Technical Support Document (TSD) 

For the Proposed Toxics Rule 

Docket No. EPA-HQ-OAR-2009-0234 

Emissions Inventories 

DRAFT

3/4/2011 

U.S. Environmental Protection Agency 

Office of Air and Radiation 

March 2011 
ii

TABLE OF CONTENTS 

Acronyms ....................................................................................................................................... iii

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) ..................................................... 11

3.2 Stationary Source Projections: non-EGU sectors (ptnonipm, nonpt, ag, afdust) ......... 15

3.2.1 Livestock emissions growth (ag, afdust) .............................................................................................. 15

3.2.2 Residential wood combustion growth (nonpt) ...................................................................................... 16

3.2.3 Gasoline Stage II growth and control (nonpt, ptnonipm) ..................................................................... 17

3.2.4 Portable fuel container growth and control (nonpt) .............................................................................. 18

3.2.5 Aircraft growth (ptnonipm) .................................................................................................................. 19

3.2.6 Stationary Source control programs, consent decrees & settlements, and plant closures (ptnonipm, 

nonpt) ................................................................................................................................................... 20

3.2.6.1 Reductions from the Portland Cement NESHAP .................................................................................. 22

3.2.6.2 Boiler MACT reductions ....................................................................................................................... 23

3.2.6.3 Summary of Mercury Reductions at nonEGU stationary sources- (ptnonipm) .................................... 26

3.2.7 Oil and gas projections in TX, OK, and non-California WRAP states (nonpt) .................................... 27

3.2.8 Future Year VOC Speciation for gasoline-related sources (ptnonipm, nonpt) ..................................... 29

3.3 Mobile source projections ............................................................................................... 29

3.3.1 Onroad mobile (on_noadj, on_moves_runpm, on_moves_startpm) ..................................................... 29

3.3.2 Nonroad mobile (nonroad) ................................................................................................................... 32

3.3.3 Locomotives and Class 1 & 2 commercial marine vessels (alm_no_c3) ............................................. 33

3.3.4 Class 3 commercial marine vessels (seca_c3) ...................................................................................... 35

3.3.5 Future Year VOC Speciation (on_noadj, nonroad) .............................................................................. 36

3.4 Canada, Mexico, and Offshore sources (othar, othon, othpt, othar_hg, and othpt_hg) 37

4 EGU Control Case for 2016 ..................................................................................................38

5 Emission Summaries for the Base Cases and Control Case ...............................................38

6 References ..............................................................................................................................53
iii

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 

IMO International Marine Organization 

IPM Integrated Planning Model 

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 

NEEDS National Electric Energy Database System

NEI National Emission Inventory 

NLEV National Low Emission Vehicle 

NMIM National Mobile Inventory Model 

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 

PFC Portable Fuel Container 

RIA Regulatory Impact Analysis 

RICE Reciprocating Internal Combustion Engine 

RFS2 Revised annual Renewable Fuel Standard 

RWC Residential Wood Combustion
iv

SMOKE Sparse Matrix Operator Kernel Emissions

SCC Source Category Code 

SPPD Sector Policies and Programs Division

TAF Terminal Area Forecast 

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 ..................................................................................... 5

Figure 3-1. Approach to Compute Monthly Emissions from the IPM data ................................ 15

Figure 3-2. MOVES exhaust temperature adjustment functions for 2005and 2016 .................... 31

Figure 3-3. Tier 2 Fraction of Light Duty Vehicles ..................................................................... 37

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 .......................................................................... 8

Table 3-2. Adjustment to IPM emissions due to application of Boiler MACT. .......................... 13

Table 3-3. Growth factors from year 2005 to future years for Animal Operations ..................... 16

Table 3-4. Projection Factors for growing year 2005 Residential Wood Combustion 

Sources to 2016 ........................................................................................................ 17

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 ............................................................................................. 21

Table 3-7. Quantification of Missing Closures in 2016 the nonEGU (ptnonipm) sector ............ 22

Table 3-8. Future-year ISIS-based cement industry annual reductions (tons/yr) for the 

nonEGU (ptnonipm) sector ...................................................................................... 23

Table 3-9. Default pollutant fuel reductions applied NEI boilers not covered by the ICR 

database. ................................................................................................................... 24

Table 3-10. Crosswalk of NEI fuels to ICR fuels ........................................................................ 25

Table 3-11. Summary of Boiler MACT reductions applied to the ptnonipm sector. .................. 26

Table 3-12. State-level non-MACT Boiler Reductions from ICR Data Gathering .................... 26

Table 3-13. Anthropogenic mercury emissions and projections in the Continental United 

States ........................................................................................................................ 27
v

Table 3-14. 2005, 2008 and estimated 2016 emissions for nonpoint Oklahoma and Texas oil and 

gas sources ............................................................................................................... 28

Table 3-15. WRAP Oil and Gas Emissions: 2005, 2018 WRAP, and 2016 with additional 

reductions due to the RICE NESHAP ..................................................................... 28

Table 3-16. Summary of the impact of PM2.5 errors in the onroad sectors ................................. 31

Table 3-17. Factors applied to year 2005 emissions to project locomotives and Class 1 and 

Class 2 Commercial Marine Vessel Emissions ....................................................... 34

Table 3-18. Factors to Project Class 3 Commercial Marine Vessel emissions to 2016 .............. 36

Table 3-19. Future Year Profiles for Mobile Source Related Sources .........................................37

Table 4-1. Boiler MACT reductions applied to policy case ptipm sector emissions prior 

to AQ modeling........................................................................................................ 38

Table 5-1. 2016 Emissions – 2016 Base Case Compared to 2005 Base Case: VOC, NOX, CO, 

SO2, NH3, and PM ................................................................................................... 39

Table 5-2. 2016 Emissions – Base Case Compared to 2005 Base Case: mercury (species 

and total), HCL and CL2 ......................................................................................... 40

Table 5-3. Speciated Mercury and total Mercury by State and Sector for 2005 and 2016 .......... 41

Table 5-4. EGU sector (ptipm emissions) for all AQ modeling cases: NOX, SO2 and VOC ..... 46

Table 5-5. EGU sector (ptipm emissions) for all AQ modeling cases: HCL and total 

Mercury .................................................................................................................... 48

Table 5-6. EGU sector (ptipm emissions) for all AQ modeling cases: Speciated Mercury ....... 49

Table 5-7. 2016 Base Case SO2 Emissions (tons/year) for Lower 48 States by Sector .............. 50

Table 5-8. 2016 Base Case PM2.5 Emissions (tons/year) for Lower 48 States by Sector .......... 51

List of Appendices 

APPENDIX A: Inventory Data Files Used for Each TR Modeling Case – 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 NESHAPa

APPENDIX D: Mercury Emission Reductions, 2005-2016 for the NonEGU categories of : 

Electric Arc Furnaces, Mercury Cell Chlor-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 Mercury Emissions 

APPENDIX H: Details Regarding the PM2.5 Natural Gas Emission Factor error in IPM Post 

Processing 

APPENDIX I: Comparison of Emissions between the AQ-modeled emissions and Final IPM 

for 2016 Base and Policy Cases –for the IPM sector
1

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 EGU MACT/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_control1 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 v4.1 platform) document available at http://www.epa.gov/ttn/chief/emch/. 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 
2

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://ftp.epa.gov/EmisInventory/2005v4/2005_emissions_tsd_07jul2010.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 

(http://www.epa.gov/ttn/chief/emch/index.html#2005). 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. 
3

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 (Hg): 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 Hg: 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: Day-specific emissions created for input into SMOKE. 

Non-EGU sector 

(also called the 

non-IPM sector): 

ptnonipm

Point For all pollutants other than Hg: 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. 

For Hg: 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 PM2.5 from fugitive dust sources from the NEI nonpoint 

inventory (e.g., building construction, road construction, paved roads, 

unpaved roads, agricultural dust), county and annual resolution. 

Unchanged from the 2005v4 platform. 

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 nonCalifornia 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 nonEGU (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 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. 
4

Platform Sector 

2005 NEI 

Sector Description and resolution of the data input to SMOKE 

locomotive, and 

non-C3 

commercial 

marine: 

alm_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 per 

Cylinder”, usually described as the Emissions Control Area (ECA) 

study (http://www.epa.gov/otaq/oceanvessels.htm). 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: 

on_noadj

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. 

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: 

on_moves_startpm

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: 

on_moves_runpm

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: 

othpt_hg

N/A Annual year 2000 Canada speciated mercury point source emissions.

Other nonpoint 

and nonroad not 

from the NEI: 

othar

N/A Annual year 2006 Canada (province resolution) and year 1999 Mexico 

Phase III (municipio resolution) nonpoint and nonroad mobile 

inventories. 

Other nonpoint 

sources not from 

N/A Annual year 2000 Canada speciated mercury from nonpoint sources.
5

Platform Sector 

2005 NEI 

Sector Description and resolution of the data input to SMOKE 

the NEI, Hg only: 

othar_hg

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 are available at its website (http://www.smoke-model.org). 

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 (http://www.epa.gov/ttn/chief/emch/index.html#2005). 

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. 

Figure 2-1. Air quality modeling domains 

36km Domain Boundary

12km East Domain Boundary

12km West Domain Boundary
6

All three grids use a Lambert-Conformal projection, with Alpha = 33o, Beta = 45o and Gamma = -97o, with a 

center of X = -97o and Y = 40o. 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, on_moves_runpm, and on_moves_startpm 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 

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 pointsource 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. 
7

• 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): Output from the NONROAD2005 model for 2015, which was run 

using NMIM, 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. Also for California and NMIM emissions, we utilized 2015 and 2016 national NMIM run 

totals and adjusted emissions by applying factors by pollutant, source category code (SCC), and 

mode (exhaust/evap) to adjust 2015 emissions to year 2016. 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 for all 

states. 

• 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 is kept at 2005 levels. 

• 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 (on_moves_startpm): 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. 
8

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. 

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 

Non-EGU Point (ptnonipm sector) projection approaches carried forward 

from the Proposed Transport Rulea,b

MACT rules, national, VOC: national applied by SCC, MACT

Boat Manufacturing 

Wood Building Products Surface Coating 

Generic MACT II: Spandex Production, Ethylene manufacture 

Large Appliances 

Miscellaneous Organic NESHAP (MON): Alkyd Resins, Chelating Agents, Explosives, 

 Phthalate Plasticizers, Polyester Resins, Polymerized Vinylidene Chloride 

Reinforced Plastics 

Asphalt Processing & Roofing 

Iron & Steel Foundries 

Metal: Can, Coil 

Metal Furniture 

Miscellaneous Metal Parts & Products 

Municipal Solid Waste Landfills 

Paper and Other Web 

Plastic Parts 

Plywood and Composite Wood Products 

Carbon Black Production 

Cyanide Chemical Manufacturing 

Friction Products Manufacturing 

Leather Finishing Operations 

Miscellaneous Coating Manufacturing 

Organic Liquids Distribution (Non-Gasoline) 

Refractory Products Manufacturing 

Sites Remediation 

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, SO2 

Appendix B 

DOJ Settlements: plant SCC controls for: 

Alcoa, TX 

Premcor (formerly Motiva), DE 

All 1 

Refinery Consent Decrees: plant/SCC controls NOx, PM, SO2 2 

Municipal Waste Combustor Reductions –plant level PM 4 

Hazardous Waste Combustion PM 3 

Hospital/Medical/Infectious Waste Incinerator Regulations NOX, PM, SO2 EPA, 2005 
9

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, SO2 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. At the 

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 approaches carried forward from the Proposed Transport Rule

Municipal Waste Landfills: projection factor of 0.25 applied All EPA, 2007a 

Livestock Emissions Growth from year 2002 to year 2016 NH3, PM 8 

Residential Wood Combustion Growth and Changeouts from year 2005 to Year 2016 All 9 

Gasoline Stage II growth and control from year 2005 to year 2016 VOC 10 

Portable Fuel Container MSAT2 inventory growth and control from year 2005 

to year 2016 

VOC 11 

Additional projections used in the proposed Toxics Rule 

modeling for non-EGU point sources (ptnonipm sector)

a

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 ISISCement model runs for the NESHAP and NSPS analysis of July 28, 2010 and include 

closures 

Hg, NOX, SO2, 

PM, HCL 12 

NESHAP: Industrial, Institutional, Commercial Boilers (based on proposed rule 

reductions 04-15-10) -- finalized 2/2011 

Hg, SO2, 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, SO2, HCL 

Section 3.2.6 

NESHAP: Reciprocating Internal Combustion Enginesb 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 LaFarge (cement manufacturing) 

(http://www.epa.gov/compliance/resources/cases/civil/caa/lafarge-infosht.html) and St. Gobain (glass 

manufacturing) facility, (http://www.epa.gov/compliance/resources/cases/civil/caa/saintgobaininfosht0110.html#pollutants), 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 

(tons) 

NOX 

(tons) 

PM10 

(tons) 

PM2_5 

(tons) 

SO2 

(tons) 

VOC 

(tons) 

110 13,214 269 210 16,270 6 
10

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. 

Additional projections used in the proposed Toxics Rule modeling for 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, SO2, 

NOX, CO 

Section 3.2.7 

Use 2008 Oklahoma and Texas Oil and Gas, and apply RICE controls to these emissions VOC, SO2, 

NOX, CO, PM 

Section 3.2.7 

New York ozone SIP controls VOC 15 

APPROACHES/REFERENCES- Stationary Sources: 

1. For Alcoa consent decree, used http:// cfpub.epa.gov/compliance/cases/index.cfm; 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: http://www.envinfo.com/caain/June04updates/tiop_fr2.pdf

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 Section 3.2.1. 

9. Expected benefits of woodstoves change-out program: http://www.epa.gov/woodstoves/index.html. See Section 

3.2.2. 

10. VOC emission ratios of year 2016 (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 rulemakingdocket # 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): http://www.dec.ny.gov/docs/air_pdf/NYMASIP7final.pdf. See 

Section 3.2.6. 

15. The 2008 data used came from Illinois’ submittal of 2008 emissions to the NEI. 

 

Onroad mobile and nonroad mobile controls 

(list includes all key mobile control strategies but is not exhaustive)a

National Onroad Rules: all 
11

Tier 2 Rule 

2007 Onroad Heavy-Duty Rule 

Final Mobile Source Air Toxics Rule (MSAT2) 

Renewable Fuel Standard

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: 

Itinerant (ITN) operations at airports to year 2020 all 6 

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, February 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. http://www.epa.gov/airmarkt/progsregs/epa-ipm/index.html

2. Only for states submitting these inputs: http://www.epa.gov/otaq/lev-nlev.htm

3. http://www.epa.gov/nonroad-diesel/2004fr.htm

4. http://www.epa.gov/cleanschoolbus/

5. http://www.epa.gov/otaq/marinesi.htm

6. Federal Aviation Administration (FAA) Terminal Area Forecast (TAF) System, December 2007: 

http://www.apo.data.faa.gov/main/taf.asp

7. http://www.epa.gov/nonroad-diesel/2004fr.htm

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) (http://www.epa.gov/airmarkt/progsregs/epa-ipm/index.html). 

The IPM is a multiregional, dynamic, deterministic linear programming model of the U.S. electric power

sector. Version 4.10 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, 
12

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, and 

so we used the IPM results for 2015 to represent 2016. As explained in the IPM TSD, additional steps were 

taken to ensure that the results were valid for use in a 2016 model run. 

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 100 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 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 a the following criteria that does as good a job as possible at preventing 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 

NEI_UNIQUE_ID 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 

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. 

 

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. 
13

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 and18 

tons of VOC. Impacts at the state-level are shown in Table 3-2. 

Table 3-2. Adjustment to IPM emissions due to application of Boiler MACT.

 CO (2016 tons) HCL (2016 tons) PM2_5 (2016 tons) SO2 (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 
14

CO (2016 tons) HCL (2016 tons) PM2_5 (2016 tons) SO2 (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.

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 

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

 

3.2 Stationary Source Projections: non

afdust) 

To project U.S. stationary sources other 

categories within the ptnonipm, nonpt, ag and afdust platform sectors.

the data and projection methods used for these sectors. 

emissions growth does not track with economic growth 

growth” assumption is based on an examination of historical emissions and

working toward improving the projection

growth assumption for the 2005, v4.1 

in Appendix D of the Regulatory Impact Assessment f

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 distribu

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 

website (see the end of Section 1). 

3.2.1 Livestock emissions growth (ag, afdust)

Growth in ammonia (NH3) and dust (PM

ptnonipm sectors was based on projections of gro

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 NH

15

pproach to Compute Monthly Emissions from the IPM data

ionary Source Projections: non-EGU sectors (ptnonipm, nonpt, ag, 

ionary sources other than ptipm, we applied growth factors and/or

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 

does not track with economic growth for many stationary nontion is based on an examination of historical emissions and economic data. 

the projection approach in future emissions platforms, we are still using 

e 2005, v4.1 platform. More details on the rationale for

in Appendix D of the Regulatory Impact Assessment for the PM NAAQS rule (EPA, 2006

specific projection factors for year 2016 were used for creating the 2016 base case unless noted 

actors (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 

Livestock emissions growth (ag, afdust)

) 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 (pri

to Compute Monthly Emissions from the IPM data

EGU sectors (ptnonipm, nonpt, ag, 

growth factors and/or controls to certain 

subsection provides details on 

year emissions, we assumed that 

-IPM sources. This “noeconomic data. While we are 

platforms, we are still using the noon the rationale for this approach can be found 

or the PM NAAQS rule (EPA, 2006). 

specific projection factors for year 2016 were used for creating the 2016 base case unless noted 

actors (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 

tion 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 

) emissions from livestock in the ag and afdust and

wth in animal population. While there are some livestock 

emissions in ptnonipm, (very small compared to ag sector) the control packet was inadvertently not applied 

620 tons in 2016 (primarily in Kansas 
16

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. 

Appendix E in the 2002v3 platform documentation provides the animal population data and regression 

curves used to derive the growth factors: 

http://www.epa.gov/scram001/reports/Emissions%20TSD%20Vol2_Appendices_01-15-08.pdf. Appendix F 

in the same document provides the cross references of livestock sources in the ag, afdust and ptnonipm

sectors to the animal categories in Table 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 lowemitting wood stoves and the corresponding decrease in other types of wood stoves. As newer, cleaner 

woodstoves replace older, higher-polluting wood stoves, there will be an overall reduction of the emissions 

from these sources. The approach cited here was developed as part of a modeling exercise to estimate the 

expected benefits of the woodstoves change-out program (http://www.epa.gov/woodstoves/index.html). 
17

Details of this approach can be found in Section 2.3.3 of the PM NAAQS Regulatory Impact Analysis (EPA, 

2006). 

The specific assumptions we made were: 

 Fireplaces, 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. 

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

2104008010 Woodstoves: General 0.7200 

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) 
18

 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: 

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 VOC_RFL is the VOC refueling emissions for onroad sources from NMIM 

BENZENE_RFL is the BENZENE refueling emissions for onroad sources from NMIM 

Etc. 

For this 2016 projection, we obtained VOC_RFL, BENZENE_RFL and NAPHTHALENE_RFL 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 MSAT 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: Permeation 

• 2501011012 Residential Portable Fuel Containers: Evaporation 
19

• 2501011013 Residential Portable Fuel Containers: Spillage During Transport 

• 2501011014 Residential Portable Fuel Containers: Refilling at the Pump: Vapor Displacement 

• 2501011015 Residential Portable Fuel Containers: Refilling at the Pump: Spillage 

• 2501012011 Commercial Portable Fuel Containers: Permeation 

• 2501012012 Commercial Portable Fuel Containers: Evaporation 

• 2501012013 Commercial Portable Fuel Containers: Spillage During Transport 

• 2501012014 Commercial Portable Fuel Containers: Refilling at the Pump: Vapor Displacement 

• 2501012015 Commercial Portable Fuel Containers: Refilling at the Pump: Spillage 

Additional information on the PFC inventories is available in Section 2.2.3 of the documentation for the 

2002 Platform (http://www.epa.gov/ttn/chief/emch/index.html#2002). 

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 

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 Area Forecast (TAF) System: http://www.apo.data.faa.gov/main/taf.asp

(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 TAF are documented in: 

http://www.faa.gov/data_research/aviation/aerospace_forecasts/2009-

2025/media/2009%20Forecast%20Doc.pdf

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. 
20

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

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-
21

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. 

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) 

PM10 

(tons) 

PM2.5 

(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 SO2 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. 
22

Table 3-7. Quantification of Missing Closures in 2016 the nonEGU (ptnonipm) sector

State Pollutant

nonEGU emissions 

without closures 

(tons) 

nonEGU 

emissions with 

closures (tons)

Excess 

emissions 

(tons) 

Percent Overestimate 

in Total man-made 

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 SO2 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 SO2 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 

Environmental Conservation February 2008 guidance. These reductions are found in Appendix J in: 

http://www.dec.ny.gov/docs/air_pdf/NYMASIP7final.pdf. See 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 National Emissions Standards for Hazardous Air 

Pollutants (NESHAPs) and the New Source Performance Standards (NSPS) for the Portland cement 

manufacturing industry. 
23

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 nonEGU (ptnonipm) sector

Pollutant

Cement Industry 

emissions in 2005 

Decrease in cement 

industry emissions 

in 2016 vs 2005 

% decrease in 

ptnonipm from 

cement reduction

NOX 193,000 56,740 2.4%

PM2.5 14,400 7,840 1.8%

SO2 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 NEI NEI_UNIQUE_ID 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 

 

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. 
24

the Boiler MACT ICR database to the facilities in the NEI and NATA inventories (details are provided in the 

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. The approach captured 1,100 facilities boilers in the 2005 

inventory out of about 1,500 facilities in the ICR. The unmatched units from the ICR database represented 

only [X] percent of the ICR SO2 emissions. 

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 (NEI_UNIQUE_ID) 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 (SCC) 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. 
25

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

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 
26

reductions from these rules. Any inconsistencies with emissions reductions cited by the Boiler MACT Final 

Rules is the result of different base emissions assumptions for these pollutants. 

Table 3-11. Summary of Boiler MACT reductions applied 

to the ptnonipm sector.

Pollutant 

Modeled Boiler MACT 

reductions (tons) 

Mercury 1.75

SO2 391,000

PM2.5 17,000

VOC 3,000

CO 72,000

HCL 5,600

As discussed above, we examined the control data collected from the Boiler MACT ICR. 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 

State Pollutant

Pre-controlled 

Emissions 

(tons) 

Controlled 

Emissions 

(tons) 

Reductions 

(tons) 

Percent 

Reduction 

% 

Michigan NOX 907 544 363 40 

North Carolina SO2 652 65 587 90 

Virginia SO2 3379 338 3041 90 

Washington SO2 639 383 256 40 

North Carolina HCL 31 3 28 90 

3.2.6.3 Summary of Mercury Reductions at nonEGU 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 reduction

3

) In addition, the projections included reduction

of Hg emissions 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. 
27

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 24.8*

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 60

*The EGU emissions are based on a revised case, not the AQ model case which for 2016 was 28.7 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 Chlor-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 was used for 2016. 
28

Table 3-14. 2005, 2008 and estimated 2016 emissions for nonpoint Oklahoma and Texas oil and gas sources 

Oklahoma Texas 

2005 

(tons) 

2008 

(tons) 

2016*

(tons) 

2005 

(tons) 

2008 

(tons) 

2016*

(tons) 

2016 TCEQ** 

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

SO2 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 in: 

http://www.tceq.state.tx.us/assets/public/implementation/air/am/contracts/reports/ei/5820783985FY0901-

20090715-ergi-Drilling_Rig_EI.pdf

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) SO2 (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. 
29

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. 
30

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 EMFAC-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-adjust (on_noadj) 

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 AEO2009. 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 (on_moves_startpm and on_moves_runpm) 

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 

AEO2009. 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 MOVESbased 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. 
31

Figure 3-2. MOVES exhaust temperature adjustment functions for 2005and 2015

Errors in 2016 PM emissions for on_no_adj, on_moves_startpm, and on_moves_runpm 

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 overestimate 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 statelevel 2016 

PM2.5* 

All-sector 

PM2.5

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% 
32

State 

2016 PM2.5 

onroad 

corrected 

2016 PM2.5

onroad 

modeled 

Error in 

onroad 

PM2.5

Total statelevel 2016 

PM2.5* 

All-sector 

PM2.5

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 
33

projected for 2016. This approach uses the NONROAD2005 model, version nr05c. Since we did not have a

2016 NMIM data but did have 2015 NMIM data, we applied national level SCC- and pollutant/mode-based4

factors to scale 2015 to 2016 values. These factors were based on NONROAD2008a (v08a-out.mdb 

database) annual national level emissions, and were applied to the monthly 2015 NMIM data by SCC, 

pollutant, and emissions mode. 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”: (http://www.epa.gov/nonroaddiesel/2004fr.htm ), 

published June 29, 2004, and, 

• Control of Emissions From Nonroad Large Spark-Ignition Engines, and Recreational Engines 

(Marine and Land-Based), November 8, 2002 (“Pentathalon Rule”). 

• OTAQ’s Locomotive Marine Rule: (http://www.epa.gov/otaq/regs/nonroad/420f08004.htm) 

• OTAQ’s Small Engine Spark Ignition (“Bond”) Rule: (http://www.epa.gov/otaq/equip-ld.htm) 

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 NONROAD model version NONROAD2008a (see 

http://www.epa.gov/otaq/nonrdmdl.htm#model) 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. 
34

Table 3-17. Factors applied to year 2005 emissions to project locomotives and Class 1 and Class 2 

Commercial Marine Vessel Emissions 

SCC 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 PM10 0.6778

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions PM2.5 0.6896

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions SO2 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 PM10 0.6205

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations PM2.5 0.6287

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations SO2 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 PM10 0.2857

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations PM2.5 0.2882

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations SO2 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) PM10 0.5006

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) PM2.5 0.5028

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) SO2 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 PM10 0.4937

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines PM2.5 0.4937

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines SO2 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 PM10 0.9072

2285002010 Railroad Equipment;Diesel;Yard Locomotives PM2.5 0.9262

2285002010 Railroad Equipment;Diesel;Yard Locomotives SO2 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 rule (EPA, 2007c). This rule lowered diesel sulfur 

content and tightened emission standards for existing and new locomotives and marine diesel emissions to 

lower future year PM, SO2, and NOX, and is documented at: 

http://www.epa.gov/otaq/regs/nonroad/420f08004.htm. Voluntary retrofits under the National Clean Diesel 

Campaign (http://www.epa.gov/otaq/diesel/index.htm) are not included in our projections. 
35

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: http://www.epa.gov/oms/regs/nonroad/marine/ci/420f09015.htm

The resulting coordinated strategy, including emission standards under the Clean Air Act for new marine 

diesel engines with per-cylinder displacement at or above 30 liters, and the establishment of Emission

Control Areas is at: http://www.epa.gov/oms/oceanvessels.htm

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 ECA proposal technical support document: 

http://www.epa.gov/oms/regs/nonroad/marine/ci/420r09007-chap2.pdf. These regions extend up to 200 

nautical miles offshore, though less at international boundaries. North and South Pacific regions are divided 

by the Oregon-Washington border, and East Coast and Gulf Coast regions are divided east-west by roughly 

the upper Florida Keys just southwest of Miami. 

The factors to compute HAP emission are based on emissions ratios discussed in the 2005v4 documentation. 

As with the 2005 base case, this sector uses CAP-HAP VOC integration. 
36

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 SO2 

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 E10 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. Tier 2 Fraction of Light Duty VehiclesFigure 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. 
37

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 

• E0 and E10 combinations are based on AEO2007 projections of E0 and E10 fuel 

• 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

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. 

0.0

0.2

0.4

0.6

0.8

1.0

1.2

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

Fraction of Light Duty Vehicles

Calendar Year

Exhaust THC

Vehicle Miles Traveled

Population
38

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 Appendix G. 

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. 
39

Table 5-1. 2016 Emissions – 2016 Base Case Compared to 2005 Base Case: VOC, NOX, CO, SO2, NH3, and 

Sector 

[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 SO2 2005 SO2 2016 NH3 2005 NH3 

afdust ag 3,446,632 3,251,990 alm_no_c3 49,665 67,690 1,353,680 1,924,925 296,121 270,007 9,087 154,016 940 773 seca_c3 69,080 44,990 815,249 647,884 82,929 54,049 23,659 420,110 seca_c3 non-US 126,898 18,367 1,603,944 532,181 149,443 43,267 957,065 321,414 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 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 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 on_moves_runpm on_moves_startpm 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 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 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 2016cr2 non-fires 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 
40

Table 5-2. 2016 Emissions – Base Case Compared to 2005 Base Case: mercury (species and total), HCL an

Sector

[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 

afdust ag alm_no_c3

0.0411 0.0411 0.0793 0.0793 0.0212 0.0212 0.1416 0.1416 

seca_c3 0.0002 0.0002 0.0004 0.0004 0.0001 0.0001 0.0007 0.0007 seca_c3 non-US 0.0001 0.00011 0.0002 0.00018 0.0001 0.00007 0.0004 0.00036 nonpt 

1.0605 1.0605 3.1034 3.1034 0.6524 0.6524 4.8163 4.8163 29,001 

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_runpmon_moves_startpm ptipm 6.8757 21.0960 21.1598 30.1986 0.6683 1.6136 28.7038 52.9082 74,089 ptnonipm 7.9112 10.4687 16.8535 29.5686 4.4183 6.1291 29.183 46.1664 37,549 avefire2016cr2 non-fires Total 

 16.13 32.91 41.91 63.66 5.87 8.53 63.92 105.11 140,639.00 
41

Table 5-3. Speciated Mercury and total Mercury by State and Sector for 2005 and 2016 

ptipm ptnonipm nonpt onroad alm seca_c3 nonroad all US sectors

[tons/yr] [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 2005 2016

Alabama HGIIGAS 1.160 0.288 0.233 0.140 4.9E-04 4.9E-04 5.9E-04 4.5E-07 3.2E-05 1.394 0.429

Alabama HGNRVA 1.419 0.948 1.105 0.453 0.029 0.029 6.2E-03 7.5E-07 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 3.0E-07 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 1.5E-06 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 2.1E-05 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 3.5E-05 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 1.4E-05 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 7.1E-05 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 2.5E-07 2.0E-05 0.176 0.140

Connecticut HGNRVA 0.077 0.003 0.049 0.049 0.081 0.081 3.6E-03 4.2E-07 1.6E-04 0.211 0.136

Connecticut PHGI 0.004 0.000 0.036 0.036 0.024 0.024 1.6E-05 1.7E-07 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 8.4E-07 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 1.1E-06 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 1.8E-06 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 7.1E-07 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 3.6E-06 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 2.4E-10 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 4.0E-10 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 1.6E-10 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 8.0E-10 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 2.5E-05 1.4E-04 0.904 0.441

Florida HGNRVA 0.550 0.260 0.639 0.222 0.081 0.081 0.022 4.2E-05 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 1.7E-05 3.0E-05 0.265 0.199

Florida Total HG 1.173 0.486 1.170 0.621 0.093 0.093 0.024 8.4E-05 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 1.4E-06 5.7E-05 1.064 0.296

Georgia HGNRVA 0.666 0.262 0.542 0.122 0.063 0.063 0.012 2.4E-06 4.0E-04 1.282 0.459
42

ptipm ptnonipm nonpt onroad alm seca_c3 nonroad all US sectors

[tons/yr] [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 2005 2016

Georgia PHGI 0.074 0.009 0.051 0.031 8.0E-04 8.0E-04 6.3E-05 9.5E-07 1.3E-05 0.126 0.041

Georgia Total HG 1.704 0.512 0.690 0.206 0.065 0.065 0.013 4.8E-06 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 2.2E-08 9.1E-05 1.763 0.475

Illinois HGNRVA 2.819 0.361 1.227 1.007 0.087 0.087 0.012 3.7E-08 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 1.5E-08 2.6E-05 0.309 0.219

Illinois Total HG 4.242 0.488 1.853 1.553 0.108 0.108 0.013 7.3E-08 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 1.0E-08 5.0E-05 1.872 0.970

Indiana HGNRVA 1.670 1.130 1.410 1.146 0.045 0.045 7.7E-03 1.7E-08 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 6.7E-09 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 3.4E-08 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 2.0E-05 3.3E-05 0.320 0.349

Louisiana HGNRVA 0.459 0.676 1.145 0.183 0.021 0.021 4.8E-03 3.4E-05 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 1.3E-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 6.7E-05 2.9E-04 2.025 1.398

Maine HGIIGAS 0.001 0.009 0.040 0.032 0.029 0.029 1.5E-04 8.8E-07 1.5E-05 0.071 0.070

Maine HGNRVA 0.002 0.003 0.064 0.039 0.076 0.076 1.6E-03 1.5E-06 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 5.9E-07 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 2.9E-06 1.5E-04 0.255 0.226

Maryland HGIIGAS 0.535 0.031 0.242 0.198 0.032 0.032 5.9E-04 3.6E-06 3.3E-05 0.809 0.262

Maryland HGNRVA 0.301 0.077 0.328 0.152 0.076 0.076 6.2E-03 6.0E-06 2.5E-04 0.712 0.311

Maryland PHGI 0.053 0.006 0.111 0.076 0.021 0.021 3.0E-05 2.4E-06 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 1.2E-05 2.9E-04 1.707 0.677

Massachusetts HGIIGAS 0.111 0.003 0.091 0.090 0.068 0.068 6.0E-04 6.8E-06 3.3E-05 0.270 0.162

Massachusetts HGNRVA 0.055 0.006 0.111 0.110 0.204 0.204 6.3E-03 1.1E-05 2.6E-04 0.377 0.326

Massachusetts PHGI 0.016 0.000 0.035 0.034 0.041 0.041 3.0E-05 4.5E-06 6.1E-06 0.092 0.076
43

ptipm ptnonipm nonpt onroad alm seca_c3 nonroad all US sectors

[tons/yr] [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 2005 2016

Massachusetts Total HG 0.182 0.009 0.237 0.234 0.313 0.313 7.0E-03 2.3E-05 3.0E-04 0.739 0.564

Michigan HGIIGAS 0.968 0.340 0.216 0.145 0.011 0.011 1.1E-03 2.2E-06 9.4E-05 1.197 0.498

Michigan HGNRVA 0.781 1.155 0.716 0.400 0.070 0.070 1.1E-02 3.7E-06 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 1.5E-06 1.7E-05 0.237 0.107

Michigan Total HG 1.826 1.501 1.086 0.639 0.088 0.088 0.012 7.4E-06 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 6.7E-08 6.5E-05 0.920 0.836

Minnesota HGNRVA 0.603 0.124 0.826 0.760 0.029 0.029 6.0E-03 1.1E-07 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 4.4E-08 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 2.2E-07 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 2.6E-07 2.1E-05 0.215 0.209

Mississippi HGNRVA 0.156 0.264 0.190 0.156 0.015 0.015 4.4E-03 4.4E-07 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 1.8E-07 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 8.8E-07 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 5.2E-09 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 8.7E-09 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 3.5E-09 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 1.7E-08 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 4.6E-06 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 7.7E-06 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 3.1E-06 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 1.5E-05 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 alm seca_c3 nonroad all US sectors

[tons/yr] [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 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 2.8E-06 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 4.7E-06 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 1.9E-06 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 9.3E-06 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 1.1E-05 5.9E-05 1.270 0.252

North 

Carolina HGNRVA 0.494 0.357 0.413 0.349 0.067 0.067 0.010 1.9E-05 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 7.5E-06 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 3.8E-05 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 5.2E-07 8.0E-05 2.032 0.678

Ohio HGNRVA 1.841 1.074 1.513 0.859 0.090 0.090 0.012 8.7E-07 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 3.5E-07 1.9E-05 0.357 0.224

Ohio Total HG 3.662 1.576 2.059 1.238 0.110 0.110 0.013 1.7E-06 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 3.0E-06 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 5.1E-06 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 2.0E-06 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 1.0E-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-07 6.4E-05 3.556 1.038

Pennsylvania HGNRVA 1.829 1.071 1.654 1.108 0.159 0.159 0.012 1.1E-06 4.8E-04 3.655 2.351

Pennsylvania PHGI 0.294 0.083 0.395 0.300 0.041 0.041 5.8E-05 4.3E-07 1.3E-05 0.730 0.425

Pennsylvania Total HG 4.979 1.613 2.684 1.923 0.264 0.264 0.013 2.1E-06 5.6E-04 7.940 3.814

Rhode Island HGIIGAS 0.014 0.014 8.8E-03 8.8E-03 9.0E-05 5.9E-07 5.1E-06 0.023 0.023

Rhode Island HGNRVA 0.023 0.023 0.019 0.019 9.6E-04 9.8E-07 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 3.9E-07 9.0E-07 0.015 0.015

Rhode Island Total HG 0.047 0.047 0.033 0.033 1.1E-03 2.0E-06 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 4.4E-06 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 7.3E-06 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 2.9E-06 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 1.5E-05 2.6E-04 1.819 1.205
45

ptipm ptnonipm nonpt onroad alm seca_c3 nonroad all US sectors

[tons/yr] [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 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 9.2E-06 1.4E-04 2.329 1.414

Texas HGNRVA 3.584 2.501 3.269 2.592 0.071 0.071 0.024 1.5E-05 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 6.1E-06 3.5E-05 0.669 0.511

Texas Total HG 5.196 3.367 4.650 3.644 0.073 0.073 0.027 3.1E-05 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 3.2E-06 4.6E-05 0.917 0.364

Virginia HGNRVA 0.181 0.146 0.938 0.421 0.069 0.069 9.0E-03 5.4E-06 3.3E-04 1.197 0.646

Virginia PHGI 0.042 0.029 0.309 0.132 0.012 0.012 4.3E-05 2.2E-06 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 1.1E-05 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 7.2E-06 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 1.2E-05 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 4.8E-06 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 2.4E-05 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 4.9E-09 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 8.1E-09 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 3.2E-09 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 1.6E-08 8.7E-05 2.880 1.196

Wisconsin HGIIGAS 0.355 0.169 0.277 0.259 0.011 0.011 6.2E-04 1.6E-07 6.1E-05 0.644 0.439

Wisconsin HGNRVA 0.784 0.692 0.438 0.396 0.053 0.053 6.5E-03 2.7E-07 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 1.1E-07 1.2E-05 0.187 0.177
46

ptipm ptnonipm nonpt onroad alm seca_c3 nonroad all US sectors

[tons/yr] [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 2005 2016

Wisconsin Total HG 1.147 0.870 0.887 0.815 0.072 0.072 7.1E-03 5.4E-07 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 1.3E-04 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 2.2E-04 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 8.8E-05 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 4.4E-04 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 

 SO2 

 2016 

Policy SO2 

 2016 

Policy 

SO2 

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 

SO2 

 2016 

Policy SO2 

 2016 

Policy 

SO2 

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 NonEGU Nonpoint Nonroad 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 

Delaware 1,717 6,299 5,857 715 79 6 14,673 

District of Columbia 0 686 1,559 3 36 0 2,284 
51

State EGU NonEGU Nonpoint Nonroad Onroad Fires Total 

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 NonEGU Nonpoint Nonroad Onroad Fires 

Area 

Fugitive 

Dust Total 

Alabama 14,801 17,064 22,982 2,576 1,631 13,938 11,591 84,583 

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 
52

State EGU NonEGU Nonpoint Nonroad Onroad Fires 

Area 

Fugitive 

Dust Total 

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 

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. 
53

6 References

Arunachalam S., 2009: Peer Review of Source Apportionment Tools in CAMx and CMAQ, EPD-07-102. University of North Carolina, Institute for the Environment, August 2009. 

Environ, 2009: Comprehensive Air Quality Model with Extensions Version 5 User’s Guide. 

Environ International Corporation. Novato, CA. March 2009. 

EPA, 2005. Clean Air Interstate Rule Emissions Inventory Technical Support Document, U.S. 

Environmental Protection Agency, Office of Air Quality Planning and Standards, March 

005. Available at http://www.epa.gov/cair/pdfs/finaltech01.pdf. 

EPA, 2006. Regulatory Impact Analyses, 2006 National Ambient Air Quality Standards for 

Particle Pollution. U.S. Environmental Protection Agency, Office of Air Quality Planning 

and Standards, October, 2006. Docket # EPA-HQ-OAR-2001-0017, # EPAHQ-OAR-2006-

0834. Available at http://www.epa.gov/ttn/ecas/ria.html. 

EPA, 2007a. Guidance for Estimating VOC and NOx Emission Changes from MACT Rules, 

U.S. Environmental Protection Agency Office of Air Quality Planning and Standards, Air 

Quality Policy Division, Research Triangle Park, NC 27711, EPA-457/B-07-001, May 2007. 

Available at 

http://www.epa.gov/ttn/naaqs/ozone/o3imp8hr/documents/guidance/200705_epa457_b- 07-

001_emission_changes_mact_rules.pdf. 

EPA. 2007b. National Scale Modeling for the Final Mobile Source Air Toxics Rule, Office of 

Air Quality Planning and Standards, Emissions Analysis and Monitoring Division, Research 

Triangle Park, NC 27711, EPA 454/R-07-002, February 2007. Available at 

http://www.epa.gov/otaq/regs/toxics/454r07002.pdf

EPA, 2007c. Regulatory Impact Analysis for Final Rule: Control of Hazardous Air Pollutants 

from Mobile Sources, U.S. Environmental Protection Agency, Office of Transportation and 

Air Quality, Assessment and Standards Division, Ann Arbor, MI 48105, EPA420-R-07-002, 

February 2007. Available at http://www.epa.gov/otaq/regs/toxics/420r07002.pdf. 

EPA, 2009. Regulatory Impact Analysis: Control of Emissions of Air Pollution from 

Locomotive Engines and Marine Compression Ignition Engines Less than 30 Liters Per 

Cylinder. U.S. Environmental Protection Agency Office of Transportation and Air Quality, 

Assessment and Standards Division, Ann Arbor, MI 48105, EPA420-R-08-001a, May 2009. 

Available at: http://www.epa.gov/otaq/regs/nonroad/420r08001a.pdf 

EPA, 2010a RFS2 Emissions Inventory for Air Quality Modeling Technical Support Document, 

February, 2010. Available at: http://www.epa.gov/otaq/renewablefuels/420r10005.pdf. 

EPA, 2010b. Technical Support Document: The Industrial Sectors Integrated Solutions (ISIS) 

Model and the Analysis for the National Emission Standards for Hazardous Air Pollutants and 
54

New Source Performance Standards for the Portland Cement Manufacturing Industry, U.S. 

Environmental Protection Agency, Sectors Policies and Program Division and Air Pollution 

Prevention and Control Division, Research Triangle Park, NC 27711, August 2010.