## 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 configu ration ......................................... 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 (pt nonipm, 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, ptnoni pm) ............................................... ...................... 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 ...................

................................................... ................................................. 2 3

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-Californ ia WRAP states (nonpt) ............................ ........ 27

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



### 3.3 Mobile source projections .........................

................................................... ................... 29

3.3.1 Onroad mobile (on_noadj, on_moves_runpm, on_moves_s tartpm) ........................................... .......... 29

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

................................................... ...................................... 32

3.3.3 Locomotives and Class 1 & 2 commercial marine vesse ls (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 C ase ............................................... 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

CAM X 

## 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 f unctions 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 assumptio ns for creating 2016 base case emissions

inventories from the 2005 base case ...............

................................................... ........ 8 Table 3-2. Adjustment to IPM emissions due to appl ication 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 200 5 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 2 016 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 boile rs 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 appl ied to the ptnonipm sector. .................. 26

Table 3-12. State-level non-MACT Boiler Reduction s from ICR Data Gathering .................... 26 Table 3-13. Anthropogenic mercury emissions and pr ojections in the Continental United

States ............................................

................................................... ......................... 27

## v

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

gas sources .......................................

................................................... ..................... 28 Table 3-15. WRAP Oil and Gas Emissions: 2005, 201 8 WRAP, and 2016 with additional

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

................................................... . 28 Table 3-16. Summary of the impact of PM 2.5 

## 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 polic y case ptipm sector emissions prior

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

................................................... ................ 38 Table 5-1. 2016 Emissions – 2016 Base Case Compare d to 2005 Base Case: VOC, NO X 

## , CO,

SO 2 

## , NH 3 

## , 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: NO X 

## , SO 2 

## 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 SO 2 

## Emissions (tons/year) for Lower 48 States by Secto r .............. 50 Table 5-8. 2016 Base Case PM2.5 Emissions (tons/ye ar) for Lower 48 States by Sector .......... 51



# List of Appendices

APPENDIX A: Inventory Data Files Used for Each TR Modeling Ca se – SMOKE Input

Inventory Datasets

APPENDIX B: List of OECA Consent Decrees- Whereby Reductions W ere Apportioned to

Facilities in a Particular Corporation

APPENDIX C: Gold Mine Facility-Specific Mercury Reductions Due to NESHAP a



## APPENDIX D: Mercury Emission Reductions, 2005-2016 for the No nEGU categories of :

Electric Arc Furnaces, Mercury Cell Chlor-Alkali Pl ants, Hazardous Waste Combustion, and Pulp and Paper.

APPENDIX E Ptnonipm (Non EGU) Plant Closures Included in the 2 016 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 M ercury Emissions

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

Processing

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

for 2016 Base and Policy Cases –for the IPM sector

## 1 

# 1 Introduction

This document provides the details of emissions dat a 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 Stand ard (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 mo deling effort consists of three emissions cases: 20 05 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 a cid or hydrogen chloride (HCL) and benzene, acetaldehyd e, formaldehyde and methanol. The latter four are

also denoted BAFM.

Table 1-1 provides more information on these emissi ons cases. The 2016 base case modeling was used to characterize future-year air quality without implem entation of the rule. Also included in Table 1-1 a re mercury (Hg) zero-out runs that were based on the 2 016 base case and 2005 base case to quantify the contributions of mercury emissions from Electric Ge nerating Units (EGUs). This document provides no further discussion on the two simple Hg EGU zero-ou t runs, which are described in the proposed Toxics Rule Technical Support Document: “ National-scale Mercury Risk Assessment Supporting t he Appropriate and Necessary Finding for Coal- and Oil-fired Elect ric Generating Units ”. The modeling outputs for the 2016 base and control cases were then used to quant ify the benefits of the proposed Toxics Rule.

Table 1-1. List of cases run in support of the air quality mod eling for the Proposed Toxics Rule

Case Name Internal EPA

Abbreviation Description 2005 base case 2005cr_hg 2005 case created using av erage-year fires data and an average-year temporal allocation approach for EGUs, to use for computing relative response factors with 2016 t he scenario 2005 Hg EGU

zero-out 2005cr_hg_ptipm_hgzero Same as 2005 base case but zero Hg emissions for EG U (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 EG U (ptipm) sector

2016 control

case 2016cr2_hg_control1 2016 EGU “control” scenario rep resenting a MACT control

strategy

The data used in the 2005 emissions cases are the s ame as those described in the 2005-based, Version 4 .1 platform (hereafter the v4.1 platform) document ava ilable 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-s cale Air Quality (CMAQ) model. CMAQ simulates the numer ous 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 dai ly and annual PM 2.5 

## concentrations, 8-hr maximum ozone, annual total mercury deposition levels and v isibility impairment. Model predictions of PM 2.5 

## and

ozone are used in a relative sense to estimate scen ario-specific, future-year design values of PM 2.5 

## and ozone, which are combined with monitoring data to estimate population-level exposures to changes in ambient concentrations for use in estimating health and wel fare effects.

We used a 2005 base case approach for the year 2005 emissions scenario. The base case approach uses a n average-year fire emissions inventory and average-y ear EGU temporal profiles, which were based on 3 ye ars

## 2 of hourly Continuous Emissions Monitoring (CEM) dat a 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 f ires, 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 m odeling, which avoids potentially spurious year-spe cific artifacts in air quality modeling estimates. In ad dition, the 2005 Version 4.1 (v4.1) platform biogen ic emissions data is the same as the 2005v4 platform a nd 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_emissio ns_tsd_07jul2010.pdf . The only significant data changes between the year 2005 cases in Table 1-1 an d future-year cases are emission inventories and speciation approaches.

The future-year inventories, ancillary files, and d etailed 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 wit h online access through the docket, and so a more

convenient access location is the EPA Emissions Mod eling 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 des cription 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 ca se inventory, including ancillary data and issues rela ted 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 emissi ons summaries and Section 6 contains the technical

references for this document.



# 2 Development of Base Case 2005 Emission Inventorie s

As mentioned previously, the 2005 emissions modelin g 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 y ear cases as well as Toxics rule-specific issues re lated to processing emissions for CMAQ.



# 2.1 Base case 2005 overview

Table 2-1 lists the platform sectors used for the 2005 base c ase and future-year base and control cases. It

also indicates the associated sectors from the Nati onal Emissions Inventory (NEI). Subsequent section s refer to these platform sectors for identifying the emiss ions differences between the 2005 base case and the 2016 cases. The inputs to the air quality model; includ ing emissions, meteorology, initial conditions, bou ndary 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’. T he v4.1 platform contains the same modeling sectors as the 2005 v4 platform; though for some sectors, the emissions data were revised. For additional inform ation 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 P latform 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 o ther 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 20 05 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 NE EDS version 4.10. The NATA inventory is an update to t he 2005 NEI v2

and was divided into EGU and non-EGU sectors consis tent with the other pollutants. (We did not actually map the NAT A inventory to IPM, but rather applied the mapping that was done t o the 2005 NEIv2 to the NATA Hg inventory). We additionally removed Hg from

sources from the National Emission Standards for Ha zardous Air Pollutants for Industrial, Commercial, and Institut ional Boilers and Process Heaters (aka “Boiler MACT”) Information Col lection Request (ICR) database because we included these emissions in the non-EGU

sector.
For both: Day-specific emissions created for input into SMO KE.

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 airc raft emissions. Additionally updated inventory to remove duplicates , improve estimates from ethanol plants, and reflect new info rmation 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 remova l 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 NH 3 

### emissions from NEI nonpoint livestock and fertilize r application, county and annual resolution. Unchanged from the 2 005v4 platform. Area fugitive dust

sector: afdust Nonpoint PM 10 

### and PM 2.5 

### from fugitive dust sources from the NEI nonpoint inventory (e.g., building construction, road constr uction, paved roads, unpaved roads, agricultural dust), county and annua l resolution. Unchanged from the 2005v4 platform. Remaining nonpoint sector:

nonpt Nonpoint Primarily 2002 NEI nonpoint sources not otherwise i ncluded in other SMOKE sectors, county and annual resolution. Also includes updated Residential Wood Combustion emissions, year2005 nonCalifornia WRAP oil and gas Phase II inventory and year 2005 Texas and Oklahoma oil and gas emissions. Removed Hg emi ssions 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-BondBa se for all states except California. Monthly emissions fo r California created from annual emissions submitted by the California A ir 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 a re included in the Non-EGU sector (as point sources) and category 3 CM V emissions are
contained in the seca_c3 sector

C3 commercial

marine: seca_c3 Mobil e :

Nonroad Annual point source

- formatted

, year 2005 category 3 (C3) CMV

emissions, developed for the rule called “Control o f Emissions from New Marine Compression-Ignition Engines at or Above 30 Liters per Cylinder”, usually described as the Emissions Contr ol Area (ECA) study ( http://www.epa.gov/otaq/oceanvessels.htm ). Utilized final projections from 2002, developed for the C3 ECA pro posal to the International Maritime Organization (EPA-420-F-10-0 41, 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 f or all pollutants, submitted by CARB for the 2005 NEI version 2. 2) Onroad gasoline and diesel vehicle emissions from M OVES2010 not subject to temperature adjustments: exhaust CO , NO X 

### , VOC, NH3, benzene, formaldehyde, acetaldehyde, 1,3-butad iene, acrolein, naphthalene, brake and tirewear PM, exhau st 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 MOVES 2010

- based onroad gasoline emissions

subject to temperature adjustments. Limited to exh aust mode only for PM species and naphthalene. California emissions n ot 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, coun ty

- level MOVES 2010

- based onroad gasoline emissions

subject to temperature adjustments. Limited to exh aust mode only for PM species and Naphthalene. California emissions n ot included. This sector is limited to running mode emissions that co ntain different temperature adjustment curves from cold start exhau st (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 Me xico. Other point

sources not from

the NEI: othpt N/A Point sources from Canada’s 2006 inventory and Mexi co’s Phase III 1999 inventory, annual resolution. Also includes a nnual 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 sou rce emissions.

Other nonpoint

and nonroad not

from the NEI:

othar N/A Annual year 2006 Canada (province resolution) and y ear 1999 Mexico Phase III (municipio resolution) nonpoint and nonro ad mobile

inventories.

Other nonpoint

sources not from N/A Annual year 2000 Canada speciated mercury from nonp oint 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 199 9 Mexico Ph ase III (municipio resolution) onroad mobile inventorie s, annual

resolution.



## As discussed in the 2005 v4.1 platform documentatio n, we processed all emissions data with the Sparse Matrix Operator Kernel Emissions (SMOKE) modeling s ystem, version 2.6. More details about SMOKE including user documentation are available at its w ebsite ( 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 con figuration

In support of the proposed Toxics Rule, EPA modeled the air quality in the East and the West (2 separa te modeling domains), each using a 12-km horizontal gr id resolution. The 12-km modeling domains were “nested” within a modeling domain covering the lowe r 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 Eas tern 12 km domain. A map of the air quality modeli ng 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, wit h a center of X = -97o and Y = 40o. Other specific par ameters for these grids are provided in the Air Qua lity Modeling Technical Support Document (EPA, 2011) for the Toxics Rule. More details on the grid parameters are provided in the 2005v4.1 platform do cumentation.



# 3 Development of 2016 Future-Year Base Case Emissio n Inventories

This section describes the methods we used for deve loping the 2016 future-year base-case emissions. T he ancillary input data are very similar in the future -year scenarios to those in the 2005 base case exce pt for the speciation profiles used for gasoline-related sourc es, which change in the future to account for incre ased ethanol usage in gasoline. The specific speciation profile changes are discussed in Sections 3.2.8 (s tationary 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 be yond those Federal measures already promulgated, or those expected to be promulgated prior to the Toxic s Rule proposal date. For EGU emissions (ptipm sec tor),

the emissions reflect state rules and federal conse nt decrees through December 1, 2010 and incorporate information on existing controls collected through the Information Collection Request (ICR) for the To xics 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-ca se scenarios do reflect projected economic changes and fuel usage for EGU and mobile sectors. For non-EG U point (ptnonipm sector) and nonpoint stationary sou rces (nonpt, ag, and afdust sectors), any local con trol programs that might be necessary for areas to attai n the 1997 PM 2.5 

## 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 re ductions associated with the New York (NY) State Implementation Plan (SIP), which were added as part of a larger effort to start including more local c ontrol

information. This is described further in Section 3.2.6. The following bullets summarize the project ion methods used for sources in the various sectors, wh ile additional details and data sources are given i n Table

3-1:

• IPM sector (ptipm): Unit-specific estimates from I PM, 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 de crees and settlements, and one 1997 ozone NAAQS State Implementation Plan for New York. We a lso used projection approaches for pointsource livestock and aircraft and gasoline stage II emissions that are consistent with projections use d

for the sectors that contain the bulk of these emis sions. Terminal area forecast (TAF) data aggregate d

to the national level were used for aircraft to acc ount 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 contr ol.

• Agricultural sector (ag): Projection factors for l ivestock estimates based on expected changes in animal population from 2005 to 2016 using 2005 Depa rtment of Agriculture data; no growth or

control for NH 3 

## emissions from fertilizer application.

• Area fugitive dust sector (afdust): Projection fac tors for dust categories related to livestock estim ates based on expected changes in animal population; no growth or control for other categories in this

sector.

## 7

• Remaining nonpoint sector (nonpt): Projection fact ors 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 Fue l 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)-est imated VOC refueling estimates for future years. Year-specific speciation was applied to som e portions of this sector (i.e., gasoline-related emissions) and is discussed in Section 3.2.8.

• Nonroad mobile sector (nonroad): Output from the N ONROAD2005 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 t he state of California, except NH 3 

## used 2015 NMIM. Also for California and NMIM emissions, we u tilized 2015 and 2016 national NMIM run totals and adjusted emissions by applying factors b y pollutant, source category code (SCC), and mode (exhaust/evap) to adjust 2015 emissions to yea r 2016. Year-specific speciation was applied to some portions of this sector and is discussed in Se ction 3.3.5. Hg was kept at 2005 levels for all

states.

• Aircraft, locomotive, and non-Class 3 commercial ma rine sector (alm_no_c3): Projection factors for

Class 1 and Class 2 commercial marine and locomotiv es which reflect activity growth and final locomotive-marine controls. Hg was kept at 2005 le vels.

• 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 NO X 

## and SO 2 

## 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 speciatio n uses different future year values to take into account both increase in ethanol, and the existence of Tier 2 vehicles which use a different speciatio n profile. Hg is kept at 2005 levels.

• Onroad PM gasoline running mode sector (on_moves_st artpm): 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 roa d 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 in creased emissions).

• Onroad PM gasoline start mode sector (on_moves_star tpm): Cold start MOVES2010 future-year state-month estimates for PM and naphthalene, appor tioned 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 (f or elemental and organic carbon) based on grid cell hourly temperatures (lower temperatures result in i ncreased emissions).

• Other nonroad/nonpoint (othar): No growth or contr ol.

• Other nonpoint speciated mercury (othar_hg): No gr owth or control.

• Other onroad sector (othon): No growth or control.

• Other nonroad/nonpoint (othar): No growth or contr ol.

• Other point (othpt): No growth or control.

• Other point speciated mercury (othpt_hg): No growt h 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 gro wth assumptions by source type used to create the 2 016

base case emissions from the 2005 base-case invento ries. All Mexico, Canada, and offshore oil emissio ns are unchanged in all future case scenarios from tho se in the 2005 base case. Emission summaries by se ctor for 2005 and future years are provided in Section 4

.

The remainder of this section is organized either b y source sector or by specific emissions category w ithin a

source sector for which a distinct set of data were used or developed for the purpose of projections f or the proposed Toxics Rule. This organization allows con solidation of the discussion of the emissions categ ories that are contained in multiple sectors, because the data and approaches used across the sectors are co nsistent. Sector names associated with the emissions categori es are provided in parentheses. A list of inventor y 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 emis sions inventories

from the 2005 base case

Control Strategies and/or growth assumptions (grouped by affected pollutants or standard and app roach used to apply to the inventory) Pollutants

affected Approach/

reference
Non-EGU Point (ptnonipm sector) projection approach es carried forward

from the Proposed Transport Rule a,b



### MACT rules, national, VOC: national applied by SCC, MACT Boat Manufacturing Wood Building Products Surface Coating Generic MACT II: Spandex Production, Ethylene manuf acture Large Appliances Miscellaneous Organic NESHAP (MON): Alkyd Resins, C helating Agents, Explosives, Phthalate Plasticizers, Polyester Resins, Po lymerized 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 pla nts owned/operated by the Companies VOC, CO, NOx,
PM, SO 2 

### Appendix B

DOJ Settlements: plant SCC controls for: Alcoa, TX Premcor (formerly Motiva), DE All 1

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

### 2 Municipal Waste Combustor Reductions –plant level P M 4

Hazardous Waste Combustion PM 3 Hospital/Medical/Infectious Waste Incinerator Regul ations NO X 

### , PM, SO 2 

### EPA, 2005

## 9

Large Municipal Waste Combustors – growth applied t o specific plants All (including Hg) 4 MACT rules, plant-level, VOC: Auto Plants VOC 5 MACT rules, plant-level, PM & SO 2 

### : Lime Manufacturing PM, SO 2 

### 6 MACT rules, plant-level, PM: Taconite Ore PM 7

a. The implementation of these rules was changed to re flect a 2016 future year, rather than 2012 / 2014

b. We inadvertently did not apply closures that had be en applied for the Transport Rule proposal; emissio ns from these plants sum to 3,300 tons VOC, 178 tons PM 2.5 

### , 1,982 tons SO 2 

### , 1,639 tons NO X 

### , 6 tons NH 3 

### and 379 tons CO. At the state level, the largest impact is in West Virginia for both NO X 

### and SO 2 

### (717 tons NO X 

### , which is slightly under 1% of total anthropogenic NO X 

### emissions and 1,604 tons SO 2 

### which is 0.5% of total SO 2 

### emissions for that state). All other

NO X 

### and SO 2 

### errors are under 500 tons at the state level and o nly a fraction of a percent impact on total emissio ns.


### Nonpoint (nonpt sector) projection approaches carri ed forward from the Proposed Transport Rule

Municipal Waste Landfills: projection factor of 0.2 5 applied All EPA, 2007a Livestock Emissions Growth from year 2002 to year 2 016 NH3, PM 8 Residential Wood Combustion Growth and Changeouts f rom 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 b ased on Industrial Sector Integrated Solutions (ISIS) policy emissions in 201 3. The ISIS results are from the ISISCement model runs for the NESHAP and NSPS analysis of July 28, 2010 and include

closures Hg, NO X 

### , SO2,
PM, HCL 12

NESHAP: Industrial, Institutional, Commercial Boil ers (based on proposed rule reductions 04-15-10) -- finalized 2/2011 Hg, SO 2 

### , HCL,

PM Section 3.2.6

NESHAP: Gold Mine Ore Processing and Production Ar ea Source Category (based on proposed rule 04-15-10) – finalized 12/2010 Hg 13 and Appendix C NESHAP: Mercury Emissions From Mercury Cell Chlor-A lkali Plants-Final Rule (12/19/03) Hg Appendix D

Pulp and Paper Project smelter replacement for Geor gia 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) H g Appendix D

New York ozone SIP controls VOC, NO X 

### , HAP VOC 14

Additional Plant and Unit closures provided by stat e, 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 th e control data gathered from the ICR

from the ICI Boiler NESHAP. NO X 

### , SO 2 

### , HCL

Section 3.2.6

NESHAP: Reciprocating Internal Combustion Engines b 

### NO X 

### , 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) . Agenc y 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, te chnical difficulties with the projections software resulted in these red uctions not being included for the 2016 projections

. The resulting emissions are therefore too high in C A, 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 a nd policy cases and that difference is unaffected b y this omission since it was omitted from both the ba se 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 SO 2 

### reductions are expected to occur to due fuel sulfu r limits but were excluded from the projection. They were expected to reduce SO 2 

### by 27,000 tons, nationwide. This omission is expe cted to have negligible impacts on our analysis since the r eductions were omitted from both the base and polic y

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) NO X 

### , CO, VOC, PM Appendix F

Use Phase II WRAP 2018 Oil and Gas, and apply RICE controls to these emissions VOC, SO 2 

### , NO X 

### , CO Section 3.2.7

Use 2008 Oklahoma and Texas Oil and Gas, and apply RICE controls to these emissions VOC, SO 2 

### , NO X 

### , 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.go v/compliance/cases/index.cfm; for Motiva: used inf ormation sent by State of Delaware

2. Used data provided by EPA, OAQPS, Sector Policies a nd 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 S ection 1.

5. Percent reductions and plants to receive reductions based on recommendations by rule lead engineer, an d are consistent with the reference: EPA, 2007a

6. Percent reductions recommended are determined from the existing plant estimated baselines and estimate d reductions as shown in the Federal Register Notice for the rul e. SO 2 

### percent reduction are computed by 6,147/30,783 = 2 0%

and PM 10 

### and PM 2.5 

### reductions are computed by 3,786/13,588 = 28%

7. Same approach as used in the 2006 Clean Air Interst ate Rule (CAIR), which estimated reductions of “PM emissions by 10,538 tpy, a reduction of about 62%.” Used sam e list of plants as were identified based on tonnag e and SCC

from CAIR: http://www.envinfo.com/caain/June04updates/tiop_fr2 .pdf

8. Except for dairy cows and turkeys (no growth), base d on animal population growth estimates from the US Department of Agriculture (USDA) and the Food and A griculture Policy and Research Institute. See Sect ion 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 interpolat ion 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 co ntrols.

11. VOC and benzene emissions for year 2016 (linear int erpolation between 2015 and 2020) from year 2002 fr om MSAT2 rule (EPA, 2007b)

12. Data files for the cement sector provided by Elinet h Torres, EPA-SPPD, from the analysis done for the Cement NESHAP: The ISIS documentation and analysis for th e cement NESHAP/NSPS is in the docket of that rulem akingdocket # 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-specif ic emissions after the rule were estimated. Consis tent with proposed rule for Gold Mine Ore Processing NESHAP: 75 FR 22469.

14. NO X 

### 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 bu t 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 Ignit ion Engines and Recreational Engines (Marine and Land Based): “Pentathalon Rule” Clean Bus USA Program

Control of Emissions of Air Pollution from Locomoti ves 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 consum ption 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 Locomoti ves 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 En gines, 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 i n the proposed Transport Rule except for the C3 mar ine standards of April 2010, which are included in the Toxics Rule b ut 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 (pti pm)

The future-year data for the ptipm sector used in t he 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 cons ent decrees through December 1, 2010 and incorporat es information on existing controls collected through the Information Collection Request (ICR) for the proposed Toxics Rule. Units with SO 2 

## or NO X 

## advanced controls (e.g., scrubber, SCR) that were not required to run for compliance with Title IV, New S ource Review (NSR), state settlements, or state-spe cific rules were modeled by IPM to either operate those c ontrols or not based on economic efficiency paramet ers. For the modeling data files, units with advanced me rcury controls (e.g., ACI) were assumed to operate those controls in states with mercury requirements. Note that this base case includes the proposed Transpor t Rule,

## 12 which is expected to be finalized in June, 2011. S peciated mercury emissions were estimated using mer cury speciation factors, which are assigned based on coa l rank (e.g., bituminous, sub-bituminous, lignite), firing type, boiler/burner type, and post-combustion emiss ions controls. These are the same factors as were used in the Clean Air Mercury Rule and are provided in Appe ndix G. Further details on the EGU emissions inventory used for this proposal can be found in th e proposed Toxics Rule IPM Technical Support

Document.

IPM is run in 5 year increments. The IPM 2015 resu lts are valid for representing 2014, 2015, and 2016 , and so we used the IPM results for 2015 to represent 20 16. 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., PM 2.5 

## and PM 10 

## ) from the EGU sector are computed via a post processing routine which applies emission factors t o the IPM-estimated fuel throughput based on fuel, configuration and controls to compute the filterabl e and condensable components of PM. This methodolo gy is documented in the IPM TSD. For the post process ing of the IPM data used for the air quality modeli ng, 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 emit ted PM 2.5 

## emissions from the EGU sector of 85 thousand

tons in the base case. This erroneous emissions fa ctor was used in both the base and policy cases. F or areas of the country in which more natural gas is project ed to be used in the policy case, the emission fact or 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 s uch as SO 2



## .

Appendix H further details the emission factor erro r.

We adjusted the inventory files resulting from the IPM post processing to account for criteria polluta nt reductions from the proposed Boiler MACT, which imp acts only units that are the less than 25 megawatt (MW). For mercury from these units, we took a diff erent approach because we used the Boiler MACT mercury ICR data directly in our modeling data file s, 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 wi th the ICR data, which was incorporated only into t he ptnonipm sector. We could not match ICR units dire ctly 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 goo d a job as possible at preventing double counting o f the Hg emissions. Emissions of Hg were removed from the I PM outputs if a unit had a design capacity less tha n 25 MW and the facility matched a facility in the ICR d atabase. The matching was done using the NEI_UNIQUE_ID field in the NEI and IPM output files 1 

## . 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 SO 2 

## , PM10, PM 2.5 

## , CO, HCL and VOC using unit-specific

reduction information from the Boiler MACT ICR data base. 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 MA CT 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 li sted 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 facil ities in the ICR database, representing 83% of the ICR SO 2 

### 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-specifi c reductions resulted in a reduction of 821 tons of C O, 725 tons HCL, 546 tons PM 2.5 

## , 20,239 tons SO 2 

## 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 B oiler 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 70 6 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 3,7 93,365 20,239 40,775 18



## After the Boiler MACT reductions were applied, we g enerated day-specific emissions for input to SMOKE. The approach was modified from the base year to acc ount for summer and winter differences in emissions that are provided by CAMD. In particular, CAMD pro vides 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, whic h is to apply state averaged month-to-day factors f rom the 2005 CEM data for NO X 

## , SO 2 

## and heat input (the heat input is used for other p ollutants).

## Figure 3-1. A pproach



# 3.2 Stat

ionary Source Projections: non afdust)

To project U.S. stat ionary sources other categories within the ptnonipm, nonpt, ag and afdus t platform sectors. the data and projection methods used for these sect ors. emissions growth does not track with economic growth growth” assump tion is based on an examination of historical emiss ions and working toward improving the projection growth assumption for th e 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 f

actors (and control factors) are provided in the fo llowing sections where feasible. However, some sectors used growth or control factor s that varied geographically and their contents cou ld not be provided in the following sections (e.g., gasoli ne distribu thousands of records). If the growth or control fa ctors for a sector are not provided in a table in t his 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 (NH 3 

## ) and dust (PM ptnonipm sectors was based on projections of gro emissions in ptnonipm, (very small compared to ag s ector) the control packet was inadvertently not app lied

to that sector. This results in an underestimate o f 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 afdus t platform sectors. This subsection provides details on the data and projection methods used for these sect ors. In estimating future- year emissions, we assumed

does not track with economic growth for many stationary non

tion is based on an examination of historical emiss ions 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 f 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 fo llowing sections where feasible. However, some sectors used growth or control factor s that varied geographically and their contents cou ld not be provided in the following sections (e.g., gasoli ne distribu tion varies by county and pollutant and has thousands of records). If the growth or control fa ctors for a sector are not provided in a table in t his document, they are available as a “projection” or “ control” packet for input to SMOKE on the



## Livestock emissions growth (ag, afdust)

) and dust (PM 10 

## and PM 2.5 

## ) emissions from livestock in the ag and afdust and ptnonipm sectors was based on projections of gro wth in animal population. While there are some liv estock emissions in ptnonipm, (very small compared to ag s ector) the control packet was inadvertently not app lied

to that sector. This results in an underestimate o f NH 3 

## 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 fo llowing sections where feasible. However, some sectors used growth or control factor s that varied geographically and their contents cou ld not tion varies by county and pollutant and has thousands of records). If the growth or control fa ctors for a sector are not provided in a table in t his 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 liv estock emissions in ptnonipm, (very small compared to ag s ector) the control packet was inadvertently not app lied 620 tons in 2016 (pri marily in Kansas

## 16 and Minnesota for which the NH 3 

## were reported at specific farms in the point sourc e inventory), and for

PM 2.5 

## the 2016 underestimate is 3 tons. All of these om issions 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 emission s are 1.9% larger than the 2005 base case emissions. Exc ept for dairy cows and turkey production, the anima l projection factors are derived from national-level animal population projections from the U.S. Departm ent of Agriculture (USDA) and the Food and Agriculture Pol icy and Research Institute (FAPRI). For dairy cows and turkeys, we assumed that there would be no grow th 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 ani mals has declined. Thus, we do not believe that production forecasts provide representative estimat es of the future number of cows and turkeys; theref ore, we did not use these forecasts for estimating futur e-year emissions from these animals. In particular , the dairy cow population is projected to decrease in th e future as it has for the past few decades; howeve r, 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 nitr ogen excretion. With the cow numbers going down an d 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 emi ssions values therefore, our projection method proj ected

from 2002 rather than from 2005.

Appendix E in the 2002v3 platform documentation pro vides 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 low

emitting wood stoves and the corresponding decrease in other types of wood stoves. As newer, cleaner woodstoves replace older, higher-polluting wood sto ves, there will be an overall reduction of the emis sions from these sources. The approach cited here was de veloped as part of a modeling exercise to estimate the expected benefits of the woodstoves change-out prog ram ( 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 2104 008051: decrease 2%/year

 New woodstoves, SCC=2104008003, 2104008004, 2104008 030, 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 w oodstoves, 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 PM 2.5 

## . These fractions are based on the fraction of emi ssions from these processes in the states that did not have the “general woodstove s and fireplaces” SCC in the 2002v3 NEI. This appr oach results in an overall decrease of 1.056% per year f or this source category.

Table 3-4 presents the projection factors used to p roject the 2005 base case (2002 emissions) for resi dential

wood combustion.

Table 3-4. Projection Factors for growing year 2005 Resident ial Wood Combustion Sources to 2016

SCC SCC Description Projection

Factors

2016

2104008000 Total: Woodstoves and Fireplaces 0.8522

2104008001 Fireplaces: General 1.1400 210 4008070 Outdoor Wood Burning Equipment

2104008002 Fireplaces: Insert; non

- EPA certified

0.7200 2104008010 Woodstoves: General

2104008051 Non

- catalytic Woodstoves: Non

- EPA certified

2104008003 Fireplaces: Insert; EPA certified; non

- catalytic

1.28 21 04008004 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 ptno nipm sectors. The only SCC in the nonpt inventory used for gasoline Stage II emissions is 2501060100 (Stor age and Transport; Petroleum and Petroleum Product Stor age; Gasoline Service Stations; Stage II: Total). The following SIC and SCC codes are associated with gas oline Stage II emissions in the ptnonipm sector:

 SIC 5541 (Automotive Dealers & Service Stations, Ga soline Service Stations, Gasoline service stations)

 SCC 40600401 (Petroleum and Solvent Evaporation;Tra nsportation and Marketing of Petroleum Products;Filling Vehicle Gas Tanks - Stage II;Vapor Loss w/o Controls)

 SCC 40600402 (Petroleum and Solvent Evaporation;Tra nsportation and Marketing of Petroleum Products;Filling Vehicle Gas Tanks - Stage II;Liqui d Spill Loss w/o Controls)

## 18

 SCC 40600403 (Petroleum and Solvent Evaporation;Tra nsportation and Marketing of Petroleum Products;Filling Vehicle Gas Tanks - Stage II;Vapor Loss w/o Controls)

 SCC 40600499 (Petroleum and Solvent Evaporation;Tra nsportation and Marketing of Petroleum Products;Filling Vehicle Gas Tanks - Stage II;Not C lassified

We used a consistent approach across nonpt and ptno nipm 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 el ements of refueling growth and control: (1) activity growt h (due to VMT growth as input into NMIM), (2) emiss ions reductions from Stage II control programs at gasoli ne stations, and (3) emissions reductions resulting from the phase-in over time of newer vehicles with onboa rd Stage II vehicle controls. We assumed that all areas with Stage II controls in 2005 continue to have Sta ge II controls in 2016.

We computed VOC , benzene and naphthalene projectio n factors at a county-specific, annual resolution a s 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 o nroad sources from NMIM BENZENE_RFL is the BENZENE refueling emissions for onroad sources from NMIM

Etc.

For this 2016 projection, we obtained VOC_RFL, BENZ ENE_RFL and NAPHTHALENE_RFL for each county by interpolating 2015 and 2020 NMIM results.

We applied these projection factors to both nonpt a nd 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 ar e used for sources that have consistent VOC and VOC HAPs using various criteria as described in the 2005 v4.1 platform documentation, and these source s are called “integrated” sources. The nonpoint gaso line stage II emissions are an integrated source, a nd so the VOC HAPs are also projected based on ratios of futu re 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 chem ical speciation (it is not B,A,F or M) and was

therefore not used for this effort. Benzene was us ed as part of the speciation for the nonpt sector g asoline 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, bu t rather used VOC speciation to estimate benzene.



## 3.2.4 Portable fuel container growth and control (n onpt)

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 Tr ansport; Petroleum and Petroleum Product Storage” a s the beginning of the description) below.

• 2501011011 Residential Portable Fuel Containers: Pe rmeation

• 2501011012 Residential Portable Fuel Containers: Ev aporation

## 19 • 2501011013 Residential Portable Fuel Containers: S pillage During Transport

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

• 2501011015 Residential Portable Fuel Containers: R efilling at the Pump: Spillage

• 2501012011 Commercial Portable Fuel Containers: Pe rmeation

• 2501012012 Commercial Portable Fuel Containers: Ev aporation

• 2501012013 Commercial Portable Fuel Containers: Sp illage During Transport

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

• 2501012015 Commercial Portable Fuel Containers: Re filling at the Pump: Spillage



## Additional information on the PFC inventories is av ailable in Section 2.2.3 of the documentation for t he 2002 Platform ( http://www.epa.gov/ttn/chief/emch/index.html#2002 ).

The future-year emissions reflect projected increas es in fuel consumption, state programs to reduce PF C emissions, standards promulgated in the MSAT rule, and impacts of the Renewable Fuel Standard (RFS) on gasoline volatility. Future-year emissions for PFC s were available for 2010, 2015, 2020, and 2030. I n creating the inventories for 2016, we linearly inte rpolated year 2015 and year 2020 inventories. Benz ene and other VOC HAP future-year PFC emissions were also i ncluded 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 p oint source emissions are projected to future years by a pplying activity growth using data on itinerant (IT N) operations at airports. The ITN operations are def ined as aircraft take-offs whereby the aircraft lea ves the airport vicinity and lands at another airport, or a ircraft landings whereby the aircraft has arrived f rom outside the airport vicinity. We used projected ITN inform ation 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 ai rports, for all years up to 2025. We aggregated and applie d this information at the national level by summing the airport-specific (U.S. airports only) ITN operation s to national totals by year and by aircraft operat ion, for each of the four available operation types: commerc ial, general, air taxi, military. We computed grow th 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 IT N 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 emis sions. For example, year 2016 commercial aircraft emission s are 11.18% higher than year 2005 emissions.

## 20

Table 3-5. Factors used to project base case 2005 aircraft e missions 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 Air craft L & TO

Exhaust;Military;Jet Engine: JP-5 0.968

27502001 Internal Combustion Engines;Fixed Wing Air craft L & TO

Exhaust;Commercial;Piston Engine: Aviation Gas 1.1118

27502011 Internal Combustion Engines;Fixed Wing Air craft L & TO

Exhaust;Commercial;Jet Engine: Jet A 1.1118

27505001 Internal Combustion Engines;Fixed Wing Air craft L & TO

Exhaust;Civil;Piston Engine: Aviation Gas 0.9952

27505011 Internal Combustion Engines;Fixed Wing Air craft L & TO

Exhaust;Civil;Jet Engine: Jet A 0.9952

27601014 Internal Combustion Engines;Rotary Wing Ai rcraft L & TO

Exhaust;Military;Jet Engine: JP-4 0.968

27601015 Internal Combustion Engines;Rotary Wing Ai rcraft L & TO

Exhaust;Military;Jet Engine: JP-5 0.968



## We did not apply growth factors to any point source s with SCC 27602011 (Internal Combustion Engines; Rotary Wing Aircraft L & TO Exhaust; Commercial; Je t Engine: Jet A) because the plant names associated with these point sources appeared to represent indu strial facilities rather than airports. This SCC i s only in one county, Santa Barbara, California (State/County FIPS 06083).

None of our aircraft emission projections account f or any control programs. We considered the NO X 

## standard adopted by the International Civil Aviatio n Organization’s (ICAO) Committee on Aviation Environmental Protection (CAEP) in February 2004, w hich is expected to reduce NO X 

## by approximately 2% in 2015 and 3% in 2020. However, this rule has not yet been adopted as an EPA (or U.S.) rule; therefo re, the

effects of this rule were not included in the futur e-year emissions projections.



## 3.2.6 Stationary Source control programs, consent d ecrees & settlements, and

plant closures (ptnonipm, nonpt)

We applied emissions reduction factors to the 2005 emissions for particular sources in the ptnonipm an d nonpt sectors to reflect the impact of stationary-s ource 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 NO X 

## SIP call were assumed to have been implemented by 2005 and captured in the 2005 base case (2005v2 point inventory). This assumptio n was confirmed by review of the 2005 NEI that show ed reductions from Large Boiler/Turbines and Large Int ernal 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 date s 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 r elated to the MACT rules, and this information is as consistent as possible with the preamble emissio ns reduction percentages for these rules.

• We included plant closures (i.e., emissions were ze roed out for future years) where information indicated that the plant was actually closed. Howe ver, 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 a nd plant closures discussed later in Section 3.2.6.1, we also applied plant closures listed in A ppendix 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) NH 3 

### (tons) NO X 

### (tons) PM 10 

### (tons) PM 2.5 

### (tons) SO 2 

### (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 0.0002 3

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 e missions 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, 51 5 tons PM10, 418 tons PM2.5 and 2,203 to ns SO2 and 100 tons HCL

.



## We inadvertently did not apply the plant closures t hat were included for the proposed Transport Rule

because of technical difficulties with software. T hese proposed transport rule closures affect the fo llowing sources: auto plants, pulp and paper plants, large and small municipal waste combustors (LMWC and SMWC), as well as plants closed before 2008 but fol lowing 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 nonE GU (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 NO X 

### 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 NO X 

### 76,953 76,749 204 0%
Pennsylvania SO 2 

### 46,609 46,231 378 0.1%

South Carolina NO X 

### 27,793 27,675 119 0.1%

South Carolina VOC 17,132 16,928 204 0.1%

Tennessee NO X 

### 49,456 49,284 172 0%
West Virginia NO X 

### 32,180 31,463 717 0.5%
West Virginia SO 2 

### 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 effe cts of the Department of Justice Settlements and Consent Decrees on the non-EGU (ptnonipm) sector em issions. 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 expecte d 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. Additiona l 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 decree s controls at the facility and SCC level.

• We applied most of the control programs as replacem ent 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 con trol programs. Exceptions to replacement controls are “additional” controls, which ensure that the co ntrolled emissions match desired reductions regardless of the baseline control efficiencies in the NEI. We used the “additional controls” approac h for many settlements and consent decrees where spec ific plant and multiple-plant-level reductions/targets were desired and at municipal wa ste landfills where VOC was reduced 75% via a MACT control using projection factors of 0.25.

• We applied New York State Implementation Plan avail able controls for the 1997 8-hour Ozone standard for non-EGU point and nonpoint NO X 

## 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.pd f. See Section 3.2.6.



## 3.2.6.1 Reductions from the Portland Cement NESHAP

As indicated in Table 3-1, the Industrial Sectors I ntegrated Solutions (ISIS) model (EPA, 2010b) was u sed to project the cement industry component of the ptnoni pm emissions modeling sector to 2016. This approac h provided reductions of criteria and hazardous air p ollutants, including mercury. The ISIS cement emis sions were developed in support for the Portland Cement N ational 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 po licy case of multi-pollutant emissions for individual cement kilns (emission inventory units) that were relevant for years 2013 through 2017. Th ese ISIS-based emissions included information on new ce ment kilns, facility and unit-level closures, and u pdated policy case emissions at existing cement kilns.

The ISIS model results for the future show a contin uation 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 prehe ater and precalciner kilns. Multiple regulatory require ments such as the NESHAP and NSPS currently apply t o the cement industry to reduce CAP and HAP emissions . Additionally, state and local regulatory requirements might apply to individual cement facil ities depending on their locations relative to ozon e and

PM 2.5 

## nonattainment areas. The ISIS model provides the emission reduction strategy that balances: 1) optimal (least cost) industry operation, 2) cost-ef fective controls to meet the demand for cement, and 3) emission reduction requirements over the time perio d of interest. Table 3-8 shows the magnitude of th e 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 p oint 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

NO X 

## 193,000 56,740 2.4%

PM 2.5 

## 14,400 7,840 1.8%

SO 2 

## 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 pt nonipm sector based on the Boiler MACT proposal, wi th additional adjustments that made it similar to the final Boiler MACT. In particular, we applied reduc tions 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) boiler s in the inventory that were not in the Boiler MACT ICR database, but that the Boiler MACT project team ind icated were part of the category.

Different approaches were used for applying the red uctions for mercury versus the other pollutants bec ause the mercury emissions from the ICR were used direct ly in the ptnonipm sector, whereas the other pollut ants
used the 2005v2 NEI data 2



## .

For mercury, the Boiler MACT ICR-based unit-level e missions inventory, using facility and unit IDs fro m 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 facilitie s in

2 

### This approach was based on exploring the sources o f the NEI versus ICR data, particularly the SO 2 

### values. We determined that a large portion of the ICR data utilized average emis sion factor that did not account for the sulfur con tent 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 t he NEI and NATA inventories (details are provided i n the 2004v4.1 platform documentation). The amount of me rcury from facilities that did not match summed to 0.177 tons (about 4%); these facilities were not in cluded in base-case 2005 ptnonipm sector. For the units included in the sector, we applied unit-specific re ductions provided by the rule developers to the ICR -based base-case emissions. These reductions were provide d 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 i n 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 facil ity 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 th e 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 boile rs not covered by the ICR database.

Fuel SO 2 

### %

reduction VOC %

reduction PM 2.5 

### %

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 SO 2 

## , PM 2.5 

## , 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 represe nted only [X] percent of the ICR SO 2 

## emissions.

In addition to the Hg reductions, we reduced SO 2 

## , PM 2.5 

## , CO and VOC using facility-fuel based reduction

information from the Boiler MACT ICR database. Bec ause we were unable to match the specific units fro m the Boiler MACT ICR to the units in the 2005 invent ory, we used the following approach. First we matc hed the 2005 inventory and the Boiler MACT ICR data at the facility (NEI_UNIQUE_ID) level. At the facilit y level, we were able to match facilities that captur ed 97% of the total Boiler MACT ICR data SO 2 

## emissions. For facilities that matched, we applied the Boiler MACT ICR reductions only to inventory boiler proces ses from these facilities that have the same fuel type (SCC) as the fuel listed in the ICR Boiler MACT dat abase. Because the ICR fuel types do not match the fuel de finitions 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 possibilit ies for ICR fuels to match to address the potential ove rlap of multiple ICR fuel categories and to increas e the number of facility/processes in the NEI that matche d units in the ICR database. The second choice was only

used when units in the ICR database did not match a ny units in the inventory at that facility.

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

Inventory fuel category ICR fuel category, 1 st 

### choice ICR fuel category, 2 nd 

### choice

Bagasse Bagasse

C oal Coal

Coal

- based Synfuel Heavy Liquid Light Liquid

C rude oil Heavy Liquid

Digester Gas Gas 2

Distillate Oil Light Liquid

Distillate Oil (Diesel) Light Liquid

G as 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)

O il 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

U nknown Gas 1 (NG Only) P etroleum 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 IC R database were applied to the records in the inven tory 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 t he 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 1.7 5 tons of mercury shown in the table is fairly consis tent with the 1.45 ton mercury reduction cited by t he final Major and Area Boiler MACT Rules, especially consid ering 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 w ith emissions reductions cited by the Boiler MACT F inal

Rules is the result of different base emissions ass umptions 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

SO 2 

## 391,000

PM 2.5 

## 17,000
VOC 3,000
CO 72,000
HCL 5,600

As discussed above, we examined the control data co llected from the Boiler MACT ICR. We extracted all controls that were installed after 2005, determined a percent reduction, and verified that these contr ols were being used. In many situations we were told that t he controls were on site but were not being utilize d. 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 NO X 

## 907 544 363 40

North Carolina SO 2 

## 652 65 587 90
Virginia SO 2 

## 3379 338 3041 90
Washington SO 2 

## 639 383 256 40

North Carolina HCL 31 3 28 90



## 3.2.6.3 Summary of Mercury Reductions at nonEGU sta tionary sources-

(ptnonipm)

The mercury emission projections included NESHAP fo r non-EGU source categories that were finalized or expected to be finalized prior to the proposed Toxi cs rule including the Boiler MACT (1.75 tons reduct ion), Portland Cement NESHAP (6.4 tons reduction), Gold M ines 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 reductio n 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 shou ld have been 0.2 tons, but due to an error in the p ercentage 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: Ele ctric Arc Furnaces 7.0 4.6

Industrial, Commercial, Institutional Boilers & Pro cess 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 wer e discussed above. The Gold Mine reductions were provided by the SPPD project lead and are summarize d 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 C ombustion, the pulp and paper plant reduction, and methodology used to apply them.



## 3.2.7 Oil and gas projections in TX, OK, and non-Ca lifornia 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, beca use it was the best available data to represent 201 6.

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, NO X 

## , and VOC to the 2008 Texas and Oklahoma data. SO 2 

## reductions associated with the

RICE NESHAP were not included due to lack of time t o include. They would impact 2310000220 (Industrial Processes;Oil and Gas Production: SIC 1 3; Drill rigs) for which we estimate a reduction in 2005 emissions of about 4400 tons, nationwide.

We discovered after emissions processing was comple te, 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 3-1 4 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 nonpoin t 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



## NO X 

### 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



## SO 2 

### 1,014 2 

## 2 5,977 956 956 37



## PM 10 

### 1,918 2,231 2,231 3,036 2,543 2 ,543 1,320



## PM 2.5 

### 1,918 2,231 2,231 2,945 2,467 2,467 1,280

* 2016 emissions estimated as 2008 emissions with R ICE reductions, which affects only CO, NO X , and VOC

** 

### Table 1.1 in: http://www.tceq.state.tx.us/assets/public/implement ation/air/am/contracts/reports/ei/5820783985FY0901- 20090715-ergi-Drilling_Rig_EI.pdf



## As with the 2005 v4 platform, the v4.1 platform uti lizes 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 Transp ort Rule, in which we used the 2005 for both 2005 a nd the future years (which were 2012 and 2014), we wer e able to obtain the WRAP 2018 Phase II emissions f or 2016 for the Proposed Toxics Rule. These data beca me available in time for us to incorporate them int o this platform, and were the best available data to repre sent 2016. Like the Texas and Oklahoma data, we ap plied

RICE reductions to the 2018 WRAP data for use in 20 16. Table 3-15 shows the WRAP oil and gas emissions in 2005, and future year, before and afte r the RICE reductions. The emissions used in 2016 are the 2018 WRAP-supplied emissions with the RICE redu ctions applied.

Table 3-15. WRAP Oil and Gas Emissions: 2005, 2018 WRAP, and 2 016 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 2,8 42 39

* 2016 emissions are 2018 WRAP emissions with RICE NESHAP reductions. These reductions apply only to CO, NO X , and VOC.

## 29 

## 3.2.8 Future Year VOC Speciation for gasoline-relat ed 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 emissio n sources. Such sources include gasoline stage II, portable fuel containers (PFCs), and finished fuel storage and transport-related sources related to bu lk terminals (where the ethanol may be mixed) and down stream to the pump. We identified this last group of sources as “btp” (from bulk terminals to pumps). W hile 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 pr ofiles; 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 Depar tment 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 f uels are the same for 2012 and years beyond (even though the quantities of the two fuels change over these year s). The national level proportions were allocated to co unties across the country using fuel modeling at th e EPA Office of Transportation and Air Quality. All gaso line stage II and “btp” sources used the same combi nation of E0 and E10 headspace profiles as were used for e xhaust and evaporative profiles.



# 3.3 Mobile source projections

Mobile source monthly inventories of onroad and non road mobile emissions were created for 2016 using a

combination of the NMIM and MOVES2010 models. Futu re-year emissions reflect onroad mobile control programs including the Light-Duty Vehicle Tier 2 Ru le, the Onroad Heavy-Duty Rule, and the Mobile Source Air Toxics (MSAT2) final rule. Nonroad mobi le 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 emissio ns standards.

Onroad mobile sources are comprised of several comp onents and are discussed in the next subsection (3. 3.1). Monthly nonroad mobile emission projections are dis cussed in subsection 3.3.2. Locomotives and Class 1 and Class 2 commercial marine vessel (C1/C2 CMV) pr ojections are discussed in subsection 3.3.3, and Cl ass 3 (C3) CMV projected emissions are discussed in sub section 3.3.4.



## 3.3.1 Onroad mobile (on_noadj, on_moves_runpm, on_m oves_startpm)

The onroad emissions were primarily based on the 20 10 version of the Motor Vehicle Emissions Simulator (MOVES2010) – the same version as was used for 2005 . The same MOVES-based PM 2.5 

## temperature adjustment factors were applied as were used in 200 5 for running mode emissions; however, cold start emissions used year-specific temperature adjustment factors. The temperature adjustments have the min or limitation that they were based on the use of MOVES national default inputs rather than county-specifi c inputs, because a county-specific database for inpu t to MOVES was not available at the time this appro ach was needed. However, the PM 2.5 

## temperature adjustments are fairly insensitive to the county-specific inputs, which is why this is only a minor limitation. Merc ury was the only onroad HAP used in 2016 that did n ot come from MOVES, since the capability for mercury f rom 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 NH 3 

## emissions are from MOVES runs for California, disaggregated to the county level using NMIM. For all other pollutants, we did not use MOVES to gener ate future-year onroad emissions for California, becaus e 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 maintai n 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 interp olated between year 2014 and year 2020) by the

EMFAC2007-based emissions for 2005. These EMFAC-ba sed 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 w ere scaled to either of three pollutants: exhaust P M 2.5 

## (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 su ch 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, whic h came from 2005 NMIM. VMT were projected using growth rates from the Department of Energy’s AEO200 9. We used MOVES2010 to create emissions by state, SCC, pollutant, emissions mode and month. W e then allocated these emissions to counties based on 2015 NMIM county-level data by state, SCC, pollutan t, and emissions mode. 2016 NMIM data were not available for this effort, but the 2015 NMIM can re asonably be expected to be sufficient for this purp ose. While EPA will eventually replace this approach wit h 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 c onsist of gasoline exhaust speciated PM and naphthalene. These pre-temperature-adjusted emissi ons 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 MOV ES2010 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 v 4.1 platform documentation), we applied MOVESbased temperature adjustment factors to gridded, ho urly emissions using gridded, hourly meteorology. Figure 3-2 illustrates the increase in PM emissions associated with decreasing temperatures for runnin g exhaust and starting exhaust in 2005 and 2015. For the running mode in 2016, we used the same temperat ure adjustment factors as the 2005 base case. However, cold start temperature adjustment factors decrease slightly in future years, and for year 2016 process ing, we updated the temperature adjustment curves f or these cold start emissions to use the 2015 temperat ure adjustments which were the best available set o f adjustments at the time the work was done. The cha nge from 2005 to 2015 adjustment factors has little impact, reducing cold-start mode temperature-adjust ed PM and naphthalene by under 4% for temperatures

down to 0 ° F. Therefore, we were comfortable using 2015 adjus tment 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_move s_startpm, and on_moves_runpm

After completion of the modeling, two errors were d iscovered in the PM emissions from the onroad secto r. The startpm and runpm sectors were impacted by the same error: they were doubly adjusted to account f or cold temperatures. Instead of processing 72°F PM e missions 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 ove restimate PM emissions particularly for the colder p arts of the country which have the lowest temperatu res and highest adjustment factors. The on_noadj PM wa s also found to be incorrect. The error occurred d uring SMOKE processing and resulting in dropping emission s of pre-speciated exhaust PM 2.5 

## from diesel vehicles due to incorrect SMOKE input speciation profiles. The impact of this error was to underestimate the P M 2.5



## . These errors impacted all states except California, which used other data as described above. Table 3 -16 summarizes the impact of the PM 2.5 

## errors in the onroad sectors.

Table 3-16. Summary of the impact of PM 2.5 

## errors in the onroad sectors

State 2016 PM 2.5 

### onroad

corrected 2016 PM 2.5



### onroad

modeled Error in

onroad

PM 2.5 

### Total statelevel 2016

PM 2.5 

### * All-sector

PM 2.5 

### 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 PM 2.5 

### onroad

corrected 2016 PM 2.5



### onroad

modeled Error in

onroad

PM 2.5 

### Total statelevel 2016

PM 2.5 

### * All-sector

PM 2.5 

### 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 underesti mate, positive means overestimate



## 3.3.2 Nonroad mobile (nonroad)

This sector includes monthly exhaust, evaporative a nd refueling emissions from nonroad engines (not including commercial marine, aircraft, and locomoti ves) derived from NMIM for all states except Califo rnia. Like the onroad emissions, NMIM provides nonroad em issions for VOC by three emission modes: exhaust, evaporative and refueling. Unlike the onroad secto r, 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 fo r the nonroad sector (defined as the equipment type s covered by NMIM) were created using a consistent NM IM-based approach as was used for 2005, but

## 33 projected for 2016. This approach uses the NONROAD 2005 model, version nr05c. Since we did not have a 2016 NMIM data but did have 2015 NMIM data, we appl ied national level SCC- and pollutant/mode-based 4



## factors to scale 2015 to 2016 values. These factor s 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 NH 3 

## , we used 2015 values for 2016 because there were no adjustment factors available for these poll utants in time for this effort.

These future-year emissions account for increases i n activity (based on NONROAD model default growth estimates of future year equipment population) and changes in fuels and engines that reflect implement ation of national regulations and local control programs that impact each year differently due to engine tur nover.

The national regulations incorporated in the modeli ng are those promulgated prior to December 2009, an d 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-Ignit ion Engines, and Recreational Engines (Marine and Land-Based), November 8, 2002 (“Pentath alon 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 pro grams 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 NON ROAD2008a (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 gene rate future-year nonroad emissions for California,

other than for NH 3 

## . We used NMIM for California future nonroad NH 3 

## emissions because CARB did not provide these data for any nonroad vehicle types. As we did for onroad emissions, we chose a projecti on approach that would maintain consistency between th e base year and future-year emissions for nonroad

emissions in California.

California year 2016 nonroad CAP emissions were com puted by linearly interpolating year 2014 and 2020 inventories. And 2016 HAP emissions were also comp uted 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 commercia l marine vessel (CMV) emissions were calculated using projection factors that were computed based o n national, annual summaries of locomotive emission s in 2002 and future years. These national summaries we re used to create national by-pollutant, by-SCC projection factors; these factors include final loc omotive-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 e missions CO 0.9431

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port e missions NH 3 

### 1.1340

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port e missions NO X 

### 0.7334

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port e missions PM 10 

### 0.6778

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port e missions PM 2.5 

### 0.6896

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port e missions SO 2 

### 0.1106

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port e missions VOC 0.8260 2285002006 Railroad Equipment;Diesel;Line Haul Loco motives: Class I Operations CO 1.3128 2285002006 Railroad Equipment;Diesel;Line Haul Loco motives: Class I Operations NH 3 

### 1.3040 2285002006 Railroad Equipment;Diesel;Line Haul Loco motives: Class I Operations NO X 

### 0.6577 2285002006 Railroad Equipment;Diesel;Line Haul Loco motives: Class I Operations PM 10 

### 0.6205 2285002006 Railroad Equipment;Diesel;Line Haul Loco motives: Class I Operations PM 2.5 

### 0.6287 2285002006 Railroad Equipment;Diesel;Line Haul Loco motives: Class I Operations SO 2 

### 0.0051 2285002006 Railroad Equipment;Diesel;Line Haul Loco motives: Class I Operations VOC 0.6473 2285002007 Railroad Equipment;Diesel;Line Haul Loco motives: Class II / III Operations CO 0.3226 2285002007 Railroad Equipment;Diesel;Line Haul Loco motives: Class II / III Operations NH 3 

### 1.3040 2285002007 Railroad Equipment;Diesel;Line Haul Loco motives: Class II / III Operations NO X 

### 0.3512 2285002007 Railroad Equipment;Diesel;Line Haul Loco motives: Class II / III Operations PM 10 

### 0.2857 2285002007 Railroad Equipment;Diesel;Line Haul Loco motives: Class II / III Operations PM 2.5 

### 0.2882 2285002007 Railroad Equipment;Diesel;Line Haul Loco motives: Class II / III Operations SO 2 

### 0.0012 2285002007 Railroad Equipment;Diesel;Line Haul Loco motives: Class II / III Operations VOC 0.3098 2285002008 Railroad Equipment;Diesel;Line Haul Loco motives: Passenger Trains (Amtrak) CO 1.0629 2285002008 Railroad Equipment;Diesel;Line Haul Loco motives: Passenger Trains (Amtrak) NH 3 

### 1.3040 2285002008 Railroad Equipment;Diesel;Line Haul Loco motives: Passenger Trains (Amtrak) NO X 

### 0.5251 2285002008 Railroad Equipment;Diesel;Line Haul Loco motives: Passenger Trains (Amtrak) PM 10 

### 0.5006 2285002008 Railroad Equipment;Diesel;Line Haul Loco motives: Passenger Trains (Amtrak) PM 2.5 

### 0.5028 2285002008 Railroad Equipment;Diesel;Line Haul Loco motives: Passenger Trains (Amtrak) SO 2 

### 0.0047 2285002008 Railroad Equipment;Diesel;Line Haul Loco motives: Passenger Trains (Amtrak) VOC 0.5304 2285002009 Railroad Equipment;Diesel;Line Haul Loco motives: Commuter Lines CO 1.0483 2285002009 Railroad Equipment;Diesel;Line Haul Loco motives: Commuter Lines NH 3 

### 1.3040 2285002009 Railroad Equipment;Diesel;Line Haul Loco motives: Commuter Lines NO X 

### 0.5179 2285002009 Railroad Equipment;Diesel;Line Haul Loco motives: Commuter Lines PM 10 

### 0.4937 2285002009 Railroad Equipment;Diesel;Line Haul Loco motives: Commuter Lines PM 2.5 

### 0.4937 2285002009 Railroad Equipment;Diesel;Line Haul Loco motives: Commuter Lines SO 2 

### 0.0047 2285002009 Railroad Equipment;Diesel;Line Haul Loco motives: Commuter Lines VOC 0.5231 2285002010 Railroad Equipment;Diesel;Yard Locomotiv es CO 1.3203 2285002010 Railroad Equipment;Diesel;Yard Locomotiv es NH 3 

### 1.3040 2285002010 Railroad Equipment;Diesel;Yard Locomotiv es NO X 

### 1.1194 2285002010 Railroad Equipment;Diesel;Yard Locomotiv es PM 10 

### 0.9072 2285002010 Railroad Equipment;Diesel;Yard Locomotiv es PM 2.5 

### 0.9262 2285002010 Railroad Equipment;Diesel;Yard Locomotiv es SO 2 

### 0.0056 2285002010 Railroad Equipment;Diesel;Yard Locomotiv es VOC 1.4964



## The future-year locomotive emissions account for in creased fuel consumption based on Energy Informatio n Administration (EIA) fuel consumption projections f or freight rail, and emissions reductions resulting from

emissions standards from the Final Locomotive-Marin e rule (EPA, 2007c). This rule lowered diesel sulf ur content and tightened emission standards for existi ng and new locomotives and marine diesel emissions to lower future year PM, SO 2 

## , and NO X 

## , and is documented at: http://www.epa.gov/otaq/regs/nonroad/420f08004.htm . Voluntary retrofits under the National Clean Die sel 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 VO C projection factors to obtain 1,3-butadiene, acetaldehyde, acrolein, benzene, and formaldehyde. Mercury, not already provided, was held at base-ye ar levels for 2016 (i.e., we used the 2002 emissions e stimates used in the 2005 base case).

Class 1 and 2 CMV gasoline emissions (SCC = 2280004 000) were not changed for future year processing. C1/C2 diesel emissions (SCC = 2280002100 and 228000 2200) were projected based on the Final Locomotive Marine rule national-level factors provi ded in Table 3-17. Similar to locomotives, VOC HAP s were projected based on the VOC factor and mercury was held at levels in the 2005 (2002 inventory) bas e

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 SO 2 

## Emissions Control Area-International Marine Organiz ation (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 cont rols. An overview of the ECA-IMO project and future year goals for reduction of NO X 

## , SO 2 

## , and PM C3

emissions can be found at: http://www.epa.gov/oms/regs/nonroad/marine/ci/420f0 9015.htm

The resulting coordinated strategy, including emiss ion standards under the Clean Air Act for new marin e 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 geograph ic region and pollutant; where VOC HAPs and all criteria air pollutants except for NO X 

## are assigned region-specific growth rates and NO X 

## receives different

rates.

The projection factors used to create the 2016 base case seca_c3 sector emissions are provided in Tabl e 3-18.

Note that these factors are relative to 2002. Fact ors relative to 2005 can be computed from the 2002- 2005

factors.

The geographic regions are described in the ECA pro posal technical support document: http://www.epa.gov/oms/regs/nonroad/marine/ci/420r0 9007-chap2.pdf . These regions extend up to 200 nautical miles offshore, though less at internation al 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 roughl y the upper Florida Keys just southwest of Miami.

The factors to compute HAP emission are based on em issions ratios discussed in the 2005v4 documentatio n. As with the 2005 base case, this sector uses CAP-HA P VOC integration.

## 36

Table 3-18. Factors to Project Class 3 Commercial Marine Vess el emissions to 2016

Adjustments Relative to 2002

NOx PM10 PM2.5 VOC/HC CO SO2

Alaska East (AE) 1.39411 0.19817 0.19630 1.578 63 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 1.8 7014 0.06677
Gulf Coast (GC) 1.15962 0.20333 0.20120 1.48548 1.4 8642 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 comb ination profiles use proportions of E0 and E10 expe cted in the future based on AEO 2007 projections of E10 and E0 fuel use. The proportions of E0 and E10 are th e same for 2012 and years beyond (even though the qua ntities of the two fuels change over these years). The national proportions were allocated to counties acr oss the country using the same fuel modeling done f or the stationary source gasoline speciation, as discussed in Section 3.2.8.

The speciation of onroad exhaust VOC additionally a ccounts 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 e xhaust VOC, a combination of pre-Tier 2 E0, pre-Tier 2 E10 , Tier 2 E0 and Tier 2 E10 profiles are used. Figu re 3-3. Tier 2 Fraction of Light Duty Vehicles Figure 3-3 shows the Tier 2 fraction of Light Duty Vehicles for different future years in terms of different metric s. For previous modeling applications, we based th e fraction on the population of vehicles. However, s ince these vehicles emit a smaller portion of VOC, a more appropriate metric for use in weighting the speciat ion profiles is the fraction of exhaust total hydro carbons (THC) which is used in the 2016 case described here . The fraction of Tier 2 emissions used here for 2 016 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 Sour ces

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 us ing 10% Ethanol

Nonr oad

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 et hanol 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 lig ht-duty vehicles using

0% Ethanol

8757 : Composite Profile - Gasoline Exhaust - Tier 2 lig ht-duty vehicles using

10% Ethanol

• E0 and E10 combinations are based on AEO2007 projec tions 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, ot hon, othpt, othar_hg, and othpt_hg)

Emissions for Canada, Mexico, and offshore sources were not projected to future years, and are therefo re the

same as those used in the 2005 base case. Therefor e, the Mexico emissions are based on year 1999, off shore oil is based on year 2005, and Canada is based on y ear 2006. For both Mexico and Canada, their respon sible agencies could not provide future year emissions th at 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 201 6 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 20 29 2030Fraction 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. Howev er, due to timing constraints for the Toxics Rule Proposal, the actual control case used in the air quality mo deling did not allow for the final version of IPM policy c ase to be used. More information on the IPM used c an 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 m odel and the emissions from the “final” IPM cases f or select pollutants is presented in Appendix G.

As with the base case, we applied the Boiler MACT r eductions associated with units in the ptipm sector , and we removed the Hg associated with Boiler MACT sourc es. 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 ptnonip m 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

PM 10 

## 698

PM 2

. 5 

## 606

SO 2 

## 18,747

VOC 19



# 5 Emission Summaries for the Base Cases and Control Case

The following tables summarize emissions difference s between the 2005 base case, 2016 base case and th e 2016 policy case.

Table 5-1 provides 48-state summed emissions by sec tor in 2016 and 2005for criteria pollutants, and Ta ble 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 prov ided in Table 5-3.

For the 2016 policy case, only the ptipm sector is different from the 2016 base case. State emissions of NO X 

## , SO 2 

## 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 provide d 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 SO 2 

## 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 PM 2.5



## .39Table 5-1.2016 Emissions – 2016 Base Case Compared to 2005 Base Case: VOC, NOX, CO, SO2, NH3, and PMSector[tons/yr][tons/yr][tons/yr][tons/yr][tons/yr][tons/yr][tons/yr][tons/yr][tons/yr][tons/yr][tons/yr][tons/yr][tons/yr][tons/yr]2016 VOC 2005 VOC 2016 NOx 2005 NOx 2016 CO 2005 CO2016 SO2 2005 SO2 2016 NH3 2005 NH3 2016 PM102005 PM102016PM2_52005PM2_5afdust8,8613,385 8,858,992 1,030,6311,030,391ag3,446,632 3,251,990alm_no_c349,665 67,690 1,353,680 1,924,925 296,121 270,007 9,087 154,016 940 773 38,789 59,366 37,604 56,687seca_c369,08044,990815,249647,88482,92954,04923,659420,11011,32453,91810,32449,541seca_c3non-US 126,898 18,367 1,603,944 532,181 149,443 43,267 957,065 321,414 131,137 43,326 120,617 39,810nonpt7,322,980 7,530,564 1,668,679 1,699,532 6,973,593 7,413,762 1,250,300 1,259,635 133,428 134,080 1,302,733 1,354,638 1,029,916 1,081,816nonroad1,577,015 2,691,844 1,271,892 2,115,408 13,431,07619,502,718 2,870 197,341 2,345 1,972 129,217 211,807 121,215 201,138on_noadj2,064,152 3,949,362 4,285,847 9,142,274 25,930,93243,356,130 26,784 177,977 82,013 156,528 114,073 308,497 40,587 236,927on_moves_runpm88,929 54,071 81,887 49,789on_moves_startpm71,509 22,729 65,846 20,929ptipm40,84540,9501,769,7643,726,459691,310601,5643,577,69810,380,78636,65521,684523,504615,095384,320508,903ptnonipm1,180,794 1,310,784 2,061,353 2,238,002 3,038,429 3,221,388 1,349,038 2,089,836 158,242 158,837 597,324 653,048 404,926 440,714avefire1,958,992 1,958,992 189,428 189,428 8,554,551 8,554,551 49,094 49,094 36,777 36,777 796,229 796,229 684,035 684,0352016cr2non-firesTotal12,431,42915,654,55114,830,40822,026,66550,593,83374,462,8857,196,50115,001,1153,860,2563,725,86411,869,92412,235,4873,327,8743,716,64540Table 5-2.2016 Emissions – Base Case Compared to 2005 Base Case: mercury (species and total), HCL and CL2Sector[tons/yr][tons/yr][tons/yr][tons/yr][tons/yr][tons/yr][tons/yr][tons/yr][tons/yr][tons/yr][tons/yr][tons/yr]2016HGIIGAS2005HGIIGAS2016HGNRVA2005HGNRVA2016PHGI2005PHGI2016Total Hg(sum of 3species)2005Total Hg(sum of 3species)2016HCL2005HCL2016CL22005CL2afdustagalm_no_c30.0411 0.0411 0.0793 0.0793 0.0212 0.0212 0.1416 0.1416 1.381.38seca_c30.0002 0.0002 0.0004 0.0004 0.0001 0.0001 0.0007 0.0007seca_c3 non-US0.0001 0.00011 0.0002 0.00018 0.0001 0.00007 0.00040.00036nonpt1.0605 1.0605 3.1034 3.1034 0.6524 0.6524 4.8163 4.8163 29,001 29,001 2,108 2,135nonroad0.1041 0.1041 0.2105 0.2105 0.0533 0.0533 0.3679 0.3679on_noadj0.1402 0.1402 0.5036 0.5036 0.0599 0.0599 0.7037 0.7037on_moves_runpmon_moves_startpmptipm6.8757 21.0960 21.1598 30.1986 0.6683 1.6136 28.7038 52.9082 74,089 351,592 99ptnonipm7.9112 10.4687 16.8535 29.5686 4.4183 6.1291 29.18346.1664 37,549 48,630 3,941 4,174avefire2016cr2 non-firesTotal16.1332.9141.9163.665.878.5363.92105.11140,639.00429,223.006,050.386,409.38

## 41 Table 5-3. Speciated Mercury and total Mercury by State and Se ctor 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 0.23 3 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 onro ad 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 H G 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 0.2 44 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 onro ad 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

Nev ada 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 onro ad 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 7.7 E

- 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 0.02 0 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

- 0 3 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 onro ad 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 1.2 E

- 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

- 0 3 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 onro ad 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 ca ses: NO X 

## , SO 2 

## 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 59,118 460,12 3 172,198 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 4,74 0 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 12 3

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 54,686 417,32 1 122,123 57,439 2,054 1,821 1,832

Georgia 111,017 37,138 32,293 616,05 4 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 45,501 330,38 2 148,934 47,403 1,586 2,169 2,260

Indiana 213,503 94,741 80,329 878,97 8 229,248 111,741 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 64,619 502,73 1 123,010 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 2 27

## 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 55,318 349,87 7 169,853 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 39,012 284,38 4 172,031 32,412 1,597 1,774 1,545

Montana 39,858 18,898 21,564 19,715 13,234 9,071 396 26 1 289

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 7 30 136 110 100

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 54 2 537

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 1,0 29 917 897

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 4 5

South Carolina 52,657 34,193 30,342 218,781 128,070 35,223 533 629 625

South Dako ta 15,650 14,249 13,641 12,215 29,711 7,490 106 12 7 118

Tennessee 102,934 31,885 20,003 266,148 106,762 44,110 79 8 940 764

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 9 23 886

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 ca ses: 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 ca ses: 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 0.0 000 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 0.1 139 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 0.13 40 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 SO 2 

## Emissions (tons/year) for Lower 48 States by Secto r

State EGU NonEGU Nonpoint Nonroad Onroad Fires Tota l
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 12 1,938
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 Tota l
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 2,010 194,65 2
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 3,979 402 892 252,82 4
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 47,5 97 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 86,05 5
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 1,303 113 177,31 8
North Carolina 82,544 45,264 21,992 2,177 811 696 1 53,484
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 32 269,3 32
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 4 9,094 6,288,529

*Non-US seca_c3 component not included. These emiss ions are 957,065 tons/yr.



## Table 5-8. 2016 Base Case PM2.5 Emissions (tons/year) for Lowe r 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 12,80 6 76,788

Arkansas 3,805 9,905 22,683 2,191 1,108 10,315 11,6 81 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 11,7 94 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 58,8 64 115,808

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 71,6 82

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 3 8,665

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 48,20 2 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 2 8,049

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 13,6 47 83,242

North Carolina 12,185 11,775 36,080 4,746 3,172 9,8 70 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 44,24 3 113,412
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 1 3,344 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 9,16 3 9,162 55,421

South Dakota 768 2,145 3,959 1,445 1,128 7,062 29,2 15 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 8,19 4 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 1,029,916 169,144 188,3 20 684,035 1,030,631 3,891,291
*Non-US seca_c3 component not included. These emiss ions are 120,617 tons/yr.

## 53



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