office of Air Quality Planning and Standards
Air Quality Assessment Division
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Ill
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TABLE OF CONTENTS
LIST OF TABLES v
LIST OF APPENDICES vi
ACRONYMS vii
1 Introduction to the Modeling Platform 1
2 2005 Emission Inventories and Their Preparation 2
2.1 Custom configuration for emissions modeling for MATS 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 13
2.3 Nonroad mobile sources (nonroad, alm_no_c3, seca_c3) 14
2.3.1 Emissions generated with the NONROAD model (nonroad) 14
2.3.2 Locomotives and commercial marine vessels (alm_no_c3, seca_c3) 18
2.4 2005 point sources (ptipm and ptnonipm sectors) 18
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
2.7 Emissions summaries for 2005 base case 21
3 VOC Speciation Changes that Represent Fuel Changes 23
4 2017 Reference Case 27
4.1 Stationary source projections: EGU sector (ptipm) 33
4.2 Stationary source proj ections: non-EGU sectors (ptnonipm, nonpt, ag, afdust) 34
4.2.1 Ethanol plants (ptnonipm) 35
4.2.2 Biodiesel plants (ptnonipm) 35
4.2.3 Portable fuel containers (nonpt) 37
4.2.4 Cellulosic fuel production (nonpt) 37
4.2.5 Ethanol transport and distribution (nonpt) 38
4.2.6 Onroad refueling (nonpt) 38
4.2.7 Refinery adjustments (ptnonipm) 39
4.2.8 Ethanol transport gasoline and blends (ptnonipm, nonpt) 39
4.2.9 Upstream agricultural adjustments (afdust, ag, nonpt, ptnonipm) 39
4.2.10 Livestock emissions growth (ag, afdust) 40
4.2.11 Residential wood combustion growth (nonpt) 40
4.2.12 Aircraft growth (ptnonipm) 41
4.2.13 Stationary source control programs, consent decrees & settlements, and plant closures
(ptnonipm, nonpt) 42
4.2.14 Oil and gas projections in TX, OK, and non-California WRAP states (nonpt) 47
4.3 Onroad mobile source projections (onroad) 47
4.3.1 California LEV 48
4.4 Nonroad mobile source projections (nonroad, alm_no_c3, seca_c3) 48
4.4.1 Emissions generated with the NONROAD model (nonroad) 48
4.4.2 Locomotives and Class 1 & 2 commercial marine vessels (alm_no_c3) 49
iv
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4.4.3 Class 3 commercial marine vessels (seca_c3) 51
4.5 Canada, Mexico, and offshore sources (othar, othon, and othpt) 52
4.6 Reference case emission summaries 52
5 MATS Control Case 58
6 References 62
LIST OF TABLES
Table 1-1. List of cases run in support of the MATS air quality modeling for the RIA 2
Table 2-1. Sectors used in emissions modeling for the final MATS 2005v4.3 platform 3
Table 2-2. Model species produced by SMOKE for CB05 with SO A for the MATS platform 5
Table 2-3. Description of differences in ancillary data (unrelated to SMOKE to MOVES) between the
MATS 2005 case and the 2005v4.2 platform 6
Table 2-4. Allocation of states to the Petroleum Administration for Defense Districts 8
Table 2-5. Gasoline parameter categories 9
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 17
Table 2-12. Summary of NONROAD modeling components 18
Table 2-13. 2005 ethanol plant emissions 19
Table 2-14. 2005 U.S. emissions (tons/year) by sector 21
Table 2-15. 2005 base year SO2 emissions (tons/year) for states by sector 21
Table 2-16. 2005 base year PM2.5 emissions (tons/year) for states by sector 22
Table 3-1. Summary of VOC speciation profile approaches by sector across cases 25
Table 4-1. Control strategies and growth assumptions for creating the 2017 reference case emissions
inventories from the 2005 base case 30
Table 4-2. MATS reference case mobile source-related projection methods 34
Table 4-3. 2017 reference case corn ethanol plant emissions 35
Table 4-4. 2017 biodiesel plant emissions 37
Table 4-5. PFC emissions for 2017 37
Table 4-6. 2017 cellulosic plant emissions 38
Table 4-7. VOC losses (Emissions) due to ethanol transport and distribution 38
Table 4-8. Onroad gasoline and diesel refueling emissions 39
Table 4-9. Impact of refinery adjustments on 2017 emissions 39
Table 4-10. Upstream agricultural emission increases due to RFS2 fuels in 2017 39
Table 4-11. Growth factors from year 2005 to 2017 for animal operations 40
Table 4-12. Projection factors for growing year 2005 residential wood combustion sources 41
Table 4-13. Impact of year 2017 projection factor error on residential wood combustion estimates 41
Table 4-14. Factors used to project 2005 base-case aircraft emissions to 2017 42
Table 4-15. Summary of non-EGU emission reductions applied to the 2005 inventory due to unit and plant
closures 43
Table 4-16. Future-year ISIS-based cement industry annual reductions (tons/yr) for the non-EGU
(ptnonipm) sector 45
Table 4-17. State-level non-MACT boiler reductions from ICR data gathering 45
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Table 4-18. National impact of RICE controls on non-EGU projections 46
Table 4-19. Impact of fuel sulfur (862) controls on 2017 non-EGU projections 46
Table 4-20. Oil and gas NOx and 862 emissions for 2005 and 2017 including additional reductions due to
the RICE NESHAP 47
Table 4-21. Factors applied to year 2005 emissions to project locomotives and class 1 and class 2
commercial marine vessel emissions to 2017 50
Table 4-22. Additional class 1 railroad and C1/C2 CMV emissions from RFS2 fuel volume changes 51
Table 4-23. NOX, SO2, and PM2.5 factors to project class 3 CMV emissions for 2017 52
Table 4-24. Summary of modeled base case SC>2 and PM2.5 annual emissions (tons/year) for 48 states by
sector 53
Table 4-25. Reference case 862 emissions (tons/year) for states by sector 54
Table 4-26. Reference case PM2.5 emissions (tons/year) for states by sector 55
Table 4-27. Future year baseline EGU CAP emissions (tons/year) by state 56
Table 5-1. Summary of emissions changes for the MATS AQ modeling in the lower 48 states 58
Table 5-2. EGU emissions totals for the Mmdeled MATS control case in the lower 48 states 58
Table 5-3. State-specific changes in annual EGU SC>2 for the lower 48 states 59
Table 5-4. State-specific changes in annual EGU PM2.5 for the lower 48 states 61
LIST OF APPENDICES
APPENDIX A: Ancillary Datasets and Parameters Used for Each MATS Modeling Case
APPENDIX B: Inventory Data Files Used for Each MATS Modeling Case - SMOKE Input Inventory
Datasets
APPENDIX C: Summary of MATS Rule 2017 Base Case Non-EGU Control Programs, Closures and
Projections
VI
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ACRONYMS
AEO Annual Energy Outlook
BEIS Biogenic Emission Inventory System
bps Buly 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
HAP Hazardous Air Pollutant
HDGHG Heavy Duty Greenhouse Gas
HONO HNO2, nitrous acid
IPM Integrated Planning Model
LDGHG Light Duty Greenhouse Gas
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
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
TSD Technical Support Document
VOC Volatile Organic Compound
WRAP Western Regional Air Partnership
vii
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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 Mercury and Air Toxics Standards (MATS). This document provides the details of
emissions modeling done to support the development of the Regulatory Impact Assessment (RIA) for the
MATS. The emissions processing described herein and the corresponding air quality modeling were used to
develop benefit-per-ton scaling factors for the benefits calculation as described in the RIA. More
information on this approach can be found in Appendix 5C of the RIA and in the Air Quality Modeling
Technical Support Document (TSD). The emissions inventories were using the Sparse Matrix Operator
Kernel Emissions (SMOKE) modeling system (http://www.smoke-model.org/index.cfm) version 2.7
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 and visibility impairment. 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 baseline emissions, and (4) the development of the future year control
case emissions.
A modeling platform is the collection of the inputs to an air quality model, including the settings and data
used for the model, including emissions data, meteorology, initial conditions, and boundary conditions. The
2005-based air quality modeling platform used for the proposed utility NESHAP RIA includes 2005 base
year emissions and 2005 meteorology for modeling ozone and PM2.5 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 the 2005v4.3 modeling platform used
for the RIA are described in the air quality modeling TSD.
The 2005-based air quality modeling platform used in support of the RIA is version 4.3 and is referred to as
the 2005v4.3 platform. It is an update to the 2005-based platform, version 4.1 (i.e., 2005v4.1) used for the
proposal modeling and for the appropriate and necessary finding. The Technical Support Document
"Preparation of Emissions Inventories for the Version 4.1, 2005-based Platform" (see
http://www.epa.gov/ttn/chief/emch/index.html#toxics) provides information on the platform used for the
proposed version of this rule and for the appropriate and necessary finding. 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 and incorporated changes made in response to public comments on the proposed
version of that rule.
Table 1-1 provides a high-level summary of the three emissions cases that were modeled in support of the
final rule RIA. 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
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and Security Act of 2007 (EISA) and the Energy Policy Act of 2005 (EPAct) on mobile source fuels.
Table 1-1. List of cases run in support of the MATS air quality modeling for the RIA
Case Name
Internal EPA
Abbreviation
Description
2005 base case
2005ct
2005 calendar year case / scenarios that uses 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 reference case
2017ct ref
2017 future year baseline scenario with EGU emissions
that represent the implementation of the Cross-State Air
Pollution Rule (CSAPR) and mobile sources representing
the implementation of the EISA/EPAct fuel supply
(RFS2 Rule) along with average year fire data.
2017 control case
2017ct ref mats
2017 "control" or remedy case scenario with EGU
emissions that represent the implementation of both
CSAPR and MATS, and mobile sources representing
implementation of the EISA/EPAct fuel supply (RFS2),
along with average year fire data.
In the remainder of this document, we provide a description of the approaches taken for the emissions in
support of air quality modeling for the MATS. 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
Reference (i.e., future year baseline) case as compared to the 2005 base case. Appendix A provides a
comparison of the ancillary datasets and parameters used for the various MATS emissions cases, and
Appendix B compares the emissions inventory and other input data files used for each of the MATS cases.
2 2005 Emission Inventories and Their Preparation
As mentioned previously, the 2005 emissions modeling approach for MATS 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 MATS 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 2.2 and 2.65 provide differences for the nonpoint and nonpoint (area)
inventories, respectively.
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 MATS in contrast to what was done for the 2005v4.2 platform.
In MATS, we used a 2005 base case approach for the year 2005 emissions scenario. This approach is very
similar to that CSAPR Final Rule (formerly known as the "Transport Rule"). A base case approach uses
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average year fires and EGU temporal profiles from three years of EGU data. We use a base case approach
because we want 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 and therefore do not introduce potentially spurious year-specific
artifacts into the air quality modeling estimates. For MATS, the same biogenic emissions data as the
2005v4.2 platform was used for the 2005 case, and also for both future-year cases. The only significant data
changes between the 2005 and the 2017 future-year MATS case are the emission inventories and speciation
approaches.
Table 2-1 below lists the platform sectors used for the MATS 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 MATS 2005v4.3-based platform.
Table 2-1. Sectors used in emissions modeling for the final MATS 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:
alm_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.
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 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 is 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 MA TS
Unlike the 2005v4.2 platform, the configuration for MATS 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 MATS 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 MATS platform had a few additional custom aspects in the 2005 cases. Table 2-3
lists the datasets used by the 2005v4.3 platform that are different from the 2005v4.2 platform.
Another consideration is the speciation across the MATS 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 in MATS processing, 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 MATS 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 ancillary data (unrelated to SMOKE to MOVES) between the
MATS 2005 case and the 2005v4.2 platform
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 MATS platform
The MATS 2005v4.3 data files are configured to support the multi-pollutant
(MP) version of CMAQ, whereas the 2005v4.2 platform data file is configured
to support only the non-MP version. Therefore, the MATS data files include
profiles for additional VOC HAP species.
Added MATS-specific VOC to TOG and nonHAP VOC to nonHAP TOG
assignments
Added onroad diesel SCCs representing start and idle modes (223007X000)
The MATS 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 then 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 then 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 MATS, EPA used a version of the MOVES 2010a model that was enhanced for the proposed Tier 3 rule.
This 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 2012, 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 create maximum temperature ranges, average relative humidity, and a series of
diurnal temperature profiles. MOVES was run for each temperature bin and diurnal profile. See Sections
2.2.4.1 and 2.2.4.3 for details.
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
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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 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 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) for calculation of 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 MOBILE6.2 and 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 database 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 database 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. This has been accomplished in this rule 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 model only one county in the group (the "representing" county) to determine the
rates. These representative rates 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, in order to generate onroad mobile emissions, MOVES was run in conjunction
with the EPA SMOKE model to generate "grid" level inventories for use 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" 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 database 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 on the EPA web site 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.
Table 2-5. Gasoline parameter categories
9
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Gasoline Parameter
Reid Vapor Pressure (psi)
Sulfur (ppm)
Ethanol (volume percent)
Benzene (volume percent)
Category ID
1
2
3
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 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.
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
10
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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 in 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 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 air quality analysis inventory. If MOVES 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. The MOVES runs for each representative county and fuel month were performed
independently of one another on different computer processors 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 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), SMOKE-MOVES uses the change in temperature over the day, 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 will span the full range of temperatures for that representative county and fuel month. For SMOKE
processing of RPP, met4moves creates a minimum and maximum temperature range for each county in the
domain. Note that these temperature ranges are county specific, 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 MATS runs, met4moves was run in daily mode.
In addition to the lookup tables of emission rates produced by MOVES, SMOKE requires county 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) has our most recent estimates of 2005
VMT and our best estimates of allocation of VMT from national to the county level. Accordingly, for the
2005 base year, our 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
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
12
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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 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 MATS. The tool then packaged the information into a form that could be used by the
compute server.
4) OTAQ issued the command to kick off the required MOVES runs for each county and fuel month on
the compute server.
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 is 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.
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).
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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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 temporalized VMT to calculate the emissions
for that grid cell and hour. This is repeated for each pollutant and SCC in that grid cell.
The emission process RPV is for modeling the off-network emissions. This includes the following modes:
vehicle exhaust, evaporative, and permeation (????). For RPV, the activity data is vehicle population
(VPOP). Movesmrg reads the temperature from the gridded hourly data and uses the temperature plus SCC
and the hour of the day to look up the appropriate EF from the representative county's EF table. It then
multiplies this EF by the VPOP for that SCC and FIPS to calculate the emissions for that grid cell and hour.
This repeats for each pollutant and SCC in that grid cell.
The emission process RPP is for modeling the off-network emissions for parked vehicles. This includes the
mode vehicle evaporative (fuel vapor venting). For RPP, the activity data is VPOP. Movesmrg reads the
county based diurnal temperature range 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/version3.0/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 on national 12km domain, to support the CMAQ runs they were
further processed to create an aggregated 36km sector specific model-ready file and 2 12km domains
(12EUS1 and 12WUS1).
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 5, nonroad
upstream impacts also impact the post-EPAct/EISA/RFS2/reference case (anti-backsliding) 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 are were estimated using the EPA's NONROAD model, as run by the EPA's
NMIM. NONROAD is EPA's model for calculating emissions from nonroad equipment, except for aircraft,
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).
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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 to develop national inventories. Inputs for NMIM runs were stored in the NMIM
County Database (NCD). The NCD version is NCD20101201Tier3. This NCD is based on NCD20101201,
which is the version of the NCD that includes all updates from the 2008 National Emission Inventory
process: http://www.epa.gov/ttn/chief/net/2008inventory.html.
In particular, for this analysis, we made updates to the underlying fuel supply for the post EISA/EPAct
reference case. The NCD20101201Tier3 contained special versions of countyyearmonth, gasoline, and
diesel, which were copied into the standard versions of these tables in order to run the model. The fuels in
NCD2010201Tier3 were developed from the fuels used for onroad vehicles, as described in Section 2.2.1.
Similarly, 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." We also made a minor change 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-but, 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 ertiary 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
In addition, a special countymap 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 using SMOKE
15
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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 estimate for the rule, the NMIM county database (NCD)
developed for the 2005 NEI, with one exception of the county-specific fuel properties, was used to calculate
nonroad emissions. Fuels were developed for MOVES (onroad mobile) for the MATS Rule (see Section
2.2) and were converted to NMIM fuels. Practically, this means converting the fuelsupply and
fuelformulation tables from MOVES into the countyyearmonth, gasoline, and diesel tables in the NCD.
In 2005, onroad and nonroad gasoline formulations are assumed to be identical.
While 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 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 of 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.
Table 2-11. NONROAD NMIM runs
Case
Base
Reference
Year
2005
2017
Run Name
Tier3Base2005Nr
Tier3Ref2017elONr
MOVES fuels were used for future year cases and converted to NMIM format. However, for 2017, EPA
18
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assumed that nonroad equipment would use only E10. The details of the fuels conversion from MOVES to
NMIM was discussed above. Table 2-12 describes the components in the NONROAD/NMIM system
common to all MATS 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,
(available at
http ://www. epa. sov/otaq/nonrdmdl . htm#model)
except it was modified to allow modeling of
emissions on El 5 fuels. 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 from these 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/transportrulefmal_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.
2.4 2005 point sources (ptipm and ptnonipm sectors)
Point sources are sources of emissions for which specific geographic coordinates (e.g., latitude/longitude) are
specified, as in the case of an individual facility. A facility may have multiple emission points, which may
be characterized as units such as boilers, reactors, spray booths, kilns, etc. A unit may have multiple
processes (e.g., a boiler that sometimes burns residual oil and sometimes burns natural gas). 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 MATS modeling. Discussion of the seca_c3 and othpt sector emissions can be found in
the Final CSAPR TSD referenced in Section 2.3.2.
19
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After removing offshore oil platforms (othpt sector), we created two platform sectors from the remaining
2005v2 NEI point sources for input 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. 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 CL2 (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 (basis for the Final CSAPR and Heavy Duty Greenhouse Gas (HDGHG) FRM) emission modeling
platform. However, for the ptnonipm sector for all MATS scenarios, including year 2005 emissions, we
included additional known ethanol plants that were not previously included 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)
We replaced all ethanol plants that OTAQ had supplied from the RFS2 rule -see Section 2.1.2 in the CSAPR
Final TSD- with those recently compiled for the 2005 case for MATS. These plants represent a "Low
Ethanol" scenario needed to produce only 4 billion gallons of ethanol, essentially a scenario without a future
year RFS2 mandate (or MATS). All ethanol plants were assigned or corrected (after quality assurance
analyses) 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 fuel
type (e.g., coal, natural gas). Finally, because benzene, acetaldehyde and formaldehyde (BAF) emissions
were directly computed for these sources, unlike the rest of the ptnonipm sector, we treated these ethanol
plants as VOC integrate sources. A summary of the ethanol plant emissions used in the 2005 scenario is
provided in Table 2-13.
Table 2-13. 2005 ethanol plant emissions
Pollutant
1,3 -Butadiene
Acrolein
Formaldehyde
Benzene
Acetaldehyde
CO
NOX
PMio
PM2.5
S02
VOC
Emissions
0.0003
10.5
13.3
5.7
314.4
7,023
8,204
10,107
3,691
9,001
10,754
20
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2.5 2005 nonpoint sources (afdust, ag, avefire, nonpt)
The year 2005 area-source fugitive dust (afdust), agricultural animal and fertilizer NHs (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 MATS modeling. The 2005
nonpoint sources that change 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 reference case ethanol emissions by the ratio of 2005 to 2017 base total VOC emissions to
compute year 2005 ethanol emissions:
Ethanol_2005 = Ethanol_2017reference * (VOC_2005 / VOC_2017reference)
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 and 2017 for MATS 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 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, Inspection/Maintenance 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
County YearMonthHour table of the NMIM County Database (NCD) NCD20100602 NMEVI 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.
21
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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 MATS
scenarios and years.
2.7 Emissions summaries for 2005 base case
Once developed, the emissions inventories were processed to provide the hourly, gridded emissions for the
model-species needed by CMAQ. Table 2-14 provides summaries of the 2005 U.S. emissions inventories
modeled for this rule by sector. Table 2-15 and Table 2-16 provide state-level summaries of SO2, and PM2.5
by sector. Note that the nonroad columns include emissions from traditional nonroad sources that are found
"on-land", along with commercial marine sources. The nonpoint columns include area fugitive dust,
agriculture, and other nonpoint emissions.
Table 2-14. 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
Table 2-15. 2005 base year SO2 emissions (tons/year) for states by sector
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
ECU
460,123
52,733
66,384
601
64,174
10,356
32,378
1,082
417,321
616,063
0
330,382
878,979
130,264
136,520
502,731
109,875
3,887
283,205
NonEGU
66,373
23,966
13,039
33,097
1,550
1,831
34,859
686
57,429
52,827
17,151
131,357
86,337
41,010
12,926
25,808
165,705
18,512
34,988
Nonpoint
52,325
2,571
27,260
77,672
6,810
18,455
1,030
1,559
70,490
56,829
2,915
5,395
59,775
19,832
36,381
34,229
2,378
9,969
40,864
Nonroad
5,622
6,151
5,678
40,222
4,897
2,557
2,657
414
31,190
9,224
2,304
19,305
9,437
8,838
8,035
6,943
25,451
1,625
9,353
Onroad
3,554
3,622
1,918
4,526
2,948
1,337
486
205
12,388
6,939
902
6,881
4,641
2,036
1,978
3,240
2,902
963
3,016
Fires
983
2,888
728
6,735
1,719
4
6
0
7,018
2,010
3,845
20
24
25
103
364
892
150
32
Total
588,980
91,931
115,008
162,852
82,098
34,540
71,416
3,947
595,836
743,893
27,117
493,339
1,039,194
202,004
195,943
573,315
307,202
35,106
371,458
22
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State
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Total
ECU
84,234
349,877
101,678
75,047
284,384
19,715
74,955
53,363
51,445
57,044
30,628
180,847
512,231
137,371
1,116,095
110,081
12,304
1,002,203
176
218,781
12,215
266,148
534,949
3
34,813
9
220,287
3,409
469,456
180,200
89,874
10,380,883
NonEGU
19,620
76,510
24,603
29,892
78,308
11,056
7,910
2,253
3,155
7,639
7,831
58,426
59,433
9,582
115,155
40,482
9,825
83,375
2,743
31,495
1,702
65,693
223,625
1,511
9,132
902
69,401
24,211
46,710
66,807
22,321
2,030,759
Nonpoint
25,261
42,066
14,747
6,796
44,573
2,600
7,659
12,477
7,408
10,726
3,193
125,158
22,020
6,455
19,810
8,556
9,845
68,349
3,365
13,489
10,347
32,714
115,192
0
3,577
5,385
32,923
7,254
14,589
6,369
6,721
1,216,362
Nonroad
6,524
14,626
10,409
5,930
10,464
3,813
9,199
2,880
789
13,321
3,541
15,666
8,766
5,986
15,425
5,015
5,697
11,999
816
7,719
3,412
6,288
34,944
0
2,439
385
10,095
18,810
2,133
7,163
2,674
446,831
Onroad
2,669
8,253
2,934
2,590
4,901
874
1,510
656
746
3,038
1,801
6,258
6,287
533
7,336
3,039
1,790
6,266
254
3,589
623
5,538
16,592
0
1,890
342
4,600
3,343
1,378
3,647
721
168,480
Fires
93
91
631
1,051
186
1,422
105
1,346
38
61
3,450
113
696
66
22
469
4,896
32
1
646
498
277
1,178
0
1,934
49
399
407
215
70
1,106
49,094
Total
138,402
491,423
155,002
121,306
422,816
39,480
101,337
72,975
63,580
91,830
50,445
386,468
609,433
159,994
1,273,843
167,642
44,357
1,172,224
7,354
275,719
28,797
376,659
926,480
1,515
53,784
7,073
337,705
57,433
534,481
264,256
123,417
14,292,410
Table 2-16. 2005 base year PM2.s emissions (tons/year) for states by sector
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
ECU
23,366
7,418
1,688
347
4,342
562
2,169
17
24,217
28,057
0
16,585
34,439
8,898
5,549
NonEGU
19,498
3,940
10,820
21,517
7,116
224
1,810
172
25,193
12,666
2,072
15,155
14,124
6,439
7,387
Nonpoint
35,555
21,402
34,744
94,200
25,340
11,460
1,590
589
52,955
63,133
41,492
74,045
74,443
54,312
138,437
Nonroad
4,142
4,486
3,803
22,815
3,960
1,740
818
277
15,035
6,504
2,140
12,880
6,515
6,969
5,719
Onroad
5,775
6,920
3,102
26,501
4,377
2,544
922
367
16,241
12,449
1,402
12,574
7,585
3,468
3,109
Fires
13,938
37,151
10,315
97,302
24,054
56
87
0
99,484
24,082
52,808
277
344
349
1,468
Total
102,273
81,316
64,472
262,682
69,189
16,586
7,397
1,421
233,125
146,892
99,914
131,516
137,450
80,436
161,669
23
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State
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Total
ECU
19,830
5,599
52
15,417
3,110
11,022
3,262
2,029
6,471
2,398
1,246
3,341
2,586
4,625
5,583
9,648
16,967
6,397
53,572
1,411
412
55,547
10
14,455
390
12,856
21,464
0
5,055
37
12,357
2,396
26,377
5,233
8,068
496,877
NonEGU
10,453
32,201
3,783
6,768
2,245
12,926
10,538
10,602
6,966
2,729
2,340
4,095
568
2,588
1,460
4,994
12,665
598
12,847
6,246
8,852
16,263
256
4,779
2,982
21,912
37,563
1,569
3,595
337
11,455
4,618
5,154
7,967
10,298
433,346
Nonpoint
31,245
28,164
15,037
23,323
31,116
47,722
73,990
34,217
76,419
30,096
45,661
9,920
13,316
13,623
50,698
48,540
49,551
41,504
52,348
90,047
58,145
44,607
1,289
26,598
33,678
32,563
194,036
0
14,761
6,943
38,140
45,599
14,778
37,277
31,645
2,110,298
Nonroad
4,762
9,440
1,363
3,410
3,293
8,561
8,541
4,133
7,230
2,654
5,848
2,212
907
5,042
1,959
8,607
6,272
4,552
9,847
3,765
3,741
7,565
394
3,491
2,910
5,072
21,361
0
1,627
479
5,968
6,697
1,702
6,083
1,455
268,745
Onroad
5,566
4,288
1,759
5,504
5,913
13,006
6,842
4,195
7,665
1,347
2,620
1,290
1,512
5,963
2,861
11,139
8,939
976
11,785
4,559
3,375
11,058
577
5,061
1,056
8,514
29,859
0
2,703
605
6,661
6,721
1,930
6,783
1,103
301,073
Fires
5,155
12,647
2,127
531
1,324
1,283
8,943
14,897
2,636
17,311
1,483
19,018
534
865
48,662
1,601
9,870
934
316
6,644
65,350
454
14
9,163
7,062
3,934
21,578
0
27,412
696
5,659
4,487
3,050
994
15,686
684,035
Total
77,010
92,339
24,120
54,952
47,001
94,520
112,116
70,074
107,388
56,536
59,198
39,876
19,423
32,707
111,224
84,529
104,264
54,962
140,715
112,672
139,874
135,494
2,540
63,548
48,079
84,851
325,861
1,569
55,153
9,098
80,241
70,519
52,991
64,337
68,254
4,294,373
3 VOC Speciation Changes that Represent Fuel Changes
A significant detail that is different in each of the MATS modeling cases than in 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 MATS 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
24
-------
ftp://ftp.epa.gov/EtnisInventorv/2005v4/2005 emissions tsd __07jul2010.pdf. In the MATS 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 is referred to as an
"E-profile", for example pre-Tier 2 vehicles E10 gasoline exhaust speciation profile 8751 versus 875 IE. 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 because the nonpoint inventories do not include ethanol specifically.
The onroad sector has some additional changes to VOC speciation. Instead of speciating VOC, 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
and evaporative modes. For the MATS 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 case.
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 combination
of profiles between the base and future cases. The speciation changes from fuels in the nonpt sector are for
refueling, 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 sources6. Refinery to bulk terminal (rbt) fuel distribution speciation does not
change across the modeling cases because this is considered upstream from the introduction of ethanol into
the fuel7. Mapping of fuel distribution SCCs to btp and rbt emissions categories can be found in Appendix A
of the revised annual Renewable Fuel Standard (RFS2) Emissions Inventory for Air Quality Modeling
Technical Support Document (EPA Report No. 420-R-10-005, January 2010,
http://www.epa.gov/otaq/renewablefuels/420rl0005.pdf).
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 MATS 2005 case is also
included. Appendix A lists ancillary input data set names used for MATS emissions.
5 This was an oversight in the 2005v4.2 platform corrected for this modeling effort.
6 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).
7 We also identified bulk plant storage (bps) as an upstream source that is pre-addition of ethanol and uses the same speciation
profile as rbt.
25
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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
Refueling
2005v4.2
2005 MATS
2017 MATS reference
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
E10 evap perm
COMBO
8869E
8870E
EO Headspace
E10 Headspace
COMBO
875 IE
8757E
8758E
Pre-Tier 2 E10 exhaust
Tier 2 E10 Exhaust
Tier 2 E15 Exhaust
COMBO (All evap except permeation)
8754E
8872E
E10 Evap
E15 Evap
COMBO (Permeation evap)
8769E
8770E
E10 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 E10 exhaust
COMBO
8753
8754
8762
EO evap
E10 evap
EO Headspace composite
COMBO
8750
8751
Pre-Tier 2 EO exhaust
Pre-Tier 2 E10 exhaust
COMBO
8753
8754
EO evap
E10 evap
COMBO
8869
8870
EO Headspace
E10 Headspace
8751
8754
8870
Pre-Tier 2 E10 exhaust
E10 Evap
E10 Headspace
26
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Category
Nonroad Diesel
Exhaust
Evaporative
Refueling
PFC
Aircraft
Locomotives
Marine
BTP
RBT/BPS
Ethanol Plants
2005v4.2
2005 MATS
2017 MATS reference
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 Proda
Ethanol Fuel Prodb
a corn ethanol and biodiesel ptnonipm
b cellulosic ethanol & cellulosic diesel nonpt
27
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4 2017 Reference Case
The 2017 reference case represents the future, including the implementation of emissions impacts of the fuel
volumes mandated by the 2005 EPAct and 2007 EISA and finalized in the RFS2 program. The reference
case includes MSAT2 and LDGHG but does 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. 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, onroad mobile including refueling sources, OTAQ-generated
emissions were provided to reflect the reference case fuels.
The 2017 reference case uses 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 MATS and CSAPR use the same 2005v4.2-based emissions
inventories. There are some differences between the 2012 and 2014 base case projections in CSAPR and the
2017 reference case for MATS:
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).
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 MATS
modeling, rather than NEI emissions.
4) There is a new dataset of ethanol plants that replace a limited set of NEI ethanol plants in 2005v4.2-
based CSAPR 2012 and 2014 projections. These MATS reference case emissions are the same for
the 2005 and 2017 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).
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
described above.
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
programs that might have been necessary for areas to attain the 1997 PM2.5 NAAQS annual standard, 2006
PM NAAQS (24-hour) standard, and the 1997 ozone NAAQS are generally not included in the future base-
case projections for most states. One exception are some NOx and VOC reductions associated with the New
York, Virginia, and Connecticut State Implementation Plans (SIP), 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
28
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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 NH3
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 2017 are
included. The only differences between the MATS future case runs are the fuels used, specifically,
the ratio of E10 and E15 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, incorporating CSAPR Proposal comments and controls based on Emissions Control Area
(EGA) and International Marine Organization (IMO) global NOx and SO2 controls.
• Onroad mobile sector uses a version MOVES developed for the Tier 3 Proposal that incorporates new
car and light truck greenhouse gas emissions standards (LDGHG) affecting model years 2012 and
later (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.
29
-------
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.
30
-------
Table 4-1 summarizes the control strategies and growth assumptions by source type that were used to create
the 2017 reference-case emissions from the 2005v4.2 base-case inventories. These future year base case
projections and controls are also included in the MATS reference and control cases. All Mexico, Canada,
and offshore oil emissions are unchanged in all future cases from those in the 2005 base case.
Lists of the control, closures, projection packets (datasets) used to create the MATS 2017 future case
inventories from the 2005 MATS base case are provided in Appendix C.
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
MATS Rule. 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.
31
-------
Table 4-1. Control strategies and growth assumptions for creating the 2017 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 year 20 17 (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.
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 Industrial/Commercial/Institutional
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
NOX, SO2, HC1
EPA, 2007a
1
2
o
J
4
5
EPA, 2005
5
6
7
8
9
10; EPA,
2010
11
12
Section
4.2.13.2
32
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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
NOX, CO, PM,
SO2
All
SO2
13
14
15
Nonpoint (nonpt sector) projection approaches
Municipal Waste Landfills: projection factor of 0.25 applied
Livestock Emissions Growth from year 2002 to 2017
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 2017
Gasoline and diesel fuel Stage II refueling via MOVES2010a month-specific inventories
for 2017 with assumed RFS2 and LDGHG fuels
Portable Fuel Container Mobile Source Air Toxics Rule 2 (MSAT2) inventory growth
and control from year 2005 to 2017
Use Phase II WRAP 2018 Oil and Gas
Use 2008 Oklahoma and Texas Oil and Gas, and apply year 2017 projections for TX, 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, S02, 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,
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.nv.gov/docs/airjdf/NYMASrP7final.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.
33
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15. Based on available, enforceable state sulfur rules as of November, 2010:
http://wwwilta.org/LegislativeandRegulatorvMVNRLM/NEUSASulfur%20Rules 09.2010.pdf,
http://www.mainelegislature.org/legis/bills/bills 124th/billpdfs/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 based on MSAT2 rule and ethanol fuel assumptions (EPA, 2007b)
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 2017
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
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
VOC
all
all
all
all
1
2
3,4,5
6
EPA, 2009;
3; 4; 5
7, 3; EPA,
2009
APPROACHES/REFERENCES - Mobile Sources
1. http://epa.gov/otaq/hwv.htm
2. Only for states submitting these inputs: http ://www. epa. gov/otaq/lev-nlev. htm
34
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3. http://www.epa.gov/nonroad-diesel/2004fr.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.html).
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 reference
case, also known as the future year baseline, emissions do not reflect the final Mercury and Air Toxics
(MATS) rule, but the control case emissions do reflect the rule. Neither case reflects the Boiler MACT
regulatory assumptions.
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 4.1 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 case modeled
with CMAQ assumed that 100% of the HC1 found in the coal was emitted into the 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 case modeled in IPM for this rule includes estimates of emissions reductions that will result from
the Cross-State Air Pollution Rule. 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 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.
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.
35
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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 MATS 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 MATS 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#final) were updated to reflect year 2017.
Year-specific projection factors for years 2017 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 the year
2017 RFS2 mandate impacts on emissions to the reference case. 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 4.2.13. All
stationary non-EGU emissions in the 2017 reference case are unchanged in the 2017 control case (see
Section 5); therefore, we will simply note that these emissions are "year 2017" rather than the more
cumbersome "year 2017 reference case".
Table 4-2. MATS reference case mobile source-related projection methods
Input
Corn ethanol plants
Biodiesel plants
Cellulosic fuel
production
Ethanol transport
and distribution
Portable Fuel
Containers (PFCs)
Type
SMOKE ORL file that
replaces 2005 base case
ORL file
SMOKE ORL file
SMOKE ORL file
SMOKE ORL file
SMOKE ORL
Sector(s)
ptnonipm
ptnonipm
nonpt
nonpt
nonpt
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.
36
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Onroad refueling
Refinery
adjustments
Ethanol transport
gasoline & ethanol
blends
Upstream
agricultural
adjustments
SMOKE ORL file
Projection factors
Projection factors
Projection factors
nonpt
ptnonipm
nonpt,
ptnonipm
afdust, ag,
nonpt,
ptnonipm
MOVES-based gasoline and diesel fuel
spillage and displacement vapor losses.
County-level (nonpoint) format, monthly
resolution.
Not in base cases, accounts for changes in
various refinery processes due to
incorporation of RFS2 fuels.
Not in base cases, accounts for RFS impacts
on emissions from bulk plant storage, refinery
to bulk terminal, and bulk terminal to pump.
Not in base cases, 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 recently compiled for 2005 and a year 2017
without the RFS2 mandate (not separately modeled for this rule). 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 the reference case. Table 4-3 provides the summaries for the corn ethanol plants
in the 2017 reference case.
Table 4-3. 2017 reference case corn ethanol plant emissions
Pollutant
1,3 -Butadiene
Acrolein
Formaldehyde
Benzene
Acetaldehyde
CO
NOX
PMio
PM2.5
SO2
VOC
Tons
0.0011
41.7
45.2
20.3
643.2
14,847
20,035
21,639
6,825
11,299
35,459
4.2.2 Biodiesel plants (ptnonipm)
OTAQ developed an inventory of existing biodiesel plants for 2017 that were sited at existing plant locations
in support of producing biodiesel fuels for the RFS2 mandate. The RFS2 calls for 1.45 billion gallons per
year (Bgal) of biodiesel fuel production by year 2017. Only plants with current production capacities were
assumed to be operating in 2017. 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 a scalar adjustment (of 1.41) to each individual biodiesel plant
to match the 2017 production target of 1.45 Bgal. Once facility-level production capacities were scaled,
emission factors were applied based on an assumed natural gas combustion process. Inventories were
37
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modeled as point sources with Google Earth and web searching validating facility coordinates and correcting
state-county FIPS.
38
-------
Table 4-4 provides the 2017 biodiesel plant emissions estimates.
39
-------
Table 4-4. 2017 biodiesel plant emissions
Pollutant
Acrolein
Formaldehyde
Benzene
Acetaldehyde
CO
NOX
PMio
PM2.5
SO2
VOC
Tons
3.09E-04
2.23E-03
4.71E-05
3.59E-04
726
1,171
99
99
9
64
4.2.3 Portable fuel containers (nonpt)
OTAQ provided year 2017 PFC emissions that include estimated Reid Vapor Pressure (RVP) and oxygenate
impacts on VOC emissions, and more importantly, large increases in ethanol emissions from RFS2. These
emission estimates also include refueling from the NONROAD model for gas can vapor displacement,
changes in tank permeation and diurnal emissions from evaporation. Because these PFC inventories contain
ethanol, we developed a VOC E-profile that integrated ethanol, see Section 3 for more details. Emissions for
2017 are provided in Table 4-5.
Table 4-5. PFC emissions for 2017
Pollutant
VOC
Benzene
Ethanol
Tons
123,186
1,368
11,565
4.2.4 Cellulosic fuel production (nonpt)
OTAQ developed county-level inventories for cellulosic diesel and cellulosic ethanol production for 2017 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. Total county production capacities was over 16 Bgal; therefore, OTAQ
applied a scalar adjustment (of 0.246) to each counties production capacity to match the 2017 production
target of 3.93 Bgal (2.2 Bgal diesel and 1.69Bgal ethanol). Once county-level cellulosic production
capacities were scaled down to match the 2017 target, emission factors were applied based on an assumed
natural gas combustion process.
40
-------
Table 4-6 provides the year 2017 cellulosic plant emissions estimates.
41
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Table 4-6. 2017 cellulosic plant emissions
Pollutant
Acrolein
Formaldehyde
Benzene
Acetaldehyde
CO
Ethanol
NH3
NOX
PMio
PM2.5
S02
VOC
Tons
21
58
27
786
42,839
1,875
0.5
64,062
7,533
3,796
4,973
5,336
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.
4.2.5 Ethanol transport and distribution (nonpt)
OTAQ developed county-level inventories for ethanol transport and distribution for 2017 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. VOC losses (Emissions) due to ethanol transport and distribution
SCC
30205031
30205052
30205053
Description
Denatured Ethanol Storage Working Loss
Ethanol Loadout to Truck
Ethanol Loadout to Railcar
Tons
27,763
19,069
9,610
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 MATS scenarios.
VMT, fleet age distribution and speed distribution were developed for 2017. 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 onroad mobile refueling emissions is provided in
42
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Table 4-8.
43
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Table 4-8. Onroad gasoline and diesel refueling emissions
Fuel Type
Gasoline
Diesel
Gasoline
Gasoline
Pollutant
voc
voc
Benzene
Ethanol
Tons
63,759
12,962
161
8,735
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 Table 4-9.
Table 4-9. Impact of refinery adjustments on 2017 emissions
Pollutant
CO
NOX
PMio
PM2.5
SO2
VOC
Reductions (tons)
12,674
20,183
4,367
2,525
13,846
3,693
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 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
Pollutant
CO
NH3
NOX
PMio
PM2.5
S02
VOC
Increases (tons)
302
45,272
363
42,934
6,500
69
16
44
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4.2.10 Livestock emissions growth (ag, afdust)
Growth in ammonia (NH3) 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. Table 4-1 liable 4-11 provides
the growth factors from the 2005 base-case emissions to all MATS year 2017 scenarios for animal categories
applied to the ag, afdust, and ptnonipm sectors for livestock-related SCCs.
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 Table 4-11 Table 4-11.
Table 4-11. Growth factors from year 2005 to 2017 for animal operations
Animal Category
Dairy Cow
Beef
Pork
Broilers
Turkeys
Layers
Poultry Average
Overall Average
Projection Factor
1.0000
1.0206
1.0893
1.3442
1.0000
1.2406
1.2674
1.0935
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:
45
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• 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 4-13shows 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 Factor
0.45
0.65
0.36
0.74
Correct
2017 Factor
0.84
1.15
0.70
1.30
Table 4-13. Impact of year 2017 projection factor error on residential wood combustion estimates
Pollutant
NOX
PM2.5
SO2
VOC
2005
Emissions
38,292
381,362
5,302
569,950
Erroneous 2017
Emissions
18,023
174,769
2,529
242,126
Erroneous 2017
Reductions
20,270
206,593
2,773
327,824
Correct 2017
Emissions
33,545
326,706
4,697
450,990
Correct 2017
Reductions
4,747
54,656
605
118,959
4.2.12 Aircraft growth (ptnonipm)
These 2005 point-source emissions are projected to future years by applying activity growth using data on
itinerant (ITN) operations at airports. The ITN operations are defined as aircraft take-offs whereby the
aircraft leaves the airport vicinity and lands at another airport, or aircraft landings whereby the aircraft has
arrived from outside the airport vicinity. We used projected ITN information available from the Federal
Aviation Administration's (FAA) Terminal Area Forecast (TAP) System:
http://www.apo.data.faa.gov/main/taf.asp (publication date 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
46
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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: low-ethanol, reference and control.
Table 4-14. Factors used to project 2005 base-case aircraft emissions to 2017
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;Iet Engine: IP-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;Iet Engine: let 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;Iet Engine: let A
Internal Combustion Engines;Rotary Wing Aircraft L &
TO Exhaust;Military;Iet Engine: IP -4
Internal Combustion Engines;Rotary Wing Aircraft L &
TO Exhaust;Military;Iet Engine: IP-5
Projection Factor
1.0229
1.1288
0.8918
0.8620
1.0229
1.1288
1.1288
0.8918
0.8918
1.0229
1.0229
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).
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 MATS 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
47
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Appendix D of the CSAPR TSD:
ftp://ftp.epa.gov/EmisInventory/2005v4 2/transportrulefmal 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.
• Evolutionary information gathering of plant closures (i.e., emissions were zeroed out for future years)
were also included where information indicated that the plant was actually closed after the 2005 base
year and prior to CSAPR and MATS 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 reference 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 MATS 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
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 decrees controls at the facility and SCC level (collected through internal
coordination on refineries by the EPA).
Fuel sulfur fuel limits were enforceable for Maine, New Jersey and New York.
48
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• Criteria air pollutant (cap) reductions a cobenefit to RICE NESHAP controls, including SC>2 RICE
cobenefit controls.
• 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.nv.gov/docs/air_pdf/NYMASIP7fmal.pdf See Section 3.2.6 in the CSAPRTSD:
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, so 2013 estimates were used for all future year MATS 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
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.
49
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Table 4-16
50
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Table shows the magnitude of the ISIS-based cement industry reductions in the future-year emissions that
represent 2013 (and 2017 for MATS), and the impact that these reductions have on total stationary non-EGU
point source (ptnonipm) emissions.
51
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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
(tons)
193,000
14,400
128,400
6,900
2,900
Reductions in
2017
(tons)
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 data base
developed for that rulemaking entitled "survey_database_2008_results2.mdb" (EPA-HQ-OAR-2002-0058-
0788) which is posted under the Technical Information for the Boiler MACT major source rule
(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 MATS future year scenarios.
Table 4-17. State-level non-MACT boiler reductions from ICR data gathering
State
Michigan
North Carolina
Virginia
Washington
North Carolina
Pollutant
NOX
SO2
S02
S02
HC1
Pre-controlled
Emissions
(tons)
907
652
3379
639
31
Controlled
Emissions
(tons)
544
65
338
383
3
Reductions in
2017 (tons)
363
587
3041
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 reference case emissions projection.
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
52
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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 MATS 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 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 MATS emissions processing:
http://www.ilta.org/LegislativeandRegulatoiYMVNRLM/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 15ppm
of sulfur, thus reducing SC>2 emissions by 99.5% for post-2012 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 MATS 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 the 2017 scenario. The impact of these fuel
sulfur content reductions on 862 is shown in Table 4-19. These year-specific reductions are the same for all
MATS scenarios: low-ethanol, reference and control.
Table 4-19. Impact of fuel sulfur (802) controls on 2017 non-EGU projections
State
Maine
New Jersey
New York
Total
Reductions (tons)
8,323
998
54,431
63,751
53
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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 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. 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 2017. 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 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 all MATS scenarios: low-ethanol, reference and control. Table
4-20 shows the 2005 and 2017 NOx and SO2 emissions including RICE reductions for Oklahoma.
Table 4-20. Oil and gas NOx and SO2 emissions for 2005 and 2017 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
PM25
2005
1,918
2,945
4,862
2017
2,231
1,085
3,316
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
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
4.3 Onroad mobile source projections (onroad)
The same version of MOVES and SMOKE-MOVES Integration Tool was used to create all MATS 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 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
54
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configurations (Section 2.2.4) were previously discussed and were the same for all MATS 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 VMT inventory, 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. 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 and 2017) 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 emissions used for MATS reflect onroad mobile control programs including the Light-
Duty Vehicle Tier 2 Rule and the Mobile Source Air Toxics (MSAT2) final rule.
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 (NET) 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
year 2017. 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 MATS include year-specific regulations affecting locomotives, various nonroad engines
including diesel engines and various marine engine types, fuel sulfur content, and 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 in renewable fuels in the 2017 reference case.
4.4.1 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
55
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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 MATS
scenarios. For 2017, El0 and El5 are available in every county, but nonroad equipment is assumed to burn
only E10. To generate the NMEVI fuels, the E10 fuel was copied from MOVES to NMIM, and the E10
oxygenate was assigned a market share of 1. Highway diesel fuel sulfur levels are copied directly from
MOVES to NMIM. Nonroad diesel fuel sulfur levels are retained from NMIM.
Section 2.3.1.3 provides a cross-walk of the nonroad mobile NMIM emission scenarios used in MATS; as
previously discussed, the only difference between these 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. Although the NONROAD Model estimates changes in VOC production from E15, NMIM calculates
toxics as if the fuel were E10. 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 programs in our projections such as programs encouraging either no
refueling or evening refueling on Ozone Action Days and diesel retrofit programs. The national regulations
incorporated in all MATS 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/regs/nonroad/420f08004 .htm)
• OTAQ's Small Engine Spark Ignition ("Bond") Rule, November 2008:
(http ://www. epa. gov/otaq/equip-ld. 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,
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
create year 2017 estimates, are discussed in Section 4.2. The remaining 2005 NEI emissions for locomotives and Class 1
and Class 1 and Class 2 commercial marine vessel (C1/C2 CMV) use year-specific projection estimates. Base future year
Base future year locomotive and C1/C2 CMV emissions were calculated using projection factors that were computed based
computed based on national, annual summaries of emissions in 2002 and 2017. Some additional emissions were then
were then factored in due to changes in fuels. These national summaries were used to create national by-pollutant, by-SCC
pollutant, by-SCC projection factors; these factors include final locomotive-marine controls and are provided in Table
in Table 4-21. Modest additive Class I railroad and C1/C2 CMV emissions that account for RFS2 volume increases in
56
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the MATS reference scenario 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
57
-------
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
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
2285002009
2285002009
2285002010
2285002010
2285002010
2285002010
2285002010
2285002010
2285002010
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
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
CO
NH3
NOX
PM10
PM25
S02
voc
CO
NH3
NOX
PM10
PM25
S02
VOC
CO
NH3
NOX
PM10
PM25
S02
VOC
CO
NH3
NOX
PM10
PM25
S02
VOC
CO
NH3
NOX
PM10
PM25
S02
VOC
CO
NH3
NOX
PM10
PM25
S02
VOC
Projection
Factor
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
0.005
0.469
1.341
1.325
1.128
0.914
0.934
0.006
1.509
58
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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
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, 2009). This rule lowered diesel sulfur
content and tightened emission standards for existing and new locomotives and marine diesel emissions to
lower future-year PM, 862, and NOx, and is documented at:
http://www.epa.gov/otaq/regs/nonroad/420f08004.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 inventory and override the
C1/C2 projection factors in Table 4-21.
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 SO2
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, which includes ECA-IMO controls. An overview of the ECA-IMO project and
future-year goals for reduction of NOx, SO2, and PM C3 emissions can be found at:
http ://www. epa. gov/oms/regs/nonroad/marine/ci/420f09015 .htm
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
59
-------
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 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
(ftp://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, and PM2.5 factors to project class 3 CMV emissions for 2017
Region
Alaska East
Alaska West
East Coast
Gulf Coast
Hawaii East
Hawaii West
North Pacific
South Pacific
Great Lakes
Outside ECA
NOX
1.409
1.469
1.435
1.120
1.539
1.725
1.240
1.573
1.106
1.585
SO2
0.062
1.571
0.070
0.055
0.078
2.037
0.064
0.084
0.046
1.891
PM25
0.203
1.571
0.264
0.207
0.268
2.035
0.222
0.293
0.171
1.891
VOC
1.631
1.571
1.955
1.529
2.036
2.037
1.644
2.114
1.302
1.891
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 MATS 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.
4.6 Reference case emission summaries
60
-------
Table 4-24 shows a summary of the 2005 and modeled reference case emissions for the lower 48 states.
61
-------
Table 4-25 and Table 4-26 provide summaries of SC>2 and PM2.5 in the 2017 baseline for each sector by state.
Table 4-27shows the future year baseline EGU emissions by state for all criteria air pollutants.
62
-------
Table 4-24. Summary of modeled base case SC>2 and PM2.5 annual emissions (tons/year) for 48 states by
sector
Source Sector SO2 Emissions
EGU Point
Non-EGU Point
Nonpoint
Nonroad
On-road
Average Fire
Total SO2, All Sources
Source Sector PMi.s Emissions
EGU Point
Non-EGU Point
Nonpoint
Nonroad
On-road
Average Fire
2005
10,380,883
2,030,759
1,216,362
446,831
168,480
49,094
14,292,410
2005
496,877
433,346
2,110,298
268,745
301,073
684,035
2017
3,281,364
1,534,991
1,125,985
15,759
29,288
49,094
6,036,480
2017
276,430
411,437
1,912,757
150,221
129,416
684,035
Total PM2.5, All Sources
4,294,373
3,564,296
63
-------
Table 4-25. Reference case SC>2 emissions (tons/year) for states by sector
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
ECU
186,084
36,996
92,804
5,346
74,255
3,581
2,835
5
117,702
96,712
182
118,217
200,969
85,178
45,740
116,927
142,447
2,564
29,786
15,133
163,168
52,380
34,865
178,143
24,018
70,910
14,140
6,719
9,042
10,211
14,653
71,113
105,344
180,935
141,433
13,211
126,316
0
103,694
29,711
33,080
249,748
0
34,912
264
51,004
5,569
84,344
50,777
NonEGU
63,053
24,191
12,160
21,046
1,415
1,833
4,770
686
49,082
44,248
17,133
81,683
73,930
22,865
10,288
23,530
129,730
14,285
33,562
17,077
48,697
24,742
24,284
33,757
7,212
6,885
2,132
2,471
6,700
7,813
45,222
58,517
9,915
93,600
27,873
9,790
64,697
2,745
28,536
1,655
59,145
129,667
676
6,599
902
50,387
19,780
32,458
61,080
Nonpoint
52,341
2,467
26,801
67,846
6,210
18,149
1,018
1,505
70,073
55,946
2,894
5,650
59,771
19,929
36,140
33,852
2,669
2,007
40,642
24,907
42,185
14,635
6,635
44,680
1,875
7,899
12,028
7,284
9,528
2,719
71,060
21,713
5,559
19,777
7,731
9,508
67,650
3,338
13,310
10,301
32,624
108,633
0
3,365
5,283
32,439
6,885
14,322
6,260
Nonroad
146
59
123
3,311
50
100
500
o
6
1,255
192
23
295
150
86
57
215
1,449
72
494
266
448
220
208
191
25
58
27
21
686
26
659
197
36
373
49
218
427
33
294
23
154
1,146
0
27
8
275
881
64
122
Onroad
569
724
314
2,087
532
311
91
38
2,111
1,158
162
1,107
760
324
294
463
447
149
593
565
995
558
396
722
106
202
200
137
757
262
1,466
890
71
1,093
501
361
1,060
85
500
86
757
2,483
0
291
129
849
633
178
633
Fires
983
2,888
728
6,735
1,719
4
6
0
7,018
2,010
3,845
20
24
25
103
364
892
150
32
93
91
631
1,051
186
1,422
105
1,346
38
61
3,450
113
696
66
22
469
4,896
32
1
646
498
277
1,178
0
1,934
49
399
407
215
70
Total
303,177
67,324
132,929
106,370
84,181
23,978
9,220
2,237
247,241
200,266
24,240
206,971
335,604
128,407
92,622
175,350
277,634
19,226
105,110
58,041
255,584
93,164
67,440
257,679
34,657
86,058
29,873
16,671
26,774
24,480
133,173
153,125
120,991
295,799
178,056
37,985
260,183
6,202
146,980
42,273
126,037
492,855
676
47,128
6,634
135,353
34,156
131,582
118,941
64
-------
State
Wyoming
Total
ECU
48,198
3,281,364
NonEGU
20,491
1,534,991
Nonpoint
5,944
1,125,985
Nonroad
18
15,759
Onroad
87
29,288
Fires
1,106
49,094
Total
75,844
6,036,480
Table 4-26. Reference case PM2.5 emissions (tons/year) for states by sector
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal
Utah
ECU
13,154
3,889
2,838
475
3,845
400
434
1
12,723
13,445
36
8,587
22,354
4,298
3,199
12,078
3,093
355
3,969
1,465
8,102
2,598
2,201
7,061
3,870
2,358
2,505
1,130
2,452
3,153
2,331
9,983
5,870
18,920
3,530
381
16,727
4
9,997
737
5,053
21,677
1
4,524
NonEGU
17,052
3,809
10,527
20,693
7,037
222
772
172
24,620
12,105
2,076
13,471
13,570
7,000
6,895
10,353
30,865
3,543
6,382
2,123
11,688
9,867
10,492
6,384
2,562
2,834
4,032
464
2,520
1,442
4,859
12,656
795
12,353
5,695
8,869
14,874
256
4,527
2,399
21,553
34,648
1,568
3,530
Nonpoint
33,235
20,214
33,486
73,607
19,868
6,838
1,207
536
50,472
59,412
40,288
70,775
72,501
51,684
136,633
26,811
27,082
8,213
18,960
23,729
43,055
68,121
31,474
69,722
28,479
44,904
9,351
8,981
8,559
49,789
44,334
43,398
40,802
47,811
88,862
39,503
38,523
1,070
23,430
32,697
28,449
187,604
0
13,978
Nonroad
2,403
2,674
2,042
14,875
2,350
1,038
383
139
8,100
3,803
1,186
6,885
3,491
3,348
2,872
2,717
5,107
881
1,975
1,914
4,696
4,483
2,337
3,954
1,332
2,967
1,319
576
2,929
1,148
5,032
3,583
2,126
5,302
2,029
2,148
4,582
222
1,932
1,339
2,939
11,901
0
963
Onroad
2,217
2,762
1,242
13,492
2,387
1,414
375
154
7,652
4,863
714
4,926
3,380
1,519
1,268
2,059
1,673
750
2,492
2,590
4,949
2,882
1,525
3,059
492
919
857
663
3,244
1,103
6,723
3,521
383
5,013
2,006
1,627
4,854
383
1,929
416
3,057
9,289
0
1,318
Fires
13,938
37,151
10,315
97,302
24,054
56
87
0
99,484
24,082
52,808
277
344
349
1,468
5,155
12,647
2,127
531
1,324
1,283
8,943
14,897
2,636
17,311
1,483
19,018
534
865
48,662
1,601
9,870
934
316
6,644
65,350
454
14
9,163
7,062
3,934
21,578
0
27,412
Total
81,999
70,498
60,450
220,443
59,540
9,968
3,259
1,002
203,050
117,711
97,108
104,922
115,640
68,198
152,335
59,173
80,467
15,869
34,310
33,145
73,773
96,893
62,926
92,816
54,048
55,465
37,083
12,348
20,569
105,298
64,879
83,011
50,910
89,715
108,767
117,877
80,014
1,949
50,978
44,650
64,985
286,698
1,569
51,724
65
-------
State
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Total
ECU
67
4,529
1,444
13,602
5,323
5,662
276,430
NonEGU
336
10,165
4,421
4,281
7,853
10,225
411,437
Nonpoint
4,930
32,254
35,706
12,951
27,656
30,812
1,912,757
Nonroad
307
3,507
3,328
1,048
3,161
850
150,221
Onroad
653
3,446
2,874
762
3,148
392
129,416
Fires
696
5,659
4,487
3,050
994
15,686
684,035
Total
6,989
59,561
52,259
35,695
48,135
63,626
3,564,296
Table 4-27. Future year baseline EGU CAP emissions (tons/year) by state
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
CO
27,024
16,797
9,925
45,388
9,006
9,180
4,256
67
72,915
16,537
1,532
51,862
30,587
8,316
5,066
37,287
32,626
12,789
13,446
7,128
25,856
9,365
9,704
16,499
5,266
4,691
9,677
5,667
25,831
9,079
19,731
17,367
7,437
33,481
26,165
5,905
38,767
1,748
10,305
NOX
64,064
36,971
36,297
20,910
50,879
2,738
2,452
11
83,174
43,778
613
56,128
106,881
42,698
25,163
71,259
33,509
6,121
17,933
7,991
66,846
36,867
27,319
52,464
20,946
28,898
15,627
4,908
11,178
65,189
21,172
44,141
53,778
93,150
47,454
10,828
123,501
456
37,516
voc
1,524
825
658
1,031
636
139
132
2
2,253
1,293
41
3,091
2,295
791
683
1,604
852
306
533
279
1,497
746
440
1,714
338
542
438
206
823
574
731
1,076
867
2,005
957
203
2,023
44
726
SO2
186,084
36,996
92,804
5,346
74,255
3,581
2,835
5
117,702
96,712
182
118,217
200,969
85,178
45,740
116,927
142,447
2,564
29,786
15,133
163,168
52,380
34,865
178,143
24,018
70,910
14,140
6,719
9,042
10,211
14,653
71,113
105,344
180,935
141,433
13,211
126,316
0
103,694
NH3
1,472
1,163
560
2,519
398
313
119
3
3,997
903
57
1,437
1,317
452
305
928
1,427
269
301
395
874
460
469
740
198
292
953
207
747
570
1,076
654
383
1,317
1,073
381
1,522
136
515
PM10
16,686
5,038
3,507
580
4,605
431
580
1
19,098
18,668
38
9,926
33,816
5,735
3,996
16,279
3,677
366
5,322
1,915
11,056
3,034
3,113
9,093
6,117
2,948
3,095
1,234
2,948
3,833
3,248
13,368
6,757
25,688
4,457
446
22,117
7
14,469
PM25
13,154
3,889
2,838
475
3,845
400
434
1
12,723
13,445
36
8,587
22,354
4,298
3,199
12,078
3,093
355
3,969
1,465
8,102
2,598
2,201
7,061
3,870
2,358
2,505
1,130
2,452
3,153
2,331
9,983
5,870
18,920
3,530
381
16,727
4
9,997
66
-------
State
South Dakota
Tennessee
Texas
Tribal
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
CO
742
10,693
78,317
601
5,632
1,868
30,205
7,183
15,496
19,247
9,087
873,344
NOX
14,293
16,982
145,182
73
67,476
458
39,408
14,284
54,247
35,179
71,380
1,930,769
voc
129
862
4,975
15
526
52
821
326
1,320
1,137
970
46,050
SO2
29,711
33,080
249,748
0
34,912
264
51,004
5,569
84,344
50,777
48,198
3,281,364
NH3
48
406
5,304
47
279
25
1,115
346
658
649
481
40,259
PM10
764
6,313
31,404
2
5,843
69
5,404
1,706
18,415
6,503
7,385
371,101
PM25
737
5,053
21,677
1
4,524
67
4,529
1,444
13,602
5,323
5,662
276,430
Emission estimates apply to all fossil Electric Generating Units, including those with capacity < 25MW
67
-------
5 MATS Control Case
For the future year control case (i.e., policy case) air quality modeling, the emissions for all sectors were
unchanged from the base case modeling except for those from EGUs. The IPM model was used to prepare
the future year policy case for EGU emissions. The air quality modeling for MATS relied on EGU emission
projections from an interim IPM platform based on the Cross-state Air Pollution Rule version
4.10_FTransport, and was subsequently updated during the rulemaking process. The updates made include:
updated assumptions regarding the removal of HC1 by alkaline fly ash in subbituminous and lignite coals; an
update to the fuel-based mercury emission factor for petroleum coke, which was corrected based on re-
examination of the 1999 ICR data; updated capital cost for new nuclear capacity and nuclear life extension
costs; corrected variable operating and maintenance cost (VOM) for ACT retrofits; adjusted coal rank
availability for some units, consistent with EIA From 923 (2008); updated state rules in Washington and
Colorado; and numerous unit-level revisions based on comments received through the notice and comment
process. Additional details on the version of IPM used to develop the control case are available in Chapter 3.
The changes in EGU 862, and PM2.5 emissions as a result of the policy case for the lower 48 states are
summarized in Table 5-1. Table 5-2 shows the CAP emissions for the modeled MATS control case by State.
State-specific difference summaries of EGU SC>2 and PM2.5 for the sum of the lower 48 states are shown in
Table 5-3 and Table 5-4 respectively.
Table 5-1. Summary of emissions changes for the MATS AQ modeling in the lower 48 states
Future Year EGU Emissions
Base Case EGU Emissions (tons)
Control Case EGU Emissions (tons)
Reductions to Base Case in Control case (tons)
Percentage Reduction of Base EGU Emissions
Total Man-made Emissions*
Total Base Case Emissions (tons)
Total Control Case Emissions (tons)
Percentage Reduction of All Man-made
Emissions
SO2
3,281,364
1,866,247
1,415,117
43%
6,036,480
4,621,363
23%
PM25
276,430
223,320
53,110
19%
3,564,296
3,511,186
1%
* In this table, man-made emissions includes average fires.
Table 5-2. EGU emissions totals for the Modeled MATS control case in the lower 48 states
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Idaho
Illinois
Indiana
CO
20,873
13,238
9,036
56,360
8,219
8,017
1,312
66,378
14,217
1,523
24,365
17,061
NOX
61,863
34,804
35,788
27,159
44,409
2,800
2,527
61,676
41,006
609
50,655
102,045
voc
1,313
749
642
1,307
582
136
67
2,055
1,197
41
2,353
1,872
SO2
68,517
23,459
35,112
5,041
19,564
1,400
4,160
64,791
78,197
182
103,867
156,781
NH3
1,235
921
490
2,548
358
313
93
3,482
790
56
1,050
1,110
PM10
9,734
4,264
1,696
1,057
3,492
439
3,056
16,434
11,165
38
7,309
29,683
PM25
7,844
3,494
1,593
942
2,859
412
1,455
11,377
9,742
36
6,588
20,388
68
-------
State
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
CO
7,340
4,683
25,911
28,171
10,992
4,283
5,408
18,792
8,699
8,782
12,249
2,223
4,493
7,178
6,781
8,350
7,987
18,725
15,195
7,266
29,956
26,687
6,002
24,865
1,721
9,826
641
5,551
71,475
266
4,003
1,868
26,778
6,334
13,923
16,124
7,516
707,640
NOX
41,247
22,136
70,126
31,655
5,683
16,554
7,211
60,982
34,942
20,749
52,755
19,758
28,180
14,382
4,862
7,699
64,922
20,863
35,309
53,267
85,565
44,725
9,671
104,906
443
37,849
14,290
16,931
138,086
32
65,286
458
37,255
3,834
47,836
32,865
71,135
1,789,790
voc
747
623
1,476
767
302
400
226
1,215
709
410
1,605
264
533
336
232
315
545
699
1,033
858
1,852
892
198
1,645
43
725
117
723
4,444
7
474
52
707
179
1,263
1,012
932
40,875
SO2
48,030
22,767
125,430
30,509
1,372
18,091
5,033
82,834
33,214
15,975
95,965
6,399
34,631
6,372
2,102
6,404
9,984
28,174
59,551
23,889
139,208
44,602
3,565
93,606
0
40,901
2,483
42,666
105,958
0
17,007
264
33,704
854
66,857
28,322
28,456
1,866,247
NH3
410
282
882
1,261
267
211
344
718
430
397
686
133
277
725
232
546
554
1,086
602
371
1,229
970
379
1,349
134
459
41
334
4,774
21
241
25
748
254
632
578
467
35,493
PM10
3,318
2,504
12,544
2,003
342
3,851
1,702
8,261
3,332
1,949
5,216
2,637
2,152
2,626
1,336
2,020
2,961
3,123
8,885
5,940
19,599
2,293
241
17,330
7
9,627
260
6,721
25,359
1
4,755
69
5,306
183
14,321
4,725
5,946
281,811
PM25
2,947
2,263
10,635
1,899
331
3,143
1,267
6,893
2,936
1,720
4,809
1,727
1,828
2,073
1,264
1,583
2,750
2,350
7,988
5,051
15,823
2,056
233
14,080
4
6,963
245
5,272
17,601
1
3,896
67
4,506
176
11,572
3,969
4,671
223,320
Table 5-3. State-s
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
pecific changes in annual EGU 862 for the lower 48 states
Future year
baseline SO2
(tons)
186,084
36,996
92,804
5,346
74,255
3,581
2,835
Future Year
Policy Case
SO2 (tons)
68,517
23,459
35,112
5,041
19,564
1,400
4,160
EGU SO2
reduction
(tons)
117,568
13,537
57,692
305
54,690
2,181
-1,324
EGU SO2
reduction (%)
63%
37%
62%
6%
74%
61%
-47%
69
-------
District of Columbia
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
5
117,702
96,712
182
118,217
200,969
85,178
45,740
116,927
142,447
2,564
29,786
15,133
163,168
52,380
34,865
178,143
24,018
70,910
14,140
6,719
9,042
10,211
14,653
71,113
105,344
180,935
141,433
13,211
126,316
0
103,694
29,711
33,080
249,748
0
34,912
264
51,004
5,569
84,344
50,777
48,198
3,281,364
0
64,791
78,197
182
103,867
156,781
48,030
22,767
125,430
30,509
1,372
18,091
5,033
82,834
33,214
15,975
95,965
6,399
34,631
6,372
2,102
6,404
9,984
28,174
59,551
23,889
139,208
44,602
3,565
93,606
0
40,901
2,483
42,666
105,958
0
17,007
264
33,704
854
66,857
28,322
28,456
1,866,247
5
52,911
18,515
0
14,350
44,189
37,148
22,973
-8,503
111,938
1,191
11,695
10,100
80,334
19,165
18,890
82,177
17,618
36,279
7,768
4,618
2,638
228
-13,521
11,562
81,455
41,727
96,831
9,646
32,710
0
62,793
27,228
-9,586
143,790
0
17,905
0
17,300
4,716
17,488
22,454
19,742
1,415,117
100%
45%
19%
0%
12%
22%
44%
50%
-7%
79%
46%
39%
67%
49%
37%
54%
46%
73%
51%
55%
69%
29%
2%
-92%
16%
77%
23%
68%
73%
26%
N/A
61%
92%
-29%
58%
N/A
51%
0%
34%
85%
21%
44%
41%
70
-------
Table 5-4. State-specific changes in annual EGU PM2.s for the lower 48 states
State
Alabama
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
District of Columbia
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Tribal
Utah
Future year
baseline
PM2.5 (tons)
13,154
3,889
2,838
475
3,845
400
434
1
12,723
13,445
36
8,587
22,354
4,298
3,199
12,078
3,093
355
3,969
1,465
8,102
2,598
2,201
7,061
3,870
2,358
2,505
1,130
2,452
3,153
2,331
9,983
5,870
18,920
3,530
381
16,727
4
9,997
737
5,053
21,677
1
4,524
Future Year
Policy Case
PM2.5 (tons)
7,844
3,494
1,593
942
2,859
412
1,455
0
11,377
9,742
36
6,588
20,388
2,947
2,263
10,635
1,899
331
3,143
1,267
6,893
2,936
1,720
4,809
1,727
1,828
2,073
1,264
1,583
2,750
2,350
7,988
5,051
15,823
2,056
233
14,080
4
6,963
245
5,272
17,601
1
3,896
EGU PM2 5
reduction
(tons)
5,310
395
1,246
-467
985
-12
-1,021
1
1,346
3,703
0
2,000
1,966
1,351
936
1,443
1,193
24
826
198
1,210
-339
481
2,252
2,143
530
432
-134
868
403
-19
1,995
819
3,097
1,474
148
2,646
0
3,033
492
-219
4,077
1
627
EGU PM2 5
reduction (%)
40%
10%
44%
-98%
26%
-3%
-235%
100%
11%
28%
0%
23%
9%
31%
29%
12%
39%
7%
21%
14%
15%
-13%
22%
32%
55%
22%
17%
-12%
35%
13%
-1%
20%
14%
16%
42%
39%
16%
2%
30%
67%
-4%
19%
56%
14%
71
-------
State
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
TOTAL
Future year
baseline
PM2.5 (tons)
67
4,529
1,444
13,602
5,323
5,662
276,430
Future Year
Policy Case
PM2.5 (tons)
67
4,506
176
11,572
3,969
4,671
223,320
ECU PM2 5
reduction
(tons)
0
24
1,268
2,031
1,354
991
53,110
ECU PM2 5
reduction (%)
0%
1%
88%
15%
25%
17%
6 References
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/fmaltechO 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/454r07002.pdf
EPA, 2009. 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 2009. Available at:
http ://www. epa. gov/otaq/regs/nonroad/420r08001 a.pdf
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.
72
-------
APPENDIX A
Ancillary Datasets and Parameters Used for Each MATS Modeling Case
The ancillary data files used for the MATS 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
reference case.
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 MATS modeling cases. 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. The values for the control case are
not shown, but they are the same as those used for the 2017 reference case.
Table A-l. List of ancillary data sets associated with the MATS 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
Environment
Variable
ARTOPNT
B3FAC
BGPRO
BGPRO
BELD3 A
BELD3 A
BELD3 B
BELD3 B
BELD3 TOT
BELD3 TOT
Program
smkinven
TmpbeisS
Smkmerge
Smkmerge
NormbeisS
NormbeisS
NormbeisS
NormbeisS
NormbeisS
NormbeisS
Sector
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]
2017 Reference Case
artopnt 2002detroit [vO]
beis3 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_279X240
(/garnet/oaqps) [vO]
LAND_TOTALS_US36_148X1 12
(/garnet/oaqps) [vO]
-------
Bioseasons file 12EUS1
Bioseasons file 36US1 mcip v3.4 beta4 b
CEM annually summed data
Combination profiles
Combination profiles - nonpt
Combination profiles - nonroad
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
Holidays table
BIOSEASON
BIOSEASON
CEMSUM
GSPRO COM
BO
GSPRO COM
BO
GSPRO COM
BO
GSPRO COM
BO
GSPRO COM
BO
COSTCY
PELVCONFI
G
PELVCONFI
G
GRIDDESC
SRGPRO
SRGPRO
SRGPRO
SRGPRO
SRGPRO
SRGPRO
GSCNV
GSPROTMP
L
GSREFTMP
L
HOLIDAYS
TmpbeisS
TmpbeisS
smkinven
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
smkinven
Laypoint
Laypoint
Grdmat
Grdmat
Grdmat
Grdmat
Grdmat
Grdmat
Grdmat
Spcmat
Spcmat
Spcmat
Temporal
beis
beis
ptipm
nonpt
nonroad
onroad
ptnonipm
seca c3
othon
othar
othar
othon
bioseason. cmaq. 2005b_ 1 2km
(/garnet/oaqps) [vO]
bioseason.cmaq.2005b 36km
(/garnet/oaqps) [vO]
cemsum_ptipm 2005
(/orchid/share) [vO]
gspro combo 2005 [v6]
gspro_combo_tier3_2005_base_no
npt v2 [v2]
bioseason. cmaq.2005b_ 1 2km
(/garnet/oaqps) [vO]
bioseason.cmaq.2005b 36km
(/garnet/oaqps) [vO]
cemsum_ptipm 2005 (/garnet/oaqps)
[vO]
gspro combo 2005 [v6]
gspro combo tier3 2017 ref nonpt
[vl]
gspro combo tier3 2005 base nonroad v2 [vO]
gspro_combo_tier3_2005_base_onr
oad v2 [vO]
gspro combo tier3 2005 base non
pt 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]
holidays [vO]
gspro combo tier3 2017 ref onroa
d[v2]
gspro_combo_tier3_2005_base_non
pt 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 [v3]
gspro speciated_pm [v3]
gsref speciated_pm [v2]
holidays [vO]
-------
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
List of sectors for mrggrid
List of sectors for mrggrid
MACT Description
Meteorology temperature profiles
Mobile codes file default
MOVES county cross-reference
MOVES Emission Factor Table list
MOVES Emission Factor Table list
MOVES Emission Factor Table list
MOVES Emission Factor Tables
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
INVTABLE
INVTABLE
INVTABLE
INVTABLE
INVTABLE
SECTORLIST
SECTORLIST
SECTORLIST
MACTDESC
METMOVES
MCODES
MCXREF
MRCLIST
MRCLIST
MRCLIST
EFTABLES
EFTABLES
MEPROC
MEPROC
MEPROC
MFMREF
NAICSDESC
NHAPEXCLU
DE
NHAPEXCLU
DE
NHAPEXCLU
DE
smkinven
smkinven
smkinven
smkinven
smkinven
Mrggrid
Mrggrid
Mrggrid
Smkreport
movesmrg
smkinven
movesmrg
movesmrg
movesmrg
movesmrg
movesmrg
movesmrg
movesmrg
movesmrg
movesmrg
movesmrg
Smkreport
smkinven
smkinven
smkinven
onroad
nonpt
avefire
ptipm
onroad
onroad
onroad
onroad
onroad
onroad
onroad
onroad
onroad
onroad
onroad
aim no c3
avefire
nonpt
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]
sectorlist 2005ct 05b [v2]
sectorlist 2005ct 05b [vl]
mactdesc 2002v3 [vl]
SMOKE DAILY 12MERGEUS1
2005 [vO]
mcodes [vl]
MCXREF tier3 [vO]
mrclist RPV 05jul2011 2005ct 05
b[vO]
mrclist RPD 20may2011 2005ct
05b [vO]
mrclist RPP 20may2011 2005ct 0
5b [vO]
EFtables 20110520 Tier3Base2005
[vO]
EFtables 20110705 Tier3Base2005
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]
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 2017ct ref 05b [vl]
sectorlist 2017ct ref 05b [vO]
sectorlist 2017ct ref 05b [vO]
mactdesc 2002v3 [vl]
SMOKE DAILY 12MERGEUS1
2005 [vO]
mcodes [vl]
MCXREF tier3 [vO]
mrclist RPV Oljul2011 2017ct ref
05b [vO]
mrclist RPD 10jun2011 2017ct re
f_05b [vO]
mrclist RPP 10jun2011 2017ct ref
05b [vO]
EFtables 20110610 Tier3Ref2017
[vO]
EFtables 20110701 Tier3Ref2017
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 addpesticid
es [vS]
-------
NHAPEXCLUDE NONROAD
NHAPEXCLUDE ptnonipm
NHAPEXCLUDE seca c3
nonpoint & nonroad surrogate xref
onroad surrogate xref default
ORIS Description
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
NHAPEXCLU
DE
NHAPEXCLU
DE
NHAPEXCLU
DE
AGREF
MGREF
OPJSDESC
SCCDESC
SICDESC
MRGDATE F
ILES
GSPROTMP
O
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
smkinven
smkinven
smkinven
Grdmat
Grdmat
smkinven
smkinven
Smkreport
Run script
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
nonroad
ptnonipm
seca c3
onroad
othpt
beis
onroad
nonpt
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]
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]
gsprojig [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]
nhapexclude nonroad_pf4 [vO]
nhapexclude_ptnonipm include 30
125010 [vO]
nhapexclude nothing [vO]
amgref us can mex revised [v!3]
amgref us can mex revised [v!3]
orisdesc [vO]
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]
gsprojig [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]
-------
Speciation profiles static
Speciation xref CAP static
Speciation xref for Canada PM
Speciation xref for Integrate-HAPs static
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
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)
GSPROTMP
A
GSREFTMP
A
GSREFTMP
N
GSREFTMP J
GSREFTMP
H
GSREFTMP
H
GSREFTMP I
GSREFTMP I
GSREFTMP
D
GSREFTMP
E
GSREFTMP
P
GSREFTMP
O
GSREFTMP
B
GSREFTMP
M
GSREFTMP
M
GSREFTMP
F
GSREFTMP
G
GSREFTMP
K
GSREFTMP
C
PSTK
SRGDESC
SRGDESC
SRGDESC
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
Spcmat
smkinven
Grdmat
Grdmat
Grdmat
othpt
nonpt
nonpt
onroad
onroad
onroad
othpt
othon
othar
gspro static cmaq [v!2]
gsref static cap_pf4 [vl]
gsref_pm25 Canada 2006_point
[v3]
gsref static integratehap emv4
[v2]
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]
gsref hg [v8]
gsref static nox hono_pf4 [v6]
pstk [vO]
srgdesc 36km revised [vl]
srgdesc 12km [v2]
srgdesc_36km_revised [vl]
gspro static cmaq [v!2]
gsref static cap_pf4 [vl]
gsref_pm25 Canada 2006_point
[v3]
gsref static integratehap emv4 [v2]
gsref nonhapvoc general hdghg
[v3]
gsref nonhapvoc general hdghg
[v2]
gsref nonhapvoc 2017 ref tier3
[vl]
gsref nonhapvoc 2017 ref tierS
[v2]
gsref no dieselpm [v3]
gsref_pm25_pf4 nondiesel [v!4]
gsref new for smoke-
moves otherthantog [vO]
gsref 2017 for smoke moves tog
[vl]
gsref sulf [vO]
gsref speciated voc [v2]
gsref speciated voc [v2]
gsref voc general hdghg [v3]
gsref voc 2017 ref tier3 [v3]
gsref hg [v8]
gsref static nox hono_pf4 [v6]
pstk [vO]
srgdesc 36km revised [vl]
srgdesc 12km [v2]
srgdesc_36km_revised [vl]
-------
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
ATPRO
PTPRO
MTPRO
ATREF
MTREF
PTREF
PTREF
PTREF
Temporal
Temporal
Temporal
Temporal
Temporal
Temporal
Temporal
Temporal
othpt
Ptipm
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]
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 MATS 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
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
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
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
Sector
beis
beis
beis
ptfire
ptnonipm
ptipm
aim no c3
nonpt
othon
othpt
othar
ptfire
beis
ptipm
onroad
2005 Base Case
123456789 10 11 12
3.14
340
B10C5
N
N
N
Y
N
N
N
N
RC
9
Y
Y
100
2017 Reference Case
123456789 10 11 12
3.14
340
B10C5
N
N
N
Y
N
N
N
N
N
N
RC
10
Y
Y
100
-------
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
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 full SCCs
Maximum errors printed
Maximum warnings printed
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
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
afdust
ptipm
nonpt
ptipm
ptnonipm
nonroad
othpt
seca c3
seca c3
ptnonipm
nonpt
ptipm
onroad
onroad
nonroad
nonpt
ptfire
ag
nonroad
onroad
340
0
0
0
0
Y
Y
N
N
model_performance
#NAME?
MOVES
Y
N
Y
Y
PMC=PM10-PM2 5
EXH PMC=EXH PM10-
EXH PM2 5
N
19
Y
Y
10000
10
340
0
0
0
0
0
0
Y
Y
N
N
model_performance
#NAME?
MOVES
Y
N
Y
Y
PMC=PM10-PM2 5
EXH PMC=EXH PM10-
EXH PM2 5
N
19
Y
Y
10000
10
-------
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
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
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
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
ptnonipm
seca c3
othpt
ptipm
ptnonipm
ptfire
avefire
ag
afdust
onroad
nonptfire
othpt
othon
seca c3
beis
nonpt
ptnonipm
avefire
nonroad
onroad
aim no c3
seca c3
mrggrid
beis
onroad
onroad
MCIP v3.4beta4
P
P
P
Mwdss
All
Mwdss
All
Aveday
Aveday
Week
All
Aveday
Mwdss
Week
Aveday
All
$EMF_AQM
voc
voc
voc
voc
TOG
VOC
VOC
10
.ncf
Y
Y
N
Y
Y
MCIP v3.4beta4
P
P
P
mwdss
all
mwdss
all
aveday
aveday
week
all
aveday
mwdss
week
aveday
all
$EMF_AQM
VOC
voc
voc
voc
TOG
VOC
VOC
10
.ncf
Y
Y
N
Y
-------
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
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
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
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
onroad
beis
onroad
beis
beis
ptipm
beis
ag
avefire
seca c3
aim no c3
othon
othpt
othar
nonptfire
afdust
seca c3
onroad
onroad
onroad
beis
Y
Y
Y
N
N
0
v4.3
Y
0
PRSFC
2005ck
RGRND
Y
Y
N
N
Y
N
N
N
N
N
N
Y
Both
Only
Y
tons/dy
RPD
RPP
RPV
SOIM1
Y
Y
Y
N
0
v4.3
Y
0
PRSFC
2005ck
RGRND
Y
Y
N
N
Y
N
N
N
N
N
N
Y
both
only
Y
RPD
RPP
RPV
SOIM1
-------
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
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
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
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
beis
beis
othpt
avefire
ptipm
beis
onroad
ptipm
ptfire
avefire
ag
afdust
onroad
nonptfire
othon
seca c3
beis
aim no c3
nonpt
nonroad
ptipm
ptfire
ptfire
aim no c3
seca c3
onroad
nonroad
SOIT1
SLTYP
Y
Y
Y
$EMF SPC
10
3
TEMP2
TEMP2
Mwdss
All
All
Aveday
Aveday
Week
All
Aveday
Week
Aveday
All
Y
Y
Y
N
Y
Y
Y
PARTIAL
ALL
ALL
PARTIAL
SOIT1
SLTYP
Y
Y
Y
$EMF SPC
10
3
TEMP2
TEMP2
mwdss
all
all
aveday
aveday
week
all
aveday
week
aveday
all
Y
Y
Y
N
Y
Y
Y
PARTIAL
ALL
ALL
PARTIAL
-------
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
Future Year
Last Modified Date
Meteorological Year
Model
Modeling Region
# of emission layers
# of met layers
Speciation
Start Date
Version
SMK PROCESS HAPS
SMK PROCESS HAPS
SMK PROCESS HAPS
POLLUTANT CONVERSI
ON
WEST HSPHERE
WRITE ANN ZERO
WRITE ANN ZERO
GZIP OUTPUTS
avefire
ptnonipm
nonpt
ptfire
ptipm
mrggrid
NONE
PARTIAL
PARTIAL
Y
Y
Y
Y
Y
2005
CMAQ v4.7 N5c
12/31/200523:59
0
13:57.7
2005
SMOKE
National
14
14
cmaq_cb05 tx
1/1/2005 0:00
2.7
NONE
PARTIAL
PARTIAL
Y
Y
Y
Y
Y
2005
CMAQ v4.7 N5c
12/31/200523:59
2017
22:39.1
2005
SMOKE
National
14
14
cmaq_cb05 tx
1/1/2005 0:00
2.7
-------
APPENDIX B
oo
Inventory Data Files Used for Each MATS Modeling Case - SMOKE Input Inventory Datasets
The emissions inventory data files used for the MATS 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 control case are identical to the 2017 reference case,
except for the following replacements:
• Inventory ptipm CAP used PTINV_EPA41 OFINAL_BC_226_summer_2015_01SEP2011_ORL [vO],
• Inventory ptipm daily data (CEM sources) used ptday_ptipm_cem_2017ct_ref_mats_05b [vO], and
• Inventory ptipm daily data (nonCEM sources) used ptday_ptipm_noncem_2017ct_ref_mats_05b [vO].
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 MATS 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 fire list
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
Sector
afdust
Ag
aim no c3
aim no c3
avefire
avefire
aim no c3
ptfire
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]
ptfire 2005ag tox [vO]
pfc 2002 caphap wETOH [vl]
nonpt_pf4 cap nopfc [v6]
nonpt cap 2005 TCEQ Oklahoma OilGas
[vO]
2017 Reference Case
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 25jul2011 [vO]
ptfire 2005ag tox [vO]
pfc 2017 ref caphap 23aug2011 [vO]
cellulosic ETOH Biodiesel 2017ct ref caphap 29
jul2011 [vO]
Ethanol transport vapor 2017ct ref caphap 25jul
2011 [vO]
nonpt_pf4 cap nopfc 2017ct ref [vO]
nonpt cap 2017ct lowE TCEQ Oklahoma OilGa
s[vO]
-------
Inventory nonpt CAP: WRAP Oil and Gas
Inventory nonpt HAP (no PFC, no refueling)
Inventory nonpt Refueling from MOVES, April
Inventory nonpt Refueling from MOVES, August
Inventory nonpt Refueling from MOVES,
December
Inventory nonpt Refueling from MOVES,
February
Inventory nonpt Refueling from MOVES, January
Inventory nonpt Refueling from MOVES, July
Inventory nonpt Refueling from MOVES, June
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
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
nonpt
nonpt
nonpt
nonpt
nonpt
nonpt
nonpt
nonpt
nonpt
nonpt
nonpt
nonpt
nonpt
nonpt
nonroad
nonroad
nonroad
nonroad
nonroad
nonroad
nonroad
nonroad
nonpt cap 2005 WRAP OilGas [vO]
nonpt_pf4 hap nopfc nobafmpesticidesplus
[v4]
rfl moves wETOH 2005ct apr 18may2011
[vO]
rfl moves wETOH 2005ct aug 18may2011
[vO]
rfl moves wETOH 2005ct dec 18may2011
[vO]
rfl moves wETOH 2005ct feb 18may2011
[vO]
rfl moves wETOH 2005ctjan 18may2011
[vO]
rfl moves wETOH 2005ctjul 18may2011
[vO]
rfl moves wETOH 2005ctjun 18may2011
[vO]
rfl moves wETOH 2005ct mar 18may2011
[vO]
rfl moves wETOH 2005ct may 18may2011
[vO]
rfl moves wETOH 2005ct nov 18may2011
[vO]
rfl moves wETOH 2005ct oct 18may2011
[vO]
rfl moves wETOH 2005ct sep 18may2011
[vO]
nonroad cmaq_lite 2005ct apr 19may2011
[vO]
nonroad cmaq_lite 2005ct aug 19may2011
[vO]
nonroad cmaq_lite 2005ct dec 19may2011
[vO]
nonroad cmaq_lite 2005ct feb 19may2011
[vO]
nonroad cmaq_lite 2005ctjan 19may2011
[vO]
nonroad cmaq_lite 2005ctjul 19may2011
[vO]
nonroad cmaq_lite 2005ctjun 19may2011
[vO]
nonroad cmaq_lite 2005ct mar 19may2011
[vO]
nonpt cap 2018PhaseII WRAP OilGas [vO]
nonpt_pf4 hap nopfc nobafmpesticidesplus 2017c
t_ref [vO]
rfl moves wETOH 2017ct ref apr 27jul2011 [vO]
rfl moves wETOH 2017ct ref aug 27jul2011
[vO]
rfl moves wETOH 2017ct ref dec 27jul2011
[vO]
rfl moves wETOH 2017ct ref feb 27jul2011 [vO]
rfl moves wETOH 2017ct ref Jan 27jul2011 [vO]
rfl moves wETOH 2017ct refjul 27jul2011 [vO]
rfl moves wETOH 2017ct refjun 27jul2011 [vO]
rfl moves wETOH 2017ct ref mar 27jul2011
[vO]
rfl moves wETOH 2017ct ref may 27jul2011
[vO]
rfl moves wETOH 2017ct ref nov 27jul2011
[vO]
rfl moves wETOH 2017ct ref oct 27jul2011 [vO]
rfl moves wETOH 2017ct ref sep 27jul2011
[vO]
nonroad cmaqjite 2017ct ref apr 20jul2011 [vO]
nonroad cmaq_lite 2017ct ref aug 20jul2011 [vO]
nonroad cmaqjite 2017ct ref dec 20jul2011 [vO]
nonroad cmaqjite 2017ct ref feb 20jul2011 [vO]
nonroad cmaq lite 2017ct ref jan 20jul2011 [vO]
nonroad cmaqjite 2017ct refjul 20jul2011 [vO]
nonroad cmaq_lite 2017ct refjun 20jul2011 [vO]
nonroad cmaq_lite 2017ct ref mar 20jul2011
[vO]
-------
Inventory nonroad cap+CMAQ-lite 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 P^PD
Inventory onroad P^PD
Inventory onroad PvPD
Inventory onroad P^PD
Inventory onroad P^PD
Inventory onroad PvPP
Inventory onroad PvPV
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
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 CAP
Inventory ptipm CAP
Inventory ptipm daily data (CEM sources)
Inventory ptipm daily data (nonCEM sources)
nonroad
nonroad
nonroad
nonroad
onroad
onroad
onroad
onroad
onroad
onroad
onroad
othar
othar
othar
othar
othon
othon
othon
othpt
othpt
othpt
ptipm
ptipm
ptipm
ptipm
ptipm
nonroad cmaq_lite 2005ct may 19may2011
[vO]
nonroad cmaq_lite 2005ct nov 19may2011
[vO]
nonroad cmaq_lite 2005ct oct 19may2011
[vO]
nonroad cmaq_lite 2005ct sep 19may2011
[vO]
VMT tierS 2005 [vO]
VMT tierS 2005 [vO]
VMT tierS 2005 [vO]
SPEED tierS [vO]
VMT tierS 2005 [vO]
VPOP tierS 2005 [vO]
VPOP tierS 2005 [vO]
nonpt mexicoborder!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]
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_2005cs_05b
(/garnet/oaqps) [vO]
nonroad cmaq_lite 2017ct ref may 20jul2011
[vO]
nonroad cmaqjite 2017ct ref nov 20jul2011 [vO]
nonroad cmaqjite 2017ct ref oct 20jul2011 [vO]
nonroad cmaqjite 2017ct ref sep 20jul2011 [vO]
VMT tierS 2017 ref cntl [vS]
VMT tierS 2017 ref cntl [vS]
VMT tierS 2017 ref cntl [vS]
SPEED tierS [vO]
VMT tierS 2017 ref cntl [vS]
VPOP tierS 2017 [vO]
VPOP 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]
Canada onroad cap 2006 [vO]
mexico border99 [vl]
mexico interior99 [vO]
ptnonipm offshore oil cap2005v2 20nov2008 [vO]
PTINV EPA410FINAL BC 58 summer 2020 21
MAY2011 ORL[vO]
ptday_ptipm caphap cem 2017ct 05b [vO]
ptday_ptipm caphap noncem 2017ct 05b [vO]
-------
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
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
ptipm
ptnonipm
ptnonipm
ptnonipm
ptnonipm
ptnonipm
aim no c3
seca c3
seca c3
seca c3
seca c3
othar
othar
othar
othar
othar
othar
othar
othpt
othpt
othpt
ptipm 2005cs hap 27dec2010.txt [vO]
ptnonipm xportfrac cap2005v2 2005cs orl
[v7]
ethanol_plants 2005ct 2017ct lowE caphap
[vO]
ptnonipm hap2005v2 2005cs orl [v6]
eca imo CANADA SCC fix vochaps 2005 0
9DEC2010 [vO]
eca imo fixFIPS US andSCC fix vochaps 2
005 09DEC2010 [vO]
eca imo CANADA SCC fix caps 2005 09D
EC2010 [vO]
eca imo fixFIPS US wDE andSCC 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]
ptnonipm xportfrac cap2017ct ref [vO]
biodiesel_plants 2017ct ref caphap 29jul2011
[vO]
ethanol_plants 2017ct ref caphap 19jul2011 [vO]
ptnonipm capHG cementlSIS 2016cr 16AUG201
0[vO]
ptnonipm hap2017ct ref [vO]
rail additional 2017ct ref caphap 26jul2011 [vO]
eca imo CANADA SCC fix vochaps 2017 [vO]
eca imo fixFIPS US andSCC fix vochaps 2017
[vO]
eca imo CANADA SCC fix caps 2017 [vO]
eca imo fixFIPS US wDE andSCC fix caps 20
17 [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]
-------
Appendix C
Summary of MATS Rule 2017 Base Case Non-EGU Control Programs, Closures and Projections
Lists of control, closure and projection packet datasets used to create MATS year 2017 base case inventories from the 2005 MATS base case are
provided in Tables C-l and C-2.
Table C-l. Datasets used to create MATS 2017 reference case inventories for non-EGU point sources
Name
CLOSURES LotusNotes, ABCG, plus Timin
2016cr
CLOSURES TR1 comments and consent decrees
2014cs
CLOSURES cement ISIS 2013 policy
closures: 2005 to 2012ck
CONTROL ADDITIONAL OECA 2005cr to
2016cr
CONTROL REPLACE DOJ 2005cr to 2016cr
CONTROL REPLACE HWI 2005cr to 2016cr
CONTROL REPLACE IndustrialBoiler
nonMACT 2005cr to 2016cr
CONTROL REPLACE LMWC 2005cr to 2016cr
CONTROL REPLACE MACT 2005cr to 2016cr
CONTROL REPLACE NY SIP 2005cr to 2016cr
CONTROL REPLACE Refineries 2005cr to
2016cr
CONTROL RICE 20 16cr 05b
CONTROL RICE SO2 2014cs 05b
CONTROL SULF rules: ME, NY, NJ 2017
ONLY
Type
Plant
Closure
Plant
Closure
Plant
Closure
Plant
Closure
Control
Control
Control
Control
Control
Control
Control
Control
Control
Control
Control
Dataset
CLOSURES LotusNotes Linda Timin 2016
cr 23AUG2010
CLOSURES TR1 2014cs 01FEB2011
CLOSURES cementlSIS 2016cr 17AUG201
0
CLOSURES_2005ck_to_2012ck_CoST_form
at
CONTROLS additional NEIpf4 OECA 200
5cr 2016cr 29JUL2010
CONTROLS replacement NEIpf4 DOJ 200
5cr 2016cr 02AUG2010.txt
CONTROLS replacement NEIpf4 HWI 200
5cr 2016cr 02AUG2010.txt
CONTROLS replacement IndBoilers nonM
ACT by2008 20AUG2010
CONTROLS replacement NEIpf4 LMWC
2005cr 2016cr 02AUG2010.txt
CONTROLS replacement NEIpf4 MACT 2
005cr 2016cr 02AUG2010.txt
CONTROLS replacement NYSIP O3 SCC
2016cr 26AUG2010
CONTROLS replacement NEIpf4 refineries
2005cr 2016cr 02AUG2010.txt
CONTROLS replacement RICE 2016cr 21
SEP2010
CONTROLS replacement RICE SO2 2014c
s 05JAN2011
CONTROLS SULF rules 2017only 03FEB
2011
Version
1
0
1
0
1
0
1
0
0
0
0
1
1
1
0
Description
Plant and unit closures identified through EPA
review.
Plant and unit closures through 2014 identified as
a result of Transport Rule comments.
Cement plant and unit closures identified via the
ISIS 2013 policy case.
Plant and unit closures identified 2008 or before.
Controls that implement OECA consent decrees.
Controls resulting from the 2002v3 DOJ Texas
settlement.
Hazardous Waste Incinerator controls for CAPs
and Haps carried over from 2002v3 1.
Industrial boiler controls not related to application
of the MACT but derived from the Boiler MACT
ICR database dated 4/30/10.
Controls for large municipal combustors carried
over from 2002v31.
MACT controls carried over from 2002v3 and
updated as appropriate.
Controls that reflect enforceable controls for NOx
and VOC from the New York ozone SIP.
Controls for refineries specified by EPA expert
refinery staff.
Controls for 2014 and 2016 that represent three
separate RICE NESHAPs
SO2 reductions from the Ultra-low Sulfur Diesel
requirement for CI engines
SO2 reductions due to state sulfur content rules for
fuel oil.
-------
CONTROL St Gobain and LaFarge 2017
CONTROL TR1 Final CONTROL packet: 2017
CONTROL TR1 Final consent decrees 20XX
CONTROL cement ISIS 2013 policy
PROJECTION 2005 to 2017 ag emissions
PROJECTION LMWC 2005cr to 2016cr
PROJECTION TR1 comments 2005cs to 20XXcs
-ptnonipm
PROJECTION aircraft 2005 to 2017 JAN2010
FAATAF
PROJECTION cement ISIS 2013 policy
PROJECTION RWC and landfills 2005 to 2017
BAD
PROJECTION TierS Proposal 2017 low-E to
2017 REF transport EPS BTP RBT
PROJECTION Tier3 Proposal 2017 low-E to
2017REF-CTLag
PROJECTION TierS Proposal 2017 low-E to
20 17 REF-CTL refineries
Control
Control
Control
Control
Projecti
on
Projecti
on
Projecti
on
Projecti
on
Projecti
on
Projecti
on
Projecti
on
Projecti
on
Projecti
on
CONTROLS rep Lafarge StGobain 2017cs
25JAN2011.txt
CONTROLS TR1 2017
CONTROLS_additional_TRlfmal_consent_d
ecrees 2005cs to 20XXcs.csv
CONTROLS replacement cementlSIS 2016
cr 17AUG2010
PROJECTION 2005 2017 ag
PROJECTION 2005cr 2016cr LMWC 29J
UL2010
PROJECTION 2005cs 20XX TRl_ptnonip
m 01FEB2011
PROJECTION aircraft 2005 to 2017 JAN2010
FAATAF
PROJECTION cementlSIS 2016cr 17AUG2
010
PROJECTION 2005 2017 RWC landfills B
AD
PRO JECTION_20 1 7ct_REF_Tier3prop_t
ransport scalars 28jul2011
PROJECTION_2017ct_REF_Tier3prop_ag_s
calars 26jul2011
PROJECTION_2017ct_REF_Tier3prop_refm
ery scalars 26jul2011
0
0
0
0
0
0
0
0
0
0
0
0
0
Controls for NOX, SO2, PM., and HC1 resulting
from Saint Gobain and Lafarge consent decrees
Controls for TCEQ oil and gas and non-ISIS
related cement controls.
Controls related to consent decrees identified
during the Transport Rule comment period.
Controls for cement plants based on 2013 ISIS
policy case
Projection factors for agriculture based on animal
population stats.
Projection factors for Solid and Liquid Municipal
Waste Combustors.
Projection factors derived from Transport Rule
comments.
Projection factors for aircraft derived from the
FAA Terminal Area Forecast System.
Projection factors that implement the 2013 ISIS
policy case for cement.
Projection factors for residential wood combustion
Projection factors for transport of renewable fuel
blends from bulk plant to storage, refinery to bulk
terminal and bulk terminal to pump
Projection factors accounting for changes in
biofuel volumes on upstream agricultural sources
Projection factors accounting for refinery process
changes from renewable fuels
Table C-2. Datasets used to create MATS 2017 reference case inventories for nonpoint sources
Control Program Name
CONTROL REPLACE NY
SIP2005crto2016cr
CONTROL RICE
2016cr 05b
CONTROL RICE SO2
2014cs 05b
CONTROL SULF rules:
ME, NY, NJ 2017 ONLY
CONTROL TR1 Final
CONTROL packet: 20 17
PROJECTION 2005 to
20 17ag sector
Type
Control
Control
Control
Control
Control
Projection
Dataset
CONTROLS replacement NYSIP O3 SC
C 2016cr 26AUG2010
CONTROLS replacement RICE 2016cr
21SEP2010
CONTROLS replacement RICE SO2 20
14cs 05JAN2011
CONTROLS SULF rules 2017only 03FE
B2011
CONTROLS TR1 2017
PROJECTION_2005_2017_ag
Version
0
1
1
0
0
0
Description
Controls that reflect enforceable controls for NOx and VOC from the
New York ozone SIP.
Controls for 2014 and 2016 that represent three separate RICE
NESHAPs
SO2 reductions from the Ultra-low Sulfur Diesel requirement for CI
engines
SO2 reductions due to state sulfur content rules for fuel oil.
Controls for TCEQ oil and gas and non-ISIS related cement controls.
Projection factors for agriculture based on animal population stats.
-------
PROJECTION RWC and
landfills 2005 to 2017 BAD
PROJECTION aircraft 2005
to 2017 JAN2010 FAATAF
PROJECTION TierS
Proposal 2017 low-E to 2017
REF transport EPS BTP
RBT
PROJECTION TierS
Proposal 2017 low-E to 2017
REF-CTL ag
PROJECTION TierS
Proposal 2017 low-E to 2017
REF-CTL refineries
Projection
Projection
Projection
Projection
Projection
PROJECTION 2005 2017 RWC landfills
BAD
PROJECTION aircraft 2005 to 2017
JAN20 10 FAATAF
PROJECTION_2017ct_REF_Tier3prop_tra
nsport scalars 28jul2011
PROJECTION 2017ct REF TierSprop ag
scalars 26jul2011
PROJECTION_2017ct_REF_Tier3prop_ref
inery scalars 26jul2011
0
0
0
0
0
Projection factors for residential wood combustion and landfills.
Projection factors for aircraft derived from the FAA Terminal Area
Forecast System.
Projection factors for transport of renewable fuel blends from bulk
plant to storage, refinery to bulk terminal and bulk terminal to pump
Projection factors accounting for changes in biofuel volumes on
upstream agricultural sources
Projection factors accounting for refinery process changes from
renewable fuels
-------
United States Office of Air Quality Planning and Standards Publication No. EP A-454/R-11-011
Environmental Protection Air Quality Assessment Division December, 2011
Agency Research Triangle Park, NC
------- |