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Technical Support Document for the Proposed
Toxics Rule: Emissions Inventories
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EPA-454/B-20-006
March 2011
Technical Support Document For the Proposed Toxics Rule: Emissions Inventories
U.S. Environmental Protection Agency
Office of Air Quality Planning and Standards
Air Quality Assessment Division
Research Triangle Park, NC
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TABLE OF CONTENTS
Acronyms hi
List of Figures iv
List of Tables iv
List of Appendices v
1 Introduction 1
2 Development of Base Case 2005 Emission Inventories 2
2.1 Base case 2005 overview 2
2.2 2005 Proposed Toxics Rule custom processing configuration 5
3 Development of 2016 Future- Year Base Case Emission Inventories 6
3.1 Stationary Source Projections: EGU sector (ptipm) 12
3.2 Stationary Source Projections: non-EGU sectors (ptnonipm, nonpt, ag, afdust) 16
3.2.1 Livestock emissions growth (ag, afdust) 16
3.2.2 Residential wood combustion growth (nonpt) 17
3.2.3 Gasoline Stage II growth and control (nonpt, ptnonipm) 18
3.2.4 Portable fuel container growth and control (nonpt) 19
3.2.5 Aircraft growth (ptnonipm) 20
3.2.6 Stationary Source control programs, consent decrees & settlements, and plant closures (ptnonipm,
nonpt) 21
3.2.6.1 Reductions from the Portland Cement NESHAP 23
3.2.6.2 Boiler MACT reductions 24
3.2.6.3 Summary of Mercury Reductions at non-EGU stationary sources- (ptnonipm) 27
3.2.7 Oil and gas projections in TX, OK, and non-California WRAP states (nonpt) 28
3.2.8 Future Year VOC Speciation for gasoline-related sources (ptnonipm, nonpt) 30
3.3 Mobile source projections 30
3.3.1 Onroad mobile (onnoadj, onmovesrunpm, onmovesstartpm) 30
3.3.2 Nonroad mobile (nonroad) 33
3.3.3 Locomotives and Class 1 & 2 commercial marine vessels (alm_no_c3) 34
3.3.4 Class 3 commercial marine vessels (seca_c3) 36
3.3.5 Future Year VOC Speciation (on noadj, nonroad) 37
3.4 Canada, Mexico, and Offshore sources (othar, othon, othpt, othar hg, and othpt_hg)38
4 EGU Control Case for 2016 39
5 Emission Summaries for the Base Cases and Control Case. 39
6 References 55
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Acronyms
AEO Annual Energy Outlook
BEIS
Biogenic Emission Inventory System
C3
Category 3 (commercial marine vessels)
CAIR
Clean Air Interstate Rule
CAMD
EPA"s Clean Air Markets Division
CAMx
Comprehensive Air Quality Model with Extensions
CAP
Criteria Air Pollutant
CARB
California Air Resources Board
CEM
Continuous Emissions Monitoring
CMAQ
Community Multiscale Air Quality
CMV
Commercial Marine Vessel
DOE
Department of Energy
ECA
Emissions Control Area
EGU
Electric Generating Unit
EISA
Energy Independence and Security Act of 2007
EMFAC
CARB"s Emission Factors mobile model
FAA
Federal Aviation Administration
FIPS
Federal Information Processing Standard
HAP
Hazardous Air Pollutant
HWI
Hazardous Waste Incinerator
ICR
Information Collection Request
IMO
International Marine Organization
IPM
Integrated Planning Model
ISIS
Industrial Sector Integrated Solutions
ITN
Itinerant (aircraft operations)
MACT
Maximum Achievable Control Technology
MOBILE6
Mobile Source Emission Factor Model, version 6
MOVES
Motor Vehicle Emissions Simulator
MSAT2
Final Mobile Source Air Toxics Rule
MWC
Municipal Waste Combustor
NAAQS
National Ambient Air Quality Standards
NATA
National Air Toxics Assessment
NEEDS
National Electric Energy Database System
NEI
National Emission Inventory
NESHAP
National Emissions Standards for Hazardous Air Pollutants
NLEV
National Low Emission Vehicle
NMIM
National Mobile Inventory Model
NSPS
New Source Performance Standard
NSR
New Source Review
OAQPS
EPA"s Office of Air Quality Planning and Standards
OECA
EPA"s Office of Enforcement and Compliance
ORL
One Record per Line (a SMOKE input format)
OTC
Ozone Transport Commission
MP
Multipollutant
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PFC
Portable Fuel Container
RIA
Regulatory Impact Analysis
RICE
Reciprocating Internal Combustion Engine
RFS2
Revised annual Renewable Fuel Standard
RWC
Residential Wood Combustion
SMOKE
Sparse Matrix Operator Kernel Emissions
see
Source Classification Code
SPPD
Sector Policies and Programs Division
TAF
Terminal Area Forecast
TCEQ
Texas Commission on Environmental Quality
TPY
Tons per Year
TR
Federal Transport Rule
TSD
Technical Support Document
VOC
Volatile Organic Compound
WRAP
Western Regional Air Partnership
List of Figures
Figure 2-1. Air quality modeling domains 6
Figure 3-1. Approach to Compute Monthly Emissions from the IPM data 15
Figure 3-2. MOVES exhaust temperature adjustment functions for 2005and 2015 32
Figure 3-3. Tier 2 Fraction of Light Duty Vehicles 38
List of Tables
Table 1-1. List of cases run in support of the air quality modeling for the Proposed Toxics Rule 1
Table 2-1. Sectors Used in the 2005v4.1 Emissions Modeling Platform and Description of the
2005 Base Year Data 3
Table 3-1. Control strategies and growth assumptions for creating 2016 base case emissions
inventories from the 2005 base case 9
Table 3-2. Adjustment to IPM emissions due to application of Boiler MACT 14
Table 3-3. Growth factors from year 2005 to future years for Animal Operations 17
Table 3-4. Projection Factors for growing year 2005 Residential Wood Combustion Sources to
2016 18
Table 3-5. Factors used to project base case 2005 aircraft emissions to future years 20
Table 3-6. Summary of Emission Reductions Applied to the 2005 Base Year Inventory Due To
Plant Closures 22
Table 3-7. Quantification of Missing Closures in 2016 the non-EGU (ptnonipm) sector 23
Table 3-8. Future-year ISIS-based cement industry annual reductions (tons/yr) for the non-EGU
(ptnonipm) sector 24
Table 3-9. Default pollutant fuel reductions applied NEI boilers not covered by the ICR
database 25
Table 3-10. Crosswalk of NEI fuels to ICR fuels 25
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Table 3-11. Summary of Boiler MACT reductions applied to the ptnonipm sector 27
Table 3-12. State-level non-MACT Boiler Reductions from ICR Data Gathering 27
Table 3-13. Anthropogenic mercury emissions and projections in the Continental United States 28
Table 3-14. 2005, 2008 and estimated 2016 emissions for nonpoint Oklahoma and Texas oil and
gas sources 29
Table 3-15. WRAP Oil and Gas Emissions: 2005, 2018 WRAP, and 2016 with additional
reductions due to the RICE NESHAP 29
Table 3-16. Summary of the impact of PM2.5 errors in the onroad sectors 32
Table 3-17. Factors applied to year 2005 emissions to project locomotives and Class 1 and Class
2 Commercial Marine Vessel Emissions 35
Table 3-18. Factors to Project Class 3 Commercial Marine Vessel emissions to 2016 37
Table 3-19. Future Year Profiles for Mobile Source Related Sources 38
Table 4-1. Boiler MACT reductions applied to policy case ptipm sector emissions prior to AQ
modeling 39
Table 5-1. 2016 Emissions - 2016 Base Case Compared to 2005 Base Case: VOC, NOx, CO,
SO:. MI3. and PM 40
Table 5-2. 2016 Emissions - Base Case Compared to 2005 Base Case: mercury (species and
total), HCL and CL2 41
Table 5-3. Speciated Mercury and total Mercury by State and Sector for 2005 and 2016 42
Table 5-4. EGU sector (ptipm emissions) for all AQ modeling cases: NOx, SO2 and VOC 47
Table 5-5. EGU sector (ptipm emissions) for all AQ modeling cases: HCL and total Mercury 49
Table 5-6. EGU sector (ptipm emissions) for all AQ modeling cases: Speciated Mercury 50
Table 5-7. 2016 Base Case SO2 Emissions (tons/year) for Lower 48 States by Sector 51
Table 5-8. 2016 Base Case PM2.5 Emissions (tons/year) for Lower 48 States by Sector 52
List of Appendices
APPENDIX A: Inventory Data Files Used for Each Proposed Toxics Rule Air Quality
Modeling Cases - SMOKE Input Inventory Datasets
APPENDIX B: List of OECA Consent Decrees- Whereby Reductions Were Apportioned to
Facilities in a Particular Corporation
APPENDIX C: Gold Mine Facility-Specific Mercury Reductions Due to NESHAP
APPENDIX D: Mercury Emission Reductions, 2005-2016 for the Non-EGU categories of:
Electric Arc Furnaces, Mercury Cell Chi or-Alkali Plants, Hazardous Waste
Combustion, and Pulp and Paper.
APPENDIX E Ptnonipm (Non EGU) Plant Closures Included in the 2016 Base Case and the
Resulting Emissions Changes Due to the Closures
APPENDIX F: Methodology to Apply Reductions for the Stationary Reciprocating Internal
Combustion Engine (RICE) NESHAP
APPENDIX G: Mercury Speciation Fractions Used to Speciate the Future Year EGU Mercury
Emissions
APPENDIX H: Details Regarding the PM2.5 Natural Gas Emission Factor error in IPM Post
Processing
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1 Introduction
This document provides the details of emissions data processing done in support of the Environmental
Protection Agency's (EPA) rulemaking effort for the proposed Toxics Rule which is also referred to as the
Utility Maximum Achievable Control Technology Standard (MACT) and New Source Performance Standard
(NSPS). The air quality modeling results were used in the Appropriate and Necessary Analysis and the
Regulatory Impact Assessment. The emissions and modeling effort consists of three emissions cases: 2005
base case, 2016 base case, and 2016 Control case. The emissions consisted of Criteria Air Pollutants (CAPs)
and the following select Hazardous Air Pollutants (HAPs): mercury (Hg), chlorine (CL2), hydrochloric acid
or hydrogen chloride (HCL) and benzene, acetaldehyde, formaldehyde and methanol. The latter four are
also denoted BAFM.
Table 1-1 provides more information on these emissions cases. The 2016 base case modeling was used to
characterize future-year air quality without implementation of the rule. Also included in Table 1-1 are
mercury (Hg) zero-out runs that were based on the 2016 base case and 2005 base case to quantify the
contributions of mercury emissions from Electric Generating Units (EGUs). This document provides no
further discussion on the two simple Hg EGU zero-out runs, which are described in the proposed Toxics
Rule Technical Support Document: "National-scale Mercury Risk Assessment Supporting the Appropriate
and Necessary Finding for Coal- and Oil-fired Electric Generating Units'". The modeling outputs for the
2016 base and control cases were then used to quantify the benefits of the proposed Toxics Rule.
Table 1-1. List of cases run in support of the air quality modeling for the Proposed Toxics Rule
Case Name
Internal EPA
Abbreviation
Description
2005 base case
2005cr hg
2005 case created using average-year fires data and an
average-year temporal allocation approach for EGUs, to use
for computing relative response factors with 2016 the scenario
2005 Hg EGU
zero-out
2005cr hg_ptipm hgzero
Same as 2005 base case but zero Hg emissions for EGU
(ptipm) sector
2016 base case
2016cr2 hg
2016 "baseline" scenario, representing the best estimate for the
future year without implementation of the EGUMACT/NSPS.
2016 base with
Hg zero-out
2016cr2 hg_ptipmhgzero
Same as 2016 base case but zero Hg emissions for EGU
(ptipm) sector
2016 control
case
2016cr2 hg control 1
2016 EGU "control" scenario representing a MACT control
strategy
The data used in the 2005 emissions cases are the same as those described in the 2005-based, Version 4.1
platform (hereafter the "2005v4.1" platform) document available at Clearinghouse for Inventories and
Emissions Factors (CHIEF). The 2005 and future-year emissions scenarios were processed in a form that is
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, CMAQ was used to calculate daily and
annual PM2.5 concentrations, 8-hr maximum ozone, annual total mercury deposition levels 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, which are combined with monitoring data to estimate
population-level exposures to changes in ambient concentrations for use in estimating health and welfare
effects.
We used a 2005 base case approach for the year 2005 emissions scenario. The base case approach uses an
average-year fire emissions inventory and average-year EGU temporal profiles, which were based on 3 years
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of hourly Continuous Emissions Monitoring (CEM) data for EGUs. We use a base case approach to reduce
year-specific variability in fires and EGUs between 2005 and the future years. For example, each year has
different days and different locations with large fires, unplanned EGU shutdowns, and periods of high
electricity demand. By using a base-case approach, the temporal and spatial aspects of the inventory for
these sources are maintained into the future year modeling, which avoids potentially spurious year-specific
artifacts in air quality modeling estimates. In addition, the 2005 Version 4.1 (v4.1) platform biogenic
emissions data is the same as the 2005v4 platform and was held constant between the 2005 case and the 2016
future-year cases. The 2005v4 emissions processing technical support document is available at:
ftp://newftp.epa.gov/Air/emismod/2005/2005v4/20Q5 emissions tsd 07iul2010.pdf. The only significant
data changes between the year 2005 cases in Table 1-1 and future-year cases are emission inventories and
speciation approaches.
The future-year inventories, ancillary files, and detailed projection data used for this modeling are available
as part of the Toxics Rulemaking, available in the docket at EPA-HQ-OAR-2009-0234. Since the data are
large, the data files themselves are not posted with online access through the docket, and so a more
convenient access location is the EPA Emissions Modeling Clearinghouse website for its 2005 platform.
The Toxics Rule data files are provided as a subheading under this main link.
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 Toxics rule. In Section 2, we briefly review the 2005 base case
inventory, including ancillary data and issues related to CMAQ support. In Section 3, we describe the
development of the future year 2016 base case. In Section 4 we provide data summaries comparing the
modeling cases. Finally, Section 5 provides emissions summaries and Section 6 contains the technical
references for this document.
2 Development of Base Case 2005 Emission Inventories
As mentioned previously, the 2005 emissions modeling approach for the proposed Toxics Rule used the
same data and approaches as the 2005 v4.1 base case platform. In this section, we briefly discuss the
modeling sectors in the 2005 base case and future year cases as well as Toxics rule-specific issues related to
processing emissions for CMAQ.
2.1 Base case 2005 overview
Table 2-1 lists the platform sectors used for the 2005 base case and future-year base and control cases. It
also indicates the associated sectors from the National Emissions Inventory (NEI). Subsequent sections refer
to these platform sectors for identifying the emissions differences between the 2005 base case and the 2016
cases. The inputs to the air quality model; including emissions, meteorology, initial conditions, boundary
conditions; along with the methods used to produce the inputs and the configuration of the air quality model
are collectively known as a modeling platform". The v4.1 platform contains the same modeling sectors as
the 2005 v4 platform; though for some sectors, the emissions data were revised. For additional information
on the revisions made, see the 2005-based, Version 4.1 platform document.
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Table 2-1. Sectors Used in the 2005v4.1 Emissions Modeling Platform and Description of the 2005 Base
Year Data
Platform Sector
2005 NEI
Sector
Description and resolution of the data input to SMOKE
EGU sector (also
called the IPM
sector): ptipm
Point
For all pollutants other than mercury (Ha): 2005 NEI v2 point source
EGUs mapped to the Integrated Planning Model (IPM) model using
the National Electric Energy Database System (NEEDS) 2006 version
4.10 database. A few revisions were made to the 2005 NEI v2 annual
emission estimates as discussed in the 2005-based, Version 4.1
platform document.
For Ha: 6/18/2010 version of the inventory used for the 2005 National
Air Toxics Assessment (NATA) mapped to IPM using NEEDS
version 4.10. The NATA inventory is an update to the 2005 NEI v2
and was divided into EGU and non-EGU sectors consistent with the
other pollutants. (We did not actually map the NATA inventory to
IPM, but rather applied the mapping that was done to the 2005 NEIv2
to the NATA Hg inventory). We additionally removed Hg from
sources from the National Emission Standards for Hazardous Air
Pollutants for Industrial, Commercial, and Institutional Boilers and
Process Heaters (aka "Boiler MACT") Information Collection Request
(ICR) database because we included these emissions in the non-EGU
sector.
For both: Dav-specific emissions created for input into SMOKE.
Non-EGU sector
(also called the
non-IPM sector):
ptnonipm
Point
For all pollutants other than Ha: All 2005 NEI v2 point source records
not matched to the ptipm sector. Includes all aircraft emissions.
Additionally updated inventory to remove duplicates, improve
estimates from ethanol plants, and reflect new information collected
from industry from the ICR for the Boiler MACT. Includes point
source fugitive dust emissions for which county-specific PM
transportable fractions were applied.
For Ha: The 6/18/2010 version of NATA inventory was used except
for modifications to gold mine emissions and removal of Hg from
facilities that closed prior to 2005. In addition, Hg emissions
developed for the Boiler MACT were used
For both: Annual resolution.
Average-fire
sector: avefire
N/A
Average-year wildfire and prescribed fire emissions, unchanged from
the 2005v4 platform; county and annual resolution.
Agricultural
sector: ag
Nonpoint
NH3 emissions from NEI nonpoint livestock and fertilizer application,
county and annual resolution. Unchanged from the 2005v4 platform.
Area fugitive dust
sector: afdust
Nonpoint
PM10 and PM25 from fugitive dust sources (e.g., building construction,
road construction, paved roads, unpaved roads, agricultural dust) from
the NEI nonpoint inventory after application of county-specific PM
transportable fractions. Includes county and annual resolution.
Remaining
nonpoint sector:
nonpt
Nonpoint
Primarily 2002 NEI nonpoint sources not otherwise included in other
SMOKE sectors, county and annual resolution. Also includes
updated Residential Wood Combustion emissions, year 2005 non-
California WRAP oil and gas Phase II inventory and year 2005 Texas
and Oklahoma oil and gas emissions. Removed Hg emissions from
boilers to avoid double counting with Hg emissions added to the non-
EGU (ptnonipm) sector from the Boiler MACT.
Nonroad sector:
nonroad
Mobile:
Nonroad
Monthly nonroad emissions from the National Mobile Inventory
Model (NMIM) using NONROAD2005 version nr05c-BondBase
(equivalent to NONROAD2008a, since it incorporated Bond rule
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Platform Sector
2005 NEI
Sector
Description and resolution of the data input to SMOKE
revisions to some of the base case inputs and the Bond rule controls
did not take effect until later) for all states except California. Monthly
emissions for California created from annual emissions submitted by
the California Air Resources Board (CARB) for the 2005v2 NEI.
locomotive, and
non-C3
commercial
marine:
aim no c3
Mobile:
Nonroad
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 included in the
Non-EGU sector (as point sources) and category 3 CMV emissions are
contained in the seca c3 sector
C3 commercial
marine: seca_c3
Mobile :
Nonroad
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 oer
Cylinder", usuallv described as the Emissions Control Area (ECA)
study. Utilized final projections from 2002, developed for the C3
ECA proposal to the
International Maritime Organization (EPA-420-F-10-041, August
2010).
Onroad
California,
NMIM-based, and
MOVES sources
not subject to
temperature
adjustments:
onnoadj
Mobile:
onroad
Three, monthly, county-level components:
1) California onroad, created using annual emissions for all pollutants,
submitted by CARB for the 2005 NEI version 2. NH3 (not
submitted by CARB) from MOVES2010.
2) Onroad gasoline and diesel vehicle emissions from MOVES2010
not subject to temperature adjustments: exhaust CO, NOx, VOC,
NH3, benzene, formaldehyde, acetaldehyde, 1,3-butadiene,
acrolein, naphthalene, brake and tirewear PM, exhaust diesel PM,
and evaporative VOC, benzene, and naphthalene.
3) Onroad emissions for Hg from NMIM using MOBILE6.2, other
than for California.
Onroad cold-start
gasoline exhaust
mode vehicle from
MOVES subject
to temperature
adjustments:
onmovesstartpm
Mobile:
onroad
Monthly, county-level MOVES2010-based onroad gasoline emissions
subject to temperature adjustments. Limited to exhaust mode only for
PM species and naphthalene. California emissions not included. This
sector is limited to cold start mode emissions that contain different
temperature adjustment curves from running exhaust (see
on_moves_runpm sector).
Onroad running
gasoline exhaust
mode vehicle from
MOVES subject
to temperature
adjustments:
onmovesrunpm
Mobile:
onroad
Monthly, county-level MOVES2010-based onroad gasoline emissions
subject to temperature adjustments. Limited to exhaust mode only for
PM species and Naphthalene. California emissions not included. This
sector is limited to running mode emissions that contain different
temperature adjustment curves from cold start exhaust (see
on_moves_startpm sector).
Biogenic: biog
N/A
Hour-specific, grid cell-specific emissions generated from the
BEIS3.14 model -includes emissions in Canada and Mexico.
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.
Other point
sources not from
the NEI, Hg only:
othpthg
N/A
Annual year 2000 Canada speciated mercury point source emissions.
Other nonpoint
N/A
Annual year 2006 Canada (province resolution) and year 1999 Mexico
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Platform Sector
2005 NEI
Sector
Description and resolution of the data input to SMOKE
and nonroad not
from the NEI:
othar
Phase III (municipio resolution) nonpoint and nonroad mobile
inventories.
Other nonpoint
sources not from
the NEI, Hg only:
otharhg
N/A
Annual year 2000 Canada speciated mercury from nonpoint sources.
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.
As discussed in the 2005 v4.1 platform documentation, we processed all emissions data with the Sparse
Matrix Operator Kernel Emissions (SMOKE) modeling system, version 2.6. More details about SMOKE
including user documentation.
For the 2005 base case, all inventory and ancillary input data files used as inputs for this rule can be found at
the 2005-based platform website Clearinghouse for Inventories and Emission Factors (CHIEF).
2.2 2005 Proposed Toxics Rule custom processing configuration
In support of the proposed Toxics Rule, EPA modeled the air quality in the East and the West (2 separate
modeling domains), each using a 12-km horizontal grid resolution. The 12-km modeling domains were
"nested" within a modeling domain covering the lower 48 states using a 36-km grid resolution. The air
quality predictions from the 36 km Continental US (CONUS) domain were used to provide incoming
"initial" and "boundary" concentrations for the Eastern 12 km domain. A map of the air quality modeling
domains is in Figure 2-1.
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Figure 2-1. Air quality modeling domains
36km Domain Boundary
12km West Domain Boundar
idary
T_
12km East Domain Boundary
All three grids use a Lambert-Conformal projection, with Alpha = 33°, Beta = 45° and Gamma = -97°, with a
center of X = -97° and Y = 40°. Other specific parameters for these grids are provided in the Air Quality
Modeling Technical Support Document (EPA, 2011) for the Toxics Rule. More details on the grid
parameters are provided in the 2005v4.1 platform documentation.
3 Development of 2016 Future-Year Base Case Emission Inventories
This section describes the methods we used for developing the 2016 future-year base-case emissions. The
ancillary input data are very similar in the future-year scenarios to those in the 2005 base case except for the
speciation profiles used for gasoline-related sources, which change in the future to account for increased
ethanol usage in gasoline. The specific speciation profile changes are discussed in Sections 3.2.8 (stationary
source impacts) and 3.3.5 (mobile source impacts).
The future base case projection methodologies vary by sector. The 2016 base case represents predicted
emissions in the absence of any further controls beyond those Federal measures already promulgated, or
those expected to be promulgated prior to the Toxics Rule proposal date. For EGU emissions (ptipm sector),
the emissions reflect state rules and federal consent decrees through December 1, 2010 and incorporate
information on existing controls collected through the Information Collection Request (ICR) for the Toxics
Rule. For mobile sources (on noadj, onmovesrunpm, and onmovesstartpm sectors), all national
measures for which data were available at the time of modeling have been included. The future base-case
scenarios do reflect projected economic changes and fuel usage for EGU and mobile sectors. For non-EGU
point (ptnonipm sector) and nonpoint stationary sources (nonpt, ag, and afdust sectors), any local control
programs that might be 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
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projections. One exception are some NOx and VOC reductions associated with the New York (NY) State
Implementation Plan (SIP), which were added as part of a larger effort to start including more local control
information. This is described further in Section 3.2.6. The following bullets summarize the projection
methods used for sources in the various sectors, while additional details and data sources are given in Table
3-1:
IPM sector (ptipm): Unit-specific estimates from IPM, version 4.10 (interim version developed for
the Toxics Rule air quality modeling).
Non-IPM sector (ptnonipm): Projection factors and percent reductions reflect emission reductions
due to control programs, plant closures, consent decrees and settlements, and one 1997 ozone
NAAQS State Implementation Plan for New York. We also used projection approaches for point-
source livestock and aircraft and gasoline stage II emissions that are consistent with projections used
for the sectors that contain the bulk of these emissions. Terminal area forecast (TAF) 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 is discussed in
Section 3.2.8.
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 to 2016 using 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 reflect emission reductions due to control
programs. Residential wood combustion projections based on growth in lower-emitting stoves and
retirement of higher emitting stoves. Portable Fuel Container (PFC) projection factors reflecting
impact of the final Mobile Source Air Toxics (MSAT2) rule. Gasoline stage II projection factors
based on National Mobile Inventory Model (NMIM)-estimated VOC refueling estimates for future
years. Year-specific speciation was applied to some portions of this sector (i.e., gasoline-related
emissions) and is discussed in Section 3.2.8.
Nonroad mobile sector (nonroad): Other than for California, adjusted output from a 2015 run of
NMIM that utilized the NR05d-Bond-final version of NONROAD (which is equivalent to
NONROAD2008a) to the year 2016. Adjustment to 2016 was made by applying factors by
pollutant, source category code (SCC), and mode (exhaust/evap). Factors were computed as the ratio
of 2016 to 2015 national-level emissions from NMIM runs. Adjustment not made for NH3 which
used the 2015 NMIM data. Includes final controls from the final locomotive-marine and small spark
ignition OTAQ rules. California-specific data provided by the state of California, except NH3 used
2015 NMIM. Year-specific speciation was applied to some portions of this sector and is discussed in
Section 3.3.5. Hg was kept at 2005 levels.
Aircraft, 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 activity growth and final
locomotive-marine controls. Hg was kept at 2005 levels.
Class 3 commercial marine vessel sector (seca_c3): base year 2005 emissions grown and controlled
to 2016, incorporating controls based on Emissions Control Area (ECA) and International Marine
Organization (IMO) global NOx and SO2 controls. Hg was dropped from this sector for both 2005
and 2016.
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Onroad no-adjustment for temperature mobile sector (on noadj): MOVES2010 run (state-month) for
2016 and results disaggregated to the county level using NMIM 2015. California-specific data
provided by the state of California. VOC speciation uses different future year values to take into
account both increase in ethanol, and the existence of Tier 2 vehicles which use a different speciation
profile. Hg is kept at 2005 levels.
Onroad PM gasoline running mode sector (onmovesstartpm): Running mode MOVES2010 future
year state-month estimates for PM and naphthalene, apportioned to the county level using NMIM
2015 state-county ratios matched to vehicle and road types. Use future year temperature adjustment
file for adjusting the 72°F-supplied emissions (for elemental and organic carbon) based grid cell
hourly temperature (lower temperatures result in increased emissions).
Onroad PM gasoline start mode sector (on moves startpm): Cold start MOVES2010 future-year
state-month estimates for PM and naphthalene, apportioned to the county level using NMIM 2015
state-county ratios of local urban and rural roads by vehicle type. Use future-year temperature
adjustment file for adjusting the 72°F emissions (for elemental and organic carbon) based on grid cell
hourly temperatures (lower temperatures result in increased emissions).
Other nonroad/nonpoint (othar): No growth or control.
Other nonpoint speciated mercury (othar hg): 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.
Other point speciated mercury (othpt hg): No growth or control.
Biogenic: 2005 emissions used for all future-year scenarios to be consistent with 2005 meteorology
used for all scenarios.
Table 3-1 summarizes the control strategies and growth assumptions by source type used to create the 2016
base case emissions from the 2005 base-case inventories. All Mexico, Canada, and offshore oil emissions
are unchanged in all future case scenarios from those in the 2005 base case. Emission summaries by sector
for 2005 and future years are provided in Section 4.
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
proposed Toxics 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.
Sector names associated with the emissions categories are provided in parentheses. A list of inventory
datasets used for this and all cases is provided in Appendix A.
13
-------
Table 3-1. Control strategies and growth assumptions for creating 2016 base 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
\on-K(il Point (plnonipm sector) projection :ippro;iches Ciirried I'orwnrd
from the Proposed Transport Rule'1'
MACT rules, national. VOC: national aoolicd bv 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
VOC
EPA, 2007a
Consent decrees on Companies (based on information from the Office of Enforcement
and Compliance Assurance - OECA) apportioned to plants owned/operated by the
Companies
VOC, CO, NOx,
PM, S02
Appendix B
DOJ Settlements: plant SCC controls for:
Alcoa, TX
Premcor (formerly Motiva), DE
All
1
Refinery Consent Decrees: plant/SCC controls
NOx, PM, S02
2
Municipal Waste Combustor Reductions -plant level
PM
4
Hazardous Waste Combustion
PM
3
Hospital/Medical/Infectious Waste Incinerator Regulations
NOx, PM, S02
EPA, 2005
Large Municipal Waste Combustors - growth applied to specific plants
All (including Hg)
4
MACT rules, plant-level, VOC: Auto Plants
VOC
5
MACT rules, plant-level, PM & SO2: Lime Manufacturing
PM, S02
6
MACT rules, plant-level, PM: Taconite Ore
PM
7
a. The implementation of these rules was changed to reflect a 2016 future year, rather than 2012 / 2014
b. We inadvertently did not apply closures that had been applied for the Transport Rule proposal; emissions from these
plants sum to 3,300 tons VOC, 178 tons PM2 5, 1,982 tons SO2, 1,639 tons NOx, 6 tons NH3 and 379 tons CO. Atthe
state level, the largest impact is in West Virginia for both NOx and SO2 (717 tons NOx, which is slightly under 1% of
total anthropogenic NOx emissions and 1,604 tons SO2 which is 0.5% of total SO2 emissions for that state). All other
NOx and SO2 errors are under 500 tons at the state level and only a fraction of a percent impact on total emissions.
Nonpoint (nonpt sector) projection :ippro;ichcs carried forward from the Proposed Transport Rule
Municipal Waslo LundlilK projection facior of 0.25 applied
All
LP A, 200"a
Livestock Emissions Growth from year 2002 to year 2016
NH3, PM
8
Residential Wood Combustion Growth and Change-outs from year 2005 to Year 2016
All
9
14
-------
Gasoline Stage II growth and control from year 2005 to year 2016
VOC
10
Portable Fuel Container Mobile Source Air Toxics Rule 2 (MSAT2) inventory growth
and control from year 2005
to year 2016
VOC
11
Addilioiuil projections used in (ho proposed Toxics Rule
modeling lor non-Kdl point sources (ptnonipiii sector)'1
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
Hg, NOx, S02,
PM, HCL
12
NESHAP: Industrial, Commercial, Institutional (ICI), Boilers (based on proposed rule
reductions 04-15-10) ~ finalized 2/2011
Hg, S02, HCL,
PM
Section 3.2.6
NESHAP: Gold Mine Ore Processing and Production Area Source Category (based on
proposed rule 04-15-10) - finalized 12/2010
Hg
13 and
Appendix C
NESHAP: Mercury Emissions From Mercury Cell Chlor-Alkali Plants-Final Rule
(12/19/03)
Hg
Appendix D
Pulp and Paper Project smelter replacement for Georgia Pacific plant in VA (12/2009)
Hg
Appendix D
NESHAP: Electric Arc Furnace Steelmaking Facilities (12/28/2007)
Hg
Appendix D
NESHAP: Hazardous Waste Combustion (12/19/2005)
Hg
Appendix D
New York ozone SIP controls
VOC, NOx,
HAP VOC
14
Additional Plant and Unit closures provided by state, regional, and EPA agencies and
additional consent decrees
All
Appendix E
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 ICR
from the ICI Boiler NESHAP.
NOx, S02, HCL
Section 3.2.6
NESHAP: Reciprocating Internal Combustion Engines (RICE)b
NOx, CO, PM
Appendix F
Replaced 2005 with 2008 emissions for Corn Products International, Cook Cty, Illinois,
due to the shutdown of 3 boilers and addition of a new boiler (subject to Prevention of
Significant Deterioration and Requirements). Agency Identifier: 031012ABI(ILEPA)
15
a. We gathered the data on the consent decrees for the LaFaree (cement manufacturing) and St. Gobain
(glass manufacturing) facility, both of which were signed in Jan 2010. However, technical difficulties
with the projections software resulted in these reductions not being included for the 2016 projections.
The resulting emissions are therefore too high in CA, IL, IN, KS, LA, MA, MI, MO, NC, OH, OK, PA,
TX, WA, and WI, and are summarized nationally below. Although these missed reductions are large,
they have a minimal impact on our overall analysis because the modeling analysis for the RIA captures
an appropriate difference between the future base and policy cases and that difference is unaffected by
this omission since it was omitted from both the base and the policy cases.
CO NOX PM10 PM25 S02 VOC
(tons) (tons) (tons) (tons) (tons) (tons)
110 13,214 269 210 16,270 6
b. Note that SO2 reductions are expected to occur to due fuel sulfur limits but were excluded from the
projection. They were expected to reduce SO2 by 27,000 tons, nationwide. This omission is expected to
have negligible impacts on our analysis since the reductions were omitted from both the base and policy
cases.
Additioiiiil projections used in the proposed Toxics Rule modeling lor Nonpoint sources (nonpt sector)
NESHAP: Reciprocating Internal Combustion Engines (RICE) (as with ptnonipm, we
excluded the low sulfur fuel requirements)
NOx, CO,
VOC, PM
Appendix F
Use Phase II WRAP 2018 Oil and Gas, and apply RICE controls to these emissions
VOC, S02,
NOx, CO
Section 3.2.7
Use 2008 Oklahoma and Texas Oil and Gas, and apply RICE controls to these emissions
VOC, S02,
NOx, CO, PM
Section 3.2.7
New York ozone SIP controls
VOC
15
15
-------
APPROACHES/REFERENCES- Stationary Sources:
1 ¦ For Alcoa consent decree, for Motiva: used information sent by State of Delaware
2. Used data provided by EPA, OAQPS, Sector Policies and Programs Division (SPPD) -see Section 1.
3. Obtained from Anne Pope, US EPA - Hazardous Waste Incinerators criteria and hazardous air pollutant controls
carried over from 2002 Platform, v3.1.
4. Used data provided by EPA, OAQPS SPPD expert -see Section 1.
5. Percent reductions and plants to receive reductions based on recommendations by rule lead engineer, and are
consistent with the reference: EPA, 2007a
6. 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 PM2 5 reductions are computed by 3,786/13,588 = 28%
7. 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.
8. 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 Section3.2.1.
9. Growth and Decline in woodstove types based on industry trade group data, See Section. See Section 3.2.2.
10. VOC emission ratios ofyear2016 (linear interpolation between 2015 and 2020) -specific from year 2005 from the
National Mobile Inventory Model (NMIM) 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.
11. VOC and benzene emissions for year 2016 (linear interpolation between 2015 and 2020) from year 2002 from
MSAT2 rule (EPA, 2007b)
12. Data files for the cement sector provided by Elineth Torres, 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
13. Data provided by Chuck French, US EPA, plant-specific emissions after the rule were estimated. Consistent with
proposed rule for Gold Mine Ore Processing NESHAP: 75 FR 22469.
14. NOx and VOC reductions obtained from Appendix J in NY Department of Environmental Conservation
Implementation Plan for Ozone (February 2008) Section 3.2.6.
15. The 2008 data used came from Illinois" submittal of 2008 emissions to the NEI.
Oiii'»;hI mobile mid nonrond mobile controls
(list includes nil key mobile control strategies hut is not exhaustive)"
National Onroad Rules:
Tier 2 Rule
2007 Onroad Heavy-Duty Rule
Final Mobile Source Air Toxics Rule (MSAT2)
Renewable Fuel Standard
all
Local Onroad Programs:
National Low Emission Vehicle Program (NLEV)
Ozone Transport Commission (OTC) LEV Program
VOC
2
National Nonroad Controls:
Clean Air Nonroad Diesel Final Rule - Tier 4
Control of Emissions from Nonroad Large-Spark Ignition Engines and Recreational
Engines (Marine and Land Based): "Pentathalon Rule"
Clean Bus USA Program
Control of Emissions of Air Pollution from Locomotives and Marine Compression-Ignition
Engines Less than 30 Liters per Cylinder
all
3,4,5
Aircraft:
all
6
16
-------
Itinerant (ITN) operations at airports to year 2020
Locomotives:
Energy Information Administration (EIA) fuel consumption projections for freight rail
Clean Air Nonroad Diesel Final Rule - Tier 4
Locomotive Emissions Final Rulemaking, December 17, 1997
Control of Emissions of Air Pollution from Locomotives and Marine
all
EPA, 2009;
7:4
Commercial Marine:
Category 3 marine diesel engines Clean Air Act and International Maritime Organization
standards (April, 30, 2010)
EIA fuel consumption projections for diesel-fueled vessels
OTAQ ECA C3 Base 2020 inventory for residual-fueled vessels
Clean Air Nonroad Diesel Final Rule - Tier 4
Emissions Standards for Commercial Marine Diesel Engines, December 29, 1999
Tier 1 Marine Diesel Engines, Februaiy 28, 2003
all
7; EPA, 2009
a. These control programs are the same as were used in the proposed Transport Rule except for the C3 marine standards of
April 2010, which are included in the Toxics Rule but were not included in the proposed transport rule.
APPROACHES/REFERENCES - Mobile Sources
1- Clean Air Markets
2. Only for states submitting these inputs: Transportation. Air Pollution, and Climate Change
3 Regulations for Emissions from Vehicles and Engines
4. Clean School Bus
5, Overview of EPA's Emission Standards for Marine Engines
6- Federal,
7- Regulations for Emissions from Vehicles and Engine
, December 2007.
3.1 Stationary Source Projections: EGU sector (ptipm)
The future-year data for the ptipm sector used in the air quality modeling were created by an interim version
4.10 of the Integrated Planning Model (IPM) (Clean Air Markets). The IPM is a multiregional, dynamic,
deterministic linear programming model of the U.S. electric power sector. Version 4.10 reflects state rules
and consent decrees through December 1, 2010 and incorporates information on existing controls collected
through the Information Collection Request (ICR) for the proposed Toxics Rule. 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. For the modeling data files, units with advanced
mercury controls (e.g., ACI) were assumed to operate those controls in states with mercury requirements.
Note that this base case includes the proposed Transport Rule, which is expected to be finalized in June,
2011. Speciated mercury emissions were estimated using mercury speciation factors, which are assigned
based on coal rank (e.g., bituminous, sub-bituminous, lignite), firing type, boiler/burner type, and post-
combustion emissions controls. These are the same factors as were used in the Clean Air Mercury Rule and
are provided in Appendix G. Further details on the EGU emissions inventory used for this proposal can be
found in the proposed Toxics Rule IPM Technical Support Document.
IPM is run in 5 year increments. The IPM 2015 results are valid for representing 2014, 2015, and 2016. As
explained in the IPM TSD, additional steps were taken to ensure that the results were valid for use in a 2014,
2015 or 2016 model run.
17
-------
Directly emitted PM emissions (i.e., PM2.5 and PM10) from the EGU sector are computed via a post
processing routine which applies emission factors to the IPM-estimated fuel throughput based on fuel,
configuration and controls to compute the filterable and condensable components of PM. This methodology
is documented in the IPM TSD. For the post processing of the IPM data used for the air quality modeling, an
erroneous natural gas emission factor that was too high by about a factor of 75 was used. This was
responsible for an over-prediction in directly emitted PM2.5 emissions from the EGU sector of 85 thousand
tons in the base case. This erroneous emissions factor was used in both the base and policy cases. For areas
of the country in which more natural gas is projected to be used in the policy case, the emission factor error
resulted in an overestimate of PM emissions in the policy case. This error does not impact secondary PM
formation which is due to emissions of precursors such as SO2.
Appendix H further details the emission factor error.
We adjusted the inventory files resulting from the IPM post processing to account for criteria pollutant
reductions from the proposed Boiler MACT, which impacts only units that are the less than 25 megawatt
(MW). For mercury from these units, we took a different approach because we used the Boiler MACT
mercury ICR data directly in our modeling data files, as explained next.
For mercury, we removed the emissions from the IPM sector data for sources matched to the Boiler MACT
ICR data to avoid double counting the ptipm sector boiler Hg with the ICR data, which was incorporated
only into the ptnonipm sector. We could not match ICR units directly to NEI or IPM units because the
identification codes used in the ICR database were different from those in the NEI and IPM outputs.
Consequently, we developed the following criteria to prevent double counting of the Hg emissions.
Emissions of Hg were removed from the IPM outputs if a unit had a design capacity less than 25 MW and
the facility matched a facility in the ICR database. The matching was done using the NEIUNIQUEID
field in the NEI and IPM output files1. This approach resulted in the removal of 0.124 tons of Hg from the
2016 base year emissions ptipm sector emissions.
For the other pollutants, we applied reductions to ptipm data intended to represent the major Boiler National
Emissions Standards for Hazardous Air Pollutants (NESHAP). In particular, we adjusted unit level SO2,
PM10, PM2.5, CO, HCL and VOC using unit-specific reduction information from the Boiler MACT ICR
database. Because we were unable to match the specific units from the Boiler MACT ICR to the units in the
ptipm file, we used the following approach:
(1) Match the ptipm sector inventory and the Boiler MACT data at the facility level.
(2) For facilities that match, use only units that are less than 25 MW and adjust only processes
from these units that have the same fuel type as listed in the ICR boiler MACT database.
Since the boiler MACT ICR fuels do not match one-to-one with the fuels described by the source
classification codes in the IPM outputs, we used a fuels crosswalk. Application of these unit-specific
reductions resulted in a reduction of 821 tons of CO, 725 tons HCL, 546 tons PM2.5, 20,239 tons SO2 and 18
tons of VOC. Impacts at the state-level are shown in Table 3-2.
1 The NEI UNIQUE ID was added to the 7,740 records (approximately 1,500 facilities) of the ICR data and this allowed
matching of the boiler MACT ICR data to the NEI at a facility level. Roughly 1,250 of the 1,500 facilities in the ICR database,
representing 83% of the ICR SO2 emissions were mapped at the facility level.
18
-------
Table 3-2. Adjustment to IPM emissions due to application of Boiler MACT.
CO (2016 tons)
HCL (2016 tons)
PM2_5 (2016 tons)
S02 (2016 tons)
VOC (2016 tons)
IPM
Base
Boiler
MACT %
Reduct.
IPM Base
Boiler
MACT %
Reduct.
IPM Base
Boiler
MACT %
Reduct.
IPM Base
Boiler
MACT %
Reduct.
IPM Base
Boiler
MACT %
Reduct.
AL
17,518
0
9,086
0
13,821
0
172,198
0
1,297
0
AZ
10,789
0
422
0
8,243
0
25,235
0
867
0
AR
10,380
0
8,265
0
4,358
0
88,049
0
651
0
CA
41,700
0
242
0
10,693
0
4,886
0
818
0
CO
9,428
18
5,191
52
4,469
3
79,129
915
682
1
CT
8,958
0
667
0
1,715
0
2,715
0
108
0
DE
1,544
0
29
0
606
0
1,975
0
47
0
FL
64,017
0
6,777
0
26,822
0
172,059
0
1,929
0
GA
12,583
0
2,555
0
14,721
0
91,901
0
1,175
0
ID
553
0
0
0
189
0
0
0
14
0
IL
19,707
0
11,375
0
10,903
25
166,000
0
1,968
0
IN
22,623
75
17,443
16
21,258
9
229,802
2,077
2,100
1
IA
7,828
16
9,064
51
5,290
22
98,632
1,276
781
2
KS
5,146
0
3,361
0
4,607
0
61,664
0
699
0
KY
32,492
0
3,132
0
13,446
0
123,098
0
1,570
0
LA
22,730
0
7,020
0
4,400
0
85,987
0
656
0
ME
2,224
0
23
0
165
0
289
0
18
0
MD
9,852
0
1,743
0
3,979
0
37,665
0
463
0
MA
7,702
0
402
0
3,495
0
9,340
0
265
0
MI
17,493
18
17,396
41
6,475
18
169,757
428
1,264
2
MN
6,169
33
2,077
155
9,250
70
51,124
3,728
648
4
MS
7,764
0
4,022
0
2,777
0
56,006
0
409
0
MO
14,359
63
19,201
126
7,464
114
172,031
4,870
1,682
1
MT
3,616
0
512
0
2,317
0
12,565
0
247
0
NE
4,699
0
8,122
0
2,693
0
77,965
0
546
0
NV
5,783
0
460
0
10,665
0
11,429
0
336
0
NH
2,415
0
191
0
1,174
0
4,723
0
107
0
NJ
8,669
0
112
0
3,600
0
7,769
0
328
0
NM
7,969
0
103
0
5,782
0
11,353
0
544
0
NY
20,012
0
1,674
0
7,098
0
35,139
0
671
0
NC
10,933
509
1,396
156
11,935
22
77,091
5,456
942
5
ND
7,268
0
2,836
0
5,923
0
119,480
0
858
0
OH
27,806
29
12,205
18
21,828
240
202,779
156
1,929
2
OK
26,742
0
10,618
0
7,290
0
139,801
0
909
0
OR
2,204
0
1,130
0
876
0
11,102
0
100
0
PA
31,503
0
3,278
0
21,826
0
152,951
0
1,871
0
RI
1,594
0
0
0
544
0
0
0
40
0
SC
9,670
0
2,586
0
10,917
0
105,085
0
706
0
SD
679
0
1,390
0
704
0
28,170
0
127
0
19
-------
CO (2016 tons)
HCL (2016 tons)
PM2_5 (2016 tons)
S02 (2016 tons)
VOC (2016 tons)
IPM
Base
Boiler
MACT %
Reduct.
IPM Base
Boiler
MACT %
Reduct.
IPM Base
Boiler
MACT %
Reduct.
IPM Base
Boiler
MACT %
Reduct.
IPM Base
Boiler
MACT %
Reduct.
TN
7,351
0
7,304
0
6,855
0
106,762
0
948
0
TX
105,672
0
26,701
0
38,275
0
421,102
0
5,051
0
UT
4,090
0
949
0
5,074
0
32,636
0
487
0
VT
0
0
0
0
0
0
0
0
0
0
VA
14,037
0
1,199
0
7,470
0
45,726
0
563
0
WA
3,544
0
2,968
0
1,534
0
29,165
0
206
0
WV
11,783
0
2,361
0
16,131
0
127,788
0
1,223
0
WI
14,789
61
7,166
110
6,259
24
77,675
1,333
993
1
WY
7,499
0
2,582
0
7,362
0
55,571
0
923
0
Tribal data
273
0
0
0
93
0
0
0
7
0
Grand Total
694,158
821
227,335
725
383,370
546
3,793,365
20,239
40,775
18
After the Boiler MACT reductions were applied, we generated day-specific emissions for input to SMOKE.
The approach was modified from the base year to account for summer and winter differences in emissions
that are provided by CAMD. In particular, CAMD provides the average-day summer and average-day
winter emissions. These are used in the process of developing monthly emissions as shown in Figure 3-1.
Daily emissions use the same approach as 2005, which is to apply state averaged month-to-day factors from
the 2005 CEM data for NOx, SO2 and heat input (the heat input is used for other pollutants).
Figure 3-1. Approach to Compute Monthly Emissions from the IPM data
If month > May and month < September:
Emissions, (tons/month) = annualized_summer_emissioris * (5/12) * summer_monthly_fractionm
Otherwise:
Emissions,,, (tons/month) = annualized_winter_emissions * (7/12) * winter_monthly_fractionm
Where:
m = month of the year
Annualized_summer_emissions = the emissions value in the summer SMOKE input file from post-processed
IPM data provided by CAMD
Annualized_winter_emissions = the emissions value in the winter SMOKE input file from post-processed
IPM data provided by CAMD
Winter_monthly_fraction is computed as:
fm
/>..
mi December
n-January n^-October
Summer_monthty_fraction is computed as: J
D,
September
n - May
Where:
D = NOx monthly CEM fractions for allocating NOx emissions which are
based on state averaged values of the CEM data for 2004, 2005 and 2006
S02 monthly CEM fractions for allocating S02 emissions which are
based on state averaged values of the CEM data for 2004, 2005 and 2006,
Heat input monthly CEM fractions for allocating all other pollutants
which are based on state averaged values of the CEM data for 2004, 2005 and
2006
20
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3.2 Stationary Source Projections: non-EGU sectors (ptnonipm, nonpt, ag,
afdust)
To project U.S. stationary sources other than ptipm, we applied growth factors and/or controls to certain
categories within the ptnonipm, nonpt, ag and afdust platform sectors. This subsection provides details on
the data and projection methods used for these sectors. In estimating future-year emissions, we assumed that
emissions growth does not track with economic growth for many stationary non-IPM sources. This "no-
growth" assumption is based on an examination of historical emissions and economic data. While we are
working toward improving the projection approach in future emissions platforms, we are still using the no-
growth assumption for the 2005, v4.1 platform. More details on the rationale for this approach can be found
in Appendix D of the Regulatory Impact Assessment for the PM NAAQS rule (EPA, 2006).
Year-specific projection factors for year 2016 were used for creating the 2016 base case unless noted
otherwise. 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 county and pollutant and has
thousands of records). If the growth or control factors for a sector are not provided in a table in this
document, they are available as a "projection" or "control" packet for input to SMOKE on the v4.1 platform
website (see the end of Section 1).
3.2.1 Livestock emissions growth (ag, afdust)
Growth in ammonia (NH3) and dust (PM10 and PM2.5) emissions from livestock in the ag and afdust and
ptnonipm sectors was based on projections of growth in animal population. While there are some livestock
emissions in ptnonipm, (very small compared to ag sector) the control packet was inadvertently not applied
to that sector. This results in an underestimate of NH3 of roughly 1,620 tons in 2016 (primarily in Kansas
and Minnesota for which the NH3 were reported at specific farms in the point source inventory), and for
PM2.5 the 2016 underestimate is 3 tons. All of these omissions have a negligible impact on the results of this
analysis because the mass impacted is so small and because the omissions were made in both the future base
and policy cases.
Table 3-3 provides the growth factors from the base case 2005 emissions to 2016 for animal categories
applied to the ag and afdust sectors for livestock-related SCCs. For example, year 2016 beef emissions are
1.9% larger than the 2005 base case emissions. Except for dairy cows and turkey production, the animal
projection factors are derived from national-level animal population projections from the U.S. Department of
Agriculture (USDA) and the Food and Agriculture Policy and Research Institute (FAPRI). 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.
21
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Appendix E in the 2002v3 platform documentation provides the animal population data and regression
curves used to derive the growth factors. 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 3-3.
Table 3-3. Growth factors from year 2005 to future years for Animal Operations
Animal Category
2016 Projection
Factors
Dairy Cow
1.000
Beef
1.019
Pork
1.083
Broilers
1.321
Turkeys
1.000
Layers
1.224
Poultry Average
1.250
Overall Average
1.087
3.2.2 Residential wood combustion growth (nonpt)
We projected residential wood combustion 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 and values cited here was developed with assistance from the EPA team
working on the woodstoves change-out program. They worked with the Hearth, Patio, and Barbecue
Association to see what the best-available data were for projecting this sector into the future.
The specific assumptions we made were:
¦ Fireplaces, 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 (these emissions have not been updated for the 2005v4 platform used for the Transport Rule
proposal) emissions 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.
Table 3-4 presents the projection factors used to project the 2005 base case (2002 emissions) for residential
wood combustion.
22
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Table 3-4. Projection Factors for growing year 2005 Residential Wood Combustion Sources to 2016
SCC
SCC Description
Projection
Factors
2016
2104008000
Total: Woodstoves and Fireplaces
0.8522
2104008001
Fireplaces: General
1.1400
2104008070
Outdoor Wood Burning Equipment
2104008002
Fireplaces: Insert; non-EPA certified
0.7200
2104008010
Woodstoves: General
2104008051
Non-catalytic Woodstoves: Non-EPA certified
2104008003
Fireplaces: Insert; EPA certified; non-catalytic
1.28
2104008004
Fireplaces: Insert; EPA certified; catalytic
2104008030
Catalytic Woodstoves: General
2104008050
Non-catalytic Woodstoves: EPA certified
2104008052
Non-catalytic Woodstoves: Low Emitting
2104008053
Non-catalytic Woodstoves: Pellet Fired
3.2.3 Gasoline Stage II growth and control (nonpt, ptnonipm)
Emissions from Stage II gasoline operations in the 2005 base case are contained in both nonpt and ptnonipm
sectors. The only SCC in the nonpt inventory used for gasoline Stage II emissions is 2501060100 (Storage
and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage II: Total). The
following SIC and SCC codes are associated with gasoline Stage II emissions in the ptnonipm sector:
¦ SIC 5541 (Automotive Dealers & Service Stations, Gasoline Service Stations, Gasoline service
stations)
¦ SCC 40600401 (Petroleum and Solvent Evaporation;Transportation and Marketing of Petroleum
Products;Filling Vehicle Gas Tanks - Stage II;Vapor Loss w/o Controls)
¦ SCC 40600402 (Petroleum and Solvent Evaporation;Transportation and Marketing of Petroleum
Products;Filling Vehicle Gas Tanks - Stage II;Liquid Spill Loss w/o Controls)
¦ SCC 40600403 (Petroleum and Solvent Evaporation;Transportation and Marketing of Petroleum
Products;Filling Vehicle Gas Tanks - Stage II;Vapor Loss w/o Controls)
¦ SCC 40600499 (Petroleum and Solvent Evaporation;Transportation and Marketing of Petroleum
Products;Filling Vehicle Gas Tanks - Stage II;Not Classified
We used a consistent approach across nonpt and ptnonipm to project these gasoline stage II emissions. The
approach involved computing VOC-specific projection factors from the NMIM results for onroad refueling,
using ratios of future-year emissions to 2005 base-case emissions. The approach accounts for three elements
of refueling growth and control: (1) activity growth (due to VMT growth as input into NMIM), (2) emissions
reductions from Stage II control programs at gasoline stations, and (3) emissions reductions resulting from
the phase-in over time of newer vehicles with onboard Stage II vehicle controls. We assumed that all areas
with Stage II controls in 2005 continue to have Stage II controls in 2016.
We computed VOC, benzene and naphthalene projection factors at a county-specific, annual resolution as
shown below:
23
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PF [county, future year] = VOC_RFL[county, future year]/VOC_RFL[county, 2005]
PF [county, future year] = BENZENE_RFL[county, future year]/ BENZENE _RFL[county, 2005]
PF [county, future year] = NAPHTHALENE_RFL[county, future year]/ NAPHTHALENE _RFL[county, 2005]
Where VOCRFL is the VOC refueling emissions for onroad sources from NMIM,
BENZENE_RFL is the BENZENE refueling emissions for onroad sources from NMIM, and
NAPHTHALENE RFL is the NAPHTHALENE refueling emissions for onroad sources from
NMIM.
For this 2016 projection, we obtained VOC RFL, BENZENERFL and NAPHTHALENERFL for each
county by interpolating 2015 and 2020 NMIM results.
We applied these projection factors to both nonpt and ptnonipm sector gasoline stage II sources.
Chemical speciation uses certain VOC HAPs for some sources, specifically, benzene, acetaldehyde,
formaldehyde, and methanol (BAFM). The VOC HAPs are used for sources that have consistent VOC and
VOC HAPs using various criteria as described in the 2005 v4.1 platform documentation, and these sources
are called "integrated" sources. The nonpoint gasoline stage II emissions are an integrated source, and so the
VOC HAPs are also projected based on ratios of future year and base year VOC. The only two VOC HAPs
emitted from refueling are benzene and naphthalene, and both of these were projected consistently with
VOC. However, naphthalene was not used in the chemical speciation (it is not B,A,F or M) and was
therefore not used for this effort. Benzene was used as part of the speciation for the nonpt sector gasoline
stage II sources. The ptnonipm is a "no-integrate" sector, so ptnonipm gasoline stage II sources did not use
the projected benzene as part of the speciation, but rather used VOC speciation to estimate benzene.
3.2.4 Portable fuel container growth and control (nonpt)
We obtained future-year VOC emissions from Portable Fuel Containers (PFCs) from inventories developed
and modeled for EPA"s MS AT rule (EPA, 2007b). The 10 PFC SCCs are summarized below (full SCC
descriptions for these SCCs include "Storage and Transport; Petroleum and Petroleum Product Storage" as
the beginning of the description) below.
2501011011 Residential Portable Fuel Containers:
2501011012 Residential Portable Fuel Containers:
2501011013 Residential Portable Fuel Containers:
2501011014 Residential Portable Fuel Containers:
2501011015 Residential Portable Fuel Containers:
2501012011 Commercial Portable Fuel Containers
2501012012 Commercial Portable Fuel Containers
2501012013 Commercial Portable Fuel Containers
2501012014 Commercial Portable Fuel Containers
2501012015 Commercial Portable Fuel Containers
Permeation
Evaporation
Spillage During Transport
Refilling at the Pump: Vapor Displacement
Refilling at the Pump: Spillage
Permeation
: Evaporation
Spillage During Transport
: Refilling at the Pump: Vapor Displacement
: Refilling at the Pump: Spillage
Additional information on the PFC inventories is available in Section 2.2.3 of the documentation for the
2002 Platform (Clearinghouse for Inventories and Emissions Factors (CHIEF)).
The future-year emissions reflect projected increases in fuel consumption, state programs to reduce PFC
emissions, standards promulgated in the MSAT rule, and impacts of the Renewable Fuel Standard (RFS) on
gasoline volatility. Future-year emissions for PFCs were available for 2010, 2015, 2020, and 2030. In
24
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creating the inventories for 2016, we linearly interpolated year 2015 and year 2020 inventories. Benzene and
other VOC HAP future-year PFC emissions were also included in the interpolation. Benzene was used in
VOC speciation for CMAQ through the modification of VOC speciation profiles calculations (no other
BAFM HAPs are emitted from PFCs).
3.2.5 Aircraft growth (ptnonipm)
As with the 2005v4 platform, aircraft emissions are contained in the ptnonipm inventory. 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 Arm h «n'cast < I \stem (publication date December 2008). This
information is available for approximately 3,300 individual airports, for all years up to 2025. We aggregated
and applied this information at the national level by summing the airport-specific (U.S. airports only) ITN
operations to national totals by year and by aircraft operation, for each of the four available operation types:
commercial, general, air taxi, military. We computed growth factors for each operation type by dividing
future-year 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 are documented.
Table 3-5 provides the national growth factors for aircraft; all factors are applied to year 2005 emissions.
For example, year 2016 commercial aircraft emissions are 11.18% higher than year 2005 emissions.
Table 3-5. Factors used to project base case 2005 aircraft emissions to future years
SCC
SCC Description
Year 2016
factor
2275001000
Military aircraft
0.968
2275020000
Commercial aircraft
1.1118
2275050000
General aviation
0.9952
2275060000
Air taxi
0.9235
27501015
Internal Combustion Engines; Fixed Wing Aircraft L & TO
Exhaust;Military;Jet Engine: JP-5
0.968
27502001
Internal Combustion Engines; Fixed Wing Aircraft L & TO
Exhaust;Commercial;Piston Engine: Aviation Gas
1.1118
27502011
Internal Combustion Engines; Fixed Wing Aircraft L & TO
Exhaust;Commercial;Jet Engine: Jet A
1.1118
27505001
Internal Combustion Engines; Fixed Wing Aircraft L & TO
Exhaust;Civil;Piston Engine: Aviation Gas
0.9952
27505011
Internal Combustion Engines; Fixed Wing Aircraft L & TO
Exhaust;Civil;Jet Engine: Jet A
0.9952
27601014
Internal Combustion Engines; Rotary Wing Aircraft L & TO
Exhaust;Military;Jet Engine: JP-4
0.968
27601015
Internal Combustion Engines; Rotary Wing Aircraft L & TO
Exhaust;Military;Jet Engine: JP-5
0.968
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 plant names associated
25
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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 has not yet been adopted as an EPA (or U.S.) rule; therefore, the
effects of this rule were not included in the future-year emissions projections.
3.2.6 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. Here we describe the complete contents of the controls and closures for the
2016 base cases.
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.
We included plant closures (i.e., emissions were zeroed out for future years) where information
indicated that the plant was actually closed. However, plants projected to close in the future (post-
2008) were not removed in the future years because these projections can be inaccurate due to
economic improvements. Not including cement kiln and plant closures discussed later in Section
3.2.6.1, we also applied plant closures listed in Appendix E. The magnitude of these non-cement
plant closures is shown in Table 3-6 below.
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Table 3-6. Summary of Emission Reductions Applied to the 2005 Base Year
Inventory Due To Plant Closures
State
CO
(tons)
nh3
(tons)
NOx
(tons)
PMio
(tons)
pm25
(tons)
so2
(tons)
voc
(tons)
HG
(tons)
CL2
(tons)
HCL
(tons)
Alabama
7,649
6
153
1,885
1,708
594
571
0.0250
Florida
28
230
461
252
159
8,469
0.0045
Georgia
1,751
60
41
27
482
Illinois*
187
2,452
726
640
19,572
517
0.0134
206
Indiana
438
0
541
193
82
4,530
94
0.0094
9.21
Iowa
374
789
213
37
3,227
10
0.00023
Louisiana
3,035
127
1,878
1,026
787
4,114
4,061
0.0013
0.001
39.03
Maine
35
12
1,145
340
143
3,572
146
0.00095
Massachusetts
32
6
385
68
35
1,303
39
0.00028
Michigan
1,366
4
2,606
509
332
2,076
398
0.034
0.16
99.07
New Hampshire
101
212
75
65
669
10
0.015
0.02
0.50
New York
5
1
48
16
11
217
14
7.9E-5
North Carolina
12
1
94
25
17
379
7
0.00017
Ohio
29
809
74
32
6,705
4
0.0048
72.98
Tennessee
47
0
2,057
169
94
5,377
9,059
0.012
0.005
91.10
Texas
220
71
38
35
35
18
West Virginia
3,770
1
498
48
46
4,893
207
Wisconsin
479
28
1,953
436
297
7,672
349
0.0063
96.66
Grand Total
19,557
415
16,213
6,135
4,546
73,886
15,504
0.1275
0.18
614.54
* For one "closure" in Illinois, we added in 2008 emissions due to a replacement of several units with a new PSD unit. The added emissions
are: 246 tons CO, 0.52 tons NH3, 719.3 tons NOX, 515 tons PM10, 418 tons PM2.5 and 2,203 tons S02 and 100 tons HCL.
We inadvertently did not apply the plant closures that were included for the proposed Transport Rule
because of technical difficulties with software. These proposed transport rule closures affect the following
sources: auto plants, pulp and paper plants, large and small municipal waste combustors (LMWC and
SMWC), as well as plants closed before 2008 but following the release of the 2005v2 point inventory. The
EPA OAQPS SPPD provided the closures information. The impact of not applying the additional closures is
shown below in Table 3-7.
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Table 3-7. Quantification of Missing Closures in 2016 the non-EGU (ptnonipm) sector
non-EGU
non-EGU
Excess
Percent Overestimate
emissions without
emissions with
emissions
in Total man-made
State
Pollutant
closures (tons)
closures (tons)
(tons)
Emissions
Georgia
VOC
33,214
31,572
1,642
0.4%
Illinois
CO
85,413
85,305
109
0%
Michigan
NOx
78,730
78,566
164
0%
Missouri
VOC
20,067
19,775
291
0.1%
New Jersey
VOC
11,226
10,230
996
0.5%
Pennsylvania
NOx
76,953
76,749
204
0%
Pennsylvania
S02
46,609
46,231
378
0.1%
South Carolina
NOx
27,793
27,675
119
0.1%
South Carolina
VOC
17,132
16,928
204
0.1%
Tennessee
NOx
49,456
49,284
172
0%
West Virginia
NOx
32,180
31,463
717
0.5%
West Virginia
S02
23,305
21,701
1,604
1.0%
West Virginia
VOC
11,879
11,678
201
0.2%
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 C, and the detailed data used are available at the website listed in Section 1.
The EPA OAQPS SPPD provided refinery consent decrees controls at the facility and SCC level.
We applied most of the control programs 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 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 of 0.25.
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
Environmer iservation February 2008 guidance. These reductions are found in Appendix J in:
Section 3.2.6.
3.2.6.1 Reductions from the Portland Cement NESHAP
As indicated in Table 3-1, the Industrial Sectors Integrated Solutions (ISIS) model (EPA, 2010b) was used to
project the cement industry component of the ptnonipm emissions modeling sector to 2016. 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.
28
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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 ISIS model results for the future show a continuation of the recent trend in the cement sector of the
replacement of lower capacity, inefficient wet and long dry kilns with bigger and more efficient preheater
and precalciner kilns. Multiple regulatory requirements such as the NESHAP and NSPS currently apply to
the cement industry to reduce CAP and HAP emissions. Additionally, state and local regulatory
requirements might apply to individual cement facilities depending on their locations relative to ozone and
PM2.5 nonattainment areas. The ISIS model provides the emission reduction strategy that balances: 1)
optimal (least cost) industry operation, 2) cost-effective controls to meet the demand for cement, and 3)
emission reduction requirements over the time period of interest. Table 3-8 shows the magnitude of the
ISIS-based cement industry reductions in the future-year emissions that represent 2016, and the impact that
these reductions have on total stationary non-EGU point source (ptnonipm) emissions.
Table 3-8. Future-year ISIS-based cement industry annual reductions (tons/yr)
for the non-EGU (ptnonipm) sector
Cement Industry
Decrease in cement
% decrease in
emissions in 2005
industry emissions
ptnonipm from
Pollutant
in 2016 vs 2005
cement reduction
NOx
193,000
56,740
2.4%
PM2.5
14,400
7,840
1.8%
S02
128,400
106,000
5.0%
VOC
6,900
5,570
0.4%
HCL
2,900
2,220
4.5%
Hg
8.87
6.63
15.2%
3.2.6.2 Boiler MACT reductions
We applied emission reductions to boilers in the ptnonipm sector based on the Boiler MACT proposal, with
additional adjustments that made it similar to the final Boiler MACT. In particular, we applied reductions to
(1) boilers in the inventory that could be matched to facility/fuel (2005 NEINEIUNIQUEID and SCC
fields) combinations in the ICR data and (2) boilers in the inventory that were not in the Boiler MACT ICR
database, but that the Boiler MACT project team indicated were part of the category.
Different approaches were used for applying the reductions for mercury versus the other pollutants because
the mercury emissions from the ICR were used directly in the ptnonipm sector, whereas the other pollutants
used the 2005v2 NEI data2.
For mercury, the Boiler MACT ICR-based unit-level emissions inventory, using facility and unit IDs from
the boiler MACT ICR database, was incorporated into the base-case 2005 ptnonipm sector. Modeling
parameters such as geographic coordinates and stack parameters were obtained by matching the facilities in
the Boiler MACT ICR database to the facilities in the NEI and NATA inventories (details are provided in the
2 This approach was based on exploring the sources of the NEI versus ICR data, particularly the SO2 values. We determined that a
large portion of the ICR data utilized average emission factor that did not account for the sulfur content of the fuel nor any permit
limitations that could impact emissions. Also, for boilers with CEMs, the boiler ICR utilized a month or two of CEM data whereas
the NEI emissions (primarily state reported data) uses the entire year of CEM data where available.
29
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2004v4.1 platform documentation). The amount of mercury from facilities that did not match summed to
0.177 tons (about 4%); these facilities were not included in base-case 2005 ptnonipm sector. For the units
included in the sector, we applied unit-specific reductions provided by the rule developers to the ICR-based
base-case emissions. These reductions were provided to a modified database developed between the
proposal and final rule.
In addition, we applied Hg reductions to boilers at 18 additional facilities (that were not included in the
Boiler MACT ICR data) in consultation with the rule developers, resulting in an additional 0.013 tons of Hg
reductions. These reductions were applied by facility and fuel type. A default percent reduction was used
for these sources, which we computed for each fuel type from the unit-specific reductions. We used the
mode of the data (i.e., most frequent data value), excluding zeros. These values along with the modal values
for the other pollutants are shown in Table 3-9. The non-Hg pollutants also used the mode of the reductions
in the ICR data as the percent reduction.
Table 3-9. Default pollutant fuel reductions applied NEI boilers not covered by the ICR database.
Fuel
SO2 %
reduction
VOC %
reduction
PM2.5 %
reduction
CO%
reduction
HCL %
reduction
Hg %
reduction
coal
95
89
59
89
82
62
gas 1 (other)
1
1
1
1
1
1
gas 2
95
98
0
98.1
99.76
82
bagasse
95
73
89
73
41
76
dry biomass
95
10.5
94
10.5
22
8
Gas 1 (NG)
1
1
1
1
1
n/a
heavy liquid
95
99.1
94.5
99.1
86.96
1
light liquid
95
99.95
80.5
99.9
68.5
1
wet biomass
95
39
75
38.5
11.5
34
In addition to the Hg reductions, we reduced SO2, PM2.5, CO and VOC using facility-fuel based reduction
information from the Boiler MACT ICR database. Because we were unable to match the specific units from
the Boiler MACT ICR to the units in the 2005 inventory, we used the following approach. First we matched
the 2005 inventory and the Boiler MACT ICR data at the facility (NEIUNIQUEID) level. At the facility
level, we were able to match facilities that captured 97% of the total Boiler MACT ICR data SO2 emissions.
For facilities that matched, we applied the Boiler MACT ICR reductions only to inventory boiler processes
from these facilities that have the same fuel type (per the SCC description) as the fuel listed in the ICR
Boiler MACT database. Because the ICR fuel types do not match the fuel definitions in the inventory, we
used a cross walk to match the ICR and inventory fuels, shown below in Table 3-10. For some NEI fuels,
we allowed two possibilities for ICR fuels to match to address the potential overlap of multiple ICR fuel
categories and to increase the number of facility/processes in the NEI that matched units in the ICR database.
The second choice was only used when units in the ICR database did not match any units in the inventory at
that facility.
Table 3-10. Crosswalk of NEI fuels to ICR fuels
Inventory fuel category
ICR fuel category, 1st choice
ICR fuel category, 2nd choice
Bagasse
Bagasse
Coal
Coal
30
-------
Inventory fuel category
ICR fuel category, 1st choice
ICR fuel category, 2nd choice
Coal-based Synfuel
Heavy Liquid
Light Liquid
Crude oil
Heavy Liquid
Digester Gas
Gas 2
Distillate Oil
Light Liquid
Distillate Oil (Diesel)
Light Liquid
Gas
Gas 2
Gasified Coal
Gas 1 (Other)
Gas 2
Gasoline
Light Liquid
Heavy Liquid
Hydrogen
Gas 1 (Other)
Kerosene
Light Liquid
Kerosene/Naphtha (Jet Fuel)
Light Liquid
Landfill Gas
Gas 2
Liquid Waste
Heavy Liquid
Heavy Liquid
Liquified Petroleum Gas (LPG)
Gas 1 (Other)
LPG
Gas 1 (Other)
Methanol
Heavy Liquid
Natural Gas
Gas 1 (NG Only)
Oil
Light Liquid
Heavy Liquid
Other Oil
Light Liquid
Heavy Liquid
Petroleum Coke
Coal
Process Gas
Gas 2
propane/butane
Gas 1 (Other)
Refinery Gas
Gas 1 (Other)
Residual Oil
Heavy Liquid
Solid Waste
Wet Biomass
Dry Biomass
Unknown
Gas 1 (NG Only)
Petroleum industry process heater,
(SCC 30600199)
Waste Coal
Coal
Waste oil
Heavy Liquid
Light Liquid
Wood
Dry Biomass
Wet Biomass
Wood/Bark Waste
Wet Biomass
Dry Biomass
Unit- and pollutant-specific reductions from the ICR database were applied to the records in the inventory
that matched facility and fuel type for boilers and process heaters. In addition, default reductions were
applied, based on Table 3-9, to units at the 18 facilities which were not in the Boiler MACT ICR, but were
determined to be part of the Boiler MACT category.
The resulting emissions reductions from the boiler MACT are shown in Table 3-11. The reduction of 1.75
tons of mercury shown in the table is fairly consistent with the 1.45 ton mercury reduction cited by the final
Major and Area Boiler MACT Rules, especially considering that the rule was several months away from
finalization when these emissions projections were done. The Boiler MACT proposal cited 8.25 tons of
reductions from these rules. Any inconsistencies with emissions reductions cited by the Boiler MACT Final
Rules are the result of different base emissions assumptions for these pollutants.
31
-------
Table 3-11. Summary of Boiler MACT reductions applied
to the ptnonipm sector.
Modeled Boiler MACT
Pollutant
reductions (tons)
Mercury
1.75
S02
391,000
PM2.5
17,000
voc
3,000
CO
72,000
HCL
5,600
3.2.6.3 Boiler reductions not associated with the MACT rule
The Boiler MACT ICR collected data on existing controls. We used an early version of the data base
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. We extracted all controls that were installed
after 2005, determined a percent reduction, and verified that these controls were being used. In many
situations we were told that the controls were on site but were not being utilized. As a result, plant-unit
specific reductions resulted in the state-level changes summarized in Table 3-12.
Table 3-12. State-level non-MACT Boiler Reductions from ICR Data Gathering
Pre-controlled
Controlled
Percent
Emissions
Emissions
Reductions
Reduction
State
Pollutant
(tons)
(tons)
(tons)
%
Michigan
NOx
907
544
363
40
North Carolina
S02
652
65
587
90
Virginia
S02
3379
338
3041
90
Washington
S02
639
383
256
40
North Carolina
HCL
31
3
28
90
3.2.6.3 Summary of Mercury Reductions at non-EGU stationary sources-
(ptnonipm)
The mercury emission projections included NESHAP for non-EGU source categories that were finalized or
expected to be finalized prior to the proposed Toxics rule including the Boiler MACT (1.75 tons reduction),
Portland Cement NESHAP (6.4 tons reduction), Gold Mines NESHAP (1.8 tons reduction), Electric Arc
Furnaces NESHAP (2.4 tons reduction), Mercury Cell Chlor-Alkali NESHAP (2.8 tons reduction) and
Hazardous Waste Combustion NESHAP (1.1 ton reduction3) In addition, the projections included reduction
of Hg emissions (0.7 ton reduction) due to the replacement of a smelter with a recovery boiler at a pulp and
paper plant. Table 3-13 provides a summary of key mercury sectors and their 2005 and 2016 emissions.
3 Actual reduction for hazardous waste reduction should have been 0.2 tons, but due to an error in the percentage applied, a higher
value was reduced.
32
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Table 3-13. Anthropogenic mercury emissions and projections in the Continental United States
Category
2005 Mercury
(tons)
2016 Mercury
(tons)
Electric Generating Units
52.9
28.7*
Portland Cement Manufacturing
7.5
1.1
Stainless and Nonstainless Steel Manufacturing: Electric Arc Furnaces
7.0
4.6
Industrial, Commercial, Institutional Boilers & Process Heaters
6.4
4.6
Chemical Manufacturing
3.3
3.3
Hazardous Waste Incineration
3.2
2.1
Mercury Cell Chlor-Alkali Plants
3.1
0.3
Gold Mining
2.5
0.7
Municipal Waste Combustors
2.3
2.3
Sum of other source categories (each of which emits less than 2 tons)
17
16
Total
105
64
*The EGU emissions utilize the interim IPM4.10, which were used in the Air Quality Modeling. The final
IPM future year base case Hg total is 24.9 tons.
Reductions from Portland Cement and Boiler MACT were discussed above. The Gold Mine reductions were
provided by the SPPD project lead and are summarized in Appendix C.
Appendix F details the source of the data used for the reductions for Electric Arc Furnaces NESHAP,
Mercury Cell Chi or-Alkali NESHAP, Hazardous Waste Combustion, the pulp and paper plant reduction, and
methodology used to apply them.
3.2.7 Oil and gas projections in TX, OK, and non-California WRAP states (nonpt)
For the 2005v4.1 platform, we received updated 2005 oil and gas emissions from Texas and Oklahoma. We
also received 2008 data that we used for 2016, because it was the best available data to represent 2016.
We applied emissions reductions from the Stationary Reciprocating Internal Combustion Engine (RICE)
NESHAP, which we assumed has some applicability to this industry (see Appendix F). We applied these
reductions of CO, NOx, and VOC to the 2008 Texas and Oklahoma data. SO2 reductions associated with the
RICE NESHAP were not included due to lack of time to include. They would impact 2310000220
(Industrial Processes; Oil and Gas Production: SIC 13; Drill rigs) for which we estimate a reduction in 2005
emissions of about 4400 tons, nationwide.
We discovered after emissions processing was complete, that 2016 drill rig emissions estimates were
available for each year for TX. These correct 2016 emissions, provided by the Texas Commission on
Environmental Quality (TCEQ) are shown in Table 3-14 along with Oklahoma data for 2005, 2008, and
adjusted 2008 using the RICE reductions, which were used for 2016.
33
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Table 3-14. 2005, 2008 and estimated 2016 emissions for nonpoint Oklahoma and Texas oil and gas sources
Oklahoma
Texas
2005
2008
2016*
2005
2008
2016*
2016 TCEQ**
(tons)
(tons)
(tons)
(tons)
(tons)
(tons)
estimate (tons)
CO
32,821
32,830
31,484
15,878
13,400
10,079
11,558
NOx
39,668
42,402
39,808
42,854
48,317
41,395
36,440
VOC
155,908
163,598
163,209
4,337
4,326
4,326
3,320
S02
1,014
2
2
5,977
956
956
37
PM10
1,918
2,231
2,231
3,036
2,543
2,543
1,320
PM2.5
1,918
2,231
2,231
2,945
2,467
2,467
1,280
*2016 emissions estimated as 2008 emissions with RICE reductions, which affects only CO, NOx, and VOC
** Table 1.1.
As with the 2005 v4 platform, the v4.1 platform utilizes the Phase II WRAP oil and gas emissions data for
the non-California Western Regional Air Partnership (WRAP) states for 2005. However, unlike the
projection methodology used for the proposed Transport Rule, in which we used the 2005 for both 2005 and
the future years (which were 2012 and 2014), we were able to obtain the WRAP 2018 Phase II emissions for
2016 for the Proposed Toxics Rule. These data became available in time for us to incorporate them into this
platform, and were the best available data to represent 2016. Like the Texas and Oklahoma data, we applied
RICE reductions to the 2018 WRAP data for use in 2016. Table 3-15 shows the WRAP oil and gas
emissions in 2005, and future year, before and after the RICE reductions. The emissions used in 2016 are
the 2018 WRAP-supplied emissions with the RICE reductions applied.
Table 3-15. WRAP Oil and Gas Emissions: 2005, 2018 WRAP, and 2016 with additional reductions due to
the RICE NESHAP
CO (tons)
NOX (tons)
VOC (tons)
S02 (tons)*
State
2005
2018
WRAP
2016*
2005
2018
WRAP
2016*
2005
2018
WRAP
2016*
2005
2016*
Alaska
0
0
0
836
453
396
68
12
12
62
1
Arizona
1
2
2
13
15
14
37
49
49
Colorado
9,134
9,661
8,785
32,188
33,517
32,412
35,500
43,639
43,285
350
11
Montana
1,159
1,542
1,541
10,617
13,880
13,032
9,187
14,110
14,009
640
6
Nevada
0
0
0
71
63
55
105
163
163
1
0
New Mexico
32,004
44,011
36,251
61,674
74,648
67,984
215,636
267,846
266,681
369
12
North Dakota
38
172
172
6,040
20,869
18,356
8,988
17,968
17,968
688
4
Oregon
2
2
2
61
44
40
19
14
14
South Dakota
15
16
16
566
557
496
370
562
562
43
0
Utah
1,426
1,347
1,305
6,896
6,297
6,158
43,403
81,890
81,869
149
1
Wyoming
10,004
8,540
7,807
36,172
34,142
32,695
166,939
304,748
304,637
541
3
Grand Total
53,784
65,292
55,880
155,133
184,486
171,640
480,252
731,002
729,250
2,842
39
* 2016 emissions are 2018 WRAP emissions with RICE NESHAP reductions. These reductions apply only to CO, NOx, and VOC.
34
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3.2.8 Future Year VOC Speciation for gasoline-related sources (ptnonipm,
nonpt)
To account for the future projected increase in the ethanol content of fuels, we used different future-year
VOC speciation for certain gasoline-related emission sources. Such sources include gasoline stage II,
portable fuel containers (PFCs), and finished fuel storage and transport-related sources related to bulk
terminals (where the ethanol may be mixed) and downstream to the pump. We identified this last group of
sources as "btp" (from bulk terminals to pumps). While most of these sources are in the nonpt sector, there
are also some in the ptnonipm sector. In the 2005 base year we used zero percent ethanol (E0) fuel profiles;
however, for the 2016 profiles we used combinations of E0 and ten percent ethanol (E10) fuel profiles. The
fuel type fraction was developed based on the Department of Energy Annual Energy Outlook (AEO) 2007
projections of ethanol fuels for the year 2022. In the AEO 2007 data, the proportions of E0 and E10 fuels are
the same for 2012 and years beyond (even though the quantities of the two fuels change over these years).
The national level proportions were allocated to counties across the country using fuel modeling at the EPA
Office of Transportation and Air Quality. All gasoline stage II and "btp" sources used the same combination
of E0 and E10 headspace profiles as were used for exhaust and evaporative profiles.
3.3 Mobile source projections
Mobile source monthly inventories of onroad and nonroad mobile emissions were created for 2016 using a
combination of the NMIM and MOVES2010 models. Future-year emissions reflect onroad mobile control
programs including the Light-Duty Vehicle Tier 2 Rule, the Onroad Heavy-Duty Rule, and the Mobile
Source Air Toxics (MSAT2) final rule. Nonroad mobile emissions reductions for these years include
reductions to locomotives, various nonroad engines including diesel engines and various marine engine
types, fuel sulfur content, and evaporative emissions standards.
Onroad mobile sources are comprised of several components and are discussed in the next subsection (3.3.1).
Monthly nonroad mobile emission projections are discussed in subsection 3.3.2. Locomotives and Class 1
and Class 2 commercial marine vessel (C1/C2 CMV) projections are discussed in subsection 3.3.3, and Class
3 (C3) CMV projected emissions are discussed in subsection 3.3.4.
3.3.1 Onroad mobile (on_noadj, on_moves_runpm, on_moves_startpm)
The onroad emissions were primarily based on the 2010 version of the Motor Vehicle Emissions Simulator
(MOVES2010) - the same version as was used for 2005. The same MOVES-based PM2.5 temperature
adjustment factors were applied as were used in 2005 for running mode emissions; however, cold start
emissions used year-specific temperature adjustment factors. The temperature adjustments have the minor
limitation that they were based on the use of MOVES national default inputs rather than county-specific
inputs, because a county-specific database for input to MOVES was not available at the time this approach
was needed. However, the PM2.5 temperature adjustments are fairly insensitive to the county-specific inputs,
which is why this is only a minor limitation. Mercury was the only onroad HAP used in 2016 that did not
come from MOVES, since the capability for mercury from MOVES was not available at the time this work
was completed. For mercury, we used the 2005 NMIM-based emissions for 2016.
California onroad (on noadj)
Like year 2005 emissions, future-year California NH3 emissions are from MOVES runs for California,
disaggregated to the county level using NMIM. For all other pollutants, we did not use MOVES to generate
future-year onroad emissions for California, because the 2005 base year emissions were provided by
CARB"s Emission Factors mobile model (EMFAC), which CARB submitted for the 2005 NEI. For
California, we chose an approach that would maintain consistency between the 2005 and 2016 emissions.
35
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This approach involved computing projection factors from a consistent set of future and 2005-year data
based on the EMFAC2007 model provided by CARB. We generated projection factors by dividing the
EMFAC2007-based emissions for 2016 (linearly interpolated between year 2014 and year 2020) by the
EMFAC2007-based emissions for 2005. These EMF AC-based emissions were provided in March 2007.
California does not specify road types, so we first used NMIM California ratios to break out vehicle
emissions to the match the more detailed NMIM level before projecting to 2016.
HAP emissions were computed as 2005v2-based HAP-CAP ratios applied at the pollutant and Level 3 SCC
(first 7 characters) to 2016 CAP emissions. HAPs were scaled to either of three pollutants: exhaust PM2.5
(e.g., metals), exhaust VOC (e.g., exhaust mode VOC HAPs such as acetaldehyde and formaldehyde), or
evaporative VOC (e.g., evaporative mode VOC HAPs such as benzene).
MOVES-based no-adiust (onnoadj)
As discussed in the 2005v4.1 platform documentation, the MOVES2010 model was used for all vehicles,
road types, and pollutants other than mercury, which came from 2005 NMIM. VMT were projected using
growth rates from the Department of Energy's AE02009. We used MOVES2010 to create emissions by
state, SCC, pollutant, emissions mode and month. We then allocated these emissions to counties based on
2015 NMIM county-level data by state, SCC, pollutant, and emissions mode. 2016 NMIM data were not
available for this effort, but the 2015 NMIM can reasonably be expected to be sufficient for this purpose.
While EPA will eventually replace this approach with a county-specific implementation of MOVES, it was
the best available approach for this modeling.
MOVES-based cold start and running mode (onmovesstartpm and onmovesrunpm)
MOVES-based cold start and running mode emissions consist of gasoline exhaust speciated PM and
naphthalene. These pre-temperature-adjusted emissions are projected to year 2016 from year 2005
inventories using a 2016-specific run of MOVES2010. VMT were projected using growth rates from the
AE02009. As with the on noadj sector, the 2016 MOVES2010 data were created at the state-month level,
and the 2015 NMIM results were used to disaggregate the state level results to the county level.
As part of the SMOKE processing (described in the v4.1 platform documentation), we applied MOVES-
based temperature adjustment factors to gridded, hourly emissions using gridded, hourly meteorology.
Figure 3-2 illustrates the increase in PM emissions associated with decreasing temperatures for running
exhaust and starting exhaust in 2005 and 2015. For the running mode in 2016, we used the same temperature
adjustment factors as the 2005 base case. However, cold start temperature adjustment factors decrease
slightly in future years, and for year 2016 processing, we updated the temperature adjustment curves for
these cold start emissions to use the 2015 temperature adjustments which were the best available set of
adjustments at the time the work was done. The change from 2005 to 2015 adjustment factors has little
impact, reducing cold-start mode temperature-adjusted PM and naphthalene by under 4% for temperatures
down to 0 F. Therefore, we were comfortable using 2015 adjustment factors rather than 2016.
36
-------
Figure 3-2. MOVES exhaust temperature adjustment functions for 2005and 2015
Start Exhaust:
2015
Start Exhaust:
2005
Run Exhaust:
All Years
Temperature (F)
Errors in 2016 PM emissions for on no adj. on moves startpm. and on moves mnpm
After completion of the modeling, two errors were discovered in the PM emissions from the onroad sector.
The startpm and runpm sectors were impacted by the same error: they were doubly adjusted to account for
cold temperatures. Instead of processing 72°F PM emissions through SMOKE and doing the temperature
adjustment on the gridded hourly emissions, the PM emissions were already adjusted for state-average
monthly temperatures and then additional adjustment factors were. The impact of this error was to over-
estimate PM emissions particularly for the colder parts of the country which have the lowest temperatures
and highest adjustment factors. The on noadj PM was also found to be incorrect. The error occurred during
SMOKE processing and resulting in dropping emissions of pre-speciated exhaust PM2.5 from diesel vehicles
due to incorrect SMOKE input speciation profiles. The impact of this error was to underestimate the PM2.5.
These errors impacted all states except California, which used other data as described above. Table 3-16
summarizes the impact of the PM2.5 errors in the onroad sectors.
Table 3-16. Summary of the impact of PM2.5 errors in the onroad sectors
State
2016 PM2.5
onroad
corrected
2016 PM2.5
onroad
modeled
Error in
onroad
pm2.5
Total state-
level 2016
PM2.5*
All-sector
pm25
error**
Alabama
2,438
1,630
-33%
84,583
-1%
Arizona
3,755
1,821
-51%
76,788
-3%
Arkansas
1.399
1,113
-20%
61,689
0%
California
17,687
17,687
0%
253,741
0%
Colorado
2,692
4,383
63%
67,544
3%
Connecticut
1,465
2,995
104%
16,308
9%
Delaware
413
516
25%
5,801
2%
District of Columbia
185
229
24%
1,121
4%
Florida
7,540
4,177
-45%
210,183
-2%
Georgia
5,393
3,814
-29%
120,309
-1%
Idaho
895
1,559
74%
99,060
1%
Illinois
6,760
10,081
49%
115,808
3%
Indiana
3,988
5,599
40%
117,919
1%
Iowa
1,854
3,824
106%
71,682
3%
37
80
70
60
50
40
30
20
10
0
-20 -10 0 10 20 30 40 50 60 70
-------
State
2016 PM2.5
onroad
corrected
2016 PM2.5
onroad
modeled
Error in
onroad
pm25
Total state-
level 2016
PM2.5*
All-sector
pm25
error**
Kansas
1,391
1,741
25%
156,315
0%
Kentucky
2,422
2,349
-3%
63,917
0%
Louisiana
1,797
1,003
-44%
89,669
-1%
Maine
820
1,881
129%
22,114
5%
Maryland
2,646
3,594
36%
38,665
2%
Massachusetts
2,977
5,288
78%
43,343
5%
Michigan
6,352
10,976
73%
81,470
6%
Minnesota
3,466
10,935
215%
115,600
6%
Mississippi
1,780
879
-51%
63,451
-1%
Missouri
3,721
4,347
17%
99,315
1%
Montana
604
1,242
106%
54,412
1%
Nebraska
1,273
1,764
39%
56,593
1%
Nevada
565
735
30%
45,843
0%
New Hampshire
777
1,591
105%
16,578
5%
New Jersey
3,243
5,496
70%
28,049
8%
New Mexico
1,238
1,182
-5%
108,648
0%
New York
7,274
13,497
86%
83,242
7%
North Carolina
3,929
3,183
-19%
88,990
-1%
North Dakota
461
1,738
277%
51,940
2%
Ohio
5,813
8,444
45%
97,759
3%
Oklahoma
2,134
1,862
-13%
113,412
0%
Oregon
1,731
1,923
11%
135,881
0%
Pennsylvania
5,517
8,859
61%
90,961
4%
Rhode Island
361
760
110%
3,124
13%
South Carolina
2,227
1,553
-30%
55,421
-1%
South Dakota
506
1,131
123%
45,722
1%
Tennessee
3,896
3,043
-22%
69,254
-1%
Texas
11,683
6,116
-48%
304,737
-2%
Utah
1,418
2,334
65%
53,877
2%
Vermont
567
1,252
121%
9,018
8%
Virginia
3,759
4,327
15%
67,861
1%
Washington
3,346
3,675
10%
61,680
1%
West Virginia
843
1,088
29%
40,423
1%
Wisconsin
3,631
8,440
132%
63,379
8%
Wyoming
485
969
100%
66,505
1%
Total
102,170
188,630
85%
3,891,291
2%
includes onroad, nonroad, ptnonipm, ptipm, afdust, avefire, alm_no_c3, seca_c3
**Negative means modeled emissions are an underestimate, positive means overestimate
3.3.2 Nonroad mobile (nonroad)
This sector includes monthly exhaust, evaporative and refueling emissions from nonroad engines (not
including commercial marine, aircraft, and locomotives) derived from NMIM for all states except California.
Like the onroad emissions, NMIM provides nonroad emissions for VOC by three emission modes: exhaust,
evaporative and refueling. 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.
With the exception of California, U.S. emissions for the nonroad sector (defined as the equipment types
covered by NMIM) were created using a consistent NMIM-based approach as was used for 2005, but
38
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projected for 2016. Since we did not have readily available 2016 NMIM data, we used the output of a 2015
run of NMIM. The 2015 NMIM run utilized the NR05d-Bond-final version of NONROAD (which is
equivalent to NONROAD2008a). We adjusted the 2015 NMIM data to 2016 by applying annual, national
level SCC- and pollutant/mode-based4 factors to the monthly 2015 NMIM data by SCC, pollutant, and
emissions mode. These factors were generated from NONROAD2008a (v08a-out.mdb database) annual
national level emissions. For nonroad mercury and NH3, we used 2015 values for 2016 because there were
no adjustment factors available for these pollutants in time for this effort.
These future-year emissions account for 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.
The national regulations incorporated in the modeling are those promulgated prior to December 2009, and
beginning about 1990. Recent rules include:
"Clean Air Nonroad Diesel Final Rule - Tier 4". 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.
OTAQ"s Small Engine Spark Ignition ("Bond") Rule
We have not included voluntary programs such as programs encouraging either no refueling or evening
refueling on Ozone Action Days and diesel retrofit programs. NMIM version 20071009, with county
database NCD20070912, and NO'.' todel version NO'NROAD2008a was used to create
NMIM inventories for 2015.
California nonroad emissions
Similar to onroad mobile, NMIM was not used to generate future-year nonroad emissions for California,
other than for NH3. We used NMIM for California future nonroad NH3 emissions because CARB did not
provide these data for any nonroad vehicle types. As we did for onroad emissions, we chose a projection
approach that would maintain consistency between the base year and future-year emissions for nonroad
emissions in California.
California year 2016 nonroad CAP emissions were computed by linearly interpolating year 2014 and 2020
inventories. And 2016 HAP emissions were also computed using the same 2005-based CAP-HAP ratios
used to create 2016 HAP emissions.
3.3.3 Locomotives and Class 1 & 2 commercial marine vessels (alm_no_c3)
Future locomotive and Class 1 and Class 2 commercial marine vessel (CMV) emissions were calculated
using projection factors that were computed based on national, annual summaries of locomotive emissions in
2002 and future years. These national summaries were used to create national by-pollutant, by-SCC
projection factors; these factors include final locomotive-marine controls and are provided in Table 3-17.
4 VOC factors were provided for exhaust, evaporative and refueling modes.
39
-------
Table 3-17. Factors applied to year 2005 emissions to project locomotives and Class 1 and Class 2
Commercial Marine Vessel Emissions
see
SCC Description
Pollutant
Year 2016
Factor
2280002X00
Marine Vessels, Commercial;Diesel;Underway & port emissions
CO
0.9431
2280002X00
Marine Vessels, Commercial;Diesel;Underway & port emissions
nh3
1.1340
2280002X00
Marine Vessels, Commercial;Diesel;Underway & port emissions
NOx
0.7334
2280002X00
Marine Vessels, Commercial;Diesel;Underway & port emissions
PMio
0.6778
2280002X00
Marine Vessels, Commercial;Diesel;Underway & port emissions
pm25
0.6896
2280002X00
Marine Vessels, Commercial;Diesel;Underway & port emissions
S02
0.1106
2280002X00
Marine Vessels, Commercial;Diesel;Underway & port emissions
voc
0.8260
2285002006
Railroad Equipment;Diesel;Line Haul Locomotives
Class I Operations
CO
1.3128
2285002006
Railroad Equipment;Diesel;Line Haul Locomotives
Class I Operations
nh3
1.3040
2285002006
Railroad Equipment;Diesel;Line Haul Locomotives
Class I Operations
NOx
0.6577
2285002006
Railroad Equipment;Diesel;Line Haul Locomotives
Class I Operations
PMio
0.6205
2285002006
Railroad Equipment;Diesel;Line Haul Locomotives
Class I Operations
pm25
0.6287
2285002006
Railroad Equipment;Diesel;Line Haul Locomotives
Class I Operations
S02
0.0051
2285002006
Railroad Equipment;Diesel;Line Haul Locomotives
Class I Operations
voc
0.6473
2285002007
Railroad Equipment;Diesel;Line Haul Locomotives
Class II / III Operations
CO
0.3226
2285002007
Railroad Equipment;Diesel;Line Haul Locomotives
Class II / III Operations
nh3
1.3040
2285002007
Railroad Equipment;Diesel;Line Haul Locomotives
Class II / III Operations
NOx
0.3512
2285002007
Railroad Equipment;Diesel;Line Haul Locomotives
Class II / III Operations
PMio
0.2857
2285002007
Railroad Equipment;Diesel;Line Haul Locomotives
Class II / III Operations
pm25
0.2882
2285002007
Railroad Equipment;Diesel;Line Haul Locomotives
Class II / III Operations
S02
0.0012
2285002007
Railroad Equipment;Diesel;Line Haul Locomotives
Class II / III Operations
voc
0.3098
2285002008
Railroad Equipment;Diesel;Line Haul Locomotives
Passenger Trains (Amtrak)
CO
1.0629
2285002008
Railroad Equipment;Diesel;Line Haul Locomotives
Passenger Trains (Amtrak)
nh3
1.3040
2285002008
Railroad Equipment;Diesel;Line Haul Locomotives
Passenger Trains (Amtrak)
NOx
0.5251
2285002008
Railroad Equipment;Diesel;Line Haul Locomotives
Passenger Trains (Amtrak)
PMio
0.5006
2285002008
Railroad Equipment;Diesel;Line Haul Locomotives
Passenger Trains (Amtrak)
pm25
0.5028
2285002008
Railroad Equipment;Diesel;Line Haul Locomotives
Passenger Trains (Amtrak)
S02
0.0047
2285002008
Railroad Equipment;Diesel;Line Haul Locomotives
Passenger Trains (Amtrak)
voc
0.5304
2285002009
Railroad Equipment;Diesel;Line Haul Locomotives
Commuter Lines
CO
1.0483
2285002009
Railroad Equipment;Diesel;Line Haul Locomotives
Commuter Lines
nh3
1.3040
2285002009
Railroad Equipment;Diesel;Line Haul Locomotives
Commuter Lines
NOx
0.5179
2285002009
Railroad Equipment;Diesel;Line Haul Locomotives
Commuter Lines
PMio
0.4937
2285002009
Railroad Equipment;Diesel;Line Haul Locomotives
Commuter Lines
pm25
0.4937
2285002009
Railroad Equipment;Diesel;Line Haul Locomotives
Commuter Lines
S02
0.0047
2285002009
Railroad Equipment;Diesel;Line Haul Locomotives
Commuter Lines
voc
0.5231
2285002010
Railroad Equipment;Diesel;Yard Locomotives
CO
1.3203
2285002010
Railroad Equipment;Diesel;Yard Locomotives
nh3
1.3040
2285002010
Railroad Equipment;Diesel;Yard Locomotives
NOx
1.1194
2285002010
Railroad Equipment;Diesel;Yard Locomotives
PMio
0.9072
2285002010
Railroad Equipment;Diesel;Yard Locomotives
pm25
0.9262
2285002010
Railroad Equipment;Diesel;Yard Locomotives
S02
0.0056
2285002010
Railroad Equipment;Diesel;Yard Locomotives
voc
1.4964
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 mi. c i \ .M* 1 This rule lowered diesel sulfur
content and tightened emission standards for existing and new locomotives and marine diesel emissions to
lower future year PM, SO:, and NOx. Voluntary retrofits under the National Clean Diesel Campaign are not
included in our projections.
40
-------
We applied HAP factors for VOC HAPs by using the VOC projection factors to obtain 1,3-butadiene,
acetaldehyde, acrolein, benzene, and formaldehyde. Mercury, not already provided, was held at base-year
levels for 2016 (i.e., we used the 2002 emissions estimates used in the 2005 base case).
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 3-17. Similar to locomotives, VOC HAPs
were projected based on the VOC factor and mercury was held at levels in the 2005 (2002 inventory) base
case.
3.3.4 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 year 2016 for the future base case. This future base case 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: Vehicles and Engines.
The resulting coordinated strategy, including emission standards under the Clean. Air Act for new marine
diesel engines with per-cvlinder displacement at or above 30 liters, and the establishment of Emission
Control Areas is at.
These projection factors vary depending on geographic region and pollutant; where VOC HAPs and all
criteria air pollutants except for NOx are assigned region-specific growth rates and NOx receives different
rates.
The projection factors used to create the 2016 base case seca_c3 sector emissions are provided in Table 3-18.
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 EC A proposal technical support document: Vehicles and
Engines. 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.
As with the 2005 base case, this sector uses CAP-HAP VOC integration. Mercury, although present in the
2005v4 inventory was not used for either the 2005 base case in the 2005v4.1 platform or the 2016 case. The
2005v4 platform Hg emissions total for the sector, including U.S. and non-U.S. sources, is less than 0.001
tons/yr.
41
-------
Table 3-18. Factors to Project Class 3 Commercial Marine Vessel emissions to 2016
Adjustments Relative to 2002
NOx
PM10
PM2.5
VOC/HC
CO
S02
Alaska East (AE)
1.39411
0.19817
0.19630
1.57863
1.57803
0.06025
Alaska West (AW)
1.42713
1.52045
1.52076
1.52054
1.52057
1.52050
East Coast (EC)
1.46173
0.25428
0.25255
1.87044
1.87014
0.06677
Gulf Coast (GC)
1.15962
0.20333
0.20120
1.48548
1.48642
0.05321
Hawaii East (HE)
1.54794
0.25771
0.25518
1.93871
1.94003
0.07430
Hawaii West (HW)
1.66069
1.93956
1.93762
1.94025
1.93952
1.94001
North Pacific (NP)
1.26904
0.21948
0.21521
1.59156
1.59099
0.06167
South Pacific (SP)
1.59215
0.28052
0.27800
2.00973
2.00573
0.07933
Great Lakes (GL)
1.10725
0.16932
0.16770
1.28006
1.27933
0.04518
Outside ECA
1.53081
1.80933
1.80933
1.80933
1.80933
1.80933
3.3.5 Future Year VOC Speciation (on_noadj, nonroad)
We used speciation profiles for VOC in the nonroad and on noadj sectors that account for the increase in
ethanol content of fuels in future years. The combination profiles use proportions of E0 and E10 expected in
the future based on AEO 2007 projections of E10 and E0 fuel use. The proportions of E0 and El0 are the
same for 2012 and years beyond (even though the quantities of the two fuels change over these years). The
national proportions were allocated to counties across the country using the same fuel modeling done for the
stationary source gasoline speciation, as discussed in Section 3.2.8.
The speciation of onroad exhaust VOC additionally accounts for a portion of the vehicle fleet meeting Tier 2
standards; different exhaust profiles are available for pre-Tier 2 versus Tier 2 vehicles. Thus for exhaust
VOC, a combination of pre-Tier 2 E0, pre-Tier 2 E10, Tier 2 E0 and Tier 2 E10 profiles are used. Figure 3-3
shows the Tier 2 fraction of Light Duty Vehicles for different future years in terms of different metrics. For
previous modeling applications, we based the fraction on the population of vehicles. However, since these
vehicles emit a smaller portion of VOC, a more appropriate metric for use in weighting the speciation
profiles is the fraction of exhaust total hydrocarbons (THC) which is used in the 2016 case described here.
The fraction of Tier 2 emissions used here for 2016 is 0.358.
Table 3-19 summarizes the profiles combined for the source categories and VOC emission modes used.
42
-------
Table 3-19. Future Year Profiles for Mobile Source Related Sources
Sector
Type of profile
Profile Codes Combined for the Future Year Speciation
Stationary
headspace
8762: Composite Profile - Gasoline Headspace Vapor using 0% Ethanol
8763: Composite Profile - Gasoline Headspace Vapor using 10% Ethanol
Nonroad
exhaust
Pre-Tier 2 vehicle
exhaust
8750: Gasoline Exhaust - Reformulated gasoline
8751: Gasoline Exhaust - E10 ethanol gasoline
Onroad and
Nonroad
evap*
Evaporative
8753: Gasoline Vehicle - Evaporative emission - Reformulated gasoline
8754: Gasoline Vehicle - Evaporative emission - E10 ethanol gasoline
Nonroad
refueling
headspace
Same as Stationary
Onroad
exhaust
Pre-Tier 2 vehicle
exhaust and Tier 2
vehicle exhaust
8750: Gasoline Exhaust - Reformulated gasoline
8751: Gasoline Exhaust - E10 ethanol gasoline
8756: Composite Profile - Gasoline Exhaust - Tier 2 light-duty vehicles using
0% Ethanol
8757: Composite Profile - Gasoline Exhaust - Tier 2 light-duty vehicles using
10% Ethanol
h() and E10 combinations are based on AE02007 projections ot EO and E10 lucl
Tier 2 and pre-Tier 2 combinations are based on the 2016 contribution of Tier 2 exhaust emissions
Figure 3-3. Tier 2 Fraction of Light Duty Vehicles
"5 0.8
0.4
Exhaust THC
Vehicle Miles Traveled
Population
0.2
0.0
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
3.4 Canada, Mexico, and Offshore sources (othar, othon, othpt, othar hg,
and othpt_hg)
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. 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 could not provide future year emissions that were consistent with the base year emissions.
43
-------
4 EGU Control Case for 2016
The Toxics Rule Control Case (also known as "policy case") is intended to represent the implementation of
acid gas, mercury and associated criteria pollutant reductions associated with the Toxics Rule. However, due
to timing constraints for the Toxics Rule Proposal, the actual control case used in the air quality modeling
did not allow for the final version of IPM policy case to be used. More information on the IPM used can be
found in the technical support document for the IPM modeling done for the Toxics Rule. A comparison
between the IPM emissions used in the air quality model and the emissions from the "final" IPM cases for
select pollutants is presented in the Regulatory Impacts Assessment for the Proposed Toxics Rule.
As with the base case, we applied the Boiler MACT reductions associated with units in the ptipm sector, and
we removed the Hg associated with Boiler MACT sources. This resulted in the removal of 0.128 tons/year of
Hg was removed from the control case data provided by CAMD, which prevented double counting Hg
emissions from boilers accounted for in the ptnonipm sector. The impacts on the non-Hg pollutants of the
Boiler MACT controls are provided in Table 4-1.
Table 4-1. Boiler MACT reductions applied to policy case
ptipm sector emissions prior to AQ modeling
Tons reduced due to Boiler MACT
HCL
675
CO
845
PM10
698
PM2.5
606
SO2
18,747
VOC
19
5 Emission Summaries for the Base Cases and Control Case
The following tables summarize emissions differences between the 2005 base case, 2016 base case and the
2016 policy case.
Table 5-1 provides 48-state summed emissions by sector in 2016 and 2005for criteria pollutants, and Table
5-2 provides the same information for mercury, HCL, and CL2. The speciated mercury emissions by state
and sector in the 2005 and 2016 base cases are provided in Table 5-3.
For the 2016 policy case, only the ptipm sector is different from the 2016 base case. State emissions of NOx,
SO2 and VOC for the ptipm sector for 2005, 2016 base, and the 2016 policy case are provided in Table 5-4.
The same information for mercury and HCL is provided in Table 5-5, and Table 5-6 provides speciated
mercury by state for the 2005 and 2016 base cases.
Table 5-7 provides sector-specific SO2 emissions (except for biogenic emissions) by state for the 2016 base
case, and Table 5-8 provides the same resolution of information as Table 5-7, but for PM2.5.
44
-------
Table 5-1. 2016 Emissions - 2016 Base Case Compared to 2005 Base Case: VOC, NOx, CO, SO2, NH3, and PM
Sector
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
2016 VOC
2005 VOC
2016 NOx
2005 NOx
2016 CO
2005 CO
2016 S02
2005 S02
2016 NH3
2005 NH3
2016 PM10
2005 PM10
2016
pm25
2005
pm25
afdust
8.8613.385
8.858.992
1.030.631
1.030.391
ag
3.446.632
3.251.990
aim no
c3
49.665
67.690
1.353.680
1.924.925
296.121
270.007
9.087
154.016
940
773
38.789
59.366
37.604
56.687
seca_c3
69.080
44.990
815.249
647.884
82.929
54.049
23.659
420.110
11.324
53.918
10.324
49.541
seca c3
non-US
126.898
18.367
1.603.944
532.181
149.443
43.267
957.065
321.414
131.137
43.326
120.617
39.810
nonpt
7.322.980
7.530.564
1.668.679
1.699.532
6.973.593
7.413.762
1.250.300
1.259.635
133.428
134.080
1.302.733
1.354.638
1.029.916
1.081.816
nonroad
1.577.015
2.691.844
1.271.892
2.115.408
13.431.076
19.502.718
2.870
197.341
2.345
1.972
129.217
211.807
121.215
201.138
on_noadj
2.064.152
3.949.362
4.285.847
9.142.274
25.930.932
43.356.130
26.784
177.977
82.013
156.528
114.073
308.497
40.587
236.927
on_move
s runpm
88.929
54.071
81.887
49.789
on_move
s_startp
m
71.509
22.729
65.846
20.929
ptipm
40.845
40.950
1.769.764
3.726.459
691.310
601.564
3.577.698
10.380.786
36.655
21.684
523.504
615.095
384.320
508.903
ptnonipm
1.180.794
1.310.784
2.061.353
2.238.002
3.038.429
3.221.388
1.349.038
2.089.836
158.242
158.837
597.324
653.048
404.926
440.714
avefire
1.958.992
1.958.992
189.428
189.428
8.554.551
8.554.551
49.094
49.094
36.777
36.777
796.229
796.229
684.035
684.035
2016cr2
non-flres
Total
12.431.429
15.654.551
14.830.408
22.026.665
50.593.833
74.462.885
7.196.501
15.001.115
3.860.256
3.725.864
11.869.924
12.235.487
3.327.874
3.716.645
40
-------
Table 5-2. 2016 Emissions - Base Case Compared to 2005 Base Case: mercury (species and total), HCL and CL2
Sector
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
2016
HGIIGAS
2005
HGIIGAS
2016
HGNRVA
2005
HGNRVA
2016
PHGI
2005
PHGI
2016
Total Hg
(sum of 3
species)
2005
Total Hg
(sum of 3
species)
2016
HCL
2005
HCL
2016
CL2
2005
CL2
afdust
ag
aim no c3
0.0411
0.0411
0.0793
0.0793
0.0212
0.0212
0.1416
0.1416
1.38
1.38
seca_c3
*
*
*
*
*
*
*
*
seca c3 non-US
*
*
*
*
*
*
*
*
nonpt
1.0605
1.0605
3.1034
3.1034
0.6524
0.6524
4.8163
4.8163
29.001
29.001
2.108
2.135
nonroad
0.1041
0.1041
0.2105
0.2105
0.0533
0.0533
0.3679
0.3679
on_noadj
0.1402
0.1402
0.5036
0.5036
0.0599
0.0599
0.7037
0.7037
on_moves_ranpm
on_moves_startpm
ptipm
6.8757
21.0960
21.1598
30.1986
0.6683
1.6136
28.7038
52.9082
74.089
351.592
99
ptnonipm
7.9112
10.4687
16.8535
29.5686
4.4183
6.1291
29.183
46.1664
37.549
48.630
3.941
4.174
avefire
2016cr2 non-flres
Total
16.13
32.91
41.91
63.66
5.87
8.53
64
105
140.639
429.223
6.050
6.409
*due to uncertainty in mercury emissions from this sector, they were removed from the inventories and not used. The amount removed from the 2005 data was on the order of
0.001 tons total mercury for the sum of U.S. and non-U.S. components
41
-------
Table 5-3. Speciated Mercury and total Mercury by State and Sector for 2005 and 2016
ptipm
ptnonipm
nonpt
onroad
aim
nonroad
all US sectors
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
State
Species of
2005
2016
2005
2016
2005
2016
2005=
2016
2005=
2016
2005=
2016
2005
2016
Alabama
HGIIGAS
1.160
0.288
0.233
0.140
4.9E-04
4.9E-04
5.9E-04
3.2E-05
1.394
0.429
Alabama
HGNRVA
1.419
0.948
1.105
0.453
0.029
0.029
6.2E-03
2.4E-04
2.56
1.44
Alabama
PHGI
0.084
0.019
0.161
0.094
2.4E-04
2.4E-04
3.2E-05
6.6E-06
0.245
0.113
Alabama
Total HG
2.663
1.255
1.499
0.687
0.029
0.029
6.8E-03
2.8E-04
4.20
1.98
Arizona
HGIIGAS
0.052
0.133
0.035
0.017
2.4E-03
2.4E-03
6.1E-04
3.9E-05
0.089
0.153
Arizona
HGNRVA
0.657
0.609
0.145
0.044
0.027
0.027
6.4E-03
2.6E-04
0.836
0.686
Arizona
PHGI
0.007
0.007
0.028
0.012
8.3E-04
8.3E-04
3.5E-05
9.4E-06
0.036
0.020
Arizona
Total HG
0.716
0.749
0.208
0.074
0.030
0.030
7.0E-03
3.1E-04
0.961
0.860
Arkansas
HGIIGAS
0.144
0.198
0.221
0.164
4.6E-04
4.6E-04
3.3E-04
2.4E-05
0.365
0.363
Arkansas
HGNRVA
0.364
0.525
0.364
0.303
0.016
0.016
3.5E-03
1.6E-04
0.748
0.848
Arkansas
PHGI
9.3E-04
1.1E-03
0.110
0.085
1.8E-04
1.8E-04
1.8E-05
6.4E-06
0.111
0.087
Arkansas
Total HG
0.509
0.725
0.694
0.552
0.017
0.017
3.8E-03
1.9E-04
1.224
1.298
California
HGIIGAS
0.001
0.042
0.757
0.599
0.462
0.462
0.113
0.037
1.0E-01
1.472
1.355
California
HGNRVA
0.003
0.052
2.118
1.068
0.728
0.728
0.218
0.071
2.0E-01
3.336
2.335
California
PHGI
0.001
0.039
0.514
0.360
0.298
0.298
0.058
0.019
5.3E-02
0.943
0.827
California
Total HG
0.005
0.132
3.389
2.027
1.488
1.488
0.390
0.127
3.5E-01
5.751
4.517
Colorado
HGIIGAS
0.103
0.026
0.167
0.154
2.5E-04
2.5E-04
5.0E-04
3.4E-05
0.271
0.181
Colorado
HGNRVA
0.321
0.055
0.458
0.372
6.4E-03
6.4E-03
5.3E-03
2.3E-04
0.791
0.438
Colorado
PHGI
0.005
0.003
0.120
0.108
1.7E-04
1.7E-04
2.5E-05
8.4E-06
0.125
0.111
Colorado
Total HG
0.429
0.083
0.746
0.634
6.8E-03
6.8E-03
5.8E-03
2.7E-04
1.187
0.730
Connecticut
HGIIGAS
0.040
0.004
0.099
0.099
0.037
0.037
3.4E-04
2.0E-05
0.176
0.140
Connecticut
HGNRVA
0.077
0.003
0.049
0.049
0.081
0.081
3.6E-03
1.6E-04
0.211
0.136
Connecticut
PHGI
0.004
0.000
0.036
0.036
0.024
0.024
1.6E-05
3.5E-06
0.064
0.061
Connecticut
Total HG
0.121
0.007
0.184
0.184
0.142
0.142
3.9E-03
1.8E-04
0.451
0.337
Delaware
HGIIGAS
0.109
0.006
0.029
0.008
2.5E-05
2.5E-05
9.8E-05
5.3E-04
5.7E-06
0.139
0.015
Delaware
HGNRVA
0.055
0.003
0.173
0.022
6.8E-05
6.8E-05
1.0E-03
1.0E-03
4.4E-05
0.230
0.027
Delaware
PHGI
0.016
0.000
0.016
0.006
1.8E-05
1.8E-05
5.1E-06
2.7E-04
1.2E-06
0.032
0.006
Delaware
Total HG
0.180
0.009
0.219
0.035
1.1E-04
1.1E-04
1.1E-03
1.8E-03
5.1E-05
0.402
0.047
District of
Columbia
HGIIGAS
0.001
0.000
0.000
6.1E-04
6.1E-04
4.2E-05
2.3E-06
0.002
0.001
District of
Columbia
HGNRVA
0.001
0.000
0.000
3.0E-03
3.0E-03
4.5E-04
1.1E-05
0.005
0.004
District of
Columbia
PHGI
0.001
0.000
0.000
4.1E-04
4.1E-04
2.1E-06
7.9E-07
0.001
0.001
District of
Columbia
Total HG
0.003
0.001
0.001
4.0E-03
4.0E-03
4.9E-04
1.4E-05
0.008
0.005
Florida
HGIIGAS
0.544
0.145
0.350
0.284
9.0E-03
9.0E-03
2.1E-03
1.4E-04
0.904
0.441
Florida
HGNRVA
0.550
0.260
0.639
0.222
0.081
0.081
0.022
1.1E-03
1.293
0.585
Florida
PHGI
0.079
0.081
0.182
0.115
3.2E-03
3.2E-03
1.1E-04
3.0E-05
0.265
0.199
Florida
Total HG
1.173
0.486
1.170
0.621
0.093
0.093
0.024
1.2E-03
2.46
1.225
Georgia
HGIIGAS
0.964
0.241
0.097
0.052
1.7E-03
1.7E-03
1.1E-03
5.7E-05
1.064
0.296
Georgia
HGNRVA
0.666
0.262
0.542
0.122
0.063
0.063
0.012
4.0E-04
1.282
0.459
42
-------
ptipm
ptnonipm
nonpt
onroad
aim
nonroad
all US sectors
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
State
Species of
2005
2016
2005
2016
2005
2016
2005=
2016
2005=
2016
2005=
2016
2005
2016
Georgia
PHGI
0.074
0.009
0.051
0.031
8.0E-04
8.0E-04
6.3E-05
1.3E-05
0.126
0.041
Georgia
Total HG
1.704
0.512
0.690
0.206
0.065
0.065
0.013
4.7E-04
2.47
0.796
Idaho
HGIIGAS
0.115
0.113
1.3E-04
1.6E-05
0.115
0.113
Idaho
HGNRVA
0.195
0.188
1.4E-03
1.1E-04
0.196
0.190
Idaho
PHGI
0.077
0.075
7.0E-06
3.8E-06
0.077
0.075
Idaho
Total HG
0.386
0.376
1.6E-03
1.3E-04
0.388
0.378
Illinois
HGIIGAS
1.367
0.124
0.382
0.338
0.012
0.012
1.1E-03
9.1E-05
1.763
0.475
Illinois
HGNRVA
2.819
0.361
1.227
1.007
0.087
0.087
0.012
5.6E-04
4.145
1.468
Illinois
PHGI
0.056
0.003
0.245
0.207
8.3E-03
8.3E-03
0.000
2.6E-05
0.309
0.219
Illinois
Total HG
4.242
0.488
1.853
1.553
0.108
0.108
0.013
6.8E-04
6.217
2.162
Indiana
HGIIGAS
1.141
0.417
0.726
0.548
4.6E-03
4.6E-03
7.4E-04
5.0E-05
1.872
0.970
Indiana
HGNRVA
1.670
1.130
1.410
1.146
0.045
0.045
7.7E-03
3.1E-04
3.134
2.330
Indiana
PHGI
0.068
0.011
0.394
0.308
2.8E-03
2.8E-03
4.0E-05
1.4E-05
0.465
0.321
Indiana
Total HG
2.879
1.558
2.530
2.002
0.053
0.053
8.5E-03
3.8E-04
5.471
3.622
Iowa
HGIIGAS
0.279
0.193
0.159
0.088
2.2E-03
2.2E-03
3.3E-04
4.6E-05
0.440
0.284
Iowa
HGNRVA
0.876
0.802
0.431
0.210
0.022
0.022
3.4E-03
2.6E-04
1.332
1.038
Iowa
PHGI
0.003
0.004
0.115
0.062
1.3E-03
1.3E-03
1.7E-05
1.4E-05
0.120
0.067
Iowa
Total HG
1.158
0.999
0.705
0.360
0.026
0.026
3.8E-03
3.2E-04
1.893
1.389
Kansas
HGIIGAS
0.167
0.114
0.259
0.176
6.1E-04
6.1E-04
3.0E-04
3.0E-05
0.427
0.291
Kansas
HGNRVA
0.834
0.837
0.189
0.120
0.019
0.019
3.2E-03
1.4E-04
1.045
0.980
Kansas
PHGI
0.007
0.004
0.101
0.068
2.4E-04
2.4E-04
1.6E-05
1.1E-05
0.108
0.072
Kansas
Total HG
1.008
0.955
0.548
0.364
0.020
0.020
3.5E-03
1.8E-04
1.580
1.343
Kentucky
HGIIGAS
0.799
0.131
0.133
0.100
2.7E-03
2.7E-03
5.0E-04
2.7E-05
0.935
0.234
Kentucky
HGNRVA
0.897
0.682
0.471
0.303
0.024
0.024
5.2E-03
1.8E-04
1.398
1.015
Kentucky
PHGI
0.062
0.015
0.090
0.063
1.7E-03
1.7E-03
2.8E-05
7.0E-06
0.155
0.079
Kentucky
Total HG
1.759
0.828
0.694
0.466
0.029
0.029
5.8E-03
2.2E-04
2.49
1.33
Louisiana
HGIIGAS
0.148
0.244
0.171
0.104
4.8E-04
4.8E-04
4.6E-04
3.3E-05
0.320
0.349
Louisiana
HGNRVA
0.459
0.676
1.145
0.183
0.021
0.021
4.8E-03
2.5E-04
1.630
0.886
Louisiana
PHGI
0.002
0.099
0.073
0.064
1.9E-04
1.9E-04
2.5E-05
6.8E-06
0.075
0.163
Louisiana
Total HG
0.609
1.019
1.388
0.352
0.022
0.022
5.3E-03
2.9E-04
2.025
1.398
Maine
HGIIGAS
0.001
0.009
0.040
0.032
0.029
0.029
1.5E-04
1.5E-05
0.071
0.070
Maine
HGNRVA
0.002
0.003
0.064
0.039
0.076
0.076
1.6E-03
1.3E-04
0.143
0.120
Maine
PHGI
8.9E-04
7.9E-04
0.023
0.018
0.017
0.017
8.3E-06
2.0E-06
0.041
0.036
Maine
Total HG
0.004
0.013
0.127
0.089
0.122
0.122
1.8E-03
1.5E-04
0.255
0.226
Maryland
HGIIGAS
0.535
0.031
0.242
0.198
0.032
0.032
5.9E-04
3.3E-05
0.809
0.262
Maryland
HGNRVA
0.301
0.077
0.328
0.152
0.076
0.076
6.2E-03
2.5E-04
0.712
0.311
Maryland
PHGI
0.053
0.006
0.111
0.076
0.021
0.021
3.0E-05
6.4E-06
0.185
0.103
Maryland
Total HG
0.890
0.114
0.681
0.426
0.129
0.129
6.8E-03
2.9E-04
1.707
0.677
Massachusetts
HGIIGAS
0.111
0.003
0.091
0.090
0.068
0.068
6.0E-04
3.3E-05
0.270
0.162
Massachusetts
HGNRVA
0.055
0.006
0.111
0.110
0.204
0.204
6.3E-03
2.6E-04
0.377
0.326
Massachusetts
PHGI
0.016
0.000
0.035
0.034
0.041
0.041
3.0E-05
6.1E-06
0.092
0.076
43
-------
ptipm
ptnonipm
nonpt
onroad
aim
nonroad
all US sectors
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
State
Species of
2005
2016
2005
2016
2005
2016
2005=
2016
2005=
2016
2005=
2016
2005
2016
Massachusetts
Total HG
0.182
0.009
0.237
0.234
0.313
0.313
7.0E-03
3.0E-04
0.739
0.564
Michigan
HGIIGAS
0.968
0.340
0.216
0.145
0.011
0.011
1.1E-03
9.4E-05
1.197
0.498
Michigan
HGNRVA
0.781
1.155
0.716
0.400
0.070
0.070
1.1E-02
7.6E-04
1.579
1.637
Michigan
PHGI
0.077
5.8E-03
0.153
0.094
0.007
0.007
5.7E-05
1.7E-05
0.237
0.107
Michigan
Total HG
1.826
1.501
1.086
0.639
0.088
0.088
0.012
8.7E-04
3.013
2.242
Minnesota
HGIIGAS
0.096
0.036
0.814
0.791
9.2E-03
9.2E-03
5.7E-04
6.5E-05
0.920
0.836
Minnesota
HGNRVA
0.603
0.124
0.826
0.760
0.029
0.029
6.0E-03
4.5E-04
1.464
0.919
Minnesota
PHGI
0.009
0.001
0.336
0.319
5.3E-03
5.3E-03
3.0E-05
1.6E-05
0.350
0.326
Minnesota
Total HG
0.707
0.161
1.977
1.869
0.043
0.043
6.6E-03
5.3E-04
2.734
2.080
Mississippi
HGIIGAS
0.126
0.140
0.088
0.069
2.7E-04
2.7E-04
4.2E-04
2.1E-05
0.215
0.209
Mississippi
HGNRVA
0.156
0.264
0.190
0.156
0.015
0.015
4.4E-03
1.5E-04
0.365
0.439
Mississippi
PHGI
0.010
0.001
0.052
0.042
1.2E-04
1.2E-04
2.4E-05
5.0E-06
0.063
0.043
Mississippi
Total HG
0.292
0.405
0.330
0.267
0.015
0.015
4.8E-03
1.8E-04
0.643
0.692
Missouri
HGIIGAS
0.702
0.472
0.452
0.277
1.2E-03
1.2E-03
7.2E-04
4.6E-05
1.156
0.752
Missouri
HGNRVA
1.129
1.471
0.560
0.414
2.0E-03
2.0E-03
7.5E-03
3.0E-04
1.698
1.895
Missouri
PHGI
0.024
0.005
0.199
0.128
8.1E-04
8.1E-04
3.9E-05
1.2E-05
0.224
0.134
Missouri
Total HG
1.854
1.949
1.211
0.819
4.1E-03
4.1E-03
8.3E-03
3.6E-04
3.078
2.780
Montana
HGIIGAS
0.030
0.010
0.024
0.020
1.0E-03
1.0E-03
1.1E-04
1.3E-05
0.055
0.032
Montana
HGNRVA
0.468
0.076
0.053
0.037
6.3E-03
6.3E-03
1.2E-03
6.7E-05
0.528
0.121
Montana
PHGI
0.007
0.011
0.017
0.014
5.4E-04
5.4E-04
6.5E-06
4.6E-06
0.025
0.025
Montana
Total HG
0.504
0.097
0.095
0.071
7.9E-03
7.9E-03
1.3E-03
8.5E-05
0.608
0.177
Nebraska
HGIIGAS
0.118
0.168
0.028
0.021
6.0E-04
6.0E-04
2.0E-04
2.5E-05
0.146
0.190
Nebraska
HGNRVA
0.224
0.252
0.108
0.073
9.9E-03
9.9E-03
2.1E-03
1.1E-04
0.345
0.337
Nebraska
PHGI
0.002
0.002
0.021
0.015
3.0E-04
3.0E-04
1.2E-05
9.4E-06
0.023
0.018
Nebraska
Total HG
0.344
0.423
0.157
0.109
0.011
0.011
2.3E-03
1.4E-04
0.514
0.545
Nevada
HGIIGAS
0.091
0.011
0.021
0.019
0.001
0.001
2.0E-04
1.8E-05
0.114
0.032
Nevada
HGNRVA
0.217
0.075
2.554
0.751
0.011
0.011
2.2E-03
1.2E-04
2.784
0.839
Nevada
PHGI
0.002
0.002
0.019
0.017
0.001
0.001
9.6E-06
4.9E-06
0.022
0.020
Nevada
Total HG
0.310
0.087
2.594
0.788
0.013
0.013
2.4E-03
1.4E-04
2.919
0.891
New
Hampshire
HGIIGAS
0.015
0.001
0.015
0.010
0.013
0.013
1.4E-04
2.9E-05
1.1E-05
0.043
0.025
New
Hampshire
HGNRVA
0.012
0.009
0.019
0.011
0.028
0.028
1.5E-03
5.6E-05
9.7E-05
0.060
0.050
New
Hampshire
PHGI
3.9E-03
3.2E-04
8.5E-03
5.4E-03
8.6E-03
8.6E-03
7.4E-06
1.5E-05
1.8E-06
0.021
0.014
New
Hampshire
Total HG
0.030
0.011
0.043
0.027
0.050
0.050
1.6E-03
1.0E-04
1.1E-04
0.125
0.090
New Jersey
HGIIGAS
0.064
0.011
0.238
0.228
0.087
0.087
7.9E-04
4.9E-05
0.390
0.327
New Jersey
HGNRVA
0.061
0.014
0.403
0.321
0.108
0.108
8.4E-03
3.9E-04
0.580
0.451
New Jersey
PHGI
0.009
0.001
0.119
0.109
0.039
0.039
3.8E-05
8.7E-06
0.167
0.149
New Jersey
Total HG
0.133
0.026
0.761
0.658
0.233
0.233
9.3E-03
4.5E-04
1.137
0.927
New Mexico
HGIIGAS
0.042
0.010
4.7E-03
1.6E-03
5.8E-04
5.8E-04
2.5E-04
9.7E-06
0.048
0.012
New Mexico
HGNRVA
0.975
0.284
0.026
8.1E-03
9.3E-03
9.3E-03
2.6E-03
6.8E-05
1.013
0.304
44
-------
ptipm
ptnonipm
nonpt
onroad
aim
nonroad
all US sectors
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
State
Species of
2005
2016
2005
2016
2005
2016
2005=
2016
2005=
2016
2005=
2016
2005
2016
New Mexico
PHGI
0.010
1.8E-03
4.3E-03
1.4E-03
2.1E-04
2.1E-04
1.4E-05
2.3E-06
0.015
0.003
New Mexico
Total HG
1.027
0.296
0.035
0.011
0.010
0.010
2.9E-03
8.0E-05
1.076
0.320
New York
HGIIGAS
0.219
0.013
0.351
0.305
0.096
0.096
1.5E-03
9.4E-05
0.667
0.416
New York
HGNRVA
0.218
0.037
0.407
0.231
0.456
0.456
1.6E-02
7.5E-04
1.098
0.741
New York
PHGI
0.029
1.2E-03
0.158
0.124
0.062
0.062
7.3E-05
1.7E-05
0.249
0.187
New York
Total HG
0.465
0.051
0.916
0.660
0.614
0.614
1.8E-02
8.6E-04
2.014
1.344
North
Carolina
HGIIGAS
1.122
0.118
0.132
0.119
0.015
0.015
9.1E-04
5.9E-05
1.270
0.252
North
Carolina
HGNRVA
0.494
0.357
0.413
0.349
0.067
0.067
0.010
4.2E-04
0.985
0.783
North
Carolina
PHGI
0.100
0.013
0.092
0.081
9.3E-03
9.3E-03
4.9E-05
1.4E-05
0.202
0.103
North
Carolina
Total HG
1.716
0.487
0.638
0.549
0.091
0.091
0.011
4.9E-04
2.456
1.138
North Dakota
HGIIGAS
0.130
0.119
0.014
0.009
0.002
1.75E-03
7.7E-05
2.2E-05
0.145
0.129
North Dakota
HGNRVA
0.988
0.800
0.022
0.013
0.009
8.5E-03
8.1E-04
8.2E-05
1.019
0.823
North Dakota
PHGI
0.006
0.017
0.009
0.005
0.001
1.1E-03
4.4E-06
9.2E-06
0.016
0.023
North Dakota
Total HG
1.123
0.936
0.045
0.027
0.011
0.011
8.9E-04
1.1E-04
1.180
0.976
Ohio
HGIIGAS
1.687
0.440
0.331
0.224
0.013
0.013
1.1E-03
8.0E-05
2.032
0.678
Ohio
HGNRVA
1.841
1.074
1.513
0.859
0.090
0.090
0.012
5.5E-04
3.456
2.035
Ohio
PHGI
0.134
0.062
0.216
0.155
7.0E-03
7.0E-03
6.0E-05
1.9E-05
0.357
0.224
Ohio
Total HG
3.662
1.576
2.059
1.238
0.110
0.110
0.013
6.5E-04
5.845
2.937
Oklahoma
HGIIGAS
0.206
0.288
0.077
0.063
5.8E-04
5.8E-04
4.8E-04
2.8E-05
0.285
0.352
Oklahoma
HGNRVA
0.719
0.739
0.244
0.154
0.020
0.020
0.005
1.8E-04
0.988
0.918
Oklahoma
PHGI
1.8E-03
1.8E-03
0.057
0.044
2.5E-04
2.5E-04
2.6E-05
7.2E-06
0.059
0.046
Oklahoma
Total HG
0.927
1.028
0.379
0.260
0.020
0.020
5.6E-03
2.2E-04
1.332
1.315
Oregon
HGIIGAS
0.025
0.002
0.215
0.049
0.012
0.012
3.5E-04
2.1E-05
2.7E-05
0.252
0.064
Oregon
HGNRVA
0.056
0.005
1.154
0.206
0.040
0.040
3.7E-03
4.0E-05
1.9E-04
1.254
0.255
Oregon
PHGI
1.9E-04
1.1E-05
0.192
0.040
7.7E-03
7.7E-03
1.8E-05
1.1E-05
6.0E-06
0.200
0.047
Oregon
Total HG
0.081
7.5E-03
1.561
0.295
0.060
0.060
4.1E-03
7.1E-05
2.2E-04
1.706
0.367
Pennsylvania
HGIIGAS
2.856
0.459
0.635
0.514
0.064
0.064
1.1E-03
6.4E-05
3.556
1.038
Pennsylvania
HGNRVA
1.829
1.071
1.654
1.108
0.159
0.159
0.012
4.8E-04
3.655
2.351
Pennsylvania
PHGI
0.294
0.083
0.395
0.300
0.041
0.041
5.8E-05
1.3E-05
0.730
0.425
Pennsylvania
Total HG
4.979
1.613
2.684
1.923
0.264
0.264
0.013
5.6E-04
7.940
3.814
Rhode Island
HGIIGAS
0.014
0.014
8.8E-03
8.8E-03
9.0E-05
5.1E-06
0.023
0.023
Rhode Island
HGNRVA
0.023
0.023
0.019
0.019
9.6E-04
4.1E-05
0.043
0.043
Rhode Island
PHGI
9.3E-03
9.3E-03
5.7E-03
5.7E-03
4.0E-06
9.0E-07
0.015
0.015
Rhode Island
Total HG
0.047
0.047
0.033
0.033
1.1E-03
4.7E-05
0.081
0.081
South
Carolina
HGIIGAS
0.319
0.166
0.314
0.231
3.1E-03
3.1E-03
5.1E-04
3.1E-05
0.636
0.400
South
Carolina
HGNRVA
0.233
0.167
0.712
0.467
0.025
0.025
5.3E-03
2.2E-04
0.976
0.664
South
Carolina
PHGI
0.029
0.015
0.176
0.124
1.9E-03
1.9E-03
2.7E-05
6.7E-06
0.207
0.141
South
Carolina
Total HG
0.581
0.347
1.202
0.822
0.030
0.030
5.8E-03
2.6E-04
1.819
1.205
45
-------
ptipm
ptnonipm
nonpt
onroad
aim
nonroad
all US sectors
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
State
Species of
2005
2016
2005
2016
2005
2016
2005=
2016
2005=
2016
2005=
2016
2005
2016
South Dakota
HGIIGAS
0.015
0.021
0.013
0.006
1.0E-03
1.0E-03
8.5E-05
1.7E-05
0.028
0.028
South Dakota
HGNRVA
0.033
6.4E-03
0.049
0.013
7.1E-03
7.1E-03
8.9E-04
6.9E-05
0.090
0.028
South Dakota
PHGI
8.2E-05
3.5E-04
0.010
4.4E-03
6.5E-04
6.5E-04
4.9E-06
6.5E-06
0.011
0.005
South Dakota
Total HG
0.048
0.027
0.071
0.024
8.8E-03
8.8E-03
9.8E-04
9.2E-05
0.129
0.061
Tennessee
HGIIGAS
0.634
0.194
0.285
0.173
2.0E-03
2.0E-03
7.1E-04
3.9E-05
0.922
0.370
Tennessee
HGNRVA
0.571
0.547
1.309
0.501
0.031
0.031
7.4E-03
2.7E-04
1.919
1.087
Tennessee
PHGI
0.046
0.001
0.152
0.096
1.2E-03
1.2E-03
4.1E-05
8.9E-06
0.200
0.099
Tennessee
Total HG
1.251
0.743
1.746
0.771
0.034
0.034
8.2E-03
3.2E-04
3.040
1.556
Texas
HGIIGAS
1.520
0.804
0.805
0.606
1.2E-03
1.2E-03
2.3E-03
1.4E-04
2.329
1.414
Texas
HGNRVA
3.584
2.501
3.269
2.592
0.071
0.071
0.024
9.0E-04
6.949
5.188
Texas
PHGI
0.092
0.063
0.576
0.446
8.1E-04
8.1E-04
1.4E-04
3.5E-05
0.669
0.511
Texas
Total HG
5.196
3.367
4.650
3.644
0.073
0.073
0.027
1.1E-03
9.947
7.112
Tribal Data
HGIIGAS
3.2E-04
3.2E-04
0.000
0.000
Tribal Data
HGNRVA
5.4E-04
5.3E-04
0.001
0.001
Tribal Data
PHGI
2.1E-04
2.1E-04
0.000
0.000
Tribal Data
Total HG
1.1E-03
1.1E-03
0.001
0.001
Utah
HGIIGAS
0.063
0.062
0.085
0.059
4.0E-04
4.0E-04
2.6E-04
1.6E-05
0.149
0.122
Utah
HGNRVA
0.079
0.114
0.232
0.115
0.014
0.014
2.7E-03
1.2E-04
0.328
0.246
Utah
PHGI
0.006
0.008
0.052
0.032
1.9E-04
1.9E-04
1.4E-05
3.5E-06
0.058
0.040
Utah
Total HG
0.148
0.184
0.369
0.206
0.015
0.015
3.0E-03
1.4E-04
0.536
0.408
Vermont
HGIIGAS
1.7E-03
2.4E-04
2.4E-04
9.4E-03
9.4E-03
7.5E-05
5.7E-06
0.011
0.010
Vermont
HGNRVA
2.8E-03
6.2E-04
6.2E-04
0.018
0.018
7.9E-04
4.7E-05
0.022
0.019
Vermont
PHGI
1.1E-03
1.6E-04
1.6E-04
5.5E-03
5.5E-03
4.1E-06
9.4E-07
0.007
0.006
Vermont
Total HG
5.6E-03
1.0E-03
1.0E-03
0.033
0.033
8.7E-04
5.3E-05
0.040
0.035
Virginia
HGIIGAS
0.401
0.108
0.496
0.235
0.019
0.019
8.5E-04
4.6E-05
0.917
0.364
Virginia
HGNRVA
0.181
0.146
0.938
0.421
0.069
0.069
9.0E-03
3.3E-04
1.197
0.646
Virginia
PHGI
0.042
0.029
0.309
0.132
0.012
0.012
4.3E-05
1.0E-05
0.363
0.174
Virginia
Total HG
0.624
0.284
1.743
0.789
0.100
0.100
9.9E-03
3.9E-04
2.477
1.183
Washington
HGIIGAS
0.105
0.005
0.047
0.030
6.1E-03
6.1E-03
5.7E-04
3.6E-03
4.3E-05
0.162
0.045
Washington
HGNRVA
0.234
0.161
0.123
0.057
0.041
0.041
5.9E-03
6.9E-03
3.1E-04
0.412
0.272
Washington
PHGI
5.3E-04
7.1E-04
0.032
0.018
3.1E-03
3.1E-03
3.1E-05
1.8E-03
9.8E-06
0.037
0.024
Washington
Total HG
0.339
0.167
0.202
0.104
0.050
0.050
6.5E-03
0.012
3.6E-04
0.611
0.341
West Virginia
HGIIGAS
1.449
0.226
0.097
0.071
2.6E-03
2.6E-03
2.0E-04
9.7E-06
1.549
0.300
West Virginia
HGNRVA
0.827
0.610
0.301
0.202
0.015
0.015
2.1E-03
7.5E-05
1.144
0.829
West Virginia
PHGI
0.129
0.024
0.056
0.041
1.7E-03
1.7E-03
1.1E-05
1.8E-06
0.186
0.067
West Virginia
Total HG
2.404
0.860
0.454
0.314
0.019
0.019
2.3E-03
8.7E-05
2.880
1.196
Wisconsin
HGIIGAS
0.355
0.169
0.277
0.259
0.011
0.011
6.2E-04
6.1E-05
0.644
0.439
Wisconsin
HGNRVA
0.784
0.692
0.438
0.396
0.053
0.053
6.5E-03
4.8E-04
1.282
1.149
Wisconsin
PHGI
7.7E-03
0.010
0.172
0.160
7.1E-03
7.1E-03
3.3E-05
1.2E-05
0.187
0.177
46
-------
ptipm
ptnonipm
nonpt
onroad
aim
nonroad
all US sectors
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
[tons/yr]
State
Species of
2005
2016
2005
2016
2005
2016
2005=
2016
2005=
2016
2005=
2016
2005
2016
Wisconsin
Total HG
1.147
0.870
0.887
0.815
0.072
0.072
7.1E-03
5.5E-04
2.114
1.765
Wyoming
HGIIGAS
0.072
0.134
0.076
0.056
4.0E-04
4.0E-04
9.2E-05
5.9E-06
0.149
0.191
Wyoming
HGNRVA
0.872
1.119
0.147
0.098
3.6E-03
3.6E-03
9.5E-04
4.3E-05
1.023
1.222
Wyoming
PHGI
0.004
0.006
0.052
0.038
2.3E-04
2.3E-04
5.3E-06
1.3E-06
0.057
0.045
Wyoming
Total HG
0.949
1.260
0.275
0.192
4.2E-03
4.2E-03
1.1E-03
5.0E-05
1.23
1.46
Total (48
state)
HGIIGAS
21.096
6.876
10.469
7.911
1.060
1.060
0.140
0.041
0.104
32.91
16.13
Total (48
state)
HGNRVA
30.199
21.160
29.569
16.853
3.103
3.103
0.504
0.079
0.211
63.66
41.91
Total (48
state)
PHGI
1.614
0.668
6.129
4.418
0.652
0.652
0.060
0.021
0.053
8.53
5.87
Total (48
state)
Total HG
52.908
28.704
46.166
29.183
4.816
4.816
0.704
0.142
0.368
105.10
63.92
Table 5-4. EGU sector (ptipm emissions) for all AQ modeling cases: NOx, SO2 and VOC
EGU sector emissions (AQ modeling cases) [tons/yr
State
2005
NOX
2016
Base NOX
2016
Policy
NOX
2005
S02
2016
Policy S02
2016
Policy
S02
2005
VOC
2016
Base
VOC
2016
Policy
VOC
Alabama
133,051
59,339
59,118
460,123
172,198
38,346
1,366
1,375
1,311
Arizona
79,776
60,265
60,279
52,733
23,140
21,632
577
892
887
Arkansas
35,407
20,095
15,563
66,384
93,754
7,314
480
619
505
California
6,916
13,640
13,454
601
4,740
4,148
822
752
740
Colorado
73,909
55,507
55,555
64,174
55,588
19,698
914
643
672
Connecticut
6,865
2,410
2,406
10,356
2,643
2,041
307
106
123
Delaware
11,917
1,699
2,580
32,378
1,717
3,359
99
55
102
District of
Columbia
492
1,082
3
Florida
217,263
55,818
54,686
417,321
122,123
57,439
2,054
1,821
1,832
Georgia
111,017
37,138
32,293
616,054
91,885
40,767
1,303
1,176
1,140
Idaho
19
100
100
0
0
0
0
14
14
Illinois
127,923
47,093
45,501
330,382
148,934
47,403
1,586
2,169
2,260
Indiana
213,503
94,741
80,329
878,978
229,248
111,741
2,508
2,088
1,930
Iowa
72,806
40,921
40,720
130,264
98,518
22,208
536
781
790
Kansas
90,220
24,943
20,198
136,520
61,622
12,781
948
703
603
Kentucky
164,743
68,087
64,619
502,731
123,010
97,707
1,487
1,566
1,360
Louisiana
63,791
30,380
31,244
109,851
98,808
32,624
1,017
720
753
Maine
1,100
921
316
3,887
1,123
0
60
60
51
Maryland
62,574
17,076
15,904
283,205
36,211
11,528
483
475
447
Massachusetts
25,135
4,611
4,222
84,234
4,236
2,556
584
226
227
47
-------
EGU sector emissions (AQ modeling cases) [tons/yr
State
2005
NOX
2016
Base NOX
2016
Policy
NOX
2005
S02
2016
Policy S02
2016
Policy
S02
2005
VOC
2016
Base
VOC
2016
Policy
VOC
Michigan
120,005
59,629
55,318
349,877
169,853
27,922
1,230
1,197
1,065
Minnesota
83,836
26,063
27,015
101,666
51,952
27,805
633
643
649
Mississippi
45,166
19,460
17,915
75,047
55,317
10,595
574
412
419
Missouri
127,431
48,179
39,012
284,384
172,031
32,412
1,597
1,774
1,545
Montana
39,858
18,898
21,564
19,715
13,234
9,071
396
261
289
Nebraska
52,426
28,822
29,767
74,955
74,642
34,551
675
545
558
Nevada
47,297
13,936
13,426
53,363
11,283
4,735
524
358
355
New
Hampshire
8,827
1,807
1,074
51,445
4,348
730
136
110
100
New Jersey
30,114
9,335
9,449
57,044
8,507
6,997
1,188
322
308
New Mexico
75,483
63,427
63,819
30,628
11,370
9,357
576
542
537
New York
63,315
16,540
15,689
180,847
28,911
13,468
801
709
643
North Carolina
111,576
41,827
37,686
512,231
82,544
34,946
936
970
894
North Dakota
76,381
57,537
27,243
137,371
76,081
11,955
763
847
419
Ohio
258,688
90,904
74,788
1,116,084
204,291
77,852
1,751
1,855
1,485
Oklahoma
86,204
45,639
41,722
110,081
139,800
14,196
1,029
917
897
Oregon
9,383
8,139
8,139
12,304
11,102
1,423
141
157
157
Pennsylvania
176,870
107,224
97,220
1,002,201
152,929
73,714
1,153
1,822
1,492
Rhode Island
545
264
271
176
0
0
35
44
45
South Carolina
52,657
34,193
30,342
218,781
128,070
35,223
533
629
625
South Dakota
15,650
14,249
13,641
12,215
29,711
7,490
106
127
118
Tennessee
102,934
31,885
20,003
266,148
106,762
44,110
798
940
764
Texas
176,170
127,355
121,488
534,949
334,636
81,000
3,851
5,025
4,941
Utah
65,261
68,049
61,311
34,813
31,343
14,261
368
489
467
Vermont
297
0
0
9
0
0
22
0
0
Virginia
62,512
29,140
33,922
220,248
45,345
16,029
646
577
560
Washington
17,634
10,424
10,425
3,409
2,804
2,804
248
201
201
West Virginia
159,947
57,493
51,936
469,456
127,826
44,129
1,141
1,210
1,171
Wisconsin
72,170
32,755
26,950
180,200
77,871
24,481
980
999
880
Wyoming
89,315
71,795
67,968
89,874
55,636
25,831
848
923
886
Tribal Data
78
11
11
3
0
0
133
2
2
Total
3,726,459
1,769,764
1,618,199
10,380,783
3,577,698
1,220,379
40,950
40,845
38,217
48
-------
Table 5-5. EGU sector (ptipm emissions) for all AQ modeling cases: HCL and total Mercury
EGU sector emissions (AQ modeling cases'
[tons/yr]
State
2005 HCL
2016
Base
HCL
2016
Cntl
HCL
(tons)
2005
Total Hg
2016
Base
Total Hg
2016
Cntl
Total
Hg
Alabama
12431
6713
181
3.0126
1.2550
0.1919
Arizona
9323
129
69
0.8839
0.7487
0.0891
Arkansas
8381
1355
13
0.7112
0.7246
0.0664
California
9
14
16
0.1375
0.1322
0.0842
Colorado
269
757
196
0.4710
0.0832
0.0902
Connecticut
823
188
8
0.1273
0.0069
0.0064
Delaware
876
59
71
0.1974
0.0090
0.0210
District of Columbia
0.0028
0.0000
0.0000
Florida
14351
3323
176
1.4855
0.4859
0.1926
Georgia
21811
1205
411
2.0396
0.5115
0.2153
Idaho
0.0000
0.0000
0.0000
Illinois
12888
2118
325
4.4081
0.4879
0.2868
Indiana
24637
4171
980
3.4409
1.5583
0.3800
Iowa
1890
1482
185
1.3696
0.9994
0.1524
Kansas
12010
733
62
1.1291
0.9551
0.0973
Kentucky
18930
3818
375
2.0195
0.8278
0.3134
Louisiana
9648
1155
74
1.0124
1.0188
0.1656
Maine
0
12
0
0.0140
0.0129
0.0000
Maryland
6380
685
104
0.9711
0.1144
0.1158
Massachusetts
1124
29
15
0.1892
0.0094
0.0104
Michigan
15921
2615
292
2.1998
1.5010
0.1739
Minnesota
489
413
172
0.7504
0.1610
0.0743
Mississippi
1150
787
169
0.4469
0.4048
0.0526
Missouri
2775
2783
199
2.3493
1.9487
0.2424
Montana
6793
104
37
0.5311
0.0968
0.0446
Nebraska
6137
1203
99
0.5419
0.4228
0.0844
Nevada
5185
222
76
0.3315
0.0874
0.0563
New Hampshire
1009
60
11
0.0363
0.0108
0.0108
New Jersey
1896
206
22
0.1576
0.0257
0.0258
New Mexico
8786
55
64
1.0439
0.2958
0.0867
New York
4692
421
191
0.4932
0.0510
0.0425
North Carolina
28180
2749
550
1.9284
0.4868
0.2072
North Dakota
15088
526
50
1.2685
0.9364
0.0626
Ohio
32,396
6065
1038
4.4451
1.5759
0.6395
Oklahoma
130
1867
31
1.2226
1.0285
0.1046
Oregon
116
1
0.0841
0.0075
0.0075
Pennsylvania
26637
5482
489
5.7308
1.6132
0.5168
Rhode Island
0.0000
0.0000
0.0000
49
-------
EGU sector emissions (AQ modeling cases'
[tons/yr]
State
2005 HCL
2016
Base
HCL
2016
Cntl
HCL
(tons)
2005
Total Hg
2016
Base
Total Hg
2016
Cntl
Total
Hg
South Carolina
8900
5339
512
0.8209
0.3472
0.1422
South Dakota
1065
118
27
0.0766
0.0272
0.0119
Tennessee
12685
1283
146
1.4991
0.7427
0.1526
Texas
3119
4339
388
6.1284
3.3674
0.5362
Utah
362
51
0.2488
0.1838
0.0784
Vermont
1811
0.0056
0.0000
0.0000
Virginia
31
2538
154
0.8099
0.2842
0.1142
Washington
5729
3
3
0.3457
0.1666
0.0198
West Virginia
41
4368
336
2.8543
0.8600
0.5046
Wisconsin
3365
1534
275
1.3562
0.8701
0.1464
Wyoming
1630
584
158
1.1254
1.2596
0.2197
Tribal Data
172
0.0000
0.0000
0.0000
Total
351,592
74,089
8,802
62.4551
28.7038
6.8376
Table 5-6. EGU sector (ptipm emissions) for all AQ modeling cases: Speciated Mercury
EGU sector emissions (AQ modeling cases) [tons/yr|
State
2005
PHGI
2016
Base
PHGI
2016
PHGI
cntl
2005
HGIIGAS
2016
Base
HGIIGAS
2016
Cntl
HGIIGAS
2005
HGNRVA
2016
Base
HGNRVA
2016 Cntl
HGNRVA
Alabama
0.0836
0.0187
0.0069
1.1605
0.2878
0.0361
1.4190
0.9485
0.1489
Arizona
0.0068
0.0071
0.0048
0.0516
0.1330
0.0232
0.6575
0.6086
0.0611
Arkansas
0.0009
0.0011
0.0004
0.1438
0.1982
0.0023
0.3644
0.5253
0.0637
California
0.0010
0.0391
0.0289
0.0009
0.0415
0.0232
0.0028
0.0516
0.0321
Colorado
0.0048
0.0029
0.0028
0.1028
0.0257
0.0108
0.3211
0.0546
0.0765
Connecticut
0.0036
0.0002
0.0005
0.0396
0.0039
0.0020
0.0774
0.0027
0.0039
Delaware
0.0158
0.0002
0.0011
0.1090
0.0061
0.0105
0.0547
0.0027
0.0094
District of Columbia
0.0006
0.0008
0.0014
Florida
0.0795
0.0814
0.0256
0.5435
0.1449
0.0603
0.5503
0.2596
0.1067
Georgia
0.0738
0.0086
0.0139
0.9645
0.2413
0.0717
0.6659
0.2616
0.1298
Idaho
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
Illinois
0.0562
0.0035
0.0059
1.3672
0.1235
0.0328
2.8189
0.3609
0.2481
Indiana
0.0682
0.0106
0.0206
1.1407
0.4173
0.1131
1.6704
1.1304
0.2463
Iowa
0.0035
0.0042
0.0014
0.2788
0.1932
0.0128
0.8757
0.8020
0.1382
Kansas
0.0066
0.0036
0.0006
0.1674
0.1141
0.0032
0.8336
0.8375
0.0935
Kentucky
0.0624
0.0146
0.0199
0.7987
0.1310
0.0954
0.8974
0.6822
0.1980
Louisiana
0.0021
0.0987
0.0238
0.1478
0.2443
0.0364
0.4593
0.6757
0.1054
Maine
0.0009
0.0008
0.0000
0.0013
0.0088
0.0000
0.0022
0.0033
0.0000
Maryland
0.0532
0.0064
0.0085
0.5354
0.0314
0.0347
0.3014
0.0766
0.0725
Massachusetts
0.0159
0.0004
0.0007
0.1107
0.0027
0.0038
0.0551
0.0063
0.0059
50
-------
EGU sector emissions (AQ modeling cases) [tons/yr]
State
2005
PHGI
2016
Base
PHGI
2016
PHGI
cntl
2005
HGIIGAS
2016
Base
HGIIGAS
2016
Cntl
HGIIGAS
2005
HGNRVA
2016
Base
HGNRVA
2016 Cntl
HGNRVA
Michigan
0.0771
0.0058
0.0035
0.9683
0.3404
0.0239
0.7808
1.1548
0.1465
Minnesota
0.0087
0.0012
0.0006
0.0957
0.0357
0.0058
0.6027
0.1241
0.0679
Mississippi
0.0103
0.0012
0.0009
0.1258
0.1399
0.0132
0.1557
0.2637
0.0385
Missouri
0.0236
0.0050
0.0024
0.7019
0.4725
0.0154
1.1286
1.4712
0.2247
Montana
0.0070
0.0108
0.0026
0.0297
0.0103
0.0030
0.4677
0.0757
0.0391
Nebraska
0.0015
0.0020
0.0009
0.1180
0.1684
0.0268
0.2244
0.2525
0.0567
Nevada
0.0017
0.0015
0.0012
0.0911
0.0113
0.0077
0.2170
0.0745
0.0474
New Hampshire
0.0039
0.0003
0.0007
0.0149
0.0015
0.0036
0.0115
0.0090
0.0066
New Jersey
0.0086
0.0012
0.0014
0.0636
0.0109
0.0112
0.0607
0.0136
0.0133
New Mexico
0.0101
0.0018
0.0007
0.0423
0.0099
0.0041
0.9750
0.2841
0.0819
New York
0.0286
0.0013
0.0022
0.2188
0.0128
0.0116
0.2179
0.0370
0.0288
North Carolina
0.0999
0.0126
0.0138
1.1218
0.1175
0.0686
0.4942
0.3567
0.1249
North Dakota
0.0056
0.0169
0.0004
0.1296
0.1190
0.0092
0.9878
0.8005
0.0530
Ohio
0.1341
0.0619
0.0450
1.6872
0.4396
0.2362
1.8411
1.0744
0.3582
Oklahoma
0.0018
0.0018
0.0010
0.2063
0.2882
0.0047
0.7188
0.7386
0.0989
Oregon
0.0002
0.0000
0.0001
0.0251
0.0023
0.0003
0.0561
0.0052
0.0071
Pennsylvania
0.2941
0.0834
0.0309
2.8559
0.4587
0.1787
1.8292
1.0711
0.3072
Rhode Island
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
South Carolina
0.0287
0.0146
0.0094
0.3191
0.1660
0.0496
0.2334
0.1666
0.0832
South Dakota
0.0001
0.0004
0.0001
0.0147
0.0205
0.0078
0.0330
0.0064
0.0040
Tennessee
0.0461
0.0013
0.0087
0.6343
0.1939
0.0440
0.5707
0.5475
0.1000
Texas
0.0920
0.0631
0.0143
1.5203
0.8036
0.0513
3.5838
2.5007
0.4706
Utah
0.0058
0.0076
0.0050
0.0634
0.0623
0.0256
0.0790
0.1139
0.0478
Vermont
0.0011
0.0000
0.0000
0.0017
0.0000
0.0000
0.0028
0.0000
0.0000
Virginia
0.0423
0.0295
0.0079
0.4006
0.1083
0.0402
0.1810
0.1464
0.0661
Washington
0.0005
0.0007
0.0001
0.1046
0.0049
0.0006
0.2342
0.1610
0.0192
West Virginia
0.1286
0.0244
0.0328
1.4490
0.2259
0.1668
0.8268
0.6097
0.3050
Wisconsin
0.0077
0.0095
0.0069
0.3549
0.1686
0.0246
0.7840
0.6920
0.1148
Wyoming
0.0043
0.0064
0.0022
0.0723
0.1340
0.0341
0.8721
1.1192
0.1834
Tribal Data
0.0000
0.0000
0.0000
0.0000
0.0000
0.0000
Total
1.61
0.67
0.3620
21.10
6.88
1.6409
30.20
21.16
4.8347
Table 5-7.2016 Base Case SO2 Emissions (tons/year) for Lower 48 States by Sector
State
EGU
Non-EGU
Nonpoint
Nonroad +
Alm_no_c3+
Seca c3
Onroad
Fires
Total
Alabama
172,198
65,649
52,312
197
513
983
291,850
Arizona
23,140
24,206
2,566
52
626
2,888
53,477
Arkansas
93,754
12,910
27,255
142
286
728
135,075
California
4,740
22,148
77,610
8,489
2,216
6,735
121,938
Colorado
55,588
1,425
6,469
47
529
1,719
65,778
Connecticut
2,643
1,832
18,438
100
275
4
23,291
51
-------
State
EGU
Non-EGU
Nonpoint
Nonroad +
Alm_no_c3+
Seca c3
Onroad
Fires
Total
Delaware
1,717
6,299
5,857
715
79
6
14,673
District of Columbia
0
686
1,559
3
36
0
2,284
Florida
122,123
40,662
70,479
4,530
1,901
7,018
246,713
Georgia
91,885
42,407
56,812
430
1,108
2,010
194,652
Idaho
0
17,137
2,911
21
167
3,845
24,082
Illinois
148,934
85,834
5,380
319
1,036
20
241,524
Indiana
229,248
64,088
59,764
160
675
24
353,959
Iowa
98,518
19,010
19,816
85
291
25
137,745
Kansas
61,622
12,708
36,374
55
257
103
111,119
Kentucky
123,010
18,773
34,208
257
436
364
177,048
Louisiana
98,808
146,371
2,371
3,979
402
892
252,824
Maine
1,123
7,803
9,943
194
131
150
19,345
Maryland
36,211
13,623
40,850
1,055
513
32
92,284
Massachusetts
4,236
16,168
25,235
1,368
497
93
47,597
Michigan
169,853
24,072
42,066
440
919
91
237,440
Minnesota
51,952
18,728
14,727
252
500
631
86,789
Mississippi
55,317
22,327
6,785
244
332
1,051
86,055
Missouri
172,031
65,392
44,540
214
652
186
283,016
Montana
13,234
7,858
1,959
24
105
1,422
24,603
Nebraska
74,642
4,777
29,569
55
181
105
109,329
Nevada
11,283
2,134
12,474
25
187
1,346
27,449
New Hampshire
4,348
2,578
7,391
22
120
38
14,496
New Jersey
8,507
6,758
10,711
1,300
661
61
27,998
New Mexico
11,370
8,065
2,833
24
237
3,450
25,978
New York
28,911
20,812
125,199
979
1,303
113
177,318
North Carolina
82,544
45,264
21,992
2,177
811
696
153,484
North Dakota
76,081
9,678
5,766
35
62
66
91,688
Ohio
204,291
58,216
19,810
422
969
22
283,731
Oklahoma
139,800
31,097
7,535
45
436
469
179,382
Oregon
11,102
8,597
9,846
787
369
4,896
35,598
Pennsylvania
152,929
46,609
68,322
458
981
32
269,332
Rhode Island
0
2,725
3,364
129
72
1
6,291
South Carolina
128,070
22,746
30,001
1,037
462
646
182,963
South Dakota
29,711
1,947
10,298
22
76
498
42,552
Tennessee
106,762
39,433
32,695
173
695
277
180,036
Texas
334,636
138,883
110,147
2,103
2,084
1,178
589,030
Tribal
0
1,495
0
1,495
Utah
31,343
8,034
3,425
25
297
1,934
45,057
Vermont
0
903
5,379
7
90
49
6,428
Virginia
45,345
47,045
32,897
771
756
399
127,213
Washington
2,804
19,131
7,227
1,432
654
407
31,655
West Virginia
127,826
23,305
14,580
75
161
215
166,162
Wisconsin
77,871
18,573
6,370
123
554
70
103,561
Wyoming
55,636
22,118
6,180
18
86
1,106
85,146
Total
3,577,698
1,349,038
1,250,300
35,616
26,784
49,094
6,288,529
*Non-US seca_c3 component not included. These emissions are 957,065 tons/yr.
Table 5-8. 2016 Base Case PM2.5 Emissions (tons/year) for Lower 48 States by Sector
State
EGU
Non-EGU
Nonpoint
Nonroad +
Alm_no_c3
+Seca c3
Onroad
Fires
Area
Fugitive
Dust
Total
Alabama
14,801
17,064
22,982
2,576
1,631
13,938
11,591
84,583
52
-------
State
EGU
Non-EGU
Nonpoint
Nonroad +
Alm_no_c3
+Seca c3
Onroad
Fires
Area
Fugitive
Dust
Total
Arizona
10,196
3,804
8,178
2,836
1,817
37,151
12,806
76,788
Arkansas
3,805
9,905
22,683
2,191
1,108
10,315
11,681
61,689
California
9,718
20,859
69,736
17,963
17,777
97,302
20,386
253,741
Colorado
4,972
7,007
12,854
2,490
4,373
24,054
11,794
67,544
Connecticut
1,632
225
9,303
1,090
2,988
56
1,014
16,308
Delaware
643
1,906
1,675
477
514
87
497
5,801
District of
Columbia
0
172
407
151
229
0
162
1,121
Florida
26,114
18,264
37,931
10,096
4,168
99,484
14,126
210,183
Georgia
14,411
12,161
40,435
4,131
3,803
24,082
21,286
120,309
Idaho
187
2,067
27,023
1,267
1,555
52,808
14,154
99,060
Illinois
11,157
14,266
13,753
7,429
10,062
277
58,864
115,808
Indiana
21,198
13,572
31,618
3,769
5,586
344
41,832
117,919
Iowa
5,223
5,688
10,176
3,593
3,816
349
42,837
71,682
Kansas
4,634
7,556
82,581
3,078
1,736
1,468
55,263
156,315
Kentucky
13,598
10,341
16,928
2,899
2,342
5,155
12,655
63,917
Louisiana
5,219
36,644
17,365
6,491
1,000
12,647
10,302
89,669
Maine
712
3,143
11,958
985
1,876
2,127
1,312
22,114
Maryland
3,791
6,153
18,742
2,304
3,584
531
3,559
38,665
Massachusetts
2,754
2,127
24,749
2,531
5,278
1,324
4,580
43,343
Michigan
7,188
11,115
22,374
5,048
10,955
1,283
23,506
81,470
Minnesota
9,011
9,665
22,535
5,035
10,917
8,943
49,495
115,600
Mississippi
2,554
9,491
15,685
2,495
876
14,897
17,454
63,451
Missouri
8,040
6,334
25,550
4,217
4,335
2,636
48,202
99,315
Montana
2,453
2,528
4,925
1,427
1,239
17,311
24,528
54,412
Nebraska
2,657
1,857
8,177
3,177
1,760
1,483
37,482
56,593
Nevada
10,903
4,029
2,612
1,364
732
19,018
7,185
45,843
New Hampshire
1,138
508
11,543
610
1,588
534
658
16,578
New Jersey
3,380
2,577
11,837
3,358
5,483
865
549
28,049
New Mexico
5,785
1,445
5,006
1,220
1,178
48,662
45,353
108,648
New York
7,580
4,442
37,074
5,432
13,467
1,601
13,647
83,242
North Carolina
12,185
11,775
36,080
4,746
3,172
9,870
11,162
88,990
North Dakota
5,338
569
2,807
2,293
1,735
934
38,263
51,940
Ohio
19,844
12,251
22,428
5,908
8,425
316
28,587
97,759
Oklahoma
7,412
5,669
45,423
2,165
1,856
6,644
44,243
113,412
Oregon
1,653
8,161
47,545
2,517
1,917
65,350
8,738
135,881
Pennsylvania
21,187
13,237
29,061
4,839
8,838
454
13,344
90,961
Rhode Island
598
256
1,035
281
758
14
182
3,124
South Carolina
11,831
4,477
16,869
2,372
1,548
9,163
9,162
55,421
South Dakota
768
2,145
3,959
1,445
1,128
7,062
29,215
45,722
Tennessee
6,637
21,495
19,126
3,129
3,034
3,934
11,900
69,254
Texas
37,320
34,923
47,953
13,048
6,101
21,578
143,814
304,737
Tribal
32
1,557
0
0
1,589
Utah
5,011
3,564
8,859
1,021
2,328
27,412
5,682
53,877
Vermont
0
337
4,882
325
1,250
696
1,528
9,018
Virginia
7,141
10,840
27,774
3,938
4,315
5,659
8,194
67,861
Washington
1,927
4,197
30,049
3,737
3,665
4,487
13,617
61,680
West Virginia
16,198
4,921
10,405
1,114
1,084
3,050
3,649
40,423
Wisconsin
6,376
7,430
24,646
3,639
8,423
994
11,870
63,379
53
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State
EGU
Non-EGU
Nonpoint
Nonroad +
Alm_no_c3
+Seca c3
Onroad
Fires
Area
Fugitive
Dust
Total
Wyoming
7,406
10,207
2,620
896
967
15,686
28,723
66,505
Grand Total
384,320
404,926
1,029,916
169,144
188,320
684,035
1,030,631
3,891,291
*Non-US seca_c3 component not included. These emissions are 120,617 tons/yr.
54
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Well
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