### Technical Support Document (TSD)



## For the Proposed Toxics Rule

Docket No. EPA

- HQ

- OAR

- 2009

- 0234



# Emissions Inventories

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 2 005 Emission Inventories

................................ ......................... 2

2.1 Base case 2005 overview

................................ ................................ ................................ ........ 2

2.2 2005 Proposed Toxics Rule custom processing configuration

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



### 3 Development of 2016 Future

- Year Base Case E mission Inventories

................................ ... 6

3.1 Stationary Source Projections: EGU sector (ptipm)

................................ ....................... 12

3.2 Stationary Source Projections: non

- EGU sectors (ptnonipm, nonpt, ag, afdust)

......... 16

3 .2.1 Livestock emissions growth (ag, afdust)

................................ ................................ .............................. 16

3.2.2 Residential wood combustion growth (nonpt)

................................ ................................ ...................... 17

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

................................ ................................ ..... 18

3. 2.4 Portable fuel container growth and control (nonpt)

................................ ................................ .............. 19

3.2.5 Aircraft growth (ptnonipm)

................................ ................................ ................................ .................. 20

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

nonpt)

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

3.2.6.1 Reductions from the Portland Cement NESHAP

................................ ................................ .................. 23

3.2.6.2 Boiler MACT reductions

................................ ................................ ................................ ....................... 24

3.2.6.3 Summary of Mercury Reductions at non

- EGU stationary sourc es

- (ptnonipm)

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

3.2.7 Oil and gas projections in TX, OK, and non

- California WRAP states (nonpt)

................................ .... 28

3.2.8 Future Year VOC Speciation for gasoline

- related sources (ptnonipm, nonpt)

................................ ..... 30



### 3.3 Mobile source projections

................................ ................................ ................................ ... 30

3.3.1 Onroad mobile (on_noadj, on_moves_runpm, on_moves_startpm)

................................ ..................... 30

3.3.2 Nonroad mobile (nonroad)

................................ ................................ ................................ ................... 33

3.3.3 Locomot ives and Class 1 & 2 commercial marine vessels (alm_no_c3)

................................ ............. 34

3.3.4 Class 3 commercial marine vessels (seca_c3)

................................ ................................ ...................... 36

3.3.5 Future Year VOC Speciation (on_noadj, nonroad)

................................ ................................ .............. 37



### 3.4 Canada, Mexico, and Offshore sources (othar, othon, othpt, othar_hg, and othpt_hg) 38



### 4 EGU Control Case for 2016

................................ ................................ ................................ .. 39

5 Emission Summaries for the Base Cases and Control Case

................................ ............... 39

6 References

................................ ................................ ................................ .............................. 55

### iii 

# A cronyms

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 Divisio n

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

C MV Commercial Marine Vessel

DOE Department of Energy

ECA Emissi ons Control Area

EGU Electric Generating U nit

EISA Energy Independence and Security Act of 2007

EMFAC CARB ‟s Emission Factor s mobile model

FAA Federal Aviation Administration

FIPS Federal Information Processing Standard

HAP Hazardous Air Pollutant

HWI Haz ardous Waste Incinerator

ICR Information Collection Request

IMO International Marine Organization

IPM Integrated Planning Model

ISIS Industrial Sector Integrated Solutions

ITN Itinerant (aircraft operations)

MACT Maximum Achievable Control Technology

MOBIL E6 Mobile Source Emission Factor Model, version 6

MOVES Motor Vehicle Emissions Simulator

MSAT2 Final Mobile Source Air Toxics Rule

MWC Municipal Waste Combustor

NAAQS National Ambient Air Quality Standards

NATA National Air Toxics Assessment

NEEDS Nationa l Electric Energy Database System

NEI National Emission Inventory

NESHAP National Emissions Standards for Hazardous Air Pollutants

NLEV National Low Emission Vehicle

NMIM National Mobile Inventory Model

NSPS New Source Performance Standard

NSR New Source R eview

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

### iv

PFC Portable Fuel Container

RIA Regulatory Impac t Analysis

RICE Reciprocating Internal Combustion Engine

RFS2 R evised annual R enewable F uel S tandard

RWC Residential Wood Combustion

SMOKE Sparse Matrix Operator Kernel Emissions

SCC Source C lassification Code

SPPD Sector Policies and Programs Division

TAF Terminal Area Forecast

TCEQ Texas Commission on Environmental Quality

TPY Tons per Year

TR Federal Transport Rule

TSD Technical Support Document

VOC Volatile Organic Compound

WRAP Western Regional Air Partnership



# List of Figures

Figure 2

- 1. Air quality modeling domains

................................ ................................ ..................... 6

Figure 3

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

................................ 15

Figure 3

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

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

Figure 3

- 3. Tier 2 Fraction of Light Duty Vehicles

................................ ................................ ..... 38



# L ist of Tables

Table 1

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

Table 2

- 1. Sectors Used in the 2005v4.1 Emissions Modeling Platform and Description of the

2005 Base Year Data

................................ ................................ ................................ . 3

Table 3

- 1. Control strategies and growth assumptions for creating 2016 base case emissions

inventor ies from the 2005 base case

................................ ................................ ......... 9

Table 3

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

.......................... 14

Table 3

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

..................... 17

Table 3

- 4. Projection Factors for growing year 2005 Residential Wood Combustion Sources to

2016

................................ ................................ ................................ ......................... 18

Table 3

- 5. Factors used to project base case 2005 aircraft emissions to future years

................. 20

Table 3

- 6. Summary of Emission Reductions Applied to the 2005 Base Year Inventory Due To

Plant Closures

................................ ................................ ................................ .......... 22

Table 3

- 7. Quantification of Missing Closures in 2016 the non

- EGU (ptnonipm) sector

........... 23

Table 3

- 8. Future

- y ear ISIS

- based cement industry annual reductions (tons/yr) for the non

- EGU

(ptnonipm) sector

................................ ................................ ................................ .... 24

Table 3

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

database.

................................ ................................ ................................ .................. 25

Table 3

- 10. Crosswalk of NEI fuels to ICR fuels

................................ ................................ ........ 25

### v

Table 3

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

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

Table 3

- 12. State

- level non

- MACT Boiler Reductions from ICR Data Gathering

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

Table 3

- 13. Anthropogenic mercury emissions and p rojections in the Continental United States 28

Table 3

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

gas sources

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

Table 3

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

reductions due to the RICE NESHAP

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

Table 3

- 16. Summary of the impact of PM 2.5 

### errors in the onroad sectors

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

Table 3

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

2 Commercial Marine Vessel Emissions

................................ ................................ 35

Table 3

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

.............. 37

Table 3

- 19. Future Year Profiles for Mobile Source Related Sources

................................ ........ 38

Table 4

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

modeling

................................ ................................ ................................ .................. 39

Table 5

- 1. 2016 Emissions

– 2016 Base C ase Compared to 2005 Base Case: VOC, NO X 

### , CO,

SO 2 

### , NH 3 

### , and PM

................................ ................................ ................................ ... 40

Table 5

- 2. 2016 Emissions

– Base Case Compared to 2005 Base Case: mercury (species and

total), HCL and CL2

................................ ................................ ................................ 41

Table 5

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

.......... 42

Table 5

- 4. EGU sector (ptipm emissions) for all AQ modeling c ases: NO X 

### , SO 2 

### and VOC

..... 47

Table 5

- 5. EGU sector (ptipm emissions) for all AQ modeling cases: HCL and total Mercury 49

Table 5

- 6. E GU sector (ptipm emissions) for all AQ modeling cases: Speciated Mercury

....... 50

Table 5

- 7. 2016 Base Case SO 2 

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

.............. 51

Table 5

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

.......... 52



# List of Appendices

APPENDIX A : Inventory Data Files Used for Each Proposed Toxics Rule Air Quality

Modeli ng Cases

- SMOKE Input Inventory Datasets

APPENDIX B : List of OECA Consent Decrees

- Whereby Reductions Were Apportioned to

Facilities in a Particular Corporation

APPENDIX C: Gold Mine Facility

- Specific Mercury Reductions Due to NESHAP

APPENDIX D: Mercury E mission Reductions

, 2005

- 2016 for the Non

- EGU categories of:

Electric Arc Furnaces, Mercury Cell Chlor

- Alkali Plants, Hazardous Waste

Combustion, and Pulp and Paper.

APPENDIX E Ptnonipm (Non EGU) Plant Closures Included in the 2016 Base Case and the

Resu lting Emissions Changes Due to the Closures

APPENDIX F: Methodology to Apply Reductions for the Stationary Reciprocating Internal

Combustion Engine (RICE) NESHAP

APPENDIX G : Mercury Speciation Fractions Used to Speciate the Future Year E GU Mercury

Emissions

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

Processing

### 1 

# 1 Introduction

This document provides the details of emissions data pr ocessing done in support of the Environmental

Protection Agency‟s (EPA) rulemaking effort for the pro posed Toxics Rule which is also referred to as the

Utility Maximum Achievable Control Technology Standard (MACT) and New Source Performance Standard

(NSPS). The air quality modeling results were used in the Appropriate and Necessary Analysis and the

Regul atory Impact Assessment. The emissions and modeling effort consists of three emissions cases : 2005

base case, 2016 base case, and 2016 Control case

. The emissions consisted of Criteria Air Pollutants (CAPs)

and the following select Hazardous Air Pollutan ts (HAPs): mercury (Hg), chlorine (CL2), hydrochloric acid

or hydrogen chloride (HCL) and benzene, acetaldehyde, formaldehyde and methanol. The latter four are

also denoted BAFM.

Table 1

- 1 provides more information on these emissions cases. The 2016 b ase case modeling was used to

characterize future

- year air quality without implementation of the rule. Also included in Table 1

- 1 are

mercury (Hg) zero

- out runs that were based on the 2016 base case and 2005 base case to quantify the

contributions of merc ury emissions from Electric Generating Units (EGUs). This document provides no

further discussion on the two simple Hg EGU zero

- out runs, which are described in the proposed Toxics

Rule Technical Support Document: “ National

- scale Mercury Risk Assessment S upporting the Appropriate

and Necessary Finding for Coal

- and Oil

- fired Electric Generating Units ”. The modeling outputs for the

2016 base and control cases were then used to quantify the benefits of the proposed Toxics Rule.

Table 1

- 1

. List of cases run in support of the air quality modeling for the Proposed Toxics Rule

Case Name Internal EPA

Abbreviation Description

2005 base case 2005c r_hg 2005 case created using average

- year fires data and an

average

- yea r temporal allocation approach for EGUs, to use

for computing relative response factors with 2016 the scenario

2005 Hg EGU

zero

- out 2005cr_hg_ptipm_hgzero Same as 2005 base case but zero Hg emissions for EGU

(ptipm) sector

2016 base case 2016cr2_hg 2016 “baseline” scenario, representing the best estimate for the

future year without implementation of the EGU MACT/NSPS.

2016 base with

Hg zero

- out 2016cr2_hg_ptipmhgzero Same as 2016 base case but zero Hg emissions for EGU

(ptipm) sector

2016 control

case 2 016cr2_hg_control1 2016 EGU “control” scenario representing a MACT control

strategy

The data used in the 2005 emissions cases are the same as those d escribed in the 2005

- based, Version 4.1

platform (hereafter the “2005 v4.1 ” platform) document available a t http://www.epa.gov/ttn/chief/emch/

. The 2005 and future

- year emissions scenarios were processed in a form that is required by the Community

Multi

- scale Air Quality ( CMAQ ) model. CMAQ simulates the numero us physical and chemical processes

involved in the formation, transport, and destruction of ozone, particulate matter and air toxics. As part of

the analysis for this rulemaking, CMAQ was used to calculate daily and annual PM 2.5 

### concentrations, 8

- hr

maxim um ozone, annual total mercury deposition levels and visibility impairment. Model predictions of

PM 2.5 

### and ozone are used in a relative sense to estimate scenario

- specific, future

- year design values of PM 2.5 

### and ozone, which are combined with monitoring d ata to estimate population

- level exposures to changes in

ambient concentrations for use in estimating health and welfare effects.

We used a 2005 base case approach for the year 2005 emissions scenario. The base case approach uses an

average

- year fire e missions inventory and average

- year EGU temporal profiles, which were based on 3 years

### 2 of hourly Continuous Emissions Monitoring (CEM) data for EGUs. We use a base case approach to reduce

year

- specific variability in fires and EGUs between 2005 and the fu ture years. For example, each year has

different days and different locations with large fires, unplanned EGU shutdowns, and periods of high

electricity demand. By using a base

- case approach, the temporal and spatial aspects of the inventory for

these so urces are maintained into the future year modeling, which avoids potentially spurious year

- specific

artifacts in air quality modeling estimates. In addition, the 2005 Version 4.1 (v4.1) platform biogenic

emissions data is the same as the 2005v4 platform 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/E misInventory/2005v4/2005_emissions_tsd_07jul2010.pdf . The only significant data

changes between the year 2005 cases in Table 1

- 1 and future

- year cases are emission inventories and

speciation approaches.

The future

- year inventor ies, ancillary files, and detailed projection data used for this modeling are available

as part of the Toxics Rule making , available in the docket at EPA

- HQ

- OAR

- 2009

- 0234 . Since the data are

large, the data files themselves are not posted with online acces s through the docket, and so a more

convenient access location is the EPA Emissions Modeling Clearinghouse website for its 2005 platform

( http://www.epa.gov/ttn/chief/emch/index.html#2005 ). The Toxics Rule data files are provided as a

subheading under this main link.

In the remainder of this document, we provide a description of the approaches taken for the emissions in

support of air quality modeling for the Toxics rule. In Section 2 , we briefly review the 2005 base case

inventory, including ancillary data and issues related to CMAQ support. In Section 3, we describe the

development of the future year 2016 base case. In Section 4 we provide data summaries comparing the

modeling cases. Finally, Section 5 provides emissions summaries and Section 6 contains the technical

references for this document.



# 2 Development of Base Case 2005 Emission I nventories

As mentioned previously, the 2005 emissions modeling appro ach for the proposed Toxics Rule used the

same data and approaches as the 2005 v4.1 base case platform. In this section, we briefly discuss the

modeling sectors in the 2005 base case and future year cases as well as Toxics rule

- specific issues related to

processing emissions for CMAQ.



## 2.1 Base case 2005 overview

Table 2

- 1 lists the platform sectors used for the 2005 base case and future

- year base and control cases. It

also indicates the associated sectors from the National Emissions Inventory (NEI). Subsequent sections refer

to these platform sectors for identifying the emissions differences between the 2005 base case and the 2016

cases. The inputs to the air quality model; including emissions, meteorology, initial conditions, bound ary

conditions; along with the methods used to produce the inputs and the configuration of the air quality model

are collectively known as a „modeling platform‟. The v4.1 platform contains the same modeling sectors as

the 2005 v4 platform; though for some sectors, the emissions data were revised. For additional information

on the revisions made, see the 2005

- based, Version 4.1 platform document

.

### 3 Table 2

- 1

. Sectors Used in the 2005v4.1 Emissions Modeling Pla tfor m and Description of the 2005 Base

Year Data

Platform Sector 2005 NEI

Sector Description and resolution of the data input to SMOKE

EGU sector (also

called the IPM

sector) : ptipm Point For all pollutants other than mercury (Hg): 2005 NEI v2 point so urce

EGUs mapped to the Integrated Planning Model (IPM) model using

the National Electric Energy Database System (NEEDS) 2006 version

4.10 database. A few revisions were made to the 2005 NEI v2 annual

emission estimates as discussed in the 2005

- based, Ver sion 4.1

platform document

. For Hg : 6/18/2010 version of the inventory used for the 2005 National

Air Toxics Assessment (NATA) mapped to IPM using NEEDS

version 4.10. The NATA inventory is an update to the 2005 NEI v2

and was divided into EGU and non

- EGU sectors consistent with the

other pollutants

. (We did not actually map the NATA inventory to

IPM, but rather applied the mapping that was done to the 2005 NEIv2

to the NATA Hg inventory). We additionally removed Hg from

sources from the National Emissio n Standards for Hazardous Air

Pollutants for Industrial, Commercial, and Institutional Boilers and

Process Heaters (aka “Boiler MACT ” ) Information Collection Request

(ICR) database because we included these emissions in the non

- EGU

sector.

For both: Day

- s pecific emissions created for input into SMOKE.

Non

- EGU sector

(also called the

non

- IPM sector ) :

ptnonipm Point For all pollutants other than Hg: All 2005 NEI v2 point source records

not matched to the ptipm sector. Includes all aircraft emissions.

Ad ditionally updated inventory to remove duplicates, improve

estimates from ethanol plants, and reflect new information collected

from industry from the ICR for the Boiler MACT

. I ncludes point

source fugitive dust emissions for which county

- specific PM

tran sportable fractions were applied.

For Hg: The 6/18/2010 version of NATA inventory was used except

for modifications to gold mine emissions and removal of Hg from

facilities that closed prior to 2005. In addition, Hg emissions

developed for the Boiler MAC T 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 fertilizer application,

county and annual resolution. Unchanged from the 2005v4 platform.

Area fugitive dust

sector: afdust Nonpoint PM 10 

### and PM 2.5 

### from fugitive dust sources (e.g., building construction,

road construction, paved roads, u npaved roads, agricultural dust) from

the NEI nonpoint inventory after application of county

- specific PM

transportable fractions. Includes county and annual resolution.

Remaining

nonpoint sector:

nonpt Nonpoint Primarily 2002 NEI nonpoint sourc es not otherwise included in other

SMOKE sectors, county and annual resolution. Also includes

updated Residential Wood Combustion emissions, year 2005 non

California WRAP oil and gas Phase II inventory and year 2005 Texas

and Oklahoma oil and gas emissio ns. Removed Hg emissions from

boilers to avoid double counting with Hg emissions added to the non

EGU (ptnonipm) sector from the Boiler MACT

. Nonroad sector:

nonroad Mobile:

Nonroad Monthly nonroad emissions from the National Mobile Inventory

Model (N MIM) using NONROAD2005 version nr05c

- BondBase

( equivalent to NONROAD2008a, since it incorporated Bond rule

### 4

Platform Sector 2005 NEI

Sector Description and resolution of the data input to SMOKE

revisions to some of the base case inputs and the Bond rule controls

did not take effect until later) for all states except California. Monthly

emi ssions for California created from annual emissions submitted by

the California Air Resources Board (CARB) for the 2005v2 NEI

. locomotive, and

non

- C3

commercial

marine:

alm_no_c3 Mobile:

Nonroad 2002 NEI non

- rail maintenance locomotives, and category 1 a nd

category 2 commercial marine vessel (CMV) emissions sources,

county and annual resolution. Aircraft emissions are included in the

Non

- EGU sector (as point sources) and category 3 CMV emissions are

contained in the seca_c3 sector

C3 commercial

marine: seca_c3 Mobile :

Nonroad Annual point source

- formatted

, year 2005 category 3 (C3) CMV

emissions, developed for the rule called “ Control of Emissions from

New Marine Compression

- Ignition Engines at or Above 30 Liters per

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

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

projections from 2002

, developed for the C3 ECA proposal to the

International Maritime Organization (EPA

- 420

- F

- 10

- 041, August

2010).

Onroad

California,

NMIM

- based, and

MOVES sources

not subject to

temperature

adjustments:

on_noadj Mobile:

onroad Three, monthly, county

- level components:

1) California onroad, created using annual emissions for all pollutants,

s ubmitted by CARB for the 2005 NEI version 2

. NH 3 

### (not

submitted by CARB) from MOVES2010.

2) Onroad gasoline and diesel vehicle emissions from MOVES2010

not subject to temperature adjustments: exhaust CO, N O X 

### , VOC,

NH3, benzene, formaldehyde, acetaldehyde, 1 ,3

- butadiene,

acrolein, naphthalene, brake and tirewear PM, exhaust diesel PM,

and evaporative VOC, benzene, and naphthalene

. 3) Onroad emissions for Hg from NMIM using MOBILE6.2, other

than for California.

Onroad cold

- start

gasoline exhaust

mode vehicle f

rom

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 exhaus t mode only for

PM species and n aphthalene. California emi ssions not included. This

sector is limited to cold start mode emissions that contain different

temperature adjustment curves from running exhaust (see

on_moves_runpm sector).

Onroad running

gasoline exhaust

mode vehicle from

MOVES subject

to temperature

adjustments:

on_moves_runpm Mobile:

onroad Monthly, county

- level MOVES 2010

- based onroad gasoline emissions

subject to temperature adjustments. Limited to exhaust mode only for

PM species and Naphthalene. California emissions not included. This

sector is limited to running mode emissions that contain different

temperature adjustment curves from cold start exhaust (see

on_moves_startpm sector).

Biogenic: biog N/A Hour

- specific, grid cell

- specific emissions generated from the

BEIS3.14 model

- includes em issions in Canada and Mexico.

Other point

sources not from

the NEI: othpt N/A Point sources from Canada‟s 2006 inventory and Mexico‟s Phase III

1999 inventory, annual resolution. Also includes annual U.S. offshore

oil 2005v2 NEI point source emissions.

Other point

sources not from

the NEI, Hg only:

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

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

### 5

Platform Sector 2005 NEI

Sector Description and resolution of the data input to SMOKE

and nonroad not

from the NEI:

ot har Phas e III (municipio resolution) nonpoint and nonroad mobile

inventories.

Other nonpoint

sources not from

the NEI, Hg only:

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

Other o nroad

sources not from

the NEI: othon N/A Year 2006 Canada (province resolution) and year 1999 Mexico Phase

III (municipio resolution) onroad mobile inventories, annual

resolution.



### As discussed in the 2005 v4.1 platform documentation, we processed all emissions data with the Sparse

Matrix Operator K ernel Emissions (SMOKE) modeling system, version 2.6. More details about SMOKE

including user documentation are available at its website ( http://www.smoke

- model.org ).

For the 2005 base case, all inventory and anci llary input data files used as inputs for this rule can be found at

the 2005

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



## 2.2 2005 Proposed Toxics Rule custom processing configuration

In support of the proposed Toxics Rule, EPA modeled the air quality in the East and the West (2 separate

modeling domains), each using a 12

- km horizontal grid resolution. The 12

- km modeling domains were

“nested” within a modelin g domain covering the lower 48 states using a 36

- km grid resolution. The air

quality predictions from the 36 km Continental US (CONUS) domain were used to provide incoming

“initial” and “boundary” concentrations for the Eastern 12 km domain. A map of the air quality modeling

domains is in Figure 2

- 1

.

### 6 Figure 2

- 1

. Air quality modeling domains

All three grids use a Lambert

- Conformal projection, with Alpha = 33o, Beta = 45o and Gam ma =

- 97o, with a

center of X =

- 97o and Y = 40o. Other specific parameters for these grids are provided in the Air Quality

Modeling Technical Support Document (EPA, 2011) for the Toxics Rule. More details on the grid

parameters are provided in the 2005v 4.1 platform documentation.



# 3 Development of 2016 Future

- Year Base Case Emission Inventories

This section describes the methods we used for developing the 2016 future

- year base

- case emissions. The

ancillary input data are very similar in the future

- year sc enarios to those in the 2005 base case except for the

speciation profiles used for gasoline

- related sources, which change in the future to account for increased

ethanol usage in gasoline. The specific speciation profile changes are discussed in Sections 3.2.8 (stationary

source impacts) and 3.3.5 (mobile source impacts).

The future base case projection methodologies vary by sector. The 2016 base case represents predicted

emissions in the absence o f any further controls beyond those Federal measures already promulgated, or

those expected to be promulgated prior to the Toxics Rule proposal d ate. For EGU emissions (ptipm sector),

the emissions reflect state rules and federal consent decrees through D e cember 1, 2010 and incorporate

information on existing controls collected through the Information Collection Request ( ICR ) for the Toxics

Rule

. For mobile sources (on_noadj, on_moves_runpm, and on_moves_startpm sectors), all national

measures for which da ta were available at the time of modeling have been included. The future base

- case

scenarios do reflect projected economic changes and fuel usage for EGU and mobile sectors. For non

- EGU

point (ptnonipm sector) and nonpoint stationary sources (nonpt, ag, and afdust sectors), any local control

programs that might be necessary for areas to attain the 1997 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

### 7 projections. On e exception are some NOx and VOC reductions associated with the New York (NY) State

Implementation Plan (SIP), which were added as part of a larger effort to start including more local control

information. This is described further in Section 3.2.6. The following bullets summarize the projection

methods used for sources in the various sectors , while additional details and data sources are given in Table

3

- 1 :

IPM sector (ptipm): Unit

- specific e stimates from IPM, version 4.10 (in terim version developed for

the Toxics Rule air quality modeling).

N on

- IPM sector (ptnonipm): Projection factors and percent reductions reflect emission reductions

due to control programs, plant closures, consent decrees and settlements , and one 1997 ozon e

NAAQS State Implementation Plan for New York. We also used projection approaches for point

source livestock and aircraft and gasoline stage II emissions that are consistent with projections used

for the sectors that contain the bulk of these emissions. Terminal area forecast (TAF) data aggregated

to the national level were used for aircraft to account for projected changes in landing/takeoff

activity. Year

- specific speciation was applied to some portions of this sector and is discussed in

Section 3.2.8

. Average fires sector (avefire): No growth or control.

Agricultural sector (ag): Projection factors for livestock estimates based on expected changes in

animal population from 2005 to 2016 using 2005 Department of Agricultu re data ; no growth or

control for NH 3 

### emissions from fertilizer application.

Area fugitive dust sector (afdust): Projection factors for dust categories related to livestock estimates

based on expected changes in animal population; no growth or control for other categories in this

sector.

Remaining n onpoint sector (nonpt): Projection factors that reflect emission reductions due to control

programs. Residential wood combustion projections based on growth in lower

- emitting stoves and

retirement of higher em itting stoves. Portable Fuel Container ( PFC ) projection factors reflecting

impact of the final Mobile Source Air Toxics ( MSAT 2) rule. Gasoline stage II projection factors

based on National Mobile Inventory Model ( NMIM )

- estimated VOC refueling estimates f or future

years. Year

- specific speciation was applied to some portions of this sector (i.e., gasoline

- related

emissions) and is discussed in Section 3.2.8

.

Nonroad mobile sector (nonroad): Other than for California, adjuste d o utput from a 2015 run of

NMIM that utiliz ed the NR05d

- Bond

- final version of NONROAD (which is equivalent to

NONROAD2008a ) to the year 2016. Adjustment to 2016 w as made by applying factors by

pollutant, source category code (SCC), and mode (exhaust/eva p). Factors were computed as the ratio

of 2016 to 2015 national

- level emissions from NMIM runs

. Adjustment not made for NH3 which

used the 2015 NMIM data. I ncludes final controls from the final locomotive

- marine and small spark

ignition OTAQ rules. Cal ifornia

- specific data provided by the state of California, except NH 3 

### used

2015 NMIM. Year

- specific speciation was applied to some portions of this sector and is discussed in

Section 3.3.5

. Hg was kept at 2005 levels.

Aircraf t, locomotive, and non

- Class 3 commercial marine sector (alm_no_c3): Projection factors for

Class 1 and Class 2 commercial marine and locomotives which reflect activity growth and final

locomotive

- marine controls. Hg was kept at 2005 levels.

Class 3 comm ercial 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 wa s dropped from this s ector for both 2005

and 2016

.

### 8

Onroad no

- adjustment for temperature mobile sector (on_noadj): MOVES2010 run (state

- month) for

2016 and results disaggregated to the county level using NMIM 2015. California

- specific data

provided by the state of California. VOC speciation uses different future year values to take into

account both increase in ethanol, and the existence of Tier 2 vehicles which use a different speciation

profile. Hg is kept at 2005 levels

. Onroad PM gasoline running mode sector (on_moves_s tartpm): Running mode MOVES2010 future

year state

- month estimates for PM and naphthalene, apportioned to the county level using NMIM

2015 state

- county ratios matched to vehicle and road types. Use future year temperature adjustment

file for adjusting the 72°F

- supplied emissions (for elemental and organic carbon) based grid cell

hourly temperature (lower temperatures result in increased emissions).

Onroad PM gasoline start mode sector (on_moves_startpm): Cold start MOVES2010 future

- year

state

- month estima tes for PM and naphthalene, apportioned to the county level using NMIM 2015

state

- county ratios of local urban and rural roads by vehicle type. Use future

- year temperature

adjustment file for adjusting the 72 ° F emissions (for elemental and organic carbon) based on grid cell

hourly temperature s (lower temperatures result in increased emissions).

Other nonroad/nonpoint (othar): No growth or control.

Other nonpoint speciated mercury (othar_hg): No growth or control.

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

Other nonroad/nonpoint (othar): No growth or control.

Other p oint (othpt): No growth or control.

Other p oint speciated mercury (othpt_hg ): No growth or control.

Biogenic: 2005 emissions used for all future

- year scenarios to be consistent w ith 2005 meteorology

used for all scenarios

.

Table 3

- 1 summarizes the control strategies and growth assumptions by source type used to create the 2016

base case emissions from the 2005 base

- case inventories. All Mexico, Canada, and offshore oil emissions

are unchanged in all future case scenarios from those in the 2005 base case. Emission summaries by sector

for 2005 and future years are provided in Section 4.

The remainder of this section is organized either by source sector o r by specific emissions category within a

source sector for which a distinct set of data were used or developed for the purpose of projections for the

proposed Toxics Rule. This organization allows consolidation of the discussion of the emissions categori es

that are contained in multiple sectors, because the data and approaches used across the sectors are consistent.

Sector names associated with the emissions categories are provided in parentheses. A list of inventory

datasets used for this and all cases is provided in Appendix A

.

### 9

Table 3

- 1

. Control strategies and growth assumptions for creating 2016 base case emissions inventories

from the 2005 base case

Control Strategies and/or growth assumptions

( g roupe d by a ffected p ollutants or s tandard and a pproach u sed to

a pply to the i nventory) Pollutants

a ffected Approach/

r eference

Non

- EGU Point (pt nonipm sector) p rojection a pproaches carried f orward

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 manufacture

Large Appliances

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

Pht halate Plasticizers, Polyester Resins, Polymerized Vinylidene Chloride

Reinforced Plastics

Asphalt Processing & Roofing

Iron & Steel Foundries

Metal: Can, Coil

Metal Furniture

Miscellaneous Metal Parts & Products

Municipal Solid Waste Landfills

Paper and O ther 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)

R efractory Products Manufacturing

Sites Remediation VOC EPA, 2007a

Consent decrees on Companies (based on information from the Office of Enforcement

and Compliance Assurance

– OECA) apportioned to plants owned/operated by the

Companies VOC, CO, NOx,

PM, 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 PM 4

Hazardous Waste Combustion PM 3

Hospital/Medical/Infectious Waste Incinerator Regulations NO X , PM, SO 2 EPA, 2005

Large Municipal Waste Combustors

– growth applied to specific plants All (including Hg) 4

MACT rules, plant

- level, VOC: Auto Plants VOC 5

MACT rules, plant

- level, PM & SO 2 : Lime Manufacturing PM, SO 2 6

MACT rules, plant

- level, PM: Taconite Ore PM 7

a. The implementation of these rules w as changed to reflect a 2016 future year, rather than 2012 / 2014

b. We inadvertently did not apply closures that had been applied for the Tran sport Rule proposal; emissions 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 s lightly 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 only a fraction of a percent impact on total emissions.



### Nonpoint ( nonpt sector) p rojection a pproaches carried f orward from the Proposed Transport Rule

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

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

Residential Wood Combustion Growth and Change

- outs from year 2005 to Year 2016 All 9

### 10

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

Portable Fuel Container Mobile Source Air Toxics Rule 2 ( MSAT2 ) inventory growth

and control from year 2005

to ye ar 2016 VOC

11



### Additional p rojections u sed i n the proposed Toxics Rule

modeling for non

- EGU point sources (ptnonipm sector) a

NESHAP: Portland Cement ( 09/09/10)

– plant level based on Industrial Sector

Integrated Solutions (ISIS) policy emissions in 201 3. The ISIS results are from the ISIS

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

closures Hg, NO X , SO2,

PM, HCL 12

NESHAP: Industrial, Commercial, Institutional (ICI) , Boilers (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 Area Source Category (based on

proposed rule 04

- 15

- 10)

– finalized 12/2010 Hg 1 3 and

Appendix C

NESHAP: Mercury Emissions From Mercury Cell Chlor

- Alk ali Plants

- Final Rule

(12/19/03) Hg Appendix D

Pulp and Paper Project smelter replacement for Georgia Pacific plant in VA (12/2009) Hg Appendix D

NESHAP: Electric Arc Furnace Steelmaking Facilities (12/28/2007) Hg Appendix D

NESHAP: Hazardous Waste Comb ustion (12/19/2005) Hg Appendix D

New York o zone SIP c ontrols VOC, NO X , HAP VOC 1 4

Additional Plant and Unit closures provided by state, regional, and EPA agencies and

additional consent decrees All Appendix E

Emission Reductions resulting from contr ols put on specific boiler units (not due to

MACT) after 2005, identified through analysis of the control data gathered from the ICR

from the ICI Boiler NESHAP. NO X , SO 2 , HCL

Section 3.2.6

NESHAP: Reciprocating Internal Combustion Engines (RICE) b NO X , CO, PM Ap pendix F

Replaced 2005 with 2008 emissions for Corn Products International, Cook Cty, Illinois,

due to the shutdown of 3 boilers and addition of a new boiler (subject to Prevention of

Significant Deterioration and Requirements) . Agency Identifier: 031012 ABI (ILEPA) 1 5

a. W e gathered the data on the c onsent d ecrees 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/saintgobain

infosht011 0.html#pollutants ), both of which were signed in Jan 2010 . However, technical difficulties

with the projections software resulted in these reductions not being included for the 2016 projections.

The resulting emissions are therefore too high in CA, IL, IN, KS, LA, MA, MI, MO, NC, OH, OK, PA,

TX, WA, and WI, and are summarized nationally below. Although these missed reductions are large,

they have a minimal impact on our overall analysis because the modeling analysis for the RIA captures

an appropriate d ifference between the future base and policy cases and that difference is unaffected by

this omission since it was omitted from both the base and the policy cases.

CO

(tons) NOX

(tons) PM10

(tons) PM2_5

(tons) SO2

(tons) VOC

(tons)

110 13,214 269 210 16,2 70 6

b. Note that SO 2 reductions are expected to occur to due fuel sulfur limits but were excluded from the

projection. They were expected to reduce SO 2 by 27,000 tons, nationwide . This omission is expected to

have negligible impacts on our analysis since the reductions were omitted from both the base and policy

cases.



### Additional p rojections used i n the p roposed Toxics Rule modeling for Nonpoint sources (nonpt sector)

NESHAP: Reciprocating Internal Combustion Engines (RICE ) (as with pt non ipm, we

excl uded 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 em issions VOC, SO 2 , NO X , CO, PM Section 3.2.7

New York ozone SIP controls VOC 15

### 11

APPROACHES /REFERENCES

- Stationary Sources :

1. For Alcoa consent decree, used http:// cfpub.epa.gov/compliance/cases/index.cfm; for Motiva : used information sent

by State of Delaware

2. Used data provided by EPA, OAQPS , Sector Policies and Programs Division (SPPD)

– see Section 1

.

3. Obtained from Anne Pope , US EPA

- H azardous W aste I ncinerators criteria and hazardous air pollutant control s

carried over from 2002 Platform, v3

. 1.

4. Used data provided by EPA, OAQPS SPPD expert

– see Section 1

.

5. Percent reductions and plants to receive reductions based on recommendations by rule lead engineer, and are

consisten t with the reference: EPA, 2007a

6. Percent reductions recommended are determined from the existing plant estimated baselines and estimated reductions

as shown in the Federal Register Notice for the rule. SO 2 percent reduction are computed by 6

, 147 /30,783 = 20 %

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 Interstate Rule (CAIR) , which estimate d reductions of “ PM emissions

by 10,538 tpy, a reduction of about 62%. ” Used same list of plan ts as were identified based on tonnage and SCC

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

8. Except for dairy cows and turkeys (no growth), based o n animal populatio n growth estimates from the US

Department of Agriculture ( USDA ) and the Food and Agriculture Policy and Research Institute. See Section 3.2.1.

9. Growth and Decline in woodstove types based on industry trade group data, See S ection . See Section 3.2.2

.

10. VOC emission ratios of year 20 16 (linear interpolation between 2015 and 2020)

- specific from year 2005 from the

National Mobile Inventory Model (NMIM ) results for onroad refueling including activity growth from VMT, Stage II

contro l programs at gasoline stations, and phase in of newer vehicles with onboard Stage II vehicle controls.

11. VOC and benzene emissions for year 20 16 (linear interpolation between 2015 and 2020) from year 2002 from

MSAT2 rule (EPA, 2007b )

12. Data files for the ceme nt sector provided by Elineth Torres, EPA

- SPPD, from the analysis done for the Cement

NES H AP: The ISIS documentation and analysis for the cement NESHAP/NSPS is in the docket of that rulemaking

docket # EPA

- HQ

- OAR

- 2002

- 005. The Cement NESHAP is in the Fe deral Register: September 9, 2010 (Volume

75, Number 174

, Page 5496 9

- 55066

13. Data provided by Chuck French, US EPA, plant

- specific emissions after the rule were estimated. Consistent with

proposed rule for Gold Mine Ore Processing NESHAP: 75 FR 22469

. 14. 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 n onroad m obile c ontrols

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

National Onroad Rules:

Tier 2 Rule

2007 Onroad Heavy

- Duty Rule

Final Mobile Source Air Toxics Rule (MSAT2)

Renewable Fuel Standard all

Local Onroad Programs:

National Low Emission Vehicle Program (NLEV)

Ozone Transport Commission (OTC) LEV Program VOC 2

National Nonroad Controls:

Clean Air Nonroad Diesel Final Rule

– Tier 4

Control of Emissions from Nonroad Large

- Spark Ignition Engines and Recreational

Engines (Marine and Land Based): “Pentathalon Rule”

Clean Bus USA Program

Control of Emissions of Air Pollution from Locomotives and Mari ne Compression

- Ignition

Engines Less than 30 Liters per Cylinder all 3,4,5

Aircraft: all 6

### 12

Itinerant (ITN) operations at airports to year 2020

Locomotives:

Energy Information Administration (EIA) fuel consumption projections for freight rail

Clean Air No nroad Diesel Final Rule

– Tier 4

Locomotive Em iss ions Final Rulemaking, December 17, 1997

Control of Emissions of Air Pollution from Locomotives and Marine all EPA, 2009;

7; 4

Commercial Marine:

Category 3 marine diesel engines Clean Air Act and Internati onal 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 Ma rine Diesel Engines, December 29, 1999

Tier 1 Marine Diesel Engines, February 28, 2003 all 7; EPA, 2009

a. These control programs are the same as were used in the proposed Transport Rule except for the C3 marine standards of

April 2010, which are included i n the Toxics Rule but were not included in the proposed transport rule.

APPROACHES/REFERENCES

– Mobile Sources

1. http://www.epa.gov/airmarkt/progsregs/epa

- ipm/index.html

2. Only for stat es submitting these inputs: http://www.epa.gov/otaq/lev

- nlev.htm

3. http://www.epa.gov/nonroad

- diesel/2004fr.htm

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

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

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

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

7. http://www.epa.gov/nonroad

- diesel/2004fr.htm



## 3.1 Stationary Source Projections: EGU sector (ptipm)

The future

- year data for the pt ipm sector used in the air quality modeling were created by an interim version

4.10 of the Integrated Planning Model (IPM) ( http://www.epa.gov/airmarkt/progsregs/epa

- ipm/index.html ).

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

sector. V ersion 4.10 reflects state rules and consent decrees through December 1, 2010 and incorporates

information on existing controls collected throug h 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 Source Review (NSR), state settlements, or state

- specifi c

rules were modeled by IPM to either operate those controls or not based on economic efficiency parameters

. For the modeling data files, u nits with advanced mercury controls (e.g., ACI) were assumed to operate those

controls in states with mercury require ments. Note that this base case includes the proposed Transport Rule,

which is expected to be finalized in June, 2011. Speciated mercury emissions were estimated using mercury

speciation factors, which are assigned based on coal rank (e.g., bituminous, s ub

- bituminous, lignite) , firing

type, boiler/burner type, and post

- combustion emissions controls. These are the same factors as were used in

the Clean Air Mercury R ule and are provided in Appendix G

. Further details on the EGU emissions

inventory used fo r this proposal can be found in the proposed Toxics Rule IPM T echnical S upport

D ocument

.

IPM is run in 5 year increments. The IPM 2015 results are valid for representing 2014, 2015, and 2016. As

explained in the IPM TSD, additional steps were taken to ensure that the results were valid for use in a 2014,

2015 or 2016 model run.

### 13 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 to the IPM

- estimated fuel thro ughput based on fuel,

configuration and controls to compute the filterable and condensable components of PM. This methodology

is documented in the IPM TSD. For the post processing of the IPM data used for the air quality modeling, an

erroneous natural ga s emission factor that was t oo high by about a factor of 75 was used. This was

responsible for an over

- prediction in directly emitted PM 2.5 

### emissions from the EGU sector of 85 thousand

tons in the base case. This erroneous emissions factor was used in bo th the base and policy cases. For areas

of the country in which more natural gas is projected to be used in the policy case, the emission factor error

resulted in an overestimate of PM emissions in the policy case. This error does not impact secondary PM

formation which is due to emissions of precursors such as SO 2



### .

Appendix H further details the emission factor error.

We adjusted the inventory files resulting from the IPM post processing to account for criteria pollutant

reductions from the proposed Boil er MACT, which impacts only units that are the less than 25 megawatt

(MW). For mercury from these units, we took a different approach because we used the Boiler MACT

mercury ICR data directly in our modeling data files, as explained next.

For mercury, we removed the emissions from the IPM sector data for sources matched to the Boiler MACT

ICR data to avoid double counting the ptipm sector boiler Hg with the ICR data, which was incorporated

only into the ptnonipm sector. We could not match ICR units dir ectly to NEI or IPM units because the

identification codes used in the ICR database were different from those in the NEI and IPM outputs.

Consequently, we developed the following criteria to prevent double counting of the Hg emissions.

Emissions of Hg we re removed from the IPM outputs if a unit had a design capacity less than 25 MW and

the facility matched a facility in the ICR database. The matching was done using the NEI_UNIQUE_ID

field in the NEI and IPM output files 1



### . This approach resulted in the r emoval of 0.124 tons of Hg from the

2016 base year emissions ptipm sector emissions.

For the other pollutants, we applied reductions to ptipm data intended to represent the major Boiler National

Emissions Standards for Hazardous Air Pollutants ( NESHAP )

. In particular, we adjusted unit level SO 2 

### , PM10, PM 2.5 

### , CO, HCL and VOC using unit

- specific reduction information from the Boiler MACT ICR

database. Because we were unable to match the specific units from the Boiler MACT ICR to the units in the

ptipm file , we used the following approach:

(1) Match the ptipm sector inventory and the Boiler MACT data at the facility level.

(2) For facilities that match, use only units that are less than 25 MW and adjust only processes

from these units that have the same fuel type as listed in the ICR boiler MACT database.

Since the boiler MACT ICR fuels do not match one

- to

- one with the fuels described by the source

classification codes in the IPM outputs , we used a fuels crosswalk. Application of these unit

- specific

reductions r esulted in a reduction of 821 tons of CO, 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

.

1 The NEI_UNIQUE_ID was added to the 7,740 records (approximately 1,500 facilities) of the ICR data and this allowed

matching of the boiler MACT ICR data to the NEI at a facility level. Roughly 1,250 of the 1,500 facilities in the ICR database,

representing 83% of the ICR SO 2 emissions were mapped at the facility level.

### 14

Table 3

- 2

. Adjustment to IPM emissions due to application of Boiler MACT.

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

IPM

Base Boiler

MACT %

Reduct. IPM Base Boiler

MACT %

Reduct. IPM Base Boiler

MACT %

Reduct. IPM Base Boiler

MACT %

Reduct. IPM Base Boiler

MACT %

Reduct.

AL 17,518 0 9,086 0 13,821 0 172,198 0 1,297 0

AZ 10,789 0 422 0 8,243 0 25,235 0 867 0

AR 10,380 0 8,265 0 4,358 0 88,049 0 651 0

CA 41,700 0 242 0 10,693 0 4,886 0 818 0

CO 9,428 18 5,191 52 4,469 3 79,129 91 5 682 1

CT 8,958 0 667 0 1,715 0 2,715 0 108 0

DE 1,544 0 29 0 606 0 1,975 0 47 0

FL 64,017 0 6,777 0 26,822 0 172,059 0 1,929 0

GA 12,583 0 2,555 0 14,721 0 91,901 0 1,175 0

ID 553 0 0 0 189 0 0 0 14 0

IL 19,707 0 11,375 0 10,903 25 166,000 0 1,968 0

IN 22,623 75 17,443 16 21,258 9 229,802 2,077 2,100 1

IA 7,828 16 9,064 51 5,290 22 98,632 1,276 781 2

KS 5,146 0 3,361 0 4,607 0 61,664 0 699 0

KY 32,492 0 3,132 0 13,446 0 123,098 0 1,570 0

LA 22,730 0 7,020 0 4,400 0 85,987 0 656 0

ME 2,224 0 23 0 165 0 289 0 18 0

MD 9,852 0 1,743 0 3,979 0 37,665 0 463 0

MA 7,702 0 402 0 3,495 0 9,340 0 265 0

MI 17,493 18 17,396 41 6,475 18 169,757 428 1,264 2

MN 6,169 33 2,077 155 9,250 70 51,124 3,728 648 4

MS 7,764 0 4,022 0 2,777 0 56,006 0 409 0

MO 14 ,359 63 19,201 126 7,464 114 172,031 4,870 1,682 1

MT 3,616 0 512 0 2,317 0 12,565 0 247 0

NE 4,699 0 8,122 0 2,693 0 77,965 0 546 0

NV 5,783 0 460 0 10,665 0 11,429 0 336 0

NH 2,415 0 191 0 1,174 0 4,723 0 107 0

NJ 8,669 0 112 0 3,600 0 7,769 0 328 0

NM 7,969 0 103 0 5,782 0 11,353 0 544 0

NY 20,012 0 1,674 0 7,098 0 35,139 0 671 0

NC 10,933 509 1,396 156 11,935 22 77,091 5,456 942 5

ND 7,268 0 2,836 0 5,923 0 119,480 0 858 0

OH 27,806 29 12,205 18 21,828 240 202,779 156 1,929 2

OK 26,742 0 10,6 18 0 7,290 0 139,801 0 909 0

OR 2,204 0 1,130 0 876 0 11,102 0 100 0

PA 31,503 0 3,278 0 21,826 0 152,951 0 1,871 0

RI 1,594 0 0 0 544 0 0 0 40 0

SC 9,670 0 2,586 0 10,917 0 105,085 0 706 0

SD 679 0 1,390 0 704 0 28,170 0 127 0

### 15 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.

TN 7,351 0 7,304 0 6,8 55 0 106,762 0 948 0

TX 105,672 0 26,701 0 38,275 0 421,102 0 5,051 0

UT 4,090 0 949 0 5,074 0 32,636 0 487 0

VT 0 0 0 0 0 0 0 0 0 0

VA 14,037 0 1,199 0 7,470 0 45,726 0 563 0

WA 3,544 0 2,968 0 1,534 0 29,165 0 206 0

WV 11,783 0 2,361 0 16,131 0 127 ,788 0 1,223 0

WI 14,789 61 7,166 110 6,259 24 77,675 1,333 993 1

WY 7,499 0 2,582 0 7,362 0 55,571 0 923 0

Tribal data 273 0 0 0 93 0 0 0 7 0

Grand Total 694,158 821 227,335 725 383,370 546 3,793,365 20,239 40,775 18



### After the Boiler MACT reductions were applied, we generated day

- specific emissions for input to SMOKE.

The approach was modified from the base year to account for summer and winter differences in emissions

that are provided by CAMD. In particular, CAMD provides the average

- day summer a nd average

- day

winter emissions. These are used in the process of developing monthly emissions as shown in Figure 3

- 1

. Daily emissions use the same approach as 2005, which is to apply state averaged month

- to

- day factors from

the 2005 CEM data for NO X 

### , SO 2 

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

Figure 3

- 1

. A pproach to Compute Monthly Emissions from the IPM data

### 16 

## 3.2 Stationary Source Projections: non

- EGU sectors (ptnonipm, nonpt, a g,

afdust)

To project U.S. stat ionary sources other than ptipm , we applied growth factors and/or controls to certain

categories within the ptnonipm, nonpt, ag and afdust platform sectors. This subsection provides details on

the data and projection methods used for these sectors. In estimating future

- year emissions, we assumed that

emissions growth does not track with economic growth for many stationary non

- IPM sources. This “no

growth” assumption is based on an examination of historical emissions and eco nomic data. While we are

working toward improving the projection approach in future emissions platforms, we are still using the no

growth assumption for th e 2005, v4.1 platform. More details on the rationale for this approach can be found

in Appendix D o f the Regulatory Impact Assessment f or the PM NAAQS rule (EPA, 2006 ).

Year

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

otherwise. Growth factors (and control factors) are provided in the following sect ions where feasible.

However, some sectors used growth or control factors that varied geographically and their contents could not

be provided in the following sections (e.g., gasoline distribution varies by county and pollutant and has

thousands of record s). If the growth or control factors for a sector are not provided in a table in this

document, they are available as a “projection” or “control” packet for input to SMOKE on the v4.1 platform

website (see the end of Section 1 )

.



## 3.2.1 Livestock emissions growth (ag, afdust)

Growth in ammonia (NH 3 

### ) and dust (PM 10 

### and PM 2.5 

### ) emissions from livestock in the ag and afdust and

ptnonipm sectors was based on projections of growth in animal population. While there are some livestock

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

to that sector. This results in an underestimate of NH 3 

### of roughly 1

, 620 tons in 2016 (primarily in Kansas

and Minnesota for which the NH 3 

### were reported at specific farms in the point source inventory)

, and for

PM 2.5 

### the 2016 underestimate is 3 tons. All of these omissions have a negligible impact on the results of this

analysis because the mass impacted is so small and because the omissions were made in both the future base

and policy cases.

Table 3

- 3 provides the growth factors from the base case 2005 emissions to 2016 for animal categories

applied to the ag and afdust sectors for livestock

- related SCCs. For example, year 2016 beef emissions are

1.9% larger than the 2005 base case emissions. Except for dairy cows and turkey production, the animal

projection factors are derived from national

- level animal population projections from the U.S. Department of

Agriculture (USDA) and the Food and Agriculture Policy and Research Institute (FAPRI). For dairy cows

and turkeys

, we assumed that there would be no growth in emissions. This assumption was based on an

analysis of historical trends in the number of such animals compared to pro duction rates. Although

productions rates have increased, the number of animals has declined. Thus, we do not believe that

production forecasts provide representative estimates of the future number of cows and turkeys; therefore,

we did not use these for ecasts for estimating future

- year emissions from these animals. In particular, the

dairy cow population is projected to decrease in the future as it has for the past few decades; however, milk

production will be increasing over the same period. Note that the ammonia emissions from dairies are not

directly related to animal population but also nitrogen excretion. With the cow numbers going down and the

production going up we suspect the excretion value will be changing, but we assumed no change because we

did not have a quantitative estimate.

The inventory for livestock emissions used 2002 emissions values therefore, our projection method projected

from 2002 rather than from 2005.

### 17 Appendix E in the 2002v3 platform documentation provides the animal popu lation data and regression

curves used to derive the growth factors:

http://www.epa.gov/scram001/reports/Emissions%20TSD%20Vol2_Appendices_01

- 15

- 08.pdf . Appe ndix 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 Resi dential 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 r eplace older, higher

- polluting wood stoves, there will be an overall reduction of the emissions

from these sources. The approach and values cited here was developed with assistance from the EPA team

working on the woodstoves change

- out program ( http://www.epa.gov/woodstoves/index.html ). They

worked with the Hearth, Patio, and Barbecue Association to see what the best

- available data were for

projecting this sector into the future.

The specific assu mptions we made were:

 Fireplaces, SCC=2104008001: increase 1%/y ea r

 Old woodstoves, SCC=2104008002, 2104008010, or 2104008051: decrease 2%/y ea r

 New woodstoves, SCC=2104008003, 2104008004, 2104008030, 2104008050, 2104008052 or

2104008053: increase 2%/y ea r

F or the general woodstoves and fireplaces category (SCC 2104008000) we computed a weighted average

distribution based on 19.4% fireplaces, 71.6% old woodstoves, 9.1% new woodstoves using 2002v3

Platform (these emissions have not been updated for the 2005v4 platform used for the Transport Rule

proposal) emissions for PM 2.5 

### . These fractions are based on the fraction of emissions from these processes in

the states that did not have the “general woodstoves and fireplaces” SCC in the 2002 v3 NEI. This approach

r esults in an overall decrease of 1.056% per year for this source category.

Table 3

- 4 presents the projection factors used to project the 2005 base case (2002 emissions) for residential

wood combustion.

### 18

Table 3

- 4

. Projection Factors for growing year 2005 Residential Wood Combustion Sources to 2016

SCC SCC Description Projection

Factors

2016

2104008000 Total: Woodstoves and Fireplaces 0.8522

2104008001 Fireplaces: G eneral 1.1400 2104008070 Outdoor Wood Burning Equipment

2104008002 Fireplaces: Insert; non

- EPA certified

0.7200 2104008010 Woodstoves: General

2104008051 Non

- catalytic Woodstoves: Non

- EPA certified

2104008003 Fireplaces: Insert; EPA certified; non

- catalytic

1.28 2104008004 Fireplaces: Insert; EPA certified; catalytic

2104008030 Catalytic Woodstoves: General

2104008050 Non

- catalytic Woodstoves: EPA certified

2104008052 Non

- catalytic Woodstoves: Low Emitting

2104008053 Non

- catalytic Woodstove s: Pellet Fired



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

Emissions from Stage II gasoline operations in the 2005 base case are contained in both nonpt and ptnonipm

sectors. The only SCC in the nonpt inventory used for gasoline Stage II em issions is 2501060100 (Storage

and Transport; Petroleum and Petroleum Product Storage; Gasoline Service Stations; Stage II: Total). The

following SIC and SCC codes are associated with gasoline Stage II emissions in the ptnonipm sector:

 SIC 5541 (Automoti ve Dealers & Service Stations, Gasoline Service Stations, Gasoline service

stations)

 SCC 40600401 (Petroleum and Solvent Evaporation;Transportation and Marketing of Petroleum

Products;Filling Vehicle Gas Tanks

- Stage II;Vapor Loss w/o Controls)

 SCC 406004 02 (Petroleum and Solvent Evaporation;Transportation and Marketing of Petroleum

Products;Filling Vehicle Gas Tanks

- Stage II;Liquid Spill Loss w/o Controls)

 SCC 40600403 (Petroleum and Solvent Evaporation;Transportation and Marketing of Petroleum

Products ;Filling Vehicle Gas Tanks

- Stage II;Vapor Loss w/o Controls)

 SCC 40600499 (Petroleum and Solvent Evaporation;Transportation and Marketing of Petroleum

Products;Filling Vehicle Gas Tanks

- Stage II;Not Classified

We used a consistent approach across nonp t and ptnonipm to project these gasoline stage II emissions. The

approach involved computing VOC

- specific projection factors from the NMIM results for onroad refueling,

using ratios of futur e

– year emissions to 2005 base

- case emissions. The approach accou nts for three elements

of refueling growth and control: (1) activity growth (due to VMT growth as input into NMIM), (2) emissions

reductions from Stage II control programs at gasoline stations, and (3) emissions reductions resulting from

the phase

- in over time of newer vehicles with onboard Stage II vehicle controls. We assumed that all areas

with Stage II controls in 2005 continue to have Stage II controls in 2016

.

We computed VOC, benzene and naphthalene projection factors at a county

- specific, annual r esolution as

shown below:

### 19

PF [county, future year] 

### = VOC_RFL [county, future year] 

### /VOC_RFL [county, 2005]



### PF [county, future year] 

### = BENZENE_RFL [county, future year] 

### / BENZENE _RFL [county, 2005]



### PF [county, future year] 

### = NAPHTHALENE_RFL [county, future year] 

### / NAPHTHALENE _RFL [county, 2005]



### Where VOC_RFL is the VOC refueling emissions for onroad sources from NMIM

, BENZENE_RFL is the BENZENE refueling emissions for onroad sources from NMIM , and

NAPHTHALENE_RFL is the NAPHTHALENE refueling emissions for onr oad sources from

NMIM

.

For this 2016 projection, we obtained VOC_RFL, BENZENE_RFL and NAPHTHALENE_RFL for each

county by interpolating 2015 and 2020 NMIM results.

We applied these projection factors to both nonpt and ptnonipm sector gasoline stage II sou rces.

Chemical speciation uses certain VOC HAPs for some sources, specifically, benzene, acetaldehyde,

formaldehyde, and methanol (BAFM). The VOC HAPs are used for sources that have consistent VOC and

VOC HAPs using various criteria as described in the 2 005 v4.1 platform documentation, and these sources

are called “integrated” sources. The nonpoint gasoline stage II emissions are an integrated source, and so the

VOC HAPs are also projected based on ratios of future year and base year VOC. The only two V OC HAPs

emitted from refueling are benzene and naphthalene, and both of these w ere projected consistently with

VOC. However, naphthalene was not used in the chemical speciation (it is not B,A,F or M) and was

therefore not used for this effort. Benzene wa s used as part of the speciation for the nonpt sector gasoline

stage II sources. The ptnonipm is a “no

- integrate” sector, so ptnonipm gasoline stage II sources did not use

the projected benzene as part of the speciation, but rather used VOC speciation to estimate benzene.



## 3.2.4 Portable fuel container growth and control (nonpt)

We obtained future

- year VOC emissions from Portable Fuel Containers (PFCs) from inventories developed

and modeled for EPA‟s MSAT rule (EPA, 2007b). The 10 PFC SCCs are summarized below ( full SCC

descriptions for these SCCs include “Storage and Transport; Petroleum and Petroleum Product Storage” as

the beginning of the description) below.

2501011011 Residential Portable Fuel Containers: Permeation

2501011012 Residential Portable Fuel Co ntainers: Evaporation

2501011013 Residential Portable Fuel Containers: Spillage During Transport

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

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

2501012011 Commercial Portable Fuel Containers: Permeation

2501012012 Commercial Portable Fuel Containers: Evaporation

2501012013 Commercial Portable Fuel Containers: Spillage During Transport

2501012014 Commercial Portable Fuel Contain ers: Refilling at the Pump: Vapor Displacement

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



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

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

The future

- year emissions reflect projected increases in fuel consumption, state programs to reduce PFC

emissions, standards promulgated in the MSAT rule, and impacts of the Renewable Fuel Standard (RFS) on

gasoline volatility. Future

- year emissions for PFCs were available for 2010, 2015, 2020, and 2030. In

### 20 creating the inventories for 2016, we linearly interpolated year 2015 and year 2020 inventories. Benzene and

other VOC HAP future

- year PFC emissions were also included in the interpolation. Benzene was used in

VOC speciation for CMAQ through the modification of VOC speciation profiles calculations (no other

BAFM HAPs are emitted from PF Cs).



## 3.2.5 Aircraft growth (ptnonipm)

As with the 2005v4 platform, aircraft emissions are contained in the ptnonipm inventory. These 2005 point

source emissions are projected to future years by applying activity growth using data on itinerant (ITN)

operations at airports. The ITN operations are defined as aircraft take

- offs whereby the aircraft leaves the

airport vicinity and lands at another airport, or aircraft landings whereby the aircraft has arrived from outside

the airport vicinity. We used projected IT N information available from the Federal Aviation

Administration‟s (FAA) Terminal Area Forecast (TAF) System: http://www.apo.data.faa.gov/main/taf.asp

(publication date December 2008). This informatio n is available for approximately 3

, 300 individual airports,

for all years up to 2025. We aggregated and applied this information at the national level by summing the

airport

- specific (U.S. airports only) ITN operations to national totals by year and by ai rcraft operation, for

each of the four available operation types: commercial, general, air taxi, military. We computed growth

factors for each operation type by dividing future

- year ITN by 2005

- year ITN. We assigned factors to

inventory SCCs based on the operation type.

The methods that the FAA used for developing the ITN data in the TAF are documented in:

http://www.faa.gov/data_researc h/aviation/aerospace_forecasts/2009

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

Table 3

- 5 provides the national growth factors for aircraft; all factors are applied to year 2005 emissions.

For example, year 201 6 commer cial aircraft emissions are 11.18 % higher than year 2005 emissions.

Table 3

- 5

. Factors used to project base case 2005 aircraft emissions to future years

SCC SCC Description Year 201 6

factor

2275001000 Mil itary aircraft 0.968

2275020000 Commercial aircraft 1.1118

2275050000 General aviation 0.9952

2275060000 Air taxi 0.9235

27501015 Internal Combustion Engines;Fixed Wing Aircraft L & TO

Exhaust;Military;Jet Engine: JP

- 5 0.968

27502001 Internal Combusti on Engines;Fixed Wing Aircraft L & TO

Exhaust;Commercial;Piston Engine: Aviation Gas 1.1118

27502011 Internal Combustion Engines;Fixed Wing Aircraft L & TO

Exhaust;Commercial;Jet Engine: Jet A 1. 1118

27505001 Internal Combustion Engines;Fixed Wing Aircra ft L & TO

Exhaust;Civil;Piston Engine: Aviation Gas 0.9 952

27505011 Internal Combustion Engines;Fixed Wing Aircraft L & TO

Exhaust;Civil;Jet Engine: Jet A 0.9 952

27601014 Internal Combustion Engines;Rotary Wing Aircraft L & TO

Exhaust;Military;Jet Engine : JP

- 4 0.968

27601015 Internal Combustion Engines;Rotary Wing Aircraft L & TO

Exhaust;Military;Jet Engine: JP

- 5 0.968



### We did not apply growth factors to any point sources with SCC 27602011 (Internal Combustion Engines;

Rotary Wing Aircraft L & TO Exhaus t; Commercial; Jet Engine: Jet A) because the plant names associated

### 21 with these point sources appeared to represent industrial facilities rather than airports. This SCC is only in

one county, Santa Barbara, California (State/County FIPS 06083).

None of o ur aircraft emission projections account for any control programs. We considered the NO X 

### standard adopted by the International Civil Aviation Organization‟s (ICAO) Committee on Aviation

Environmental Protection (CAEP) in February 2004, which is expected t o 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; therefore, the

effects of this rule were not included in the future

- year emissions projections.



## 3.2.6 Stationary Source control progra ms, consent decrees & settlements, and

plant closures (ptnonipm , nonpt )

We applied emissions reduction factors to the 200 5 emissions for particular sources in the ptnonipm and

nonpt sectors to reflect the impact of stationary

- source control programs includ ing 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 (2005 v2 point inventory). This assumption was confirmed by review of the 2005 NEI that showed

reductions from Large Boiler/Turbines and Large Internal Combustion Engines in the Northeast states

covered by the NOx SIP call. The future

- year base contr ols consist of the following:

We did not include MACT rules where compliance dates were prior to 2005

, because we assumed

these were already reflected in the 2005 inventory

. The EPA OAQPS Sector Policies and Programs

Division (SPPD) provided all controls information related to the MACT rules , and this information is

as consistent as possible with the preamble emissions reduction percentages for these rules

.

We included plant closures (i.e., emissions were zeroed out for future years) where information

ind icated that the plant was actually closed. However, plants projected to close in the future (post

200 8 ) were not removed in the future years because these projections can be inaccurate due to

economic improvements

. Not including cement kiln and plant clo sures discussed later in Section

3.2.6.1, w e also applied plant closures listed in Appendix E

. The magnitude of the se non

- cement

plant closures is shown in Table 3

- 6 below.

### 22

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 0.02 50

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 0.009 4 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 0.001 3 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 Y ork 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 0.0 05 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. 1 2 75 0.18 614.54

* For one “closure” in Illinois, we added in 2008 emissions due to a replacement of several units with a new PSD unit. The added emissions

are: 246 tons CO, 0.52 tons NH3, 719.3 tons NOX, 515 tons PM10, 418 tons PM2.5 and 2,203 tons SO2 and 100 tons HCL

.



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

because of technica l difficulties with software

. These proposed transport rule closures affect the following

sources: auto plants, pulp and paper plants, large and small municipal waste combustors (LMWC and

SMWC), as well as plants closed before 2008 but following the relea se of the 2005 v2 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

.

### 23

Table 3

- 7

. Q uantification of Missing Closures in 2016 the non

- EGU ( ptnonipm ) sector

S tate Pollutant non

- EGU

emissions without

closures (tons) non

- EGU

emissions with

closures (tons) Excess

emissions

(tons) Percent Overestimate

in Total man

- made

Emissions

Georgia VOC 3 3,214 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 effects of the Department of Justice Settlements and

Consent Decrees on the non

- EGU ( ptnonipm ) sector emissions

. We also included estimated impacts

of HAP standards per Section 112, 129 of the Clean Air Act on the non

- E GU ( ptnonipm ) and

nonpoint ( nonpt ) sector emissions, based on expected CAP co

- benefits to sources in these sectors.

Numerous controls have compliance dates beyond 2008; these include refinery and the Office of

Compliance and Enforcement (OECA) consent decr ees, Department of Justice (DOJ) settlements, as

well as most national VOC MACT controls. Additional OECA consent decree information is

provided in Appendix C, and the detail ed data used are available at the website listed in Section 1.

The EPA OAQPS SPPD provided refinery consent decrees cont rols at the facility and SCC level.

We applied most of the control programs as replacement controls, which means that any existing

percent reductions (“baseline control efficiency”) reported in the NEI were removed pr ior to the

addition of the percent reductions due to these control programs. Exceptions to replacement controls

are “additional” controls, which ensure that the controlled emissions match desired reductions

regardless of the baseline control efficiencies in the NEI. We used the “additional controls” approach

for many settlements and consent decrees where specific plant and multiple

- plant

- level

reductions/targets were desired and at municipal waste landfills where VOC was reduced 75% via a

MACT control usi ng projection factors of 0.2 5

. We applied New York State Implementation Plan available 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 g uidance. These reductions are found in Appendix J in :

http://www.dec.ny.gov/docs/air_pdf/NYMASIP7final.pdf. See Section 3.2.6.



## 3.2.6.1 Reductions from the Portland Cement NESHAP

As indicated in Table 3

- 1 , the Industrial Secto rs Integrated Solutions (ISIS) model (EPA, 2010b) was used to

project the cement industry component of the ptnonipm emissions modeling sector to 2016. This approach

provided reductions of criteria and hazardous air pollutants, including mercury. The ISIS cement emissions

were developed in support for the Portland Cement NESHAPs and the NSPS for the Portland cement

manufacturing industry.

### 24 The ISIS model produced a Portland Cement NESHAP policy case of multi

- pollutant emissions for

individual cement kilns (emission inventory units) that were relevant for years 2013 through 2017. These

ISIS

- based emissions included information on new cement kilns, facility and unit

- level closures, and updated

policy case emissions at existing cement kilns.

The ISIS model results for the future show a continuation of the recent trend in the cement sector of the

replacement of lower capacity, inefficient wet and long dry kilns with bigger and more efficient preheater

and precalciner kilns. Multiple regulatory requirements s uch as the NESHAP and NSPS currently apply to

the cement industry to reduce CAP and HAP emissions. Additionally, state and local regulatory

requirements might apply to individual cement facilities depending on their locations relative to ozone and

PM 2.5 

### n onattainment areas. The ISIS model provides the emission reduction strategy that balances: 1)

optimal (least cost) industry operation, 2) cost

- effective controls to meet the demand for cement, and 3)

emission reduction requirements over the time period of interest. Table 3

- 8 shows the magnitude of the

ISIS

- based cement industry reductions in the future

- year emissions that represent 2016 , and the impact that

these reductions have on total stationary non

- EGU point source (ptnonipm) emissions.

Table 3

- 8

. Future

- year ISIS

- based cement industry annual reductions (tons/yr)

for the non

- EGU ( ptnonipm ) sector

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 r eductions to boilers in the ptnonipm sector based on the Boiler MACT proposal , with

additional adjustments that made it similar to the final Boiler MACT

. In particular

, we applied reductions to

(1) boilers in the inventory that could be matched to facilit y/fuel (2005 NEI NEI_UNIQUE_ID and SCC

fields) combinations in the ICR data and (2) boilers in the inventory that were not in the Boiler MACT ICR

database

, but that the Boiler MACT project team indicated were part of the category.

Different approaches w ere used for applying the reductions for mercury versus the other pollutants because

the mercury emissions from the ICR were used directly in the ptnonipm sector

, whereas the other pollutants

used the 2005 v2 NEI data 2



### .

For mercury, the Boiler MACT ICR

- b ased unit

- level emissions inventory, using facility and unit IDs from

the boiler MACT ICR database, was incorporated into the base

- case 2005 ptnonipm sector. Modeling

parameters such as geographic coordinates and stack parameters were obtained by matching the facilities in

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

2 This approach was based on exploring the sources of the NEI versus ICR data, particularly the SO 2 val ues. We determined that a

large portion of the ICR data utilized average emission factor that did not account for the sulfur content of the fuel nor an y 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.

### 25 2004v4.1 platform documentation). The amount of mercury from facilities that did not match summed to

0.177 tons (about 4%); thes e facilities were not included in base

- case 2005 ptnonipm sector. For the units

included in the sector, we applied unit

- specific reductions provided by the rule developers to the ICR

- based

base

- case emissions. These reductions were provided to a modified database developed between the

proposal and final rule.

In addition, we applied Hg reductions to boilers at 18 additional facilities (that were not included in the

Boiler MACT ICR data ) in consultation with the rule developers, resulting in an additional 0.013 tons of Hg

reductions

. These reductions were applied by facility and fuel type. A default percent reduction was used

for these sources , which we computed for each fuel type from the unit

- specific reductions. W e used the

mode of the data (i.e., mo st 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 th e percent reduction.

Table 3

- 9

. Default pollutant fuel reductions applied NEI boilers not covered by the ICR database.

F uel SO 2 %

reduction VOC %

reduction PM 2.5 %

reduction CO %

reduction HCL %

reducti on 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. Because we were unable to match the specific units from

the Boiler MACT ICR to the units in the 2005 inventory, we used the following approach. First we matched

the 2005 inventory and the Boiler MACT ICR data at the facility (NEI_UNIQUE_ID) level. At the facility

level, we were able to match facilities that c aptured 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 processes

from these facilities that have the same fuel type ( per the SCC description ) as the fuel listed in the ICR

B oiler MACT database. Because the ICR fuel types do not match the fuel definitions in the inventory , we

used a cross walk to match the ICR and inventory fuels , shown below in Table 3

- 10

. F or some NEI fuels

,

we allowed two possib ilities for ICR fuels to match to address the potential overlap of multiple ICR fuel

categories and to increase the number of facility/processes in the NEI that ma t ched units in the ICR database

. The second choice was only used when uni ts in the ICR database did not match any units in the inventory at

that facility.

Table 3

- 10

. Crosswalk of NEI fuels to IC R fuels

Inventory fuel category ICR f uel c ategory , 1 st 

### choice ICR fuel c ategory , 2 nd 

### choice

Bagasse Bagasse

C oal Coal

### 26

Inventory fuel category ICR f uel c ategory , 1 st 

### choice ICR fuel c ategory , 2 nd 

### choice

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 W et 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 ICR database were applied to the records in the inventory

that matched facility and fuel type for boilers and process heaters

. In addition

, default reductions were

applied, based on Table 3

- 9

, to units at the 18 facilities which were not in the Boiler MACT ICR, but were

determined to be part of the Boiler MACT category.

The resulting emissions reductions from the boiler MACT are shown in Tabl e 3

- 11

. The reduction of 1 .75

tons of mercury shown in the table is fairly consistent with the 1.45 ton mercury reduction cited by the final

Major and Area Boiler MACT Rules, especially considering that the rule was several months away from

finalization when these emissions project ions were done. The Boiler MACT proposal cited 8.25 tons of

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

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

### 27 Tabl e 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



## 3.2.6.3 Boiler reductions not associated with the MACT rule

The Boiler MACT ICR collected data on existing controls. We used an early version of the data base

entitled “ survey_database_2008_results 2 .mdb ” ( EPA

- HQ

- OAR

- 2002

- 0058

- 0788) which is posted under the

Tech nical I nformation for the Boiler MACT m ajor source rule

( http://www.epa.gov/ttn/atw/boiler/boilerpg.html ) We extracted all controls that were installed after 2005,

determined a percent reducti on, and verified that these controls were being used. In many situations we were

told that the controls were on site but were not being utilized. As a result, plant

- unit specific reductions

resulted in the state

- level change s 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 Su mmary of Mercury Reductions at non

- EGU stationary sources

-

( ptnoni pm )

The mercury emission projections included NESHAP for non

- EGU source categories that were finalized or

expected to be finalized prior to the proposed Toxics rule including the Boiler MACT (1. 75 tons reduction),

Portland Cement NESHAP (6.4 tons reduction ), Gold Mines NESHAP (1.8 tons reduction), Electric Arc

Furnaces NESHAP (2.4 tons reduction) , Mercury Cell Chlor

- Alkali NESHAP (2. 8 tons reduction) and

Hazardous Waste Combustion NESHAP (1.1 ton reduction 3 

### ) In addition, the projections included reductio n

of Hg emissions (0.7 ton reduction) due to the replacement of a smelter with a recovery boiler at a pulp and

paper plant

. Table 3

- 13 provides a summary of key mercury sectors and their 2005 and 2016 emissions.

3 Actual reduction for hazardous waste reduction should have been 0.2 tons, but due to an error in the percentage applie d, a higher

value was reduced.

### 28

Table 3

- 13

. Anthropogenic mercury emissions and projections in the Continental United States

Category 2005 Mercury

(tons) 2016 Mercury

(tons)



### Electric Generating Units 52.9 28.7 *

Portland Cement Manufacturing 7.5 1.1

Stain less and Nonstainless Steel Manufacturing: Electric Arc Furnaces 7.0 4.6

Industrial, Commercial, Institutional Boilers & Process Heaters 6.4 4.6

Chemical Manufacturing 3.3 3.3

Hazardous Waste Incineration 3.2 2.1

Mercury Cell Chlor

- Alkali Plants 3.1 0. 3

Gold Mining 2.5 0.7

Municipal Waste Combustors 2.3 2.3

Sum of other source categories (each of which emits less than 2 tons) 17 16

Total 105 6 4

*T he EGU emissions utilize the interim IPM4.10, which were used in the Air Quality Modeling. The final

I PM future year base case Hg total is 24.9 tons.

Reductions from Portland Cement and Boiler MACT were discussed above. The Gold Mine reductions were

provided by the SPPD project lead and are summarized in Appendix C.

Appendix F details the source of th e data used for the reductions for Electric Arc Furnaces NESHAP,

Mercury Cell Chlor

- Alkali NESHAP

, Hazardous Waste Combustion

, the pulp and paper plant reduction, and

methodology used to apply them.



## 3.2.7 O il and gas projections in TX, OK, and non

- California WR AP states (nonp t )

For the 2005v4.1 platform, we received updated 2005 oil and gas emissions from Texas and Oklahoma. We

also received 2008 data that we used for 2016, because it was the best available data to represent 2016.

We applied emissions reduct ions 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 

### reduction s associated with the

RICE NESHAP were not included due to lack of time to include. They would impact 2310000220

(Industrial Processes;Oil and Gas Production: SIC 13; Drill rigs) for which we estimate a reduction in 2005

emissions of about 4400 tons , nati onwide

.

We discovered after emissions processing was complete, that 2016 drill rig emissions estimates were

available for each year for TX . T hese correct 2016 emissions , provided by the Texas Commission on

Environmental Quality (TCEQ) are shown in Table 3

- 14 along with Ok lahoma data for 2005, 2008, and

adjusted 2008 using the RICE reductions, which were used for 2016.

### 29 Table 3

- 14

. 2005 , 2008 and estimated 2016 e missions for nonpoi nt Oklahoma and Texas oil and gas s ources

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 4 1,395 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 RICE reduc tions , which affects only CO, NO X , and VOC

** Table 1.1 in:

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

-

20090715

- ergi

- Drilling_Rig_EI.pdf



### As with the 2005 v4 platform, the v4.1 platform utilizes the Phase II WRAP oil and gas emissions data for

the non

- California Western Regional Air Partnership (WRAP) states for 2005. However, unlike the

projection methodology used for the proposed Transport Rule, in which we used the 2005 for both 2005 and

the future years (which were 2012 and 2014), we were able to obtain the WRAP 2018 Phase II emissions for

2016 for the Proposed Toxics Rule. These data became available in time for us to incorporate them into this

platform, and were the best available data to represent 2016. Like the Texas and Oklahoma data, we applied

RICE reductions to the 2018 WRAP data for use i n 2016. Table 3

- 15 shows the WRAP oil and gas

emissions in 2005, and future year, before and after the RICE reductions. The emissions used in 2016 are

the 2018 WRAP

- supplied emissions with the RICE reductions applied.

Table 3

- 15

. WRAP Oil a nd Gas Emissions: 2005 , 2018 WRAP, and 2016 with additional r eductions due to

the RICE NESHAP

CO (tons) NOX (tons) VOC (tons) SO2 (tons) *

State 2005 2018

WRAP 2016 * 2005 2018

WRAP 2016 * 2005 20 18

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 1 6 566 557 496 370 562 562 43 0

Utah 1,426 1,347 1,305 6,896 6,297 6,158 43,403 81,890 81,869 149 1

Wyoming 10,004 8,540 7,807 36,172 34,142 32,695 166,939 304,748 304,637 541 3

Grand Total 53,784 65,292 55,880 155,133 184,486 171,640 480,252 731,002 729 ,250 2,842 39

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

### 30 

## 3.2.8 Future Year VOC Speciation for gasoline

- related sources (ptnonipm,

nonpt)

To account for the future projected increase in the ethanol content of fuels, we used different future

- year

VOC speciation for certain gasoline

- related emission sources . Such so urces include gasoline stage II,

portable fuel containers ( PFCs )

, and finished fuel storage and transport

- related sources r elated to bulk

terminals (where the ethanol may be mixed) and downstream to the pump

. We ide ntified th is last group of

sources as “btp ” (from bulk terminals to pumps). While most of these sources are in the nonpt sector, there

are also some in the ptnoni pm sector

. In the 2005 base year we used zero percent ethanol (E0) fuel profiles;

however, for t he 2016 profiles we used combinations of E0 and ten percent ethanol ( E10 ) fuel profiles. The

fuel type fraction w as developed based on the Department of Energ y Annual Energy Outlook ( AEO ) 2007

projections of ethanol fuels for the year 2022. In the AEO 2007 data, t he proportions of E0 and E10 fuels are

the same for 2012 and years beyond (even though the quantities of the two fuels change over these years )

. The national level proportions were allocated to counties across the country using fuel modeling at the EPA

Office of Transportation and Air Quality

. All gasoline stage II and “btp” sources used the same combination

of E0 and E10 headspace profiles as were u sed for exhaust and evaporative profiles

.



## 3.3 Mobile source projections

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

combination of the NMIM and MOVES 2010 models. Future

- year e missions reflect onroa d mobile control

programs including the Light

- Duty Vehicle Tier 2 Rule, the Onroad Heavy

- Duty Rule, and the Mobile

Source Air Toxics (MSAT 2 ) final rule. Nonroad mobile emissions reductions for these years include

reductions to locomotives, various nonroad engines including diesel engines and various marine engine

types, fuel sulfur content, and evaporative emissions standards.

Onroad mobile sources are comprised of several components and are discussed in the next subsection ( 3.3.1 ).

Monthly nonroad mobile emission projections are discussed in subsection 3.3.2 . Locomotives and Class 1

and Class 2 commercial marine vessel (C1/C2 CMV) projections are discussed in subsection 3.3.3 , and Class

3 (C3) CMV projected emissions are discussed in subsection 3.3.4

.



## 3.3.1 Onroad mobile ( on_noadj , on_moves_runpm, on_moves_startpm )

The onroad emissions were primarily based on the 2010 version of the Motor Vehicle Emissions Simulator

( MOVES 2010 )

– the same version as was used for 2005. The same MOVES

- based PM 2.5 

### temperature

adjustment factors were applied as were used in 2005 for running mode emissions; however, cold start

emissions used year

- specific temperature adjustment factors. The temperature adjustments have the minor

limitation that they were based on the use of MOVES national default inputs rather than county

- specific

inputs, because a county

- specific database for input to MOVES was not available at the t ime this approach

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. Mercury was the only onroad HAP used in 2016 that did not

come from MOVES, since the capability for mercury from MOVES was not available at the time this work

was completed . For mercury, we used the 2005 NMIM

- based emissions for 2016.

California onroad (on_noadj)

Like year 2005 emissions, future

- year California NH 3 

### emissions are from MOVES runs for California

, disaggregated to the county level using NMIM

. For all other pollutants, w e did not use MOVES to generate

future

- year onroad emissions for California, because the 200 5 base year emissions were provided by

CARB ‟s Emission Factor s mobile model ( EMFAC ) , which CARB submitt ed for the 2005 NEI. For

California, we chose an approach that would maintain consistency between the 2005 and 2016 emissions.

### 31 This approach involved computing projection factors from a consistent set of future an d 2005

- year data

based on the EMFAC2007 model provided by CARB. We generated projection factors by dividing the

EMFAC2007

- based emissions for 2016 (linearly interpolated between year 2014 and year 2020) by the

EM FAC2007

- based emissions for 2005

. These EM FAC

- based emissions were provided in March 2007.

California does not specify road types, so we first used NMIM California ratios to break out vehicle

emissions to the match the more detailed NMIM level before projecting to 2016

.

HAP emissions were comput ed as 2005v2

- based HAP

- CAP ratios applied at the pollutant and Level 3 SCC

(first 7 characters) to 2016 CAP emissions. HAPs were scaled to either of three pollutants: exhaust PM 2.5 

### (e.g., metals), exhaust VOC (e.g., exhaust mode VOC HAPs such as acetaldeh yde and formaldehyde), or

evaporative VOC (e.g., evaporative mode VOC HAPs such as benzene).

MOVES

- based no

- adjust (on_noadj)

As discussed in the 2005v4 .1 platform documentation, the MO VES 2010 model was used for all vehicles

, road types, and pollutants ot her than mercury

, which came from 2005 NMIM

. VMT were projected using

growth rates from the Department of Energy‟s AEO2009. W e used MOVES 2010 to create emissions by

state , SCC, pollutant, emissions mode and month . We then allocated these emissions to co unties based on

20 1 5 NMIM county

- level data by state, SCC, pollutant, and emissions mode

. 2016 NMIM data were not

available for this effort, but the 2015 NMIM can reasonably be expected to be sufficient for this purpose.

While EPA will eventually replace this approach with a county

- specific implementation of MOVES, it was

the best available approach for this modeling.

MOVES

- based cold start and running mode (on_moves_startpm and on_moves_runpm)

MOVES

- based cold start and running mode emissions consist of gasoline exhaust speciated PM and

naphthalene. These pre

- temperature

- adjusted emissions are projected to year 2016 from year 2005

inventories using a 2016

- specific run of MOVES2010. VMT were projected using growth rates from the

AEO2009. As with the on_noadj sector , the 2016 MOVES2010 data were created at the state

- month level

, and the 2015 NMIM results were used to disaggregate the state level results to the county level.

As part of the SMOKE processing (described in the v4.1 platform documentation) , we applied MOVES

based temperature adjustment factors to gridded, hourly emissions using gridded, hourly meteorology.

Figure 3

- 2 illustrates the increase in PM emissions associated with decreasing temperatures for running

exhau st and starting exhaust in 2005 and 2015. For the running mode in 2016, w e used the same temperature

adjustment factors as the 2005 base case. However, cold start temperature adjustment factors decrease

slightly in future years, and for year 201 6 processi ng, we updated the temperature adjustment curves for

these cold start emissions to use the 2015 temperature adjustments which were the best available set of

adjustments at the time the work was done . The change from 2005 to 2015 adjustment factors ha s lit tle

impact, reducing cold

- start mode temperature

- adjusted PM and naphthalene by under 4% for temperatures

down to 0 F. Therefore, we were comfortable using 2015 adjustment factors rather than 2016.

### 32

Figure 3

- 2

. MOVES exhaust temperature adjustment functions for 2005and 201 5

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

After completion of the modeling, two errors were discovered in the PM emissions from the onroad sector

. The startpm and runpm sectors were impacted by the same error: they were doubly adjusted to account for

cold temperatures. Instead of processing 72°F PM emissions through SMOKE and doing the temperature

adjustment on the gridded hourly emissions , the PM emissions were already adjusted for state

- average

monthly temperatures and then additional adjustment factors were. The impact of this error was to over

estimate PM emissions particularly for the colder parts of the country which have the lowest temperatures

and highest adjustment factors. The on_noadj PM was also found to be incorrect. The error occurred during

SMOKE processing and resulting in dropping emissions of pre

- speciated exhaust PM 2.5 

### from diesel vehicles

due to incorrect SMOKE input s peciation profiles. The impact of this error was to underestimate the PM 2.5



### . T hese 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 i mpact of PM 2.5 

### errors in the onroad sectors

S tate 2016 PM 2.5

onroad

corrected 2016 PM 2.5

onroad

modeled Error in

onroad

PM 2.5 Total state

level 2016

PM 2.5 * Al l

- sector

PM 2.5

e rror**

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%

Delaw are 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%

### 33 S tate 2016 PM 2.5

onroad

corrected 2016 PM 2.5

onroad

modeled Error in

onroad

PM 2.5 Total state

level 2016

PM 2.5 * Al l

- sector

PM 2.5

e rror**

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%

O klahoma 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

- 2 2% 69,254

- 1%

Texas 11

, 683 6

, 116

- 48% 304,737

- 2%

Utah 1

, 418 2

, 334 65% 53,877 2%

Vermont 567 1

, 252 121% 9,018 8%

Virginia 3

, 759 4

, 327 15% 67,861 1%

Washington 3

, 346 3

, 675 10% 61,680 1%

West Virginia 843 1

, 088 29% 40,423 1%

Wisconsin 3

, 631 8

, 440 132% 63,379 8%

Wyoming 485 969 100% 66,505 1%

Total 102

, 170 188

, 630 85% 3,891,291 2%

*includes onroad, nonroad, ptnonipm, ptipm, afdust, avefire, alm_no_c3, seca_c3

**Negative means modeled emissions are an underestimate, positive means overestimate



## 3.3.2 Nonro ad mobile (nonroad)

This sector includes mont hly exhaust, evaporative and refueling emissions from nonroad engines (not

including commercial marine, aircraft, and locomotives) derived from NMIM for all states except California.

Like the onroad emissions, NMIM provides nonroad emissions for VOC by three emission modes: exhaust,

evaporative and refueling. Unlike the onroad sector, nonroad refueling emissions for nonroad sources are

not included in the nonpoint (nonpt) sector and so are retained in this sect or

.

With the exception of California, U.S. emissions for the nonroad sector (defined as the equipment types

covered by NMIM) were created using a consistent NMIM

- based approach as was used for 2005, but

### 34 projected for 2016

. Since we did not have readily a vailable 2016 NMIM data, we used t he output of a 2015

run of NMIM. The 2015 NMIM run utilized the NR05d

- Bond

- final version of NONROAD (which is

equivalent to NONROAD2008a ). We adjusted the 2015 NMIM data to 2016 by applying annual, national

level SCC

- an d pollutant

/mode

- based 4 

### factors to the monthly 2015 NMIM data by SCC, pollutant, and

emissions mode

. These factors were generated from NONROAD2008a (v08a

- out.mdb database) annual

national level emissions

. For nonroad mercury and NH 3 

### , we used 2015 values for 2016 because there were

no adjustment factors available for these pollutants in time for this effort

.

These future

- year emissions account for increases in activity (based on NONROAD model default growth

estimates of future year equipment population ) a nd changes in fuels and engines that reflect implementation

of national regulations and local control programs that impact each year differently due to engine turnover

.

The national regulations incorporated in the modeling are those promulgated prior to D ecember 200 9

, and

beginning about 1990. Recent rules include:

“Clean Air Nonroad Diesel Final Rule

- Tier 4” : ( http://www.epa.gov/nonroaddiesel/2004fr.htm ),

published June 29, 2004, and,

Control of Emissions f

rom Nonroad Large Spark

- Ignition Engines, and Recreational Engines (Marine

and Land

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

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

OTAQ‟s Small Engine S park Ignition (“Bond”) Rule : ( http://www.epa.gov/otaq/equip

- ld.htm )

We have not included volu ntary programs such as programs encouraging either no refueling or evening

refueling on Ozone Action Days and d iesel retrofit programs. NMIM version 20071009, with county

datab a se NCD20070912, and NONROAD model version NONROAD2008a (see

http://www.epa.gov/otaq/nonrdmdl.htm#model ) was used to create NMIM inventories for 2015.

California nonroad emissions

Similar to onroad mobile, NMIM was not used to generate future

- year nonroad emissions for California,

other than for NH 3



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

### emissions because CARB did not

pr ovide these data for any nonroad vehicle types. As we did for onroad emissions, we chose a projection

approach that would maintain consistency between the base year and future

- year emissions for nonroad

emissions in California.

California year 201 6 nonro ad CAP emissions were computed by linearly interpolating year 20 14 and 20 20

inventories. A nd 2016 HAP emissions were also computed using the same 2005

- based CAP

- HAP ratios

used to create 201 6 HAP emissions.



## 3.3.3 Locomotives and Class 1 & 2 commercial marine ve ssels (alm_no_c3)

Future locomotive and Class 1 and Class 2 commercial marine vessel (CMV) emissions were calculated

using projection factors that were computed based on national

, annual summaries of locomotive emissions in

2002 and future years. These na tional summaries were used to create national by

- pollutant, by

- SCC

pr ojec tion factors; these factors include final loco motive

- marine controls and are provided in Table 3

- 17

.

4 VOC factors were provided for exhaust, evaporative and refueling modes.

### 35

Table 3

- 17

. Factors applied to year 2005 emissions to project locomotives and C lass 1 and Class 2

Commercial Marine Vessel Emissions

SCC SCC Description Poll utant Year 201 6

Factor

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emis sions CO 0.9 431

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions NH 3 1.1340

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions NO X 0.7334

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions PM 10 0. 6778

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions PM 2.5 0. 6896

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions SO 2 0. 1106

2280002X00 Marine Vessels, Commercial;Diesel;Underway & port emissions VOC 0. 8260

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations CO 1. 3128

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations NH 3 1. 3040

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I O perations NO X 0. 6577

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations PM 10 0. 6205

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations PM 2.5 0. 6287

2285002006 Railroad Equipment;Diesel;Line Haul Lo comotives: Class I Operations SO 2 0. 0051

2285002006 Railroad Equipment;Diesel;Line Haul Locomotives: Class I Operations VOC 0. 6473

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations CO 0. 3226

2285002007 Railroad Equipm ent;Diesel;Line Haul Locomotives: Class II / III Operations NH 3 1. 3040

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations NO X 0.3 512

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations PM 10 0.28 57

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations PM 2.5 0.28 82

2285002007 Railroad Equipment;Diesel;Line Haul Locomotives: Class II / III Operations SO 2 0.0 012

2285002007 Railroad Equipment;Diesel;Line Hau l Locomotives: Class II / III Operations VOC 0. 3098

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) CO 1.0 629

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) NH 3 1. 3040

22850020 08 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) NO X 0. 5251

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) PM 10 0. 5006

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passeng er Trains (Amtrak) PM 2.5 0. 5028

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) SO 2 0. 0047

2285002008 Railroad Equipment;Diesel;Line Haul Locomotives: Passenger Trains (Amtrak) VOC 0. 5304

2285002009 Railroad Equipme nt;Diesel;Line Haul Locomotives: Commuter Lines CO 1.0 483

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines NH 3 1. 3040

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines NO X 0. 5179

2285002009 Railroad Equ ipment;Diesel;Line Haul Locomotives: Commuter Lines PM 10 0. 4937

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines PM 2.5 0. 4937

2285002009 Railroad Equipment;Diesel;Line Haul Locomotives: Commuter Lines SO 2 0. 0047

2285002009 Rail road Equipment;Diesel;Line Haul Locomotives: Commuter Lines VOC 0. 5231

2285002010 Railroad Equipment;Diesel;Yard Locomotives CO 1. 3203

2285002010 Railroad Equipment;Diesel;Yard Locomotives NH 3 1. 3040

2285002010 Railroad Equipment;Diesel;Yard Locomotives NO X 1.1 194

2285002010 Railroad Equipment;Diesel;Yard Locomotives PM 10 0.9 072

2285002010 Railroad Equipment;Diesel;Yard Locomotives PM 2.5 0.9 262

2285002010 Railroad Equipment;Diesel;Yard Locomotives SO 2 0. 0056

2285002010 Railroad Equipment;Diesel;Yard Locomotives VOC 1. 4964



### The future

- year locomotive emissions account for increased fuel consumption based on Energy Information

Administration (EIA) fuel consumption projections for freight rail, and emissions reductions resulting from

emissions standards from the Final Locomotive

- Marine rule (EPA, 2007c)

. This rule lowered diesel sulfur

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

lower future year PM, SO 2 

### , and NO X 

### , and is documented at:

http://www.epa.gov/otaq/regs/nonroad/420f08004.htm

. Voluntary retrofits under the National Clean Diesel

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

### 36 We applied HAP factors for VOC HAPs by using the VOC projection factors to obtain 1,3

- butadiene,

acetaldehyde, acro lein, benzene, and formaldehyde. Mercury, not already provided

, w as held at base

- year

levels for 2016 ( i.e., we used the 2002 emissions estimat es used in the 2005 b ase case)

.

Class 1 and 2 CMV gasoline emissions (SCC = 2280004000) were not changed for future year processing.

C1/C2 diesel emissions (SCC = 2280002100 and 2280002200) were p rojected based on the Final

L ocomotive Marine rule nationa l

- level factors provided in Table 3

- 17

. Similar to locomotives, VOC HAPs

were projected based on the VOC factor and mercury was held at levels in the 2005 (2002 inventory) b ase

case.



## 3.3.4 Class 3 commercial marine vess els (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 Organization ( ECA

- IMO ) project began in 2005. The (S)ECA

Categ ory 3 ( C3 ) commercial marine vesse l 2002 base case emissions w ere projected to year 2005 for the

2005 b ase case and to year 2016 for the future base case

. This future base case include s ECA

- IMO controls

. 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/420f09015.htm

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

diesel engines with per

- cylinder displacement at or above 30 liters , and the establishment of Emission

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

These projectio n factors vary depending on geographic 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 b ase ca se seca_c3 sector emissions are provided in Table 3

- 18

. Note that these factors are relative to 2002. Factors relative to 2005 can be computed from the 2002

- 2005

factors.

T he geographic regions are described in the ECA propos al technical support document:

http://www.epa.gov/oms/regs/nonroad/marine/ci/420r09007

- chap2.pdf . These regions extend up to 200

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

by the Oregon

- Washington border, and East Coast and Gulf Coast regions are divided east

- west by roughly

the upper Florida Keys just southwest of Miami.

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

As with the 2005 base case, this sector uses CAP

- HAP VOC integration. Mercury, although present in the

2005v4 inventory was not used for either the 2005 base case in the 2005v4.1 platform or the 2016 case. The

2005v4 pla t form Hg emissions total for the sector, including U.S. and non

- U.S. sources, is less than 0.001

tons/yr.

### 37

Table 3

- 18

. Factors to Project C lass 3 Commercial Marine Vessel emis sions to 2016

Adjustments Relative to 2002

NOx PM10 PM2.5 VOC/HC CO SO2

Alaska East (AE) 1.39411 0.19817 0.19630 1.57863 1.57803 0.06025

Alaska West (AW) 1.42713 1.52045 1.52076 1.52054 1.52057 1.52050

East Coast (EC) 1.46173 0.25428 0.25255 1.87 044 1.87014 0.06677

Gulf Coast (GC) 1.15962 0.20333 0.20120 1.48548 1.48642 0.05321

Hawaii East (HE) 1.54794 0.25771 0.25518 1.93871 1.94003 0.07430

Hawaii West (HW) 1.66069 1.93956 1.93762 1.94025 1.93952 1.94001

North Pacific (NP) 1.26904 0.21948 0.2 1521 1.59156 1.59099 0.06167

South Pacific (SP) 1.59215 0.28052 0.27800 2.00973 2.00573 0.07933

Great Lakes (GL) 1.10725 0.16932 0.16770 1.28006 1.27933 0.04518

Outside ECA 1.53081 1.80933 1.80933 1.80933 1.80933 1.80933



## 3.3.5 Future Year VOC Speciation (on_ noadj, nonroad)

We used speciation profiles for VOC in the nonroad and on_noadj sectors that account for the increase in

ethanol content of fuels in future years

. The combination profiles use proportions of E0 and E10 expected in

the future based on AEO 2 007 projections of E10 and E0 fuel use. The proportions of E0 and E10 are the

same for 2012 and years beyond (even though the quantities of the two fuels change over these years ) . The

national proportions were allocated to counties across the country usi ng the same fuel modeling done for the

stationary source gasoline speciation, as discussed in Section 3.2.8

.

The speciation of onroad exhaust VOC additionally accounts for a portion of the vehicle fleet meeting Tier 2

standa rds; different exhaust profiles are available for pre

- Tier 2 versus Tier 2 vehicles. Thus for exhaust

VOC, a combination of pre

- Tier 2 E0, pre

- Tier 2 E10, Tier 2 E0 and Tier 2 E10 profiles are used. Figure 3

- 3

shows the Tier 2 f

raction of Light Duty Vehicles for different future years in terms of different metrics. For

previous modeling applications, we based the fraction on the population of vehicles. However, since these

vehicle s emit a smaller portion of VOC, a more appropri ate metric for use in weighting the speciation

profiles is the fraction of exhaust total hydrocarbons (THC) which is used in the 2016 case described here

. The fraction of Tier 2 emissions used here for 2016 is 0.358.

Table 3

- 19 summarizes the profiles combined for the source categories and VOC emission modes used.

### 38 Table 3

- 19

. Future Year Profiles for Mobile Source Related Sources

Sector Type of profile Profile Codes Combined for t he Future Year Speciation

Stationary headspace 8762 : Composite Profile

- Gasoline Headspace Vapor using 0% Ethanol

8763 : Composite Profile

- Gasoline Headspace Vapor using 10% Ethanol

Nonroad

exhaust Pre

- Tier 2 vehicle

exhaust 8750 : Gasoline Exhau st

- Reformulated gasoline

8751 : Gasoline Exhaust

- E10 ethanol gasoline

Onroad and

Nonroad

evap* Evaporative 8753 : Gasoline Vehicle

- Evaporative emission

- Reformulated gasoline

8754 : Gasoline Vehicle

- Evaporative emission

- E10 ethanol gasoline

No nroad

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

- Ti er 2 light

- duty vehicles using

0% Ethanol

8757 : Composite Profile

- Gasoline Exhaust

- Tier 2 light

- duty vehicles using

10% Ethanol

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

Tier 2 and pre

- Tier 2 combinations are based on the 2016 contribution of Tier 2 exhaust emissions



### Figure 3

- 3

. Tier 2 Fraction of Light Duty Vehicles



## 3.4 Canada, Mexico, and Offshore sources (othar, othon, othpt, othar_hg,

and othpt_hg)

Emissions for Cana da, Mexico, and offshore sources were not projected to future years, and are therefore the

same as those used in the 2005 base case. Th erefore, the Mexic o emissions are based on year 1999

, offshore

oil is based on year 2005, and Canada is based on year 20 06. For both Mexico and Canada, their responsible

agencies could not provide f uture year emissions that were consistent with the base year emissions.

### 39 

# 4 EGU Control Case for 201 6

The Toxics Rule Control C ase (also known as “policy case”) i s intended to rep resent the implementation of

acid gas, mercury and associated criteria pollutant reductions associated with the Toxics Rule. However, due

to timing constraints for the Toxics Rule Proposal

, the actual control case used in the air quality modeling

did not allow for the final version of IPM policy case to be used. More information on the IPM used can be

found in the technical support document for the IPM modeling done for the Toxics Rule

. A comparison

between the IPM emissions used in the air quality model and the emissions from the “final” IPM cases for

select pollutants is presented in the Regulatory Impacts Assessment for the Proposed Toxics Rule

.

As with the base case, we applied the B oiler MACT reductions associated with units in the ptipm sector, and

we removed the Hg associated with Boiler MACT sources

. This resulted in the removal of 0.128 tons/year of

Hg was removed from the control case data provided by CAMD , which prevented double count ing Hg

emissions from boilers accounted for in the ptnonipm s ector

. The impacts on the non

- Hg pollutants of the

Boiler MACT controls a re provided in Table 4

- 1

.

Table 4

- 1

. Boiler MACT reductions applied to policy case

ptipm sector emissions p rior to AQ modeling

Tons reduced due to B oiler MACT

HCL 675

CO 845

PM 10 

### 698

PM 2

. 5 

### 606

SO 2 

### 18

, 747

VOC 19



# 5 Emission Summaries for the Base Case s and Control Case

The following tables summarize emissions differences between the 2005 base case, 20 16 b ase case and the

201 6 policy case

.

Table 5

- 1 provides 48

- state summed emissions by sector in 2016 and 2005for criteria pollutants, and Table

5

- 2 provides the same information for mercury, HCL, and CL2

. The speciated mercury emissions by state

and sector in the 2005 and 2016 base cases are provided in Table 5

- 3

.

For the 2016 policy case, only the ptipm sector is different from the 2016 base case

. S tate 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 provided in Table 5

- 5 , and Table 5

- 6 provides speciated

mercury by state for the 2005 and 2016 base cases.

Table 5

- 7 provide s sector

- specific SO 2 

### emissions (except for biogenic emissions) by state for the 2016 base

case , and Table 5

- 8 pr ovides the same resolution of information as Table 5

- 7 , but for PM 2.5



### .

### 40

Table 5

- 1

. 2016 Emissions

– 2016 Base Case Compared to 2005 Base Case : VOC, NO X 

### , CO, SO 2 

### , NH 3 

### , and PM

Sector [ tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr]

2016 VOC 2005 VOC 2016 NOx 2005 NOx 2016 CO 2005 CO 2016 SO2 2005 SO2 2016 NH3 2005 NH3 2016 PM 10 2005 PM 10 2016

PM 2_5 2005

PM 2_5

afdust 8, 8613,385 8,858,992 1,030

, 631 1,030,391

ag 3,446,632 3,251,990

alm_no_

c3 49,665 67,690 1,353,680 1,924,925 296,121 270,007 9,087 154,016 940 773 38,789 59,366 37,604 56,687

seca_c3 69,0 80 44,990 815,249 647,884 82,929 54,049 23,659 420,110 11,324 53,918 10,324 49,541

seca_c3

non

- US 126,898 18,367 1,603,944 532,181 149,443 43,267 957,065 321,414 131,137 43,326 120,617 39,810

nonpt 7,322,980 7,530,564 1,668,679 1,699,532 6,973,593 7,413,762 1,250,300 1,259,635 133,428 134,080 1,302,733 1,354,638 1,029,916 1,081,816

nonroad 1,577,015 2,691,844 1,271,892 2,115,408 13,431,076 19,502,718 2,870 197,341 2,345 1,972 129,217 211,807 121,215 201,138

on_noadj 2,064,152 3,949,362 4,285,847 9,142,274 25,930,932 43,356,130 26,784 177,977 82,013 156,528 114,073 308,497 40,587 236,927

on_move

s_runpm 88,929 54,071 81,887 49,789

on_move

s_startp

m 71,509 22,729 65,846 20,929

ptipm 40,845 40,950 1,769,764 3, 726,459 691,310 601,564 3,577,698 10,380,786 36,655 21,684 523,504 615,095 384,320 508,903

ptnonipm 1,180,794 1,310,784 2,061,353 2,238,002 3,038,429 3,221,388 1,349,038 2,089,836 158,242 158,837 597,324 653,048 404,926 440,714

avefire 1,958,992 1,958,99 2 189,428 189,428 8,554,551 8,554,551 49,094 49,094 36,777 36,777 796,229 796,229 684,035 684,035

2016cr2

non

- fires

Total 12,431,429 15,654,551 14,830,408 22,026,665 50,593,833 74,462,885 7,196,501 15,001,115 3,860,256 3,725,864 11,869,924 12,235,487 3,32 7,874 3,716,645

### 41

Table 5

- 2

. 2016 Emissions

– Base Case Compared to 2005 Base Case : mercury (species and total), HCL and CL2

Sector [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons /yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr]

2016

HGIIGAS 2005

HGIIGAS 2016

HGNRVA 2005

HGNRVA 2016

PHGI 2005

PHGI 2016

Total Hg

(sum of 3

species) 2005

Total Hg

(sum of 3

species) 2016

HCL 2005

HCL 2016

CL2 2005

CL2

afdust

ag

alm_no_c3 0.0411 0.0411 0.0793 0.0793 0.0212 0.0212 0.1416 0.1416 1.38 1.38

seca_c3 * * * * * * * *

seca_c3 non

- US * * * * * * * *

nonpt 1.0605 1.0605 3.1034 3.1034 0.6524 0.6524 4.8163 4.8163 29,001 29,001 2,108 2,135

nonroad 0.1041 0.1041 0.2105 0.2105 0.0533 0.0533 0.3679 0.3679

on_noadj 0.1402 0.1402 0.5036 0.5036 0.0599 0.0599 0.7037 0.7037

on_moves_runpm

on_moves_startpm

ptipm 6.8757 21.0960 21.1598 30.1986 0.6683 1.6136 28.7038 52.9082 74,089 351,592 99

ptnonipm 7.9112 10.4687 16.8535 29.5686 4.4183 6.1291 29.183 46.1664 37,549 48,630 3,941 4,174

avefire

2016cr2 non

- fires

Total 16.13 32.91 41.91 63.66 5.87 8.53 64 105 140,639 429,223 6,050 6,409

* due to uncertain ty in mercury emissions from this sector, they were removed from the inventories and not used. The amount removed from the 2 005 data was on the order of

0.001 tons total mercury for the sum of U.S. and non

- U.S. components

### 42 Table 5

- 3

. Speciated Mercury and total Mercury by State and Sector for 2005 and 2016

ptipm ptnonipm nonpt onroad alm nonroad all US sectors

[tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr]

State Species of 2005 2016 2005 2016 2005 2016 2005=

2016 2005=

2016 2005=

2016 2005 2016

Alabama HGIIGAS 1.160 0.288 0.233 0.140 4.9E

- 04 4.9E

- 04 5.9E

- 04 3.2E

- 05 1.394 0.429

Alabama HGNRVA 1.419 0.948 1.105 0.453 0.029 0.029 6.2E

- 03 2.4E

- 04 2.56 1.44

Alabama PHGI 0.084 0.0 19 0.161 0.094 2.4E

- 04 2.4E

- 04 3.2E

- 05 6.6E

- 06 0.245 0.113

Alabama Total HG 2.663 1.255 1.499 0.687 0.029 0.029 6.8E

- 03 2.8E

- 04 4.20 1.98

Arizona HGIIGAS 0.052 0.133 0.035 0.017 2.4E

- 03 2.4E

- 03 6.1E

- 04 3.9E

- 05 0.089 0.153

Arizona HGNRVA 0.657 0.609 0 .145 0.044 0.027 0.027 6.4E

- 03 2.6E

- 04 0.836 0.686

Arizona PHGI 0.007 0.007 0.028 0.012 8.3E

- 04 8.3E

- 04 3.5E

- 05 9.4E

- 06 0.036 0.020

Arizona Total HG 0.716 0.749 0.208 0.074 0.030 0.030 7.0E

- 03 3.1E

- 04 0.961 0.860

Arkansas HGIIGAS 0.144 0.198 0.221 0. 164 4.6E

- 04 4.6E

- 04 3.3E

- 04 2.4E

- 05 0.365 0.363

Arkansas HGNRVA 0.364 0.525 0.364 0.303 0.016 0.016 3.5E

- 03 1.6E

- 04 0.748 0.848

Arkansas PHGI 9.3E

- 04 1.1 E

- 03 0.110 0.085 1.8E

- 04 1.8E

- 04 1.8E

- 05 6.4E

- 06 0.111 0.087

Arkansas Total HG 0.509 0.725 0.694 0.552 0.017 0.017 3.8E

- 03 1.9E

- 04 1.224 1.298

California HGIIGAS 0.001 0.042 0.757 0.599 0.462 0.462 0.113 0.037 1.0E

- 01 1.472 1.355

California HGNRVA 0.003 0.052 2.118 1.068 0.728 0.728 0.218 0.071 2.0E

- 01 3.336 2.335

California PHGI 0.001 0.039 0.514 0.360 0.298 0.298 0.058 0.019 5.3E

- 02 0.943 0.827

California Total HG 0.005 0.132 3.389 2.027 1.488 1.488 0.390 0.127 3.5E

- 01 5.751 4.517

Colorado HGIIGAS 0.103 0.026 0.167 0.154 2.5E

- 04 2.5E

- 04 5.0E

- 04 3.4E

- 05 0.271 0.181

Colorado HGNRVA 0.321 0.055 0.458 0.372 6.4E

- 03 6.4E

- 03 5.3E

- 03 2.3E

- 04 0.791 0.438

Colorado PHGI 0.005 0.003 0.120 0.108 1.7E

- 04 1.7E

- 04 2.5E

- 05 8.4E

- 06 0.125 0.111

Colorado Total HG 0.429 0.083 0.746 0.634 6.8E

- 03 6.8E

- 03 5.8E

- 03 2.7E

- 04 1.187 0.730

Connecticut HGIIGAS 0.040 0.004 0.099 0.099 0.037 0.037 3.4E

- 04 2.0E

- 05 0.176 0.140

Connecticut HGNRVA 0.077 0.003 0.049 0.049 0.081 0.081 3.6E

- 03 1.6E

- 04 0.211 0.136

Connecticut PHGI 0.004 0.000 0.036 0.036 0.024 0.024 1.6E

- 05 3.5E

- 06 0.064 0.061

Connecticut Total HG 0.121 0 .007 0.184 0.184 0.142 0.142 3.9E

- 03 1.8E

- 04 0.451 0.337

Delaware HGIIGAS 0.109 0.006 0.029 0.008 2.5E

- 05 2.5E

- 05 9.8E

- 05 5.3E

- 04 5.7E

- 06 0.139 0.015

Delaware HGNRVA 0.055 0.003 0.173 0.022 6.8E

- 05 6.8E

- 05 1.0E

- 03 1.0E

- 03 4.4E

- 05 0.230 0.027

Delaware P HGI 0.016 0.000 0.016 0.006 1.8E

- 05 1.8E

- 05 5.1E

- 06 2.7E

- 04 1.2E

- 06 0.032 0.006

Delaware Total HG 0.180 0.009 0.219 0.035 1.1E

- 04 1.1E

- 04 1.1E

- 03 1.8E

- 03 5.1E

- 05 0.402 0.047

District of

Columbia HGIIGAS 0.001 0.000 0.000 6.1E

- 04 6.1E

- 04 4.2E

- 05 2.3E

- 06 0.002 0.001

District of

Columbia HGNRVA 0.001 0.000 0.000 3.0E

- 03 3.0E

- 03 4.5E

- 04 1.1E

- 05 0.005 0.004

District of

Columbia PHGI 0.001 0.000 0.000 4.1E

- 04 4.1E

- 04 2.1E

- 06 7.9E

- 07 0.001 0.001

District of

Columbia Total HG 0.003 0.001 0.001 4.0E

- 03 4 .0E

- 03 4.9E

- 04 1.4E

- 05 0.008 0.005

Florida HGIIGAS 0.544 0.145 0.350 0.284 9.0E

- 03 9.0E

- 03 2.1E

- 03 1.4E

- 04 0.904 0.441

Florida HGNRVA 0.550 0.260 0.639 0.222 0.081 0.081 0.022 1.1E

- 03 1.293 0.585

Florida PHGI 0.079 0.081 0.182 0.115 3.2E

- 03 3.2E

- 03 1 .1E

- 04 3.0E

- 05 0.265 0.199

Florida Total HG 1.173 0.486 1.170 0.621 0.093 0.093 0.024 1.2E

- 03 2.46 1.225

Georgia HGIIGAS 0.964 0.241 0.097 0.052 1.7E

- 03 1.7E

- 03 1.1E

- 03 5.7E

- 05 1.064 0.296

Georgia HGNRVA 0.666 0.262 0.542 0.122 0.063 0.063 0.012 4.0 E

- 04 1.282 0.459

### 43 ptipm ptnonipm nonpt onroad alm nonroad all US sectors

[tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr]

State Species of 2005 2016 2005 2016 2005 2016 2005=

2016 2005=

2016 2005=

2016 2005 2016

Georgia PHGI 0.074 0.009 0.051 0.031 8.0E

- 04 8.0E

- 04 6.3E

- 05 1.3E

- 05 0.126 0.041

Georgia Total HG 1.704 0.512 0.690 0.206 0.065 0.065 0.013 4.7E

- 04 2.47 0.796

Idaho HGIIGAS 0.115 0.113 1.3E

- 04 1.6E

- 05 0.115 0.113

Idaho HGNRVA 0 .195 0.188 1.4E

- 03 1.1E

- 04 0.196 0.190

Idaho PHGI 0.077 0.075 7.0E

- 06 3.8E

- 06 0.077 0.075

Idaho Total HG 0.386 0.376 1.6E

- 03 1.3E

- 04 0.388 0.378

Illinois HGIIGAS 1.367 0.124 0.382 0.338 0.012 0.012 1.1E

- 03 9.1E

- 05 1.763 0.475

Illinois HGN RVA 2.819 0.361 1.227 1.007 0.087 0.087 0.012 5.6E

- 04 4.145 1.468

Illinois PHGI 0.056 0.003 0.245 0.207 8.3E

- 03 8.3E

- 03 0.000 2.6E

- 05 0.309 0.219

Illinois Total HG 4.242 0.488 1.853 1.553 0.108 0.108 0.013 6.8E

- 04 6.217 2.162

Indiana HGIIGAS 1.141 0. 417 0.726 0.548 4.6E

- 03 4.6E

- 03 7.4E

- 04 5.0E

- 05 1.872 0.970

Indiana HGNRVA 1.670 1.130 1.410 1.146 0.045 0.045 7.7E

- 03 3.1E

- 04 3.134 2.330

Indiana PHGI 0.068 0.011 0.394 0.308 2.8E

- 03 2.8E

- 03 4.0E

- 05 1.4E

- 05 0.465 0.321

Indiana Total HG 2.879 1.558 2 .530 2.002 0.053 0.053 8.5E

- 03 3.8E

- 04 5.471 3.622

Iowa HGIIGAS 0.279 0.193 0.159 0.088 2.2E

- 03 2.2E

- 03 3.3E

- 04 4.6E

- 05 0.440 0.284

Iowa HGNRVA 0.876 0.802 0.431 0.210 0.022 0.022 3.4E

- 03 2.6E

- 04 1.332 1.038

Iowa PHGI 0.003 0.004 0.115 0.062 1.3E

- 03 1.3E

- 03 1.7E

- 05 1.4E

- 05 0.120 0.067

Iowa Total HG 1.158 0.999 0.705 0.360 0.026 0.026 3.8E

- 03 3.2E

- 04 1.893 1.389

Kansas HGIIGAS 0.167 0.114 0.259 0.176 6.1E

- 04 6.1E

- 04 3.0E

- 04 3.0E

- 05 0.427 0.291

Kansas HGNRVA 0.834 0.837 0.189 0.120 0.019 0.019 3.2 E

- 03 1.4E

- 04 1.045 0.980

Kansas PHGI 0.007 0.004 0.101 0.068 2.4E

- 04 2.4E

- 04 1.6E

- 05 1.1E

- 05 0.108 0.072

Kansas Total HG 1.008 0.955 0.548 0.364 0.020 0.020 3.5E

- 03 1.8E

- 04 1.580 1.343

Kentucky HGIIGAS 0.799 0.131 0.133 0.100 2.7E

- 03 2.7E

- 03 5.0E

- 04 2.7E

- 05 0.935 0.234

Kentucky HGNRVA 0.897 0.682 0.471 0.303 0.024 0.024 5.2E

- 03 1.8E

- 04 1.398 1.015

Kentucky PHGI 0.062 0.015 0.090 0.063 1.7E

- 03 1.7E

- 03 2.8E

- 05 7.0E

- 06 0.155 0.079

Kentucky Total HG 1.759 0.828 0.694 0.466 0.029 0.029 5.8E

- 03 2.2E

- 04 2.49 1.33

Louisiana HGIIGAS 0.148 0.244 0.171 0.104 4.8E

- 04 4.8E

- 04 4.6E

- 04 3.3E

- 05 0.320 0.349

Louisiana HGNRVA 0.459 0.676 1.145 0.183 0.021 0.021 4.8E

- 03 2.5E

- 04 1.630 0.886

Louisiana PHGI 0.002 0.099 0.073 0.064 1.9E

- 04 1.9E

- 04 2.5E

- 05 6.8E

- 06 0.075 0.163

Louisiana Total HG 0.609 1.019 1.388 0.352 0.022 0.022 5.3E

- 03 2.9E

- 04 2.025 1.398

Maine HGIIGAS 0.001 0.009 0.040 0.032 0.029 0.029 1.5E

- 04 1.5E

- 05 0.071 0.070

Maine HGNRVA 0.002 0.003 0.064 0.039 0.076 0.076 1.6E

- 03 1.3E

- 04 0.143 0.120

Maine PHGI 8.9E

- 04 7.9E

- 04 0.023 0.018 0.017 0.017 8.3E

- 06 2.0E

- 06 0.041 0.036

Maine Total HG 0.004 0.013 0.127 0.089 0.122 0.122 1.8E

- 03 1.5E

- 04 0.255 0.226

Maryland HGIIGAS 0.535 0.031 0.242 0.198 0.032 0.032 5.9E

- 04 3.3E

- 05 0.809 0.262

Maryland HGNRVA 0.301 0.077 0.328 0.152 0.076 0.076 6.2E

- 03 2.5E

- 04 0.712 0.311

Maryland PHGI 0.053 0.006 0.111 0.076 0.021 0.021 3.0E

- 05 6.4E

- 06 0.185 0.103

Maryland Total HG 0.890 0.114 0.681 0.426 0.129 0.129 6.8E

- 03 2.9E

- 04 1.707 0.677

Massachusetts HGIIG AS 0.111 0.003 0.091 0.090 0.068 0.068 6.0E

- 04 3.3E

- 05 0.270 0.162

Massachusetts HGNRVA 0.055 0.006 0.111 0.110 0.204 0.204 6.3E

- 03 2.6E

- 04 0.377 0.326

Massachusetts PHGI 0.016 0.000 0.035 0.034 0.041 0.041 3.0E

- 05 6.1E

- 06 0.092 0.076

### 44 ptipm ptnonipm nonpt onroad alm nonroad all US sectors

[tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr]

State Species of 2005 2016 2005 2016 2005 2016 2005=

2016 2005=

2016 2005=

2016 2005 2016

Massachusetts T otal HG 0.182 0.009 0.237 0.234 0.313 0.313 7.0E

- 03 3.0E

- 04 0.739 0.564

Michigan HGIIGAS 0.968 0.340 0.216 0.145 0.011 0.011 1.1E

- 03 9.4E

- 05 1.197 0.498

Michigan HGNRVA 0.781 1.155 0.716 0.400 0.070 0.070 1.1E

- 02 7.6E

- 04 1.579 1.637

Michigan PHGI 0.0 77 5.8E

- 03 0.153 0.094 0.007 0.007 5.7E

- 05 1.7E

- 05 0.237 0.107

Michigan Total HG 1.826 1.501 1.086 0.639 0.088 0.088 0.012 8.7E

- 04 3.013 2.242

Minnesota HGIIGAS 0.096 0.036 0.814 0.791 9.2E

- 03 9.2E

- 03 5.7E

- 04 6.5E

- 05 0.920 0.836

Minnesota HGNRVA 0.60 3 0.124 0.826 0.760 0.029 0.029 6.0E

- 03 4.5E

- 04 1.464 0.919

Minnesota PHGI 0.009 0.001 0.336 0.319 5.3E

- 03 5.3E

- 03 3.0E

- 05 1.6E

- 05 0.350 0.326

Minnesota Total HG 0.707 0.161 1.977 1.869 0.043 0.043 6.6E

- 03 5.3E

- 04 2.734 2.080

Mississippi HGIIGAS 0.12 6 0.140 0.088 0.069 2.7E

- 04 2.7E

- 04 4.2E

- 04 2.1E

- 05 0.215 0.209

Mississippi HGNRVA 0.156 0.264 0.190 0.156 0.015 0.015 4.4E

- 03 1.5E

- 04 0.365 0.439

Mississippi PHGI 0.010 0.001 0.052 0.042 1.2E

- 04 1.2E

- 04 2.4E

- 05 5.0E

- 06 0.063 0.043

Mississippi Total HG 0.292 0.405 0.330 0.267 0.015 0.015 4.8E

- 03 1.8E

- 04 0.643 0.692

Missouri HGIIGAS 0.702 0.472 0.452 0.277 1.2E

- 03 1.2E

- 03 7.2E

- 04 4.6E

- 05 1.156 0.752

Missouri HGNRVA 1.129 1.471 0.560 0.414 2.0E

- 03 2.0E

- 03 7.5E

- 03 3.0E

- 04 1.698 1.895

Missouri PHGI 0.024 0.005 0.199 0.128 8.1E

- 04 8.1E

- 04 3.9E

- 05 1.2E

- 05 0.224 0.134

Missouri Total HG 1.854 1.949 1.211 0.819 4.1E

- 03 4.1E

- 03 8.3E

- 03 3.6E

- 04 3.078 2.780

Montana HGIIGAS 0.030 0.010 0.024 0.020 1.0E

- 03 1.0E

- 03 1.1E

- 04 1.3E

- 05 0.055 0.032

Montana HGNR VA 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 HGII GAS 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 Tot al HG 0.344 0.423 0.157 0.109 0.011 0.011 2.3E

- 03 1.4E

- 04 0.514 0.545

Nevada HGIIGAS 0.091 0.011 0.021 0.019 0.001 0.001 2.0E

- 04 1.8E

- 05 0.114 0.032

Nevada HGNRVA 0.217 0.075 2.554 0.751 0.011 0.011 2.2E

- 03 1.2E

- 04 2.784 0.839

Nevada PHGI 0.002 0.002 0.019 0.017 0.001 0.001 9.6E

- 06 4.9E

- 06 0.022 0.020

Nevada Total HG 0.310 0.087 2.594 0.788 0.013 0.013 2.4E

- 03 1.4E

- 04 2.919 0.891

New

Hampshire HGIIGAS 0.015 0.001 0.015 0.010 0.013 0.013 1.4E

- 04 2.9E

- 05 1.1E

- 05 0.043 0.025

New

Hampshire HGNRVA 0.0 12 0.009 0.019 0.011 0.028 0.028 1.5E

- 03 5.6E

- 05 9.7E

- 05 0.060 0.050

New

Hampshire PHGI 3.9E

- 03 3.2E

- 04 8.5E

- 03 5.4E

- 03 8.6E

- 03 8.6E

- 03 7.4E

- 06 1.5E

- 05 1.8E

- 06 0.021 0.014

New

Hampshire Total HG 0.030 0.011 0.043 0.027 0.050 0.050 1.6E

- 03 1.0E

- 04 1.1E

- 04 0.125 0.090

New Jersey HGIIGAS 0.064 0.011 0.238 0.228 0.087 0.087 7.9E

- 04 4.9E

- 05 0.390 0.327

New Jersey HGNRVA 0.061 0.014 0.403 0.321 0.108 0.108 8.4E

- 03 3.9E

- 04 0.580 0.451

New Jersey PHGI 0.009 0.001 0.119 0.109 0.039 0.039 3.8E

- 05 8.7E

- 06 0.16 7 0.149

New Jersey Total HG 0.133 0.026 0.761 0.658 0.233 0.233 9.3E

- 03 4.5E

- 04 1.137 0.927

New Mexico HGIIGAS 0.042 0.010 4.7E

- 03 1.6E

- 03 5.8E

- 04 5.8E

- 04 2.5E

- 04 9.7E

- 06 0.048 0.012

New Mexico HGNRVA 0.975 0.284 0.026 8.1E

- 03 9.3E

- 03 9.3E

- 03 2.6E

- 03 6.8E

- 05 1.013 0.304

### 45 ptipm ptnonipm nonpt onroad alm nonroad all US sectors

[tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr]

State Species of 2005 2016 2005 2016 2005 2016 2005=

2016 2005=

2016 2005=

2016 2005 2016

New Mexico PHGI 0.010 1.8E

- 03 4.3E

- 03 1.4E

- 03 2.1E

- 04 2.1E

- 04 1.4E

- 05 2.3E

- 06 0.015 0.003

New Mexico Total HG 1.027 0.296 0.035 0.011 0.010 0.010 2.9E

- 03 8.0E

- 05 1.076 0.320

New York HGIIGAS 0.219 0.013 0.351 0.305 0.096 0.096 1.5 E

- 03 9.4E

- 05 0.667 0.416

New York HGNRVA 0.218 0.037 0.407 0.231 0.456 0.456 1.6E

- 02 7.5E

- 04 1.098 0.741

New York PHGI 0.029 1.2E

- 03 0.158 0.124 0.062 0.062 7.3E

- 05 1.7E

- 05 0.249 0.187

New York Total HG 0.465 0.051 0.916 0.660 0.614 0.614 1.8E

- 02 8. 6E

- 04 2.014 1.344

North

Carolina HGIIGAS 1.122 0.118 0.132 0.119 0.015 0.015 9.1E

- 04 5.9E

- 05 1.270 0.252

North

Carolina HGNRVA 0.494 0.357 0.413 0.349 0.067 0.067 0.010 4.2E

- 04 0.985 0.783

North

Carolina PHGI 0.100 0.013 0.092 0.081 9.3E

- 03 9.3E

- 03 4. 9E

- 05 1.4E

- 05 0.202 0.103

North

Carolina Total HG 1.716 0.487 0.638 0.549 0.091 0.091 0.011 4.9E

- 04 2.456 1.138

North Dakota HGIIGAS 0.130 0.119 0.014 0.009 0.002 1.75E

- 03 7.7 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 8.0E

- 05 2.032 0.678

Ohio HGNRVA 1.841 1.074 1.513 0.859 0.090 0.090 0.012 5.5E

- 04 3.456 2.035

Ohio PHGI 0.134 0.062 0.216 0.155 7.0E

- 03 7.0E

- 03 6.0E

- 05 1.9E

- 05 0.357 0.224

Ohio Total HG 3.662 1.576 2.059 1.238 0.110 0.110 0.013 6.5E

- 04 5.845 2.937

Oklahoma HGIIGAS 0.206 0.288 0.077 0.063 5.8E

- 04 5.8E

- 04 4.8E

- 04 2.8E

- 05 0.285 0.352

Oklahoma HGNRVA 0.719 0.739 0.244 0.154 0.020 0.020 0.005 1.8E

- 04 0.988 0.918

Oklahoma PHGI 1.8E

- 03 1.8E

- 03 0.057 0.044 2.5E

- 04 2.5E

- 04 2.6E

- 05 7.2E

- 06 0 .059 0.046

Oklahoma Total HG 0.927 1.028 0.379 0.260 0.020 0.020 5.6E

- 03 2.2E

- 04 1.332 1.315

Oregon HGIIGAS 0.025 0.002 0.215 0.049 0.012 0.012 3.5E

- 04 2.1E

- 05 2.7E

- 05 0.252 0.064

Oregon HGNRVA 0.056 0.005 1.154 0.206 0.040 0.040 3.7E

- 03 4.0E

- 05 1.9E

- 0 4 1.254 0.255

Oregon PHGI 1.9E

- 04 1.1E

- 05 0.192 0.040 7.7E

- 03 7.7E

- 03 1.8E

- 05 1.1E

- 05 6.0E

- 06 0.200 0.047

Oregon Total HG 0.081 7.5E

- 03 1.561 0.295 0.060 0.060 4.1E

- 03 7.1E

- 05 2.2E

- 04 1.706 0.367

Pennsylvania HGIIGAS 2.856 0.459 0.635 0.514 0.064 0.064 1.1E

- 03 6.4E

- 05 3.556 1.038

Pennsylvania HGNRVA 1.829 1.071 1.654 1.108 0.159 0.159 0.012 4.8E

- 04 3.655 2.351

Pennsylvania PHGI 0.294 0.083 0.395 0.300 0.041 0.041 5.8E

- 05 1.3E

- 05 0.730 0.425

Pennsylvania Total HG 4.979 1.613 2.684 1.923 0.264 0.264 0.013 5.6E

- 04 7.940 3.814

Rhode Island HGIIGAS 0.014 0.014 8.8E

- 03 8.8E

- 03 9.0E

- 05 5.1E

- 06 0.023 0.023

Rhode Island HGNRVA 0.023 0.023 0.019 0.019 9.6E

- 04 4.1E

- 05 0.043 0.043

Rhode Island PHGI 9.3E

- 03 9.3E

- 03 5.7E

- 03 5.7E

- 03 4.0E

- 06 9.0E

- 07 0.0 15 0.015

Rhode Island Total HG 0.047 0.047 0.033 0.033 1.1E

- 03 4.7E

- 05 0.081 0.081

South

Carolina HGIIGAS 0.319 0.166 0.314 0.231 3.1E

- 03 3.1E

- 03 5.1E

- 04 3.1E

- 05 0.636 0.400

South

Carolina HGNRVA 0.233 0.167 0.712 0.467 0.025 0.025 5.3E

- 03 2.2E

- 04 0.976 0.664

South

Carolina PHGI 0.029 0.015 0.176 0.124 1.9E

- 03 1.9E

- 03 2.7E

- 05 6.7E

- 06 0.207 0.141

South

Carolina Total HG 0.581 0.347 1.202 0.822 0.030 0.030 5.8E

- 03 2.6E

- 04 1.819 1.205

### 46 ptipm ptnonipm nonpt onroad alm nonroad all US sectors

[tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr]

State Species of 2005 2016 2005 2016 2005 2016 2005=

2016 2005=

2016 2005=

2016 2005 2016

South Dakota HGIIGAS 0.015 0.021 0.013 0.006 1.0E

- 03 1.0E

- 03 8. 5E

- 05 1.7E

- 05 0.028 0.028

South Dakota HGNRVA 0.033 6.4E

- 03 0.049 0.013 7.1E

- 03 7.1E

- 03 8.9E

- 04 6.9E

- 05 0.090 0.028

South Dakota PHGI 8.2E

- 05 3.5E

- 04 0.010 4.4E

- 03 6.5E

- 04 6.5E

- 04 4.9E

- 06 6.5E

- 06 0.011 0.005

South Dakota Total HG 0.048 0.027 0.071 0. 024 8.8E

- 03 8.8E

- 03 9.8E

- 04 9.2E

- 05 0.129 0.061

Tennessee HGIIGAS 0.634 0.194 0.285 0.173 2.0E

- 03 2.0E

- 03 7.1E

- 04 3.9E

- 05 0.922 0.370

Tennessee HGNRVA 0.571 0.547 1.309 0.501 0.031 0.031 7.4E

- 03 2.7E

- 04 1.919 1.087

Tennessee PHGI 0.046 0.001 0.152 0. 096 1.2E

- 03 1.2E

- 03 4.1E

- 05 8.9E

- 06 0.200 0.099

Tennessee Total HG 1.251 0.743 1.746 0.771 0.034 0.034 8.2E

- 03 3.2E

- 04 3.040 1.556

Texas HGIIGAS 1.520 0.804 0.805 0.606 1.2E

- 03 1.2E

- 03 2.3E

- 03 1.4E

- 04 2.329 1.414

Texas HGNRVA 3.584 2.501 3.269 2.592 0.071 0.071 0.024 9.0E

- 04 6.949 5.188

Texas PHGI 0.092 0.063 0.576 0.446 8.1E

- 04 8.1E

- 04 1.4E

- 04 3.5E

- 05 0.669 0.511

Texas Total HG 5.196 3.367 4.650 3.644 0.073 0.073 0.027 1.1E

- 03 9.947 7.112

Tribal Data HGIIGAS 3.2E

- 04 3.2E

- 04 0.000 0.000

Tribal Data HGNRVA 5.4E

- 04 5.3E

- 04 0.001 0.001

Tribal Data PHGI 2.1E

- 04 2.1E

- 04 0.000 0.000

Tribal Data Total HG 1.1E

- 03 1.1E

- 03 0.001 0.001

Utah HGIIGAS 0.063 0.062 0.085 0.059 4.0E

- 04 4.0E

- 04 2.6E

- 04 1.6E

- 05 0.149 0.122

Utah HGNR VA 0.079 0.114 0.232 0.115 0.014 0.014 2.7E

- 03 1.2E

- 04 0.328 0.246

Utah PHGI 0.006 0.008 0.052 0.032 1.9E

- 04 1.9E

- 04 1.4E

- 05 3.5E

- 06 0.058 0.040

Utah Total HG 0.148 0.184 0.369 0.206 0.015 0.015 3.0E

- 03 1.4E

- 04 0.536 0.408

Vermont HGIIGAS 1.7E

- 03 2. 4E

- 04 2.4E

- 04 9.4E

- 03 9.4E

- 03 7.5E

- 05 5.7E

- 06 0.011 0.010

Vermont HGNRVA 2.8E

- 03 6.2E

- 04 6.2E

- 04 0.018 0.018 7.9E

- 04 4.7E

- 05 0.022 0.019

Vermont PHGI 1.1E

- 03 1.6E

- 04 1.6E

- 04 5.5E

- 03 5.5E

- 03 4.1E

- 06 9.4E

- 07 0.007 0.006

Vermont Total HG 5.6E

- 03 1.0E

- 03 1.0E

- 03 0.033 0.033 8.7E

- 04 5.3E

- 05 0.040 0.035

Virginia HGIIGAS 0.401 0.108 0.496 0.235 0.019 0.019 8.5E

- 04 4.6E

- 05 0.917 0.364

Virginia HGNRVA 0.181 0.146 0.938 0.421 0.069 0.069 9.0E

- 03 3.3E

- 04 1.197 0.646

Virginia PHGI 0.042 0.029 0.309 0.132 0.012 0.012 4.3E

- 05 1.0E

- 05 0.363 0.174

Virginia Total HG 0.624 0.284 1.743 0.789 0.100 0.100 9.9E

- 03 3.9E

- 04 2.477 1.183

Washington HGIIGAS 0.105 0.005 0.047 0.030 6.1E

- 03 6.1E

- 03 5.7E

- 04 3.6E

- 03 4.3E

- 05 0.162 0.045

Washington HGNRVA 0.234 0.161 0.1 23 0.057 0.041 0.041 5.9E

- 03 6.9E

- 03 3.1E

- 04 0.412 0.272

Washington PHGI 5.3E

- 04 7.1E

- 04 0.032 0.018 3.1E

- 03 3.1E

- 03 3.1E

- 05 1.8E

- 03 9.8E

- 06 0.037 0.024

Washington Total HG 0.339 0.167 0.202 0.104 0.050 0.050 6.5E

- 03 0.012 3.6E

- 04 0.611 0.341

West Virgi nia HGIIGAS 1.449 0.226 0.097 0.071 2.6E

- 03 2.6E

- 03 2.0E

- 04 9.7E

- 06 1.549 0.300

West Virginia HGNRVA 0.827 0.610 0.301 0.202 0.015 0.015 2.1E

- 03 7.5E

- 05 1.144 0.829

West Virginia PHGI 0.129 0.024 0.056 0.041 1.7E

- 03 1.7E

- 03 1.1E

- 05 1.8E

- 06 0.186 0.067

West Virginia Total HG 2.404 0.860 0.454 0.314 0.019 0.019 2.3E

- 03 8.7E

- 05 2.880 1.196

Wisconsin HGIIGAS 0.355 0.169 0.277 0.259 0.011 0.011 6.2E

- 04 6.1E

- 05 0.644 0.439

Wisconsin HGNRVA 0.784 0.692 0.438 0.396 0.053 0.053 6.5E

- 03 4.8E

- 04 1.282 1.149

Wisconsin PHGI 7.7E

- 03 0.010 0.172 0.160 7.1E

- 03 7.1E

- 03 3.3E

- 05 1.2E

- 05 0.187 0.177

### 47 ptipm ptnonipm nonpt onroad alm nonroad all US sectors

[tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr] [tons/yr]

State Species of 2005 2016 2005 2016 2005 2016 2005=

2016 2005=

2016 2005=

2016 2005 2016

Wisconsin Total HG 1.147 0.870 0.887 0.815 0.072 0.072 7.1E

- 03 5.5E

- 04 2.114 1.765

Wyoming HGIIGAS 0.072 0.134 0.076 0.056 4.0E

- 04 4.0E

- 04 9.2E

- 05 5.9E

- 06 0.149 0.1 91

Wyoming HGNRVA 0.872 1.119 0.147 0.098 3.6E

- 03 3.6E

- 03 9.5E

- 04 4.3E

- 05 1.023 1.222

Wyoming PHGI 0.004 0.006 0.052 0.038 2.3E

- 04 2.3E

- 04 5.3E

- 06 1.3E

- 06 0.057 0.045

Wyoming Total HG 0.949 1.260 0.275 0.192 4.2E

- 03 4.2E

- 03 1.1E

- 03 5.0E

- 05 1.23 1.46

Total (48

state) HGIIGAS 21.096 6.876 10.469 7.911 1.060 1.060 0.140 0.041 0.104 32.91 16.13

Total (48

state) HGNRVA 30.199 21.160 29.569 16.853 3.103 3.103 0.504 0.079 0.211 63.66 41.91

Total (48

state) PHGI 1.614 0.668 6.129 4.418 0.652 0.652 0.060 0. 021 0.053 8.53 5.87

Total (48

state) Total HG 52.908 28.704 46.166 29.183 4.816 4.816 0.704 0.142 0.368 105.10 63.92



### Table 5

- 4

. EGU sector (ptipm emissions) for all AQ modeling cases: 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 59,118 460,123 172,198 38,346 1,366 1,375 1,311

Arizona 79,776 60,265 60,279 52,733 23,140 21,632 577 892 887

Arkansas 35,407 20,095 15,563 66,384 93,754 7,314 480 619 505

California 6,916 13,640 13,454 601 4,740 4,148 822 752 740

Colorado 73,909 55,507 55,555 64,174 55,588 19,698 914 643 672

Connecticut 6,865 2,410 2,406 10,356 2,643 2,041 307 106 123

Delaware 11,917 1,699 2,580 32,378 1,717 3,359 99 55 102

District of

Columbia 492 1,082 3

Florida 217,263 55,818 54,686 417,321 122,123 57,439 2,054 1,821 1,832

Georgia 111,017 37,138 32,293 616,054 91, 885 40,767 1,303 1,176 1,140

Idaho 19 100 100 0 0 0 0 14 14

Illinois 127,923 47,093 45,501 330,382 148,934 47,403 1,586 2,169 2,260

Indiana 213,503 94,741 80,329 878,978 229,248 111,741 2,508 2,088 1 ,930

Iowa 72,806 40,921 40,720 130,264 98,518 22,208 536 781 790

Kansas 90,220 24,943 20,198 136,520 61,622 12,781 948 703 603

Kentucky 164,743 68,087 64,619 502,731 123,010 97,707 1,487 1,566 1,360

Louisiana 63,791 30,380 31,244 109,851 98,808 32,624 1,017 720 753

Maine 1,100 921 316 3,887 1,123 0 60 60 51

Maryland 62,574 17,076 15,904 283,205 36,211 11,528 483 475 447

Massachusetts 25,135 4,611 4,222 84,234 4,236 2,556 584 226 227

### 48 EGU sector emissions (AQ modeling cases) [tons/yr]

State 2005

NOX 2016

Base NOX 2016

Policy

NOX 2005

SO2 2016

Policy SO2

2016

Policy

SO2 2005

VOC 2016

Base

VOC 2016

Policy

VOC

Michigan 120,005 59,629 55,318 349,877 169,853 27,922 1,230 1,197 1,065

Minnesota 83,836 26,063 27,015 101,666 51,952 27,805 633 643 649

Mississippi 45,166 19,460 17, 915 75,047 55,317 10,595 574 412 419

Missouri 127,431 48,179 39,012 284,384 172,031 32,412 1,597 1,774 1,545

Montana 39,858 18,898 21,564 19,715 13,234 9,071 396 261 289

Nebraska 52,426 28,822 29,767 74 ,955 74,642 34,551 675 545 558

Nevada 47,297 13,936 13,426 53,363 11,283 4,735 524 358 355

New

Hampshire 8,827 1,807 1,074 51,445 4,348 730 136 110 100

New Jersey 30,114 9,335 9,449 57,044 8,507 6,997 1,188 3 22 308

New Mexico 75,483 63,427 63,819 30,628 11,370 9,357 576 542 537

New York 63,315 16,540 15,689 180,847 28,911 13,468 801 709 643

North Carolina 111,576 41,827 37,686 512,231 82,544 34,946 936 970 894

North Dakota 76,381 57,537 27,243 137,371 76,081 11,955 763 847 419

Ohio 258,688 90,904 74,788 1,116,084 204,291 77,852 1,751 1,855 1,485

Oklahoma 86,204 45,639 41,722 110,081 139,800 14,196 1,029 917 897

Oregon 9,383 8,139 8,139 12,304 11,102 1,423 141 157 157

Pennsylvania 176,870 107,224 97,220 1,002,201 152,929 73,714 1,153 1,822 1,492

Rhode Island 545 264 271 176 0 0 35 44 45

South Carolina 52,657 34,193 30,3 42 218,781 128,070 35,223 533 629 625

South Dakota 15,650 14,249 13,641 12,215 29,711 7,490 106 127 118

Tennessee 102,934 31,885 20,003 266,148 106,762 44,110 798 940 764

Texas 176,170 127,355 121,488 534, 949 334,636 81,000 3,851 5,025 4,941

Utah 65,261 68,049 61,311 34,813 31,343 14,261 368 489 467

Vermont 297 0 0 9 0 0 22 0 0

Virginia 62,512 29,140 33,922 220,248 45,345 16,029 646 577 560

Washingt on 17,634 10,424 10,425 3,409 2,804 2,804 248 201 201

West Virginia 159,947 57,493 51,936 469,456 127,826 44,129 1,141 1,210 1,171

Wisconsin 72,170 32,755 26,950 180,200 77,871 24,481 980 999 880

Wyoming 89,315 71,795 67,968 89,874 55,636 25,831 848 923 886

Tribal Data 78 11 11 3 0 0 133 2 2

Total 3,726,459 1,769,764 1,618,199 10,380,783 3,577,698 1,220,379 40,950 40,845 38,217

### 49 Table 5

- 5

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

EGU sector emissions (AQ modeling cases) [tons/yr]

State 2005 HCL 2016

Base

HCL 2016

Cntl

HCL

(tons) 2005

Total Hg 2016

Base

Total Hg 2016

Cntl

Total

Hg

Alabama 12431 6713 181 3.0126 1.2550 0.1919

Arizona 9323 129 69 0.8839 0.7487 0.0891

Arkansas 8381 1355 13 0.7112 0.7246 0.0664

California 9 14 16 0.1375 0.1322 0.0842

Colo rado 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 0.1 656

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 1.043 9 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 1.57 59 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

### 50 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 36 2 51 0.2488 0.1838 0.0784

Vermont 1811 0.0056 0.0000 0.0000

Virginia 31 2538 154 0.8099 0.2842 0.1142

Washington 5729 3 3 0.3457 0.1666 0.0198

West Virginia 41 4368 336 2.8543 0.8600 0 .5046

Wisconsin 3365 1534 275 1.3562 0.8701 0.1464

Wyoming 1630 584 158 1.1254 1.2596 0.2197

Tribal Data 172 0.0000 0.0000 0.0000

Total 351,592 74,089 8,802 62.4551 28.7038 6.8376



### Table 5

- 6

. EGU sector (ptipm emissions) for all AQ modeling cases: Speciated Mercury

EGU sector emissions (AQ modeling cases) [tons/yr]

State 2005

PHGI 2016

Base

PHGI

2016

PHGI

cntl 200 5

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

Distric t 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 0.0 000 0.0000 0.0000 0.0000 0.0000 0.0000

Illinois 0.0562 0.0035 0.0059 1.3672 0.1235 0.0328 2.8189 0.3609 0.2481

Indiana 0.0682 0.0106 0.0206 1.1407 0.4173 0.1131 1.6704 1.1304 0.2463

Iowa 0.0035 0. 0042 0.0014 0.2788 0.1932 0.0128 0.8757 0.8020 0.1382

Kansas 0.0066 0.0036 0.0006 0.1674 0.1141 0.0032 0.8336 0.8375 0.0935

Kentucky 0.0624 0.0146 0.0199 0.7987 0.1310 0.0954 0.8974 0.6822 0.1980

Louisiana 0.0021 0.0987 0.0238 0.1478 0.2443 0.0364 0.4593 0.6757 0.1054

Maine 0.0009 0.0008 0.0000 0.0013 0.0088 0.0000 0.0022 0.0033 0.0000

Maryland 0.0532 0.0064 0.0085 0.5354 0.0314 0.0347 0.3014 0.0766 0.0725

Massachu setts 0.0159 0.0004 0.0007 0.1107 0.0027 0.0038 0.0551 0.0063 0.0059

### 51 EGU sector emissions (AQ modeling cases) [tons/yr]

State 2005

PHGI 2016

Base

PHGI

2016

PHGI

cntl 200 5

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 0.067 9

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 0.0 834 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 0.0 058 0.0076 0.0050 0.0634 0.0623 0.0256 0.0790 0.1139 0.0478

Vermont 0.0011 0.0000 0.0000 0.0017 0.0000 0.0000 0.0028 0.0000 0.0000

Virginia 0.0423 0.0295 0.0079 0.4006 0.1083 0.0402 0.1810 0.1464 0.0661

Washington 0.0005 0.0007 0.0001 0.1046 0.0049 0.0006 0.2342 0.1610 0.0192

West Virginia 0.1286 0.0244 0.0328 1.4490 0.2259 0.1668 0.8268 0.6097 0.3050

Wisconsin 0.0077 0.0095 0.0069 0.3549 0.1686 0.0246 0.7840 0.6920 0.1148

Wy oming 0.0043 0.0064 0.0022 0.0723 0.1340 0.0341 0.8721 1.1192 0.1834

Tribal Data 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Total 1.61 0.67 0.3620 21.10 6.88 1.6409 30.20 21.16 4.8347



### Table 5

- 7

. 

### 2016 Base Case SO 2 

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

State EGU Non

- EGU Nonpoint Nonroad +

Alm_no_c3+

Seca_c3 Onroad Fires Total

Alabama 172,198 65,649 52,312 197 513 983 291,850

Arizona 23,140 24,206 2,566 52 626 2,888 53,477

Arkansas 93,754 12,910 27,255 142 286 728 135,075

California 4,740 22,148 77,610 8,489 2,216 6,735 121,938

Colorado 55,588 1,425 6,469 47 529 1,719 65,778

Connecticut 2,643 1,832 18,438 100 275 4 23,291

### 52

State EGU Non

- EGU Nonpoint Nonroad +

Alm_no_c3+

Seca_c3 Onroad Fires Total

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

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

Florida 122,123 40,662 70,479 4,530 1,901 7,018 246,713

Georgia 91,885 42,407 56,812 430 1,108 2,010 194,652

Idaho 0 17,137 2,911 21 167 3,845 24,082

Illinois 148,934 85,834 5,380 319 1,036 20 241,524

Indiana 229,248 64,088 59,764 160 675 24 353,959

Iowa 98,518 19,010 19,816 85 291 25 137,745

Kansas 61,622 12,708 36,374 55 257 103 111,119

Kentucky 123,010 18,773 34,208 257 436 364 177,048

Louisiana 98,808 146,371 2,371 3,979 402 892 252 ,824

Maine 1,123 7,803 9,943 194 131 150 19,345

Maryland 36,211 13,623 40,850 1,055 513 32 92,284

Massachusetts 4,236 16,168 25,235 1,368 497 93 47,597

Michigan 169,853 24,072 42,066 440 919 91 237,440

Minnesota 51,952 18,728 14,727 252 500 631 86,789

Mississippi 55,317 22,327 6,785 244 332 1,051 86,055

Missouri 172,031 65,392 44,540 214 652 186 283,016

Montana 13,234 7,858 1,959 24 105 1,422 24,603

Nebraska 74,642 4,777 29,569 55 181 105 109,329

Nevada 11,283 2,134 12,474 25 187 1,346 27,449

New Hampshire 4,348 2,578 7,391 22 120 38 14,496

New Jersey 8,507 6,758 10,711 1,300 661 61 27,998

New Mexico 11,370 8,065 2,833 24 237 3,450 25,978

New York 28,911 20,812 125,199 979 1,303 113 177,318

North Carolina 82,544 45,264 21,992 2,177 811 696 153 ,484

North Dakota 76,081 9,678 5,766 35 62 66 91,688

Ohio 204,291 58,216 19,810 422 969 22 283,731

Oklahoma 139,800 31,097 7,535 45 436 469 179,382

Oregon 11,102 8,597 9,846 787 369 4,896 35,598

Pennsylvania 152,929 46,609 68,322 458 981 32 269,332

R hode 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 58 9,030

Tribal 0 1,495 0 1,495

Utah 31,343 8,034 3,425 25 297 1,934 45,057

Vermont 0 903 5,379 7 90 49 6,428

Virginia 45,345 47,045 32,897 771 756 399 127,213

Washington 2,804 19,131 7,227 1,432 654 407 31,655

West Virginia 127,826 23,305 14,580 75 161 215 166,162

Wisconsin 77,871 18,573 6,370 123 554 70 103,561

Wyoming 55,636 22,118 6,180 18 86 1,106 85,146

Total 3,577,698 1,349,038 1,250,300 35,616 26,784 49,094 6,288,529

* Non

- US seca_c3 component not included. These emissions are 957,065 tons /yr.



### Table 5

- 8

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

State EGU Non

- EGU Nonpoint Nonroad +

Alm_no_c3

+Seca_c3 Onroad Fires A rea

Fugitive

D ust Total

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

### 53

State EGU Non

- EGU Nonpoint Nonroad +

Alm_no_c3

+Seca_c3 Onroad Fires A rea

Fugitive

D ust Total

Arizona 10,196 3,804 8,178 2,836 1,817 37,151 12,806 76,788

Arkansas 3,805 9,905 22,683 2,191 1,108 10,315 11,681 61,689

California 9,718 20,859 69,736 17,963 17,777 97,302 20,386 253,741

Colorado 4,972 7,007 12,8 54 2,490 4,373 24,054 11,794 67,544

Connecticut 1,632 225 9,303 1,090 2,988 56 1,014 16,308

Delaware 643 1,906 1,675 477 514 87 497 5,801

District of

Columbia 0 172 407 151 229 0 162 1,121

Florida 26,114 18,264 37,931 10,096 4,168 99,484 14,126 210,183

Georgia 14,411 12,161 40,435 4,131 3,803 24,082 21,286 120,309

Idaho 187 2,067 27,023 1,267 1,555 52,808 14,154 99,060

Illinois 11,157 14,266 13,753 7,429 10,062 277 58,864 115,808

Indiana 21,198 13,572 31,618 3,769 5,586 344 41,832 117,919

Iowa 5,22 3 5,688 10,176 3,593 3,816 349 42,837 71,682

Kansas 4,634 7,556 82,581 3,078 1,736 1,468 55,263 156,315

Kentucky 13,598 10,341 16,928 2,899 2,342 5,155 12,655 63,917

Louisiana 5,219 36,644 17,365 6,491 1,000 12,647 10,302 89,669

Maine 712 3,143 11,958 985 1,876 2,127 1,312 22,114

Maryland 3,791 6,153 18,742 2,304 3,584 531 3,559 38,665

Massachusetts 2,754 2,127 24,749 2,531 5,278 1,324 4,580 43,343

Michigan 7,188 11,115 22,374 5,048 10,955 1,283 23,506 81,470

Minnesota 9,011 9,665 22,535 5,035 10,91 7 8,943 49,495 115,600

Mississippi 2,554 9,491 15,685 2,495 876 14,897 17,454 63,451

Missouri 8,040 6,334 25,550 4,217 4,335 2,636 48,202 99,315

Montana 2,453 2,528 4,925 1,427 1,239 17,311 24,528 54,412

Nebraska 2,657 1,857 8,177 3,177 1,760 1,483 37, 482 56,593

Nevada 10,903 4,029 2,612 1,364 732 19,018 7,185 45,843

New Hampshire 1,138 508 11,543 610 1,588 534 658 16,578

New Jersey 3,380 2,577 11,837 3,358 5,483 865 549 28,049

New Mexico 5,785 1,445 5,006 1,220 1,178 48,662 45,353 108,648

New York 7,580 4,442 37,074 5,432 13,467 1,601 13,647 83,242

North Carolina 12,185 11,775 36,080 4,746 3,172 9,870 11,162 88,990

North Dakota 5,338 569 2,807 2,293 1,735 934 38,263 51,940

Ohio 19,844 12,251 22,428 5,908 8,425 316 28,587 97,759

Oklahoma 7,412 5 ,669 45,423 2,165 1,856 6,644 44,243 113,412

Oregon 1,653 8,161 47,545 2,517 1,917 65,350 8,738 135,881

Pennsylvania 21,187 13,237 29,061 4,839 8,838 454 13,344 90,961

Rhode Island 598 256 1,035 281 758 14 182 3,124

South Carolina 11,831 4,477 16,869 2 ,372 1,548 9,163 9,162 55,421

South Dakota 768 2,145 3,959 1,445 1,128 7,062 29,215 45,722

Tennessee 6,637 21,495 19,126 3,129 3,034 3,934 11,900 69,254

Texas 37,320 34,923 47,953 13,048 6,101 21,578 143,814 304,737

Tribal 32 1,557 0 0 1,589

Utah 5 ,011 3,564 8,859 1,021 2,328 27,412 5,682 53,877

Vermont 0 337 4,882 325 1,250 696 1,528 9,018

Virginia 7,141 10,840 27,774 3,938 4,315 5,659 8,194 67,861

Washington 1,927 4,197 30,049 3,737 3,665 4,487 13,617 61,680

West Virginia 16,198 4,921 10,405 1 ,114 1,084 3,050 3,649 40,423

Wisconsin 6,376 7,430 24,646 3,639 8,423 994 11,870 63,379

### 54

State EGU Non

- EGU Nonpoint Nonroad +

Alm_no_c3

+Seca_c3 Onroad Fires A rea

Fugitive

D ust Total

Wyoming 7,406 10,207 2,620 896 967 15,686 28,723 66,505

Grand Total 384,320 404,926 1,029,916 169,144 188,320 684,035 1,030,631 3,891,291

* Non

- US seca_c3 component not included. These emissions are 120,617 tons/yr.

### 55



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