### west virginia department of environmental protection



# Appendix C

On-Road Mobile Emissions Inventory Documentation

Promoting a healthy environment.

## [This page intentionally left blank.]

# B ROOKE 

# - H ANCOCK 

# - J EFFERSON



# T RANSPORTATION 

# S TUDY

TECHNICAL MEMORANDUM 201 1

- 1

Transportation Air Quality Analysis a nd Technical Documentation

For the Weirton

- Steubenville , WV

- OH

State Implementation Plan Inventory Mobile Emission Estimates

For the U.S. EPA 1997 Annual PM 2.5 

## National Ambient Air Quality Standard

Submitted: May 2011 Revised: February 2012

## Production of t his Document paid for by funds from the U.S. Federal Highway Administration, Federal

Transit Administration, Ohio Department of Transportation, West Virginia Department of Transportation and dues from BHJ member governments.

Prepared By:

Brooke

- Hancock

- Jefferson Metropolitan Planning Commission

Th e Metropolitan Planning Organization for Steubenville, Ohio

- Weirton, WV Area 124 North Fourth Street, 2 nd 

## Floor Steubenville, OH 43952

740/282

- 3685

304/797

- 9666

740/282

- 1821 FAX

Email: mikepap@bhjmpc.org Website: www.bhjmpc.org

### Transp ortation Air Quality Analysis And Technical Documentation Weirton

- Steubenville

, WV

- OH

For the U.S. EPA 1997 Annual PM 2.5 

### National Ambient Air Quality Standard May 2011 ; Revised February 2012 Page i



## BROOKE

- HANCOCK

- JEFFERSON TRANSPORTATION STUDY

Transportation Air Quality Analysis and Technical Documentation

For the Weirton

- Steubenville, WV

- OH State Implementation Plan Inventory M obile Emission Estimates

For the U.S. EPA 1997 Annual PM 2.5 

## National Ambient Air Quality Standard

TABLE OF CONTENTS

Introduction

................................ ................................ ................................ ................................ ..... Page 1

On Road Mobile Emission Summary

................................ ................................ ............................. Page 3

Latest Planning Assumptions

................................ ................................ ................................ .......... Page 5

Travel Demand Modeling

- Analysis Years

................................ ................................ ................... Page 5

Social

- Economic Demographics

................................ ................................ ................................ ..... Page 5

Emission Factor Generation

................................ ................................ ................................ ............ Page 9

Temperature and Relative Humidity

................................ ................................ ............................... Page 11

Ramp Fraction

................................ ................................ ................................ ................................ . Page 12

Source Type Population

................................ ................................ ................................ .................. Page 12

Vehicle Age Distribution

................................ ................................ ................................ ................ Page 12

Road Type Distribution

................................ ................................ ................................ ................... Page 21

Post Process ing

................................ ................................ ................................ ............................... Page 22

Appendix A

– Interagency Consultation Documentation

### Transp ortation Air Quality Analysis And Technical Documentation Weirton

- Steubenville

, WV

- OH

For the U.S. EPA 1997 Annual PM 2.5 

### National Ambient Air Quality Standard May 2011 ; Revised February 2012 Page ii 

## LIST OF TABLES

Table 1 BHJ Region On

- Road Mobile Emissions by State

................................ ................. Page 3

Table 2 BHJ Region On

- Road Mobile Emissions by County

................................ ............. Page 4

Table 3 BHJ Region Representative Social

- Economic Demographic Variables

By County 2005

- 2020

................................ ................................ ............................. Page 7

Table 4 Moves Inputs

................................ ................................ ................................ ........... Page 9

Table 5 Average Temperature and Relative Humidity Data

................................ ................ Page 11

Table 6 Ramp Fractions

................................ ................................ ................................ ....... Page 11

Table 7 Source Type Population for Year 2005

................................ ................................ .. Page 12

Table 8 Vehicle Age Distribution for Jefferson County, OH

................................ .............. P age 13

Table 9 Vehicle Age Distribution for Brooke/Hancock County, WV

................................ . Page 17

Table 10 Road Type Distribution for Brooke

- Hancock

- Jefferson Non

- Attainment Area

..... Page 21

LIST OF FIGURES

Figure 1 BHJ Geographic Area Fine Particulate Matter Non

- Attainment A rea Boundary

................................ ................................ ................................ ................................ . Page 2

Figure 2 BHJ Geographi c Area Covered by Travel Demand Model and Traffic Analysis Zone

Structure

................................ ................................ ................................ .................. Page 8

### Transp ortation Air Quality Analysis And Technical Documentation Weirton

- Steubenville

, WV

- OH

For the U.S. EPA 1997 Annual PM 2.5 

### National Ambient Air Quality Standard May 2011 ; Revised February 2012 Page iii



## SELECTED ABBREVIATIONS & ACRONYMS

BHJ

– B rooke

- Hancock

- Jefferson Metropolitan Planning Commission

CFR

– Code of Federal Regulations

FHWA

– F ederal Highway Administration ;

FTA

– F ederal Transit Admi nistration ;

LRTP

– Long Range Transportation Plan

MOBILE6

– Mobile Source Emission Factor

Model

MOU

– Memorandum of Understanding

MPO

– Metropolitan Planning Organization

MVEB

– Motor Vehicle Emissions Budget

NAAQS

– National Ambient Air Quality

Standard

NO X 

## – Nitrogen Oxides

ODOT

– Ohio Department of Transportation

OEPA

– Ohio Environmental Protection Agency

PM

– Particulate Matter

PM 2.5 

## - Particulate Matter with an aerodynamic diameter less than 2.5 microns (often referred to

as Fine Partic ulate Matter) PM 10



## – Particulate Matter with an aerodynamic

diameter less than 10 microns

SIP

– State Implementation Plan

STIP

– Statewide Transportation Improvement Program

TAZs

– Traffic Analysis Zones

TCM

– Transportation Control Measure

TDM

– Travel Demand Model

TIP

– Transportation Improvement Program in a Metropolitan Planning Area

USEPA

– United States Environmental Protection Agency

VMT

– Vehicle Miles of Travel

VOC

– Volatile Organic Compounds

WVDEP

– West Virginia Department of

Envir onmental Protection

WVDOT

– West Virginia Department of Transportation

## REVISION HISTORY

February 24, 2012

Edit Page 1 ; Introduction; Paragraph #1 Revise paragraph into two sentences.

Edit P a ge 3 ; On Road Emission Summary Paragraph #1 ; Insert Maintenance Year 2022 with 2025

Revise ; Table 1; BHJ Region on

- Road Mobile Emissions b y State ; Add Maintenance Year 2025

Revise Pag e 4 ; Table 2 ; BHJ Region on

- Road Mobile Emissions b y County ; Add Maintenance Year

2025

Revise Page 5 ; Travel Demand Modeling

- Analysis Years ; Add Analysis Year 2025

– Maintenance

Year

Revise Page 9; Emission Factor Generation ; Paragraph #1 Add 2025

### Transp ortation Air Quality Analysis And Technical Documentation Weirton

- Steubenville

, WV

- OH

For the U.S. EPA 1997 Annual PM 2.5 

### National Ambient Air Quality Standard May 2011 ; Revised February 2012 Page 1 

## Transportation Air Quality Analysis a nd Technical Documentation

For the Weirton

- Steubenville, WV

- OH State Implementation Plan Inventory Mobile Emission Estimates

For the U.S. EPA 1997 Annual PM 2.5 

## National Ambient Air Quality Standard

INTRODUCTION

This memorandum document s the air quality analyses and underlying planning assumptions performed

for the Annual PM2.5 on

- road mobile source emission inv entories for the Weirton

- Steubenville, WV

OH Metropolitan area State Implementation Plan (SIP) . T he Ohio Department of Transportation (ODOT), Division of Transportation System Development

- Modeling and Forecasting Section and the

Brooke

- Hancock

- Jefferson Planning Commission (BHJM PC) completed these analyses in coordination with the Ohio Environmental Protection Agency (OEPA) and West Virginia Department of Environmental Protection (WVDEP)

.

The BHJ Region is compr ised of the counties of Brooke and Hancock in the north panhandle of West Virginia and Jefferson in east

- central Ohio. On January 5, 2005, the USEPA designated all three counties within the BHJMPO Planning Area as non

- attainment for the PM 2.5 

## air quality standard. The designation became effective on April 5, 2005; 90 days after USEPA published action in the Federal Registry

. Figure 1 displays the geographic extent of the Steubenville

- Weirton, OH

- WV non

- attainment

area

The BHJMPC serves as t he Metropolitan Planning Organization (MPO) . The MPO submitted the necessary Travel Demand Model networks along with all land use and social

- economic demographics. The ODOT Modeling and Forecasting section performed the MOVES runs to generate travel dema nd model based emission factors as well as the complete air quality analyses for t he metropolitan area

.

### Transp ortation Air Quality Analysis And Technical Documentation Weirton

- Steubenville

, WV

- OH

For the U.S. EPA 1997 Annual PM 2.5 

### National Ambient Air Quality Standard May 2011 ; Revised February 2012 Page 2 

## Figure 1

BHJ GEOGRAPHIC AREA

FINE PARTICULATE MATTER

NON

- ATTAINMENT AREA BOUNDARY

### Transp ortation Air Quality Analysis And Technical Documentation Weirton

- Steubenville

, WV

- OH

For the U.S. EPA 1997 Annual PM 2.5 

### National Ambient Air Quality Standard May 2011 ; Revised February 2012 Page 3 

## ON ROAD MOBILE EMISSION SUMMARY

The Table 1 and 2 below present a summary of the pollutant emissions including Fine Particulate Matter ( PM 2.5 

## ) , Nitrogen Oxides (NO

x 

## ), Sulfur Dioxide (SO 2 

## ) modeled for each county that make up the BHJ Region . The Model Years for the demonstration includes the Base Year 20 05, Attainment Year 2008 (a leap year)

, Interim Year 2015 , and Maintenance Year 202 5

.

Table 1

BHJ REGION ON

- ROAD MOBILE EMISSIONS BY STATE

### Transp ortation Air Quality Analysis And Technical Documentation Weirton

- Steubenville

, WV

- OH

For the U.S. EPA 1997 Annual PM 2.5 

### National Ambient Air Quality Standard May 2011 ; Revised February 2012 Page 4 

## Table 2

BHJ REGION O N

- ROAD MOBILE EMISSIONS BY COUNTY

### Transp ortation Air Quality Analysis And Technical Documentation Weirton

- Steubenville

, WV

- OH

For the U.S. EPA 1997 Annual PM 2.5 

### National Ambient Air Quality Standard May 2011 ; Revised February 2012 Page 5 

## LATEST PLANNING ASSUMPTIONS

The annual PM2.5 inventory runs meet the latest planning assumption requirement. This report will

present the latest population and land use data available that calibrated the modeling process used to

calculate the vehicle emissions for the mobile emissions budgets as well as the input values for U.S.

EPA’s most recent emissions software MOVES for this attainment demonstration.

A series of conference calls held in November 2010 and February 2011 by the Interagency Consultancy Group established two parameters. First, that this re

- designation effort will require the use of U.S.

EPA’s most recent emissions software MOVES for all mobile source emission analyses and second, the

annual emi ssion estimates will be based a single

- season app roach . The group also decided that s ince travel demand models produce average daily conditions, the daily emissions estimates are multiplied by 365 days to produce annual emissions estimates expressed in tons per year.

TRAVEL DEMAND MODELING

- ANALYSIS YEARS

A Travel Demand Model (TDM) is the traditional forecasting tool used to examine potential changes in future travel patterns for a specific study area, in this case the Brooke

- Hancock

- Jefferson Metropolitan Area. The BHJ MPO, with the assistance of ODOT Modeling & Forecasting, maintains a validated region

- wide TDM that employs a four

- step modeling process consisting of trip generation, trip distribution, mode choice, and route assignment performed with the Cube Voyager software package

. The model Ou tputs generated from the TDM are link

- by

- link directional 24

- hour traffic volumes for simulating Base Year and Horizon Year travel patterns generated by the LRTP transportation network.

The current BHJ TDM Validation Year is 2000. The model uses compar able Average Daily Traffic count data, updated social

- economic variables for each of the analysis years by either updating existing or known land use commitment for 2008, 2009, and 2014, or projected 2014 and 2020 variables based on a straight

- line extrapo lation between 2009 set of variables and the Horizon Year 2030 variables. These networks represent all planned federal

- aid projects as well as any regionally significant projects found in the BHJ TIP and LRTP expected to be open for traffic by the end of e ach respective analysis

year.

T he interagency consultation process, as previously discussed, established the following model years for

Brooke and Hancock counties , WV and Jefferson County, OH that reflected the most recent correspondence from the U.S. EPA :

Analysis Year 2005

– Baseline Emissions Analysis Year 20 08

– Attainment Year Analysis Year 2015

– Interim Year Analysis Year 2022

– Maintenance Year Analysis Year 2025

– Maintenance Year

SOCIAL

- ECONOMIC DEMOGRAPHICS

Identifying projected growth centers and understanding urban and rural population changes are essential to determine future transportation needs in a given study area. Critical elements include an understanding of the past and anticipated future shifts in the region’s economy, population, land use

### Transp ortation Air Quality Analysis And Technical Documentation Weirton

- Steubenville

, WV

- OH

For the U.S. EPA 1997 Annual PM 2.5 

### National Ambient Air Quality Standard May 2011 ; Revised February 2012 Page 6 

## patterns, and other environmental factors over time. In turn, these factors are useful for predicting future transportation patterns and justify transportation improvements over the next twenty years.

Travel fo recasting procedures require the user delineate the TDM study area into geographic areas called Traffic Analysis Zones (TAZs). Typically, TAZs are based on factors such as land use, area types (urban, suburban or rural), or political government units such as cities, villages, or townships. TAZs represent centers of travel generators or attractors based on a set of demographic variables. The BHJMPO collects and reviews the TDM independent variables that characterize current and future

estimates of the met ropolitan area’s social and economic activity that may influence land

- use development patterns. In all, there are 376 TAZs in the BHJ model represented by 60 TAZs for Brooke County, 78 TAZs for Hancock County, and 237 TAZs for Jefferson County. Figure 2 displays the BHJ

MPO geography covered by the Steubenville

- Weirton Travel Demand Model (the entire three

- county area) including the Traffic Analysis Zone structure. The computer

- based TDM for the Steubenville

Weirton Metropolitan Area highway network empl oys the following land use variables:

[ AREA_TYPE ] ≡ Area Type
[ AVG_PARK ] ≡ Average parking cost
[ ENROLL ] ≡ School en rollment classified by Private [ENROLL_PRIV], Public [ENROLL_PUB] and Post

- secondary [ENROLL_UNIV] schools
[ HOTEL_RM ] ≡ Hotel Rooms
[ MED_HHINC ] ≡ Median household income
[ POP ] ≡ Population
[ POP_18 ] ≡ Population 18 years or less
[ POP_GRP ] ≡ Population residing in Group Quarters
[ TOTEMP ] ≡ Total Employment grouped by the North American Industrial Classification System (NAICS)
[ TOT_HH ] ≡ O ccupied housing
[ TOT_VEH ] ≡ Vehicles available per household
[ WORKERS ] ≡ Workers per household

Table 3 is a set of demographic variables developed for the most recent 2006 24

- Hour PM2.5 National Ambient Air Quality Standard for the Weirton

- Steubenville, W V

- OH non

- attainment area compiled September 2010.

### Transp ortation Air Quality Analysis And Technical Documentation Weirton

- Steubenville

, WV

- OH

For the U.S. EPA 1997 Annual PM 2.5 

### National Ambient Air Quality Standard May 2011 ; Revised February 2012 Page 7 

## Table 3

BHJ REGION

REPRESENTATIVE SOCIAL

- ECONOMIC DEMOGRAPHIC

VARIABLES BY COUNTY 2005

- 2020

### Transp ortation Air Quality Analysis And Technical Documentation Weirton

- Steubenville

, WV

- OH

For the U.S. EPA 1997 Annual PM 2.5 

### National Ambient Air Quality Standard May 2011 ; Revised February 2012 Page 8 

## Figure 2

BHJ GEOGRAPHIC AREA

COVERED BY TRAVEL DEMAND MODEL

AND TRAFFIC ANALYSIS ZONE STRUCTURE

### Transp ortation Air Quality Analysis And Technical Documentation Weirton

- Steubenville

, WV

- OH

For the U.S. EPA 1997 Annual PM 2.5 

### National Ambient Air Quality Standard May 2011 ; Revised February 2012 Page 9 

## EMISSION FACTOR GENERATION

The MOVES model generated the emission factor files were for base year

- 2 005 and attainment year

2008 represent ing the transportation improvement programs implemented in the BHJ Region. The

model also generated emission factors for two future year scenarios 2015

, 2022 , and 2025

.

Table 4 summarizes the settings used in the MOVES run specification file and the MOVES County

Data Manager. The subsequent tables provide the specific inputs that a re not using the MOVES default

values

.

Table 4

MOVES INPUTS

### Transp ortation Air Quality Analysis And Technical Documentation Weirton

- Steubenville

, WV

- OH

For the U.S. EPA 1997 Annual PM 2.5 

### National Ambient Air Quality Standard May 2011 ; Revised February 2012 Page 10 

## Table 4 (continued)

MOVES INPUTS

TEMPERATURE AND RELATIVE HUMIDITY

The single season approach for temperature and relative humidity uses weather data collected by the National Oceanic and Atmospheric Administration (NOAA) National Climatic Data Center (NCDC)

. The data used in this report

, taken from the Pittsburgh International Airport collection center, is

rep resentative of 12 months in 2009

. Data entered into a spreadsheet provided by U.S. EPA convert ed

the Mobile6 to get the correct data for the MOVES model

. Listed in the table below are the average annual hourly temperature s and relative humidity distribution profile s for the Weir ton

- Steubenville,

WV

- OH metropolitan area

.

### Transp ortation Air Quality Analysis And Technical Documentation Weirton

- Steubenville

, WV

- OH

For the U.S. EPA 1997 Annual PM 2.5 

### National Ambient Air Quality Standard May 2011 ; Revised February 2012 Page 11 

## Table 5

AVERAGE TEMPERATURE AND RELATIVE HUMIDITY DATA

RAMP FRACTIO N

The Base Year Travel Demand Model used the V ehicles H our of T ravel (VHT) fractions to derive the Ramp F ractions shown in Table 6

. The future year networks also used the b ase year fractions

.

Table 6

RAMP FRACTIONS

### Transp ortation Air Quality Analysis And Technical Documentation Weirton

- Steubenville

, WV

- OH

For the U.S. EPA 1997 Annual PM 2.5 

### National Ambient Air Quality Standard May 2011 ; Revised February 2012 Page 12 

## SOURCE TYPE POPULATION

A combination of local and MOVES default data is the Source Type P opulation for vehicle

classifications

. The MOVES default values provided the data for Source Type Population 51, 52, 53, 61, and 62 while local data from Ohio and West Virginia motor vehicle registration s accounted for all other Source Type Population needed to run the MOVES m odel. Table 7 shows the Source Type Population identifications, the corresponding Source Type Name, and the number of vehicles analyzed for Jefferson County, OH and Brooke/Hancock County, WV combined

.

Table 7

SOURCE TYPE POPULATION FOR YEAR 2005

VEHICLE AGE DISTRIBUTION

A grouping of data from Ohio and West Virginia sources along with the MOVES model default s make up the Vehicle Age Distribution. MOVES default values included Vehicle Type ID 41, 42, 51, 52, 53, 61, and 62. Local data from Ohio and West Virginia motor vehicle registration s accounted for all other Vehicle Type ID. Table 8 and Table 9 show th e Vehicle Age Distribution for Jefferson County, OH and Brooke/Hancock County, WV respectively

.

### Transp ortation Air Quality Analysis And Technical Documentation Weirton

- Steubenville

, WV

- OH

For the U.S. EPA 1997 Annual PM 2.5 

### National Ambient Air Quality Standard May 2011 ; Revised February 2012 Page 13 

## Table 8
VEHICLE AGE DISTRIBUTION FOR JEFFERSON COUNTY, OH

### Transp ortation Air Quality Analysis And Technical Documentation Weirton

- Steubenville

, WV

- OH

For the U.S. EPA 1997 Annual PM 2.5 

### National Ambient Air Quality Standard May 2011 ; Revised February 2012 Page 14 

## Table 8 (continued)
VEHICLE AGE DISTRIBUTION FOR JEFFERSON COUNTY, OH

### Transp ortation Air Quality Analysis And Technical Documentation Weirton

- Steubenville

, WV

- OH

For the U.S. EPA 1997 Annual PM 2.5 

### National Ambient Air Quality Standard May 2011 ; Revised February 2012 Page 15 

## Table 8 (continued)
VEHICLE AGE DISTRIBUTION FOR JEFFERSON COUNTY, OH

### Transp ortation Air Quality Analysis And Technical Documentation Weirton

- Steubenville

, WV

- OH

For the U.S. EPA 1997 Annual PM 2.5 

### National Ambient Air Quality Standard May 2011 ; Revised February 2012 Page 16 

## Table 8 (continued)
VEHICLE AGE DISTRIBUTION FOR JEFFERSON COUNTY, OH

### Transp ortation Air Quality Analysis And Technical Documentation Weirton

- Steubenville

, WV

- OH

For the U.S. EPA 1997 Annual PM 2.5 

### National Ambient Air Quality Standard May 2011 ; Revised February 2012 Page 17 

## Table 9

VEHICLE AGE DISTRIB UTION FOR BROOKE/HANCOCK COUNTY, WV

### Transp ortation Air Quality Analysis And Technical Documentation Weirton

- Steubenville

, WV

- OH

For the U.S. EPA 1997 Annual PM 2.5 

### National Ambient Air Quality Standard May 2011 ; Revised February 2012 Page 18 

## Table 9 (continued)
VEHICLE AGE DISTRIBUTION FOR BROOKE/HANCOCK COUNTY, WV

### Transp ortation Air Quality Analysis And Technical Documentation Weirton

- Steubenville

, WV

- OH

For the U.S. EPA 1997 Annual PM 2.5 

### National Ambient Air Quality Standard May 2011 ; Revised February 2012 Page 19 

## Table 9 (continued)
VEHICLE AGE DISTRIBUTION FOR BROOKE/HANCOCK COUNTY, WV

### Transp ortation Air Quality Analysis And Technical Documentation Weirton

- Steubenville

, WV

- OH

For the U.S. EPA 1997 Annual PM 2.5 

### National Ambient Air Quality Standard May 2011 ; Revised February 2012 Page 20 

## Table 9 (continued)
VEHICLE AGE DISTRIBUTION FOR BROOKE/HANCOCK COUNTY, WV

### Transp ortation Air Quality Analysis And Technical Documentation Weirton

- Steubenville

, WV

- OH

For the U.S. EPA 1997 Annual PM 2.5 

### National Ambient Air Quality Standard May 2011 ; Revised February 2012 Page 21 

## ROAD TYPE DISTRIBUTION

The ODOT and WV Division of Highway county summary V ehicle Miles Traveled (V MT ) data categorized by federal functional class for the three county non

- attainment areas is the basis for Road Type Di stribution Fraction

. Table 10 illustrates Road Type Distribution

.

Table 10

ROAD TYPE DISTRIBUTION FOR BROOKE

- HANCOCK

- JEFFERSON

NON

- ATTAIN MENT AREA

### Transp ortation Air Quality Analysis And Technical Documentation Weirton

- Steubenville

, WV

- OH

For the U.S. EPA 1997 Annual PM 2.5 

### National Ambient Air Quality Standard May 2011 ; Revised February 2012 Page 22 

## POST PROCESSING

Several custom programs created by ODOT were computed the t otal emissions. The process uses data on daily and directional traffic distributions as well as more up to date volume/delay functions from the 2000 Highway Capacity Manual (HCM). This process, described below and illustrated in Figure 4, also

uses rewri tten code able to handle the newer model network formats and MOVES generated emission

factors.

The first step in the process involves running postcms.exe to calculate hourly link volumes based on the percentage of the daily volume (travel demand model output) determined by a link’s facility and area type. The analysis does not use the l ink speeds from the travel demand model. Using a link’s volume

to

- capacity ratio and link group code

, a post

- process to the model based on HCM methods estimates the

li nk speeds

.

The second step (mmoves.exe) uses a combination of the MOVES emission factors and the hourly link volumes that are output of the postcms.exe p rogram. The hourly volumes multiplied by the MOVES emission factor for the corresponding hour of d ay, speed bin, and road type ; calculate emissions for every network link for each hour. The final link on road vehicle emissions for the area is the sum of all

individual link

- hour emissions.

The third step, (vehcalm.exe), calculates vehicle

- based emis sions for each source type for each hour of the day. A combination of local and default data is the source for the vehicle source type . The final vehicle emissions for each county are the sum of all individual hourly emissions for all vehicle types.

Since the intrazonal trips are not loaded onto the network, the fourth step in the process requires a separate method to account for those trips that use local roads to travel within a zone. The intracalm.exe program uses intrazonal trips to estimate VMT using the area in square miles and intrazonal trips of each

zone. The computer program assumes that the zone is circular and uses the radius of the circle as the average trip length for these intrazonal trips. By combining MOVES generated emissions with

estimated intrazonal VMT , the i ntrazonal emissions are then calculated. The emission rates are the same

as those used to calculated link based emissions.

The final step is to summarize link, vehicle, and intrazonal emissions for each county, pollutant, and analyzed year, and to multiply annual average daily emissions by 365 to produce an annual estimate.

## APPENDIX A

INTERAGENCY CONSULTATION DOCUMENTATION1 

## Durham, William F

From: Durham, William F

Sent: Wednesday, February 29, 2012 3:23 PM

To: 'Mike Paprocki (mikepap@bhjmpc.org)'; 'Nino Brunello

(Nino.Brunello@dot.state.oh.us)'; 'Dave Moor e (Dave.Moore1@dot.state.oh.us)'; 'Sam

Granato (Sam.Granato@dot.state.oh.us)'; 'P aul Braun (paul.braun@epa.state.oh.us)'; 'Leigh.Oesterling@fhwa.dot.gov'; 'Martin Ko tsch (kotsch.martin@epa.gov)'; 'Patricia
Morris (morris.patricia@epa.gov)'; S edosky, Timothy B; 'Kevin Burgess (Kevin.Burgess@fhwa.dot.gov)'; 'kathleen.zubrzycki@dot.gov'

Cc: 'Jennifer Hunter Dines - OH EPA (Jennifer. Dines@epa.state.oh.us)'; Crowder, Laura M
Subject: Proposed Weirton, WV 1997 PM2.5 Redesignation

Attachments: William Frederick Durham.vcf; TranExcerpt - Proposed Weirton PM2 5 Redesignation 2012-02-28.pdf



### Dear Folks:

Below is the public notice for the Weirton, WV 1997 PM 2.5 

### Redesignation and Maintenance Plan. The complete

documentation is available on the website listed in the notice. I have attached the transportation section for your

convenience. The narrative borrows very heavily from the Transportation Air Quality Analysis and Technical

Documentation For the Weirton

‐ Steubenville, WV

‐ OH State Implementation Plan Inventory Mobile Emission Estimates

For the U.S. EPA 1997 Annual PM 2.5 

### National Ambient Air Quality Standard (revised February 2012).

For the reasons outlined in the attached excerpt, the DEP is proposing to make a finding that regional highway emissions

of PM 2.5 

### , NOx and SO 2 

### are insignificant contributors to the nonattainment problem for the Weirton area. The finding will

become final if EPA concurs and approves this SIP revision. Therefore, no transportation conformity Motor Vehicle

Emission Budgets (MVEB) should be necessary. Please contact me if you have any problems opening the attachment or accessing our website.

Thank you.

Fred

>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>

NOTICE OF PUBLIC COMMENT AND HEARING

The West Virginia Department of Environmental Protection (DEP) is offering an opportunity for public comment, and will

hold a public hearing, on the Proposed Redesignation Request and Maintenance Plan for the West Virginia Portion of the

Steubenville

‐ Weirton, OH

‐ WV 1997 PM 2.5 

### Nonattainment Area

. The proposal concerns two inter

‐ related actions
regarding the West Virginia portion of the nonattainment area, comprising Brooke and Hancock Counties. First, DEP is

requesting that the U.S. Environmental Protection Agency (EPA) redesignate the area to attainment pursuant to the

provisions of the Clean Air Act, section 107. Second, DEP is asking that EPA concurrently approve, as a revision to the

State Implementation Plan (SIP), the related maintenance plan which demonstrates that the area will continue to meet2 

### the PM 2.5 

### air quality standards for at least ten more years. DEP is expressly soliciting comment on whether the proposed

maintenance plan adequately meets the requirements of the Clean Air Act, section 175A, as well as any relevant

comments regarding the incorporation of the finalized maintenance plan into the West Virginia SIP. The comment period will begin March 2, 2012 and end on April 3, 2012.

The area was designated as nonattainment with the 1997 PM 2.5 

### annual National Ambient Air Quality Standard (NAAQS)

in the January 5, 2005 Federal Register [70 FR 944], effective April 5, 2005. On September 14, 2011, in accordance with

Section 179(c)(1) of the CAA, EPA determined that the Steubenville

‐ Weirton, OH

‐ WV PM 2.5 

### nonattainment area had

attained the 1997 annual PM 2.5 

### NAAQS by the applicable attainment date of April 5, 2010 [76 FR 75465]. DEP

demonstrates that the measured air quality improvement has been achieved through federally enforceable emission

reduction measures. Further, DEP shows that with expected growth and federally enforceable controls, the area will

continue attainment of the NAAQS for at least ten years.

Written and oral comments will be accepted until the close of the public hearing and will be made part of the formal

record. The public hearing shall be held beginning at 6:00 p.m. on Tuesday, April 3, 2012 at DEP headquarters: 601 57 th



### Street, SE, Charleston, WV 25304. Written comments will also be accepted for inclusion in the formal record by mail,

facsimile (304

‐ 926

‐ 0479), e

‐ mail ( Tammy.L.Mowrer@wv.gov ) or other delivery to the Division of Air Quality if received
by the close of business on April 3, 2012. Send written comments to be included in the formal record to John A.
Benedict, Director, Division of Air Quality at the address above. Please identify the document to which the comments apply, the commenter’s name, address and telephone number.

Copies of the proposed SIP revision and documentation may be viewed on weekdays between 8:30 a.m. and 4:30 p.m.

at the Division of Air Quality, 601 57 th 

### Street, SE, Charleston, WV 25304. Copies may alternatively be reviewed at the

Brooke

‐ Hancock

‐ Jefferson Metropolitan Planning Commission at 124 North Fourth Street, Second Floor, Steubenville,

OH 43952

‐ 4498, phone: (740) 282

‐ 3685. The relevant documents may also be accessed via the following web address: http://www.dep.wv.gov/daq/General/P ages/PublicNoticeandComment.aspx1 

## Durham, William F

From: Durham, William F

Sent: Tuesday, April 10, 2012 4:00 PM

To: 'Mike Paprocki (mikepap@bhjmpc.org)'; 'Nino Brunello

(Nino.Brunello@dot.state.oh.us)'; 'Dave Moor e (Dave.Moore1@dot.state.oh.us)'; 'Sam

Granato (Sam.Granato@dot.state.oh.us)'; 'P aul Braun (paul.braun@epa.state.oh.us)'; 'Leigh.Oesterling@fhwa.dot.gov'; 'Martin Ko tsch (kotsch.martin@epa.gov)'; 'Patricia
Morris (morris.patricia@epa.gov)'; S edosky, Timothy B; 'Kevin Burgess (Kevin.Burgess@fhwa.dot.gov)'; 'kathleen.zubrzycki@dot.gov'

Cc: 'Jennifer Hunter Dines - OH EPA (Jennifer. Dines@epa.state.oh.us)'; Crowder, Laura M
Subject: Proposed Weirton, WV 2006 PM2.5 Redesignation

Attachments: Weirton PM2 5 Redesignation 2012-04-10.pdf



### Dear Folks:

Below is the public notice for the Weirton, WV 2006 PM 2.5 

### Redesignation and Maintenance Plan. The complete

documentation is available on the website listed in the notice. I have attached the narrative for your convenience. DEP

has previously proposed redesignation of the area under the separate 1997 PM 2.5 

### NAAQS.

For the reasons outlined in the attached narrative, the DEP is proposing to make a finding that regional highway

emissions of PM 2.5 

### , NOx and SO 2 

### are insignificant contributors to the nonattainment problem for the Weirton area. The
finding will become final if EPA concurs and approves this SIP revision. Therefore, no transportation conformity Motor

Vehicle Emission Budgets (MVEB) should be necessary. Additionally, DEP reviewed the PM 2.5 

### exceedances under EPA’s
policy and determined that annual inventories are sufficient to characterize the emission contributions. Therefore, the

emissions analysis is the same as that presented in the previous 1997 PM 2.5 

### Redesignation/ Maintenance Plan. Please

contact me if you have any problems opening the attachment or accessing our website.

Thank you.

Fred

>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>


## NOTICE OF PUBLIC COMMENT AND HEARING

The West Virginia Department of Environmental Protection (DEP ) is offering an opportunity for public comment, and will hold a public hearing, on the Proposed Redesignation Request and Maintenance Plan for the West Virginia Por tion of the Steubenville-Weirton, OH-WV 2006

PM 2.5 

## Nonattainment Area . The proposal concerns two inter-r elated actions regarding the West Virginia portion of the nonattainment area, co mprising Brooke and Hancock Counties. First, DEP is requesting that the U.S. Environmental Protection Agency (EPA) redesignate the area to2 

## attainment pursuant to the provi sions of the Clean Air Act, se ction 107. Second, DEP is asking that EPA concurrently approve, as a revision to the State Implemen tation Plan (SIP), the related maintenance plan which demonstrates that the area will continue to meet the PM 2.5 

## air quality standards for at least ten more years. DEP is expressly soliciting comment on whether the currently proposed maintenance plan adequately meets the requirements of the Clean Air Act, section 175A, as well as any relevant comments regarding the incorpor ation of the finalized maintenance plan into the West Virginia SIP. DEP has previously proposed redesignation of the area with respect to the separate 1997 PM 2.5 

## NAAQS. The comment period will begin April 13, 2012 and end on May 15, 2012.

The area was designated as nonattainment with the 2006 PM 2.5 

## annual National Ambient Air Quality Standard (NAAQS) in the November 13, 2009 Federal Register [74 FR 58688], effective
December 14, 2009. On October 4, 2011, in accordan ce with Section 179(c)(1 ) of the CAA, EPA proposed to determine that th e Steubenville-Weirton, OH-WV PM 2.5 

## nonattainment area had

attained the 2006 24-hour PM 2.5 

## NAAQS [76FR61291]. DEP demonstrat es that the measured air quality improvement has been achieved thr ough federally enforceable emission reduction measures. Further, DEP shows that with expected growth and federally enforceable controls, the area will continue attainment of the NAAQS for at least ten years.

Written and oral comments will be accepted during the public hearing and will be made part of the formal record. The public hearing shall be held beginning at 6:00 p.m. on Tuesday, May 15, 2012 at DEP headquarters: 601 57 th 

## Street, SE, Charleston, WV 25304. Written comments will also be accepted for inclusion in the formal record by mail, facsimile (304-926-0479), e-mail
( Tammy.L.Mowrer@wv.gov ) or other delivery to the Division of Air Quality if received by the close of business on May 15, 2012. Send written comments to be included in the formal record to
John A. Benedict, Director, Divi sion of Air Quality at the ad dress above. Please identify the document to which the comments apply, the comme nter’s name, address and telephone number.

Copies of the proposed SIP revision and docum entation may be viewed on weekdays between 8:30 a.m. and 4:30 p.m. at the Division of Air Quality, 601 57 th 

## Street, SE, Charleston, WV 25304. Copies may alternatively be reviewed at the Brooke-Hancock-Jefferson Metropolitan Planning Commission at 124 Nort h Fourth Street, Second Floor, Steubenville, OH 43952-4498, phone: (740) 282-3685. The relevant documents may also be accessed via the following web

address: http://www.dep.wv.gov/daq/General/Page s/PublicNoticeandComment.aspx

### w est virg i n i a departm ent of env i ronm ental prot ect i on



# A p p e n d i x D

L A D C O

D o c u m e n t a t i o n

Prom oti ng a h eal thy e nvi ro nm ent

.

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## T a b l e o f C o n t e n t s

2

. 5



## A ppen di x D1: Re gi o n al A i

r Qual i t y A n al y s es f o r Ozo n e, PM

, an d Re gi o n al Haz e:

F i n al T ech n i cal Suppo rt Do cum

en t

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 5

A ppen di x D2: Dev el o pm

en t o f 2005 Ba s e Year Gro wt h an d Co n t ro l F act o r f

o r

L ake Mi ch i gan A i

r Di rect o rs Co n s o rt i

um (LA DC O) F i

n al Re po rt

. . . . . . . . . . . . . Page 177

## [This page intentionally left blank.]

# A p p e n d i x D 1 :

2. 5 

# R e g i o n a l A i r Q u a l i t y A n a l y s e s f o r O z o n e
, P M

, a n d

R e g i o n a l H a z e : F i n a l T e c h n i c a l S u p p o r t D o c u m e n t

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. pdf

## [This page intentionally left blank.]

# Regional Air Quality Analyses for

Ozone, PM 2. 5 

# , and Regional Haze:

F inal Technical Support Document

April 25 , 2008

States of Illinois, Indiana, Michigan, Ohio, and Wisconsin

### ii 

# Table of Contents

Section

- Title Pa ge

Executive Summary i i i

1

. 0 Introduction 1

1

. 1 SIP Requirements 1

1

. 2 Organization 2

1

. 3 Technical Work: Overview 3

2

. 0 Ambient Data Analyses 4

2

. 1 Ozone 4

2

. 2 PM 2. 5 

### 2 1

2

. 3 Regional Haze 35

3

. 0 Air Quality Model ing 4 6

3

. 1 Selection of Base Year 46

3

. 2 Future Years of Interest 46

3

. 3 Modeling System 4 7

3

. 4 Domain/Grid Resolution 47

3

. 5 Model Inputs: Meteorology 48

3

. 6 Model Inputs: Emissions 51

3

. 7 Base Year Modeling Results 60

4

. 0 Attainment De monstration for Ozone and PM 2.5 

### 7 1

4

. 1 Future Year Modeling Results 71

4

. 2 Supplemental Analyses 82

4

. 3 Weight of Evidence Determination for Ozone 82

4

. 4 Weight of Evidence Determination for PM 2. 5 

### 90

5

. 0 Reasonable Progress Assessment for Regional Haze 9 3

5

. 1 Class I Areas Impacted 93

5

. 2 Future Year Modeling Results 96

5

. 3 Weight of Evidence Determination for Regional Haze 1 04

6

. 0 Summary 1 11

7

. 0 References 1 1 4

Appendix I Ozone and PM 2.5 

### Modeling Results Appendix II Ozone Source Apportio nment Modeling Results Appendix III PM 2.5 

### Source Apportionment Modeling Results Appendix IV Haze Source Apportionment Modeling Results

### iii 

### EXECUTIVE SUMMARY

States in the upper Midwest face a number of air quality challenges. More than 5 0 counties are

cu rrently classified as nonattainment for the 8

- hour ozone standard and 60 for the f ine particle ( PM 2.5 

### ) standard (1997 version s ). A map of these nonattainment areas is provided in the figure

below. In addition, visibility impairment due to regional haze is a problem in the larger national parks and wilderness areas (i.e., Class I areas). There are 156 Class I areas in the U.S., including two in northern Michigan

.

Figure i

. Current nonattainment counties for ozone (left) and PM 2. 5 

### (right )



### T o support the development of State Implementation Plans (SIPs) for ozone, PM 2.5 

### , and regional haze in the States of Illinois, Indiana, Michigan, Ohio, and Wisconsin, technical analyses were conducted by the Lake Michigan Air Directors Consortium (LADCO ), its member states, and various contractors

. The analyses include preparation of regional emissions inventories and meteorological data, evaluation and application of regional chemical transport
models, and collection and analysis of ambient monitoring data.

M onitoring data were analyzed to produce a conceptual understanding of the air quality problems. Key findings of the analyses include:

Ozone

• Current monitoring data (2005

- 2007) show about 20 sites in violation of the 8

- hour

ozone standard of 85 parts per billion (ppb) . Historical ozone data show a steady downward trend over the past 15 years, especially since 2001

- 2003, due likely t o federal and state emission control programs.

• Ozone concentrations are strongly influenced by meteorological con ditions, with more high ozone days and higher ozone levels during summers with above normal temperatures.

### iv



### • Inter

- and intra

- regional t ransport of ozone and ozone precursors aff ects many portions of the five states , and is the principal cause of nonattainme nt in some areas far from population or industrial centers

.

PM 2.5



### • Current monitoring data (200 5

- 200 7 ) show 3 0 sites in violation of the annual PM 2.5 

### standard of 15 ug/m 3



### . N onattainment sites are characterized by an elevated regional background ( about 12

– 14 ug/m 3 

### ) and a significant local (urban) increment ( about 2

– 3 ug/m 3 

### ). Historical PM 2.5 

### data show a slight downward trend since deployment of the PM 2.5 

### monitoring network in 1999.

• PM 2.5 

### concentrations are also influenced by meteorology , but the re lationship is more complex and less well understood compared to ozone.

• O n an annual average basis, PM 2.5 

### chemical composition consists mostly of sulfate, nitrate, and organic carbon in similar proportions.

Haze

• Current monitoring data (2000

- 2004) show visibility levels in the Class I areas in northern Michigan are on the order of 2 2

– 2 4 deciviews . The goal of EPA’s visibility program is to achieve natural conditions, which is about 12 deciviews for these

Class I areas, by the year 2064.

• Visibility im pairment is dominated by sulfate and nitrate.

Air quality models were applied to s upport the regional planning efforts

. Two base years were used in the modeling analyses: 2002 and 2005. B asecase modeling was conducted to evaluate model performance (i.e., assess the model's ability to reproduce observed concentrations ) . This

exercise wa s intended to build confidence in the model prior to its use in examining co ntrol strategies. M odel performance for ozone and PM 2.5 

### was found to be generally acceptable.

Future year strategy modeling was conducted to determine whether existing (“on the books”) controls would be sufficient to provide for attainment of the standards for ozone and PM 2.5 

### and if not, then what additional emission reductions would be necessary f or attainment. Based on th e modeling and other supplemental analyses , the following general conclusions can be made:

• E xisting controls are expected to produce significant improvement in ozone and



## PM 2.5 

### concentrations and visibility levels

.

• The choice of the base year affects the future year model projections. A key

difference between the base years of 2002 and 2005 is meteorology. 2002 was more ozone conducive than 2005. The choice of which base year to use as the basis for the SIP is a policy decisio n (i.e., how much safeguard to incorporate).

• M odeling suggests that mo st sites are expected to meet the current 8

- hour ozone standard by the applicable attainment date, except for sites in western Michigan and, possibly, in eastern Wisconsin a nd northeast ern Ohio.

### v



### • Modeling suggests that m ost sites a re expected to meet the current PM 2.5 

### standard by the applicable attainment date, except for sites in Detroit, Cleveland, and Granite City

.

T he regiona l modeling for PM 2.5 

### does not include air quality benefit s expected from local controls. States are conducting local

- scale analyses and will us e these results, in conjunction with the regional

- scale modeling, to support their

attainment demonstrations for PM 2.5



### .

• These findings of residual nonattainment for ozon e and PM 2.5 

### are supported by current (2005

– 2007) monitoring data which show significant nonattainment in the region (e.g., peak ozone design values on the order of 90

– 93 ppb, and peak

PM 2.5 

### d esign values on the order of 16

- 17 ug/m 3 

### ). It is unlikely that sufficient emission reductions will occur in the next couple of years to provide for

attainment at all sites.

• Attainment at most sites by the applicable attainment date is dependent on actual future year meteorology (e.g., if the weather conditions a re consistent with [or less severe than] 2005, then attainment is likely) and actual future year emissions (e.g., if the emission reductions associated with the existing controls are achieved, then attainment is likely). I f either of these conditions is n ot met, then attainment may be less likely.

• M ode ling s uggests that the new PM 2.5 

### 24

- hour standard and the new lower

ozone standard will not be met at several sites , even by 2018, with existing

controls.

• Visibility levels in a few Class I areas in the eas tern U.S. are expected to be greater than

(less improved than) the uniform rate of visibility improvement

value s in 2018 based on existing controls , including those in northern Michigan

and s ome in the northeastern U.S. Visibility levels in m any other Cla ss I areas in the eastern U.S. are expected to be less than (more improved than) the uniform rate of visibility improvement value s in 2018. These results, along with information on the costs of compliance, time necessary for compliance, energy

and non air

quality environmental impacts of compliance, and remaining useful life of existing sources, should be considered by the states in setting reasonable progress goals for regional haze.

### 1 

### Section 1. 0 Introduction

This Technical Support Document summarizes the final air quality analyses conducted by the

Lake Michigan Directors Consortium (LADCO) 1 

### and its contractors to support the development of State Implementation Plans (SIPs) for ozone, fine particles ( PM 2.5 

### ), and regional haze in the States of Illinois, Indiana, Michigan, Ohio, and Wisconsin

. The analyses include preparation of regional emissions inventories and meteorological modeling data for two base years (2002 and 2005), evaluation and application of regional chemical transport models, and analysis of ambient monitoring data.

Two aspects of the analyses should be emphasize d. First, a regional , multi

- pollutant approach was taken in addressing ozone, PM 2.5 

### , and haze f or technical reasons (e.g., common ality in precursors, emission sources, atmosphe ric processes, transport influences, and geographic areas of concern), and practical reasons (e.g., more efficient use of program resources). Furthermore, EPA has consistently encouraged multi

- pollutant planning in its rule for the haze program (64 FR 357 19), and its implementation guidance for ozone (70 FR 71663) and PM 2.5 

### (72 FR 20609). Second, a weight

- of

- evidence approach was taken in considering the results of the various analyses (i.e., two sets of modeling results

-- one for a 2002 base year and on e for a 2005 base year

-- and ambient data analyses) in order to provide a more robust assessment of expected future year air quality.

The report is orga nized in the following sections. This I ntroduction p rovides an overview of regulatory requirements and background information on regional planning . Section 2 r eviews the ambient monitoring data and presents a conceptual model of ozone, PM 2.5 

### , and haze for the region . Section 3 d iscusses the air quality modeling analyses, including development of the k ey model inputs (emissions inventory and meteorological data), and basecase model performance

evaluation . A modeled a ttainment d emonstration for o zone and PM 2.5 

### is presented in Section 4, along with relevant data analyses considered as part of the weight

- of

- evidence de termination

. Section 5 documents the r easonable p rogress a ssessment for r egional h aze

, a long with relevant data analyses considered as part of the weight

- of

- evidence determination . Finally, ke y study findings are reviewed and summarized in Section 6.

1.1 SIP Requirements For ozone, EPA promulgated designations on April 15, 2004 (69 FR 23858, April 30, 2004). In

the 5

- state region, m ore than 100 counties were designated as nonattainment

. 2 

### The designations became effective on June 15, 2004. SIPs for ozone we re due no later than three years from the effective date of the nonattainment designations (i.e., by June 2007). The attainment date for ozone varies as a function of nonattainment classification. For the region,

the attainment dates a re either June 2007 (marginal nonattainment areas), June 2009 (basic nonattainment areas), or June 2010 (moderate nonattainment areas).

1 

### A sub

- entity of LADCO, known as the Midwest Regional Planning Organization (MRPO), is responsible

for the regional haze activities of the multi

- state organization.

2 

### Based on more recent air quality data, many counties in Indiana, Michigan, and Ohio were subsequently redesignated as attainment. As of December 31 , 2007, there are 53 counties designated

as n onattainment in the region.

### 2



### For PM 2.5 

### , EPA promulgated designations on December 17, 2004 (70 FR 944, January 5, 2005).

In the 5

- state region, 70 c ounties were designated as nonattainment. 3 

### The designations bec a me effective on April 5, 2005. SIPs for PM 2.5 

### are due no later than three years from the effective date of the nonattainment designations (per section 172(b) of the Clean Air Act) (i.e., by A pril 2008) and for haze no later than three years after the date on which the Administrator promulgate d the PM 2.5 

### designations (per the Omnibus Appropriations Act of 2004) (i.e., by December 2007). The applicable attainment date for PM 2.5 

### nonattainment ar eas is five years from the date of the nonattainment designation (i.e., by April 2010).

For haze, the Clean Air Act sets “as a national goal the prevention of any future, and the remedying of any existing, impairment of visibility in Class I ar eas which impairment results from manmade air pollution.” T here are 156 Class I areas, including two in northern Michigan: Isle Royale National Park and Seney National Wildlife Refuge 4



### . EPA’s visibility rule (64 FR 35714, July 1, 1999) require s reasonabl e progress in achieving “natural conditions” by the year 2064. As noted above, the first regional haze SIP wa s due in December 2007 and must address the

initial 10

- year implementation period (i.e., reasonable progress by the year 2018). SIP requirements (pursuant to 40 CFR 51.308(d)) include setting reasonable progress goals, determining baseline conditions, determining natural conditions, providing a long

- term control strategy, providing a monitoring strategy (air quality and emissions), and establishing BART emissions limitations and associated compliance schedule.

1.2 Organization

LADCO wa s established by the States of Illinois, Indiana, Michigan, and Wisconsin in 1989. T he four states and EPA signed a Memorandum of Agreement (MOA) that initiated t he Lake Michigan Ozone Study (LMOS) and identified LADCO as the organization to oversee th e study. Additional MOAs were signed by the States in 1991 (to establish the Lake Michigan Ozone Control Program)

, January 2000 (to broaden LADCO’s responsibilities) , and June 2004 (to update LADCO’s mission and reaffirm the commitment to regional planning). In March 2004, Ohio joined LADCO. LADCO consists of a Board of Directors (i.e., the State Air Directors), a technical staff, and various workgroups. The main p urpose s of LADCO are to provide technical

assessments for and assistance to its member states

, and to provide a forum for its member states to discuss regional air quality issues.

MRPO is a similar entity led by the five LADCO States and involves the fe derally recognized tribes in Michigan and Wisconsin

, EPA

, and Federal Land Managers (i.e., National Park Service, U.S. Fish & Wildlife Agency, and U.S. Forest Service )

. In October 2000, the States of Illinois, Indiana, Michigan, Ohio, and Wisconsin signed a n MOA that established the MRPO. An

o perating principles document for M RPO, which describe the roles and responsibilities of states, tribes, federal agencies, and stakeholders, w as issued in March 2001

. M RPO has a similar

purpose as LADCO , but is focus ed on visibility impairment due to regional haze in the Federal

Class I areas located inside the borders of the five states, and the impact of emissions from the five states on visibility impairment due to regional haze in the Federal Class I areas located

outside the borders of the five states. MRPO works cooperatively with the R egional Planning Organizations (R POs ) representing other parts of the country. The RPOs sponsored several

3 

### USEPA subsequently adjusted the final designations, which resulted in 63 counties in the region being designate d as nonattainment (70 FR 19844, April 15, 2005)

.

4 

### Although Rainbow Lake in northern Wisconsin is also a Class I area, the visibility rule does not apply because the Federal Land Manager determined that visibility is not an air quality related value there

.

### 3



### joint projects and, with assistance by EPA, maintain regular contact on technical and policy

matters.

1.3 Technical Work: Overview To ensure the reliability and effectiveness of its planning process, LADCO has made data

collec tion and analysis a priority. M ore than $ 7 M in RPO grant funds were used for special purpose monitor ing, preparing and improving emissions inventories, and conducting air quality analyses 5 

### . An overview of the technical work is provided below.

Monitoring: Numerous monitoring projects were conducted to supplement on

- going state and local air pollution mo nitoring. These projects include rural monitoring (e.g., comprehensive sampling in the Seney National Wildlife Refuge and in Bondville, IL ) ; urban monitoring (e.g., continuation of the St. Louis Supersite); aloft (aircraft) measurements; regional ammonia monitoring; and organic speciation sampling in Seney, Bondville, and five urban areas.

Emissions: B aseyear emissions inventor ies were prepared for 2002 and 2005 . States provided point source and area source emissions data , and MOBILE6 input files and mob ile source activity data. LADCO and its contractors developed the emissions data for other source categories (e.g., select nonroad sources, ammonia, fires, and biogenics) and processed the data for input into an air quality model. To support control stra tegy modeling, future year inventories were prepared. The future years of interest include 2008 (planning year to address the 2009 attainment year for basic ozone nonattainment ares), 2009 ( planning year to address the 2010 attainment year for P M 2.5 

### and m oderate ozone nonattainment areas ), 2012 ( planning

to address a 201 3 alternative attainment date), and 2018 (first milestone year for regional haze).

Air Quality Analyses: The weight

- of

- evidence approach relies on data analysis and modeling. Air quality data analyses were used to provide both a conceptual model (i.e., a qualitative description of the o zone

, PM 2.5 

### , and regional haze problems) and supplemental information for

the attainment demonstration . Given uncertainties in emissions inventories and mo deling

, especially for PM 2. 5 

### , these data analyses are a necessary part of the overall technical support.

Modeling includes baseyear analyses for 2002 and 2005 to evaluate model performance and future year strategy analyses to assess candidate control stra tegies. The analyses wer e
conducted in accordance with EPA’s modeling guidelines ( EPA, 2007a)

. The PM/haze modeling cover s the full calendar year (2002 and 2005 ) for an eastern U.S. 36 km domain, while the ozone modeling focus es on the summer period (200 2 and 2005 ) for a Midwest 12 km subdomain. The same model (CAMx) was used for ozone, PM 2.5 

### , and regional haze

.

5 

### Since 1999, MRPO has received almost $10M in RPO grant funds from USEPA.

### 4 

### Section 2.0 Ambient Data Analyses

An extensive network of air quality monitors in the 5

- state region provides data for ozone (and its precurs ors)

, PM 2. 5 

### ( both total mass and individual chemical species), and visibility. These

data are used to determine attainment/nonattainment designations, support SIP development, and provide air quality information to public (see, for example, www.airnow.gov ).

Analys e s of the data were conducted to produce a conceptual model, which is a qualitative

summar y of the physical, chemical

, and m eteorological processes that control the formation and distribution of pollutants i n a given region. This section reviews the relevant data analyses and describes our understanding of ozone, PM 2.5, 

### and r egional haze with respect to c urrent

co nd i tions

, d ata variability ( spatial, temporal, and chemical)

, i nfluence of meteorology ( includin g

t ransport patterns )

, p recursor s ensi tivity, and s ource culpability.

2.1 Ozone In 1979, EPA adopted an ozone standard of 0.12 ppm, averaged over a 1

- hour period. This

standard i s attained when the number of days per calendar year with maximum hourly av erage concentrations above 0.12 ppm is equal to or less than 1.0, averaged over a 3

- year period

, which generally reflects a design value (i.e., the 4 th 

### highest daily 1

- hour value over a 3

- year period) less than 0.12 ppm.

In 1997, EPA tightened the ozone s tandard to 0.08 ppm, averaged over an 8

- hour period 6



### . T he

standard is attain ed if the 3

- year average of the 4th

- highest daily maximum 8

- hour average

ozone concentrations ( i.e., the design value) measured at each monitor within an area is less

than 0.08 pp m (or 85 ppb)

.

Current C onditions : A map of the 8

- hour ozone design values at each monitoring site in the region for the 3

- year period 2005

- 2007 is shown in Figure 1. The “hotter” colors represent higher concentrations , where yellow and orange dots re present sites w ith design values above

the standard

. Currently, there are 1 9 sites in violation of the 8

- hour oz one NAAQS in the 5

- state region, including sites in the Lake Michigan area, Detroit, Cleveland, Cincinnati, and Columbus.

Table 1 provides the 4 th



### - highest daily 8

- hour ozone values and the associated design values

since 2001 for several high monitoring sites throughout the region.

6 

### On March 12, 2008 , USEPA further t ighten ed the 8

- hour ozone standard to increase public health protection and prevent environmental damage from ground

- level ozone. USEPA set the primary (health)

standard and secondary (welfare) standard at the same level : 0.075 ppm (75 ppb), averaged ove r an 8

- hour period.

### 5 Figure 1

. 8

- h our o zone d esign v alues ( 200 5

- 2007 )Key Sites

'01 '02 '03 '04 '05 '06 '07 '01-'03 '02-'04 '03-'05 '04-'06 '05-'07

Lake Michigan Area

Chiwaukee 99 116 88 78 93 79 85 101 94 86 83 85

Racine 92 111 82 69 95 71 77 95 87 82 78 81 Milwaukee-Bayside 93 99 92 73 93 73 83 94 88 86 79 83 Harrington Beach 102 93 99 72 94 72 84 98 88 88 79 83

Manitowoc 97 83 92 74 95 78 85 90 83 87 82 86 Sheboygan 102 105 93 78 97 83 88 100 92 89 86 89

Kewaunee 90 92 97 73 88 76 85 93 87 86 79 83 Door County 95 95 93 78 101 79 92 94 88 90 86 90

Hammond 90 101 81 67 87 75 77 90 83 78 76 79 Whiting 64 88 81 88 77 85 Michigan City 90 107 82 70 84 75 73 93 86 78 76 77

Ogden Dunes 85 101 77 69 90 70 84 87 82 78 76 81

Holland 92 105 96 79 94 91 94 97 93 89 88 93

Jenison 86 93 91 69 86 83 88 90 84 82 79 85 Muskegon 95 96 94 70 90 90 86 95 86 84 83 88

Indianapolis Area

Noblesville 88 101 101 75 87 77 84 96 92 87 79 82

Fortville 89 101 92 72 80 75 81 94 88 81 75 78

Fort B. Harrison 87 100 91 73 80 76 83 92 88 81 76 79

Detroit Area

New Haven 95 95 102 81 88 78 93 97 92 90 82 86

Warren 94 92 101 71 89 78 91 95 88 87 79 86

Port Huron 84 100 87 74 88 78 89 90 87 83 80 85

Cleveland Area Ashtabula (Conneaut) 97 103 99 81 93 86 92 99 94 91 86 90 Notre Dame (Geauga) 99 115 97 75 88 70 68 103 95 86 77 75 Eastlake (Lake) 89 104 92 79 97 83 74 95 91 89 86 84 Akron (Summit) 98 103 89 77 89 77 91 96 89 85 81 85

Cincinnati Area Wilmington (Clinton) 93 99 96 78 83 81 82 96 91 85 80 82 Sycamore (Hamilton) 88 100 93 76 89 81 90 93 89 86 82 86 Hamilton (Butler) 83 100 94 75 86 79 91 92 89 85 80 85 Middleton (Butler) 87 98 83 76 88 76 91 89 85 82 80 85 Lebanon ( Warren) 85 98 95 81 92 86 88 92 91 89 86 88

Columbus Area London (Madison) 84 97 90 75 81 76 83 90 87 82 77 80 New Albany (Franklin) 90 103 94 78 92 82 87 95 91 88 84 87 Franklin (Franklin) 83 99 84 73 86 79 79 88 85 81 79 81

Ohio Other Areas Marietta ( Washington) 85 95 80 77 88 81 86 86 84 81 82 85

St. Louis Area W. Alton (MO) 85 99 91 77 89 91 89 91 89 85 85 89 Orchard (MO) 88 98 90 76 92 92 83 92 88 86 86 89 Sunset Hills (MO) 88 98 88 70 89 80 89 91 85 82 79 86 Arnold (MO) 86 93 82 70 92 79 87 87 81 81 80 86 Margaretta (MO) 80 98 90 72 91 76 91 89 86 84 79 86 Maryland Heights (MO) 88 84 94 88 4th High 8-hour Value Design Values Table 1. Ozone Data for Select Sites in 5-State Region

### 7



### Meteorology

and Transport

: Most pollutants exhibit some dependence on meteorological factors, especially wind direction, because that governs which sources are upwind and thus most influential on a given sample. Ozone is even more dependent, since its production is driven by high temperatures and sunligh t

, as well as precursor concentrations (see, for example, Figure 2 )

.

Figure 2. Number of h ot d ays and 8

- h our “ e xceedance” d ays in 5

- state region



### Qualitatively, ozone episodes in the region are associated with hot weather, clear skies (sometimes hazy ), low wind speeds, high solar radiation, and southerly to southwesterly winds.

These conditions are often a result of a s low

- moving high pressure system to the east of the region. The relative importance of various meteorological factors is discussed la ter in this

section.

Transport of ozone (and its precursors) is a significant factor and occurs on several spatial scales. Regionally, o ver a multi

- day period, somewhat stagnant summertime conditions can

lead to the build

- up in ozone and ozone precursor concentrations over a large spatial area

. Th is pollutant air mass can be advected long distances, resulting in elevated ozone levels in

locations far downwind. An example of such an episode is shown in Figure 3.

Figure 3. Example of e levated r egional o zone c oncentrations (June 23

– 25, 2005)

Note: hotter colors represent higher concentrations, with orange representing concentrations above the 8

- hour standard

### 8



### Local ly, emissions from urban areas add to the regional background leading to ozone

concentration hot spots downwind. Depending on the synoptic wind patterns (and local land

lake breezes), different downwind areas are affected (see, for example, Figure 4).

Figure 4. Examples of r ecent h igh o zone d a ys in the Lake Michigan a rea

Note: hotter colors represent higher concentrations, with orange representing concentrations above the 8

- hour standard



### Aloft (aircraft) measurements in the Lake Michigan area also provide evidence of elevated regional backgro und concentrations and “plumes” from urban areas. For one example summer day (August 20, 2003

– see Figure 5), the incoming background ozone levels were on the order

of 80

– 100 ppb and the downwind ozone levels over Lake Michigan were on the order of 100

- 150 ppb (STI, 2004)

.

Figure 5. Aircraft o zone m easurements over Lake Michigan (left) and a long u pwind b oundary (right)

– August 20, 2003 (Note: aircraft measurements reflect instantaneous values)

### 9



### As discussed in Section 4, residual nonattainment is projected in at least one area in the 5

- state region

– i.e., western Michigan. To understand the source regions likely impacting high ozone concentrations in western Michigan and e stimate the impact of these source regions , two s imple transport

- related analyses were performed

.

First, back trajectories were constructed using the HYSPLIT model for high ozone days (8

- hour peak > 80 ppb) during the period 2002

- 2006 in western Michigan to characterize general transport patterns. Comp osite trajectory plots for all high ozone days based on data from three sites (Cass County, Holland, and Muskegon) are provided in Figure 6. The plots point back to

areas located to the south

- southwest (especially, northeastern Illinois and northwestern I ndiana) as being upwind on these high ozone days.

Figure 6 Back t rajectory a nalysis s howing u pwind a reas a ssociated with h igh o zone

c oncentrations



### Second, to assess the impact from Chicago /NW Indiana , Blanchard (2005 a ) compar ed ozone
concentrat ions upwind (Braidwood, IL), within Chicago (ten sites in the City), and downwind (Holland and Muskegon) for days in 1999

– 2002 with southwesterly winds

- i.e., transport towards western Michigan. Figure 7 shows the distribution of daily peak 8

- hour ozon e concentrations by day

- of

- week, with a line connecting the mean values. The difference between day

- of

- week mean values at downwind and upwind sites indicates that Chicago /NW

Indiana contribute s about 10

- 15 ppb to downwind ozone levels.

### 10

Figure 7 . Mean day

- of

- week peak 8

- hour ozone concentrations at sites upwind, within, and downwind of Chicago, 1999

– 2002 (southwesterly wind days)



### Based on this information, the following key findings related to transport can be made:

• 

### Ozone transport is a problem af fecting many portions of the eastern U.S. The Lake Michigan area (and other areas in the LADCO region ) both receive high levels of incoming (transported) ozone and ozone precursors from upwind source areas on many hot summer days, and contribute to the hi gh levels of ozone and ozone precursors affecting downwind receptor areas.

• 

### The presence of a large body of water (i.e., Lake Michigan) influences for the formation and transport of ozone in the Lake Michigan area . Depending on large

- scale synoptic

winds and local

- scale lake breezes, different parts of the area experience high ozone concentrations. For example, under southerly flow, high ozone can occur in eastern Wisconsin, and under southwesterly flow, high ozone can occur in western Michigan.

• 

### Downwi nd shoreline areas around Lake Michigan are affected by both regional transport of ozone and subregional transport from major cities in the Lake Michigan area

. Counties along the western shore of Michigan (from Benton Harbor to Traverse City, and

even as far north as the Upper Peninsula) are impacted by high levels of incoming (transported) ozone.

### 11



### Data V ariability : Since 1980, considerable progress has been made to meet the previous 1

- hour ozone standard. Fig ure 8 shows the decline in both the 1

- hour a nd 8

- hour design values

for the 5

- state LADCO region over the last 25 years.

Figure 8 Oz one d esign v alue t rends in 5

- State r egion



### The trend i s more dramatic for the higher ozone sites in the 5

- state region (see Figure 9 ) . This plot shows a pronoun ced downward trend in the design value since the 2001

- 2003 period , due, in part, to the very low 4 th 

### high values in 2004.

Figure 9. Trend in o zone d esign v alues and 4 th 

### h igh v alues for higher ozone s ites in r egion



### The imp rovement in ozone concentrations is also seen in the decrease in the number of sites measuring nonattainment over the past 15 years in the Lake Michigan area (see Figure 10). 75 85 95 105

'95-'97 '96-'98 '97-'99 '98-'00 '99-'01 '00-'02 '01-'03 '02-'04 '03-'05 '04-'06 '05-'07

65 75 85 95 105 115

'95 '96 '97 '98 '99 '00 '01 '02 '03 '04 '05 '06 '07 

### Design Values

4 th 

### High Values

### 12 Figure 10 . Ozone d esign v alue m aps for 1995

- 1 997, 2000

- 2002, and 2 00 5

- 200 7

### 13



### Given the effect of meteorology on ambient ozone levels, year

- to

- year variations in meteorology can make it difficult to assess trends in ozone air quality. Two approaches were considered to adjust ozone trends for meteorological influences : an air quality

- meteorology statistical m odel developed by EPA (i.e., Cox method), and statistical grouping of meteorological variables performed by LADCO (i.e., Classification and Regression Trees, or CART).

Cox M ethod

: This method uses a statistical m odel to ‘remove’ the annual effect of meteorology on ozone (Cox and Chu, 1993 ). A regression model was fit to the 1997

- 2007 data to relate daily peak 8

- hour ozone concentrations to six daily meteorological variables plus seasonal and

annual factors (Kensk i, 2008a) . Meteorological variables included were daily maximum temperature, mid

- day average relative humidity, morning and afternoon wind speed and wind direction. The model is then used to predict 4 th 

### high ozone values. By holding the meteorological e ffects constant, the long term trend can be examined independently of meteorology. Presumably

, any trend reflects changes in emissions of ozone precursors.

Figure 11a shows the meteorologically

- adjusted 4 th 

### high ozone concentrations for several

monitor s near major urban areas in the region . The plots indicate a general downward trend since the late 1990s for most cities, indicating that recent emission reductions have had a positive effect in improving ozone air quality.

A similar model was run to e xamine meteorologically adjusted trends in seasonal average ozone. This model incorporates more meteorological variables, including rain and long

- distance transport (direction and distance). Model development was docume nted in Camalier et al.,

2007. The seasonal average trend s are shown in Figure 11b. Trends determined by seasonal model for the same set of sites examined above are consistent with those developed by the 4 th


### high model

.

### 14 Chiwaukee, WI Sheboygan, WI

Cleveland (Ashtabula ), OH Cincinnati (Sycamore) , OH

Detroit (New Haven), MI St. Louis, MO

Indianapolis, IN

Figure 11 a

. Trends in meteorologically adjusted 4 th 

### high 8

- hour ozone

concentrations for seven Mid western sites (199 7

– 200 7 )

### 15 Chiwaukee, WI Sheboygan, WI

1998 2000 2002 2004 2006 20 40 60 80Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

1998 2000 2002 2004 2006 20 40 60 80Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather



### Cleveland (Ashtabula), OH Cincinnati (Sycamore) , OH

1998 2000 2002 2004 2006 20 40 60 80Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

1998 2000 2002 2004 2006 20 40 60 80Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather



### Detroit (New Haven), MI St. Louis, MO

1998 2000 2002 2004 2006 20 40 60 80Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

1998 2000 2002 2004 2006 20 40 60 80Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather



### Indianapolis, IN

1998 2000 2002 2004 2006 20 40 60 80Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather 

### Figure 11b

. Trends in seasonal 8

- hour ozone

concentrations for seven Mid western sites (199 7

– 200 7 )

### 16



### CART : Classification and Regression Tree (CART) analysis is another statistical technique which partitions data sets into similar groups (Breiman et al., 1984). CART analysis was performed using data for the period 1995

- 2007 for 22 selected ozone monitors w ith current 8

- h ou r design values close to or above the standard (Kenski, 2008 b )

. T he CART model searches through 60 meteorological variables to determine which are most efficient in predicting ozone. Although the exact sel ection of predictive variables changes from site to site, the most common predictors we re temperature, wind direction, and relative humidity. Only occasionally were upper air variables, transport time or distance, lake breeze, or other variables s ignifica nt. (Note,

the o zone and met eorological data for the CART analysis a re the same as used in the EPA/Cox analysis.)

For each monitor, regression trees were developed that classify each summer day (May

September) by its meteorological conditions. Similar d ays are assigned to nodes, which are equivalent to branches of the regression tree. Ozone time series for the higher concentration nodes are plotted for select sites in Fig ure 12 . By grouping days with similar meteorology, the influence of meteorological variability on the trend in ozone concentrations is partially removed; the remaining trend is presumed to be due to trends in precursor emissions or other non

meteorological influences. Trends over the 13

- year period at most sites were found to be

declin ing, with the exception of Detroit which showed fairly flat trends. C omparison of the average of the high concentration node values for 2001

- 2003 v. 2005

- 2007 showed an improvement of about 5 ppb across all sites (even Detroit)

.

The effect of meteorology was further examined by using an ozone conduciveness index (Kenski, 2008 b ) . This metric reflects the variability from the 1 3

- year average in the number of days in the higher ozone concentration nodes (see Figure 13)

. Examination of these plots

indicates :

• 

### 2002 and 2005 w

ere both above normal

, with 2002 tending to be more severe ; and

• 

### 2001

- 2003 and 2005

- 2007 were both above normal, with no clear pattern in which period was more severe (i.e., ozone conduciveness values were similar at most sites,

2001

- 2003 values were higher at a few sites, and 2005

- 2007 values were higher at a few sites)

.

Given the similarity in ozone conduciveness between 2001

- 2003 and 2005

- 2007, the improvement in ozone levels noted above is presumed to be due to non

- meteorological fact ors (i.e., emission reductions).

In conclusion, a ll three statistical approaches ( CART and the two nonlinear regression models)

show a similar result ; ozone in the urban areas of the LADCO region has declined during the

1997

- 2007 period, even when meteoro logical variability is accounted for. The decreases are present whether seasonal average ozone, peak values (annual 4 th 

### highs), or a subset of high days with similar meteorology are considered . The consistency in results across models is a good indicatio n that these trends reflect impacts of emission control programs

.

### 17 Chiwaukee, WI Sheboygan, WI

Cleveland (Ashtabula), OH Cincinnati (Sycamore) , OH

Detroit (New Haven), MI St. Louis, MO

Indianapolis, IN

Figure 12

. Trends for higher o zone C ART groups (average ozone > 6 5 ppb) for seven

Midwestern sites (199 5

– 20 0 7 )

Note: line represents linear best fit

### 18 Figure 13 . Ozone c onduciveness i ndex (and number of high ozone days) f or s even
M idwestern site (199 5

– 200 7 )

### 19



### Pr ecursor S ensitivity : O zone i s formed from the reactions of hydrocarbons and nitrogen oxides under meteorological conditions that are conducive to such reactions (i.e., warm temperatures and strong sunlight )

. In areas with high VOC/NO x ratios, typical of r ural environments ( with low

NO x), ozone tends to be more responsive to reductions in NO x . Conversely, in areas with low

VOC/NO x

ratios, typical of urban environments (with h i gh NOx) , ozone tends to be more responsive to VOC reductions.

An analysis of V OC and NO

x 

### - limitation was conducted with the ozone MAPPER program, which

is based on the Smog Production (SP) algorithm (Blanchard

, et al., 200 3 ) . The “Extent of Reaction” parameter in the SP algorithm provides an indication of VOC and NOx sensitivity:

Extent Range Precursor Sensitivity

< 0 .6 VOC

- sensitive

0. 6

– 0.8 Transitional

> 0.8 NOx

- sensitive



### A map of the Extent of Reaction values for high ozone days is provided in Figure 1 4 . As can be

seen, ozone is usually VOC

- l imited in cities and NOx

- limited in rural areas. ( Data from aircraft measurements suggest that ozone is usually NO

x 

### - limited over Lake Michigan and away from urban centers on days when ozone in the urban centers is VOC

- limited. ) T he highest ozone days we re found t o be NO

x 

### - limite d. This analysis suggests that a NOx reduction strategy would be effective in reducing ozone levels. Examination of day

- of

- week concentrations, however, raises some question about the effectiveness of NOx reductions.

Figure 1 4 . Mean afternoon extent of reaction (1998

– 2002)

### 20



### Blanchard (2004 and 2005 a ) examin ed weekend

- weekday differences in ozone and NO

x 

### in the

Midwest. All urban areas in th e s e two stud ies exhibited substantially lower (40

- 60%) weekend

concentrations of NO

x 

### compared to weekday concentrations. Despite lower weekend NO

x 

### concentrations, weekend ozone concentrations were not lower; in fact, most urban sites had higher concentrations of ozone

, although the increase was generally not statistically significant ( see Figure 1 5 ). This small but counterproductive change in local ozone concentrations suggests that local urban

- scale NO

x 

### reductions alone may not be very effective.

Figure 1 5 . Weekday/w eekend d ifferences in 8

- h our o zone

– number of sites with weekend

inc rease (positive values) v. number of sites with weekend decreases (negative values)



### Two additional analyses, however, demonstrate the positive effect of NOx emission reductions
on downwind ozone concentrations. First, Blanchard (2005 a ) looked at the effe ct of changes in precursor emissions in Chicago on downwind ozone levels in western Michigan. For the transport days of interest (i.e., southwesterly flow during the summers of 1999

– 2002), mean NOx concentrations in Chicago are about 50% lower and mean ozone concentrations at the (downwind) western Michigan sites are about 1.5

– 5.2 ppb (3

– 8 %) lower on Sunday compared to Wednesday. This degree of change in downwind ozone levels suggests a positive, albeit non

- linear response to urban area emission re ductions.

Second, Environ (2007a) examined the effect of differences in day

- of

- week emissions in southeastern Michigan on downwind ozone levels. This modeling study found that w eekend changes in ozone precursor emissions cause both increases and decrease s in Southeast Michigan ozone, depending upon location and time :

### 21

• 

### Weekend increases in 8

- hour maximum ozone occur in and immediately downwind of

the Detroit urban area (i.e., in VOC

- sensitive areas).

• 

### Weekend decreases in 8

- hour maximum ozone occur outside and downwind of the

Detroit urban area (i.e., in NOx

- sen sitive areas).

• 

### At the location of the peak 8

- hour ozone downwind of Detroit, ozone was lower on

weekends than weekdays.

• 

### Ozone benefits (reductions) due to weekend emission changes in Southeast Michiga n
can be transported downwind for hundreds of miles.

• 

### Southeast Michigan benefits from lower ozone transported into the region on Saturday through Monday because of weekend emission changes in upwind areas.

In summary, these analyses suggest that urban VOC reductions and regional (urban and rural) NOx reductions will be effective in lowering ozone concentrations. Local NOx reductions can lead to local ozone increases (i.e., NOx disbenefits), but this effect does not appear to pose a problem with respect to attainment of the standard. It should also be noted that urban VOC and regional NOx reductions are likely to have multi

- pollutant benefits (e.g., both lower ozone and

P M 2. 5 

### impacts).

2.2 P M 2.5 

### In 1997, EPA adopted the PM 2. 5 

### standard s of 15 ug/ m 3 

### (annu al average) and 65 ug/m3 (24

- hour average). T he annual standard is attained if the 3

- year average of the annual average PM 2. 5 

### concentration is less than or equal to the level of the standard. The daily standard is attained if the 98th percentile of 24

- h our PM 2. 5 

### concentrations in a year, averaged over three years, is less than or equal to the level of the standard

.

In 2006, EPA revised the PM 2. 5 

### standard s to 15 ug/m 3 

### (annual average) and 35 ug/m 3 

### (24

- hour average).

Current C onditions : Maps of annual and 24

- hour PM 2. 5 

### design values for the 3

- year period

200 5

- 200 7 are shown in Figure 1 6 . The “hotter” colors represent higher concentrations , where

red dots represent sites w ith design values above the annual standard

. Currently, there are 3 0

sites in vio lation of the annual PM 2. 5 

### standard.

Table 2 provides the annual PM 2. 5 

### concentrations and associated design values since 200 3 for several high monitoring sites throughout the region.

### 22

Figure 1 6

. PM 2.5 

### d esign v alues

- a nnual a verage (top) and 24

- h o ur a verage (bottom) ( 200 5

- 2007)2005 BY 2002 BY

Key Site County Site ID '03 '04 '05 '06 '07 '03 - '05 '04 - '06 '05 - '07 Average

w/ 2007 Average

Chicago - Washington HS Cook 170310022 15.6 14.2 16.9 13.2 15.7 15.6 14.8 15.3 15.2 15.9
Chicago - Mayfair Cook 170310052 15.9 15.3 17.0 14.5 15.5 16.1 15.6 15.7 15.8 17.1
Chicago - Springfield Cook 170310057 15.6 13.8 16.7 13.5 15.1 15.4 14.7 15.1 15.0 15.6
Chicago - Lawndale Cook 170310076 14.8 14.2 16.6 13.5 14.3 15.2 14.8 14.8 14.9 15.6

Blue Island Cook 170312001 14.9 14.1 16.4 13.2 14.3 15.1 14.6 14.6 14.8 15.6

Summit Cook 170313301 15.6 14.2 16.9 13.8 14.8 15.6 15.0 15.2 15.2 16.0

Cicero Cook 170316005 16.8 15.2 16.3 14.3 14.8 16.1 15.3 15.1 15.5 16.4
Granite City Madison 171191007 17.5 15.4 18.2 16.3 15.1 17.0 16.6 16.5 16.7 17.3

E. St. Louis St. Clair 171630010 14.9 14.7 17.1 14.5 15.6 15.6 15.4 15.7 15.6 16.2

Jeffersonville Clark 180190005 15.8 15.1 18.5 15.0 16.5 16.5 16.2 16.7 16.4 17.2
Jasper Dubois 180372001 15.7 14.4 16.9 13.5 14.4 15.7 14.9 14.9 15.2 15.5
Gary Lake 180890031 16.8 13.3 14.5 16.8 15.1 14.9 15.6
Indy - Washington Park Marion 180970078 15.5 14.3 16.4 14.1 15.8 15.4 14.9 15.4 15.3 16.2
Indy - W 18th Street Marion 180970081 16.2 15.0 17.9 14.2 16.1 16.4 15.7 16.1 16.0
Indy - Michigan Street Marion 180970083 16.3 15.0 17.5 14.1 15.9 16.3 15.5 15.8 15.9 16.6

Allen Park Wayne 261630001 15.2 14.2 15.9 13.2 12.8 15.1 14.4 14.0 14.5 15.8

Southwest HS Wayne 261630015 16.6 15.4 17.2 14.7 14.5 16.4 15.8 15.5 15.9 17.3

Linwood Wayne 261630016 15.8 13.7 16.0 13.0 13.9 15.2 14.2 14.3 14.6 15.5

Dearborn Wayne 261630033 19.2 16.8 18.6 16.1 16.9 18.2 17.2 17.2 17.5 19.3
Wyandotte Wayne 261630036 16.3 13.7 16.4 12.9 13.4 15.5 14.3 14.2 14.7 16.6

Middleton Butler 390170003 17.2 14.1 19.0 14.1 15.4 16.8 15.7 16.2 16.2 16.5

Fairfield Butler 390170016 15.8 14.7 17.9 14.0 14.9 16.1 15.5 15.6 15.8 15.9

Cleveland-28th Street Cuyahoga 390350027 15.4 15.6 17.3 13.0 14.5 16.1 15.3 14.9 15.4 16.5

Cleveland-St. Tikhon Cuyahoga 390350038 17.6 17.5 19.2 14.9 16.2 18.1 17.2 16.8 17.4 18.4
Cleveland-Broadway Cuyahoga 390350045 16.4 15.3 19.3 14.0 15.3 17.0 16.2 16.2 16.5 16.7
Cleveland-E14 & Orange Cuyahoga 390350060 17.2 16.4 19.4 15.0 15.9 17.7 16.9 16.8 17.1 17.6
Newburg Hts - Harvard Ave Cuyahoga 390350065 15.6 15.2 18.6 13.1 15.8 16.5 15.6 15.8 16.0 16.2
Columbus - Fairgrounds Franklin 390490024 16.4 15.0 16.4 13.6 14.6 15.9 15.0 14.9 15.3 16.5

Columbus - Ann Street Franklin 390490025 15.3 14.6 16.4 13.6 14.7 15.4 14.9 14.9 15.1 16.0
Columbus - Maple Canyon Franklin 390490081 14.9 13.6 14.6 12.9 13.1 14.4 13.7 13.5 13.9 16.0
Cincinnati - Seymour Hamilton 390610014 17.0 15.9 19.8 15.5 16.5 17.6 17.1 17.3 17.3 17.7

Cincinnati - Taft Ave Hamilton 390610040 15.5 14.6 17.5 13.6 15.1 15.9 15.2 15.4 15.5 15.7

Cincinnati - 8th Ave Hamilton 390610042 16.7 16.0 19.1 14.9 15.9 17.3 16.7 16.6 16.9 17.3

Sharonville Hamilton 390610043 15.7 14.9 16.9 14.5 14.8 15.8 15.4 15.4 15.6 16.0

Norwood Hamilton 390617001 16.0 15.3 18.4 14.4 15.1 16.6 16.0 15.9 16.2 16.3

St. Bernard Hamilton 390618001 17.3 16.4 20.0 15.9 16.1 17.9 17.4 17.3 17.6 17.3

Steubenville Jefferson 390810016 17.7 15.9 16.4 13.8 16.2 16.7 15.4 15.5 15.8 17.7
Mingo Junction Jefferson 390811001 17.3 16.2 18.1 14.6 15.6 17.2 16.3 16.1 16.5 17.5

Ironton Lawrence 390870010 14.3 13.7 17.0 14.4 15.0 15.0 15.0 15.4 15.2 15.7
Dayton Montgomery 391130032 15.9 14.5 17.4 13.6 15.6 15.9 15.2 15.5 15.5 15.9

New Boston Scioto 391450013 14.7 13.0 16.2 14.3 14.0 14.6 14.5 14.8 14.7 17.1

Canton - Dueber Stark 391510017 16.8 15.6 17.8 14.6 15.9 16.7 16.0 16.1 16.3 17.3

Canton - Market Stark 391510020 15.0 14.1 16.6 11.9 14.4 15.2 14.2 14.3 14.6 15.7

Akron - Brittain Summit 391530017 15.4 15.0 16.4 13.5 14.4 15.6 15.0 14.8 15.1 16.4
Akron - W. Exchange Summit 391530023 14.2 13.9 15.7 12.8 13.7 14.6 14.1 14.1 14.3 15.6 Annual Average Conc. Design Values 

### Table 2. PM2.5 Data for Select Sites in 5-State Region

### 24

When 

### EPA initially set the 2 4

- hour s tandard at 65 μg/m 3



### , it also adopted the following concentration ranges for its Air Quality Index (AQI) scale:

Good < 1 5 ug/m 3



### Moderate 15

- 40 μg/m 3


### Unhealthy for Sensitive Groups (USG) 40

- 65 μg/m 3



### Unhealthy 65

- 150 μg/m 3



### Fig ure 1 7 shows the frequency of these AQI categories for major metropolitan areas in the region. Daily average concentrations are often in the moderate range and occasionally in the

USG range. Moder ate and USG levels can occur any time of the year.

Figure 1 7 . Percent of d ays in AQI c ategories for PM 2. 5 

### (2002

- 2004)



### Data Variability : PM 2. 5 

### concentrations vary spatially, temporally, and chemically in the region. This variability is discussed furt her below.

On an annual basis, PM 2.5 

### exhibits a distinct and consistent spatial pattern. As seen in Figure

1 6 , a cross the Midwest, annual concentrations follow a gradient from low values (5

- 6 μg/m 3 

### ) in

no rthern and western areas ( Minnesota and northern W isconsin ) to high values (17

- 18 μg/m 3 

### ) in Ohio and along the Ohio River. In addition, c oncentrations in urban areas are higher than in upwind rural areas, indicating that local urban sources add a significant increment of 2

- 3 μg/m 3



### to the regional backgro und of 12

- 14 μg/m 3 

### (see Figure 1 8 ).

Figure 1 8

. Regional (lighter shading) v. local components (darker shading) of a nnual a verage PM 2. 5 

### concentrations

### 25



### Because monitoring for PM 2. 5 

### only began in earnest in 1999, after promulgation of the PM 2. 5 

### standar d

, limited data are available to assess trends

. T ime series based on federal reference method (FRM) PM 2. 5 

### - mass data show a downward trend in each state (see Figure 19 ) 7



### .

Figure 19

. PM 2. 5 

### t rends i n a nnual average (top) and d aily c oncentrations (bottom)

7 

### Despite the general downward trend since 1999, all states experienced an increase during 2005. Further analyses are underway to understand this increase (e.g., examination of meteorological and emissions effects).

### 26



### A statistical analysis of PM 2. 5 

### trends was performed using the n onparametric Theil test for slope (Hollander and Wolfe, 1973). Trends were generally consistent around the region, for both PM

mass a nd for the individual components of mass. Figure 2 0 shows trends for PM 2. 5 

### based on

FRM data at sites with six or more years of data since 1999. The size and direction of each arrow shows the size and direction of the trend for each site; solid arrows sh ow statistically significant trends and open arrows show trends that are not significant. Region

- wide decreases are widespread and consistent; all sites had decreasing concentration trends ( 13 of the 38 were statistically significant ) . The average decrea se for this set of sites is

- 0.24 ug/m 3 

### /year.

Figure 2 0

. Annual t rends in PM 2. 5 

### m ass ( 1999

– 2006 )



### Seasonal trends show mostly similar patterns (Fig ure 2 1 ). Trends were downward at most sites and seasons, with overall seasonal averages varying b etween

- 0.15 to

- 0.56 ug/m 3 

### /year. The strongest and most significant decreases took place during the winter quarter (Jan uary

- March ). No statistically significant increasing trends were observed.

### 27 Figure 2 1 . Seasonal t rend s in PM 2. 5 

### m ass ( 1999

– 2006 )



### PM 2.5 

### shows a slight variation from weekday to weekend , as seen in Figure 2 2

. Although m ost cities have slightly lower concentrations on the weekend , t he difference is usually less than 1 μg/m 3 

### . The re is a more pronounced w eekday/weekend difference at monitoring sites that are strongly source

- influenced. Rural monitors tend to show less of a weekday/weekend pattern

than urban monitors.

Figure 2 2 Day

- of

- w eek v ariability in PM 2. 5 

### (2002

- 2004)

### 28



### In the Midwest, PM 2.5 

### is made u p of mostly ammonium sulfate, ammonium nitrate , and organic

carbon in approximately equal proportions on an annual average basis. Elemental carbon and

crustal matter (also referred to as soil) contribute less than 5% each.

Figure 2 3 . Spatial map of P M 2. 5 

### chemical composition in the Midwest (2002

- 2003)



### The three major components vary spatially (Fig ure 23 ), including notable urban and rural

differences (Figure 24). The components also vary seasonally (Figure 25). T hese patterns

account for much of th e annual variability in PM 2.5 

### mass noted above.

Figure 2 4 . Average regional (lighter shading) v. local (darker shading) of PM 2. 5 

### che mical species

### 29 Figure 2 5 Seasonal and s patial v ariability in PM 2. 5 

### c omponents

### 30



### Ammonium sulfate peaks in the summer and is highest in the southern and eastern parts of the Midwest, closest to the Ohio River Valley. Sulfate is primarily a regional pollutant; concentrations are similar in rural and urban areas and highly correlated over large distances. It

is for med when sulfuric acid (an oxidation product of sulfur dioxide) and ammonia react in the atmosphere, especially in cloud droplets. Coal combustion is the primary source of sulfur dioxide; ammonia is emitted primarily from animal husbandry operations and f ertilizer use.

Ammonium nitrate has almost the opposite spatial and seasonal pattern, with the highest concentrations occurring in the winter and in the northern parts of the region. Nitrate seems to have both regional and local sources, because urban co ncentrations are higher than rural upwind concentrations. Ammonium nitrate forms when nitric acid reacts with ammonia, a process that is enhanced when temperatures are low and humidity is high. Nitric acid is a product of the oxidation of nitric oxide, a pollutant that is emitted by combustion processes.

Organic carbon is more consistent from season to season and city to city , although concentrations are generally slightly higher in the summe r . Like nitrate, organic carbon has both regional and local co mponents. Particulate organic carbon can be emitted directly from cars and other fuel combustion sources or formed in a secondary process as volatile organic gases react and condense. In rural areas, summer organic carbon has significant contributions

fr om biogenic sources.

Precursor S ensitivity:

Data from the Midwest ammonia monitoring network were analyzed with thermodynamic equilibrium models to assess the effect of changes in precursor gas concentrations on PM 2.5 

### concentrations (Blanchard, 200 5 b ). These analyses indicate that particle formation responds in varying degrees to reductions in sulfate, nitric acid, and ammonia

. Based on Figure 2 6 , which shows PM 2.5 

### concentrations as a function of sulfate, nitric acid (HNO 3), and ammonia (NH3), several k ey findings should be noted:

• 

### P M 2. 5 

### mass is sensitive to reductions in sulfate at all times of the year and all parts of the region . Even though sulfate reductions cause more ammonia to be available to form

ammonium nitrate ( PM

- nitrate increases slightly when sulfate is reduced ) , this increase is generally offset by the sulfate reductions, such that PM 2.5 

### mass decreases.

• 

### PM 2. 5 

### mass is also sensitive to reductions in nitric acid and ammonia . The greatest PM 2. 5 

### decrease in response to nitric acid reductio ns occurs during the winter , when nitrate is a significant fraction of PM 2. 5



### .

• 

### Under conditions with lower sulfate levels (i.e., prox y of future year conditions), P M 2. 5 

### is more sensitive to reductions in nitric acid compared to reductions in ammonia.

• 

### Ammo nia becomes more limiting as one moves from west to east across the region.

Examination of weekend/weekday difference in PM

- nitrate and NOx concentrations in the

Midwest demonstrate that reductions in local (urban) NO x lead to reductions , albeit non

propo rtional reductions, in PM

- nitrate (Blanchard, 200 4 )

. This result is consistent with analyses

of continuous PM

- nitrate from several US cities, including St. Louis (Millstein, et al, 2007).

### 31

Figure 2 6

. Predicted mean PM fine mass con centrations at Bondville, IL (top) and Detroit (Allen Park), MI (bottom) as functions of changes in sulfate, nitric acid (HNO3), and ammoni a (NH3)

Note: starting at the baseline values (represented by the red star), either moving downward (reductions in n itric acid) or moving leftward (reductions in sulfate or ammonia) results in lower PM 2.5 

### values

### 32



### Meteorology : PM 2 .5 

### concentrations are not as strongly influenced by meteorolog y as ozone, but the two pollutants share some similar meteorological dependencies. In the summer, conditions that are conducive to ozone (hot temperatures, stagnant air masses

, and low wind speeds due to stationary high pressure systems) also frequently give rise to high PM 2.5 

### . In the case of PM

, the reason is two

- fold: (1) stagnation and limited mixing under these conditions cause PM 2. 5 

### to build up, usually over several days, and (2) these conditions generally promote higher conversion of important precursors (SO 2 

### to SO 4 

### ) and higher emissions of some precursors, especially biogenic ca rbon. Wind direction is another strong determinant of PM 2.5 

### ; air transported from polluted source regions has higher concentrations.

Unlike ozone, PM 2.5 

### has occasional winter episodes. Conditions are similar to those for summer episodes, in that station ary high pressure and ( seasonally ) warm temperatures are usually factors. Winter episodes are also fueled by high humidity and low mixing heights.

PM 2. 5 

### chemical species show noticeable transport influences. T rajectory analyses have

demonstrated that high PM

- sulfate is associated with air masses that traveled through the

sulfate

- rich Ohio River Valley ( Poirot, et al, 2002 and Kenski

, 2004). Likewise, high PM

- nitrate is associated with air masses that traveled through the ammonia

- rich Midwest. Figure 2 7 shows results from an ensemble trajectory analysis of 17 rural eastern IMPROVE sites.

Figure 2 7

. Sulfate and n itrate s ource r egions based on e nsemble t rajectory a nalysis



### When these results are considered together with analyses of precursor sens itivity ( e.g., Fig ure
2 6 )

, one possible conclusion is that ammonia control in the Midwest could be effective at reducing nitrate concentrations. The thermodynamic equilibrium modeling shows that ammonia

reductions would reduce PM concentrations in the Mid west, but that nitric acid reductions are more effective when the probable reductions in future sulfate levels are considered.

### 33



### Source Culpability :

Three source apportionment studies were performed using speciated PM 2. 5 

### monitoring data and statistical analysis methods (Hopke, 2005, STI, 2006, and STI, 200 8 ). Figure 2 8 summarizes the source contributions from these studies. The studies show that a large portion of PM 2. 5 

### mass consists of secondary, regional impacts, which cannot be attributed

to indivi dual facilities or sources (e.g., secondary sulfate, secondary nitrate, and secondary organic aerosols). Nevertheless, wind analyses (e.g., Figure 2 7 ) provide information on likely source regions. Regional

- or national

- scale control programs may be the m ost effective way to deal with these impacts. EPA's C AIR , for example, will provide for substantial reductions in SO2 emissions over the eastern half of the U.S., which will reduce sulfate (and PM 2. 5 

### ) concentrations and improve visibility levels.

The stu dies also show that a smaller, yet significant portion of PM 2. 5 

### mass is due to emissions from nearby (local) sources. Local (urban) excesses occur in many urban areas for organic and elemental carbon, crustal matter, and, in some cases, sulfate. The stat istical analysis methods

h elp to identify local sources and quantify their impact. This information is valuable to s tates wishing to develop control programs to address local impacts. A combination of national/regional

- scale and local

- scale emission redu ctions may be necessary to provide for

attainment.

The carbon sources are not easily identified in complex urban environments. LADCO’s Urban Organics Study (STI, 2006) identified four major sources of organic carbon: mobile sources, burning, industrial s ources, and secondary organic aerosols. Additional sampling and analysis is underway in Cleveland and Detroit to provide further information on sources of organic

carbon.

### 34 0 1 2 3 4 5 6 7CoalCombustionSecondaryNitrateMobileIndustrialSoilBurningug/m3 Northbrook (Jan 03-Mar 05)
Cincinnati (Dec 03-Mar 05)

Indianapolis (Jan 02-Mar 05)

Allen Park (2002-2004)

Dearborn (Jun 02-May 05)

Bondville (2002-2004)
St. Louis (2004)

Granite City (2002-2004)

Tikhon (Jan 03-Jun05)

Orange (Apr 01-Mar 05)

Lorain (Feb 02-Dec04)

0 2 4 6 8 10

sulfate nitrate OM diesel soil steel ind. zinc ind. copper mixed ind. Allen Park

Dearborn

La w ndale

Springfield

Tikhon

Craig

Taft



### Figure 2 8

. Major Source Contributions in the Midwest based on Hopke

, 2005 (upper left), STI, 2006 (upper right), and STI, 200 8 (lower left) (Note: the labeling of similar source types varies between studies

– e.g., organic carbon/mobile sources are named gasoline and diesel by Hopke

, mobile b y STI 2006, and OM and diesel by STI 2008 )

### 35



### 2.3 Haze

Section 169A of the Clean Air Act sets as a national goal “the prevention of any future

, and the remedying of any existing, impairment of visibility in mandatory Class I Federal areas which imp airment re sults from manmade air pollution” . To impl ement this provision, in 1999, EPA adopted regulations to address regional haze visibility impairment (USEPA, 1999)

. EPA’s rule requires s tates to “make reasonable progress toward meeting the national goal” . Spe cifically,

states must establish reasonable progress goals , which provide for improved visibility on the most impaired (20% worst) days sufficient to achieve natu ral conditions by the year 2064, and for no degradation on the least impaired (20% best) days

.

The primary cause of impaired visibility in the Class I areas is pollution by fine particles that scatter light. The degree of impairment, w hich is expressed in terms of visual range, light extinction (1/Mm)

, or deciviews (dv), depends not just on the t otal PM 2. 5 

### mass concentration, but

also on the chemical composition of the particles and meteorological conditions.

Current C onditions : A map of the average l ight extinction values for the most impaired (20% worst) visibility days for the 5

- year baseline period ( 2000

- 2004 ) is shown in Figure 2 9

.

Figure 2 9 . Baseline Visibility Levels for 20% Worst Days (2000

– 2004) , units: Mm - 1

### 36



### Initially, the baseline (2000

– 2004) visibility condition values were derived using the average for

the 20% worst and 20% best days for each year, as reported on the VIEWS website: http://vista.cira.colostate.edu/views/Web/IMPROVE/SummaryData.aspx . These values were calculated using the original IMPROVE equation for reconstructed light extinction

.

Three changes were made to the baseline calculations to produce a new set of values. First, the reconstructed light extinction equation was revised by the IMPROVE Steering Committee in

2005

. Th e new IMPROVE equation was used to calculate updated baseline values.

Second, d ue to sampler problems, the 2002

- 2004 data for Boundary Waters were invalid for certain chemical species. (Note, sulfate and nitrate data were valid.) A “substituted” dat a set was developed by using values from Voyageurs for the invalid species



## .

Third, LADCO identified a number of days during 2000

- 2004 where data capture at the Class I monitors was incomplete ( Kenski, 2007b ). The missing data cause the se days to be exclu ded from the baseline calculations. However, the light extinction due to the remaining measured species is significant ( i.e., above the 80 th 

### percentile). It makes sense to include these days in

the baseline calculations, because they are largely dominate d by anthropogenic sources. ( Only one of these days is driven by high organic carbon, which might indicate non

- anthropogenic

aerosol from wildfires. ) As seen in Table 3 , inclusion of these days i n the baseline calculation

results in a s mall

, but measurab le

, effect on the baseline values (i.e., v alues increase from 0.2

to 0.8 dv )

.

Table 3 . Average of 20% worst days, with and without missing data days

Average Worst Day DV, per RHR Average Worst Day DV,

with Missing Data Days Difference

BOWA 19.59 19.8 6 0.27

ISLE 20.74 21.59 0.85

SENE 24.16 24.38 0.22

VOYA 19.27 19.48 0.21



### A summary of the initial and updated baseline values for the Class I areas in northern Michigan

and northern Minnesota are presented in Table 4

. The updated baseline values reflect the most current, complete understanding of visibility impairing effects and, as such, will be used for SIP planning purposes.

### 37 Table 4 . Summary of v isibility m etrics (deciviews) for n orthern Class I a reas

Old IMPROVE Equation (Cite: VIEWS, November 2005)

20% Worst Days

2000 2001 2002 2003 2004 Baseline

Value 2018

UR I Value Natural

Conditions

Voyageurs 18.50 18.00 19.00 19.20 17.60 18.46 16.74 11.0 9

BWCA 19.85 19.99 19.68 19.73 17.65 19.38 17.47 11.21

Isle Royale 20.00 22.00 20.80 19.50 19.10 20.28 18.17 11.22

Seney 22.60 24.90 24.00 23.80 22.60 23.58 20.73 11.37

20% Best Days

2000 2001 2002 2003 2004 Baseline

Value Natur al
Conditions

Voyageurs 6.30 6.20 6.70 7.00 5.40 6.32 3.41

BWCA 5.90 6.52 6.93 6.67 5.61 6.33 3.53

Isle Royale 5.70 6.40 6.40 6.30 5.30 6.02 3.54

Seney 5.80 6.10 7.30 7.50 5.80 6.50 3.69

New IMPROVE Equation (Cite: VIEWS, March 2006)

20% Worst Days

2000 2001 2002 2003 2004 Baseline

Value 2018

UR I Value Natural

Conditions

Voyageurs 19.55 18.57 20.14 20.25 18.87 19.48 17.74 12.05

BWCA 20.20 20.04 20.76 20.13 18.18 19.86 17.94 11.61

Isle Royale 20.53 23.07 2 1.97 22.35 20.02 21.59 19.43 12.36

Seney 22.94 25.91 25.38 24.48 23.15 24.37 21.64 12.65

20% Best Days

2000 2001 2002 2003 2004 Baseline

Value Natural

Conditions

Voyageurs 7.01 7.12 7.53 7.68 6.37 7.14 4.26

BWCA 6.00 6.92 7.00 6 .45 5.77 6.43 3.42

Isle Royale 6.49 7.16 7.07 6.99 6.12 6.77 3.72
Seney 6.50 6.78 7.82 8.01 6.58 7.14 3.73

Notes: (1) BWCA values for 2002

- 2004 reflect "substituted" data. (2) New IMPROVE equation values include Ken s ki, 200 7 adjustment for missing days

UR I

= uniform rate of improvement

### 38



### As noted above, the goal of the visibility program is to achieve natural conditions. Initially, the

natural conditions values for each Class I area were taken directly from EP A guidance (EPA, 2003). These values were calculated using the original IMPROVE equation. This equation was revised by the IMPROVE Steering Committee in 2005, and the new IMPROVE equation was used to calculate updated natural conditions values. The upda ted values are reported on the

VIEWS website.

A summary of the initial and updated natural conditions values are presented in Table 4 . The updated natural conditions values (based on the new IMPROVE equation) will be used for SIP planning purposes.

Data Variability : For the four northern Class I areas, the most important PM 2. 5 

### chemical species are ammonium sulfate, ammonium nitrate, and organic carbon. The contribution of these species on the 20% best and 20% worst visibility days (based on 2000

– 2004 data) is provided in Figure 30 . For the 20% worst visibility days, the contributions are: sulfate = 35

- 55%, nitrate =

25

- 30%, and organic carbon = 12

- 22%. Although the chemical c omposition is similar , sulfate increases in importance from west to east and concentrations are highest at Seney ( the

easternmost site )

. It should also be noted that sulfate and nitrate contribute more to light

extinction than to PM 2. 5 

### mass because of their hygroscopic properties.

20% Best Days 20% Worst Days



### 0 20 40 60 80 100 120

VOYA BOWA ISRO Seney VOYA BOWA ISRO Seney sea salt

soil

elem. carbon

coarse mass

organic carbon

nitrate

sulfate

Figure 30 . Chemical composition of light extinction for 20% best visibility days (left) and 20% worst visibility days (right) in terms of Mm - 1



### Analysis of PM 2. 5 

### mass and chemical species for rural IMPROVE (and IMPROVE

- protocol) sites

in the ea stern U.S. showed a high degree of correlation between PM 2. 5 

### - mass , sulfate, and nitrate levels (see Figure 3 1 ). The Class I sites in northern Michigan and northern Minnesota , in particular, are highly correlated for PM 2. 5 

### mass, sulfates, and org anic carbo n mass (AER, 2004)

.

### 39

Figure 3 1

. Correlations a mong IMPROVE (and IMPROVE

- protocol) m onitoring s ites in Eastern U.S.



### Lo ng

- term trends at Boundary Waters (the only regional site with a sufficient data record) show significant decreases in total PM 2. 5 

### (

- 0.005 ug/year) and SO4 (

- 0.04 ug/year) and an increase in NO3 (+0.01 ug/year). These PM 2. 5 

### and SO4 trends are generally consistent with long

- term
trends at other IMPROVE sites in the eastern U.S. , which have shown widespre ad decreases in

SO4 and PM 2. 5 

### (DeBell, et al, 2006 ). Detecting changes in nitrate has been hampered by uncertainties in the IMPROVE data for particular years and

, thus

, this estimate should be

considered tentative.

Haze in the Midwest Class I areas has no strong seasonal pattern. P oor visibility days occur throughout the year, as indicated in Fig ure 3 2

. (Note, in contrast, other parts of the country

, such as Shenandoah National Park in Virginia, show a strong tendency for the worst air quality days to occur in the summer months. ) This figure and Figure 3 3 (which presents the monthly average light extinction values based on all sampling days) also show that sulfate and organic carbon concentrations are higher in the summer, and nitrate concentrations are higher in the winter, sugge sting the importance of different sources and meteorological conditions at different times of the year.

### 40

Figure 3 2 . Daily light extinction values for 20% worst days at Boundary Waters (2000

– 2004)

### 41 Figure 3 3 . Monthly average light extinction values for northern Class I areas

### 42



### Precursor S ensitivity: Results from two analyses using t hermodynamic equilibrium model s provide information on the effect of changes in precursor concentrations on PM 2.5 

### concentrations (and, in turn , visibility levels) in the northern Class I areas . First, a preliminary analysis using data collected at Seney indicate d that PM 2.5 

### there is most sensitive to reductions in sulfate, but is also sensitive to reductions in nitric acid (Blanchard

, 2004).

Second, an analysis was performed using data from the Midwest ammonia monitoring network

for a site in Minnesota

-- Great River Bluffs, which is the closest ammonia monitoring site to the

northern Class I areas (Blanchard, 2005 b )

. F igure 34 shows PM 2.5 

### co ncentrations as a function

of sulfate, nitric acid ( HNO3 ) , and ammonia ( NH3 ) . Reductions in sulfate (i.e., movement to the left of baseline value [represented by the red star]), as well as reductions in nitric acid (i.e., movement downward) and NH3 (i.e., movement to the left), result in lower PM 2.5 

### concentrations. Thus, reductions in sulfate, nitric acid, and ammonia will lower PM 2.5 

### concentrations and improve visibility in the northern Class I areas.

Figure 3 4 . Predicted PM 2.5 

### mass concentrations at Great River Bluffs, MN as functions of changes

in sulfate, nitric acid, and ammonia



### Meteorology and T

ransport : The role of meteorology in haze is complex. Wind speed and wind direction govern the movement of air masses from polluted areas to the cl eaner wilderness areas. As noted above, increasing humidity increases the efficiency with which sulfate and nitrate aerosols scatter light. Temperature and humidity together govern whether ammonium nitrate can form from its precursor gases, nitric acid a nd ammonia. Temperature and sunlight also play an indirect role in emissions of biogenic organic species that condense to form particulate organic matter; emissions increase in the summer daylight hours.

Trajectory analyses were performed to understan d transport patterns for the 20% worst and 20% best visibility days. The composite results for the four northern Class I areas are provided in Figure 3 5

. The o range areas are where the air is most likely to come from, and the green

areas are where the ai r is least likely to come from . As can be seen, bad air days are generally associated with transport from regions located to the south, and good air days with transport

from Canada.

### 43

Figure 3 5 . Composite back trajectories f or light extinction

- 20% best visibility days (left) and 20% worst visibility days (right) ( 2000

– 2005 )



### Source Culpability : Air quality data analyses (including the trajectory analyses above) and dispersion modeling were used to provide information on source region and source sector contributions to regional haze in the northern Class I areas (see MRPO, 200 8 ). Based on this information, the most important contributing states are Michigan, Minnesota, and Wisconsin, as

well as Missouri, North Dakota, Iow a, Indiana and Illinois (see, for example, Figure 3 5 above). The most important contributing pollutants and source sectors are SO2 emissions from electrical generating units (EGUs) and certain non

- EGUs, which lead to sulfate formation, and

NOx emissions f rom a variety of source types (e.g., motor vehicles), which lead to nitrate formation. Ammonia emissions from livestock waste and fertilizer applications are also important, especially for nitrate formation.

A source apportionment study was performed usi ng monitoring data from Boundary Waters and statistical analysis methods (DRI, 2005). The study shows that a large portion of PM 2.5 

### mass consists of secondary, regional impacts, which cannot be attributed to individual facilities or sources (e.g., seconda ry sulfate, secondary nitrate, and secondary organic aerosols). Industrial

sources contribute about 3

- 4% and mobile sources about 4

- 7 % to PM 2.5 

### mass.

A special study was performed in Seney to identify sources of organic carbon ( Sheesley, et al,

2004 ). As seen in Figure 3 6 , t he highest PM 2.5 

### concentrations occurred during the summer,

with organic carbon being the dominant species. The higher summer organic carbon concentrations were attributed mostly to secondary organic aerosols of biogenic origin b ec ause of the lack of primary emission markers, and concentrations of know biogenic

- related species (e.g., pinonic acid

– see Figure 36) were also high during the summer.

### 44

Figure 3 6 . Monthly concentrations of PM 2.5 

### species (top )

, and secondary and b iogenic

- related organic carbon species in Seney (bottom )

### 45



### Although the Seney study showed that biomass burning was a relatively small contributor to organic carbon on an annual average basis, episodic impacts are apparent (see, for example, high organic carbon days in Figure 32). To assess further whether burning is a significant contributor to visibility impairment in the northern Class I areas, the PM 2.5 

### chemical speciation data were examined for days with high organic carbon and elemental carbon conc entrations, which are indicative of biomass burning impacts. Only a handful of such days were identified:

Table 5 . Days with high OC and EC concentrations in northern Class I areas

Site 2000 2001 2002 2003 2004

Voyageurs

--- --- Jun 1 Aug 25 Jul 17

Jun 28

Jul 19

Boundary Waters

--- --- Jun 28 Aug 25 Jul 17

Jul 19

Isle Royale

--- --- Jun 1 Aug 25

---
Jun 28
Seney

--- --- Jun 28

--- ---



### Back trajectories on these days point mostly to wildfi res in Canada. Elimination of these high organic carbon concentration days has a small effect in lowering the baseline visibility levels in

the northern Class I areas (i.e., Minnesota Class I areas change by about 0.3 deciviews and Michigan Class I areas change by less than 0.2 deciviews). This suggests that fire activity, although significant on a few days, is on average a relatively small contributor to visibility impairment in the northern Class I areas.

In summary, these analyses show that organic ca rbon in the northern Class I is largely

uncontrollable.

### 46 

### Section 3.0 Air Quality Modeling

Air quality models are relied on by federal and state regulatory agencies to support their planning efforts

. Used properly, models can assist policy makers in dec iding which control programs are most effective in improving air quality, and meeting specific goals and objectives. For example, models can be used to conduct “what if” analyses, which provide information for policy makers on the effectiveness of candida te control programs.

The modeling analyses wer e conducted in accordance with EPA’s modeling guidelines (EPA,

2007 a ). Further details of the modeling are provided in two protocol documents : LADCO, 2007a and LADCO, 2007b.

This section reviews the develop ment and evaluation of the modeling system used for the multi

pollutant analyses. Application of the modeling system (i.e., attainment demonstration for ozone

and PM 2. 5 

### , and reasonable progress assessment for haze) is covered in the following sections.

3.1 Selection of Base Year

Two base year s were used in the modeling analyses: 2002 and 2005. EPA’s modeling guidance recommends using 2002 as the baseline inventory year, but also allows for use of an alternative baseline inventory year, especially a more recent ye ar. Initially , LADCO conducted modeling with a 2002 base year (i.e., Base K/Round 4 modeling, which was completed in 2006)

. A decision was subsequently made to conduct modeling with a 2005 base year (i.e., Base M/Round 5, which was completed in 2007). As discussed in the previous section, 2002 and 2005 both had above normal ozone conducive conditions, although 200 2 was more severe compared to 200 5 . Examination of multiple base years provides for a more complete technical

assessment. Both sets of model runs are discussed in this document.

3.2 Future Years of Interest To address the multiple attainment requirements f or ozone and PM 2.5 

### , and reasonable progress goals for regional haze, several future years are of interest:

2008 Planning year for oz one basic nonattainment areas (attainment date 2009) 8



### 2009 Planning year for ozone moderate nonattainment areas and PM 2.5 

### nonattainment areas (attainment date 2010)

2012 Planning year for ozone moderate nonattainment areas and PM 2.5 

### nonattainment

areas , with 3

- yea r extension (attainment date 2013)

2018 First milestone year for regional haze planning

8 

### According to USE PA’ s ozone implementation rule (USEPA, 2005), emission reductions needed for attainment must be implemented by the beginning of the ozone season immediately preceding the area’s

attainment date. The PM2.5 implementation rule contains similar provisions

– i.e., emission reductions should be in place by the beginning of the year preceding the attainment date (USEPA, 2007 c ). The logic for requiring emissions reductions by the year (or season) immediately preceding the attainment year follows from language in the Clean Air Act , and the ability for an area to receive up to two 1

- year extensions. Therefore, emissions in the year preceding the attainment year should be at a level that is consistent with attainment. It also follows that the year preceding the att ainment year should be modeled for attainment planning purposes.

### 47



### Detailed emissions inventories were developed for 2009 and 2018. To support modeling for other future years, less rigorous emissions processing w as conducted (e.g., 20 12 emissions

were estimated for several source sectors by interpolating between 2009 and 2018 emissions).

3.3 Modeling System The air quality analyses were conducted with the CAMx model, with emissions and meteorology generated using EMS (and CONCEPT ) and MM5, respectively. The selection of CAMx as the primary model is based on several factors: performance, operator considerations (e.g., ease of application and resource requirements), technical support and documentation, model extensions (e.g., 2

- way nest ed grids, process analysis, source apportionment, and plume

- in

grid), and model science

. CAMx model set

- up for Base M and Base K is summarized below:

Base M (2005) Base K (2002)

• CAMx v4.50 * CAMx 4.30 • CB05 gas phase chemistry * CB

- IV wi th updated gas

- phase chemistry • SOA chemistry updates * No SOA chemistry updates • AERMOD dry deposition scheme * Wesley

- based dry deposition • ISORROPIA inorganic chemistry • ISORROPIA inorganic chemistry • SOAP organic chemistry • SOAP organic chemistry • RADM aqueous phase chemistry • RADM aqueous phase chemistry • PPM horizontal transport • PPM horizontal transport



### 3. 4 Domain/Grid Resolution The National RPO grid projection w as used for this modeling. A subset of the RPO domain was

us ed for the LADCO modeling. For PM 2. 5 

### and haze , the large eastern U.S. grid at 36 km (see box on right side of Figure 3 6 ) was used . A PM 2. 5 

### sensitivity run was also performed for this

domain at 12 km. For ozone , the smaller grid at 12 km (see shaded port ion of the box on the right side of Figure 3 7 ) w as used for most model runs . An ozone sensitivity run was also performed with a 4km sub

- grid over the Lake Michigan a rea and Detroit/Cleveland.

The vertical resolution in the air quality model consists of 16 layers extending up to 15 km , with higher resolution in the boundary layer.

Figure 3 7

. Modeling grids

– RPO domain (left) and LADCO modeling domain (right) 

## 12 km

36 km

### 48



### 3.5 Model Inputs : Meteorology Meteorological inputs w ere derived using the Fifth

- Generation NCAR/Penn State Meteorological Model (MM5)

– version 3.6.3 for the year s 2001

– 2003, and version 3.7 for the year 2 005

. The

MM5 modeling domains are consistent with the National RPO grid projections (see Figure 3 8 )

.

Figure 3 8 . MM5 m odeling domain for 2001

- 2003 (left) and 2005 (right)



### The annual 2002 36 km MM5 simulation was completed by Iowa DNR. The 36/12 km 2

- way nested simulation for the summers of 2001, 2002, and 2003 were conducted jointly by Illinois

EPA and LADCO. The 36 km n on

- summer portion of the annual 2003 simulation was conducted by Wisconsin DNR. The annual 2005 36/12 km (and summer season 4 km) MM5 modeling was completed by Alpine Geophysics

. Wisconsin DNR also completed 36/12 km MM5 runs for the

summer season of 200 5.

Model performance was assessed quantitatively wi th the METSTAT tool from Environ . The metrics used to quantify model performance include mean observat ion, mean prediction, bias

, gross error, root mean square error, and index of agreement. Model perfor mance metrics were

calculated for several sub

- region s of the modeling domain (Figure 3 9 ) and represent hourly spatial averages of multiple monitor locations. Additional analysis of rainfall is done on a monthly basis.

Figure 3 9 . Sub

- domains used for m odel performance for 2001

- 2003 (left) and 2005 (right)



### A summary of the performance evaluation results for the meteorological model ing is provided

below. Further details are provided in two summary reports (LADCO, 2005 and LADCO, 2007 c ).

### 49



### Temperature : The biggest issue with the performance in the upper Midwest is the existence of a cool diurnal temperature bias in the winter and warm temperature bias over night during the

summer (see Figure 40 ). These features are common to other annual MM5 simulations for the

central United States and do not appear to adversely affect mod el performance

.

Figure 40

. Daily temperature bias for 2002 (left ) and 2005 ( right ) with hotter colors (yellow/orange/red) representing overestimates and cooler colors (blues) represen ting

underestimates

Note: months are represented from left to right (January to December) and days are represented from top to bottom (1 to 30(31)

– i.e., upper left hand corner is January 1 and lower right hand

corner is December 31



### Wind Field

s : The win d fields are generally good . Wind speed bias is less than 0.5 m/sec and wind speed error is consistently between 1.0 and 1.5 m/sec. Wind direction error is generally

within 15

- 30 degrees.

Mixing Ratio: The mixing ratio (a measure of humidity) is over

- pr edicted in the late spring and summer months, and mixing ratio error is highest during this period. There is little bias and error during the cooler months when there is less moisture in the air.

Rainfall: The modeled and observed rainfall totals show go od agreement spatially and in terms of magnitude in the winter, fall, and early spring months. There are , however, large over

predictions of rainfall in the late spring and summer months (see Figure 4 1 )

. These over

predictions are seen spatially and in ma gnitude over the entire domain, particularly in the

Southeast United States , and are likely due to excessive convective rainfall being predicted in

MM5. This over

- prediction of rainfall in MM5 does not necessarily translate into over

- prediction of wet dep

osition in the photochemical model. CAMx does not explicitly use the convective and

non

- convective rainfall output by MM5, but estimates wet scavenging by hydromete o rs using
cloud, ice, snow, and rain water mixing ratios output by MM5. Nevertheless, this could have an effect on model performance for PM 2. 5 

### , as discussed in Section 3.7, and may warrant further

attention.

### 50

Figure 4 1

. Comparison of observed (left column) and modeled (right column) monthly rainfall for July 2002 (top) and July 2005 (bottom)

### 51



### 3.6 Model Inputs: Emissions

E mission inventories were prepared for two base years: 2002 (Base K) and 2005 (Base M), and several future years: 2008, 2009, 2012, and 2018. Further details of the emission inventories are provide d in two summary reports (LADCO, 2006 a and LADCO, 200 8 a ) and the following pages of the LADCO web site:

http://www.ladco.org/tech/emis/basek/BaseK_Reports.htm http://www.ladco.org/tech/emis/r5/round5_reports.htm

For on

- road, nonroad, ammonia, and biogenic sources, emissions were estimated by models. For the other sectors (point sources, area sources, and MAR [comme rcial marine, air craft, and railroads]), emissions were prepared using data supplied by the LADCO States and other

RPOs

.

Base Year Emissions : S tate and source sector emission summaries for 2002 ( Base K) and 2005 ( Base M) are compared in Figure 4 2

. A ddit ional detail is provided in Table s 6 a (all sectors

– tons per day) and 6b (EGUs

– tons per year)

.

VOC NOx SO2

0 2000 4000 6000 8000 10000 12000

2002

(K) 2005
(M) 2002

(K) 2005
(M) 2002

(K) 2005
(M) 

### WI

OH

MI

IN

IL

0 2000 4000 6000 8000 10000 12000

2002

(K) 2005
(M) 2002

(K) 2005
(M) 2002

(K) 2005
(M) Area

Nonroad+MAR

Onroad

Non-EGU

EGU



### Figure 4 2 . Base K and Base M e missions for 5

- s tate LADCO r egion by state (top) and source sector (bottom) , u nits: tons per summer weekday



### A summary of the base year emissions by sector for the LADCO States is provided below.VOC Base M BaseK Base M BaseK BaseK Base M NOx Base M BaseK Base M BaseK BaseK Base M SOX Base M BaseK Base M BaseK BaseK Base M PM2.5 Base M BaseK Base M BaseK BaseK Base M

July 2002 2005 2009 2009 2012 2018 2018 2002 2005 2009 2009 2012 2018 2018 2002 2005 2009 2009 2012 2018 2018 2002 2005 2009 2009 2012 2018 2018

Nonroad

IL 224 321 164 257 149 130 213 324 333 263 275 224 154 155 31 33 5 5 0.6 0.4 0.4 30 24 14

IN 125 195 94 160 95 95 128 178 191 142 158 141 141 89 17 19 3 3 3 0.3 0.2 17 13 7

MI 348 414 307 350 276 222 271 205 239 159 197 133 93 112 19 22 3 3 0.5 0.3 0.3 22 18 11

OH 222 356 161 294 145 126 238 253 304 195 246 162 109 135 23 29 4 5 0.5 0.3 0.4 27 22 13

WI 214 238 194 203 175 140 157 145 157 114 129 97 69 77 13 15 2 2 0.3 0.2 0.2 14 12 7

5-State Total 1133 1524 920 1264 840 713 1007 1105 1224 873 1005 757 566 568 103 118 17 18 4.9 1.5 1.5 110 89 52

U.S. Total 8463 9815 5442 8448 5244 6581 6041 9060 6057 8120 5832 5100 505 654 117 153 104 13 573 750 475

MAR

IL 10 11 10 10 10 10 6 277 246 201 228 195 186 165 0 22 0 19 0 0 17 7 6 4

IN 5 5 5 5 5 5 3 123 93 89 87 87 84 65 0.2 8 0.2 7 0.2 0.2 6 2 2 2

MI 7 7 7 7 7 8 7 114 87 112 82 111 110 65 0.6 21 0.7 14 0.7 0.8 8 3 3 2

OH 8 7 8 7 8 8 5 177 134 128 126 126 122 94 0.4 14 0.3 12 0.3 0.3 10 4 4 2

WI 4 4 4 4 4 4 3 79 58 59 54 59 57 41 12.7 8 9.5 6 9.5 8.7 5 2 2 1

5-State Total 34 34 34 33 34 35 24 770 618 589 577 578 559 430 13.9 73 10.7 58 10.7 10 46 18 17 11

U.S. Total 307 317 321 157 329 346 334 4968 4515 4002 1813 3964 3919 3812 620 512 509 122 509 503 290 147 57 165

OtherArea

IL 679 675 688 594 700 738 582 62 48 68 48 70 73 49 11 11 12 16 12 13 16 40 64 69

IN 354 391 365 358 373 398 384 62 56 65 58 67 69 59 158 32 150 32 151 153 32 2 2 2

MI 518 652 516 562 520 541 549 49 49 52 50 53 54 51 71 29 68 29 68 68 28 111 114 120

OH 546 604 550 506 558 593 487 50 93 59 108 60 62 108 22 6 34 15 35 35 14 19 35 34

WI 458 315 467 290 474 506 293 32 37 34 37 34 35 37 9 17 9 13 10 10 13 11 12 12

5-State Total 2555 2637 2586 2310 2625 2776 2295 255 283 278 301 284 293 304 271 95 273 105 276 279 103 183 227 237

U.S. Total 17876 21093 18638 18683 20512 24300 3856 4899 4100 4220 4418 5357 2075 2947 2062 2559 2189 2709 2735 2621 2570

On-Road

IL 446 341 314 268 260 197 151 890 748 578 528 474 300 201 9 4 3 13 10 6

IN 405 282 237 235 193 150 138 703 541 425 402 313 187 173 11 3 2 9 7 2

MI 522 351 335 269 303 217 163 926 722 680 501 619 385 204 14 4 3 12 9 3

OH 574 680 365 424 340 238 242 1035 934 609 693 512 270 274 18 4 4 16 12 4

WI 238 175 144 119 117 88 68 481 457 303 322 226 118 138 9 2 2 8 6 2

5-State Total 2185 1829 1395 1315 1213 890 762 4035 3402 2595 2446 2144 1260 990 61 17 14 58 44 17

U.S. Total 14263 7825 23499 13170

EGU

IL 9 7 8 6 8 9 7 712 305 227 275 244 231 224 1310 1158 944 958 789 810 869 13 34 77

IN 6 6 6 6 7 6 6 830 393 406 370 424 283 255 2499 2614 1267 1033 1263 1048 1036 16 73 74

MI 12 6 11 4 11 12 4 448 393 218 242 219 247 243 1103 1251 1022 667 1031 1058 725 15 25 29

OH 5 4 6 5 7 7 6 1139 408 330 280 322 271 285 3131 3405 1463 1326 994 701 983 28 94 80

WI 3 5 3 2 4 4 3 293 213 146 165 139 147 177 602 545 512 460 492 500 435 0 22 25

5-State Total 35 28 34 23 37 38 26 3422 1712 1327 1332 1348 1179 1184 8645 8973 5208 4444 4569 4117 4048 72 248 285

U.S. Total 214 140 195 124 197 215 138 14371 10316 7746 7274 7721 7007 6095 31839 34545 20163 16903 17629 14727 14133 685 1131 1571

Non-EGU

IL 313 221 286 218 305 350 258 356 330 334 218 338 343 235 373 423 251 335 257 249 346 16 17 19

IN 150 130 160 137 170 199 167 238 179 212 175 216 225 178 292 218 270 216 274 290 180 35 36 44

MI 123 116 115 119 122 139 140 216 240 208 242 214 229 271 162 158 166 148 171 185 163 20 21 25

OH 77 84 75 87 79 90 104 177 175 157 166 160 167 178 240 289 231 288 210 216 293 27 28 33

WI 88 84 97 87 104 120 106 98 97 91 93 92 94 81 163 156 154 152 155 156 85 0 0.1 0.1

5-State Total 751 635 733 648 780 898 775 1085 1021 1002 894 1020 1058 943 1230 1244 1072 1139 1067 1096 1067 98 102 121

U.S. Total 4087 3877 4409 4700 5378 6446 6730 6129 6435 6952 5759 5630 6093 6340 6970 1444 1777

IL 1681 1576 1470 1353 1432 1434 1217 2621 2010 1671 1572 1545 1287 1029 1725 1656 1212 1337 1059 1072 1251 119 155 189

IN 1045 1009 867 901 843 853 826 2134 1453 1339 1250 1248 989 819 2966 2902 1690 1294 1691 1492 1256 81 133 131

MI 1530 1546 1291 1311 1239 1139 1134 1958 1730 1429 1314 1349 1118 946 1356 1495 1260 865 1271 1312 927 183 190 190

OH 1432 1735 1165 1323 1137 1062 1082 2831 2048 1478 1619 1342 1001 1074 3416 3761 1732 1650 1240 953 1304 121 195 166

WI 1005 821 909 705 878 862 630 1128 1019 747 800 647 520 551 800 750 687 635 667 675 540 35 54 47

5-State Total 6693 6687 5702 5593 5529 5350 4889 10672 8260 6664 6555 6131 4915 4419 10263 10564 6581 5781 5928 5504 5280 539 727 723Heat Input (MMBTU/year) Scenario SO2 (tons/year) SO2 (lb/MMBTU) NOx (tons/year) NOx (lb/MMBTU)

IL 980,197,198 2001 - 2003 (average) 362,417 0.74 173,296 0.35

IPM 2.1.9 241,000 73,000

1,310,188,544 IPM3.0 (base) 277,337 0.423 70,378 0.107

IPM3.0 - will do 140,296 0.214 62,990 0.096

IPM3.0 - may do 140,296 0.214 62,990 0.096

IN 1,266,957,401 2001 - 2003 (average) 793,067 1.25 285,848 0.45

IPM 2.1.9 377,000 95,000

1,509,616,931 IPM3.0 (base) 361,835 0.479 90,913 0.120

IPM3.0 - will do 417,000 0.552 94,000 0.125

IPM3.0 - may do 417,000 0.552 94,000 0.125

MI 756,148,700 2001 - 2003 (average) 346,959 0.92 132,995 0.35

IPM 2.1.9 399,000 100,000

1,009,140,047 IPM3.0 (base) 244,151 0.484 79,962 0.158

IPM3.0 - will do 244,151 0.484 79,962 0.158

IPM3.0 - may do 244,151 0.484 79,962 0.158

OH 1,306,296,589 2001 - 2003 (average) 1,144,484 1.75 353,255 0.54

IPM 2.1.9 216,000 84,000

1,628,081,545 IPM3.0 (base) 316,883 0.389 96,103 0.118

IPM3.0 - will do 348,000 101,000

IPM3.0 - may do 348,000 101,000

WI 495,475,007 2001 - 2003 (average) 191,137 0.77 90,703 0.36

IPM 2.1.9 155,000 46,000

675,863,447 IPM3.0 (base) 127,930 0.379 56,526 0.167

IPM3.0 - will do 150,340 0.445 55,019 0.163

IPM3.0 - may do 62,439 0.185 46,154 0.137

IA 390,791,671 2001 - 2003 (average) 131,080 0.67 77,935 0.40

IPM 2.1.9 147,000 51,000

534,824,314 IPM3.0 (base) 115,938 0.434 59,994 0.224

IPM3.0 - will do 115,938 0.434 59,994 0.224

IPM3.0 - may do 100,762 0.377 58,748 0.220

MN 401,344,495 2001 - 2003 (average) 101,605 0.50 85,955 0.42

IPM 2.1.9 86,000 42,000

447,645,758 IPM3.0 (base) 61,739 0.276 41,550 0.186

IPM3.0 - will do 54,315 0.243 49,488 0.221

IPM3.0 - may do 51,290 0.229 39,085 0.175

MO 759,902,542 2001 - 2003 (average) 241,375 0.63 143,116 0.37

IPM 2.1.9 281,000 78,000

893,454,905 IPM3.0 (base) 243,684 0.545 72,950 0.163

IPM3.0 - will do 237,600 0.532 72,950 0.163

IPM3.0 - may do 237,600 0.532 72,950 0.163

ND 339,952,821 2001 - 2003 (average) 145,096 0.85 76,788 0.45

IPM 2.1.9 109,000 72,000

342,685,501 IPM3.0 (base) 41,149 0.240 44,164 0.258

IPM3.0 - will do 56,175 0.328 58,850 0.343

IPM3.0 - may do 56,175 0.328 58,850 0.343

SD 39,768,357 2001 - 2003 (average) 12,545 0.63 15,852 0.80

IPM 2.1.9 12,000 15,000

44,856,223 IPM3.0 (base) 4,464 0.199 2,548 0.114

IPM3.0 - will do 4,464 0.199 2,548 0.114

IPM3.0 - may do 4,464 0.199 2,548 0.114 Table 6b. EGU Emissions for Midwest States (2018)

### 54



### On

- road Sources : For 2002, EMS was run by LADCO using VMT and MOBILE6 inputs supplied by the LADCO States. EMS was run to generate 36 days (weekda y, Saturday, Sunday for each month) at 36 km, and 9 days (weekday, Saturday, Sunday for June

– August) at 12 km. For

2005

, CONCEPT was run by a contractor (Environ) using transportation data (e.g., VMT and

vehicle speeds) supplied by the state and local p lanning agencies in the LADCO States and Minnesota for 24 networks. These data were first processed with T3 (Travel Demand Modeling [TDM] Transformation Tool) to provide input files for CONCEPT to calculate link

- specific, hourly

emission estimates (Envir o n, 2008 ) . CONCEPT was run with meteorological data for a July and January weekday, Saturday, and Sunday (July 15

– 17 and January 16

– 18). A sp atial plot of

emissions is provided in Figure 4 3

.

VOC Emissions NOx Emissions

Figure 4 3

. M otor v ehicle emissions for VOC (left) and NOx (right) for a July weekday (2005)



### Off

- road Sources : For 2002 and 2005, NMIM and NMIM2005, respectively, were run by

Wisconsin DNR. Additional off

- road sectors (i.e., commercial marine, aircraft, and railroads [MAR] ) were handled separately. Local data for agricultural equipment, construction equipment, commercial marine, recreational marine, and railroads were prepared by contractors (Environ

, 2004, and E.H. Pechan , 2004 ). For Base M, u pdated local data for railro ads and commercial

marine w ere prepared by a contractor (Environ , 2007 b, 2007 c )

. Table 7 compares the Base M

2005 and Base K 2002 emissions. Compared to 2002, t he new 2005 emissions reflect substantially lower commercial marine emissions and lower locomo tive NOx emissions.

Table 7 . Locomotive and c ommercial m arine e missions for the five LADCO States ( 2002 v. 2005)

Railroads (TPY) Commercial Marine (TPY)

2002 2005 2002 2005

VOC 7,890 7,625 1,562 828

CO 20,121 20,017 8,823 6,727

NOx 182,226 145, 132 64,441 42,336

PM 5,049 4,845 3,113 1,413

SO2 12,274 12,173 25,929 8,637

NH3 86 85

---- ----

### 55



### Area Sources : For 2002 and 2005, E MS was run by LADCO using data supplied by the LADCO

States to produce weekday, Saturday, and Sunday emissions for e ach month. For 2005,

s pecial attention was given to two source categories: industrial adhesi ve and sealant solvents (which were dropped from the inventory to avoid double

- counting) and outdoor wood boilers (which were added to the inventory)

.

Point Sourc es : For 2002 and 2005, EMS was run by LADCO using data supplied by the LADCO

States to produce weekday, Saturday, and Sunday emissions for each month. For EGUs, t he annual and summer season emissions were temporalized for modeling purposes using profiles prepared by Scott Edick (Michigan DEQ) based on CEM data

.

Biogenic s: For Base M, a contractor (Alpine) provided an updated version of the

CONCEPT/MEGAN biogenics model. Compared to the previous (EMS/BIOME) emissions,

t here is more regional isoprene using MEGA N compared to the BIOME estimates used for Base K (see Figure 4 4 ). Also, with the secondary organic aerosol updates to the CAMx air quality model, Base M includes emissions for monoterpenes and sesquiterpenes, which are pre

cursors of secondary PM 2. 5 

### organ ic carbon mass

.

Figure 4 4 . Isoprene emissions for Base M (left) v. Base K (right)



### Ammonia : For Base M, t he CMU

- based 2002 (Base K) ammonia emissions were projected to 2005 using growth factors from the Round 4 emissions modeling. These emissions were then adjusted by applying temporal factors by month based on the process

- based ammonia

emissions model

(Zhang, et al, 2005, and Mansell, et al, 2005) . A plot of average daily emissions by state and month is provided in Figure 4 5

. A spatial plot of emissi ons is provided in Figure 4 6 , which shows high emissions densities in the central U.S.

0 300 600 900 1200 1500 1800

Jan Fed March Apr May Jun Jul Aug Sept Oct Nov Dec

Illinois Indiana Iowa Michigan

Minnesota Ohio Wisconsin 

### Figure 4 5 . Average daily ammonia emissions for Midwest States by month (2005)

- (units: average daily emissions

– tons per day)

### 56 Figure 4 6

. Ammonia emissions for a July weekday (2005)

– 12 km modeling domain



### Canadian Emissions: For Base M, Scott Edick (Michigan DEQ) processed the 2005 Canadian National Pollutant Release Inventory , Version 1.0 (NPRI). Specifically, a subset of the NPRI

data (emissions and stack par ameters) relevant to the air quality modeling were reformatted

. The resulting emissions represent a significant improvement in the base year emissions.

A spatial plot of point source SO2 and NOx emissions is provided in Figure 4 7

. Additional plots

and emission reports are available on the LADCO website ( http://www.ladco.org/tech/emis/basem/canada/index.htm ).

Figure 4 7

. Canadian point source emissions for SO2 (left) and NOx (rig ht)

### 57



### Fires: For Base K, a contractor (EC/R , 2004 ) developed a 2001, 2002, and 2003 fire emissions inventory for eight Midwest States (five LADCO states plus Iowa, Minnesota, and Missouri), including emissions from wild fires, prescribed fires, and agricultu ral burns. Projected emissions were also developed for 2010 and 2018 assuming “no smoke management” and “optimal smoke management” scenarios. A n early mode l sensitivity run showed very little difference in modeled

PM 2. 5 

### concentrations. Consequently, the fire emissions were not included in subsequent modeling runs (i.e., they were not in the Base K or Base M modeling inventories)

.

Future Year Emissions : Complete e mission inventories were developed for several future years:

Base K

– 2009 , 2012, and 2018

, and Base M

– 2009 and 2018

. In addition, 2008 (Base K and Base M) and 2012 (Base M) proxy inventories were estimated based on the 2009 and 2018

data. (Note, the EGU emissions for the Base M 2012 inventory were based on EPA’s IPM3.0 modeling.)

S ource se ctor emission summaries for the base years and future years are shown in Figure 4 8

. Additional detail is provided in Table s 6 a and 6b

.

VOC NOx SO2

0 3000 6000 9000 12000

2002 2005 2009 2012 2015 2018 2002 2005 2009 2012 2015 2018 2002 2005 2009 2012 2015 2018 Area

On-road

Nonroad

Non-EGU

EGU



### Figure 4 8 . Base year and future year emissions for 5

- State LADCO Region (TPD, July wee kday)



### For on

- road, and nonroad, the future year emissions were estimated by models (i.e.,

EMS/ CONCEPT and NMIM , respectively). One adjustment was made to the 2009 and 2018 motor vehicle emission files prepared by Environ with CONCEPT. To reflect newer transportation modeling conducted by CATS for the Chicago area, emissions were increased by 9% in 2009 and 2018. The 2005 base year and adjusted 2009 and 2018 motor vehicle emissions are provided in Table 8

.

### 58 

### Table 8 . Motor Vehicle Emissions Produced by C ONCEPT Modeling (July weekday

– ton s per day )

Year State Sum of CO Sum of TOG Sum of NO x Sum of PM2.5 Sum of SO2 Sum of NH3 Sum of VMT

2005 IL 3,684.3 341.5 748.2 12.9 9.6 35.9 344,087,819.6

IN 3,384.9 282.0 541.1 8.9 11.1 25.7 245,537,231.9

MI 4,210 .3 351.9 722.0 12.4 13.9 35.3 340,834,025.9

MN 2,569.1 218.7 380.5 6.3 7.6 17.7 170,024,599.7

OH 6,113.4 679.8 933.6 16.2 18.8 36.5 360,521,068.6

WI 2,206.0 175.1 457.5 7.8 9.2 19.7 189,123,964.3

Total 22,168.0 2,049.0 3,782.9 64.5 70.2 170.8 1,65 0,128,709.9

2009 IL 2,824.4 268.0 527.8 10.1 4.2 38.9 372,132,591.1

IN 2,839.5 234.9 401.9 6.7 2.8 26.1 249,817,026.3

MI 3,172.0 269.2 500.9 9.2 4.0 37.1 356,347,010.5

MN 2,256.8 206.3 307.5 5.1 2.3 21.5 204,443,017.8

OH 4,619.2 423.7 69 3.5 11.8 4.7 39.5 387,428,127.2

WI 1,673.4 119.4 322.1 5.7 2.3 20.6 197,729,964.9

Total 17,385.3 1,521.5 2,753.6 48.7 20.3 183.6 1,767,897,737.8

2018 IL 2,084.7 151.5 200.7 6.3 3.7 43.1 413,887,887.3

IN 2,217.3 138.4 173.0 4.4 2.6 30.2 28 8,042,232.1

MI 2,434.3 163.5 204.1 5.9 3.6 40.5 388,128,431.8

MN 1,799.6 123.1 137.1 3.6 2.2 24.9 237,022,213.7

OH 3,361.5 242.5 274.1 6.8 4.0 43.1 421,694,093.4

WI 1,255.5 68.4 138.5 3.9 2.0 22.2 218,277,167.5

Total 13,152.9 887.5 1,127.5 30.8 18.1 203.9 1,967,052,025.8

### 59



### For EGUs

, future year emissions were based on IPM2.1.9 modeling completed by the RPOs in July 2005 Base K and IPM3.0 co mpleted by EPA in February 2007 for Base M. Several CAIR

scenarios were assumed:

Base K 1a: IPM2.1.9, wi th full trading and banking 1b: IPM2.1.9, with restricted trading (compliance with state

- specific emission budgets) and full trading 1d: IPM2.1.9, with restricted trading (compliance with state

- specific emission budgets)

Base M

5a: EPA’s IPM3.0 was assum ed as the future year base for EGUs. 5b: EPA’s IPM3.0, with several “will do” adjustments identified by the States. These adjustments should reflect a legally binding commitment (e.g., signed contract, consent decree, or operating permit).

5c: EPA’s IPM 3.0, with several “may do” adjustments identified by the States. These adjustments reflect less rigorous criteria, but should still be some type of public reality (e.g., BART determination or press announcement).



### For other sectors (area, MAR, and non

- EGU point sources)

, the future year emissions for the LADCO States were derived by applying growth and control factors to the base year inventory. These factors were developed by a contractor (E.H. Pechan , 2005 and E.H. Pechan, 2007 ).

For the non

- LADCO Sta tes, future year emission files were based on dat a from other RPOs

.

Growth factors were based initially on EGAS (version 5.0), and were subsequently modified (for select, priority categories) by examining emissions activity data. Due to a lack of informa tion on future year conditions, the biogenic VOC and NOx emissions, and all Canadian emissions were assumed to remain the constant between the base year and future years.

A “base” control scenario was prepared for each future year based on the following “ on the

books” controls :

On

- Highway Mobile Sources

• Federal Motor Vehicle Emission Control Program, low

- sulfur gasoline and ultra

- low sulfur diesel fuel

• I nspection

- m aintenance programs, including IL’s vehicle emissions tests (NE IL), IN’s vehicle

emissi ons testing program (NW IN), OH’s E

- check program (NE OH), and WI’ s vehicle inspection program (SE WI)

– note: a speci al emissions modeling run was done for the Cincinnati/Dayton area to

reflect the removal of the state’s E

- check p rogram and inclusion of l ow RVP gasoline

• Refo rmulated gasoline , including in Chicago

- Gary,

- Lake County, IL,IN; and Milwaukee, Racine, WI

Off

- Highway Mobile Sources

• Federal control programs incorporated into NONROAD model (e.g., nonroad diesel rule), plus the evaporative Large Spa rk Ignition and Recreational Vehicle standards

• Heavy

- duty diesel (2007) engine standard/Low sulfur fuel

• Federal railroad/locomotive standards

• Federal commercial marine vessel engine standards

Area Sources (Base M only)

• Consumer solvents

• AIM coatings

• Aeros ol coatings

• Portable fuel containers

Power Plants

• Title IV (Phases I and II)

• NOx SIP Call

• Clean Air Interstate Rule

### 60 Other Point Sources

• VOC 2

- , 4

- , 7

- , and 10

- year MACT standards

• C ombustion turbine MACT



### Other controls included in the modeling include: c onsent decrees (refineries, ethanol plants, and ALCOA) 9



### , NOx RACT in Illinois and Ohio 10 

### , and BART for a few non

- EGU sources in Indiana

and Wisconsin

.

For Base K, several additional control scenarios were considered:

Scenario 2

– “base” controls plus ad ditional controls recommended in LADCO White Papers for stationary and mobile sources

Scenario 3

– Scenario 2 plus additional White Papers for stationary and mobile sources

Scenario 4

– “base” controls plus additional candidate control measures under discussion by State Commissioners

Scenario 5

– “base” controls plus additional candidate control measures identified by the LADCO Project Team

3. 7 Basecase Mo d el ing Results The purpose of the basecase modeling is to evaluate model performance (i.e., assess the model's ability to reproduce the observed concentrations )

. The m odel performance evaluation

focus ed on the magnitude, spatial pattern, and temporal of modeled and measured

concentrations. This exercise wa s intended to assess whether, and to w hat degree, confidence in the model is warranted (and to assess whether model improvements are necessary)

.

Model performance w as assess ed by comparing modeled and monitored concentrations. Graphical (e.g., side

- by

- side spatial plots, time series plots, a nd scatter plots) and statistical analyses were conducted

. No rigid acceptance/rejection criteria w ere used for this study. Instead, the statisti cal guidelines recommended by EPA and other modeling studies (e.g., modeling by the other RPOs) w ere used to assess the re asonableness of the results

. The model performance results presented here describe how well the model replicates observed

ozone and PM 2. 5 

### concentrations after a series of iterative improvements to model inputs.

Ozone : Spatial plots are provi ded for high ozone period s in June 2002 and June 2005 (see Figure s 4 9a and 49b ). The plot s show that the model is doing a reasonable job of reproducing the magnitude, day

- to

- day variation, and spatial pattern of ozone concentrations. There is a tendency, however, to underestimate the magnitude of regional ozone levels. This is more apparent with the 2002 modeling; the regional concentrations in the 2005 modeling agree better with observations due to model and inventory improvements.

9 

### E.H. Pechan’s original control file included control factors for three sources in Wayne County, MI. These control factors were not applied in the regional

- scale modeling to avoid double

- co unting with the

State’s local

- scale analysis for PM2.5

10 

### NOx RACT in Wisconsin is included in the 2005 basecase ( and EGU “will do” scenario ). NOx RACT in Indiana was not included in the modeling inventory.

### 61

Figure 4 9a . Modeled (top) v. m onitored (bottom) 8

- h our o zone c oncentrations: June 20

– 25, 2002

### 62

Figure 4 9 b Modeled (top) v. m onitored (bottom) 8

- h our o zone c oncentrations: June 23

– 28 2005

### 63

Standard model performance statistics were g enerated for the entire 12 km domain, and by day and by monitoring site. The domain

- wide mean normalized bias for the 2005 base year is similar to that for the 2002 base year and is ge nerally within 30% (see Figure 50 ).

Fig ure 50 . Mean bias for summer 2005 (Base M) and summer 2002 (Base K)



### Station

- average metrics (over the entire summer) are shown in Figure 51 . The bias results further demonstrate the model’s tendency to underestimate absolute ozone concentrations.

Figure 51 . Mean bias (left) and gross error (right) for summer 2005

### 64

A limited 4 km ozone analysis was performed by LADCO to address the effect of grid spacing. For this modeling, 4 km grids were placed over Lake Michigan and the Detroit

- Cleveland area (see Figure 5 2 ). Model inputs included 4 km emissions developed by LADCO (consistent with Base K/Round 4) and the 4 km meteorology developed by Alpine Geophysics.

Figure 5 2

. 4 km grids for Lake Michigan region and Detroit

- Cleveland region



### Hourly time series plots were prepared for several monitors (see Figure 5 3 ). The results are
similar at 12 km and 4 km, with some site

- by

- site and day

- by

- day differences.

Figure 5 3 . Ozone time series plots for 12 km and 4 km modeling (June 17

- 29, 2002)

### 65

An additional diag nostic analysis was performed to assess the response of the modeling system

t o changes in emissions (Baker and Kenski, 2007) . Specifically, the 2002

- to

- 2005 change in observed ozone concentrations was compared to the change in modeled ozone concentrations

based on the 95 th 

### percentile (and above) concentration values for each monitor

. This analysis was also done with the inclusion of model performance criteria which eliminated poorly performing days (i.e., error > 35%). The results show good agreement in t he modeled and

monitored ozone concentration change s (e.g., ozone improves by about 9

- 10 ppb between 2002 and 2005 according to the model and the measurements)

– see Figure 54

. This provides further support for using the model to develop ozone control str ategies.

Figure 5 4 . Comparison of change in predicted and observed ozone concentrations (2002 v. 2005)

### 66

PM 2. 5 

### : Time series plots of the monthly average mean bias and annual fractional bias for Base M and Base K are shown in Figure 5 5 . As can be seen , Base M model performance for most species is fair (i.e., close to “no bias” throughout most of the year), with two main exceptions. First, the Base M and Base K results for organic carbon are poor, suggesting the need for more work on primary organic ca rbon emissions. Second, the Base M results for sulfate, while acceptable (i.e., bias values are within 35%) , are not as good as the Base K results (e.g., noticeable underprediction during the summer months)

.

Figure 5 5 . PM 2. 5 

### Model p erformance

- m onthly a verage m ean b ias and a nnual f ractional b ias for Base M (left column) and Base K (right column) 

## Base K

Base M

### 67

Two analyses were undertaken to understand sulfate model performance for 2005 :

• Assess Meteorological Influences: The MM5 model p erformance evaluation showed that

rainfall is over

- predicted by MM5 over most of the domain during the summer months (LADCO, 2007 c ). Because CAMx does not explicitly use the rainfall output by MM5, this
may or may not result in over

- predict ion sulfate wet deposition (and under

- predict ion of sulfate concentrations) . A sensitivity run was performed with no wet deposition for July, August, and September . The resulting model performance (see green line in Figure 5 6)

showed a noticeable difference from the ba secase (i.e., higher sulfate concentrations), and suggest s that further evaluation of MM5 precipitation fields may be warranted.

• Assess Emissions Influences: The major contributor to sulfate concentrations in the region is SO2 emitted from EGUs. The base case modeling inventory for EGUs is based on annual emissions, which were allocated to a typical weekday, Saturday, and Sunday by month using CEM

- based temporal profiles. A sensitivity run was performed using day

- specific emissions . The resulting model p erformance (see purple line in Figure 56 )

showed lit tle difference from the basecase.

Figure 5 6 . Monthly sulfate bias for Base M (MRPO EGU) v. two sensitivity analyses (Note: positive

values indicate over

- prediction, negative v alues indicate under

- prediction)



### To assess the effect of the wet deposition issue on future year modeled values, another sensitivity run was conducted with no wet deposition in Quarters 2

- 3 for the base year (2005) and 2018. The resulting future year val ues were only slightly different from the current base strategy run . In general, t he future year values (without wet deposition)

were a little higher (+ 0.1 5 ug/m 3 

### or less ) in the Ohio Valley and a little lower (

- .1 0 ug/m 3


### of less) in the Great Lakes regio n. This sensitivity run provides a bound for sulfate wet deposition issue in terms of the attainment test , given that having no wet deposition is

unrealistic. The results suggest that even with an improved wet deposition treatment, the Base M strategy re sults are not expected to change very much

.

Time series plots of daily sulfate, nitrate, elemental carbon, and organic carbon concentrations

for three Mi dwest ern locations are presented in Figure s 5 7 (2002) and 5 8 (2005) . These results

are consistent wit h the model performance statistics (i.e., good agreement for sulfates and nitrates and poor agreement [large underprediction] for organic carbon). Monthly Sulfate Bias (ug/m3)

-3 -2 -1 0 1 2 3 4 5

1 2 3 4 5 6 7 8 9 10 11 12

MRPO EGU MOG daily EGU No Wet Deposition

### 68 

## SULFATE NITRATE ORGANIC CARBON Seney

Detroit

Chicago

Figure 5 7 . Time series of sulfate, nitrate, and organic carbon at three Midwest sites for 2005

### 69 

## SULFATE NITRATE ORGANIC CARBON

Seney

Detroit

Chicago

Figure 5 8 . Time series of sulfate, nit rate, and organic carbon at three Midwest sit es for 2005

### 70

In summary, model performance for ozone and PM 2. 5 

### is generally acceptable and can be

characterized as follows:

Ozone

• Good agreement between modeled and monitored concentration for higher concentration levels (> 60 ppb)

– i.e., bias within 30 %

• Regional modeled concentrations appear to be underestimated in the 2002 base year, but show better agreement (with monitored data) in the 2005 base year due to model and inventory improvements.

• Day

- to

- day and hour

- to

- hour variation in and spatial patte rns of modeled

concentrations are consistent with monitored data

• Model accurately simulates the change in monitored ozone concentrations due to reductions in precursor emissions.

PM 2. 5



### • Good agreement in the magnitude of fine particle mass, but some spec ies are

overestimated and some are underestimated (during periods of the year when it is important)

• Sulfates: good agreement in the 2002 base year, but underestimated in

the summer in the 2005 base year due probably to meteorological factors

• Nitrates: slig htly overestimated in the winter in the 2002 base year, but good a greement in the 2005 base year as a result of model and inventory improvements

• Organic Carbon: grossly underestimated in the 2002 and 2005 base

years due likely to missing primary organic ca rbon emissions and, possibly, other factors (e.g., grid resolution and model chemistry).

• Temporal variation and spatial patterns of modeled concentrations are consistent

with monitored data

Several observations should be noted on the implications of thes e model performance findings on the attainment modeling presented in the following section. First, it has been demonstrated that model performance overall is acceptable and, thus, the model can be used for air quality planning purposes. Second, consisten t with EPA guidance, the model is used in a relative

sense to project future year values. EPA suggests that this approach “should reduce some of the uncertainty attendant with using absolute model predictions alone” ( EPA, 2007a)

. Furthermore, the attainm ent modeling is supplemented by additional information to provide a weight of evidence determination.

### 71 Section 4.0 Attainment Demonstration for Ozone and PM 2./ 5



### Air quality modeling and other information were used to determine whether existing (“on the books”) controls would be sufficient to provide for attainment of the NAAQS for ozone and PM 2.5 

### and if not, then what additional emission reductions would be necessary for attainment

. Traditionally, attainment demonstrations involved a “bright line” t est in which a single modeled

value was compared to the ambient standard. To provide a more robust assessment of
expecte d future year air quality, EPA’ s modeling guidelines call for consideration of supplemental information. This section summarizes the r esults of the primary (guideline) modeling analysis and a weight of evidence de termination based on the modeling results and other supplemental analyses

.

4.1 Future Year Modeling Results The purpose of the future year modeling is to assess the effectiven ess of existing and possible additional control programs. T he model was used in a relative sens e to project future year values, which are then compared to the standard to determine attainment / nonattainment. Specifically, t he modeling test consists of the following steps:

( 1 ) Calculate base year design values : F or ozone and PM 2.5 

### , the base year design values were derived by averaging the three 3

- year periods centered on the emissions base year:

2002 base year: 2000

- 2002, 2001

- 2003, and 2002

- 2004

2005 base year: 2003

- 2005, 2004

- 2006, and 2005

- 2007 11



### ( 2 ) Estimate the expected change in air quality: For each grid cell, a relative reduction factor (RRF) is calculated by taking the ratio of the future year and baseline modeling results.

( 3 ) Calculate future year valu es: For each grid cell (with a monitor), t he RRFs are multiplied by the base year design values to project the future year values

( 4 ) Assess attainment: Future year values are compared to the NAAQS to assess

attainment or nonattainment.

A comparison of the 2 002 and 2005 base year design values for ozone and PM 2. 5 

### is provided in Figure 5 9 . In general, the figure shows that the 2005 base year design values are much lower than the 2002 base year design values, especially for ozone.

11 

### A handful of source

- oriented PM2.5 monitors in Illinois and Indiana were excluded from the annual attainment test, because these monitors are not to be used to judging attainment of the annual standard.

### 72 Figure 5 9 . 2002 v. 2005 base year design values for ozone (top) and PM 2. 5 

### (bottom) 

## 2002 2005

Statis tical Summary

# Sites > NAAQS 93 9

Peak Value 99.0 ppb 90.0 ppb

Ave Exceedance Amount 7 ppb 2 ppb



## 2002 2005

Statistical Summary

# Sites > NAAQS 58 4 1

Peak Value 19.3 ug/m 3 

### 17.7 ug /m 3



### Ave Exceedance Amount 1.2 ug/m 3 

### 0.9 ug/m 3

### 73

O zone results are pr ovided for those grid cells with ozone monitors. The RRF calculation

consider s all nearby grid cells (i.e., 3x3 for 12 km modeling ) and a threshold of 85 ppb. (If there were less than 10 days above this value , then the threshold was lowered until either there were 10 days or the threshold reached 70 ppb.) PM 2.5 

### results are provid ed for those grid cells with FRM ( PM 2.5 

### - m ass) monitors. Spatial mapping w as performed to extrapolate PM 2.5 

### - speciation

data from STN and IMPROVE sites to FRM sites. RRF values for PM 2.5 

### were derived as a function of quarter and chemical species.

Additional, hot

- spot modeling will be performed b y the states for certain PM 2.5 

### nonattainment areas (e.g., Detroit, Cleveland, and Granite City) to address primary emissions from local point

sources which may not be adequately accounted for by the regional grid modeling. This modeling will consist of Ga ussian dispersion modeling (e.g., AERMOD) performed in

accordance with EPA’s modeling guidance (see Section 5.3 of the April 2007 guidance document). Further analyses will need to be undertaken to determine how to best combine the regional modeling and th e hot

- spot modeling. This could mean some adjustment to the model results presented in this document to reflect better the regional component.

The ozone and PM 2. 5 

### modeling results are provided in Appendix I for select monitors (high concentration sites) in the 5

- state region for the following future years of interest: 2008 (ozone only), 2009, 2012, and 2018. (Note, RRF values for ozone, and for PM 2. 5 

### by season and chemical species are also included in Appendix I for key monitoring sites.) A summary of the modeling results is provided in Table 9 (ozone) and Table 1 0 (PM 2. 5 

### ) , and s patial maps of the

Base M future year concent r ations are provided in Figures 60

- 6 2

.Key Sites 2018

Round 5 Round 4 Round 5 Round 4 Round 5 Round 4 Round 5
Lake Michigan Area

Chiwaukee 550590019 82.0 93.0 82.3 92.0 80.9 90.3 76.2

Racine 551010017 77.6 85.9 77.5 84.9 76.1 82.9 71.2
Milwaukee-Bayside 550190085 79.6 85.4 79.8 84.9 78.0 82.3 72.7
Harrington Beach 550890009 80.0 86.7 80.1 85.4 78.3 82.9 72.5

Manitowoc 550710007 81.3 80.3 80.8 78.9 78.6 76.3 72.5
Sheboygan 551170006 84.4 90.0 84.0 88.9 81.8 86.4 75.4

Kewaunee 550610002 78.9 82.5 78.1 81.0 75.9 79.1 69.9
Door County 550290004 84.8 83.6 83.9 81.8 81.5 79.3 74.7

Hammond 180892008 75.4 86.9 75.4 86.6 74.6 86.3 71.6
Whiting 180890030 77.0 77.0 76.2 73.1
Michigan City 180910005 74.2 87.4 73.9 86.5 72.5 85.4 68.1
Ogden Dunes 181270020 75.7 82.3 75.6 82.8 74.5 82.0 70.8

Holland 260050003 85.6 84.9 85.3 83.4 82.8 81.0 76.1

Jenison 261390005 77.9 78.7 77.1 77.6 74.5 75.5 68.7
Muskegon 261210039 80.8 82.7 80.5 81.5 78.0 79.4 71.9

Indianapolis Area

Noblesville 189571001 78.0 85.2 78.1 83.7 75.6 82.0 68.7

Fortville 180590003 73.9 85.1 73.9 83.8 71.4 82.1 65.1

Fort B. Harrison 180970050 74.8 84.8 75.1 83.7 73.2 82.4 69.1

Detroit Area

New Haven 260990009 82.7 86.3 81.4 85.3 80.2 83.5 76.1

Warren 260991003 82.5 84.3 81.3 83.3 80.7 81.9 77.6

Port Huron 261470005 79.0 80.5 77.5 79.1 75.5 77.0 70.9

Cleveland Area

Ashtabula 390071001 84.9 84.7 83.4 82.7 81.0 80.2 75.1
Geauga 390550004 75.7 90.3 74.7 88.8 72.7 86.2 67.3

Eastlake 390850003 82.8 84.2 81.9 82.8 80.5 80.6 76.2

Akron 391530020 79.3 83.0 78.1 81.4 75.6 78.5 68.7

Cincinnati Area

Wilmington 390271002 77.8 84.8 77.5 83.5 74.9 81.1 68.3
Sycamore 390610006 81.7 85.4 81.9 84.7 80.3 82.9 74.6

Lebanon 391650007 83.6 80.1 83.0 79.0 80.7 77.0 74.2

Columbus Area

London 390970007 75.4 79.9 75.0 78.4 72.6 76.5 66.3
New Albany 390490029 82.4 84.1 81.8 82.6 79.6 80.2 73.0

Franklin 290490028 77.0 77.7 75.9 76.5 74.1 74.7 69.0

St. Louis Area
W. Alton (MO) 291831002 82.4 86.1 81.0 85.2 78.6 84.0 74.9
Orchard (MO) 291831004 83.3 83.3 82.0 82.2 80.0 80.4 76.2
Sunset Hills (MO) 291890004 79.5 82.8 78.7 81.9 77.1 80.6 73.9
Arnold (MO) 290990012 78.7 78.4 77.2 77.4 75.6 75.8 72.0
Margaretta (MO) 295100086 79.8 84.0 79.3 83.4 77.9 82.5 74.4
Maryland Heights (MO) 291890014 84.5 83.4 81.7 78.1 2009 2012 2008 Table 9. Summary of Ozone Modeling ResultsCounty Site ID Site Round 5 Round4 Round 5 Round4 Round 5 Round4

Cook 170310022 Chicago - Washington HS 14.1 14.8 14.0 14.6 13.9 14.4

Cook 170310052 Chicago - Mayfair 14.4 15.8 14.2 15.5 13.9 15.0

Cook 170310057 Chicago - Springfield 13.9 14.5 13.8 14.3 13.7 14.1

Cook 170310076 Chicago - Lawndale 13.8 14.5 13.7 14.3 13.6 14.1

Cook 170312001 Blue Island 13.7 14.5 13.6 14.3 13.4 14.1

Cook 170313301 Summit 14.2 14.8 14.0 14.6 13.9 14.4

Cook 170316005 Cicero 14.4 15.3 14.3 15.1 14.2 14.9

Madison 171191007 Granite City 15.1 16.0 14.9 15.8 14.3 15.5

St. Clair 171630010 E. St. Louis 14.1 14.9 13.9 14.7 13.4 14.5

Clark 180190005 Jeffersonville 13.8 15.5 13.7 15.0 13.4 14.4

Dubois 180372001 Jasper 12.4 13.8 12.2 13.5 11.8 13.0

Lake 180890031 Gary 13.0 12.8 12.4

Marion 180970078 Indy-Washington Park 12.8 14.5 12.6 14.2 12.0 13.7

Marion 180970083 Indy- Michigan Street 13.4 14.8 13.1 14.9 12.6 14.0

Wayne 261630001 Allen Park 13.0 14.5 12.8 14.1 12.4 13.3

Wayne 261630015 Southwest HS 14.2 15.8 13.9 15.3 13.5 14.4

Wayne 261630016 Linwood 13.1 14.1 12.8 13.7 12.5 13.0

Wayne 261630033 Dearborn 15.8 17.7 15.5 17.1 15.1 16.1

Wayne 261630036 Wyandotte 13.1 15.1 12.8 14.7 12.5 13.9

Butler 390170003 Middleton 13.5 14.2 13.2 13.7 12.8 13.1

Butler 390170016 Fairfield 13.1 13.5 12.9 12.9 12.5 12.2
Cuyahoga 390350027 Cleveland-28th Street 13.5 14.4 13.2 13.8 12.7 12.9
Cuyahoga 390350038 Cleveland-St. Tikhon 15.2 16.1 14.8 15.4 14.3 14.4
Cuyahoga 390350045 Cleveland-Broadway 14.4 14.6 14.0 14.0 13.5 13.1
Cuyahoga 390350060 Cleveland-GT Craig 15.0 15.3 14.6 14.7 14.1 13.7
Cuyahoga 390350065 Newburg Hts - Harvard Ave 14.0 14.1 13.6 13.5 13.1 12.6

Franklin 390490024 Columbus - Fairgrounds 12.9 14.6 12.6 14.0 12.0 13.0

Franklin 390490025 Columbus - Ann Street 12.7 14.1 12.4 13.5 11.9 12.5

Franklin 390490081 Columbus - Maple Canyon 11.7 14.0 11.4 13.4 10.9 12.5

Hamilton 390610014 Cincinnati - Seymour 14.5 15.5 14.3 14.8 13.8 14.0

Hamilton 390610040 Cincinnati - Taft Ave 12.8 13.6 12.6 13.0 12.2 12.3

Hamilton 390610042 Cincinnati - 8th Ave 14.0 14.6 13.8 14.0 13.4 13.2

Hamilton 390610043 Sharonville 12.9 13.6 12.7 13.0 12.3 12.2

Hamilton 390617001 Norwood 13.4 14.2 13.2 13.6 12.8 12.8

Hamilton 390618001 St. Bernard 14.7 15.2 14.4 14.6 14.0 13.8

Jefferson 390810016 Steubenville 12.8 16.3 12.5 15.9 12.7 16.2

Jefferson 390811001 Mingo Junction 13.5 15.5 13.2 15.0 13.4 15.3

Lawrence 390870010 Ironton 12.8 14.2 12.5 13.7 12.3 13.2

Montgomery 391130032 Dayton 13.2 13.7 12.9 13.2 12.4 12.3

Scioto 391450013 New Boston 12.1 15.4 11.9 14.8 11.6 14.2

Stark 391510017 Canton - Dueber 14.0 15.0 13.6 14.3 13.3 13.6

Stark 391510020 Canton - Market 12.6 13.6 12.3 13.0 11.9 12.2

Summit 391530017 Akron - Brittain 13.0 14.4 12.7 13.6 12.3 12.9

Summit 391530023 Akron - W. Exchange 12.3 13.6 12.0 13.0 11.5 12.2 2009 2012 2018 Table 10. Summary of PM2.5 Modeling Results

### 76 2005 (observed) 2009

2012 2018

Figure 60 . Observed base year and projected future year design values for ozone

– Base M

### 77 2005 (observed) 2009

2012 2018

Figure 61 . Observed base year and projected future year design values for PM 2. 5 

### (annual average)

– Base M

### 78 2005 (observed) 2009

2012 2018

Figure 6 2 . Observed base year and projected future year design values for PM 2. 5 

### (24

- hr average)

- Base M

### 79

The number of monitors with design va lues above the standard are as follows:

Table 11 . Number of sites above standard

Ozone (8 hour: 85 ppb)

State 2002 2005 2009 2012 2018

BaseK Base M BaseK Base M BaseK Base M BaseK Base M

IL 3 0 0 0 0 0 0 0

IN 22 0 0 0 0 0 0 0

MI 15 3 1 1 0 0 0 0

OH 40 4 1 0 1 0 0 0

WI 13 2 4 0 3 0 1 0

Total 93 9 6 1 4 0 1 0

PM2.5 (Annual: 15 ug/m 3 

### )

State 2002 2005 2009 2012 2018

BaseK Base M BaseK Base M BaseK Base M BaseK Base M

IL 11 7 3 1 3 0 2 0

IN 10 6 1 0 1 0 0 0

MI 6 2 3 1 2 1 0 0

OH 31 2 6 7 1 4 0 1 1

WI 0 0 0 0 0 0 2 0

Total 58 41 14 3 10 1 5 1



### The modeling results above reflect the “base” controls identified in Sect ion 3.6 , with EGU

emissions based on IPM modeling (i.e., Round 4

– IPM2.1.9, and Round 5

– IPM3.0). In addition, two sets of alternative future year EGU emissions were examined in Round 5. First, alternative control assumptions were provided for several facilities by the states (i.e., “will do” and “may do” scenarios). In general, these scenarios produced a small change in future year

ozone and PM 2. 5 

### concentrations (i.e., about 0.1 ug/ m 3 

### for PM 2. 5 

### and 0. 1

- 0.2 ppb for ozone). Second, EPA suggested adjust ments to the 2010 IPM emissions to reflect 2009 conditions. The revised (2009) SO2 emissions represent a 5

- 6% increase in domainwide SO2 emissions. The increased SO2 emissions result in slightly greater annual average PM 2. 5 

### concentrations (on the

order o f 0.1

– 0.2 ug/m 3 

### ) , but do not produce any new residual nonattainment areas.

### 80

The limited 4 km ozone modeling (based on Base K) performed by LADCO included a future year analysis for 2009. The figure below show s the 2009 values with 12 km and 4 km grid

sp acing for the LADCO modeling and similar modeling conducted by a stakeholder group (M idwest Ozone Group ).

70 75 80 85 90 95

Cook Lake

LaPorte Porter Allegan Muskegon Kenosha

Milwaukee Ozaukee Racine

Sheboygan

MOG-12km MOG-4km LADCO-12km LADCO-4km

70 75 80 85 90 95

New Haven Warren Geauga Eastlake Portage Akron

MOG-12km MOG-4km LADCO-12km LADCO-4km 

### Figure 63 . Future y ear (2009) v alues for Lake Michigan area (top) and D etroit

- Cleveland r egion (bottom)



### T hese results show that the 12 km and 4 km values are similar, with the most notable changes in northwestern Indiana and northeastern Illinois (e.g., 4 km values are as much as 4 ppb lower than 12 km values). The differences in the southern part of the Lake Michigan area are plausible, given the tight emissions gradient there (i.e., finer grid resolution appears to provide more appropriate representation).

In light of these findings, 12 km grid spacing can continue to be used for ozone modeling, but

the Base K/Round 4 resu lts for northwester n Indiana /northeastern Illinois should be viewed with caution (i.e., p robably 1

– 4 ppb too high).

### 81

In summary, the ozone modeling provide s the following information for the nonattainment areas in the region (see Table 1 2 ):

Table 1 2 . Ozone Nonattain ment Ar eas in the LADCO Region (as of December 31 , 2007)

Area Name Category Number of

C ounties Attainment

D eadline

Detroit

- Ann Arbor, MI Marginal 8 2007

Chicago

- Gary

- Lake County, IL

- IN Moderate 10 2010

Cleveland

- Akron

- Lorain, OH Moderate 8 2010

Milwaukee

- Racine, WI Moderate 6 2010

Sheboygan, WI Moderate 1 2010

St Louis, MO

- IL Moderate 4 2010

Allegan Co, MI Subpart 1 1 2009

Cincinnati

- Hamilton, OH

- KY

- IN Subpart 1 6 2009

Columbus, OH Subpart 1 6 2009

Door Co, WI Subpart 1 1 2009

Kewaunee Co, WI Subpart 1 1 2009

Manitowoc Co, WI Subpart 1 1 2009

53



### Marginal Areas (2007 attainment date): No modeling was conducted for the 2006 SIP p lanning year. Rather, 2005

– 2007 air quality data are available to determine attainment.

Basi c (Subpart 1) Areas (2009 attainment date): The modeling results for the 2008 SIP planning year show :

• Base K: all areas in attainment, except Cincinnati and Ind ianapolis

• Base M: all areas in attainment, except Holland (Allegan County)

Moderate Areas (2010 attainment date): The modeling results for the 2009 SIP planning year

show:

• Base K: all areas still in nonattainment

• Base M: all areas in attainment

The PM 2. 5 

### modeling results show:

• Base K: all areas in attainment, except for Chicago, Cincinnati, Cleveland, Detroit, Granite City (IL), Louisville, Portsmouth (OH), and Steubenville

• Base M: all areas in attainment, except for Cleveland, Detroit, and Granite City (IL)

With respect to the new lower 8

- hour ozone standard, the modeling about 30 sites in 2012 and

5 sites in 2018 with design values greater than 7 5 ppb. With respect to the new lower 24

- hour

PM 2. 5 

### standard, the modeling shows 13 sites in 2012 and 10 in 2018 with design values greater

t han 35 ug/m 3



### .

### 82

4.2 Supplemental Analyses EPA’s modeling guidelines recommend that attainment demonstrations consist of a primary (guideline) modeling analysis and supplemental analyses. Three basic types of sup plemental

an alyses are recommend ed:

• additional modeling

• analyses of trends in ambient air quality and emissions, and

• observational models and diagnostic analyses

Furthermore, according to EPA’s guid elines , if the future year model ed values are “close” to the

standar d (i.e., 82

– 87 ppb for ozone and 14.5

– 15.5 ug/m 3 

### for PM 2. 5 

### ) , then the results of the primary modeling should be reviewed along with the supplemental information in a “weight of evidence” assessment of whether each area is likely to achieve timely attai nment.

A WOE determination for ozone and PM 2. 5 

### is provided in the follow ing sections . Special attention is given to the following areas with future year modeled values that exceed or are “close” to the ambient standard (see Appendix I) :

Ozo ne PM2.5 Lake Michigan area Chicago, IL Cleveland, OH Cleveland, OH Cincinnati, OH Cincinnati, OH Granite City, IL Detroit, MI



### 4.3 Weight

- of

- Evidence De termination for Ozone

The WOE determination for ozone consists of the prim ary modeling and other supplemental analyses (some of which were discussed in Section 2)

. A summary of this information is provided below.

Primary ( Guideline

) Modeling : The guideline modeling is presented i n Section 4.1. Key findings

from th is modeling include:

• B ase M regional modeling shows attainment by 2008 and 2009 at all sites, except Holland (MI), and attainment at all sites by 2012.

• B ase K modeling results reflect generally higher future year values, and show more

sites in nonattainm ent compared to the Base M modeling. The difference in the two modeling analyses is due mostly to lower base year design values in Base M.



## • Base K and Base M modeling analyses are considered “SIP quality”, so the

attainment demonstration for ozone should reflect a weight

- of

- evidence approach, with consideration of monitoring based information.

• Base M mode ling also shows that the proposed lower 8

- hour standard wi ll not be

met at many sites , even by 2018, with existing controls.

A dditional

Modeling : Four a dditional modeling analyses were considered: (1) re

- examination of the primary modeling to estimate attainment probabilities, (2) remodeling with different assumptions, ( 3 ) an unmonitored area analysis, and ( 4 ) EPA’s latest regional ozone modeling.

Each o f these analyses is described below.

### 83

First, the primary modeling results (which were initially processed using EPA’s attainment test)

were re

- examined to estimate the probability of attaining the ozone standard (Lopez, 2007, and LADCO, 2008b) . Seven estim ates of future year ozone concentrations were calculated based

on model

- based RRFs and appropriate monitor

- based concentrations for each year between

2001 and 2007. R RF values for 2001, 2003, 2004, 2006, and 2007 were derived based on the

2002 and 2005 mo deling results . M onitor

- based concentrations reflect 4 th 

### high values, design values, or average of three design values centered on the year in question . The probability of

attainment was de termined as the percentage of these seven estimates below the sta ndard. The results indicate that sites in the Lake Michigan area (Chiwaukee, Sheboygan, Holland, Muskegon), Cleveland (Ashtabula), and St. Louis (W Alton) have a fairly low probability of attainment by 2009 (i.e., about 50% or less).

Second , the primary modeling analysis was re done with different types of assump tions for calculating base year design values (i.e., using the 3

- year period centered on base year , and using the h ighest 3

- year period that includes the base year ), and for calculating RRFs (i.e., using all days with base year modeled value > 70 ppb , and using all days with base year modeled value > 85 ppb, with at least 10 days an d “acceptable” model performance )

. The results for several high concentration sites are presented in Table s 1 3 a and 13 b for 2009. The different modeling assumptions produce eight estimates of future year ozone concentrations. The highest estimates are associated with base year design values representing the 3

- year average for 2001

- 2003, and the lowest estimates are asso ciated with base year design values representing the 3

- year average 2004

- 2006. The different RRF approaches produce little change in future year ozone concentrations. This suggests that future year concentration

estimates are most sensitive to the choice of the base year and the methodology used to derive the base year design values.

Third

, EPA’s modeling guidelines recommend that an “unmonitored area analysis” be included as a supplemental analysis , particularly in nonattainment areas where the monitori ng network just meets o r minimally exceeds the size of the network required to report data to EPA’s Air Quality System . The purpose of this analysis is to identify areas where future year values are predicte d to be greater than the NAAQS.

Based on exam ination of the spatial plots in Figures 4 9a and 49b , the most notable areas of high modeled ozone concentrations are over the Great Lakes. Over

- water monitoring, however, is not required by EPA 12



### . A cursory analysis of unmonitored areas for ozone was perf ormed by

LADCO using an earlier version of the 2002 base year modeling (i.e, Base I) (Baker, 2005). Base year and future year “observed” values were derived for unmonitored grid cells using the absolute modeled concentrations (in all grid cells) and the o bserved values (in monitored grid cells). A spatial map of the estimated 2009 values is provided in Figure 6 4 . As can be seen, there are very few (over land) grid cells where additional monitors may be desirable

. This indicates that the current modeling analysis , which focuses on monitored locations, is addressing areas o f high ozone throughout the region.

12 

### Air quality measurements over Lake Michigan were collected by LADCO previously to understand

oz one transport in the area (see, for example, Figure 5). Due to cut

- backs in USEPA funding, however,

these measurements were discontinued in 2003.

### 84 Table 1 3 a. Primary and Additional Ozone Modeling Results

– Lake Michigan and Cleveland Areas (2009)

2009 Modeling Results Lake Michigan Area C leveland Area

Chiwaukee Harr.Beach Sheboygan DoorCounty Holland Hammond MichiganCity Ashtabula Geauga Eastlake

550590019 550890009 551170006 550290004 260050003 180892008 180910005 390071001 390550004 390850003

Attainment Test (based on EPA guidan ce

- 2002 baseyear) Base Year Design Value (average of three 3

- year periods) 98.3 93.0 97.0 91.0 94.0 88.3 90.3 95.7 99.0 92.7

RRF (all days > 85 ppb, or at least 10 days) 0.935 0.918 0.916 0.899 0.888 0.980 0.958 0.865 0.897 0.894

Future Year Design Value 91.9 85.4 88.9 81.8 83.5 86.5 86.5 82.8 88.8 82.9

Attainment Test (based on EPA guidance

- 2005 baseyear) Base Year Design Value (average of three 3

- year periods) 84.7 83.3 88.0 88.7 90.0 77.7 77.0 89.0 79.3 86.3

RRF (all days > 85 ppb, or at least 10 days) 0.972 0.961 0.955 0.946 0.948 0.971 0.960 0.937 0.942 0.949

Future Year Design Value 82.3 80.1 84.0 83.9 85.3 75.4 73.9 83.4 74.7 81.9

Weight of Evidence (alternative approaches

- 2002bas eyear)

Alt 1

- Base Year Des. Value (3

- year period centered on 2002) 101.0 98.0 100.0 94.0 97.0 90.0 93.0 99.0 103.0 95.0

Alt 2

- Base Year Des. Value (Highest 3

- year period including 2002 ) 101.0 98.0 100.0 94.0 97.0 92.0 93.0 99.0 103 9 5.0

RRF (all days > 85 ppb, or at least 10 days) 0.935 0.918 0.916 0.899 0.888 0.980 0.958 0.865 0.897 0.894

Alt 1

- Future Year Projected Value 94.4 90.0 91.6 84.5 86.1 88.2 89.1 85.6 92.4 84.9

Alt 2

- Future Year Projected Value 94. 4 90.0 91.6 84.5 86.1 90.2 89.1 85.6 92.4 84.9

Alt 1

- RRF (all days > 70 ppb) 0.933 0.918 0.912 0.907 0.893 0.969 0.947 0.876 0.907 0.900

Alt 1

- Future Year Projected Value 94.2 90.0 91.2 85.3 86.6 87.2 88.1 86.7 93.4 85.5

Alt 2

- Future Year Pro

jected Value 94.2 90.0 91.2 85.3 86.6 89.1 88.1 86.7 93.4 85.5

Alt 2

- RRF (all days > 85 ppb, or at least 10 days; with acceptable model performance) 0.945 0.904 0.910 0.904 0.887 0.976 0.964 0.866 0.896 0.894

Alt 1

- Future Year Projected Value 95 .4 88.6 91.0 85.0 86.0 87.8 89.7 85.7 92.3 84.9

Alt 2

- Future Year Projected Value 95.4 88.6 91.0 85.0 86.0 89.8 89.7 85.7 92.3 84.9

Weight of Evidence (alternative approaches

- 2005baseyear)

Alt 1

- Base Year Des. Value (3

- y ear period centered on 2005) 83.0 79.0 86.0 86.0 88.0 76.0 76.0 86.0 77.0 86.0

Alt 2

- Base Year Des. Value (Highest 3

- year period including 2005) 86.0 88.0 89.0 90.0 93.0 79.0 78.0 91.0 86.0 89.0

Alt 1

- Future Year Projected Value 80.7 75.9 82.1 8 1.4 83.4 73.8 73.0 80.6 72.5 81.6

Alt 2

- Future Year Projected Value 83.6 84.6 85.0 85.1 88.2 76.7 74.9 85.3 81.0 84.5

### 85

Table 1 3 b. Primary and Additional Ozone Modeling Results

– Cincinnati, Columbus, St. Louis, Indianapolis, and Detroit (2009)

2009 M odeling Results Cincinnati Area Columbus St. Louis Area Indianapolis Area Detroit Area

Wilmington Lebanon Sycamore New Albany W. Alton OrchardFarm Noblesville Fortville New Haven

390271002 39165007 390610006 390490029 291831002 291831004 1 80571001 18059003 260990009

Attainment Test (based on EPA guidance

- 2002 baseyear) Base Year Design Value (average of three 3

- year periods) 94.3 90.7 90.7 94.0 90.0 90.0 93.7 91.3 92.3

RRF (all days > 85 ppb, or at least 10 days) 0.8 85 0.908 0.938 0.888 0.947 0.914 0.894 0.918 0.924

Future Year Design Value 83.5 82.4 85.1 83.5 85.2 82.3 83.8 83.8 85.3

Attainment Test (based on EPA guidance

- 2005 baseyear) Base Year Design Value (average of thre e 3

- year periods) 82.3 87.7 84.3 86.3 86.3 87.0 83.3 78.7 86.0

RRF (all days > 85 ppb, or at least 10 days) 0.941 0.947 0.967 0.947 0.938 0.942 0.945 0.947 0.947

Future Year Design Value 77.4 83.1 81.5 81.7 80.9 82.0 78.7 74.5 81.4

Weight of Evidence (alternative approaches

- 2002baseyear)

Alt 1

- Base Year Des. Value (3

- year period centered on 2002) 96.0 92.0 93.0 95.0 91.0 92.0 96.0 94.0 97.0

Alt 2

- Base Year Des. Value (Highest 3

- year period including 2002 ) 96.0 92.0 93.0 96.0 91.0 92.0 96.0 94.0 97.0

RRF (all days > 85 ppb, or at least 10 days) 0.885 0.908 0.938 0.888 0.947 0.914 0.894 0.918 0.924

Alt 1

- Future Year Projected Value 85.0 83.5 87.2 84.4 86.2 84.1 85.8 8 6.3 89.6

Alt 2

- Future Year Projected Value 85.0 83.5 87.2 85.2 86.2 84.1 85.8 86.3 89.6

Alt 1

- RRF (all days > 70 ppb) 0.885 0.914 0.940 0.901 0.945 0.911 0.912 0.907 0.918

Alt 1

- Future Year Projected Value 85.0 84.1 87.4 85.6 86.0 83 .8 87.6 85.3 89.0

Alt 2

- Future Year Projected Value 85.0 84.1 87.4 86.5 86.0 83.8 87.6 85.3 89.0

Alt 2

- RRF (all days > 85 ppb, or at least 10 days; with acceptable model performance) 0.880 0.911 0.940 0.886 0.951 0.913 0.894 0.916 0.935

Alt 1

- Future Year Projected Value 84.5 83.8 87.4 84.2 86.5 84.0 85.8 86.1 90.7

Alt 2

- Future Year Projected Value 84.5 83.8 87.4 85.1 86.5 84.0 85.8 86.1 90.7

Weight of Evidence (alternative approaches

- 2005baseyear)

Alt 1

- Base Year Des. Value (3

- year period centered on 2005) 80.0 86.0 81.0 84.0 85.0 86.0 80.0 76.0 82.0

Alt 2

- Base Year Des. Value (Highest 3

- year period including 2005) 85.0 89.0 86.0 88.0 89.0 89.0 87.0 81.0 90.0

Alt 1

- Future Yea r Projected Value 75.3 81.4 78.3 79.5 79.7 81.0 75.6 72.0 77.7

Alt 2

- Future Year Projected Value 80.0 84.3 83.2 83.3 83.5 83.8 82.2 76.7 85.2

### 86 Fig ure 64. Estimated Future Year Values (unmonitored grid cells)



### Finally, EPA’s latest regional ozone modeling was considered as corroborative information

. This modeling was performed as part of the June 2007 proposal to revise the ozone standard (EPA, 2007 b ). EPA applied the CMAQ model with 2001 meteorology to first estimate ozone

levels in 2020 based on the current standard and national rules in effect or proposed (i.e., the baseline), and then to evaluate strategies for attaining a more stringent (70 ppb) primary standard. Baseline (2020) ozone levels were predicted to be below the current stan dard in 481

of the 491 counties with ozone monitors . Of the 10 counties predicted to be above the standard, there is one county in the LADCO region (i.e., Kenosha County, WI at 86 ppb ) . This result is consistent with LADCO’s Base K modeling for 2018 (i.e ., Kenosha County, WI at 86.7 ppb), which is not surprising given that EPA’s modeling and LADCO’s Base K modeling have a similar base year (2001 v. 2002).

Analysis of T

rends : EPA’s modeling guid elines note that while air quality models are generally

the m ost appropriate tools for assessing the expected impacts of a change in emissions, it may also be possible to extrapolate future trends based on measured historical trends of air quality and emissions. To do so, USEPA’s guidance suggests that ambient tren ds should first be normalized to account for year

- to

- year variations in meteorological conditions ( EPA, 2002)

. Meterologically

- adjusted 4 th 

### high 8

- hour ozone con centrations were derived using th e air quality

– meteorological regression model developed by EPA (i.e., Cox method

– see Section 2.1 ).

The historical trend in these met

- adjusted ozone concentrations were extrapolated to estimate future year ozone concentrations based on historical and projected trend s in precursor

emissions. Both VOC and NOx em issions affect ozone concentrations. Given that observation

based methods show that urban areas in the region are generally VOC

- limited and rural areas in the region are NOx

- limited (see Section 2.1) , urban VOC emissions and regional NOx

emissions are con sidered important. The trends in urban VOC and regional NOx emissions were calculated to produce appropriate weighting factors.

The resulting 2009 and 2012 ozone values are provided in Figure 65, along with the primary and alternative modeling ozone va lues for key sites in the Lake Michigan, Cleveland, and

Cincinnati areas. The results reflect a fairly wide scatter, but, on balance, the supplemental information is supportive of the primary modeling results (i.e., sites in the Lake Michigan area

and Cle veland are expected to be close to the standard).

### 87 Sheboygan

75 80 85 90 95 100

2002 2005 2009 2012 

### Holland

75 80 85 90 95 100

2002 2005 2009 2012



### Sycamore

75 80 85 90 95 100

2002 2005 2009 2012 

### Ashtabula

75 80 85 90 95 100

2002 2005 2009 2012



### Figure 6 5 . Estimates of Future Year Ozone Concentrations

– Lake Michigan Area (Sheboygan and Holland), Cincinnati (Sycamore ), and Cleveland (Ashtabula)

Note: Primary (guideline) modeling valu e s (Base K and Base M results) are represented by large red diamond s , additional modeling values by small black circles, and trends

- based value s by small pink squares

### 88



### Observation al Models and Diagnostic Analyses: The observation

- based modeling (i.e.,

MAPPE R) is presented in Section 3. The key findings from this modeling are that most urban

areas are VOC

- limited and rural areas are NOx

- li mited.

The primary diagnostic analysis is source apportionment modeling with CAMx t o provide more quantitative informati on on source region (and source sector) impacts (Baker, 2007a)

. Specifically, the model estimated the impact of 18 geographic source regions (which are identified in Figure 6 6 ) and 6 source sectors (EGU point, non

- EGU point, on

- road, off

- road,
area, and b iogenic sources) at o zone monitoring site s in the region.

Figure 6 6 . Source regions (left) and key monitoring sites (right) for ozone modeling analysis



### Modeling results for 2009 (Base M ) and 20 12 ( Base K ) are provided in Appendix II for several key monitoring sites. For each monitoring site, there are two graphs: one showing sector

- level contributions, and one showing source region and sector

- level contributions in terms of percentages. (Note, in the sector

- level graph, the contribution s from NOx emissions are shown in blue, and from VOC emissions in green. )

The sector

- level results (see, for example, Figure 6 7 ) show that on

- road and nonroad NOx emissions generally have the largest contributio ns at the key monitor locations (> 15% each).

EGU and non

- EGU NOx emissions are also important contributors (> 10% each). The source group contributions vary by receptor location due to emissions inventory differences.

Figure 6 7 . Source

- sector res ults for Holland (left) and Ashtabula (right) monitors

– 20 09 (Base M) Kenosha County

Cook County

Indianapolis

Holland

St. Louis Detroit

Cincinn ati Door County

Cleveland

### 89



### The source region results (see, for example, Figure 68) show that while nearby areas generally have the highest impacts (e.g., the northeastern IL/northwestern IN/southeastern WI

nonatt ainment area contributes 25

- 35 % to high sites in the Lake Michigan area, and Cleveland

nonattainment counties contribute 20

- 25 % to high sites in northeastern Ohio)

, there is an even larger regional impact (i.e., contribution from other states).

Figure 6 8 . Source

- region results for Holland (left) and Ashtabula (right) monitors

– 20 09 (Base M)



### Summary : Air quality modeling and other supplemental analyses were performed to estimate future year ozone concentrations. Based on this information, the followi ng general conclusions

can be made:

• E xisting (“on the books”) controls are expected to produce significant improvement in ozone air quality

.

• The choice of the base year affects the future year model projections. A key difference between the base years o f 2002 and 2005 is meteorology. As noted
above, 2002 was more ozone conducive than 2005. The choice of which base year to use as the basis for the SIP is a policy decision (i.e., how much safeguard to incorporate).

• Most sites are expected to meet the cu rrent 8

- hour standard by the applicable attainment date, except, for sites in western Michigan and, possibly, in eastern

Wisconsin and northeastern Ohio.

• Current monitoring data show significant nonattainment in these areas (e.g., peak design values on th e order of 90

– 93 ppb). It is not clear whether sufficient emission reductions will occur in the next couple of years to provide for

attainment.

• Attainment by the applicable attainment date is dependent on actual future year meteorology (e.g., if the we ather conditions are consistent with [or less severe than] 2005, then attainment is likely) and actual future year emissions (e.g., if the emission reductions associated with the existing controls are achieved, then attainment is likely). On the other han d, if either of these conditions is not met, then attainment may be less likely.

### 90



### 4.3 Weight

- of

- Evidence Determination for PM 2.5 

### The WOE determination for PM 2. 5 

### consists of the primary modeling and other supplemental analyses. A summary of this informati on is provided below.

Primary ( Guideline

) Modeling : The results of the guideline modeling are presented in Section 4.1. Key findings from this modeling include:

• Base M regional modeling shows attainment by 2009 at all sites, except Detroit, Cleveland, a nd Granite City, and attainment at all sites by 2012, except for Detroit and Granite City.

T he regiona l modeling for PM 2. 5 

### does not reflect any air quality benefit expected from local controls. States are conducting local

- scale analyses and will us e thes e results, in conjunction with the regional

- scale modeling, to support their attainment

demonstrations for PM 2. 5



### • Base K modeling results reflect generally higher future year values, and show more sites in nonattainment in 2009 and 2012 compared to the Bas e M modeling. The difference in the two modeling analyses is due mostly t o lower base year design

values in Base M.



## • Base K and Base M modeling analyses are considered “SIP quality”, so the

attainment demonstration for PM 2. 5 

### should reflect a weight

- of

- evi dence approach, with consideration of monitoring based information.

• Base M mode ling also shows that the new PM 2. 5 

### 24

- hour standard wi ll not be met at many sites , even by 2018, with existing controls.

A dditional Modeling : EPA’s latest regional PM 2. 5 

### model ing was considered as corroborative

information . This modeling was performed as part of the September 2006 revision to the PM 2. 5 

### standard ( USEPA, 2006)

. EPA applied the CMAQ model with 2001 meteorology to estimate

PM 2. 5 

### levels in 20 15 and 2020 first with national rules in effect or proposed , and then with additional controls to attain the current standard (15 ug/m 3 

### annual/65 ug/m 3 

### daily). Additional analyses were performed to evaluate strategies for attaining more stringent standards in 2020 (15/35, and 14/35) . Baseline (20 15 ) PM 2. 5 

### levels were predicted to be above the current

standard in four counties in the LADCO region : Madison County, IL at 15.2 ug/m 3 

### , Wayne County, MI at 17.4, Cuyahoga County, OH at 15.4 , and Scioto County, OH at 15.6. These

resul ts are consistent with LADCO’s Base K modeling for 2012/ 2018, whic h is not surprising given that EPA’s modeling and LADCO’s Base K modeling have a similar base year (2001 v. 2002).

Observation al Models and Diagnostic Analyses : The observation

- based modeli ng (i.e., application of thermodynamic equilibrium models) is presented in Section 3. The key findings from this modeling are that PM 2. 5 

### mass is sensitive to reductions in sulfate , nitric acid, and

ammonia concentrations . Even though sulfate reductions c ause more ammonia to be available

to form ammonium nitrate ( PM

- nitrate increases slightly when sulfate is reduced ) , this increase is generally offset by the sulfate reductions, such that PM 2.5 

### mass decreases. Under conditions with lower sulfate levels (i. e., proxy of future year conditions), PM 2.5 

### is more sensitive to reductions in nitric acid compared to reductions in ammonia

.

### 91



### The primary diagnostic analysis is source apportionment modeling with CAMx t o provide more quantitative information on source reg ion (and source sector) impacts (Baker, 2007b)

. Specifically, the model estimated the impact of 18 geographic source regions (which are identified in Figure 6 9 ) and 6 source sectors (EGU point, non

- EGU point, on

- road, off

- road, area, and biogenic sources) at PM 2. 5 

### monitoring site s in the region.

Figure 6 9 . Source regions (left) and key monitoring sites (right) for PM 2. 5 

### modeling analysis



### Modeling results for 20 12 (Base K) and 201 8 (Base M) are provided in Appendix I I I for several key monitoring sites. For each monitoring site, there are two graphs: one showing sector

- level contributions, and one showing source region and sector

- level contributions in terms of absolute

modeled values.

The sect or

- level res ults (see, for example, Figure 70 ) show that EGU sulf ate, non

- EGU

- sulfate, and area organic carbon emissions generally have the largest contributions at the key monitor locations (> 15% each). Ammonia emissions are also important contributors (> 10%). The source group contributions vary by receptor location due to emissions inventory differences.

Figure 70 . Source

- sector results for Detroit (left) and Cle v eland (right) monitors

– 2018 (Base M) Cook County

Steubenville

Granite City Detroit

Cincinnati Cleveland

### 92



### The source region results (see, for examp le, Figure 71) show that while nearby areas generally have the highest impacts (e.g., Detroit nonattainment counties contribute 40% to high sites in southeastern Michigan, and Cleveland nonattainment counties contribute 35% to high sites in

northeastern Oh io), there is an even larger regional impact (i.e., contribution from other states).

Figure 71 . Source

- region results for Detroit (left) and Cleveland (right) monitors

– 2018 (Base M)



### Summary : Air quality modeling and other supplemental analyses wer e performed to estimate future year PM 2. 5 

### concentrations. Based on this information, the following general conclusions

can be made:

• E xisting (“on the books”) controls are expected to produce significant improvement in PM 2. 5 

### air quality.

• The choice of th e base year affects the future year model projections. It is not clear how much of this is attributable to differences in meteorology, because, as noted in Section 3, PM 2 .5 

### concentrations are not as strongly influenced by meteorolog

y as ozone

.

• Most sites are expected to meet the current PM 2. 5 

### standard by the applicable attainment date, except for sites in Detroit, Cleveland, and Granite City

.

• Current monitoring data show significant nonattainment in these areas (e.g., peak design values on the order of 1 6

– 17 ug/m 3 

### ). It is not clear whether sufficient emission reductions will occur in the next couple of years to provide for attainment. States are conducting local

- scale analyses for Detroit, Cleveland, and Granite City , in particular, to identify approp riate additional local controls.

• Attainment by the applicable attainment date is dependent ( possibly) on actual future year meteorology and (more likely ) on actual future year emissions (e.g., if

the emission reductions associated with the “on the books” controls are achieved, then attainment is likely). On the other hand, if either of these

conditions is not met

(especially, with respect to emissions) , then attainment may be less likely.

### 93 

### Section 5. Reasonable Progress Assessment for Regional Haze

Ai r quality modeling and other information were used to assess the improvement in visibility that would be provided by existing (“on the books”) controls and possible additional control programs. In determining reasonable progress for regional haze, Section 169A of the Clean Air

Act and EPA’s visibility rule requires states to consider five factors :

• costs of compliance

• time necessary for compliance

• energy and non

- air quality environmental impacts of compliance

• remaining useful life of any existing source su bject to such requirements

• uniform rate of visibility improvement needed to attain natural visibility condit ions by 2064

The uniform rate of visibility improvement requirement can be depicted graphically in the form of a “glide path” (see Figure 72 ).

Figure 72 . Visibility “glide paths” for northern Class I areas (units: deciviews)



### 5.1 Class I Areas Impacted EPA’s visibility

rule requires a s tate to “address regional haze in each mandatory Class I

Federal area located within t he State and in each mandatory Class I Federal area located outside the State which may be affected by emissions from within the State.” (40 CFR Part 51.308(d)) To meet this requirement, technical analyses conducted by the RPOs were

consulted to obtain i nformation on areas of influence and culpability for Class I areas in the

eastern U.S. (MRPO, 2007). A summary of this information is provided in Table 1 (MRPO, 2007). Th e table shows that every LADCO State impacts multiple Class I areas in the eastern

U .S

. 5

10 15 20 25

2004 2010 2020 2030 2040 2050 2060 2064

Voyageurs Bounday Waters

Isle Royale Seney

Voyageurs Boundry Waters



### 20% best visibility days 20% worst visibility days Baseline

Conditions

Baseline

Conditions Natural

Conditions

### 94 Table 1 4 . Draft List o f Class I Areas Impacted by LADCO States

AREA NAME IL IN MI OH WI

81.401 Alabama.

Sipsey Wilderness Area (1) (1)

81.404 Arkansas.

Caney Creek Wilderness Area (2), ( 4 ) (2), ( 4 ) (2), ( 4 )

Upper Buffalo Wi lderness Area (1), ( 2 ),( 4 ),( 5 ) ( 2) , ( 4 ) (2), ( 4 ) (2)

81.408 Georgia.

Cohotta Wilderness Area

Okefenokee Wilderness Area

Wolf Island Wilderness Area

81.411 Kentucky.

Mammoth Cave NP (1), (2), ( 5 ) (1), ( 2 ), ( 5 ) (1 ), (2) (1), ( 2 ), ( 5 )

81.412 Louisiana.

Breton Wilderness Area

81.413 Maine.

Acadia National Park (3) (3) (3) ( 3 )

Moosehorn Wilderness Area. (3) (3) (3) ( 3 )

81.414 Michigan.

Isle Royale NP. ( 1 ), ( 2 ) (1), ( 2 ) (1), ( 2 ) ( 1 ), ( 2 )

Seney Wilderness Area (1), (2) (1), ( 2 ) (1), (2) (1), (2) (1), ( 2 )

81.415 Minnesota.

Boundary Waters Canoe Area Wilderness (2) (2) (2) (1), (2)

Voyageurs NP (2) (2) ( 1 ), ( 2 )

81.416 Missouri.

Hercules

- Gl ades Wilderness Area ( 2 ), ( 4 ), ( 5 ) ( 2 ), ( 4 ), ( 5 ) (2), ( 4 ) (2)

Mingo Wilderness Area ( 2 ), ( 4 ), ( 5 ) ( 2 ), ( 4 ), ( 5 ) (2) ( 2 ), ( 4 ) (2)

81.419 New Hampshire.

Great Gulf Wilderness Area (3) ( 3 ) (3) (1), ( 3 )

Pres

. Range

- Dry River Wilderness Area.

81.42 New Jersey.

Brigantine Wilderness Area (3) (3) (1), (3) (1), ( 3 )

### 95

81.422 North Carolina.

Great Smoky Mountains NP{1} (1) (1) (1)

Joyce Kilmer

- Slickrock Wilderness Area{2}

Linville Gorge Wilderness Area.

Shining Rock Wilderness Area.

Swanquarter Wilderness Area

81.426 South Carolina.

Cape Romain Wilderness

81.428 Tennessee.

Great Smoky Mountains NP{1}. (1) (1) (1)

Joyce Kilmer

- Slickrock Wilderness{2}

81.431 Vermont.

Lye Brook Wilderness (2), ( 3 ) (2), ( 3 ) (2), ( 3 ) (1), (2), ( 3 )

81.433 Virginia.

James River Face Wilderness. (2) (2) (2) ( 2 ), ( 5 )

Shenandoah NP (2), (3) (1), ( 2 ), ( 3 ) (2), (3) (1),( 2 ),( 3 ),( 5 )

81.435 West Virginia.

Dolly Sods/Otter Creek Wilderness. (2), (3) (1), ( 2 ), ( 3 ) (1), (2), (3) (1),( 2 ),( 3 ),( 5 )



### Key (1) MRPO Back Trajectory Analyses (2) MRPO PSAT Modeling (3) MANE

- VU Contribution Assessment (4) Missouri

- Arkansas Contribution Assessment (5) VI STAS Areas of Influence

### 96



### 5. 2 Future Year Modeling Results For regional haze, the calculation of future year conditions assumed:

• baseline concentrations based on 2000

- 2004 IMPROVE data, with updated (subsitituted) data for Mingo, Boundary Waters, Voyage urs, Isle Royale, and Seney (see Section 2.3 ) ;

• u se of the new IMPROVE light extinction equation ; and

• use of EPA default values for natural conditions, based on the new IM PROVE light extinction equation.

The uniform rate of visibility improvement values for the 2018 planning year were derived (for the 20% worst visibility days) based on a straight line between baseline concentration value (plotted in the year 2004

-- end year of the 5

- year baseline period) and natural condition value (plotted in the year 2064

-- date for achieving natural conditions). Plots of these “glide paths” with the Base M modeling results are presented in Figure 73 for Class I areas in the eastern U.S. A tabular summary of measured baseline and modeled future year deciview values for

these Class I areas are provided in Table 1 5 (2002 base year) and Table 1 6 (2005 base year) 13



### .

The haze results show that several Class I areas in the eastern U.S. are expected to be greater

than (less improved than) the uniform rate of visibility impr ovement values (in 2018), including those in northern Michigan and several in the northeastern U.S. Many other Class I areas in the eastern U.S. are expected to be less than (more improved than) the uniform rate of visibility improvement values (in 2018). As noted above, states should consider these results, along with

information on th e other four factors , in setting reasonable progress goals.

A n assessment of the f ive factors was performed for LADCO and the State of Minnesota by a contractor (EC/R, 2 007). Sp ecifically, ECR examined reductions in S O 2 and NO x emissions

from EGUs and i ndustrial, commercial and institutional (ICI) boilers; NO x emissions from mobile

sources and reciprocating engines and turbines; and a mmonia emissions from agricultural

op erations. The impacts of “on the books” controls were also examined to provide a frame of reference for assessing the impacts of the additional control measures.

The results of ECR’s analysis of the five factors are summarized below:

Factor 1 (Cost of C ompliance): The average cost effectiveness values (in terms of $M
per ton ) are provided in Table 1 6 . For comparison, cost

- effectiveness estimates previously provided for “on the books” controls include:

CAIR SO2: $700

- $1,200, NOx: $1,400

– $2.600 ($/ T)

BART SO2: $300

- $963, NOx: $248

- $1,770

MACT SO2: $1,500, NOx: $7,600

Most of the cost

- effectiveness values for the additional controls are within the range of

cost

- effectiveness values for “on the books” controls.

13 

### Model results reflect the grid cell where the IMPROVE monitor is located.

### 97 Voyageurs Bou ndary Waters

Isle Royale Seney

Mammoth Cave Upper Buffalo

Figure 73 . Visibility modeling results for Class I areas in eastern U.S.

### 98 Mingo Shenandoah

Dolly Sods Bringantine

Lye Brook Acadia

Figure 73 (cont.) Visibility modeling results for Class I areas in eastern U.S

.Worst 20% 2018 2009 2012 2018 2018 2018

Site Baseline URP OTB OTB OTB EGU2
(5-state region) EGU2
(12-state region)

BOWA1 19.86 17.70 19.05 19.01 18.94 18.40 17.72

VOYA2 19.48 17.56 19.14 19.19 19.18 18.94 18.38

SENE1 24.38 21.35 22.98 22.71 22.38 21.26 20.63

ISLE1 21.59 19.21 20.46 20.28 20.04 19.09 18.64

HEGL1 26.75 22.76 24.73 24.34 23.85 23.01 22.04

MING1 28.15 24.08 25.18 24.67 24.01 22.53 21.45

CACR1 26.36 22.55 24.01 23.55 22.99 22.43 21.57

UPBU1 26.27 22.47 24.02 23.58 23.06 22.31 21.38

MACA1 31.37 26.14 28.06 27.03 25.52 24.27 22.57

DOSO1 29.04 24.23 24.86 23.59 22.42 21.60 20.15

SHEN1 29.31 24.67 24.06 22.79 21.57 20.43 19.42

JARI1 29.12 24.48 24.81 23.79 22.42 21.59 20.88

BRIG1 29.01 24.68 25.87 25.25 24.39 23.91 23.45

LYBR1 24.45 21.16 21.80 21.32 20.69 20.18 19.79

Best 20% 2018 2009 2012 2018 2018 2018

Site Baseline URP OTB OTB OTB EGU2
(5-state region) EGU2
(12-state region)

BOWA1 6.42 6.42 6.71 6.73 6.87 6.83 6.81

VOYA2 7.09 7.09 7.21 7.25 7.34 7.31 7.26

SENE1 7.14 7.14 7.19 7.19 7.23 7.06 6.91

ISLE1 6.75 6.75 6.57 6.51 6.47 6.20 6.06

HEGL1 12.84 12.84 12.61 12.62 12.61 12.43 12.02

MING1 14.46 14.46 13.96 13.93 13.94 13.74 13.33

CACR1 11.24 11.24 10.91 10.92 10.90 10.75 10.42

UPBU1 11.71 11.71 11.47 11.46 11.42 11.28 11.01

MACA1 16.51 16.51 16.06 15.91 15.54 15.18 14.75

DOSO1 12.28 12.28 11.72 11.45 11.19 10.93 10.67

SHEN1 10.93 10.93 9.73 9.53 9.17 9.05 8.90

JARI1 14.21 14.21 13.56 13.33 12.97 12.65 12.46

BRIG1 14.33 14.33 13.74 13.69 13.47 13.32 13.21

LYBR1 6.36 6.36 6.12 6.05 5.96 5.88 5.82 

### Table 15. Haze Results - Round 4 (Based on 2000-2004)Worst 20% 2018 2009 2012 2018 2018

Site Baseline URP OTB OTB OTB OTB+Will DO

BOWA1 19.86 17.94 18.45 18.33 17.94 17.92

VOYA2 19.48 17.75 18.20 18.07 17.63 17.66

SENE1 24.38 21.64 23.10 23.04 22.59 22.42

ISLE1 21.59 19.43 20.52 20.43 20.09 20.13

ISLE9 21.59 19.43 20.33 20.22 19.84 19.82

HEGL1 26.75 23.13 24.72 24.69 24.22 24.17

MING1 28.15 24.27 25.88 25.68 24.74 24.83

CACR1 26.36 22.91 23.39 23.29 22.44 22.40

UPBU1 26.27 22.82 23.34 23.27 22.59 22.55

MACA1 31.37 26.64 27.11 27.01 26.10 26.15

DOSO1 29.05 24.69 24.00 23.90 23.00 23.04

SHEN1 29.31 25.12 24.99 24.87 23.92 23.95

JARI1 29.12 24.91 25.17 25.01 24.06 24.12

BRIG1 29.01 25.05 25.79 25.72 25.21 25.22

LYBR1 24.45 21.48 22.04 21.86 21.14 21.14

ACAD1 22.89 20.45 21.72 21.72 21.49 21.49

Best 20% 2018 2009 2012 2018 2018

Site Baseline Max OTB OTB OTB OTB+Will DO

BOWA1 6.42 6.42 6.21 6.19 6.14 6.12

VOYA2 7.09 7.09 6.86 6.83 6.75 6.76

SENE1 7.14 7.14 7.57 7.58 7.71 7.78

ISLE1 6.75 6.75 6.62 6.59 6.60 6.62

ISLE9 6.75 6.75 6.56 6.55 6.52 6.50

HEGL1 12.84 12.84 12.51 12.32 11.66 11.64

MING1 14.46 14.46 14.07 13.89 13.28 13.29

CACR1 11.24 11.24 10.88 10.85 10.52 10.52

UPBU1 11.71 11.71 11.13 11.08 10.73 10.74

MACA1 16.51 16.51 15.76 15.69 15.25 15.25

DOSO1 12.28 12.28 11.25 11.23 11.00 11.01

SHEN1 10.93 10.93 10.13 10.11 9.91 9.91

JARI1 14.21 14.21 13.38 13.38 13.14 13.14

BRIG1 14.33 14.33 14.15 14.08 13.92 13.92

LYBR1 6.37 6.37 6.25 6.23 6.14 6.15

ACAD1 8.78 8.78 8.86 8.86 8.82 8.82



### Table 16. Haze Results - Round 5.1 (Based on 2000-2004)

### 101 Table 1 7 . Estimated Cost Effectiveness for Potential C ontrol Measures

Average Cost effectiveness ($/ton)

Emission category Control strategy Region SO2 NOX NH3

EGU EGU1 3

- State 1,540 2,037

9

- State 1,743 1,782

EGU2 3

- State 1,775 3,016

9

- State 1,952 2,984

ICI boilers ICI1 3

- State 2,992 2,537

9

- State 2,275 1,899

ICI Workgroup 3

- State 2,731 3,814

9

- State 2,743 2,311

3

- State 538 Reciprocating engines emitting 100 tons/year or

more 9

- State 506 Reciprocating engines

and turbines

3

- State 754 Turbines emitting 100 tons/year or m ore 9

- State 754

3

- State 1,286 Reciprocating engines emitting 10 tons/year or more 9

- State 1,023

3

- State 800 Turbines emitting 10 tons/year or more 9

- State 819

10% reduction 3

- State 31

- 2,700 Agricultural sources

9

- State 31

- 2,700

15% reduction 3

- State 31

- 2,700

9

- State 31

- 2,700

Mobile sources Low

- NOX Reflash 3

- State 241

9

- State 241

MCDI 3

- State 10,697

9

- State 2,408

Anti

- Idling 3

- State (430)

- 1,700

9

- State (430)

- 1, 700

Cetane Additive Program 3

- State 4,119

9

- State 4,119

Cement Plants Process Modification Michigan

- Conversion to dry kiln Michigan 9,848

LoToxTM Michigan 1,399

Glass Manufacturing LNB Wisconsin 1,041

Oxy

- firing Wisconsin 2,833

Electric boost Wisconsin 3,426

SCR Wisconsin 1,054

SNCR Wisconsin 1,094

Lime Manufacturing Mid

- kiln firing Wisconsin 688

LNB Wisconsin 837

SNCR Wisconsin 1,210

SCR Wisconsin 5,037

FGD Wisconsin 128

- 4,828

Oil Refinery LNB Wisconsin 3,288

SNCR Wisconsin 4,260

SCR Wisconsin 17,997

LNB+FGR Wisconsin 4,768

ULNB Wisconsin 2,242

FGD Wisconsin 1,078

### 102



### Factor 2 (Time Necessary for Compliance): All of the control measures can be implemented by 2018. Thus, this factor can be easily addressed.

Factor 3 (Energy and Non

- Air Quality Environmental Impacts): The energy and other environmental impacts are belie ved to be manageable. For example, the increased energy demand from add

- on control equipment is less than 1% of the total electricity and steam production in the region, and solid waste disposal and wastewater treatment

costs are less than 5% of the total operating costs of the pollution control equipment. It

should also be noted that the SO2 and NOx controls would have beneficial environmental impacts (e.g., reduced acid deposition and nitrogen deposition).

Factor 4 (Remaining Useful Life): The addition al control measures are intended to be

market

- based strategies applied over a broad geographic region. It is not expected that the control requirements will be applied to units that will be retired prior to the amortization period for the control equipmen t. Thus, this factor can be easily addressed.

Factor 5 (Visibility Impacts ): The estimated incremental improvement in 2018 visibility levels for the additional measures is shown in Figure 7 4 , along with the cost

effectiveness expressed in $M per deciview improvement). These results show that

a lthough EGU and ICI boiler controls have higher cost

- per

- deciview values (compared to some of the other measures), their visibility impacts are larger

.

### 103 

# Visibility Improvement (dv)

0.00 0.25 0.50 0.75 1.00

SO2 NOx NH3 SO2 NOx NH3 SO2 NOx NH3 SO2 NOx NH3 SO2 NOx NH3

Strategy 1 Strategy 2 

## EGUs ICI Boilers Rec.Eng. Mobile Ag



# Cost Effectiveness ($M/dv)

0 1000 2000 3000 4000 5000 6000

SO2 NOx NH3 SO2 NOx NH3 SO2 NOx NH3 SO2 NOx NH3 SO2 NOx NH3

Strategy 1 Strategy 2 

## EGUs ICI Boilers Rec.Eng. Mobile Ag

Figure 74. Results of ECR analysis of reasonable p rogress factors

– visibility improvement (Factor 5) is on top, and cost effectiveness (Factor 1) is on bottom

### 104



### 5. 3 Weight

- of

- Evidence Determination for Haze

The WOE determination for haze consists of the primary modeling and other supplemental analyses. A summary of this information is provided below.

Primary ( Guideline

) Modeling : The results of the guideline modeling are presented in Section 4.1. Key findings from this modeling include:

• Base M modeling results show that the northern Minnesota Class I ar eas are close to the glide path, whereas the northern Michigan Class I areas are above the glide path in 2018

. Other sites in the eastern U.S. are close to (or below) the glide path, except for Mingo (MO), Brigantine (NJ), and Acadia (ME).

• Base K modelin g results show that the northern Minnesota and northern Michigan

Class I areas are above the glide path in 2018. Other sites in the eastern U.S. are close to (or below) the glide path.

• The difference in the two modeling analyses is due mostly t o differ ences in future year emission projections , especially for EGUs (e.g., use of IPM2.1.9 v. IPM3.0).



## • Base K and Base M modeling analyses are considered “SIP quality”, so the

attainment demonstration for haze should reflect a weight

- of

- evidence approach,

with consideration of monitoring based information.

A dditional Modeling : T wo additional modeling analyses were considered: (1) the primary modeling redone with different baseline values, and (2) modeling by the State of Minnesota

which looked at different rec eptor locations in the northern Class I areas (MPCA, 2008) . Each of these analyses is described below.

First, the primary modeling analysis (Base M) was revised using an alternative baseline value. Specifically, the data for the period 2000

- 2005 were us ed to calculate the baseline, given that the Base M modeling reflects a 2005 base year. The results of this alternative analysis (see

Table 1 8 ) are generally consistent with the primary modeling (see Table 1 6 ).

Second, Minnesota’s modeling reflects a 200 2 base year and much of the data developed by LADCO for its modeling. (Note, Minnesota conducted modeling for LADCO’s domain at 36 km, and for a statewide domain at 12 km.) The purpose of the 12 km modeling was to address local scale impacts on the north ern Class I areas at several locations, not just the location of the IMPROVE monitor. Results for the Boundary Waters on the 20% worst days range from 18.3

– 19.0 dv, with an average value of 18.7 dv, which is consistent with Minnesota’s 36 km modeling

re sults at the IMPROVE monitor. This variability in visibility levels should be kept in mind when reviewing the values presented in Tables 1 5 , 1 6

, and 1 8

, which reflect results a t the IMPROVE

monitor locations.Worst 20% 2009 2012 2018 2018

Site Baseline URP OTB OTB OTB OTB+Will DO

BOWA1 20.10 18.12 18.63 18.51 18.12 18.09

VOYA2 19.62 17.86 18.27 18.15 17.70 17.72

SENE1 24.77 21.94 23.44 23.39 22.94 22.77

ISLE1 21.95 19.71 20.84 20.76 20.41 20.44

ISLE9 21.95 19.71 20.65 20.55 20.15 20.13

HEGL1 27.45 23.67 25.30 25.27 24.79 24.73

MING1 28.92 24.86 25.88 25.68 24.74 24.83

CACR1 27.05 23.44 23.88 23.78 22.92 22.86

UPBU1 26.97 23.36 23.92 23.85 23.14 23.09

MACA1 31.76 26.93 27.42 27.32 26.39 26.44

DOSO1 29.36 24.92 24.20 24.11 23.19 23.23

SHEN1 29.45 25.23 25.06 24.94 23.98 24.01

JARI1 29.40 25.13 25.32 25.17 24.22 24.28

BRIG1 29.12 25.14 25.84 25.77 25.26 25.26

LYBR1 24.71 21.69 22.22 22.06 21.36 21.36

ACAD1 22.91 20.47 21.72 21.72 21.49 21.49

Best 20% 2009 2012 2018 2018

Site Baseline URP OTB OTB OTB OTB+Will DO

BOWA1 6.40 6.40 6.20 6.17 6.13 6.10

VOYA2 7.05 7.05 6.82 6.78 6.71 6.71

SENE1 7.20 7.20 7.60 7.61 7.73 7.80

ISLE1 6.80 6.80 6.67 6.64 6.65 6.66

ISLE9 6.80 6.80 6.62 6.61 6.57 6.55

HEGL1 13.04 13.04 12.71 12.51 11.85 11.82

MING1 14.68 14.68 14.07 13.89 13.28 13.29

CACR1 11.62 11.62 11.24 11.20 10.86 10.86

UPBU1 11.99 11.99 11.41 11.36 11.01 11.02

MACA1 16.64 16.64 15.88 15.82 15.37 15.38

DOSO1 12.24 12.24 11.21 11.19 10.96 10.97

SHEN1 10.85 10.85 10.04 10.02 9.82 9.83

JARI1 14.35 14.35 13.51 13.51 13.27 13.27

BRIG1 14.36 14.36 14.17 14.10 13.94 13.94

LYBR1 6.21 6.21 6.11 6.09 6.01 6.01

ACAD1 8.57 8.57 8.67 8.66 8.62 8.62



### Table 18. Haze Results - Round 5.1 (Based on 2000-2005)

### 106



### Observation al Models and Diagnostic Analyse s : The observation

- based modeling (i.e., application of thermodynamic equilibrium models) is presented in Section 3. The key findings from this modeling are that PM 2. 5 

### mass is sensitive to reductions in sulfate, nitric acid, and

ammonia concentrations . E ven though sulfate reductions cause more ammonia to be available

to form ammonium nitrate ( PM

- nitrate increases slightly when sulfate is reduced ) , this increase is generally offset by the sulfate reductions, such that PM 2. 5 

### mass decreases and visibility

im proves

. Under conditions with lower sulfate levels (i.e., proxy of future year c onditions), PM 2.5 

### is more sensitive to reductions in nitric acid compared to reductions in ammonia.

As discussed in Section 2, t hermodynamic equilibrium modeling based on dat a collected at Seney indicates that PM 2. 5 

### there is most sensitive to reductions in sulfate, but also responsive to reductions in nitric acid (Blanchard

, 2004). An analysis using data from the Midwest ammonia monitoring network for a site in Minnesota (i.e ., Great River Bluffs, which is the closest ammonia monitoring site to the northern Class I areas) suggested that reductions in sulfate, nitric acid,

and ammonia concentrations will lower PM 2. 5 

### concentrations and improve visibility levels in the

northern C lass I areas

.

Trajectory analyses for the 20% worst visibility days for the four northern Class I areas are provided in Figure 7 5 . (Note, this figure is similar to Figure 3 4 , but the trajectory results for each

Class I area are displayed separately here

. ) The o range areas are where the air is most likely to come from, and the green areas are where the air is least likely to come from . Darker shading represents higher frequency. As can be seen, bad air days are generally associated with transport from r egions located to the south, and good air days with transport from Canada.

### 107 

### Seney Isle Royale

Boundary Waters Voyageurs

Figure 7 5 . Trajectory analysis results for northern Class I areas on 20% worst visibility days



### The primary diagnostic analysis is source apportionment modeling with CAMx t o provide more quantitative information on source region (and source sector) im pacts (Baker, 2007b)

. Specifically , the CAMx model was applied to provide source contribution information. Specifically, the model estimated the impact of 18 geographic source regions (which are identified in Figure 7 6 ) and 6 source se ctors (EGU point, n on

- EGU point, on

- road, off

- road, area, and ammonia sources) at visibility/haze monitoring site s in the eastern U.S.

### 108

Figure 7 6 . Source regions (left) and key monitoring sites (right) for haze modeling analysis



### Modeling results for 2018 (Base K and Base M) are provided in Appendix I V for several key monitoring sites ( Class I areas). For each monitoring site, there are two graphs: one showing

sector

- level contributions, and one showing source r egion and sector

- level contributions in

terms of absolute modeled values.

The sector

- level results (see, for example, Figure 7 7 ) show that EGU sulfate, non

- EGU

- sulfate, and ammonia emissions generally have the largest contributions at the key monitor loca tions. The source group contributions vary by receptor location due to emissions inventory differences.

Figure 7 7 . Source

- sector results for Seney (left) and Boundary Waters (right)

– 2018 (Base M)



### The source region results (see, for example, Fi gure 7 8 ) show that emissions from a number of nearby states contribute to regional haze levels.

### 109

Figure 7 8 . Source

- region results for Seney (left) and Boundary Waters (right)

– 2018 (Base M)



### Table 19 provides a summary of the estimated state

- level culp abilities based on the LADCO back trajectory analyses and the PSAT analyses for 2018.

Summary : Air quality modeling and other supplemental analyses were performed to estimate future year

visibility levels

. Based on this information, the following genera l conclusions can be

made:

• E xisting (“on the books”) controls are expected to improve visibility levels in the

northern Class I areas

.

• Visibility levels in a few Class I areas in the eastern U.S. are expected to be greater than (less improved than) the u niform rate of visibility improvement

value s in 2018, including those in northern Michigan and s ome in the

northeastern U.S.

• Visibility levels in m any other Class I areas in the eastern U.S. are expected to

be less than

(more improved than) the uniform rate of visibility improvement

value s in 2018.

### 110 Table 19 . State Culpabilities Based on PSAT Modeling and Trajectory Analyses

Boundary Waters Seney

LADCO

- Round 4
PSAT LADCO

-

Round 5
PSAT MPCA

PSAT CENRAP

-

PSAT LADCO

-

Traj. Analysis LADCO

- Round 4
PSAT LADCO

-

Round 5
PSAT CENRAP

-

PSAT LADCO

-

Traj. Analysis

Michigan 3.4% 4.8% 3.0% 1.9% 0.7% 13.8% 18.1% 14.7%

Minnesota 30.5% 23.5% 28.0% 30.6% 37.6% 4.8% 1.6% 3.8%

Wisconsin 10.4% 10.9% 10.0% 6.4% 10.6% 12.6% 10.9% 8.4%

Ill inois 5.2% 5.1% 6.0% 3.5% 2.7% 13.0% 14.3% 7.4%

Indiana 2.9% 3.9% 3.0% 1.8% 1.2% 9.6% 11.6% 2.2%

Iowa 7.6% 8.3% 8.0% 2.5% 7.4% 6.2% 3.8% 5.7%

Missouri 5.2% 3.4% 6.0% 2.1% 3.3% 6.5% 4.8% 3.2%

N. Dakota 5.7% 1.1% 6.0% 4.6% 5.9% 1.5% 0.1% 0.6%

Canada 1.9% 2.7% 3.0% 12.5% 15.1% 2.1% 1.2% 11.1%

CENRAP

WRAP 10.9% 13.5% 4.2% 10.1% 13.1% 10.0% 7.0%

83.6% 77.2% 73.0% 70.2% 94.6% 83.3% 76.4% 64.1%



### Voyageurs Isle Royale

LADCO

- Round 4
PSAT LADCO

-

Round 5
PSAT MPCA

PSAT CENRAP

-

PSAT LADCO

-

Traj. Analysis LADCO

- Round 4
PSAT LADCO

-

Round 5
PSAT CENRAP

-

PSAT LADCO

-

Traj. Analysis

Michigan 2.0% 4.9% 2.0% 1.0% 1.6% 12.7% 13.4%

Minnesota 35.0% 20.2% 31.0% 31.5% 36.9% 14.1% 9.5%

Wisconsin 6.3% 7.9% 6.0% 3.7% 9.7% 16.3% 14.7%

Illinois 3.0% 7.1% 3.0% 1.8% 1.2% 7.0% 8.7%

Indiana 1.6% 4.6% 2.0% 0.8% 5.6% 5.2%

Iowa 7.4% 7.1% 7.0% 2.4% 10.2% 6.9% 8.3%

Missouri 4.3% 4.0% 4.0% 1.6% 0.3% 3.9% 4.6%

N. Dakota 10.3% 1.7% 13.0% 6.1% 7.1% 3.6% 0.3 %

Canada 2.7% 3.3% 5.0% 17.2% 13.3% 2.2% 1.7%

CENRAP

WRAP 10.2% 13.7% 6.1% 16.5% 12.5% 12.6%

82.7% 74.5% 73.0% 72.2% 96.8% 84.9% 79.0%

### 111 

### Section 6. Summary

T o support the development of SIPs for ozone, PM 2.5 

### , and regional haze in the Sta tes of Illinois, Indiana, Michigan, Ohio, and Wisconsin, technical analyses were conducted by LADCO, its member states, and various contractors

. The analyses include preparation of regional emissions inventories and meteorological modeling data for two ba se years, evaluation and application of regional chemical transport models, and review of ambient monitoring data.

Analyses of monitoring data were conducted to produce a conceptual model, which is a qualitative summar y

of the physical, chemical

, and m e teorological processes that control the formation and distribution of pollutants in a given region. Key findings of the analyses include:

Ozone

• Current monitoring data show about 20 sites in violation of the 8

- hour ozone

standard of 85 ppb . Historical ozone data show a steady downward trend over the past 15 years, especially since 2001

- 2003, due likely to federal and state emission control programs.

• Ozone concentrations are strongly influenced by meteorological conditions, with more high ozone days and higher ozone levels during summers with above normal temperatures.

• Inter

- and intra

- regional transport of ozone and ozone precursors affects many portions of the five states , and is the principal cause of nonattainment in some areas far from population o r industrial centers

PM 2.5



### • Current monitoring data show 3 0 sites in violation of the annual PM 2.5 

### standard of 15 ug/m 3 

### . Nonattainment sites are characterized by an elevated regional background ( about 12

– 14 ug/m 3 

### ) and a significant local (urban) incre ment ( about 2

– 3 ug/m 3 

### ).

Historical PM 2.5 

### data show a slight downward trend since deployment of the PM 2.5 

### monitoring network in 1999.

• PM 2.5 

### concentrations are also influenced by meteorology, but the relationship is more complex and less well understood compared to ozone.

• On an annual average basis, PM 2.5 

### chemical composition consists of mostly sulfate, nitrate, and organic carbon in similar proportions.

Haze

• Current monitoring data show visibility levels in the Class I areas in northern Michigan are on the order of 2 2

– 2 4 deciviews. The goal of EPA’s visibility program is to achieve natural conditions, which is on the order of 12 deciviews for these

Class I areas, by the year 2064.

• Visibility impairment is dominated by sulfate and nitrate.

Air qu ality models were applied to support the regional planning efforts. Two base years were

used in the modeli ng analyses: 2002 and 2005. EPA’s modeling guidance recommends using

### 11 2



### 2002 as the baseline inventory year, but also allows for use of an alternative b aseline inventory year, especially a more recent year. Initially, LADCO conducted modeling with a 2002 base year (i.e., Base K modeling, which was completed in 2006). A decision was subsequently made to conduct modeling with a 2005 base year (i.e., Base M, which was completed in 2007). Statistical analyses showed that 2002 and 2005 both had above normal ozone

- conducive conditions, although 200 2 was more severe compared to 200 5 . Examination of multiple base years provides for a more complete technical as sessment. Both sets of model runs are

discussed in this document.

B asecase modeling was conducted to evaluate model performance (i.e., assess the model's ability to reproduce the observed concentrations ) . This exercise wa s intended to assess whether, a nd to degree, confidence in the model is warranted (and to assess whether model improvements are necessary) . M odel performance for ozone and PM 2.5 

### was generally acceptable and can be characterized as follows:

Ozone

• Good agreement between modeled and mon itored concentration for higher concentration levels (> 60 ppb)

– i.e., bias within 30%

• Regional modeled concentrations appear to be underestimated in the 2002 base year, but show better agreement (with monitored data) in the 2005 base year due to

model a nd inventory improvements

.

• Day

- to

- day and hour

- to

- hour variation in and spatial patterns of modeled

concentrations are consistent with monitored data

• Model accurately simulates the change in monitored ozone concentrations due to reductions in precursor e missions.

PM 2. 5



### • Good agreement in the magnitude of fine particle mass, but some species are

overestimated and some are underestimated

• Sulfates: good agreement in the 2002 base year, but underestimated in

the summer in the 2005 base year due probably to m eteorological factors

• Nitrates: slightly overestimated in the winter in the 2002 base year, but good agreement in the 2005 base year as a result of model and inventory improvements

• Organic Carbon: grossly underestimated in the 2002 and 2005 base years due likely to missing primary organic carbon emissions

• Temporal variation and spatial patterns of modeled concentrations are consistent

with monitored data

Future year strategy modeling was conducted to determine whether existing (“on the books”)

controls wo uld be sufficient to provide for attainment of the standards for ozone and PM 2.5 

### and if not, then what additional emission reductions would be necessary for attainment. Traditionally, attainment demonstrations involved a “bright line” test in which a sing le modeled value (based

on EPA guidance) was compared to the ambient standard. To provide a more robust assessment of expected future year air quality, other information was considered. Furthermore,

a ccording to EPA’s modeling guidance, if the future yea r model ed values are “close” to the

### 113



### standard (i.e., 82

– 87 ppb for ozone and 14.5

– 15.5 ug/m 3 

### for PM 2.5 

### ) , then the results of the primary modeling should be reviewed along with the supplemental information in a “weight of evidence” (WOE) assessment of w hether each area is likely to achieve timely attainment. Key findings of the WOE determination include:

• E xisting controls are expected to produce significant improvement in ozone and

PM 2.5 

### concentrations and visibility levels.

• The choice of the base yea r affects the future year model projections. A key difference between the base years of 2002 and 2005 is meteorology. 2002 was more ozone conducive than 2005. The choice of which base year to use as the basis for the SIP is a policy decision (i.e., how much safeguard to incorporate).

• Most sites are expected to meet the current 8

- hour standard by the applicable attainment date, except for sites in western Michigan and, possibly, in eastern

Wisconsin and northeastern Ohio.

• Most sites a re expected to meet the current PM 2.5 

### standard by the applicable attainment date, except for sites in Detroit, Cleveland, and Granite City

.

T he regiona l modeling for PM 2.5 

### does not reflect air quality benefit s expected from local controls. States are conducting local

- scale analyses and will us e these results, in conjunction with the regional

- scale modeling, to support their

attainment demonstrations for PM 2.5



### .

• These findings of residual nonattainment for ozone and PM 2.5 

### are supported by current (2005

– 2007) monitoring dat a which show significant nonattainment in the region (e.g., peak ozone design values on the order of 90

– 93 ppb, and peak

PM 2.5 

### d esign values on the order of 16

- 17 ug/m 3 

### ). It is unlikely that sufficient

emission reductions will occur in the next few of years to provide for attainment at

all sites.

• Attainment at most sites by the applicable attainment date is dependent on actual future year meteorology (e.g., if the weather conditions are consistent with [or less severe than] 2005, then attainment is li kely) and actual future year emissions (e.g., if the emission reductions associated with the existing controls are achieved, then attainment is likely). If either of these conditions is not met, then attainment may be less likely.

• T he new PM 2.5 

### 24

- hour s tandard and the new lower ozone standard will not be

met at several sites , even by 2018, with existing controls.

• Visibility levels in a few Class I areas in the eastern U.S. are expected to be greater than

(less improved than) the uniform rate of visibili ty improvement

value s in 2018 based on existing controls , including those in northern Michigan

and s ome in the northeastern U.S. Visibility levels in m any other Class I areas in the eastern U.S. are expected to be less than (more improved than) the unifor m rate of visibility improvement value s in 2018.

### 114 

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– Factor Analysis”, Draft Final Technical Memorandum, July 18, 2007.

E.H. Pechan, 2004, “LADCO Nonroad Emissions Inventory Project

– Development of Local Data for Construction and Agricultural Equipment”, Final Report, September 10, 2004

E. H. Pechan, 2005, “Development of Updated Growth and Control Factors for Lake Michigan Air Directors Consortium (LADCO)”, Draft Report, December 29, 2005.

E. H. Pechan, 2007, “Development of 2005 Base Year Growth and Control Factors for Lake Michigan Air D irectors Consortium (LADCO)”, Final Report, September 2007.

Environ, 2004, “LADCO Nonroad Emissions Inventory Project for Locomotive, Commercial Marine, and Recreational Marine Emission Sources, Final Report, December 2004.

Environ, 2007 a , “Modeling Week end and Weekday Ozone in Southeast Michigan”, Draft Final Report, Prepared for National Renewable Energy Laboratory, July 30, 2007.

Environ, 2007 b , “LADCO 2005 Locomotive Emissions”, Draft, February 2007.

Environ, 2007 c , “LADCO 2005 Commercial Marine Em issions”, Draft, March 2, 2007.

Environ, 2008 , “LADCO On

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.

Hollander, M. and D. Wolfe, 1973, “Nonparametric Statistical Methods”, John Wiley & Sons, New York (1973).

Hopke, P. K., 2005, Analyses of Midwest PM

- Related Measurements, Report to LADCO

, March

2005.

Kenski, D.M., 2004, Quantifying Transboundary Transport of PM 2. 5 

### : A GIS Analysis, Proc.

AWMA 97 th 

### Annual Conf., Indianapolis

, June 2004

.

Kenski, D.M., 2007a, “CART Analysis for O zone Trends and Meteorological Similarity”, May 10,

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Kenski, D.M., 2007 b , “ Impact of Missing Data on Worst Days at Midwest Northern Class 1

Areas ”, Donna Kenski

, Midwest RPO

, March 12, 2007 (revised 6/19/07)

.

Kenski, D.M., 2008a, “Updated Cox Trends Analysis”, February 2008.

Kenski, D.M. 2008 b , “Updated CART Analysis for Ozone through 2007”, January 30, 2008.

### 116



### LADCO, 2005, “Meteorological Modeling Performance Summary for Applications to

PM 2. 5 

### /Haze/Ozone Modeling Projects”, February 18, 2005

.

LADCO, 2006a, “Base K/Round 4 Strategy Modeling” Emissions”, May 16, 2006

.

LADCO, 2006b, “Base K/Round 4 Modeling: Summary”, August 31, 2006

.

LADCO, 2007a, “Modeling Protocol: 2002 Basecase Technical Details”, October 17, 2007

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LADCO, 2007 c , “An Evaluation of an Annual 2005 MM5 Simulation to Support Photochemical and Emissions Modeling Applications”, January 10, 2007

.

LADCO, 2007 d , “Base M/Round 5 Modeling: Summary (DRAFT), O ctober 10, 2007

LADCO, 2008 a , “Base M Strategy Modeling: Emissions (Revised)”, February 27, 2008

LADCO, 2008b, “ Critique: Modeled Attainment Probability (i.e., Bob Lopez’ analysis) ”, January
10, 2008

Lopez, 2007, “Assessing the Site

- Specific and Region

- w ide Probability of Attainment Level Ozone Air Quality under varying seasonal conditions using the results of 2002 (Round 4) and 2005 (Round 5) Regional Modeling”, Draft 2, Bob Lopez

, WDNR

, December 12 , 2007

Mansell, G, Z. Wang, R. Zhang, J. Fadel, T. Rams ey, H. Xin, Y. Liang, and J. Arogo, 2005, “An Improved Process

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– Emission

Estimates”

MPCA, 2008, “Technical Support Document for the Minnesota State Implementation Plan for Regional Haze”, Draft, Marc h 4, 2008

.

M RPO , 2001, “Principles for Regional Planning”, March 9, 2001.

MRPO, 2007, “Draft List of Class I Areas Located Within (or Impacted by) Midwest RPO States”, June 26, 2007.

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.

Millstein, D.E., R.A. Harley, and S.V. Hering, 2007, “Weekly cycles in fine particulate matter”, Atmospheric Environment (2007), doi:10.1016/j.atmosenv.2007.10.010.

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., 36 (4); 820

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### in Selected Midwestern Cities”, Final Report, STI

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.

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R ule”, EPA

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- 03

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- Course Review of SIPs to Meet the

1

- Hour NAAQS for Ozone”, January 2002.

EPA, 2005, Final Rule to Implement the 8

- Hour Ozone National Ambient Air Quality Standard

, 70 FR 71612, November 29, 2005

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EPA, 2007 a , "Guidance for on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM 2. 5 

### , and Regional Haze”, EPA

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Process

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– Model Development”

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http://www.ladco.org/tech/emis/basek/BaseK_Reports.htm

http://www.ladco.org/tech/emis/r5/round5_reports.htm

http://vista.cira.colostate.edu/views/

### 118 

## APPENDIX I

Ozone and PM 2. 5 

## Modeling ResultsKey Sites 2005 BY 2002 BY

'03 '04 '05 '06 '07 '03-'05 '04-'06 '05-'07 Average Average RRF Round 5

Lake Michigan Area Lake Michigan Area

Chiwaukee 550590019 88 78 93 79 85 86 83 85 84.7 98.3 0.968 82.0 Chiwaukee

Racine 551010017 82 69 95 71 77 82 78 81 80.3 91.7 0.966 77.6 Racine

Milwaukee-Bayside 550190085 92 73 93 73 83 86 79 83 82.7 91.0 0.963 79.6 Milwaukee-Bayside

Harrington Beach 550890009 99 72 94 72 84 88 79 83 83.3 93.0 0.960 80.0 Harrington Beach

Manitowoc 550710007 92 74 95 78 85 87 82 86 85.0 87.0 0.957 81.3 Manitowoc
Sheboygan 551170006 93 78 97 83 88 89 86 89 88.0 97.0 0.959 84.4 Sheboygan

Kewaunee 550610002 97 73 88 76 85 86 79 83 82.7 89.3 0.954 78.9 Kewaunee
Door County 550290004 93 78 101 79 92 90 86 90 88.7 91.0 0.956 84.8 Door County

Hammond 180892008 81 67 87 75 77 78 76 79 77.7 88.3 0.971 75.4 Hammond

Whiting 180890030 64 88 81 88 76 77 85 79.3 0.971 77.0 Whiting
Michigan City 180910005 82 70 84 75 73 78 76 77 77.0 90.3 0.964 74.2 Michigan City
Ogden Dunes 181270020 77 69 90 70 84 78 76 81 78.3 86.3 0.967 75.7 Ogden Dunes

Holland 260050003 96 79 94 91 94 89 88 93 90.0 94.0 0.951 85.6 Holland

Jenison 261390005 91 69 86 83 88 82 79 85 82.0 86.0 0.950 77.9 Jenison

Muskegon 261210039 94 70 90 90 86 84 83 88 85.0 90.0 0.951 80.8 Muskegon

Indianapolis Area Indianapolis Area

Noblesville 189571001 101 75 87 77 84 87 79 82 82.7 93.7 0.944 78.0 Noblesville

Fortville 180590003 92 72 80 75 81 81 75 78 78.0 91.3 0.948 73.9 Fortville

Fort B. Harrison 180970050 91 73 80 76 83 81 76 79 78.7 90.0 0.951 74.8 Fort B. Harrison

Detroit Area Detroit Area

New Haven 260990009 102 81 88 78 93 90 82 86 86.0 92.3 0.962 82.7 New Haven

Warren 260991003 101 71 89 78 91 87 79 86 84.0 90.0 0.982 82.5 Warren

Port Huron 261470005 87 74 88 78 89 83 80 85 82.7 88.0 0.956 79.0 Port Huron

Cleveland Area Cleveland Area

Ashtabula 390071001 99 81 93 86 92 91 86 90 89.0 95.7 0.954 84.9 Ashtabula
Geauga 390550004 97 75 88 70 68 86 77 75 79.3 99.0 0.954 75.7 Geauga

Eastlake 390850003 92 79 97 83 74 89 86 84 86.3 92.7 0.959 82.8 Eastlake

Akron 391530020 89 77 89 77 91 85 81 85 83.7 93.3 0.948 79.3

Cincinnati Area Cincinnati Area

Wilmington 390271002 96 78 83 81 82 85 80 82 82.3 94.3 0.945 77.8 Wilmington
Sycamore 390610006 93 76 89 81 90 86 82 86 84.7 90.3 0.965 81.7 Sycamore

Lebanon 391650007 95 81 92 86 88 89 86 88 87.7 87.0 0.954 83.6 Lebanon

Columbus Area Columbus Area

London 390970007 90 75 81 76 83 82 77 80 79.7 88.7 0.946 75.4 London

New Albany 390490029 94 78 92 82 87 88 84 87 86.3 93.0 0.954 82.4 New Albany

Franklin 290490028 84 73 86 79 79 81 79 81 80.3 86.0 0.958 77.0 Franklin

St. Louis Area St. Louis Area W. Alton (MO) 291831002 91 77 89 91 89 85 85 89 86.3 90.0 0.954 82.4 W. Alton (MO) Orchard (MO) 291831004 90 76 92 92 83 86 86 89 87.0 90.0 0.958 83.3 Orchard (MO) Sunset Hills (MO) 291890004 88 70 89 80 89 82 79 86 82.3 88.3 0.966 79.5 Sunset Hills (MO) Arnold (MO) 290990012 82 70 92 79 87 81 80 86 82.3 84.7 0.956 78.7 Arnold (MO) Margaretta (MO) 295100086 90 72 91 76 91 84 79 86 83.0 87.7 0.962 79.8 Margaretta (MO) Maryland Heights (MO) 291890014 88 84 94 88 86 88 87.3 0.967 84.5 Maryland Heights (MO) 4th High 8-hour Value Des. Values (truncated) 2008 - OTBKey Sites 2005 BY 2002 BY

'03 '04 '05 '06 '07 '03-'05 '04-'06 '05-'07 Average Average RRF Round 5 Round 4 RRF Round 5

Lake Michigan Area Lake Michigan Area

Chiwaukee 550590019 88 78 93 79 85 86 83 85 84.7 98.3 0.972 82.3 92.0 0.971 82.2 Chiwaukee

Racine 551010017 82 69 95 71 77 82 78 81 80.3 91.7 0.965 77.5 84.9 0.964 77.4 Racine

Milwaukee-Bayside 550190085 92 73 93 73 83 86 79 83 82.7 91.0 0.965 79.8 84.9 0.964 79.7 Milwaukee-Bayside

Harrington Beach 550890009 99 72 94 72 84 88 79 83 83.3 93.0 0.961 80.1 85.4 0.960 80.0 Harrington Beach

Manitowoc 550710007 92 74 95 78 85 87 82 86 85.0 87.0 0.951 80.8 78.9 0.949 80.7 Manitowoc
Sheboygan 551170006 93 78 97 83 88 89 86 89 88.0 97.0 0.955 84.0 88.9 0.953 83.9 Sheboygan

Kewaunee 550610002 97 73 88 76 85 86 79 83 82.7 89.3 0.945 78.1 81.0 0.943 78.0 Kewaunee
Door County 550290004 93 78 101 79 92 90 86 90 88.7 91.0 0.946 83.9 81.8 0.945 83.8 Door County

Hammond 180892008 81 67 87 75 77 78 76 79 77.7 88.3 0.971 75.4 86.6 0.970 75.3 Hammond

Whiting 180890030 64 88 81 88 76 77 85 79.3 0.971 77.0 0.970 77.0 Whiting
Michigan City 180910005 82 70 84 75 73 78 76 77 77.0 90.3 0.960 73.9 86.5 0.959 73.8 Michigan City
Ogden Dunes 181270020 77 69 90 70 84 78 76 81 78.3 86.3 0.965 75.6 82.8 0.964 75.5 Ogden Dunes

Holland 260050003 96 79 94 91 94 89 88 93 90.0 94.0 0.948 85.3 83.4 0.947 85.2 Holland

Jenison 261390005 91 69 86 83 88 82 79 85 82.0 86.0 0.940 77.1 77.6 0.939 77.0 Jenison

Muskegon 261210039 94 70 90 90 86 84 83 88 85.0 90.0 0.947 80.5 81.5 0.945 80.3 Muskegon

Indianapolis Area Indianapolis Area

Noblesville 189571001 101 75 87 77 84 87 79 82 82.7 93.7 0.945 78.1 83.7 0.946 78.2 Noblesville

Fortville 180590003 92 72 80 75 81 81 75 78 78.0 91.3 0.947 73.9 83.8 0.948 73.9 Fortville

Fort B. Harrison 180970050 91 73 80 76 83 81 76 79 78.7 90.0 0.955 75.1 83.7 0.956 75.2 Fort B. Harrison

Detroit Area Detroit Area

New Haven 260990009 102 81 88 78 93 90 82 86 86.0 92.3 0.947 81.4 85.3 0.947 81.4 New Haven

Warren 260991003 101 71 89 78 91 87 79 86 84.0 90.0 0.968 81.3 83.3 0.969 81.4 Warren

Port Huron 261470005 87 74 88 78 89 83 80 85 82.7 88.0 0.937 77.5 79.1 0.938 77.5 Port Huron

Cleveland Area Cleveland Area

Ashtabula 390071001 99 81 93 86 92 91 86 90 89.0 95.7 0.937 83.4 82.7 0.941 83.7 Ashtabula
Geauga 390550004 97 75 88 70 68 86 77 75 79.3 99.0 0.942 74.7 88.8 0.945 75.0 Geauga

Eastlake 390850003 92 79 97 83 74 89 86 84 86.3 92.7 0.949 81.9 82.8 0.954 82.4 Eastlake

Akron 391530020 89 77 89 77 91 85 81 85 83.7 93.3 0.934 78.1 81.4 0.935 78.2

Cincinnati Area Cincinnati Area

Wilmington 390271002 96 78 83 81 82 85 80 82 82.3 94.3 0.941 77.5 83.5 0.942 77.6 Wilmington
Sycamore 390610006 93 76 89 81 90 86 82 86 84.7 90.3 0.967 81.9 84.7 0.968 82.0 Sycamore

Lebanon 391650007 95 81 92 86 88 89 86 88 87.7 87.0 0.947 83.0 79.0 0.948 83.1 Lebanon

Columbus Area Columbus Area

London 390970007 90 75 81 76 83 82 77 80 79.7 88.7 0.941 75.0 78.4 0.942 75.0 London

New Albany 390490029 94 78 92 82 87 88 84 87 86.3 93.0 0.947 81.8 82.6 0.948 81.8 New Albany

Franklin 290490028 84 73 86 79 79 81 79 81 80.3 86.0 0.945 75.9 76.5 0.948 76.2 Franklin

St. Louis Area St. Louis Area W. Alton (MO) 291831002 91 77 89 91 89 85 85 89 86.3 90.0 0.938 81.0 85.2 0.932 80.5 W. Alton (MO) Orchard (MO) 291831004 90 76 92 92 83 86 86 89 87.0 90.0 0.942 82.0 82.2 0.939 81.7 Orchard (MO) Sunset Hills (MO) 291890004 88 70 89 80 89 82 79 86 82.3 88.3 0.956 78.7 81.9 0.954 78.5 Sunset Hills (MO) Arnold (MO) 290990012 82 70 92 79 87 81 80 86 82.3 84.7 0.938 77.2 77.4 0.937 77.1 Arnold (MO) Margaretta (MO) 295100086 90 72 91 76 91 84 79 86 83.0 87.7 0.955 79.3 83.4 0.955 79.3 Margaretta (MO) Maryland Heights (MO) 291890014 88 84 94 88 86 88 87.3 0.955 83.4 0.954 83.3 Maryland Heights (MO) 4th High 8-hour Value Des. Values (truncated) 2009 - Will Do 2009 - OTBKey Sites 2005 BY 2002 BY

'03 '04 '05 '06 '07 '03-'05 '04-'06 '05-'07 Average Average RRF Round 5 Round 4 RRF Round 5

Lake Michigan Area Lake Michigan Area

Chiwaukee 550590019 88 78 93 79 85 86 83 85 84.7 98.3 0.956 80.9 90.3 0.900 76.2 Chiwaukee

Racine 551010017 82 69 95 71 77 82 78 81 80.3 91.7 0.947 76.1 82.9 0.886 71.2 Racine

Milwaukee-Bayside 550190085 92 73 93 73 83 86 79 83 82.7 91.0 0.944 78.0 82.3 0.880 72.7 Milwaukee-Bayside

Harrington Beach 550890009 99 72 94 72 84 88 79 83 83.3 93.0 0.939 78.3 82.9 0.870 72.5 Harrington Beach

Manitowoc 550710007 92 74 95 78 85 87 82 86 85.0 87.0 0.925 78.6 76.3 0.853 72.5 Manitowoc
Sheboygan 551170006 93 78 97 83 88 89 86 89 88.0 97.0 0.930 81.8 86.4 0.857 75.4 Sheboygan

Kewaunee 550610002 97 73 88 76 85 86 79 83 82.7 89.3 0.918 75.9 79.1 0.845 69.9 Kewaunee
Door County 550290004 93 78 101 79 92 90 86 90 88.7 91.0 0.919 81.5 79.3 0.843 74.7 Door County

Hammond 180892008 81 67 87 75 77 78 76 79 77.7 88.3 0.960 74.6 86.3 0.922 71.6 Hammond

Whiting 180890030 64 88 81 88 76 77 85 79.3 0.960 76.2 0.922 73.1 Whiting
Michigan City 180910005 82 70 84 75 73 78 76 77 77.0 90.3 0.942 72.5 85.4 0.884 68.1 Michigan City
Ogden Dunes 181270020 77 69 90 70 84 78 76 81 78.3 86.3 0.951 74.5 82.0 0.904 70.8 Ogden Dunes

Holland 260050003 96 79 94 91 94 89 88 93 90.0 94.0 0.920 82.8 81.0 0.846 76.1 Holland

Jenison 261390005 91 69 86 83 88 82 79 85 82.0 86.0 0.909 74.5 75.5 0.838 68.7 Jenison

Muskegon 261210039 94 70 90 90 86 84 83 88 85.0 90.0 0.918 78.0 79.4 0.846 71.9 Muskegon

Indianapolis Area Indianapolis Area

Noblesville 189571001 101 75 87 77 84 87 79 82 82.7 93.7 0.914 75.6 82.0 0.831 68.7 Noblesville

Fortville 180590003 92 72 80 75 81 81 75 78 78.0 91.3 0.916 71.4 82.1 0.835 65.1 Fortville

Fort B. Harrison 180970050 91 73 80 76 83 81 76 79 78.7 90.0 0.931 73.2 82.4 0.879 69.1 Fort B. Harrison

Detroit Area Detroit Area

New Haven 260990009 102 81 88 78 93 90 82 86 86.0 92.3 0.932 80.2 83.5 0.885 76.1 New Haven

Warren 260991003 101 71 89 78 91 87 79 86 84.0 90.0 0.961 80.7 81.9 0.924 77.6 Warren

Port Huron 261470005 87 74 88 78 89 83 80 85 82.7 88.0 0.913 75.5 77.0 0.858 70.9 Port Huron

Cleveland Area Cleveland Area

Ashtabula 390071001 99 81 93 86 92 91 86 90 89.0 95.7 0.910 81.0 80.2 0.844 75.1 Ashtabula
Geauga 390550004 97 75 88 70 68 86 77 75 79.3 99.0 0.916 72.7 86.2 0.848 67.3 Geauga

Eastlake 390850003 92 79 97 83 74 89 86 84 86.3 92.7 0.932 80.5 80.6 0.883 76.2 Eastlake

Akron 391530020 89 77 89 77 91 85 81 85 83.7 93.3 0.903 75.6 78.5 0.821 68.7 Akron

Cincinnati Area Cincinnati Area

Wilmington 390271002 96 78 83 81 82 85 80 82 82.3 94.3 0.910 74.9 81.1 0.830 68.3 Wilmington
Sycamore 390610006 93 76 89 81 90 86 82 86 84.7 90.3 0.948 80.3 82.9 0.881 74.6 Sycamore

Lebanon 391650007 95 81 92 86 88 89 86 88 87.7 87.0 0.921 80.7 77.0 0.846 74.2 Lebanon

Columbus Area Columbus Area

London 390970007 90 75 81 76 83 82 77 80 79.7 88.7 0.911 72.6 76.5 0.832 66.3 London

New Albany 390490029 94 78 92 82 87 88 84 87 86.3 93.0 0.922 79.6 80.2 0.845 73.0 New Albany

Franklin 290490028 84 73 86 79 79 81 79 81 80.3 86.0 0.923 74.1 74.7 0.859 69.0 Franklin

St. Louis Area St. Louis Area W. Alton (MO) 291831002 91 77 89 91 89 85 85 89 86.3 90.0 0.911 78.6 84.0 0.868 74.9 W. Alton (MO) Orchard (MO) 291831004 90 76 92 92 83 86 86 89 87.0 90.0 0.919 80.0 80.4 0.876 76.2 Orchard (MO) Sunset Hills (MO) 291890004 88 70 89 80 89 82 79 86 82.3 88.3 0.937 77.1 80.6 0.897 73.9 Sunset Hills (MO) Arnold (MO) 290990012 82 70 92 79 87 81 80 86 82.3 84.7 0.918 75.6 75.8 0.874 72.0 Arnold (MO) Margaretta (MO) 295100086 90 72 91 76 91 84 79 86 83.0 87.7 0.939 77.9 82.5 0.896 74.4 Margaretta (MO) Maryland Heights (MO) 291890014 88 84 94 88 86 88 87.3 0.936 81.7 0.894 78.1 Maryland Heights (MO) 4th High 8-hour Value Des. Values (truncated) 2018 - OTB 2012 - OTB2005 BY 2002 BY

Key Site County Site ID '03 '04 '05 '06 '07 '03 - '05 '04 - '06 '05 - '07 Average

w/ 2007 Average

Round 5 Round4 Key Site

Chicago - Washington HS Cook 170310022 15.6 14.2 16.9 13.2 15.7 15.6 14.8 15.3 15.2 15.9 14.1 14.8 Chicago - Washington HS
Chicago - Mayfair Cook 170310052 15.9 15.3 17.0 14.5 15.5 16.1 15.6 15.7 15.8 17.1 14.4 15.8 Chicago - Mayfair
Chicago - Springfield Cook 170310057 15.6 13.8 16.7 13.5 15.1 15.4 14.7 15.1 15.0 15.6 13.9 14.5 Chicago - Springfield
Chicago - Lawndale Cook 170310076 14.8 14.2 16.6 13.5 14.3 15.2 14.8 14.8 14.9 15.6 13.8 14.5 Chicago - Lawndale

Blue Island Cook 170312001 14.9 14.1 16.4 13.2 14.3 15.1 14.6 14.6 14.8 15.6 13.7 14.5 Blue Island

Summit Cook 170313301 15.6 14.2 16.9 13.8 14.8 15.6 15.0 15.2 15.2 16.0 14.2 14.8 Summit

Cicero Cook 170316005 16.8 15.2 16.3 14.3 14.8 16.1 15.3 15.1 15.5 16.4 14.4 15.3 Cicero
Granite City Madison 171191007 17.5 15.4 18.2 16.3 15.1 17.0 16.6 16.5 16.7 17.3 15.1 16.0 Granite City

E. St. Louis St. Clair 171630010 14.9 14.7 17.1 14.5 15.6 15.6 15.4 15.7 15.6 16.2 14.1 14.9 E. St. Louis

Jeffersonville Clark 180190005 15.8 15.1 18.5 15.0 16.5 16.5 16.2 16.7 16.4 17.2 13.8 15.5 Jeffersonville

Jasper Dubois 180372001 15.7 14.4 16.9 13.5 14.4 15.7 14.9 14.9 15.2 15.5 12.4 13.8 Jasper
Gary Lake 180890031 16.8 13.3 14.5 16.8 15.1 14.9 15.6 13.0 Gary

Indy-Washington Park Marion 180970078 15.5 14.3 16.4 14.1 15.8 15.4 14.9 15.4 15.3 16.2 12.8 14.5 Indy-Washington Park
Indy-W 18th Street Marion 180970081 16.2 15.0 17.9 14.2 16.1 16.4 15.7 16.1 16.0 13.4 Indy-W 18th Street
Indy- Michigan Street Marion 180970083 16.3 15.0 17.5 14.1 15.9 16.3 15.5 15.8 15.9 16.6 13.4 14.8 Indy- Michigan Street

Allen Park Wayne 261630001 15.2 14.2 15.9 13.2 12.8 15.1 14.4 14.0 14.5 15.8 13.0 14.5 Allen Park

Southwest HS Wayne 261630015 16.6 15.4 17.2 14.7 14.5 16.4 15.8 15.5 15.9 17.3 14.2 15.8 Southwest HS

Linwood Wayne 261630016 15.8 13.7 16.0 13.0 13.9 15.2 14.2 14.3 14.6 15.5 13.1 14.1 Linwood

Dearborn Wayne 261630033 19.2 16.8 18.6 16.1 16.9 18.2 17.2 17.2 17.5 19.3 15.8 17.7 Dearborn

Wyandotte Wayne 261630036 16.3 13.7 16.4 12.9 13.4 15.5 14.3 14.2 14.7 16.6 13.1 15.1 Wyandotte

Middleton Butler 390170003 17.2 14.1 19.0 14.1 15.4 16.8 15.7 16.2 16.2 16.5 13.5 14.2 Middleton

Fairfield Butler 390170016 15.8 14.7 17.9 14.0 14.9 16.1 15.5 15.6 15.8 15.9 13.1 13.5 Fairfield

Cleveland-28th Street Cuyahoga 390350027 15.4 15.6 17.3 13.0 14.5 16.1 15.3 14.9 15.4 16.5 13.5 14.4 Cleveland-28th Street

Cleveland-St. Tikhon Cuyahoga 390350038 17.6 17.5 19.2 14.9 16.2 18.1 17.2 16.8 17.4 18.4 15.2 16.1 Cleveland-St. Tikhon
Cleveland-Broadway Cuyahoga 390350045 16.4 15.3 19.3 14.1 15.3 17.0 16.2 16.2 16.5 16.7 14.4 14.6 Cleveland-Broadway
Cleveland-E14 & Orange Cuyahoga 390350060 17.2 16.4 19.4 15.0 15.9 17.7 16.9 16.8 17.1 17.6 15.0 15.3 Cleveland-E14 & Orange

Newburg Hts - Harvard Ave Cuyahoga 390350065 15.6 15.2 18.6 13.1 15.8 16.5 15.6 15.8 16.0 16.2 14.0 14.1 Newburg Hts - Harvard Ave
Columbus - Fairgrounds Franklin 390490024 16.4 15.0 16.4 13.6 14.6 15.9 15.0 14.9 15.3 16.5 12.9 14.6 Columbus - Fairgrounds

Columbus - Ann Street Franklin 390490025 15.3 14.6 16.5 13.8 14.7 15.5 15.0 15.0 15.1 16.0 12.7 14.1 Columbus - Ann Street
Columbus - Maple Canyon Franklin 390490081 14.9 13.6 14.6 12.9 13.1 14.4 13.7 13.5 13.9 16.0 11.7 14.0 Columbus - Maple Canyon
Cincinnati - Seymour Hamilton 390610014 17.0 15.9 19.8 15.5 16.5 17.6 17.1 17.3 17.3 17.7 14.5 15.5 Cincinnati - Seymour

Cincinnati - Taft Ave Hamilton 390610040 15.5 14.6 17.5 13.6 15.1 15.9 15.2 15.4 15.5 15.7 12.8 13.6 Cincinnati - Taft Ave

Cincinnati - 8th Ave Hamilton 390610042 16.7 16.0 19.1 14.9 15.9 17.3 16.7 16.6 16.9 17.3 14.0 14.6 Cincinnati - 8th Ave

Sharonville Hamilton 390610043 15.7 14.9 16.9 14.5 14.8 15.8 15.4 15.4 15.6 16.0 12.9 13.6 Sharonville

Norwood Hamilton 390617001 16.0 15.3 18.4 14.4 15.1 16.6 16.0 16.0 16.2 16.3 13.4 14.2 Norwood

St. Bernard Hamilton 390618001 17.3 16.4 20.0 15.9 16.1 17.9 17.4 17.3 17.6 17.3 14.7 15.2 St. Bernard

Steubenville Jefferson 390810016 17.7 15.9 16.4 13.8 16.2 16.7 15.4 15.5 15.8 17.7 12.8 16.3 Steubenville

Mingo Junction Jefferson 390811001 17.3 16.2 18.1 14.6 15.6 17.2 16.3 16.1 16.5 17.5 13.5 15.5 Mingo Junction

Ironton Lawrence 390870010 14.3 13.7 17.0 14.4 15.0 15.0 15.0 15.5 15.2 15.7 12.8 14.2 Ironton

Dayton Montgomery 391130032 15.9 14.5 17.4 13.6 15.6 15.9 15.2 15.5 15.5 15.9 13.2 13.7 Dayton

New Boston Scioto 391450013 14.7 13.0 16.2 14.3 14.0 14.6 14.5 14.8 14.7 17.1 12.1 15.4 New Boston

Canton - Dueber Stark 391510017 16.8 15.6 17.8 14.6 15.9 16.7 16.0 16.1 16.3 17.3 14.0 15.0 Canton - Dueber

Canton - Market Stark 391510020 15.0 14.1 16.6 11.9 14.4 15.2 14.2 14.3 14.6 15.7 12.6 13.6 Canton - Market

Akron - Brittain Summit 391530017 15.4 15.0 16.4 13.5 14.4 15.6 15.0 14.8 15.1 16.4 13.0 14.4 Akron - Brittain

Akron - W. Exchange Summit 391530023 14.2 13.9 15.7 12.8 13.7 14.6 14.1 14.1 14.3 15.6 12.3 13.6 Akron - W. Exchange Annual Average Conc. Design Values 2009 Modeling Results2005 BY 2002 BY

Key Site County Site ID '03 '04 '05 '06 '07 '03 - '05 '04 - '06 '05 - '07 Average

w/ 2007 Average

Round 5 Round4 Key Site

Chicago - Washington HS Cook 170310022 15.6 14.2 16.9 13.2 15.7 15.6 14.8 15.3 15.2 15.9 14.0 14.6 Chicago - Washington HS
Chicago - Mayfair Cook 170310052 15.9 15.3 17.0 14.5 15.5 16.1 15.6 15.7 15.8 17.1 14.2 15.5 Chicago - Mayfair
Chicago - Springfield Cook 170310057 15.6 13.8 16.7 13.5 15.1 15.4 14.7 15.1 15.0 15.6 13.8 14.3 Chicago - Springfield
Chicago - Lawndale Cook 170310076 14.8 14.2 16.6 13.5 14.3 15.2 14.8 14.8 14.9 15.6 13.7 14.3 Chicago - Lawndale

Blue Island Cook 170312001 14.9 14.1 16.4 13.2 14.3 15.1 14.6 14.6 14.8 15.6 13.6 14.3 Blue Island

Summit Cook 170313301 15.6 14.2 16.9 13.8 14.8 15.6 15.0 15.2 15.2 16.0 14.0 14.6 Summit

Cicero Cook 170316005 16.8 15.2 16.3 14.3 14.8 16.1 15.3 15.1 15.5 16.4 14.3 15.1 Cicero
Granite City Madison 171191007 17.5 15.4 18.2 16.3 15.1 17.0 16.6 16.5 16.7 17.3 14.9 15.8 Granite City

E. St. Louis St. Clair 171630010 14.9 14.7 17.1 14.5 15.6 15.6 15.4 15.7 15.6 16.2 13.9 14.7 E. St. Louis

Jeffersonville Clark 180190005 15.8 15.1 18.5 15.0 16.5 16.5 16.2 16.7 16.4 17.2 13.7 15.0 Jeffersonville

Jasper Dubois 180372001 15.7 14.4 16.9 13.5 14.4 15.7 14.9 14.9 15.2 15.5 12.2 13.5 Jasper
Gary Lake 180890031 16.8 13.3 14.5 16.8 15.1 14.9 15.6 12.8 Gary

Indy-Washington Park Marion 180970078 15.5 14.3 16.4 14.1 15.8 15.4 14.9 15.4 15.3 16.2 12.6 14.2 Indy-Washington Park
Indy-W 18th Street Marion 180970081 16.2 15.0 17.9 14.2 16.1 16.4 15.7 16.1 16.0 13.2 Indy-W 18th Street
Indy- Michigan Street Marion 180970083 16.3 15.0 17.5 14.1 15.9 16.3 15.5 15.8 15.9 16.6 13.1 14.9 Indy- Michigan Street

Allen Park Wayne 261630001 15.2 14.2 15.9 13.2 12.8 15.1 14.4 14.0 14.5 15.8 12.8 14.1 Allen Park

Southwest HS Wayne 261630015 16.6 15.4 17.2 14.7 14.5 16.4 15.8 15.5 15.9 17.3 13.9 15.3 Southwest HS

Linwood Wayne 261630016 15.8 13.7 16.0 13.0 13.9 15.2 14.2 14.3 14.6 15.5 12.8 13.7 Linwood

Dearborn Wayne 261630033 19.2 16.8 18.6 16.1 16.9 18.2 17.2 17.2 17.5 19.3 15.5 17.1 Dearborn

Wyandotte Wayne 261630036 16.3 13.7 16.4 12.9 13.4 15.5 14.3 14.2 14.7 16.6 12.8 14.7 Wyandotte

Middleton Butler 390170003 17.2 14.1 19.0 14.1 15.4 16.8 15.7 16.2 16.2 16.5 13.2 13.7 Middleton

Fairfield Butler 390170016 15.8 14.7 17.9 14.0 14.9 16.1 15.5 15.6 15.8 15.9 12.9 12.9 Fairfield

Cleveland-28th Street Cuyahoga 390350027 15.4 15.6 17.3 13.0 14.5 16.1 15.3 14.9 15.4 16.5 13.2 13.8 Cleveland-28th Street

Cleveland-St. Tikhon Cuyahoga 390350038 17.6 17.5 19.2 14.9 16.2 18.1 17.2 16.8 17.4 18.4 14.8 15.4 Cleveland-St. Tikhon
Cleveland-Broadway Cuyahoga 390350045 16.4 15.3 19.3 14.1 15.3 17.0 16.2 16.2 16.5 16.7 14.0 14.0 Cleveland-Broadway
Cleveland-E14 & Orange Cuyahoga 390350060 17.2 16.4 19.4 15.0 15.9 17.7 16.9 16.8 17.1 17.6 14.6 14.7 Cleveland-E14 & Orange

Newburg Hts - Harvard Ave Cuyahoga 390350065 15.6 15.2 18.6 13.1 15.8 16.5 15.6 15.8 16.0 16.2 13.6 13.5 Newburg Hts - Harvard Ave
Columbus - Fairgrounds Franklin 390490024 16.4 15.0 16.4 13.6 14.6 15.9 15.0 14.9 15.3 16.5 12.6 14.0 Columbus - Fairgrounds

Columbus - Ann Street Franklin 390490025 15.3 14.6 16.5 13.8 14.7 15.5 15.0 15.0 15.1 16.0 12.4 13.5 Columbus - Ann Street
Columbus - Maple Canyon Franklin 390490081 14.9 13.6 14.6 12.9 13.1 14.4 13.7 13.5 13.9 16.0 11.4 13.4 Columbus - Maple Canyon
Cincinnati - Seymour Hamilton 390610014 17.0 15.9 19.8 15.5 16.5 17.6 17.1 17.3 17.3 17.7 14.3 14.8 Cincinnati - Seymour

Cincinnati - Taft Ave Hamilton 390610040 15.5 14.6 17.5 13.6 15.1 15.9 15.2 15.4 15.5 15.7 12.6 13.0 Cincinnati - Taft Ave

Cincinnati - 8th Ave Hamilton 390610042 16.7 16.0 19.1 14.9 15.9 17.3 16.7 16.6 16.9 17.3 13.8 14.0 Cincinnati - 8th Ave

Sharonville Hamilton 390610043 15.7 14.9 16.9 14.5 14.8 15.8 15.4 15.4 15.6 16.0 12.7 13.0 Sharonville

Norwood Hamilton 390617001 16.0 15.3 18.4 14.4 15.1 16.6 16.0 16.0 16.2 16.3 13.2 13.6 Norwood

St. Bernard Hamilton 390618001 17.3 16.4 20.0 15.9 16.1 17.9 17.4 17.3 17.6 17.3 14.4 14.6 St. Bernard

Steubenville Jefferson 390810016 17.7 15.9 16.4 13.8 16.2 16.7 15.4 15.5 15.8 17.7 12.5 15.9 Steubenville

Mingo Junction Jefferson 390811001 17.3 16.2 18.1 14.6 15.6 17.2 16.3 16.1 16.5 17.5 13.2 15.0 Mingo Junction

Ironton Lawrence 390870010 14.3 13.7 17.0 14.4 15.0 15.0 15.0 15.5 15.2 15.7 12.5 13.7 Ironton

Dayton Montgomery 391130032 15.9 14.5 17.4 13.6 15.6 15.9 15.2 15.5 15.5 15.9 12.9 13.2 Dayton

New Boston Scioto 391450013 14.7 13.0 16.2 14.3 14.0 14.6 14.5 14.8 14.7 17.1 11.9 14.8 New Boston

Canton - Dueber Stark 391510017 16.8 15.6 17.8 14.6 15.9 16.7 16.0 16.1 16.3 17.3 13.6 14.3 Canton - Dueber

Canton - Market Stark 391510020 15.0 14.1 16.6 11.9 14.4 15.2 14.2 14.3 14.6 15.7 12.3 13.0 Canton - Market

Akron - Brittain Summit 391530017 15.4 15.0 16.4 13.5 14.4 15.6 15.0 14.8 15.1 16.4 12.7 13.6 Akron - Brittain

Akron - W. Exchange Summit 391530023 14.2 13.9 15.7 12.8 13.7 14.6 14.1 14.1 14.3 15.6 12.0 13.0 Akron - W. Exchange Annual Average Conc. Design Values 2012 Modeling Results2005 BY 2002 BY

Key Site County Site ID '03 '04 '05 '06 '07 '03 - '05 '04 - '06 '05 - '07 Average

w/ 2007 Average Round 5

OTB Round 5

Will Do Round4 Key Site

Chicago - Washington HS Cook 170310022 15.6 14.2 16.9 13.2 15.7 15.6 14.8 15.3 15.2 15.9 13.9 13.8 14.4 Chicago - Washington HS
Chicago - Mayfair Cook 170310052 15.9 15.3 17.0 14.5 15.5 16.1 15.6 15.7 15.8 17.1 13.9 13.8 15.0 Chicago - Mayfair
Chicago - Springfield Cook 170310057 15.6 13.8 16.7 13.5 15.1 15.4 14.7 15.1 15.0 15.6 13.7 13.5 14.1 Chicago - Springfield
Chicago - Lawndale Cook 170310076 14.8 14.2 16.6 13.5 14.3 15.2 14.8 14.8 14.9 15.6 13.6 13.4 14.1 Chicago - Lawndale

Blue Island Cook 170312001 14.9 14.1 16.4 13.2 14.3 15.1 14.6 14.6 14.8 15.6 13.4 13.3 14.1 Blue Island

Summit Cook 170313301 15.6 14.2 16.9 13.8 14.8 15.6 15.0 15.2 15.2 16.0 13.9 13.8 14.4 Summit

Cicero Cook 170316005 16.8 15.2 16.3 14.3 14.8 16.1 15.3 15.1 15.5 16.4 14.2 14.0 14.9 Cicero
Granite City Madison 171191007 17.5 15.4 18.2 16.3 15.1 17.0 16.6 16.5 16.7 17.3 14.3 14.2 15.5 Granite City

E. St. Louis St. Clair 171630010 14.9 14.7 17.1 14.5 15.6 15.6 15.4 15.7 15.6 16.2 13.4 13.3 14.5 E. St. Louis

Jeffersonville Clark 180190005 15.8 15.1 18.5 15.0 16.5 16.5 16.2 16.7 16.4 17.2 13.4 13.4 14.4 Jeffersonville

Jasper Dubois 180372001 15.7 14.4 16.9 13.5 14.4 15.7 14.9 14.9 15.2 15.5 11.8 11.9 13.0 Jasper
Gary Lake 180890031 16.8 13.3 14.5 16.8 15.1 14.9 15.6 12.4 12.4 Gary

Indy- Washington Park Marion 180970078 15.5 14.3 16.4 14.1 15.8 15.4 14.9 15.4 15.3 16.2 12.0 12.1 13.7 Indy- Washington Park

Indy- W 18th Street Marion 180970081 16.2 15.0 17.9 14.2 16.1 16.4 15.7 16.1 16.0 12.6 12.7 Indy- W 18th Street
Indy- Michigan Street Marion 180970083 16.3 15.0 17.5 14.1 15.9 16.3 15.5 15.8 15.9 16.6 12.6 12.6 14.0 Indy- Michigan Street

Allen Park Wayne 261630001 15.2 14.2 15.9 13.2 12.8 15.1 14.4 14.0 14.5 15.8 12.4 12.4 13.3 Allen Park

Southwest HS Wayne 261630015 16.6 15.4 17.2 14.7 14.5 16.4 15.8 15.5 15.9 17.3 13.5 13.5 14.4 Southwest HS

Linwood Wayne 261630016 15.8 13.7 16.0 13.0 13.9 15.2 14.2 14.3 14.6 15.5 12.5 12.5 13.0 Linwood

Dearborn Wayne 261630033 19.2 16.8 18.6 16.1 16.9 18.2 17.2 17.2 17.5 19.3 15.1 15.1 16.1 Dearborn

Wyandotte Wayne 261630036 16.3 13.7 16.4 12.9 13.4 15.5 14.3 14.2 14.7 16.6 12.5 12.5 13.9 Wyandotte

Middleton Butler 390170003 17.2 14.1 19.0 14.1 15.4 16.8 15.7 16.2 16.2 16.5 12.8 12.8 13.1 Middleton

Fairfield Butler 390170016 15.8 14.7 17.9 14.0 14.9 16.1 15.5 15.6 15.8 15.9 12.5 12.6 12.2 Fairfield

Cleveland-28th Street Cuyahoga 390350027 15.4 15.6 17.3 13.0 14.5 16.1 15.3 14.9 15.4 16.5 12.7 12.9 12.9 Cleveland-28th Street

Cleveland-St. Tikhon Cuyahoga 390350038 17.6 17.5 19.2 14.9 16.2 18.1 17.2 16.8 17.4 18.4 14.3 14.5 14.4 Cleveland-St. Tikhon
Cleveland-Broadway Cuyahoga 390350045 16.4 15.3 19.3 14.1 15.3 17.0 16.2 16.2 16.5 16.7 13.5 13.7 13.1 Cleveland-Broadway
Cleveland-E14 & Orange Cuyahoga 390350060 17.2 16.4 19.4 15.0 15.9 17.7 16.9 16.8 17.1 17.6 14.1 14.2 13.7 Cleveland-E14 & Orange

Newburg Hts - Harvard Ave Cuyahoga 390350065 15.6 15.2 18.6 13.1 15.8 16.5 15.6 15.8 16.0 16.2 13.1 13.3 12.6 Newburg Hts - Harvard Ave
Columbus - Fairgrounds Franklin 390490024 16.4 15.0 16.4 13.6 14.6 15.9 15.0 14.9 15.3 16.5 12.0 12.1 13.0 Columbus - Fairgrounds

Columbus - Ann Street Franklin 390490025 15.3 14.6 16.5 13.8 14.7 15.5 15.0 15.0 15.1 16.0 11.9 11.9 12.5 Columbus - Ann Street
Columbus - Maple Canyon Franklin 390490081 14.9 13.6 14.6 12.9 13.1 14.4 13.7 13.5 13.9 16.0 10.9 11.0 12.5 Columbus - Maple Canyon
Cincinnati - Seymour Hamilton 390610014 17.0 15.9 19.8 15.5 16.5 17.6 17.1 17.3 17.3 17.7 13.8 13.9 14.0 Cincinnati - Seymour

Cincinnati - Taft Ave Hamilton 390610040 15.5 14.6 17.5 13.6 15.1 15.9 15.2 15.4 15.5 15.7 12.2 12.3 12.3 Cincinnati - Taft Ave

Cincinnati - 8th Ave Hamilton 390610042 16.7 16.0 19.1 14.9 15.9 17.3 16.7 16.6 16.9 17.3 13.4 13.4 13.2 Cincinnati - 8th Ave

Sharonville Hamilton 390610043 15.7 14.9 16.9 14.5 14.8 15.8 15.4 15.4 15.6 16.0 12.3 12.4 12.2 Sharonville

Norwood Hamilton 390617001 16.0 15.3 18.4 14.4 15.1 16.6 16.0 16.0 16.2 16.3 12.8 12.8 12.8 Norwood

St. Bernard Hamilton 390618001 17.3 16.4 20.0 15.9 16.1 17.9 17.4 17.3 17.6 17.3 14.0 14.1 13.8 St. Bernard

Steubenville Jefferson 390810016 17.7 15.9 16.4 13.8 16.2 16.7 15.4 15.5 15.8 17.7 12.7 12.7 16.2 Steubenville

Mingo Junction Jefferson 390811001 17.3 16.2 18.1 14.6 15.6 17.2 16.3 16.1 16.5 17.5 13.4 13.4 15.3 Mingo Junction

Ironton Lawrence 390870010 14.3 13.7 17.0 14.4 15.0 15.0 15.0 15.5 15.2 15.7 12.3 12.3 13.2 Ironton

Dayton Montgomery 391130032 15.9 14.5 17.4 13.6 15.6 15.9 15.2 15.5 15.5 15.9 12.4 12.5 12.3 Dayton

New Boston Scioto 391450013 14.7 13.0 16.2 14.3 14.0 14.6 14.5 14.8 14.7 17.1 11.6 11.6 14.2 New Boston

Canton - Dueber Stark 391510017 16.8 15.6 17.8 14.6 15.9 16.7 16.0 16.1 16.3 17.3 13.3 13.3 13.6 Canton - Dueber

Canton - Market Stark 391510020 15.0 14.1 16.6 11.9 14.4 15.2 14.2 14.3 14.6 15.7 11.9 12.0 12.2 Canton - Market

Akron - Brittain Summit 391530017 15.4 15.0 16.4 13.5 14.4 15.6 15.0 14.8 15.1 16.4 12.3 12.3 12.9 Akron - Brittain

Akron - W. Exchange Summit 391530023 14.2 13.9 15.7 12.8 13.7 14.6 14.1 14.1 14.3 15.6 11.5 11.6 12.2 Akron - W. Exchange Annual Average Conc. Design Values 2018 Modeling Results

### 24-Hour PM 2.5 Base Year

Key Site County Site ID '03 '04 '05 '06 '07 '03-'05 '04-'06 '05-'07 Average

w/ 2007 2009 2012 2018 Key Site

Chicago - Washington HS Cook 170310022 37.7 32.5 45.7 27.0 35.7 38.6 35.1 36.1 36.6 36 36 35 Chicago - Washington HS
Chicago - Mayfair Cook 170310052 37.3 38.8 48.3 31.6 39.4 41.5 39.6 39.8 40.3 36 36 36 Chicago - Mayfair
Chicago - Springfield Cook 170310057 36.4 33.1 46.5 27.7 38.9 38.7 35.8 37.7 37.4 32 32 31 Chicago - Springfield
Chicago - Lawndale Cook 170310076 32.6 39.7 45.1 29.0 37.2 39.1 37.9 37.1 38.1 35 35 34 Chicago - Lawndale

McCook Cook 170311016 43.0 39 39 38 McCook

Blue Island Cook 170312001 39.6 38.5 43.8 28.1 35.1 40.6 36.8 35.7 37.7 34 34 33 Blue Island

Schiller Park Cook 170313103 40.7 50.3 30.0 36.6 45.5 40.3 39.0 41.6 39 39 39 Schiller Park

Summit Cook 170313301 38.4 42.4 49.1 27.4 36.7 43.3 39.6 37.7 40.2 38 38 37 Summit

Maywood Cook 170316005 38.5 42.5 44.6 29.2 36.9 41.9 38.8 36.9 39.2 38 38 37 Maywood
Granite City Madison 171191007 40.8 35.4 44.1 36.3 36.0 40.1 38.6 38.8 39.2 33 33 32 Granite City

E. St. Louis St. Clair 171630010 32.6 30.2 39.6 29.2 33.1 34.1 33.0 34.0 33.7 28 28 28 E. St. Louis

Jeffersonville Clark 180190005 28.4 45.5 35.9 43.3 37.0 36.6 41.6 38.4 29 31 31 Jeffersonville

Jasper Dubois 180372001 39.5 30.0 41.2 31.6 39.5 36.9 34.3 37.4 36.2 28 29 28 Jasper
Gary - IITRI Lake 180890022 39.0 34 34 35 Gary - IITRI
Gary - Burr School Lake 180890026 39.0 33 34 32 Gary - Burr School
Gary Lake 180890031 38.7 27.1 36.2 38.7 32.9 34.0 35.2 24 24 27 Gary
Indy-West Street Marion 180970043 38.0 33 33 33 Indy-West Street

Indy-English Avenue Marion 180970066 38.0 32 32 32 Indy-English Avenue
Indy-Washington Park Marion 180970078 39.3 31.0 42.5 31.7 37.6 37.6 35.1 37.3 36.6 31 31 32 Indy-Washington Park
Indy-W 18th Street Marion 180970081 36.2 31.9 45.7 34.8 38.4 37.9 37.5 39.6 38.3 31 31 31 Indy-W 18th Street
Indy- Michigan Street Marion 180970083 36.7 31.3 40.3 33.5 37.2 36.1 35.0 37.0 36.0 28 28 29 Indy- Michigan Street

Luna Pier Monroe 261150005 34.7 35.0 49.3 32.6 32.2 39.7 39.0 38.0 38.9 32 32 31 Luna Pier

Oak Park Oakland 261250001 36.6 32.5 52.2 33.0 35.3 40.4 39.2 40.2 39.9 36 36 35 Oak Park

Port Huron St. Clair 261470005 37.2 32.2 47.6 37.9 36.3 39.0 39.2 40.6 39.6 34 34 33 Port Huron

Ypsilanti Washtenaw 261610008 38.8 31.5 52.1 31.3 34.5 40.8 38.3 39.3 39.5 35 35 34 Ypsilanti

Allen Park Wayne 261630001 40.5 36.9 43.0 34.1 35.9 40.1 38.0 37.7 38.6 35 34 33 Allen Park

Southwest HS Wayne 261630015 33.6 36.0 49.7 36.2 34.0 39.8 40.6 40.0 40.1 35 35 33 Southwest HS

Linwood Wayne 261630016 46.2 38.3 51.8 36.9 34.8 45.4 42.3 41.2 43.0 39 39 38 Linwood

E 7 Mile Wayne 261630019 37.1 35.0 52.3 36.2 33.0 41.5 41.2 40.5 41.0 38 38 37 E 7 Mile

Dearborn Wayne 261630033 42.8 39.4 50.2 43.1 36.6 44.1 44.2 43.3 43.9 40 40 39 Dearborn

Wyandotte Wayne 261630036 34.8 32.3 46.7 33.2 28.6 37.9 37.4 36.2 37.2 35 35 34 Wyandotte

Newberry Wayne 261630038 36.8 57.5 28.6 33.4 39.1 39.8 42.7 38 37 36 Newberry

FIA Wayne 261630039 43.9 32.4 34.8 37.0 39.7 33 33 31 FIA

Middleton Butler 390170003 38.6 37.2 47.6 30.2 37.1 41.1 38.3 38.3 39.3 28 28 27 Middleton

Fairfield Butler 390170016 34.8 32.2 43.4 35.2 34.5 36.8 36.9 37.7 37.1 27 28 27 Fairfield

Butler 390170017 34.6 34.3 44.9 37.9 39.6 40.8 29 29 28

Cleveland-28th Street Cuyahoga 390350027 41.3 40.9 35.7 31.5 39.0 39.3 36.0 35.4 36.9 32 32 31 Cleveland-28th Street

Cleveland-St. Tikhon Cuyahoga 390350038 47.3 42.5 51.2 36.1 39.7 44.9 47.0 42.3 44.2 36 35 34 Cleveland-St. Tikhon
Cleveland-Broadway Cuyahoga 390350045 42.2 36.1 46.2 29.5 37.0 41.5 37.3 37.6 38.8 31 30 29 Cleveland-Broadway
Cleveland-GT Craig Cuyahoga 390350060 45.5 42.2 49.5 31.0 38.7 45.7 40.9 39.7 42.1 37 37 35 Cleveland-GT Craig

Newburg Hts - Harvard Ave Cuyahoga 390350065 39.1 36.1 47.9 27.8 39.1 41.0 37.3 38.3 38.9 31 30 30 Newburg Hts - Harvard Ave
Columbus - Fairgrounds Franklin 390490024 39.2 35.1 45.0 34.0 34.2 39.8 38.0 37.7 38.5 33 32 31 Columbus - Fairgrounds

Columbus - Ann Street Franklin 390490025 37.0 35.5 44.9 34.0 35.5 39.1 38.1 38.1 38.5 31 31 30 Columbus - Ann Street

Cincinnait Hamilton 390610006 45.0 33.3 34.7 37.7 40.6 27 28 27 Cincinnait
Cincinnati - Seymour Hamilton 390610014 37.8 42.0 38.5 35.2 38.1 39.4 38.6 37.3 38.4 26 25 24 Cincinnati - Seymour

Cincinnati - Taft Ave Hamilton 390610040 31.9 30.5 45.8 32.8 34.7 36.1 36.4 37.8 36.7 24 24 23 Cincinnati - Taft Ave

Cincinnati - 8th Ave Hamilton 390610042 33.8 31.9 44.4 34.5 35.9 36.7 36.9 38.3 37.3 28 28 27 Cincinnati - 8th Ave

Sharonville Hamilton 390610043 37.3 31.4 39.9 34.9 34.0 36.2 35.4 36.3 36.0 28 28 27 Sharonville

Norwood Hamilton 390617001 37.1 34.6 47.1 34.0 33.7 39.6 38.6 38.3 38.8 30 30 29 Norwood

St. Bernard Hamilton 390618001 35.8 33.9 51.4 36.1 35.4 40.4 40.5 41.0 40.6 30 30 29 St. Bernard

Steubenville Jefferson 390810016 39.6 43.8 43.8 32.1 43.5 42.4 39.9 39.8 40.7 29 28 28 Steubenville

Mingo Junction Jefferson 390811001 40.9 51.5 44.2 32.9 35.4 45.5 42.9 37.5 42.0 30 30 30 Mingo Junction

Dayton Montgomery 391130032 42.7 32.5 45.0 30.3 36.9 40.1 35.9 37.4 37.8 30 30 30 Dayton

Canton - Dueber Stark 391510017 34.2 36.3 47.6 32.2 33.4 39.4 38.7 37.7 38.6 28 28 27 Canton - Dueber

Akron - Brittain Summit 391530017 36.9 36.9 45.2 31.5 33.3 39.7 37.9 36.7 38.1 30 30 29 Akron - Brittain

Green Bay - Est High Brown 550090005 33.5 32.3 41.5 36.9 37.1 35.8 36.9 38.5 37.1 35 34 32 Green Bay - Est High

Madison Dane 550250047 32.0 31.9 40.1 33.4 44.3 34.7 35.1 39.3 36.4 32 31 29 Madison

Milwaukee-Health Center Milwaukee 550790010 33.2 38.4 38.7 40.7 40.6 36.8 39.3 40.0 38.7 35 34 33 Milwaukee-Health Center
Milwaukee-SER Hdqs Milwaukee 550790026 29.6 28.7 41.5 42.6 39.8 33.3 37.6 41.3 37.4 34 34 33 Milwaukee-SER Hdqs
Milwaukee-Virginia FS Milwaukee 550790043 39.2 41.4 37.1 44.0 38 39.2 40.8 39.7 39.9 36 36 36 Milwaukee-Virginia FS

Milwaukee- Fire Dept Hdqs Milwaukee 550790099 33.7 38.9 37.1 38.3 40.7 36.6 38.1 38.7 37.8 33 32 32 Milwaukee- Fire Dept Hdqs

Waukesha Waukesha 551330027 29.1 38.4 41.1 28.2 33.8 36.2 35.9 34.4 35.5 31 31 29 Waukesha 98th Percentile (24-hour) Design Values Round 5 Modeling ResultsSite ID State County Season Species Species Comp. of Ave.
FRM (fraction) Species RRF

1703100521 IL Cook winter so4 0.1772 0.9342

1703100521 IL Cook winter no3 0.3099 1.0128

1703100521 IL Cook winter ocm 0.2147 0.9942

1703100521 IL Cook winter ec 0.0372 0.888

1703100521 IL Cook winter soil 0.0242 1.1674

1703100521 IL Cook winter nh4 0.1421 0.97

1703100521 IL Cook winter pbw 0.0947 0.9678

1703100521 IL Cook spring so4 0.32 0.8018

1703100521 IL Cook spring no3 0.0609 0.9385

1703100521 IL Cook spring ocm 0.2742 1.0629

1703100521 IL Cook spring ec 0.0501 0.8712

1703100521 IL Cook spring soil 0.0505 1.1796

1703100521 IL Cook spring nh4 0.1203 0.8619

1703100521 IL Cook spring pbw 0.0984 0.8492

1703100521 IL Cook summer so4 0.3089 0.725

1703100521 IL Cook summer no3 0 1.0124

1703100521 IL Cook summer ocm 0.1599 1.069

1703100521 IL Cook summer ec 0.0351 0.8683

1703100521 IL Cook summer soil 0.0318 1.204

1703100521 IL Cook summer nh4 0.0932 0.7354

1703100521 IL Cook summer pbw 0.094 0.7217

1703100521 IL Cook fall so4 0.1872 0.9151

1703100521 IL Cook fall no3 0.1628 0.9408

1703100521 IL Cook fall ocm 0.2389 1.0091

1703100521 IL Cook fall ec 0.0403 0.8623

1703100521 IL Cook fall soil 0.0284 1.1443

1703100521 IL Cook fall nh4 0.1062 0.9247

1703100521 IL Cook fall pbw 0.0614 0.9233

1711910071 IL Madison winter so4 0.213 0.9195

1711910071 IL Madison winter no3 0.2705 1.0306

1711910071 IL Madison winter ocm 0.2093 0.9289

1711910071 IL Madison winter ec 0.0434 0.9083

1711910071 IL Madison winter soil 0.0306 1.1782

1711910071 IL Madison winter nh4 0.1528 0.9513

1711910071 IL Madison winter pbw 0.0804 0.9243

1711910071 IL Madison spring so4 0.3194 0.7717

1711910071 IL Madison spring no3 0.0189 0.8611

1711910071 IL Madison spring ocm 0.2455 1.1103

1711910071 IL Madison spring ec 0.0564 1.0046

1711910071 IL Madison spring soil 0.0459 1.2252

1711910071 IL Madison spring nh4 0.1121 0.7894

1711910071 IL Madison spring pbw 0.1085 0.7783

1711910071 IL Madison summer so4 0.313 0.705

1711910071 IL Madison summer no3 0 0.884

1711910071 IL Madison summer ocm 0.153 1.1546

1711910071 IL Madison summer ec 0.0345 1.0513

1711910071 IL Madison summer soil 0.0302 1.2532

1711910071 IL Madison summer nh4 0.102 0.7409

1711910071 IL Madison summer pbw 0.1096 0.7133

1711910071 IL Madison fall so4 0.2058 0.9037

1711910071 IL Madison fall no3 0.1308 0.9426

1711910071 IL Madison fall ocm 0.259 1.0233

1711910071 IL Madison fall ec 0.0563 0.9248

1711910071 IL Madison fall soil 0.0549 1.1412

1711910071 IL Madison fall nh4 0.1073 0.9185

1711910071 IL Madison fall pbw 0.0655 0.918



### PM2.5 RRFs by Species and Season (2009)Site ID State County Season Species Species Comp. of Ave.
FRM (fraction) Species RRF

1803720011 IN Dubois winter so4 0.2669 0.8833

1803720011 IN Dubois winter no3 0.2548 0.9526

1803720011 IN Dubois winter ocm 0.1747 0.9374

1803720011 IN Dubois winter ec 0.0313 0.9319

1803720011 IN Dubois winter soil 0.0192 1.1349

1803720011 IN Dubois winter nh4 0.1646 0.9069

1803720011 IN Dubois winter pbw 0.0885 0.9006

1803720011 IN Dubois spring so4 0.4141 0.6808

1803720011 IN Dubois spring no3 0.0022 0.8106

1803720011 IN Dubois spring ocm 0.178 0.9997

1803720011 IN Dubois spring ec 0.0324 0.9083

1803720011 IN Dubois spring soil 0.0218 1.1284

1803720011 IN Dubois spring nh4 0.1432 0.7075

1803720011 IN Dubois spring pbw 0.1556 0.6916

1803720011 IN Dubois summer so4 0.3687 0.644

1803720011 IN Dubois summer no3 0 0.8029

1803720011 IN Dubois summer ocm 0.1174 1.0136

1803720011 IN Dubois summer ec 0.0207 0.913

1803720011 IN Dubois summer soil 0.0213 1.1988

1803720011 IN Dubois summer nh4 0.1168 0.6789

1803720011 IN Dubois summer pbw 0.1246 0.6613

1803720011 IN Dubois fall so4 0.2964 0.8232

1803720011 IN Dubois fall no3 0.138 0.8797

1803720011 IN Dubois fall ocm 0.2116 0.9861

1803720011 IN Dubois fall ec 0.0437 0.9019

1803720011 IN Dubois fall soil 0.03 1.1387

1803720011 IN Dubois fall nh4 0.1449 0.8444

1803720011 IN Dubois fall pbw 0.0941 0.8558

1809700811 IN Marion winter so4 0.2358 0.9192

1809700811 IN Marion winter no3 0.2729 0.9769

1809700811 IN Marion winter ocm 0.1851 0.9546

1809700811 IN Marion winter ec 0.0385 0.8647

1809700811 IN Marion winter soil 0.0239 1.0835

1809700811 IN Marion winter nh4 0.1561 0.9446

1809700811 IN Marion winter pbw 0.0877 0.944

1809700811 IN Marion spring so4 0.3745 0.6868

1809700811 IN Marion spring no3 0.0167 0.8082

1809700811 IN Marion spring ocm 0.2034 0.9881

1809700811 IN Marion spring ec 0.0447 0.8547

1809700811 IN Marion spring soil 0.0376 1.0625

1809700811 IN Marion spring nh4 0.1313 0.7182

1809700811 IN Marion spring pbw 0.1309 0.7056

1809700811 IN Marion summer so4 0.3582 0.6529

1809700811 IN Marion summer no3 0 0.8099

1809700811 IN Marion summer ocm 0.1231 1.0043

1809700811 IN Marion summer ec 0.03 0.8444

1809700811 IN Marion summer soil 0.0253 1.0918

1809700811 IN Marion summer nh4 0.1114 0.6854

1809700811 IN Marion summer pbw 0.1163 0.6674

1809700811 IN Marion fall so4 0.2751 0.8538

1809700811 IN Marion fall no3 0.149 0.9452

1809700811 IN Marion fall ocm 0.223 0.9648

1809700811 IN Marion fall ec 0.0525 0.8412

1809700811 IN Marion fall soil 0.0358 1.089

1809700811 IN Marion fall nh4 0.1378 0.8905

1809700811 IN Marion fall pbw 0.0865 0.8888Site ID State County Season Species Species Comp. of Ave.
FRM (fraction) Species RRF

2616300331 MI Wayne winter so4 0.1587 0.9206

2616300331 MI Wayne winter no3 0.2394 0.9813

2616300331 MI Wayne winter ocm 0.3193 1.0781

2616300331 MI Wayne winter ec 0.0383 0.9279

2616300331 MI Wayne winter soil 0.0541 1.0206

2616300331 MI Wayne winter nh4 0.1188 0.9518

2616300331 MI Wayne winter pbw 0.0714 0.9566

2616300331 MI Wayne spring so4 0.3383 0.7398

2616300331 MI Wayne spring no3 0.0259 0.8787

2616300331 MI Wayne spring ocm 0.3543 1.0234

2616300331 MI Wayne spring ec 0.0504 0.8671

2616300331 MI Wayne spring soil 0.0915 1.0153

2616300331 MI Wayne spring nh4 0.1191 0.7818

2616300331 MI Wayne spring pbw 0.1126 0.7619

2616300331 MI Wayne summer so4 0.3311 0.6681

2616300331 MI Wayne summer no3 0 0.8431

2616300331 MI Wayne summer ocm 0.2297 1.0029

2616300331 MI Wayne summer ec 0.0362 0.8332

2616300331 MI Wayne summer soil 0.061 1.0177

2616300331 MI Wayne summer nh4 0.1027 0.6974

2616300331 MI Wayne summer pbw 0.1073 0.6754

2616300331 MI Wayne fall so4 0.1898 0.854

2616300331 MI Wayne fall no3 0.1075 0.9367

2616300331 MI Wayne fall ocm 0.3689 1.0607

2616300331 MI Wayne fall ec 0.0546 0.8862

2616300331 MI Wayne fall soil 0.1676 1.0317

2616300331 MI Wayne fall nh4 0.0866 0.8919

2616300331 MI Wayne fall pbw 0.0553 0.8821

3903500381 OH Cuyahoga winter so4 0.2117 0.8993

3903500381 OH Cuyahoga winter no3 0.2665 0.9856

3903500381 OH Cuyahoga winter ocm 0.2048 0.9716

3903500381 OH Cuyahoga winter ec 0.0413 0.8903

3903500381 OH Cuyahoga winter soil 0.0465 1.0959

3903500381 OH Cuyahoga winter nh4 0.1459 0.9416

3903500381 OH Cuyahoga winter pbw 0.0832 0.9541

3903500381 OH Cuyahoga spring so4 0.3334 0.7145

3903500381 OH Cuyahoga spring no3 0.0374 0.8393

3903500381 OH Cuyahoga spring ocm 0.2068 1.0899

3903500381 OH Cuyahoga spring ec 0.052 0.9362

3903500381 OH Cuyahoga spring soil 0.0697 1.0601

3903500381 OH Cuyahoga spring nh4 0.1256 0.7666

3903500381 OH Cuyahoga spring pbw 0.115 0.7761

3903500381 OH Cuyahoga summer so4 0.3241 0.6303

3903500381 OH Cuyahoga summer no3 0 0.89

3903500381 OH Cuyahoga summer ocm 0.1306 1.0998

3903500381 OH Cuyahoga summer ec 0.0419 0.9354

3903500381 OH Cuyahoga summer soil 0.0583 1.0906

3903500381 OH Cuyahoga summer nh4 0.1074 0.7038

3903500381 OH Cuyahoga summer pbw 0.1183 0.6674

3903500381 OH Cuyahoga fall so4 0.2055 0.8193

3903500381 OH Cuyahoga fall no3 0.1275 0.9189

3903500381 OH Cuyahoga fall ocm 0.2234 1.0245

3903500381 OH Cuyahoga fall ec 0.0499 0.8913

3903500381 OH Cuyahoga fall soil 0.0675 1.0927

3903500381 OH Cuyahoga fall nh4 0.1034 0.8615

3903500381 OH Cuyahoga fall pbw 0.0637 0.8564Site ID State County Season Species Species Comp. of Ave.
FRM (fraction) Species RRF

3904900241 OH Franklin winter so4 0.2555 0.8622

3904900241 OH Franklin winter no3 0.2373 1.0002

3904900241 OH Franklin winter ocm 0.2082 0.974

3904900241 OH Franklin winter ec 0.0375 0.8537

3904900241 OH Franklin winter soil 0.0259 1.0844

3904900241 OH Franklin winter nh4 0.1495 0.9261

3904900241 OH Franklin winter pbw 0.0861 0.9274

3904900241 OH Franklin spring so4 0.3754 0.6615

3904900241 OH Franklin spring no3 0.0176 0.8436

3904900241 OH Franklin spring ocm 0.2069 1.062

3904900241 OH Franklin spring ec 0.0405 0.8678

3904900241 OH Franklin spring soil 0.0371 1.0551

3904900241 OH Franklin spring nh4 0.1296 0.7212

3904900241 OH Franklin spring pbw 0.128 0.6992

3904900241 OH Franklin summer so4 0.3703 0.622

3904900241 OH Franklin summer no3 0 0.9056

3904900241 OH Franklin summer ocm 0.1343 1.0654

3904900241 OH Franklin summer ec 0.0311 0.8565

3904900241 OH Franklin summer soil 0.0267 1.0667

3904900241 OH Franklin summer nh4 0.1142 0.7021

3904900241 OH Franklin summer pbw 0.1186 0.6614

3904900241 OH Franklin fall so4 0.2692 0.8119

3904900241 OH Franklin fall no3 0.1186 0.9099

3904900241 OH Franklin fall ocm 0.2489 1.019

3904900241 OH Franklin fall ec 0.0533 0.8371

3904900241 OH Franklin fall soil 0.0423 1.0924

3904900241 OH Franklin fall nh4 0.1217 0.8539

3904900241 OH Franklin fall pbw 0.0821 0.8519

3906100141 OH Hamilton winter so4 0.2685 0.8104

3906100141 OH Hamilton winter no3 0.2378 1.0886

3906100141 OH Hamilton winter ocm 0.19 0.961

3906100141 OH Hamilton winter ec 0.035 0.8969

3906100141 OH Hamilton winter soil 0.0229 1.4146

3906100141 OH Hamilton winter nh4 0.1583 0.9077

3906100141 OH Hamilton winter pbw 0.0874 0.8687

3906100141 OH Hamilton spring so4 0.3583 0.6331

3906100141 OH Hamilton spring no3 0.0025 1.0155

3906100141 OH Hamilton spring ocm 0.1986 1.0798

3906100141 OH Hamilton spring ec 0.0466 0.9228

3906100141 OH Hamilton spring soil 0.0289 1.3785

3906100141 OH Hamilton spring nh4 0.1215 0.6968

3906100141 OH Hamilton spring pbw 0.128 0.6307

3906100141 OH Hamilton summer so4 0.3722 0.577

3906100141 OH Hamilton summer no3 0 1.0923

3906100141 OH Hamilton summer ocm 0.121 1.082

3906100141 OH Hamilton summer ec 0.0309 0.9099

3906100141 OH Hamilton summer soil 0.0199 1.537

3906100141 OH Hamilton summer nh4 0.1178 0.6441

3906100141 OH Hamilton summer pbw 0.1261 0.5734

3906100141 OH Hamilton fall so4 0.2608 0.7754

3906100141 OH Hamilton fall no3 0.1184 0.9857

3906100141 OH Hamilton fall ocm 0.213 1.0235

3906100141 OH Hamilton fall ec 0.0512 0.8876

3906100141 OH Hamilton fall soil 0.0328 1.4007

3906100141 OH Hamilton fall nh4 0.1254 0.846

3906100141 OH Hamilton fall pbw 0.0828 0.8172Site ID State County Season Species Species Comp. of Ave.
FRM (fraction) Species RRF

3908110011 OH Jefferson winter so4 0.2367 0.8217

3908110011 OH Jefferson winter no3 0.1709 1.0522

3908110011 OH Jefferson winter ocm 0.3288 0.8819

3908110011 OH Jefferson winter ec 0.0435 0.9091

3908110011 OH Jefferson winter soil 0.0272 0.4368

3908110011 OH Jefferson winter nh4 0.1199 0.8904

3908110011 OH Jefferson winter pbw 0.073 0.8583

3908110011 OH Jefferson spring so4 0.3508 0.6666

3908110011 OH Jefferson spring no3 0.0154 0.9156

3908110011 OH Jefferson spring ocm 0.3078 0.9995

3908110011 OH Jefferson spring ec 0.0395 0.9853

3908110011 OH Jefferson spring soil 0.0407 0.4844

3908110011 OH Jefferson spring nh4 0.114 0.7054

3908110011 OH Jefferson spring pbw 0.1095 0.6713

3908110011 OH Jefferson summer so4 0.3779 0.6156

3908110011 OH Jefferson summer no3 0 1.0837

3908110011 OH Jefferson summer ocm 0.2098 1.0145

3908110011 OH Jefferson summer ec 0.0308 0.9689

3908110011 OH Jefferson summer soil 0.0323 0.3632

3908110011 OH Jefferson summer nh4 0.1065 0.6428

3908110011 OH Jefferson summer pbw 0.1007 0.625

3908110011 OH Jefferson fall so4 0.2315 0.7694

3908110011 OH Jefferson fall no3 0.0702 1.0302

3908110011 OH Jefferson fall ocm 0.372 0.9312

3908110011 OH Jefferson fall ec 0.051 0.9086

3908110011 OH Jefferson fall soil 0.0344 0.4555

3908110011 OH Jefferson fall nh4 0.0859 0.8284

3908110011 OH Jefferson fall pbw 0.0629 0.7951

3911300321 OH Montgomer winter so4 0.2613 0.8598

3911300321 OH Montgomer winter no3 0.2407 1.029

3911300321 OH Montgomer winter ocm 0.1954 0.9442

3911300321 OH Montgomer winter ec 0.036 0.8746

3911300321 OH Montgomer winter soil 0.0259 1.1295

3911300321 OH Montgomer winter nh4 0.1531 0.9304

3911300321 OH Montgomer winter pbw 0.0876 0.9205

3911300321 OH Montgomer spring so4 0.3659 0.6606

3911300321 OH Montgomer spring no3 0.0163 0.8639

3911300321 OH Montgomer spring ocm 0.1895 1.0976

3911300321 OH Montgomer spring ec 0.0442 0.9417

3911300321 OH Montgomer spring soil 0.0253 1.0873

3911300321 OH Montgomer spring nh4 0.1313 0.7149

3911300321 OH Montgomer spring pbw 0.1326 0.6839

3911300321 OH Montgomer summer so4 0.375 0.6234

3911300321 OH Montgomer summer no3 0 0.9474

3911300321 OH Montgomer summer ocm 0.128 1.1047

3911300321 OH Montgomer summer ec 0.029 0.9496

3911300321 OH Montgomer summer soil 0.0205 1.1299

3911300321 OH Montgomer summer nh4 0.1114 0.6931

3911300321 OH Montgomer summer pbw 0.1114 0.6482

3911300321 OH Montgomer fall so4 0.3062 0.8033

3911300321 OH Montgomer fall no3 0.1012 0.9634

3911300321 OH Montgomer fall ocm 0.2221 1.0158

3911300321 OH Montgomer fall ec 0.0514 0.877

3911300321 OH Montgomer fall soil 0.028 1.1391

3911300321 OH Montgomer fall nh4 0.1352 0.8625

3911300321 OH Montgomer fall pbw 0.0982 0.8475Site ID State County Season Species Species Comp. of Ave.
FRM (fraction) Species RRF

3915100171 OH Stark winter so4 0.2362 0.8558

3915100171 OH Stark winter no3 0.2234 1.0222

3915100171 OH Stark winter ocm 0.2478 0.9255

3915100171 OH Stark winter ec 0.0414 0.8866

3915100171 OH Stark winter soil 0.0334 1.099

3915100171 OH Stark winter nh4 0.1376 0.925

3915100171 OH Stark winter pbw 0.0802 0.9155

3915100171 OH Stark spring so4 0.3581 0.6834

3915100171 OH Stark spring no3 0.0236 0.855

3915100171 OH Stark spring ocm 0.221 1.0892

3915100171 OH Stark spring ec 0.0501 1.0017

3915100171 OH Stark spring soil 0.058 1.0528

3915100171 OH Stark spring nh4 0.1288 0.7264

3915100171 OH Stark spring pbw 0.1256 0.7009

3915100171 OH Stark summer so4 0.3621 0.6277

3915100171 OH Stark summer no3 0 0.8203

3915100171 OH Stark summer ocm 0.1483 1.0984

3915100171 OH Stark summer ec 0.0403 1.016

3915100171 OH Stark summer soil 0.037 1.0781

3915100171 OH Stark summer nh4 0.1157 0.6739

3915100171 OH Stark summer pbw 0.124 0.651

3915100171 OH Stark fall so4 0.2293 0.8041

3915100171 OH Stark fall no3 0.1262 0.9363

3915100171 OH Stark fall ocm 0.2722 1.0226

3915100171 OH Stark fall ec 0.0545 0.9202

3915100171 OH Stark fall soil 0.0461 1.0959

3915100171 OH Stark fall nh4 0.1105 0.8549

3915100171 OH Stark fall pbw 0.0706 0.8428

3915300171 OH Summit winter so4 0.2511 0.8771

3915300171 OH Summit winter no3 0.2376 1.0052

3915300171 OH Summit winter ocm 0.2185 0.9429

3915300171 OH Summit winter ec 0.0334 0.8677

3915300171 OH Summit winter soil 0.0255 1.0835

3915300171 OH Summit winter nh4 0.1489 0.9374

3915300171 OH Summit winter pbw 0.0851 0.945

3915300171 OH Summit spring so4 0.387 0.7046

3915300171 OH Summit spring no3 0.0072 0.8466

3915300171 OH Summit spring ocm 0.1901 1.0967

3915300171 OH Summit spring ec 0.035 0.9482

3915300171 OH Summit spring soil 0.0304 1.0524

3915300171 OH Summit spring nh4 0.1294 0.7521

3915300171 OH Summit spring pbw 0.1342 0.7384

3915300171 OH Summit summer so4 0.3694 0.6378

3915300171 OH Summit summer no3 0 0.8587

3915300171 OH Summit summer ocm 0.1417 1.1077

3915300171 OH Summit summer ec 0.0332 0.9506

3915300171 OH Summit summer soil 0.0198 1.0744

3915300171 OH Summit summer nh4 0.1121 0.6961

3915300171 OH Summit summer pbw 0.1146 0.6691

3915300171 OH Summit fall so4 0.2443 0.8074

3915300171 OH Summit fall no3 0.1175 0.9392

3915300171 OH Summit fall ocm 0.2636 1.0252

3915300171 OH Summit fall ec 0.0623 0.8883

3915300171 OH Summit fall soil 0.0494 1.086

3915300171 OH Summit fall nh4 0.109 0.8622

3915300171 OH Summit fall pbw 0.0723 0.8506

### 119 

## APPENDIX I I

Ozone Source Appor tionment Modeling Results

### 120 

## APPENDIX I II

PM 2.5 

## Source Apportionment Modeling Results

## Chicago ( Cicero ) , Illinois

2005 (Round 5)

2012 (Round 4)

2018 (Round 5)

## Clark County, Indiana

2005 (Round 5)

2012 (Round 4)

2018 (Round 5)

## Dearborn , Mi chigan

2005 (Round 5)

2012 (Round 4)

2018 (Round 5)

## Cincinnati, Oh io

2005 (Round 5)

2012 (Round 4)

2018 (Round 5)

## Cleveland, Ohio

2005 (Round 5)

2012 (Round 4)

2018 (Round 5)

## Steubenville, Ohio

2005 (Round 5)

2012 (Round 4)

2018 (Round 5)

### 121 

## APPENDIX I V

Haze Source Apportionment Modeling Results

## Boundary Waters, Minnesota

2005 (Round 5)

201 8 (Round 4)

2018 (Round 5)

## Voyageurs, Minnesota

2005 (Round 5)

201 8 (Round 4)

2018 (Round 5)

## Seney, Michigan

2005 (Round 5)

201 8 (Round 4)

2018 (Round 5)

## Isle Royale, Mich igan

2005 (Round 5)

201 8 (Round 4)

2018 (Round 5)

## Shenandoah, Virginia

2005 (Round 5)

2018 (Round 4)

2018 (Round 5)

## Mammoth Cave, Kentucky

2005 (Round 5)

2018 (Round 4)

2018 (Round 5)

## Lye Brook, Vermont

2005 (Round 5)

2018 (Round 4)

2018 (Round 5)

## [This page intentionally left blank.]

# A p p e n d i x D 2 :

D e v e l o p m e n t o f 2 0 0 5 B a s e Y e a r G r o w t h a n d C o n t r o l

F a c t o r f o r L a k e M i c h i g a n A i r D i r e c t o r s C o n s o r t i u m ( L A D C O ) F i n a l R e p o r t

h t t p://www. l adco

. o rg/ repo rt s /rpo /em i s s i o n s /l adco _2005_b as e_y r_gr o wt h _an d_co n t ro l s _repo rt _f

i

n al

. pdf

## [This page intentionally left blank.]

# PECHAN

3622 Lyckan Parkway

Suite 2005

Durham, NC 27707

919-493-3144 telephone

919-493-3182 facsimile

5528-B Hempstead Way Springfield, VA 22151

703-813-6700 telephone

703-813-6729 facsimile 

# DEVELOPMENT OF 2005

BASE YEAR GROWTH AND

CONTROL FACTORS FOR

LAKE MICHIGAN AIR

DIRECTORS CONSORTIUM
(LADCO)

F INAL 

# R EPORT

Prepared for:

Mr. Michael Koerber

Executive Director Lake Michigan Air Directors Consortium

9501 West Devon Avenue, Suite 701

Rosemont, IL 60018

Prepared by:

Andrew D. Bollman

James H. Wilson

E.H. Pechan & Associates, Inc. 3622 Lyckan Parkway, Suite 2005

Durham, NC 27707

September 2007

Pechan Report No. 07.09.001/9458.000

## [ This page intentionally left blank]

## PECHAN September 2007



## iii Development of 2005 Base Year Growth and Control Factors for LADCO – Final Report 

# CONTENTS

Page

TABLES ......................................................................................................................... ............... iv

ACRONYMS AND ABBREVIATIONS....................................................................................... v

SECTION I. BACKGROUND ............................................................................................... 1

SECTION II. EMISSION ACTIVITY GROWTH DATA ....................................................... 3

A. OVERVIEW .................................................................................................................... ... 3

B. AREA SOURCE/MAR CATEGORIES............................................................................. 3

C. NON-EGU POINT SOURCES......................................................................................... 14

D. NONROAD MODEL SOURCES .................................................................................... 23

SECTION III. UPDATED EMISSION CONTROL DATA.................................................... 27

A. NON-EGU POINT SOURCE CONTROLS..................................................................... 27

1. NO

x 

## SIP Call................................................................................................................. 27

2. MACT Standards .......................................................................................................... 27

3. Consent Decrees............................................................................................................ 2 7 4. On-the-Books (OTB) Control Additions ...................................................................... 35

5. Best Available Retrof it Technology (BART)............................................................... 35

B. AREA SOURCE/MAR CONTROLS............................................................................... 40

1. Area Sources ................................................................................................................ . 40 2. MAR Sources (Locomotives and Marine Vessels)....................................................... 44

SECTION IV. PREPARATION OF GROWTH AND CONTROL FILES ............................. 49

A. GROWTH FACTORS...................................................................................................... 49

B. CONTROL FACTORS..................................................................................................... 51

1. Point Source Control Factors........................................................................................ 51

2. Area Source and MAR Control Factors........................................................................ 52

SECTION V. REFERENCES ................................................................................................. 55

## PECHAN September 2007



## iv Development of 2005 Base Year Growth and Control Factors for LADCO – Final Report 

## TABLES

Table II-1. Priority Emission Activ ity Area Source/MAR Categories........................................... 4

Table II-2. Growth Indicators for Pr iority Area Source/MAR Categories ..................................... 7 Table II-3. Priority Emission Ac tivity Point Source Categories................................................... 15

Table II-4. Growth Indicators for Pr iority Point Source Categories............................................. 19 Table II-5. Priority Emission Activity NONROAD Model Source Categories............................ 24

Table II-6. Growth Indicators for Priori ty NONROAD Model Source Categories...................... 25 Table III-1. RICE Engines in Illinois Affected by NO

x 

## SIP Call ................................................ 28

Table III-2. Post-2005 MACT St andards and Expected VOC, NO

x 

## , and PM Reductions.......... 29

Table III-3. LADCO State Non- EGU Point Sources Affected by Consent Decree Requirements

and Other On-the-Books Controls ................................................................................................ 32

Table III-4. Ohio RACT Rule Summary Cleveland/Akron 8-H our Ozone Nonattainment Area 35 Table III-5. BART Eligible Non-EGU Sources........................................................................... 36

Table III-6. Residential Wood Co mbustion NSPS Emission Reductions ................................... 43

Table III-7. Locomotive Emissions Reported in EPA Draft RIA................................................ 45 Table III-8. Percentage Reduc tions Associated with Federal Locomotive Standards................. 46 Table III-9. Percentage Re ductions Associated with Federal CMV Standards........................... 47

Table III-10. Commercial Marine Vessel Rule Penetration Values ............................................ 48 Table IV-1. RPO Data Exchange Protoc ol Format for Growth/Control Data.............................. 50 Table IV-2. Fields Populated in Growth Factor File .................................................................... 51 Table IV-3. Fields Populated in Po int Source Control Factor File............................................... 53 Table IV-4. Fields Populated in Area Source/MAR Control Factor File ..................................... 54

## PECHAN September 2007



## v Development of 2005 Base Year Growth and Control Factors for LADCO – Final Report 

# ACRONYMS AND ABBREVIATIONS

AEO Annual Energy Outlook

AIM architectural and industrial maintenance BART best available retrofit technology CE control efficiency

CMV commercial marine vessel DEQ Department of Environmental Quality DOE Department of Energy EGAS Economic Growth Analysis System EGU electric generating unit EPA United States Environmental Protection Agency

FAA Federal Aviation Administration FCCUs fluid catalytic cracking units FCU fluid coking units FGR flue gas recirculation HAP hazardous air pollutant

IC internal combustion LADCO Lake Michigan Air Directors Consortium

LAER lowest achievable emission rate

LNB low NO

x 

## burner

MACT maximum achievab le control technology
MAR marine, aircraft, and railroad

MSAT mobile source air toxics NAAQS national ambient air quality standards

NH3 ammonia NMIM National Mobile Inventory Model

NO

x 

## oxides of nitrogen NSPS new source performance standard

OTB on-the-books

OWB outdoor wood boiler
Pechan E.H. Pechan & Associates, Inc. PFC portable fuel container PM particulate matter PM-2.5 particulate matter less than or equal to 2.5 micrometers RACT reasonably availa ble control technology

RE rule effectiveness REMI Regional Economic Models, Inc. RIA Regulatory Impact Analysis RICE reciprocating inte rnal combustion engines ROG reactive organic gases RP rule penetration RPO Regional Planning Organization

SCC source classification code

## PECHAN September 2007



## vi Development of 2005 Base Year Growth and Control Factors for LADCO – Final Report 

## SIC standard industrial classification SIP State Implementation Plan

tpy tons per year

SO 2 

## sulfur dioxide VOC volatile organic compound

## PECHAN Septenber 2007



## 1 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

# SECTION I. BACKGROUND

E.H. Pechan & Associates, Inc. (Pechan) is supporting the Lake Michigan Air Directors Consortium’s (LADCO) efforts to forecast anthro pogenic emissions for the purpose of assessing progress for air quality goals, including goals re lated to regional haze and attainment of the ozone national ambient air quality standards (NAAQS). Under a previous contract with LADCO, Pechan prepared emission activity growth and emission control da ta for all non-electric generating unit (EGU) point, area, and nonroad sour ce categories relative to a base year (2002) inventory supplied by LADCO. In December 2004, Pechan submitted emissions activity growth and control factor files for use by LADCO in emissions modeling. A December 14, 2004 Pechan report documents the contents a nd derivation of these files (Pech an, 2004). Revised files were later provided to LADCO in March 2005.

In September 2005, LADCO contracted with Pech an to conduct the following two tasks to develop updated growth and cont rol factors needed to support future year control strategy analyses for regional haze, particulate matter less than or equal to 2.5 micrometers (PM-2.5), and

ozone:

Task 1: Update control factors to reflect current information pertaining to: (a) Petroleum refinery cases and settlements; (b) Maximum achievable control techno logy (MACT) standard control efficiency assumptions; (c) Residential wood combustion unit lifetime; and (d) Regional Planning Organization (RPO) inventories.

Task 2: Develop non-EGAS default-base d emission activity growth factors for: (a) Priority point source categories; and (b) Priority area source categories.

A December 2005 report describes Pechan efforts to perform these tasks, which resulted in updated emissions activity growth and control factor files (Pechan, 2005). The updates reflect

the use of more recent and/or more detailed info rmation than that used in the earlier study. As with the earlier study, this effort involved the pr eparation of emission activity growth and control information relative to a 2002 base year invent ory for future years of interest. Control information was developed for 2007, 2008, 2009, 2012, and 2018 (e.g., 2018 is the first milestone for regional haze reasonable progress de monstrations). Because the incremental level of effort required to develop emission activ ity growth factors for each year over the 2003-2018 period was nominal, Pechan prepared non-E GU point and area and nonroad source growth factors for each year ov er this entire period.

For the current study, LADCO requested that Pechan develop growth and c ontrol factor files to support emission projections from a recently compiled 2005 base year inventory for the following LADCO states: Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin. LADCO requested that Pechan provide files representing changes in emission activity and emission control between the base year and 200 9, 2012, and 2018. As with the previous studies, Pechan provided updated point/area source and ma rine, aircraft, and railroad (MAR) category

## PECHAN Septenber 2007



## 2 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

## growth factors for each year over the 2006-2018 pe riod. Control factor development focused on the modeling years of interest, or in the case of point source controls, the specific anticipated implementation date within the forecast period.

This report is organized into this Background section and:

• Section II, which describes the developm ent of the emission activity growth data; • Section III, which discusses how the update d emission control data were compiled;
• Section IV, which describes the preparation of the updated growth and control factor
files; and • Section V, which presents the referen ces consulted in preparing this report.

## PECHAN Septenber 2007



## 3 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

# SECTION II. EMISSION ACTIVITY GROWTH DATA

A. OVERVIEW

As with the two previous studies, Pechan relied on the data incorporated into Version 5.0 of the Economic Growth Analysis System (EGAS) as the default growth factor data source. 1 

## The EGAS 5.0 projections data are typically derived from two main resource s: (1) version 5.5 of Regional Economic Models Incorporated (REMI )’s state-level economic models; and (2) the Department of Energy (DOE)’s Annual Energy Outlook (AEO) 2004 . While socioeconomic growth indicators from the REMI models prov ide state-level growth rates, the DOE energy forecasts provide regional or national growth rate s (e.g., the same growth rate is applied to each

LADCO state because each of these states is includ ed in the DOE’s East North Central division). Instead of relying on REMI’s population forecasts, Pechan de veloped growth factors from county-level population projections avai lable from each LADCO region state.

LADCO requested that Pechan review the grow th indicators applied to particular source classification codes (SCCs) in the 2005 base ye ar inventory. For these “priority” source categories, Pechan evaluated alternative growth methodologies and data sources before selecting a forecasting approach. The balance of this sect ion describes the emission activity growth data developed in this study. Section IV discusses how these data were compiled into the file format required by LADCO.



## B. AREA SOURCE/MAR CATEGORIES

LADCO provided Pechan with a list of priority point/area source and MAR categories for which emission activity projection improvements were to be evaluated. For these source categories,

Pechan reviewed U.S. Environmental Prot ection Agency (EPA) SCC documentation and emission estimation guidance to identify the emi ssions activity (throughput) data associated with each SCC. Pechan then investig ated the availability of LADCO state-specific projections for

these data.

Table II-1 presents the descrip tions and emissions activity for each priority area source/MAR category. The last column in this table identif ies each category’s grow th indicator assignment under the previous Pechan forecast effort (Pech an, 2005). Table II-2 displays the assigned growth indicator for each priority area/MAR s ource category and any altern ative indicators that were considered. This table also presents the pe rcentage growth rates for the assigned indicators over two forecast periods: 2005-2009 and 2005-2018.

In addition to population data from the L ADCO states and REMI employment data, the following information sources supp lied data used in estimating emission activity growth for the priority area source/MAR categories:

1 

### Information on these EGAS 5.0 data sources is provided in the report documenting the earliest study Pechan performed for LADCO (Pechan, 2004).PECHANSeptember 20074Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-1. Priority Emission Activity Area Source/MAR CategoriesPOLLUTANTSCCDESC2DESC3DESC4EMISSIONS ACTIVITYCURRENT GROWTH BASISNH3 2805047100 Agriculture Production- LivestockSwine production - deeppit house operations(unspecified animalConfinement Annual average number of swine REMI Farm sector value addedNH3 2805039200 Agriculture Production- LivestockSwine production -operations with lagoons(unspecified animalManure handling andstorageAnnual average number of swine REMI Farm sector value addedNH3 2805047300 Agriculture Production- LivestockSwine production - deeppit house operations(unspecified animalLand application ofmanureAnnual average number of swine REMI Farm sector value addedNH3 2805001100 Agriculture Production- LivestockBeef cattle - finishingoperations on feedlots(drylots)Confinement Annual average number of beefcattleREMI Farm sector value addedNH3 2805039100 Agriculture Production- LivestockSwine production -operations with lagoons(unspecified animalConfinement Annual average number of swine REMI Farm sector value addedNH3 2805003100 Agriculture Production- LivestockBeef cattle - finishingoperations onpasture/rangeConfinement Annual average number of beefcattleREMI Farm sector value addedNH3 2805001300 Agriculture Production- LivestockBeef cattle - finishingoperations on feedlots(drylots)Land application ofmanureAnnual average number of beefcattleREMI Farm sector value addedNH3 2630020000 Wastewater Treatment Public Owned Total Processed Volume of wastewater processed REMI Water and Sanitation sector outputNH3 2805007100 Agriculture Production- LivestockPoultry production - layerswith dry manuremanagement systemConfinement Annual average number of poultry Regression with Food/Kindred Productssector value added as explanatory variableNH3 2805021300 Agriculture Production- LivestockDairy cattle - scrape dairy Land application ofmanureAnnual average number of dairycattleRegression with Farm sector employment asexplanatory variableNH3 2805030000 Agriculture Production- LivestockPoultry Waste Emissions Not ElsewhereClassified (see also 28-05-007 -008-009)Annual average number of poultry REMI Farm sector value addedNH3 2805021200 Agriculture Production- LivestockDairy cattle - scrape dairy Manure handling andstorageAnnual average number of dairycattleRegression with Farm sector employment asexplanatory variableNH3 2104008070 Residential Wood Outdoor Boiler Amount of wood burnedNOX 2285002006 Railroad Equipment Diesel Line Haul Locomotives:Class I OperationsAmount of diesel consumed byClass I line-haul locomotivesAEO Freight rail distillate (nat'l) adjusted forrelative state growth in REMI Total outputNOX 2280002023 Marine VesselsCommercialDiesel Push Boats Amount of diesel consumed bycommercial push boatsAEO Shipping distillate (nat'l) adjusted forrelative state growth in REMI WaterTransportation sector outputNOX 2102006001 Industrial Natural Gas All Boiler Types Volume of natural gas burned byindustrial area source boilersAEO Industrial natural gasPECHANSeptember 20075Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-1 (continued)POLLUTANTSCCDESC2DESC3DESC4EMISSIONS ACTIVITYCURRENT GROWTH BASISNOX 2275020000 Aircraft Commercial Aircraft Total:All Types Number of commercial aircraftlanding-takeoff cyclesFederal Aviation Administration (FAA)itinerant air carrier Landing and Take-Off(LTO) forecasts by stateNOX 2285002010 Railroad Equipment Diesel Yard Locomotives Amount of diesel consumed byyard locomotivesAEO Freight rail distillate (nat'l) adjustedfor relative state growth in REMI TotaloutputNOX 2104006000 Residential Natural Gas Total: All CombustorTypesVolume of residential naturalgas consumedAEO Residential natural gasNOX 2285002009 Railroad Equipment Diesel Line HaulLocomotives:Commuter LinesAmount of diesel consumed bycommuter locomotivesAEO Commuter rail diesel (nat'l)adjusted for relative growth in populationNOX 2280003200 Marine Vessels Commercial Residual Underway emissions Amount of residual oilconsumed by CMV duringunderway operationsAEO Shipping residual oil (nat'l) adjustedfor relative state growth in REMI WaterTransportation sector outputNOX 2102006000 Industrial Natural Gas Total: Boilers and ICEnginesVolume of natural gas burnedby industrial area source boilersand IC enginesAEO Industrial natural gasNOX 2104008070 Residential Wood Outdoor Boiler Amount of wood burnedROG 2461850000 Miscellaneous Non-industrial:CommercialPesticide Application:AgriculturalAll Processes (Not listed for this SCC)Region V employment projections for"Pesticide Handlers, Sprayers"ROG 2461020000 Miscellaneous Non-industrial:CommercialAsphalt Application: AllProcessesTotal: All SolventTypesAmount of solvent used Region Vemployment projections for"Paving, Surfacing, & TampingOperators"ROG 2401200000 Surface Coating Other Special PurposeCoatingsTotal: All SolventTypesAmount of solvent used PopulationROG 2401001000 Surface Coating Architectural Coatings Total: All SolventTypesAmount of solvent used Regression with Population (inc. countylevel) as explanatory variable + projectedsolvent content changeROG 2460100000 Miscellaneous Non-industrial:Consumer and CommercialAll Personal Care Products Total: All SolventTypesAmount of solvent used Population (county-level for LADCOstates)ROG 2501011010 Petroleum and PetroleumProduct StoragePortable Gas Cans Residential Volume ofgasoline stored REMI, Gas and Oil ExpendituresROG 2501060100 Petroleum and PetroleumProduct StorageGasoline Service Stations Stage 2: Total Volume of gasoline pumped bystationsRegression with Gas and OilExpenditures as explanatory variableROG 2460800000 Miscellaneous Non-industrial:Consumer and CommercialAll FIFRA Related Products Total: All SolventTypesAmount of solvent used Regressionwith Population (county-levelfor LADCO states) as explanatoryvariableROG 2501060050 Petroleum and PetroleumProduct StorageGasoline Service Stations Stage 1: Total Volume of gasoline pumpedinto stationsREMI, Gas and Oil ExpendituresROG 2460400000 Miscellaneous Non-industrial:Consumer and CommercialAll Automotive AftermarketProductsTotal: All SolventTypesAmount of solvent used Population (county-level for LADCOstates)ROG 2425000000 Graphic Arts All Processes Total: All SolventTypesAmount of solvent used REMI, Printing and Publishing sectoroutputROG 2401005000 Surface Coating Auto Refinishing: SIC 7532 Total: All SolventTypesAmount of solvent used REMI, Automobile Parking, Repair,Services sector outputROG 2460500000 Miscellaneous Non-industrial:Consumer and CommercialAll Coatings and RelatedProductsTotal: All SolventTypesAmount of solvent used Population (county-level for LADCOstates)PECHANSeptember 20076Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-1 (continued)POLLUTANTSCCDESC2DESC3DESC4EMISSIONS ACTIVITYCURRENT GROWTH BASISROG 2460200000 Miscellaneous Non-industrial:Consumer and CommercialAll Household Products Total: All SolventTypesAmount of solvent used Population (county-level for LADCOstates)ROG 2415020000 Degreasing Fabricated Metal Products(SIC 34): All ProcessesTotal: All SolventTypesAmount of solvent used REMI, Fabricated Metals sectoremploymentROG 2415025000 Degreasing Industrial Machinery andEquipment (SIC 35): AllProcessesTotal: All SolventTypesAmount of solvent used REMI, Machinery and ComputerEquipment sector employmentROG 2460600000 Miscellaneous Non-industrial:Consumer and CommercialAll Adhesives and Sealants Total: All SolventTypesAmount of solvent used Population (county-level for LADCOstates)ROG 2420000370 Dry Cleaning All Processes Special Naphthas Amount of special naphthasusedSCC is not in current growth file; similarSCC (2420010370) is in file w/ REMILaundry sector outputROG 2630010000 Wastewater Treatment Industrial Total Processed Volume of wastewaterprocessedProjected LADCO NEEDS industrial flowdesign forecastROG 2501060101 Petroleum and PetroleumProduct StorageGasoline Service Stations Stage 2:DisplacementLoss/UncontrolledVolume of gasoline pumped viauncontrolledRegression with Gas and OilExpenditures as explanatory variableROG 2415360000 Degreasing AutoRepair Services (SIC75): Cold CleaningTotal: All SolventTypesAmount of solvent used REMI, Automobile Parking, Repair,Services sector outputROG 2401002000 Surface Coating Architectural Coatings -Solvent-basedTotal: All SolventTypesAmount of solvent used REMI, Housing expendituresROG 2401003000 Surface Coating Architectural Coatings -Water-basedTotal: All SolventTypesAmount of solvent used REMI, Housing expendituresROG 2104008070 Residential Wood Outdoor Boiler Amount of wood burnedSO2 2601020000 On-site Incineration Commercial/Institutional TotalAmount of material burned REMI, Commercial sector employmentSO2 2102004000 Industrial DistillateOil Total: Boilers and ICEnginesAmount of distillate oil burnedby area source industrialboilers/IC enginesAEO Industrial distillateSO2 2103004000 Commercial/Institutional Distillate Oil Total: Boilers and ICEnginesAmount of distillate oil burnedby area source commercialboilers/IC enginesNo growth based on historical energydataSO2 2275020000 Aircraft Commercial Aircraft Total: All Types SeeNOX entry FAA itinerant air carrier LTO forecasts bystateSO2 2102005000 Industrial Residual Oil Total: All BoilerTypesAmount of residual oil burnedby area source industrial boilersAEO Industrial residualSO2 2102002000 Industrial Bituminous/SubbituminousCoalTotal: All BoilerTypesAmount of bit/sub coal burnedby area source industrial boilersAEO Industrial steam coalSO2 2285002006 Railroad Equipment Diesel Line HaulLocomotives: Class IOperationsSee NOX entry AEO Freight rail distillate (nat'l) adjustedfor relative state growth in Total outputSO2 2104008070 Residential Wood Outdoor Boiler Amount of wood burnedPECHANSeptember 20077Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-2. Growth Indicators forPriority Area Source/MAR CategoriesTotal % ChangePollutantSCC2005-20092005-2018Growth Indicator BasisAlternatives ConsideredCommentNH3 2805047100 -1.7 6.2 Interpolated SCC/state-level animal count projections from EPA NH3inventory of Animal Husbandry OperationsForecast data are state-levelNH3 2805039200 -2.3 4.4 Interpolated SCC/state-level animal count projections from EPA NH3inventory of Animal Husbandry OperationsForecast data are state-levelNH3 2805047300 -1.6 6.4 Interpolated SCC/state-level animal count projections from EPA NH3inventory of Animal Husbandry OperationsForecast data are state-levelNH3 2805001100 5.2 16.7 Interpolated SCC/state-level animal count projections from EPA NH3inventory of Animal Husbandry OperationsForecast data are state-levelNH3 2805039100 -1.6 6.4 Interpolated SCC/state-level animal count projections from EPA NH3inventory of Animal Husbandry OperationsForecast data are state-levelNH3 2805003100 3.2 3.5 Interpolated SCC/state-level animal count projections from EPA NH3inventory of Animal Husbandry OperationsForecast data are state-levelNH3 2805001300 5.2 16.7 Interpolated SCC/state-level animal count projections from EPA NH3inventory of Animal Husbandry OperationsForecast data are state-levelNH3 2630020000 2.8 9.5 Municipal design flow forecasts from Drinking Water Infrastructure NeedsSurveyNH3 2805007100 -4.5 8.3 Interpolated SCC/state-level animal count projections from EPA NH3inventory of Animal Husbandry OperationsForecast data are state-levelNH3 2805021300 -10.2 -39.0 Interpolated SCC/state-level animal count projections from EPA NH3inventory of Animal Husbandry OperationsForecast data are state-levelNH3 2805030000 -5.3 6.8 Interpolated SCC/state-level animal count projections from EPA NH3inventory of Animal Husbandry OperationsForecast data are state-levelNH3 2805021200 -10.2 -39.0 Interpolated SCC/state-level animal count projections from EPA NH3inventory of Animal Husbandry OperationsForecast data are state-levelNH3 2104008070 78.0 84.3 Extrapolation of national 1999-2004 trend in OWB sales (exponentialgrowth) thru 2006; linear growth thru 2008; 2009+ based on ruralpopulation growth rateNOX 2285002006 0.0 0.0 No growth due to contradictory historicand forecast trends DOE 1990-2004 = -1.4% per year; AEOforecast = 1.4% per yearNote that post-2001 trend hasbeen upward and thathistorical data shows severalups and downsNOX 2280002023 4.3 9.9 AEO national Domestic Shippingsector distillate projections adjusted forLADCO region growth in REMI Water Transportation sector outputrelative to nation1998-2004 fuel consumption data forbarge traffic on regional rivers indicatessimilar annual growth rate (1.0%)Forecast data are state-levelNOX 2102006001 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = -0.01% per year;AEO forecast = 1.4% per yearNOX 2275020000 0.0 0.0 No growth due to contradictory historic and forecast trends (FederalAviation Administration commercialaircraft landing and take-offs data)FAA 1990-2005 = -0.01% per year; FAAforecast = 1.7% per yearNOX 2285002010 0.0 0.0 No growth due to contradictory historic and forecast trend (historic trendbased on 1996-2002 regional Switch and Terminal Services employment)1996-2004 = -1.6% per yearemployment decrease; AEO forecast =1.4% per yearNOX 2104006000 1.7 2.8 AEO residential natural gas consumption forecast DOE 1990-2004 = 0.5% annual; AEOforecast = 0.2%PECHANSeptember 20078Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-2 (continued)Total % ChangePollutantSCC2005-20092005-2018Growth Indicator BasisAlternatives ConsideredCommentNOX 2285002009 7.4 26.1 Annual growth rate (1.8%) from2005-2009 diesel fuel consumption projectionsreported in Marta 2007 budgetNOX 2280003200 0.4 1.3 1995-2005 Great Lakes region ton-miles trend (0.1% annual growth) AEO forecasts national DomesticShipping residual oil consumption+1.1% per yearUsed historicalgrowth rate sinceregion-specificNOX 2102006000 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = -0.01% per year;AEO forecast = 1.4% per yearNOX 2104008070 78.0 84.3 Extrapolation of national 1999-2004 trend in OWB sales (exponential growth) thru2006; linear growth thru 2008; 2009+ based on rural population growth rateROG 2461850000 4.8 16.1 Regional employment projections for "Pesticide Handlers, Sprayers" combined withprojected solvent content change from Freedonia's "Solvents to 2010-AgriculturalChemical Market for Solvents" (-0.4% per year)Forecast data arestate-levelROG 2461020000 -1.9 -6.0 No employment growth assumption due to contradictory historic and forecast trends in"Paving, Surfacing, & Tamping Operators" employment, combined with projectedsolvent content change from Freedonia's "Solvents to 2010-Asphalt ProductionMarket for Solvents" (-0.5% per year)Forecast data arestate-levelROG 2401200000 -6.5 -16.1 Population forecast combined withprojected change in paint solvent content fromFreedonia's "Solvents to 2010-Paints/CoatingsMarket for Solvents" (-1.9% per year)Forecast data arecounty-levelROG 2401001000 -9.9 -9.3 Regression with Population forecast as explanatory variable combined with Freedoniaprojected change in proportion of total Architectural coatings that are solvent-based (-2.0% per year)Forecast data arecounty-levelROG 2460100000 -3.9 -11.6 Population forecast combined with projected solvent content change from Freedonia's"Solvents to 2010-Cosmetics & Toiletries Market for Solvents" (-1.5% per year)Forecast data arecounty-levelROG 2501011010 0.2 0.3 Regression equation with Gas and Oil Expenditures as explanatory variable Forecast data arestate-levelROG 2501060100 0.2 0.3 Regression equation with Gas and Oil Expenditures as explanatory variable Forecast data arestate-levelROG 2460800000 -10.5 -15.6 Regression equation with Population as explanatory variable Forecast data arecounty-levelROG 2501060050 0.2 0.3 Regression equation with Gas and Oil Expenditures as explanatory variable Forecast data arestate-levelROG 2460400000 0.1 3.3 Population forecast combined withprojected change in solvent use/vehicle fromFreedonia's "Solvents to 2010-Transportation Markets for Solvents" (-0.4% per year)Forecast data arecounty-levelROG 2425000000 0.0 0.0 No employment growth assumption due to contradictory historic and forecast trendsfor "Printing Machine Operators" employment, and no projected change in solventcontent of ink from Freedonia's "Solventsto 2010-Printing Ink Market for Solvents"ROG 2401005000 -12.9 -38.9 Employment projections for "Automotive Body andRelated Repairers" combined w/change in proportion of automotive coatingsthat are solvent-based from Freedonia's"Automotive Coatings, Adhesives & Sealants-Automotive Coatings Demand byFormulation and Substrate" (-4.3% per year)Forecast data arestate-levelROG 2460500000 -6.5 -16.1 Population forecast combined withprojected change in paint solvent content fromFreedonia's "Solvents to 2010-Paints/CoatingsMarket for Solvents" (-1.9% per year)Forecast data arecounty-levelPECHANSeptember 20079Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-2 (continued)Total % ChangePollutantSCC2005-20092005-2018Growth Indicator BasisAlternatives ConsideredCommentROG 2460200000 0.6 6.8 Population forecast combined with projected change in cleaning product solventcontent from Freedonia's "Solvents to2010-Cleaning Product Market for Solvents" (-0.1% per year)Forecast data arecounty-levelROG 2415020000 -15.0 -35.9 Fabricated Metals sector employment forecast combined with projected change insolvent use from Freedonia's "Solvents to 2010-Metal Processing Market forSolvents" (-3.7% per year)Forecast data arestate-levelROG 2415025000 0.2 -11.4 Machinery and Computer Equipment sector employment forecast combined withprojected change in solvent use from Freedonia's "Solvents to 2010-Metal ProcessingMarket for Solvents" (-3.7% per year)Forecast data arestate-levelROG 2460600000 -10.0 -24.4 Population forecast combinedwith projected change in solvent content fromFreedonia's "Solvents to 2010-Adhesives andSealants Market for Solvents" (-2.7%per year)Forecast data arecounty-levelROG 2420000370 -1.6 -0.4 Regional employment projections for "Laundry andDry Cleaning Workers" (+0.7% peryear) combined with projected solvent content from Freedonia's "Solvents to 2010-DryCleaning and Other Markets for Solvents" (-0.7%)ROG 2630010000 4.1 13.8 Growth rate from regional industrial wastewater flow design forecast from DrinkingWater Infrastructure Needs SurveyROG 2501060101 0.2 0.3 Regression equation with Gas and Oil Expenditures as explanatory variable Forecast data arestate-levelROG 2415360000 2.4 10.3 Regional employment projections for "Automotive Service Technicians andMechanics" combined with forecast change in solvent use from "Solvents to 2010-Transportation Markets for Solvents" (-0.4% per year)ROG 2401002000 -9.9 -9.3 Regression with Population forecast as explanatory variable combined with Freedoniaprojected change in proportion of total Architectural coatings that are solvent-based (-2.0% per year)Forecast data arestate-levelROG 2401003000 3.6 12.3 Regression equation with Population as explanatory variable Forecast data arestate-levelROG 2104008070 78.0 84.3 Extrapolation of national 1999-2004 trend in OWB sales (exponential growth) thru2006; linear growth thru 2008; 2009+ based on rural population growth rateSO2 2601020000 7.2 15.0 Commercial sector employment forecast Forecast data arestate-levelSO2 2102004000 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = +0.5% per year;AEO forecast = -0.3% per yearSO2 2103004000 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = -2.0% per year;AEO forecast = 0.8% per yearSO2 2275020000 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = -0.01% per year;AEO forecast = +1.7% per yearSO2 2102005000 -49.4 -49.6 AEO forecast for industrial sectorresidual oil consumption DOE 1990-2004 = -6.6% per year;AEO forecast = -5.1% per yearSO2 2102002000 2.9 -0.6 AEO forecast for other industrial coal combustion DOE 1990-2004 = -1.5% per year;AEO forecast = <-0.1% per yearPECHANSeptember 200710Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-2 (continued)Total % ChangePollutantSCC2005-20092005-2018Growth Indicator BasisAlternatives ConsideredCommentSO2 2285002006 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = -1.4% per year;AEO forecast = +1.4% per yearNote that post2001 trend isupward & historicaldata has severalups/downsSO2 2104008070 78.0 84.3 Extrapolation of national 1999-2004 trend in OWB sales (exponential growth) thru2006; linear growth thru 2008; 2009+ based on rural population growth rate

## PECHAN September 2007



## 11 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

## • Animal Husbandry: projected number of animals from EPA’s ammonia emission forecasts for animal husbandry operations (EPA, 2004);

• Marine Vessels Commercial, Diesel–Pus h Boats: historic al (1998-2004) fuel consumption for barge traffic on rive rs in LADCO region (ENVIRON, 2007a);

• Multiple Fuel Combustion categories: DOE East North Central region energy

forecasts from AEO 2007 and 1990-2004 LADCO region energy consumption data (DOE, 2007a and 2007b);

• Commercial Aircraft: state-level i tinerant aircraft operations (FAA, 2007);

• Diesel Line Haul Locomotives–Co mmuter Lines: Metra diesel fuel expenditure/price projections (Metra, 2007);

• Pesticide Application–Agric ultural: LADCO region project ed number of “pesticide handlers, sprayers, and applic ators, vegetation” (BLS, 2007);

• Commercial Asphalt Application–All Pro cesses: LADCO region projected number of “paving, surfacing, and ta mping operators” (BLS, 2007);

• Graphic Arts –All Processes: LADC O region projected number of “printing machine operators” (BLS, 2007);

• Surface Coating–Auto Refinishing: SIC 7532: LADCO region projected number of “Automotive Body and Related Repairers” (BLS, 2007);

• Dry Cleaning, All Processes–Special Naphtha s: LADCO region pr ojected number of “Laundry and Dry Cleaning Workers” (BLS, 2007);

• Wastewater Treatment-Industr ial: LADCO region projec ted wastewater treatment industrial design flow (EPA, 2007a); and

• Degreasing–Auto Repair Services (S IC 75): Cold Cleaning: LADCO region projected number of “Automotive Servi ce Technicians and Mechanics” (BLS, 2007).

Many of the above are solvent use categories fo r which Pechan also incorporated projected solvent content changes as forecast by The Freedonia Group, Inc. (Freedonia, 2006).

In cases where energy consumption is the emissions activity, a common growth factor development approach was to compare ava ilable regional histori cal (1990-2004) energy consumption data to AEO 2007 forecast data to determine if the forecast growth rates appear suspect relative to historical trends. Pechan conducted similar historical/forecast activity trend comparisons for the non-fuel combustion priority cat egories whenever historical trend data were readily available (e.g., occupationa l employment data). In sel ecting from alternative data

## PECHAN September 2007



## 12 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

## sources/trend data, the ge neral decision-making hierarchy was as follows, listed in order of preference:

1. If the forecast and historical trends were in the same direc tion, Pechan relied on forecast data (an exception was made, however, in ca ses where forecast data were only available
on a national-level, but historical data were available for the LADCO region).

2. If the forecast and historical trends were in different dire ctions (e.g., forecast trend is for an increase in activity, but historical trend was a decrease), Pechan applied a no growth assumption.

Outdoor Wood Boilers

Residential Wood Combusti on from Outdoor Wood Boilers (SCC 2104008070) was not originally identified as a source category for gr owth indicator review be cause this category was only recently added to LADCO’ s emissions inventory. Outdoor Wood Boilers (OWBs) have become much more prevalent in the last several years as homeown ers seek ways to avoid recent large increases in natural gas and home heating oil prices. This s ource category does not exist in
EPA’s official SCC list, and there is no current EPA emission inventory preparation guidance for this sector. Pechan assumed that this category’ s emissions are based on the estimated number of wood boilers, the average amount of wood burned in each boiler, and emission factors that are

related to the amount of wood burned.

Investigations indicate little hist orical and forecast OWB data exist to assist in identifying future trends in LADCO region OWB use: state-specif ic sales from nine manufacturers obtained by
EPA from nine manufacturers, and national sa les data obtained by the New York Attorney General’s Office via subpoena of 21 manufact urers. These sales data are for 1999-2004. Because of the much greater manufacturer covera ge for the national data, and because the state estimates indicate that the majority of recent OW Bs sales have occurred in the LADCO states, Pechan focused the historical trend analysis on the national data. Th ese data indicate an extremely high average growth rate of 41 percent per year over the 1999-2004 period. Manufacturers indicate that although OWBs have been available for sale since the 1980s, the very large OWB sales growth rates are new phe nomenon. The growth rates appear to mainly

result from homeowner reactions to recent large increases in residential heating prices (e.g., between 1999 and 2004, residential natural gas and di stillate oil prices ro se 61 and 87 percent, respectively). Because DOE data indicate th at natural gas accounts for the majority of residential energy consumption in the LADCO Stat es, and increases in residential natural gas prices continued through 2006 (the average annual price for resi dential natural gas increased 28 percent between 2004 and 2006), Pechan forecas t the national number of OWBs through 2006 via extrapolation of the 1999-2004 national OWB tre nd. In particular, Pechan fit an exponential equation to the 1994-2004 data, and used the equation to estimate 2005 and 2006 OWB counts.

Next, Pechan reviewed AEO 2007 projections of residential natura l gas prices for the East North Central region (which includes 5 of the 6 LADC O region States) to identify whether recent increases are expected to continue. The AEO 2007 projects the average 2 007 price for residential

## PECHAN September 2007



## 13 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

## natural gas in the East North Central region to be 4 percent lower th an in 2006, and forecasts continued price decreases through th e 2008-2018 period (see table below).

Year Residential Natural Gas Price ($/million Btu) % Change

from 2006

2006 12.08

2007 10.92 -4.1

2008 10.80 -5.1

2009 10.28 -9.7

2010 10.02 -11.9

2011 9.61 -15.5

2012 9.48 -16.9

2013 9.28 -18.4

2014 9.32 -18.1

2015 9.27 -18.6

2016 9.37 -17.7

2017 9.60 -15.7

2018 9.56 -20.8



## Given the projected modest price decreases thru 2 008, and the fact that distillate oil prices are forecast to increase 6.1 percent between 2006 a nd 2008, and because one expects a time lag in responding to energy price changes, Pechan assumes that OWB sales will continue to increase at a significant rate through 2008. Pechan specifi cally fit a linear trend line to the 1999-2004 OWB, and projected OWBs in 2007 and 2008 by extending the trend through 2008, and applying each year’s growth rate to the estimated count of OWBs in 2006.

By 2009, Pechan projects that the larger projected declines in natural gas prices, and forecasted decreases in other heating fuel prices, will si gnificantly restrain OW B growth. In addition, because of neighborhood smoke nuisance concerns, and the need for ready access to inexpensive wood, it is expected that the market for OWBs will be generally constrained to heavily-wooded rural areas. Therefore, Pechan forecasts post-200 8 year OWB growth to more closely trend with population growth in these areas. To approxi mate this growth, Pechan compiled 1990 and 2000 total and rural area population data for the LADCO region. These da ta indicate that rural area population grew at approximately 60 percent of the rate of total popula tion over this period. Pechan estimated rural area population growth for the LADCO region by multiplying this adjustment factor by the forecasted growth rate for total population in the LADCO region. The following table displays the projected count of OWBs in the LADCO region for 2005, 2009,
2002, and 2018.

Year Estimated # of OWBs

2005 81,082

2009 144,356

2012 145,911

2018 149,421

## PECHAN September 2007



## 14 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

## It is important to note that it is particularly ch allenging to forecast OWB activity given the extremely high OWB sales growth rates that have occurred in recent years. LADCO will want to closely monitor activity and emission trends for this category given its relative importance in the emissions inventory, recent hist orical growth rates, and a dditional unique characteristics.

Finally, Pechan reviewed the complete list of area/MAR source categories in the LADCO base year inventory to identify the priority category growth indicators that could be applied to nonpriority area/MAR categories. This step yielde d priority category growth indicator assignments for an additional 26 area/MAR categories.



## C. NON-EGU POINT SOURCES

Table II-3 displays the priority point source categori es, including the description and emissions activity associated with each cate gory. The last column in this table identifies each category’s growth indicator assignment under th e previous growth and control factor contract. Table II-4 presents the assigned growth indicator for each pr iority point source category and identifies any alternative growth indicators that were considered . Pechan first considered the use of historical throughput data from LADCO stat e point source inventories to identify recent trends that provided sufficient support for extrapolation. As mentioned above, for energy consumption sectors, Pechan compared regional histor ical (1990-2004) energy consumption data to AEO 2007

forecast data to determine if the fo recast growth rates appear suspec t relative to historical trends.

In selecting from alternative growth indicat or data sources, the ge neral decision-making hierarchy was as follows, listed in order of preference:

(1) If throughput data were availa ble for states representing a majority of emissions for a given category, and these data indicated a c onsistent trend, the hist orical throughput trend
was extended thru 2009, and held constant th ereafter (two reasons for not extending the trend throughput the entire forecast period ar e that throughput data are only available for a three or a six-year period, and in some cas es the historical throughput decrease was so large that it would eventua lly result in no activity);

(2) If the forecast and historical trends were in the same direction, Pechan relied on the forecast data (an exception was made, however, if the forecast data were only available
on a national-level, but the historical data were available for the LADCO region); and

If the forecast and historical tr ends were in different directions (e.g., forecast trend is for an increase in activity, but historic al trend was a decrease), Pechan applied a no growth assumption.PECHANSeptember 200715Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-3. Priority Emission Activity Point Source CategoriesPOLLUTANTSCCDESC2DESC3DESC4EMISSIONS ACTIVITYCURRENT GROWTH BASISNH3 10200601 Industrial Natural Gas > 100 Million Btu/hr Volume of natural gas burned inindustrial pt source boilers of >100MMBtuNo growth based on historical (1990-2001)energy dataNH3 30102599 ChemicalManufacturingCellulosic Fiber Production Other Not Classified Amount of cellulosic fiber produced Avg of REMI employment & output GFs forPlastics, Materials, & Synthetics sectorNH3 10200602 Industrial Natural Gas 10-100 Million Btu/hr Volume of natural gas burned inindustrial pt source boilers of 10-100MMBtuNo growth based on historical energy dataNH3 30199999 ChemicalManufacturingOther Not Classified Specify in Comments Field Amount of (unknown) chemicalproducts producedAvg of REMI employment & output GFs forChemicals sectorNH3 10200204 Industrial Bituminous/SubbituminousCoalSpreader Stoker Amount of bituminous coal burned inspreader stoker industrial pt sourceboilersAEO Industrial steam coalNOX 20200202 Industrial Natural Gas Reciprocating Volume of natural gas burned inindustrial pt source reciprocatingenginesNo growth based on historical energy dataNOX 30500606 Mineral Products Cement Manufacturing (DryProcess)Kilns Amount of cement produced via dryprocessLADCO region historical cement productiongrowth rateNOX 30600201 PetroleumIndustryCatalytic Cracking Units Fluid Catalytic Cracking Unit Amount of fresh feed processed viafluid catalytic cracking units (FCCU)AEO Refined Petroleum Products Supplied(national)NOX 30600104 PetroleumIndustryProcess Heaters Gas-fired Volume of gas burned in petroleumindustry pt source process heatersAEO Refining sector natural gas (national)NOX 10200202 Industrial Bituminous/SubbituminousCoalPulverized Coal: Dry Bottom Amountof bituminous coal burned indry bottom industrial pt source boilersAEO Industrial steam coalNOX 10200601 Industrial Natural Gas > 100 Million Btu/hr Volume of natural gas burned inindustrial pt source boilers of >100MMBtuNo growth based on historical energy dataNOX 30300304 Primary MetalProductionBy-product CokeManufacturingQuenching Amount of coal charged tomanufacture cokeREMI output for Blast Furnaces and BasicSteel products sectorNOX 10200217 Industrial Bituminous/SubbituminousCoalAtmospheric Fluidized BedCombustion: Bubbling Bed(BituminousAmount of bituminous coal burned inbubbling bed industrial pt sourceboilersAEO Industrial steam coalNOX 10200707 Industrial Process Gas Coke Oven Gas Volume of process gas burned in cokeovensAEO Metallurgical coal consumptionprojections (national)NOX 10200602 Industrial Natural Gas 10-100 Million Btu/hr Volume of natural gas burned inindustrial pt source boilers of 10-100MMBtuNo growth based on historical energy dataPECHANSeptember 200716Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-3 (continued)POLLUTANTSCCDESC2DESC3DESC4EMISSIONS ACTIVITYCURRENT GROWTH BASISNOX 20200254 Industrial Natural Gas 4-cycle Lean Burn Volume of natural gas burnedin industrial sector 4-cyclelean burn IC enginesNo growth based on historical energy dataNOX 20100102 Electric Generation Distillate Oil (Diesel) Reciprocating Amount of distillate oil burnedin reciprocating engines forelectricityAEO Electric Generation distillate oilNOX 20200201 Industrial Natural Gas Turbine Volume of natural gas burnedin industrial sector turbinesNo growth based on historical energy dataNOX 39000689 In-process Fuel Use Natural GasGeneral Volume of industrial processpt source natural gas burnedNo growth based on historical energy dataNOX 10200201 Industrial Bituminous/SubbituminousCoalPulverized Coal: WetBottomAmount of bituminous coalburned in wet bottom industrialpt source boilersAEO Industrial steam coalNOX 10200701 Industrial Process Gas Petroleum Refinery Gas Volume of petroleum refinery(still) gas burnedNo growth based on historical energy dataROG 30100399 Chemical ManufacturingAmmonia Production Other Not Classified Amountof ammonia produced REMI output for Agricultural Chemicals sectorROG 30201916 Food and Agriculture Vegetable Oil ProcessingOil Extraction Amount of extractor feed cakeproducedREMI output for Grain Mill Products and Fatsand Oils sectorROG 40500511 Printing/Publishing General Gravure: 2754 Amount of solvent in ink usedby pt sourcesREMI output for Commercial Printing andBusiness Forms sectorROG 30199999 Chemical Manufacturing OtherNot Classified Specify in CommentsFieldAmount of (unknown)chemical products producedAvg of REMI employment & output GFs forChemicals sectorROG 30125099 Chemical Manufacturing Methanol/AlcoholProductionOther Not Classified Amount of methanol/alcoholproducedREMI output for Industrial Chemicals sectorROG 40201301 Surface CoatingOperationsPaper Coating Coating Operation Amount of solvent in coatingused by pt sourcesNo growth based on historical LADCOemissions trendROG 40200101 Surface CoatingOperationsSurface Coating Application- GeneralPaint: Solvent-base Amount of coating mix appliedby pt sourcesNo growth based on historical LADCOemissions trendROG 30102599 Chemical Manufacturing Cellulosic Fiber Production Other Not Classified Amount of cellulosic fiberproducedAvg of REMI employment & output GFs forPlastics, Materials, & Synthetics sectorROG 30500201 Mineral Products Asphalt Concrete Rotary Dryer:Conventional Plant (see3-05-002-50 to -53 forAmount of hot mix asphaltproduced by pt sourcesAvg of REMI employment & output GFs forMisc. Petroleum and Coal Products sectorROG 30201906 Food and Agriculture Vegetable Oil Processing Corn Oil: General Amount of extractor feed cakeproducedREMI output for Grain Mill Products and Fatsand Oils sectorROG 30201919 Food and Agriculture Vegetable Oil ProcessingFugitive Leaks Amount of extractor feed cakeproducedREMI output for Grain Mill Products and Fatsand Oils sectorROG 40200110 Surface CoatingOperationsSurface Coating Application- GeneralPaint: Solvent-base Amount of solvent-basedcoatings applied by pt sourcesHistorical LADCO throughput data trendROG 40201899 Surface CoatingOperationsMetal Coil Coating Other Not Classified Amount of solvent in coatingused by pt sourcesREMI output for Nonferrous Rolling andDrawing sectorROG 40388801 Petroleum ProductStorage at RefineriesFugitive Emissions Specify in CommentsFieldPetroleum product storagecapacity at refineriesAEO Refined Petroleum Products Supplied(national)ROG 40200701 Surface CoatingOperationsSurface Coating Application- GeneralAdhesive Application Amountof adhesive coatingsapplied by pt sourcesREMI output for Total Manufacturing sectorPECHANSeptember 200717Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-3 (continued)POLLUTANTSCCDESC2DESC3DESC4EMISSIONS ACTIVITYCURRENT GROWTH BASISSO2 30600201 Petroleum Industry Catalytic Cracking Units Fluid Catalytic CrackingUnitAmount of fresh feedprocessed via FCCUAEO Refined Petroleum Products Supplied(national)SO2 10200202 Industrial Bituminous/SubbituminousCoalPulverized Coal: DryBottomAmount of bituminous coalburned in dry bottom industrialpt source boilersAEO Industrial steam coalSO2 30600805 Petroleum Industry Fugitive Emissions Miscellaneous:Sampling/Non-AsphaltBlowing/Purging/etc.Barrels of refinery feedprocessedAEO Refined Petroleum Products Supplied(national)SO2 30199999 Chemical Manufacturing OtherNot Classified Specify in CommentsFieldAmount of (unknown)chemical products producedAvg of REMI employment & output GFs forChemicals sectorSO2 10200217 Industrial Bituminous/SubbituminousCoalAtmospheric FluidizedBed Combustion:Bubbling Bed (BituminousAmount of bituminous coalburned in bubbling bedindustrial pt source boilersAEO Industrial steam coalSO2 10200201 Industrial Bituminous/SubbituminousCoalPulverized Coal: WetBottomAmount of bituminous coalburned in wet bottom industrialpt source boilersAEO Industrial steam coalSO2 10200225 Industrial Bituminous/SubbituminousCoalTraveling Grate(Overfeed) Stoker(Subbituminous Coal)Amount of subbituminous coalburned in overfeed stokerindustrial pt source boilersAEO Industrial steam coalSO2 30500606 Mineral Products Cement Manufacturing (DryProcess)Kilns Amount of cement producedvia dry processLADCO region historical cement productiongrowthSO2 30600401 Petroleum Industry Blowdown Systems Blowdown System withVapor Recovery Systemwith FlaringBarrels of refinery feedprocessedAEO Refined Petroleum Products Supplied(national)SO2 10200204 Industrial Bituminous/SubbituminousCoalSpreader Stoker Amount of bituminous coalburned in spreader stokerindustrial pt source boilersAEO Industrial steam coalSO2 10300217 Commercial/Institutional Bituminous/SubbituminousCoalAtmospheric FluidizedBed Combustion:Bubbling Bed (Bitumin.)Amount of bituminous coalburned in bubbling bedcommercial pt source boilersAEO Commercial coalSO2 39000701 In-process Fuel Use Process Gas Coke Oven or BlastFurnaceVolume of coke oven or blastfurnace gas burnedAEO Metallurgical coal consumptionprojections (national)SO2 30103201 Chemical Manufacturing ElementalSulfur Production Mod. Claus: 2 Stage w/oControl (92-95%Removal)Amount of 100% sulfurproducedREMI output for Industrial Chemicals sectorSO2 10300225 Commercial/Institutional Bituminous/SubbituminousCoalTraveling Grate(Overfeed) Stoker(Subbituminous Coal)Amount of subbituminous coalburned in overfeed stokercommercial pt source boilersAEO Commercial coalSO2 10300209 Commercial/Institutional Bituminous/SubbituminousCoalSpreader Stoker(Bituminous Coal)Amount of bituminous coalburned in spreader stokercommercial pt source boilersAEO Commercial coalSO2 10200401 Industrial Residual Oil Grade 6 Oil Amount of residual oil (No. 6)burned in industrial pt sourceboilersNo growth based on historical energy dataPECHANSeptember 200718Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-3 (continued)POLLUTANTSCCDESC2DESC3DESC4EMISSIONS ACTIVITYCURRENT GROWTH BASISEMISSION TREND ANALYSIS CATEGORIES NOT LISTEDABOVE WITH CONSISTENT THROUGHPUT TRENDSVOC 40202201 Petroleum and SolventEvaporationSurface Coating OperationsPlastic Parts: CoatingOperationAmount of solvent used incoating appliedHistorical LADCO throughput data trendNOX 39000699 In Process Fuel Use Natural Gas General Amount of nat gas used inindustrial processesNo growth based on historical energy dataPECHANSeptember 200719Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-4. Growth Indicators forPriority Point Source CategoriesTotal % ChangePollutantSCCEmissionsPriorityCategory2005-20092005-2018Growth Indicator BasisAlternatives ConsideredCommentNH3 10200601 NOx -11.5 -11.5 Historic throughput trend from 3 states (-3.0% peryear) extended thru 2009; post-2009 held constant.DOE 1990-2004 = -0.01% per year; AEOforecast = +1.4% per yearNH3 30102599 4.9 18.2 Avg of REMI employment & output growth factors forPlastics, Materials, & Synthetics sectorForecast data are state-levelNH3 10200602 NOx -12.2 -12.2 Historic throughput trend from 4 states (-3.2% peryear) extended thru 2009; post-2009 held constant.DOE 1990-2004 = -0.01% per year; AEOforecast = +1.4% per yearNH3 30199999 8.9 25.6 Avg of REMI employment & output growth factors forChemicals sectorForecast data are state-levelNH3 10200204 NOx 2.9 -0.6 AEO forecast for other industrialcoal combustion DOE 1990-2004 = -1.5% per year; AEOforecast = <-0.1% per yearDid not use throughput sinceavailable states represent<50% of regional emissionsNOX 20200202 NOx 0.0 0.0 No growth due to contradictory historic and forecasttrendsDOE 1990-2004 = -0.01% per year; AEOforecast = +1.4% per yearDid not use throughput sinceavailable state represent<50% of regional emissionsNOX 30500606 8.2 29.4 LADCO region historical cement production growthrate (+2.0% per year)NOX 30600201 SO2 0.4 0.4 AEO refinery distillation projections for PetroleumAdministration District (PAD) II, which includes allLADCO states plus additional surrounding statesSimilar 1990-2005 data also includesstates not in LADCO region and showsvery small growth rateDid not use throughput sinceavailable states represent<50% of regional emissionsNOX 30600104 5.9 20.6 1991-2002 MidwestCensus region Refining sectornatural gas consumption growth rate (+1.5% peryear)AEO National Refining sector natural gasconsumption forecast is +2.7% per yearUsed historical growth ratebecause it is regional and ofsimilar direction to AEOnational forecastNOX 10200202 NOx 2.9 -0.6 AEO forecast for other industrialcoal combustion DOE 1990-2004 = -1.5% per year; AEOforecast = <-0.1% per yearDid not use throughput sinceavailable states represent<50% of regional emissionsNOX 10200601 NOx -11.5 -11.5 Historic throughput trend from 3 states (-3.0% peryear) extended thru 2009; post-2009 held constant.DOE 1990-2004 = -0.01% per year; AEOforecast = +1.4% per yearNOX 30300304 -6.2 -19.8 AEO forecast for metallurgicalcoal consumption DOE 1990-2004 = -3.0% per year; AEOforecast = -1.7% per yearNOX 10200217 2.9 -0.6 AEO forecast for other industrialcoal combustion DOE 1990-2004 = -1.5% per year; AEOforecast = <-0.1% per yearNOX 10200707 -6.2 -19.8 AEO forecast for metallurgicalcoal consumption DOE 1990-2004 = -3.0% per year; AEOforecast = -1.7% per yearNOX 10200602 NOx -12.2 -12.2 Historic throughput trend from 4 states (-3.2% peryear) extended thru 2009; post-2009 held constant.DOE 1990-2004 = -0.01% per year; AEOforecast = +1.4% per yearNOX 20200254 0.0 0.0 No growth due to contradictory historic and forecasttrendsDOE 1990-2004 = -0.01% per year; AEOforecast = +1.4% per yearPECHANSeptember 200720Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-4 (continued)Total % ChangePollutantSCCEmissionsPriorityCategory2005-20092005-2018Growth Indicator BasisAlternatives ConsideredCommentNOX 20100102 1.7 5.5 DOE 1990-2004 historic trend (+0.4%) AEO forecast = +5.0% per year thru2009, but near equivalent decreasefrom 2009 to 2018Used historic trendbecause of largedifference between 2009and 2018 forecast, &historic growth rate is inbetween the 2 forecastvaluesNOX 20200201 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = -0.01% per year;AEO forecast = +1.4% per yearNOX 39000689 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = -0.01% per year;AEO forecast = +1.4% per yearNOX 10200201 SO2 2.9 -0.6 AEO forecast for other industrial coal combustion DOE 1990-2004 = -1.5% per year;AEO forecast = <-0.1% per yearNo throughput dataavailableNOX 10200701 -1.3 -4.1 DOE 1990-2004 historic trend (-0.3% per year) AEO forecast = 2009 (-1.0% peryear) and 2018 (-0.1% per year)Used historic trendbecause is regionspecific (forecast isnational), and historicchange is in between the2009 & 2018 AEO growthratesROG 30100399 -19.7 -28.6 Freedonia's "Chemical Catalysts to 2009-Ammonia CatalystDemand" - national projections adjusted for relative state growth inREMI output for Agricultural Chemicals sectorForecast data are statelevelROG 30201916 2.2 11.9 Avg of REMI employment & output growth factors for Grain MillProducts and Fats and Oils sectorForecast data are statelevelROG 40500511 0.0 0.0 No growth due to contradictory historic trend versus forecast trendin regional employment for "Printing Machine Operators"Freedonia's "Solvents to2010-Printing Ink Marketfor Solvents" indicates noprojected change insolvent content of inkROG 30199999 8.9 25.6 Avg of REMI employment & output growth factors for ChemicalssectorForecast data are statelevelROG 30125099 1.1 5.3 Freedonia's "Chemical Catalysts to 2009-Alcohols Catalyst Demandby Application" - national projections adjusted for relative stategrowth in REMI output for Industrial Chemicals sectorForecast data are statelevelROG 40201301 0 0 No growth based on consistent historic LADCO emissions trend 2005 emissions dataconfirm previous nogrowth approachROG 40200101 VOC -6.4 -21.4 Regional employmentprojections for "Coating, Painting, andSpraying Machine Operators, and Tenders" adjusted for solventcontent of paints and coatings from Freedonia's "Solvents to 2010-Paints and Coatings Market for Solvents" (-1.9% per year)Forecast data are statelevel; adopted approachbelieved better thanavailable historicthroughput dataPECHANSeptember 200721Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-4 (continued)Total % ChangePollutantSCCEmissionsPriorityCategory2005-20092005-2018Growth Indicator BasisAlternatives ConsideredCommentROG 30102599 4.9 18.2 Avg of REMI employment& output growth factors for Plastics,Materials, & Synthetics sectorForecast data are statelevelROG 30500201 8.3 21.5 Avg of REMI employment & output growth factors for Misc.Petroleum and Coal Products sectorForecast data are statelevelROG 30201906 2.2 11.9 Avg of REMI employment & output growth factors for Grain MillProducts and Fats and Oils sectorForecast data are statelevelROG 30201919 2.2 11.9 Avg of REMI employment & output growth factors for Grain MillProducts and Fats and Oils sectorForecast data are statelevelROG 40200110 VOC -6.4 -21.4 Regional employmentprojections for "Coating, Painting, andSpraying Machine Operators, and Tenders" adjusted for solventcontent of paints and coatings from Freedonia's "Solvents to 2010-Paints and Coatings Market for Solvents" (-1.9% per year)Forecast data are statelevel; adopted approachbelieved better thanavailable historicthroughput dataROG 40201899 6.9 26.0 Freedonia's "Protective Coatings to 2009-Demand for CoilCoatings" - national projections adjusted for relative state growth inREMI output for Nonferrous Rolling and Drawing sector, adjustedfor projected solvent content information for paints and coatingsfrom "Solvents to 2010-Paints/Coatings Market for Solvents" (-1.9%per year)Forecast data are statelevel; coil coating hasseen significant growthhistorically, and suchgrowth is projected tocontinue in the futureROG 40388801 0.4 0.4 AEO refinery distillationprojections for Petroleum AdministrationDistrict (PAD) II, which includesall LADCO states plus additionalsurrounding states1990-2005 data also includes statesnot in LADCO region and showssimilar very small growth rateROG 40200701 -1.6 -1.0 Freedonia's "Solventsto 2010-Adhesives & Sealants Market forSolvents" national projections, adjusted for relative state growth inREMI output in Total Manufacturing sectorSO2 30600201 SO2 0.4 0.4 AEO refinery distillationprojections for Petroleum AdministrationDistrict (PAD) II, which includesall LADCO states plus additionalsurrounding states1990-2005 data also includes statesnot in LADCO region and showssimilar very small growth rateDid not use throughputsince available staterepresent <50% ofregional emissionsSO2 10200202 NOx 2.9 -0.6 AEO forecast for other industrial coal combustion DOE 1990-2004 = -1.5% per year;AEO forecast = <-0.1% per yearNo throughput dataavailableSO2 30600805 SO2 0.4 0.4 AEO refinery distillationprojections for Petroleum AdministrationDistrict (PAD) II, which includesall LADCO states plus additionalsurrounding states1990-2005 data also includes statesnot in LADCO region and showssimilar very small growth rateNo throughput dataavailableSO2 30199999 8.9 25.6 Avg of REMI employment & output GFs for Chemicals sector Forecast data are statelevelSO2 10200217 2.9 -0.6 AEO forecast for other industrial coal combustion DOE 1990-2004 = -1.5% per year;AEO forecast = <-0.1% per yearSO2 10200201 SO2 2.9 -0.6 AEO forecast for other industrial coal combustion DOE 1990-2004 = -1.5% per year;AEO forecast = <-0.1% per yearNo throughput dataavailableSO2 10200225 2.9 -0.6 AEO forecast for other industrial coal combustion DOE 1990-2004 = -1.5% per year;AEO forecast = <-0.1% per yearSO2 30500606 8.2 29.4 LADCO region historicalcement production growth rate (+2.0% peryear)PECHANSeptember 200722Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-4 (continued)Total % ChangePollutantSCCEmissionsPriorityCategory2005-20092005-2018Growth Indicator BasisAlternatives ConsideredCommentSO2 30600401 0.4 0.4 AEO refinery distillationprojections for Petroleum AdministrationDistrict (PAD) II, which includesall LADCO states plus additionalsurrounding states1990-2005 data also includes statesnot in LADCO region and showssimilar very small growth rateSO2 10200204 NOx 2.9 -0.6 AEO forecast for other industrial coal combustion DOE 1990-2004 = -1.5% per year;AEO forecast = <-0.1% per yearDid not use throughputsince available statesrepresent <50% ofregional emissionsSO2 10300217 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = -1.2% per year;AEO forecast = +0.0% per yearSO2 39000701 -6.2 -19.8 AEO forecast for metallurgicalcoal consumption DOE 1990-2004 = -3.0% per year;AEO forecast = -1.7% per yearSO2 301032012.5 8.2 1996-2005 recovered elemental sulfur production growth rate(+0.6% per year) for IL + MI + MN + OHSO2 10300225 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = -1.2% per year;AEO forecast = +0.0% per yearSO2 10300209 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = -1.2% per year;AEO forecast = +0.0% per yearSO2 10200401 SO2 -49.4 -49.6 AEO forecast for industrial residual oil consumption (-5.1% peryear)DOE 1990-2004 = -6.6% per year Used AEO forecast dueto similarity with AEOhistorical trend andthroughput trend (-6.1%)NON-PRIORITY CATEGORIES INCLUDED IN EMISSION TREND ANALYSIS WITH CONSISTENT THROUGHPUT TRENDS:VOC 40202201 VOC -33.5 -33.5 Historic throughput trend from 3 states (-9.7%) extended thru 2009;post-2009 held constant.NOX 39000699 NOx -15.8 -15.8 Historic throughput trend from 3 states (-4.2%) extended thru 2009;post-2009 held constant.

## PECHAN September 2007



## 23 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

## Table II-4 also presents the 2005-2009 and 2005-2018 growth rates for the final assigned point source growth indicators. In addition, Pechan re viewed the complete list of point SCCs in the LADCO base year inventor y to identify the priority category growth indicators that could be applied to non-priority point s ource categories. This step yi elded priority category growth indicator assignments for an addi tional 539 point s ource categories.



## D. NONROAD MODEL SOURCES

At LADCO’s request, Pechan analyzed potential improvements to the default growth indicators for the 25 NONROAD model priority source cate gories displayed in Table II-5. With the exception of the all-terrain vehicle, offroad motorcycle, and snowmobile categories, 1989-1996 national equipment population trends form the basi s for the NONROAD growth rates. For these other three categories, NONROA D relies on national equipment population forecasts prepared by a relevant trade association (see Table II-5 for details).

Table II-6 reports this stu dy’s growth indicator assignmen ts for priority NONROAD model source categories. Given the acknowledged sh ortcomings of the NONROAD growth rates (use of 1989-1996 national equipment populations to proj ect future equipment populations in each region of the country), the growth factor improve ments generally reflect th e use of regional/statelevel forecast data that are e xpected to correlate with use of the equipment (i.e., regional occupational employment project ions, state-level economic sector employment forecasts, or state-level landing/take-off projec tions). Table II-6 also disp lays any alternative growth

indicators that were considered. For th e three categories for which NONROAD relies on

forecasts rather than historical 1989-1996 trends , Pechan compiled available recent historical equipment population estimates. This informati on was used to revise the current national forecast approach to reflect more recent info rmation, and whenever possible, recent LADCO region-specific equipment population trends.

Although the NONROAD model growth rates are fuel -specific, Pechan was unable to develop fuel-specific forecast data. Therefore, Pechan updated a priority category’s growth rates only when the 1989-1996 national equipment populations i ndicated that the cate gory’s fuel-specific growth rate had traditionally be en similar to the overall sect or’s equipment population growth rate. Table II-6 identifies instances where the past fuel-specific growth rate substantially differed from the overall sector’s growth rate. In these cases, Pechan retained the NONROAD model fuel-specific forecast approach. Section IV.A. describes how Pechan incorporated the updated equipment population growth rates into the NONROAD modelPECHANSeptember 200724Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-5. Priority Emission Activity NONROAD Model Source CategoriesSCCDESCRIPTION SUMMARYEMISSIONS ACTIVITYCURRENT GROWTH BASIS2260001030 2-Stroke ATV Population of 2-stroke gasoline ATVs NONROAD (Motorcycle Industry Councilnational 2-stroke gasoline ATV projections)2265001030 4-Stroke ATV Population of 4-stroke gasoline ATVs NONROAD (Motorcycle Industry Councilnational 4-stroke gasoline ATV projections)2260001010 2-Stroke Offroad Motorcycles Population of 2-stroke gasoline offroad motorcycles NONROAD (Motorcycle Industry Council national off-highway motorcycle populationprojections)2265001010 4-Stroke Offroad Motorcycles Population of 4-stroke gasoline offroad motorcycles NONROAD (Motorcycle Industry Council national off-highway motorcycle populationprojections)2267006000 LPG Light Commercial Population of light commercial LPG-fueledequipmentNONROAD (national 1989-1996 LPG light commercial equipment population growth rate)2270004000 Diesel Lawn & GardenEquipmentPopulation of lawn & garden diesel-fueledequipmentNONROAD (national 1989-1996 diesel lawn& garden equipment population growth rate)2270008000 Diesel Airport ServiceEquipmentPopulation of airportservice diesel-fueledequipmentNONROAD (national 1989-1996 diesel airportservice equipment population growth rate)2267008000 LPG Airport Service Equipment Population of airport service LPG-fueled equipment NONROAD (national 1989-1996 total airport service equipment population growth rate)2268008000 CNG Airport Service Equipment Population of airport service CNG-fueled equipment NONROAD (national 1989-1996 total airport service equipment population growth rate)2260001020 2-Stroke Snowmobiles Population of 2-stroke gasoline snowmobiles NONROAD national growth (see below) with state adjustment based on real disposableincome forecasts2265001020 4-Stroke Snowmobiles Population of 4-stroke gasoline snowmobiles NONROAD (International Snowmobile Manufacturers Association national snowmobilepopulation projections)2260007000 2-Stroke Logging Equipment Population of logging 2-stroke gasoline equipmentNONROAD (national 1989-1996 gasoline logging equipment population growth rate)2265007000 4-Stroke Logging Equipment Population of logging 4-stroke gasoline equipmentNONROAD (national 1989-1996 gasoline logging equipment population growth rate)2270006000 Diesel Light Commercial Population of light commercial diesel-fueledequipmentNONROAD (national 1989-1996 diesel light commercial equipment population growth rate)2268006000 CNG Light Commercial Population of light commercial CNG-fueledequipmentNONROAD (national 1989-1996 CNG light commercial equipment population growth rate)2267007000 LPG Logging Equipment Population oflogging LPG-fueled equipment NONROAD(national 1989-1996 logging equipment population growth rate)2268007000 CNG Logging Equipment Populationof logging CNG-fueled equipment NONROAD (national 1989-1996 logging equipment population growth rate)2285002000 Diesel Railway Maintenance Population of railway maintenance diesel-fueledequipmentNONROAD (national 1989-1996 diesel railwaymaintenance equipment population growthrate)2260006000 2-Stroke Light Commercial Populationof light commercial 2-stroke gasolineequipmentNONROAD (national 1989-1996 gasoline light commercial equipment population growth rate)2265006000 4-Stroke Light Commercial Populationof light commercial 4-stroke gasolineequipmentNONROAD (national 1989-1996 gasoline light commercial equipment population growth rate)2270002000 Diesel ConstructionEquipment Population of construction diesel-fueled equipment NONROAD national growth with stateadjustment based on Construction employmentforecasts2270003000 Diesel Industrial Equipment Population of industrial diesel-fueled equipmentNONROAD (national 1989-1996 diesel industrial equipment population growth rate)2267003000 LPG Industrial Equipment Populationof industrial LPG-fueled equipment NONROAD (national 1989-1996 LPG industrial equipment population growth rate)2270001000 Diesel Recreational Vehicles Population of diesel recreational vehicles NONROAD (national 1989-1996 diesel recreational equipment population growth rate)2282020000 Diesel Recreational MarinePopulation of diesel recreational marine vessels NONROAD (national 1989-1996 diesel recreational equipment population growth rate)PECHANSeptember 200725Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-6. Growth Indicators for Priority NONROAD Model Source CategoriesTotal % ChangeSCC2005-20092005-2018Growth Indicator BasisAlternatives ConsideredComment2260001030 10.8 39.7 50% higher growth rate than overall LADCO region"Recreational Vehicle Service Technicians" employmentprojections, which is 1.7%/yr, based on premise thatATV market is less mature than market for otherrecreation vehiclesAvailable ATV/OHV registration data for MI+MN+WI indicateavg. annual growth of 14.6% between 2000 and 2005;however, 2006 data for each state is only +1 or +1.1%;NONROAD shows 2005-2006 = +10.5%. National ATVsales (not population) of +3.8% per year for 2000-2005;2005-2006 sales = -4.2%Did not use long-term historical trendas ATV market appears to be maturingbased on most recent data (this ispredicted by NONROAD model, but notuntil post-2010; NONROAD shows2010-2018 = +2.7%/yr)2265001030 10.8 39.7 50% higher growth rate than overall LADCO region"Recreational Vehicle Service Technicians" employmentprojections, which is 1.7%/yr, based on premise thatATV market is less mature than market for otherrecreation vehiclesAvailable ATV/OHV registration data for MI+MN+WI indicateavg. annual growth of 14.6% between 2000 and 2005;however, 2006 data for each state is only +1 or +1.1%;NONROAD shows 2005-2006 = +10.5%. National ATVsales (not population) of +3.8% per year for 2000-2005;2005-2006 sales = -4.2%Did not use long-term historical trendas ATV market appears to be maturingbased on most recent data (this ispredicted by NONROAD model, but notuntil post-2010; NONROAD shows2010-2018 = +2.7%/yr)2260001010 5.1 17.7 LADCO region employment projections for "MotorcycleMechanics"Unable to compile regional registration trends; national offroad motorcycle sales for 2000-2006 = +2.3%, but 2001-2006 = -1.6%Employment projections are LADCOregion-specific and fall in-betweenrecent national sales trends2265001010 5.1 17.7 LADCO region employment projections for "MotorcycleMechanics"Unable to compile regional registration trends; national offroad motorcycle sales for 2000-2006 = +2.3%, but 2001-2006 = -1.6%Employment projections are LADCOregion-specific and fall in-betweenrecent national sales trends2267006000 19.8 57.5 NONROAD (national 1989-1996 LPG light commercialequipment population growth rate)REMI Commercial sector employment forecast for LADCOregion (2005-2009=+1.8%yr; 2005-2018 = +1.1%/yr)Did not use alternative becausehistorical period indicates substantiallydifferent growth rate for LPG thanoverall sector2270004000 5.8 20.1 LADCO region employment projections for "Landscapingand Groundskeeping Workers"2270008000 -0.7 18.0 State-level FAA itinerant air carrier + air taxi landing andtake-off (LTO) forecast (updated as of December 2006)2267008000 -0.7 18.0 State-level FAA itinerant air carrier + air taxi landing andtake-off (LTO) forecast (updated as of December 2006)2268008000 -0.7 18.0 State-level FAA itinerant air carrier + air taxi landing andtake-off (LTO) forecast (updated as of December 2006)2260001020 3.5 11.9 50% lower growth rate than overall LADCO region"Recreational Vehicle Service Technicians" employmentprojections, which is 1.7%/yr, based on premise thatsnowmobile market is more mature than market for otherrecreation vehiclesAnnual growth in snowmobile registrations for statesrepresenting 92% of 2006 LADCO region registrations:2000-2006 = +0.0%No growth assumption not adoptedbecause lack of snowfall often cited asmajor contributing factor for recentstagnation in snowmobile registrations2265001020 3.5 11.9 50% lower growth rate than overall LADCO region"Recreational Vehicle Service Technicians" employmentprojections, which is 1.7%/yr, based on premise thatsnowmobile market is more mature than market for otherrecreation vehiclesAnnual growth in snowmobile registrations for statesrepresenting 92% of 2006 LADCO region registrations:2000-2006 = +0.0%No growth assumption not adoptedbecause lack of snowfall often cited asmajor contributing factor for recentstagnation in snowmobile registrationsPECHANSeptember 200726Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable II-6 (continued)Total % ChangeSCC2005-20092005-2018Growth Indicator BasisAlternatives ConsideredComment2260007000 2.2 7.32265007000 2.2 7.3 LADCO region employment projections for "LoggingEquipment Operators"2270006000 7.2 15.0 REMI Commercial sector employment forecast forLADCO region2268006000 7.2 15.0 REMI Commercial sector employment forecast forLADCO region2267007000 2.2 7.3 LADCO region employment projections for "LoggingEquipment Operators"2268007000 2.2 7.3 LADCO region employment projections for "LoggingEquipment Operators"2285002000 12.2 36.7 NONROAD (national 1989-1996 diesel railwaymaintenance equipment population growth rate)LADCO region employment projections for "Rail-Track Laying &Maintenance Equipment Operators" (-1.6%/yr)Did not use alternative becausehistorical period indicatessubstantially different growth rate fordiesel than overall sector2260006000 7.2 15.0 REMI Commercial sector employment forecast forLADCO region2265006000 7.2 15.0 REMI Commercial sector employment forecast forLADCO region2270002000 4.7 16.2 LADCO region employment projections for "OperatingEngineers and Other Construction EquipmentOperators"2270003000 0.1 0.2 LADCO region employment projections for "IndustrialMachinery Mechanics"2267003000 0.1 0.2 LADCO region employment projections for "IndustrialMachinery Mechanics"2270001000 10.4 31.5 NONROAD (national1989-1996 diesel recreationalequipment population growth rate)LADCO region employment projections for "Recreational VehicleService Technicians" (+1.7%/yr)Did not use alternative becausehistorical period indicatessubstantially different growth rate fordiesel than overall sector2282020000 10.4 31.5 NONROAD (national1989-1996 diesel recreationalequipment population growth rate)Two options: 1996-2005 LADCO region recreational boatregistration growth rate (0.8%/yr) and LADCO region employmentprojections (+1.5%/yr) for "Motorboat Mechanics"Did not use alternatives becausehistorical period indicatessubstantially different growth rate fordiesel than overall sector.

## PECHAN September 2007



## 27 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

# SECTION III. UPDATED EMISSION CONTROL DATA

A. NON-EGU POINT SOURCE CONTROLS

1. NO

x 

## SIP Call

All states in the LADCO region affected by the NO

x 

## (oxides of nitrogen) SIP (State Implementation Plan) Call requirements (OH, IN, IL, MI) indicated that their sources were complying in 2005. The only exception to this is for reciprocating internal combustion engines (RICE) in Illinois. The State of I llinois recommended that an 82 percent NO

x 

## control efficiency be applied to large RICE engines that are affected by the SIP Call. The RICE engine requirement in Illinois has a January 1, 2008 complia nce date. Table III-1 lists these engines and

the associated NO

x 

## control efficiencies applied in the emi ssion projections. This requirement is expected to affect NO

x 

## emissions in all projection years.

2. MACT Standards

Table III-2 summarizes the control factors us ed to estimate the post-2005 effects of MACT

emission standards on volatile organic compounds (VOC), NO

x 

## , and PM emissions in the projection years. The inform ation in this table was developed from EPA guidance on estimating the criteria pollutant emission benefits of MACT standards (Page, 2007). Any post2005 MACT standards that have no expected cr iteria pollutant emission reductions according to the draft EPA guidance were not included in Ta ble III-2. Table III-2 was circulated to the states for review, and Wisconsin provided it s own estimates of the expected VOC and PM

emission reductions from these MACT standards in its state. The State of Michigan concurred

with the emission reduction estimates made by Wisconsin. Those VOC and PM emission reduction percentages are shown in the two right- most columns of Table III-2. So, the control

factor file reflects the EPA estimated values for IL, IN, MN, and OH, and the Wisconsinprovided estimates for MI and WI.

3. Consent Decrees

Previous Pechan-developed control efficienci es by source (Pechan, 2005) and pollutant were merged with the LADCO state 2005 point source file and control factor s assigned accordingly. The 2005 point source control efficienci es (CEs) for sulfur dioxide (SO 2 

## ) and NO

x 

## for fluid catalytic cracking units (FCCUs) and heaters and boilers were checked to see whether there is any compliance by 2005. Pechan also added all MA CTEC revisions/additions from their earlier report to the control factor file (MACTEC incor porated cases and settleme nts control factors for

refineries that were not evaluated for the 812 st udy) and made any changes/additions that were provided by the state air pollution control agencies. Table III-3 lists all of the LADCO state Non-EGU Point Sources affected by consent decr ees. These sources all have post-2005 control factors applied in the analysis. There are tw o refineries in the st udy area who had either complied with their consent decrees or curtailed applicable operations by 2005, so no future year control factors were applied in this analysis. These two refineries are Premcor Refining in IL and the Flint Hills Refinery in MN.PECHANSeptember200728Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable III-1. RICE Engines in Illinois Affected by NOxSIP CallId Number Device Process Device Description Pollutant%Reduction Comments027807AAC 0003 01 ENGINES 09-ENG AND 10-ENG NOX 59 50/50 for 2 engines =.5+.5(.18) = 59% reduction041804AAC 0009 01 ENGINE 1213 NOX 82041804AAC 0010 01 ENGINE 1214 NOX 82041804AAC 0011 01 ENGINE 1215 NOX 82041804AAC 0012 01 ENGINE 1216 NOX 82041804AAC 0013 01 ENGINE 1217 NOX 82073816AAA 0001 01 WORTHINGTON MLV-10 COMPRESSOR & GAS FIRED ENGINE #12 NOX 82073816AAA 0004 01 CLARK TCV-10 COMPRESSOR& GAS FIRED ENGINE ENGINE #9 NOX 82073816AAA 0012 01 WORTHINGTON MLV-10 COMPRESSOR AND GAS FIRED ENGINE NO. 13 NOX 82073816AAA 0013 01 WORTHINGTON MLV-10 COMPRESSOR AND GAS FIRED ENGINE NO. 14 NOX 82073816AAA 0014 01 WORTHINGTON MLV-10 COMPRESSOR AND GAS FIRED ENGINE NO. 15 NOX 82073816AAA 0015 01 WORTHINGTON MLV-14 ENGINE #10 NOX 82085809AAA 0010 01 3 CLARKCOMPRESSORS NOX 82093802AAF 0003 01 ENGINE E-1008 NOX 82113817AAA 0002 01 ENGINE EC21 NOX 82113817AAA 0003 01 ENGINE IC11 NOX 82113821AAA 0002 01 ENTERPRISE RECIP COMP EC-21 4000 MPEF 3.3.2-1 NOX 82113821AAA 0005 01 COOPER COMPRESSOR CC22 EF 3.3.2-1 4000 HP NOX 82149820AAB 0002 01 2 RECIPROCATING ENGINES (1013 - 1014) NOX 59 50/50 for 2 engines =.5+.5(.18) = 59% reduction149820AAB 0003 01 3 RECIPROCATING ENGINES (1015 - 1017) NOX 82167801AAA 0001 01 ENGINES1116 AND 1117 NOX 82167801AAA 0003 01 1-COOPER RECIPROCATING ENGINE, 4000HP, 1115 NOX 82167801AAA 0008 01 ENGINES1118 AND 1119 NOX 59 50/50 for 2 engines =.5+.5(.18) = 59% reductionPECHANSeptember200729Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable III-2. Post-2005 MACT Standards and Expected VOC, NOx, and PM ReductionsMACT Standard – Source CategoryCode of FederalRegulationsSubpartComplianceDate (existingsources)VOC(% Reduction)NOx(% Reduction)Total PM(% Reduction)Affected SCCsMACTCodeWisconsin andMichigan ValuesVOC PMAsphalt Processing and Asphalt RoofingManufactureLLLLL5/1/20068530505001, 30500101,30500102, 30505010,30601101041810 0Auto and Light Duty TrucksIIII4/26/20074040201601 to 40201632;4020169907020 0Coke Ovens: Pushing, Quenching andBattery StacksCCCCC4/14/2006030300304; 30300303030310 0Fabric Printing, Coating & DyeingOOOO5/29/20066040201101 to 40201199;40201201; 40201210071310 0Integrated Iron and SteelFFFFF5/20/2006(5)2030301501 to 3030159603050 10Iron and Steel FoundriesEEEEE4/22/20075304003XX, 304007XX03085 0Lime ManufacturingAAAAA1/5/200723305016XX04080 10Metal CanKKKK11/13/20067040201702; 40201703 to4020179907070 0Metal FurnitureRRRR5/23/20060402020XX10 0Misc. Coating ManufacturingHHHHH12/11/200664402026XX164210 0Misc. Metal Parts and ProductsMMMM1/2/20070402025XX10 0Misc. Organic Chemical Production andProcesses (MON)FFFF11/10/200666645200XX; 30113001 to30113007; 684300XX;30101005 to 30101099;68445001; 68445010;68445013; 68445020;68445022; 68445101;68445201; 30110002 to30110099; 64820001;64820010; 64821001;64821010; 64822001;64822010; 64823001;64823010; 64823001;64823010; 64880001;64882001; 64882002;64882599; 30105001;30105101 to 30105130;30801001; 31604001;31604002; 31600403;68510001; 68510010;68510011; 68580001;68582001; 68582002;68582599; 30101837;64610301 to 64610350;64610001 to 64610050;64610101 to 64610150;164110 0PECHANSeptember2007Table III-2 (continued)30Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportMACT Standard – Source CategoryCode of FederalRegulationsSubpartComplianceDate (existingsources)VOC(% Reduction)NOx(% Reduction)Total PM(% Reduction)Affected SCCsMACTCodeWisconsin andMichigan ValuesVOC PM64610201 to 64610250;64615001 to 64615030;64620001 to 64620038;64630001 to 64630083;64631001 to 64631083;64632001 to 64632083;64680001; 64682001;64682002; 64682501;64682502; 64682599;64130001 to 64130025;64130101 to 64130125;64130201 to 64130225;64131010 to 64131030;64132001 to 64132030;64133001 to 64133030;64180001; 64182001;64182002; 64182599;64615001; 64620001;65135001Organic Liquids DistributionEEEE2/3/20077040300102, 40300104,40300106, 40300107,40301010-40301021060210 0Plastic PartsPPPP4/19/20070402022XX10 0Plywood and Composite WoodProductsDDDD10/1/200754307007XX; 30700921 to30700971; 30701001 to30701057; 30700602 to307006611624Refractory Products ManufacturingSSSSS4/17/2006810040610 0Reinforced Plastic CompositesProductionWWWW4/21/2006390133710 0Site RemediationGGGGG10/8/2006500504001XX; 50400201,50400202; 504002XX;504100XX; 504101XX;504102XX; 504103XX;504102XX; 504103XX;04104XX; 504105XX;504106XX; 504107XX;50480001; 50482001;50482002; 50482599;50480004080510 0PECHANSeptember 2007Table III-2 (continued)31Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportMACT Standard – Source CategoryCode of FederalRegulationsSubpartComplianceDate (existingsources)VOC(% Reduction)NOx(% Reduction)Total PM(% Reduction)Affected SCCsMACTCodeWisconsin andMichigan ValuesVOC PMStationary Combustion TurbinesYYYY3/5/2007131720100101, 20100201,20200101, 20200103,20200201, 20200203,20200901, 20300102,20300202, 2030020301050 0Taconite Iron Ore ProcessingRRRRR10/30/2006006232302371 to 3230239904110 10Wood Building ProductsQQQQ5/28/200663040202101 to 40202199070310 0**Based on organic hazardous air pollutant (HAP) emission reductionsPECHANSeptember200732Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable III-3. LADCO State Non-EGU Point Sources Affected by Consent Decree Requirementsand Other On-the-Books ControlsIdentification CodesFCCU RequirementsHeater/Boiler RequirementsState County Facility* CompanyLocationStateSO2NOxSO2NOx18 089 00003 BP AmocoWhitingINFCU 500: Install wet gasscrubber; FCU 600: UseSO2adsorbing catalystadditive and/orhydrotreatment.FCU 600: InstallSCR; FCU 500: LowNOxcombustionpromoter and NOxadsorbing catalystadditiveElimination of oil burningand restricting H2S inrefinery fuel gasUse qualifying controls toreduce NOxemissions by9632 tons per year (tpy).39 095 0448010246 BP AmocoToledoOHSO2catalyst additiveInstall SNCR systemElimination of oil burningand restricting H2S inrefinery fuel gasUse qualifying controls toreduce NOxemissions by9632 tpy.17 197 197090AAI CITGO GlobalRefineryLemontILNew wet gas scrubberLow NOxcombustionpromoter (20 ppmvdlimit)Comply with NSPSSubparts A and J for fuelgas combustion devices.Eliminate fuel oil burning.Use qualifying controls toreduce NOxemissions fromlisted units by at least 50% ofthe revised baseline17 119 119090AAA Conoco PhilipsGlobal RefineryRoxanna (WoodRiver)ILInstall new wet gasscrubber (25 ppmvd orlower)FCCU 1: Scrubberbased NOxemissionreduction technologyto achieve 20 ppmvdSubject to NSPS SubpartsAand J for fuel gascombustion devicesUse qualifying controls toreduce NOxemissions fromcombustion units by 4951 tpy17 119 119090AAA Conoco PhilipsGlobal RefineryHartford (WoodRiver)ILInstall new wet gasscrubber (25 ppmvd orlower)FCCU 2: EnhancedSNCRSubject to NSPS SubpartsAand J for fuel gascombustion devicesUse qualifying controls toreduce NOxemissions fromcombustion units by 4951 tpy17 197 197800AAA Exxon-MobilRefineryJolietILInstall new wet gasscrubber (25 ppmvd orlower)Install and operate anSCR systemAccept NSPS Subpart Japplicability for heatersand boilers and reduce oreliminate fuel oil firingUse qualifying controls toreduce NOxemissions fromcombustion units17 033 033808AAB MarathonAshland RefineryRobinsonILExisting wet gasscrubberCatalyst additiveAccept NSPS Subpart Japplicability for heatersand boilers and reduce oreliminate fuel oil firingReduce overall NOxemissions from the controlledheaters and boilers at MAPrefineries by 4,000 tpy.Control methods can include:SCR or SNCR; ULNB;technologies to reach 0.040lbs per MMBtu or lower;alternate SO2single burnertechnology to achieve 0.055lbs per MMBtu or lower; unitshutdowns.PECHANSeptember2007Table III-3 (continued)33Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportIdentification CodesFCCU RequirementsHeater/Boiler RequirementsState County Facility* CompanyLocationStateSO2NOxSO2NOx26 163 A9831 MarathonAshland RefineryDetroit MI SO2catalyst additive Catalyst additiveAccept NSPS Subpart Japplicability for heaters andboilers and reduce oreliminate fuel oil firingReduce overall NOxemissions from thecontrolled heaters andboilers at MAP refineries by4,000 tpy. Control methodscan include: SCR or SNCR;ULNB; technologies toreach 0.040 lbs per MMBtuor lower; alternate SO2single burner technology toachieve 0.055 lbs perMMBtu or lower; unitshutdowns.27 163 2716300003 MarathonAshland RefinerySt Paul Park MN New wet gas scrubberon unit 1; catalystadditive on other unitCatalyst additiveAccept NSPS Subpart Japplicability for heaters andboilers and reduce oreliminate fuel oil firingReduce overall NOxemissions from thecontrolled heaters andboilers at MAP refineries by4,000 tpy. Control methodscan include: SCR or SNCR;ULNB; technologies toreach 0.040 lbs per MMBtuor lower; alternate SO2single burner technology toachieve 0.055 lbs perMMBtu or lower; unitshutdowns.39 151 1576000301 MarathonAshland RefineryCanton OH SO2catalyst additive Catalyst additiveAccept NSPS Subpart Japplicability for heaters andboilers and reduce oreliminate fuel oil firingReduce overall NOxemissions from thecontrolled heaters andboilers at MAP refineries by4,000 tpy. Control methodscan include: SCR or SNCR;ULNB; technologies toreach 0.040 lbs per MMBtuor lower; alternate SO2single burner technology toachieve 0.055 lbs perMMBtu or lower; unitshutdowns.39 095 0448010246 SunocoPetroleumRefineryToledo OH Install new wet gasscrubber to meet 25ppmvd SO2Install SCR systemsor alternatetechnology to meet 20ppmvdAccept NSPS Subpart Japplicability and reduce oreliminate fuel oil burningPECHANSeptember2007Table III-3 (continued)34Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportIdentification CodesState County Facility* CompanyLocationStateNotes17 115 115015AAEADM Decatur IL Settlement agreement17 143 143065AJEADM Peoria IL Settlement agreement17 001 001815AAFADM Quincy IL Settlement agreement18 173 00002Alcoa Warrick Units 1,2,3 IN Settlement agreement*Facility identification codes are thoseused in the 2002 point source files.

## PECHAN September 2007



## 35 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

## The Michigan Department of Environmental Quality (DEQ) provided information about the expected emissions reductions associated with settlements affecting Michigan sources. The information from Michigan DEQ was provided fo r Severstal (iron and steel), US Steel, and Marathon refinery. For Seve rstal, the key information provided indicated that NO

x 

## emissions

after the summer 2007 would be reduced at the blast furnace B and C stoves via a low NO

x 

## burner (LNB) installation. A 50 percent NO

x 

## control factor was applied with 2007 implementation year based on information in th e Ozone Transport Rulemaking analysis about the expected emission reduction of LNB applied to a blast furnace. For US Steel, the Michigan DEQ-provided information indicated that PM controls would be in stalled during 2005 or 2006 on the basic oxygen furnace and blast furnace B, so it was assumed that these were base year controls and no future year control factor was ap plied. For the Marathon refinery in Michigan, the Michigan DEQ estimated that catalyst additives applied to the FCCU would reduce NO

x 

## emissions by 25-50 percent and SO 2 

## emissions by 60-80 percent. The midpoint of each range was used to estimate post-2005 control factors for th is refinery. All other expected controls at

Marathon are to reduce PM emissions and were assumed to have occurred by 2005, so no future year PM control factors were applied.

4. On-the-Books (OTB) Control Additions

Table III-3 lists on-the-books controls that were applied to individual f acilities/sources in the future year control factor file. This inform ation was developed from the OTB updated control factor file provided by LADCO from the 2002 ba se year projections. The compliance date

information in this file was used to eliminat e controls that had compliance dates of 2005 or earlier. Ohio EPA provided informati on about the expected effects of NO

x 

## Reasonably Available Control Technology (RAC T) rules in achieving post- 2005 emission reductions in the
Cleveland-Akron, Ohio 8-hour ozone nonattainment area. Table III-4 summarizes the source categories, associated emission control equipmen t to meet the requirements, and the estimated

NO

x 

## control percentages.

Table III-4. Ohio RACT Rule Summary Cleveland/Akron 8-Hour Ozone

Nonattainment Area

Source Category Unit Size (MMBtu/hour) NO

x 

### Control Estimated NO

x 

### Control Efficiency RICE Engines All Low Emission Combustion 80% ICI Boilers 20-49 Burner Tune-up 10%

ICI Boilers 50-99 LNB+FGR 61%

ICI Boilers 100-249 LNB+FGR 61%

ICI Boilers >250 LNB+FGR 61% Combustion Turbine All Dry LNB 70%

SOURCE: Ohio EPA Division of Air Pollution Control.



## 5. Best Available Retrofit Technology (BART)

Table III-5 lists the BART-eligible sources fo r the states in the LADCO study region. In instances where criteria air pollu tant control percentages (for SO 2 

## and NO

x 

## ) are listed in this table, those control percentages were applied in estimating 2018 emissions.PECHANSeptember200736Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable III-5. BART Eligible Non-EGU SourcesEst. Emission ReductionState State ID Source Name CountyCountyID Source ID BART Emission Unit ID Description Stack ID SO2NOxILLINOIS 17 Conoco Phillips Madison 11D 119090AAAILLINOIS 17 Exxon Mobil Will 197 197800AAAILLINOIS 17 CITGO Will 197 197090AAIILLINOIS 17 National Steel –Granite CityMadison 119 119813AAIINDIANA 18 AGC DIVISIONALCOA POWERGENERATINGWarrick 173 2 Boiler #2 Dry Bottom, pulverizedcoal-fired boiler241-242 95 90Boiler #3 Dry Bottom pulverizedcoal-fired boiler242 95 90Boiler #4 Dry Bottom, pulverizedcoal-fired boiler243 95 90INDIANA 18 Alcoa Inc. – Warrick Warrick 173 7 105m.1, 10 POTLINE #3. ROOMS105 AND 106 gtc105M 95 40107M, 108M POTLINE #4. ROOMS107 AND 108 GTC107M 95 40109M,110M POTLINE #5, ROOMS109 AND 110, A-398109M 95 40111M,112M, POTLINE #6 95 40130m.1,104 potline #2, Rooms 103and 104, A-398103m.1 95 40134.63 HDC FURNACECOMLEXES1EH 0 40134.71 OFFLINES #2 134.71 0 40INDIANA 18 ESSROC CEMENTCORP. (Speed)Clark 19 8 EU20 Kiln #1 95 70EU21 Kiln #2 95 70INDIANA 18 GE PLASTICS MT.VERNON INC.Posey 129 2 08-706 CO AND ORGANICSULFIDE STREAMFROM PHOSGENEFED08-70670795 009-001 B&W NATURAL GASAND OIL FIREDBOILER09-001 0 7009-001 Riley Boiler 12-001 95 7012-001 Hot Oil Heater 0 009-002 LASKER BOILER 09-002 95 7509-002 ERIE BOILER 09-002 95 75PECHANSeptember2007Table III-5 (continued)37Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportEst. Emission ReductionState State ID Source Name CountyCountyIDSource ID BART Emission Unit ID Description Stack ID SO2NOxINDIANA 18 ISG-BURNSHARBOR (FormerlyBeth. Steel)Porter 127 1 460-01 #7 Boiler 4 95 7546002 #8 Boiler 5 95 75460-03 #9 Boiler 6 95 75460-04 #10 Boiler 7 95 75460-05 Boiler #11 8 95 75460-06 #12 Boiler 9 95 75512-06 #1 COKE BATTERYPUSHING11 0 0512-08 #1 Coke BatteryUnderfire13 95 75512-14 #2 COKE BATTERYPUSHING12 0 0512-16 #2 COKE BATTERYUNDERFIRE STACK14 95 75520 BLAST FURNACEFUGITIVES0 0520-04 SINTER WINDBOXSTACK25 95 75520-18 BLAST FURNACE DCASTHOUSEEMISSIONS33 0 0520-18 C BLAST FURNACESTOVES31 0 0520-19 BLAST FURNACE DSTOVES34 0 0520-19 BLAST FURNACE CCASTHOUSE33 0 0534 STEELMAKINGFUGITIVES0 0534-01 STEELMAKING HMDSTATION #157 0 0534-02 STEELMAKING HMD#259 0 0534-10 STEELMAKINGVESSELS #1 & #262 0 0534-11 STEELMAKINGVESSELS64 0 0534-23 STEELMAKING FMBOILER65 0 0PECHANSeptember 2007Table III-5 (continued)38Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportEst. Emission ReductionState State ID Source Name CountyCountyID Source ID BART Emission Unit ID Description Stack ID SO2NOx595-24 CASTER #1 80 0 0670-05 HOT STRIP FURNACE#190 95 75670-07 HOT STRIP #3FURNACE92 95 75670-07 HOT STRIP 91 95 75673-14 160" OKATE MILLFURNACE #1112 0 75673-15 160" PLATE MILLFURNACE #2113 0 75673-16.17 160" PLATE MILLFURNACES 4&5110 0 0673-18.19 160" PLATE MILLFURNACES 6&7111 0 0673-20 160" PLATE MILLFURNACE #8114 0 0674.26,27 110" PLATE MILLFURNACES #1122 0 0MICHIGAN 26 Lafarge Midwest Inc. Alpena 7 B1477 Kilns #1-#5MICHIGAN 26 Stone Container Corp. Ontonagon 131 A5754 Riley BoilerPaper Machine #2MICHIGAN 26 Tilden Mining Co Marquette103 B4885 Pelletizing Line #1, includeskiln, furnace, cooler, dryerBoiler #2Primary crusherMICHIGAN 26 Empire Iron Mining Marquette 103B1827 Pelletizing Lines #1 - #3 furnaceBoilers #1 - #3Primary crusherMICHIGAN 26 St. Mary’s Cement(CEMEX)Charlevoix 29 B1559 Kiln and pre-calcinerMICHIGAN 26 New Page Paper(Escanaba)Delta 41 A0884 Boiler #8Boiler #9Recovery furnaceLime kilnMINNESOTA 27 Ipsat Inland St. Louis 137 2713700062MINNESOTA 27 EVTAC-Fairlane St. Louis 137 2713700113MINNESOTA 27 National Steel(Keewatin)St. Louis 137 2713700063MINNESOTA 27 Hibbing Taconite St. Louis 137 2713700061MINNESOTA 27 USS Minntac St. Louis 137 2713700005MINNESOTA 27 Northshore Mining Lake 75 2707500003PECHANSeptember 2007Table III-5 (continued)39Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportEst. Emission ReductionState State ID Source Name CountyCountyID Source ID BART Emission Unit ID Description Stack ID SO2NOxN. DAKOTA 38 Great River Energy –Coal CreekMcLean 55 17N. DAKOTA 38 Basin Electric Power –Leland OldsMercer 57 1N. DAKOTA 38 Great River Energy –StantonMercer 57 4N. DAKOTA 38 Minnkota Power – MRYoungOliver 65 1OHIO 39 Mead Paper Division Ross 67 671010028WISCONSIN 55 Georgia-PacificConsumer Products(Formerly Fort James)Brown 9 405032870 Boiler B26 stoker (coal, tire andother fuels), 350mmBtu/hrS10 85 50Boiler B27 cyclone, 615 mmBtu/hr S10 85 88

## PECHAN September 2007



## 40 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

## B. AREA SOURCE/MAR CONTROLS

1. Area Sources

Pechan worked with the LADCO states to dete rmine how to estimate the effect of federal/ state/local rules on area source category emissions. The sub-sections below describe the results

of this effort.

a. VOC Solvent Categories

For VOC emissions from consumer products and architectural and maintenance coatings, it was decided to estimate post-2005 VOC emission reducti on credits using EPA guidance to states for estimating the benefits of three Federal ru les being promulgated during calendar year 2007 (Harnett, 2007). These rules will establish or amend VOC content limits for (1) aerosol coatings (new rule), (2) architectural and industrial mainte nance (AIM) coatings (amendments), and (3) household and institutional consumer products (amendments).

EPA estimated that the aerosol coatings rule will achieve the equivalent of a 19 percent reduction

in mass VOC emissions from the 1990 baseline. The year 1990 represents the baseline, since there has been no previous Federal rulemaking for aerosol coatings. The creditable reduction that may be claimed is 0.114 pounds per capita. In the LADCO state 2005 emission inventory, this VOC emission reduction is applied to SCC 2460500000, which are Coatings and related products. A 12 percent VOC emission reduction is applied to SCC 2460500000 in each forecast analysis year (i.e., 2009, 2012, and 2018) to estimate the benefit of the federal aerosol coatings rule. This percentage is lower than the equi valent value estimated by EPA because the aerosol coatings rule is a subset of the Coatings and related products categ ory represented by SCC

2460500000.

For AIM coatings, EPA estimates that the amende d Federal AIM rule will achieve a reduction of 31 percent from the post-1998 Federa l rule baseline of 3.6 pounds per capita. This is a creditable reduction of 1.1 pounds per capita. AIM coatin g emission reductions are applied to the following SCCs in the base year LADCO i nventory for each analysis year: 2401001000;
2401002000; 2401003000; 2401008000; 2401008999. 2



## For consumer products, EPA has calculated that the amended Federal rule will achieve a VOC reduction of approximately 29 percent beyond that achieved by the 1998 Federal rule. This is a creditable reduction of 0.9 pounds per capita. Emission reductions from the Federal rule are applied to all Consumer Product source cate gories (SCCs 2460*) in each analysis year.

b. Portable Fuel Containers

For portable fuel containers ( PFCs), while there are state-by-st ate differences in likely rule adoption dates, all state’s control factors are ba sed on the EPA mobile source air toxics (MSAT) rule requirements. EPA adopted emission standa rds for portable fuel containers (such as gas

2 

### Note that subsequent to delivery of the area source/MAR control file, Wisconsin stated that this last SCC should

not be included for their state.

## PECHAN September 2007



## 41 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

## cans) under the consumer products authority of th e Clean Air Act. Starting with containers
manufactured in 2009, the standard limits eva poration and permeation emissions from these containers to 0.3 grams of hydrocarbons per gallon per day. EPA also adopted test procedures and a certification and compliance program in order to ensure that containers meet the emission standard over a range of in-use conditions.

The VOC emission reduction benefits were estimat ed assuming that the new rule affects PFC sales starting during 2009, and th at each PFC that meets the MSAT standard has 75 percent lower emissions than the PFC being replaced.

To account for the fact that grow th in the portable fuel contai ner population and turnover from old to new containers will be affected by the MSAT rule, 3 

## Pechan calculated projection year emissions using the following equation:

(Eq. 1)

where:

Q N 

## = emissions in projection year
Q

o 

## = emissions in base year

R i 

## = annual retirement rate

F

e 

## = emission factor ratio for existing sources (1.0)

G N 

## = projection year growth factor (projection year activity/base year activity)

F

n 

## = emission factor ratio for new s ources relative to existing sources t = number of years between ba se year (2002) and projection year

The first term in the equation represents ne w source growth and cont rols, the second term

accounts for retirement and controls for exis ting sources, and the third term accounts for replacement source controls. Because retirement was not estimated using a constant annual rate (5 percent were assumed to be retired in the fi rst year, with 10 percent retired in each additional forecast year), Pechan replaced the (1-R i 

## ) t 

## terms in this equation w ith the appropriate proportion of containers retired between the base year a nd the appropriate forecast year. Pechan then computed an overall emission reduction for each future year of interest by comparing the forecast year controlled emissions calculated from this equation to the forecast year uncontrolled emissions. For example, an overall VOC emissi on reduction of 26.4 percent was computed for Illinois. Pechan then back-calculated the appr opriate rule pe netration (RP) value for each forecast year based on the overall emissions reduction, the 75 percent CE value, and an rule effectiveness (RE) of 100 percen t (e.g., the calculated RP for Illinois for 2012 is 35.2 percent).

3 

### Note that to simplify the analysis P echan assumed that all post-2005 new container growth would be affected by the MSAT rule (due to low growth rates, this assumpti on does not have a significant impact on the overall emission reduction estimates of this rule). 

# ( ) 

# [ ] () () ⎭ ⎬ ⎫ ⎩ ⎨ ⎧ ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ ⎥ ⎦ ⎤ ⎢ ⎣ ⎡

− − +

− +

− = n e n 

## F t i R F t i R F N G o Q N Q 1 1 1 1

## PECHAN September 2007



## 42 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

## c. Residential Wood Heating (Woods toves and Fireplace Inserts)

Pechan developed control fact ors by pollutant and year to account for the effect of the replacement of retired wood stoves/inserts that emit at pre-residential wood heater new source performance standard (NSPS) levels,. These c ontrol factors were developed using an annual 2 percent retirement rate for wood stoves/fire places along with pre- and post-NSPS wood stove and fireplace emission factors. SCCs for "cont rolled" wood stoves and fireplace inserts have no control factors applied. Pechan developed updated residential wood combustion control factors for the LADCO states using the same algorithms applied previously (Pechan, 2004). Table III-6 displays the emission reduction, control efficiency, and rule pe netration percentages modeled.

d. Stage II Vehicle Refueling

Pechan developed updated (2005 base year) Stag e II vehicle refueling control factors via

MOBILE runs for the LADCO states. Onroad refue ling control factors were calculated based on the percentage difference between the projection year (2009, 2012, and 2018) MOBILE6 refueling emission factors and the 2005 MO BILE6 refueling emission factors.

MOBILE6 emission factors were calculated at Janua ry and July temperature and fuel conditions. July emission factors were used as the surr ogate for the five-month ozone season (May through September) and the January emission factors were used as the surrogates for the remaining seven months. Temperatures modele d were the January and July average daily monthly maximum and minimum temperatures for each state (i.e., Illi nois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin) based on 30-year average temperatur e data, as used in EPA’s second Section 812 Prospective analysis. MOBILE6 input files were created for each unique combination of: January and July Reid vapor pressure, reformul ated gasoline, oxygenated fuel, gasoline sulfur, and Stage II control programs for each of the st ates mentioned above. Fuel data and Stage II control program information for each state a nd corresponding projection year were based on EPA’s National Mobile Inventory Model (N MIM) County Database (version NDC20060201). Data extracted from NMIM’s County Database fo r these input parameters were based on January and July values.

Stage II control programs for IL, IN, and WI bega n in 1998 with a phase-in year of one year and with a percent efficiency value of 86.0 per cent for LDGVs and LDGTs in the program. Similarly, the HDGVs in the program have 86.0 pe rcent efficiency. For Ohio, these control programs began in 1993 with a two-year phase -in and 77.0 percent efficiency for both the LDGVs + LDGTs and HDGVs in the program.

Modeling these temperature, fuel, and Stage II control inputs (where applicable), Pechan

calculated MOBILE6 emission factors for calendar years 2005, 2009, 2012, and 2018.

The resulting MOBILE6 emission factors were fi rst weighted according to the default MOBILE6 VMT mix to determine the weighted average refu eling emission factor fo r all gasoline vehicle types. The resulting January and July emission factors were weighted together according to the number of days in the seven-month season (212 days) and the five-month ozone season (153).PECHANSeptember200743Development of 2005 Base YearGrowth and Control Factorsfor LADCO - Final ReportTable III-6. Residential Wood Combustion NSPS Emission Reductions(percentage values)2009 2012 2018SCC SCC Description PollutantReductionControlEfficiencyRulePenetration ReductionControlEfficiencyRulePenetration ReductionControlEfficiencyRulePenetration2104008001 Total Fireplaces CO 2.9 55.0 5.3 4.9 55.0 8.9 8.5 55.0 15.52104008010 Total Woodstoves CO 3.1 55.0 5.6 5.2 55.0 9.5 9.0 55.0 16.42104008000 Total Fireplaces & Woodstoves CO 3.0 55.0 5.5 5.1 55.0 9.3 8.9 55.0 16.22104008001 Total Fireplaces NOX 1.9 28.6 6.6 3.3 28.6 11.5 5.6 28.6 19.62104008010 Total Woodstoves NOX 2.0 28.6 7.0 3.4 28.6 11.9 5.8 28.6 20.32104008000 Total Fireplaces & Woodstoves NOX 2.0 28.6 7.0 3.3 28.6 11.5 5.8 28.6 20.32104008001 Total Fireplaces PM10-PRI 2.3 35.9 6.4 4.0 35.9 11.1 6.9 35.9 19.22104008010 Total Woodstoves PM10-PRI 2.5 35.9 7.0 4.2 35.9 11.7 7.2 35.9 20.12104008000 Total Fireplaces & Woodstoves PM10-PRI 2.4 35.9 6.7 4.1 35.9 11.4 7.1 35.9 19.82104008001 Total Fireplaces PM25-PRI 2.3 35.9 6.4 4.0 35.9 11.1 6.9 35.9 19.22104008010 Total Woodstoves PM25-PRI 2.5 35.9 7.0 4.2 35.9 11.7 7.2 35.9 20.12104008000 Total Fireplaces & Woodstoves PM25-PRI 2.4 35.9 6.7 4.1 35.9 11.4 7.1 35.9 19.82104008001 Total Fireplaces VOC 5.9 77.4 7.6 9.8 77.4 12.7 17.1 77.4 22.12104008010 Total Woodstoves VOC 5.6 77.4 7.2 9.4 77.4 12.1 16.4 77.4 21.22104008000 Total Fireplaces & Woodstoves VOC 5.4 77.4 7.0 9.2 77.4 11.9 16.0 77.4 20.7Note: Rule effectiveness (RE) of 100 percent for each SCC/year.

## PECHAN September 2007



## 44 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

## After this was done for all of the modeled years and state or sub-state ar eas, the overall control efficiency for refueling, due to fleet turnover, was calculated ba sed on the percentage difference between the 2005 and corresponding pr ojection year emission factors. These control efficiencies were then assigned to individual counties, base d on the mapping of fuel and Stage II control parameters to those modeled in the MOBILE6 files.

2. MAR Sources (Locomotives and Marine Vessels)

EPA issued a proposed rule this spring affecting future criteria pollutant emissions from railroad locomotives and commercial marine vessels (C MVs) (EPA, 2007b). These are the two off-road source categories that are addres sed in this report. Base year emissions (2005) information for these two source categories was develope d by ENVIRON under contract to LADCO (ENVIRON, 2007a and b).

Control factors for criteria air pollutants were developed using Chapter 3 (Emissions Inventory) of EPA’s “Draft Regulatory Impact Analysis: Control of Emissions of Air Pollution from Locomotive Engines and Marine Compression-Ignition Engines Less than 30 Liters per Cylinder” (EPA, 2007b). This chap ter presents EPA’s analysis of the emissions impact of the proposed rule for three source ca tegories affected: commerci al marine diesel engines, recreational marine diesel engine s, and locomotives. The proposed control requirements include NOx and PM emission standards for Category 1 and Category 2 commercial marine diesel engines (both above and below 37 kilowatts). New NOx and PM emission standards would also apply to all recrea tional diesel engines and locomotives. There are no new standards for HC or
CO; however, the PM standards are also expected to decrease HC emissions.

For locomotives, the EPA Regulatory Impact Anal ysis (RIA) chapter was used to develop 2009, 2012 and 2018 estimates of baseline and post-co ntrol emissions by po llutant by locomotive usage. This information is summarized in Ta ble III-7. The RIA examin ed the effect of the proposed rule on emissions for (1) large line haul , (2) large switch, (3) small railroads, and (4) passenger commuter trains. Each of these four usage types was assigned to the base year 2005 emission inventory SCCs. The SCC assignments are shown at the bottom of Table III-7.

## PECHAN September 2007



## 45 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

## Table III-7. Locomotive Emissions Reported in EPA Draft RIA

Large Line Haul Large Switch Small Railroads Passenger Commuter

Year Baseline Controlled Baseline Controlled Baseline Controlled Baseline Controlled

Volatile Organic Compounds

2006 43,874 5,501 2,891 1,609

2009 43,486 42,008 5,696 5,552 3,032 3,032 1,546 1546

2012 42,891 35,890 5,898 5,364 3,179 3,179 1,476 1301

2018 41,684 23,607 6,325 5,066 3,497 3,497 1,332 771

PM-2.5

2006 27,082 2,202 907 992

2009 24,216 23,661 2,120 2,070 870 870 861 861

2012 23,800 20,672 2,188 2,006 912 912 819 738

2018 22,542 14,516 2,309 1,896 991 991 719 466

PM-10

2006 27,919 2,270 935 1,023

2009 24,965 24,393 2,185 2,134 897 897 888 888

2012 24,536 21,311 2,256 2,068 940 940 845 761

2018 23,240 14,965 2,380 1,954 1,022 1,022 741 480

Oxides of Nitrogen

2006 779,842 86,861 37,690 38,466

2009 755,490 751,364 88,573 87,999 39,528 39,528 32,338 32,338

2012 730,031 692,606 88,909 86,614 41,456 41,456 27,212 25,933

2018 708,525 608,010 90,875 84,612 44,299 44,299 22,559 19,496

SCC(s) 2285002006 2285002010 2285002007 2285002008; 2285002009



## Because the federal locomotive emission standa rds modeled under previous LADCO contracts
will continue to achieve emission reductions, it was necessary for Pechan to adjust the information in the new RIA to estimate the total post-base year reductions from the effects of both the existing and proposed locomotive standa rds. Pechan computed a revised set of projected emissions for modeling years 2009, 2012, and 2018 that reflect application of EPA’s assumed locomotive growth rate (1.6 percent per year) to the base year (2006) emissions from their analysis. This step was used to estim ate the emissions for each modeling year excluding

the effects of both sets of emission standards. Next, Pechan computed the percentage reduction

in emissions between the revised emissions in each modeling year and th e controlled emissions reported in EPA’s draft RIA. Table III-8 show s how the revised baseli ne and percent reduction for each modeling year. For example, for la rge line haul railroads, the baseline 2009

uncontrolled VOC emissions are estimated to be 46,014 tons nationally. Cont rolled emissions of 42,008 tons represent an 8.7 per cent VOC reduction from this uncontrolled emission estimate
(46,014 – 42,008 = 4,010; 4,010/46,014 * 100 = 8.7).

## PECHAN September 2007



## 46 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

## Table III-8. Percentage Reductions Associated with Federal Locomotive

Standards

Large Line Haul Large Switch Small Railroads Passenger Commuter

Year Revised

Baseline %

Reduction Revised

Baseline %

Reduction Revised

Baseline %

Reduction Revised

Baseline %

Reduction

Volatile Organic Compounds

2006

2009 46,014 8.7% 5,769 3.8% 3,032 0.0% 1,687 8.4%

2012 48,258 25.6% 6,051 11.3% 3,180 0.0% 1,770 26.5%

2018 53,080 55.5% 6,655 23.9% 3,498 0.0% 1,947 60.4%

PM-2.5

2006

2009 28,403 16.7% 2,309 10.4% 951 8.5% 1,040 17.2%

2012 29,788 30.6% 2,422 17.2% 998 8.6% 1,091 32.4%

2018 32,765 55.7% 2,664 28.8% 1,097 9.7% 1,200 61.2%

PM-10

2006

2009 29,281 16.7% 2,381 10.4% 981 8.5% 1,073 17.2%

2012 30,709 30.6% 2,497 17.2% 1,028 8.6% 1,125 32.4%

2018 33,777 55.7% 2,746 28.9% 1,131 9.7% 1,238 61.2%

Oxides of Nitrogen

2006

2009 817,877 8.1% 91,097 3.4% 39,528 0.0% 40,342 19.8%

2012 857,766 19.3% 95,540 9.3% 41,456 0.0% 42,310 38.7%

2018 943,477 35.6% 105,087 19.5% 45,599 2.8% 46,537 58.1%

SCC(s) 2285002006 2285002010 2285002007 2285002008; 2285002009
Note: emissions reported in short tons.



## Analogous control factor calculations to those de scribed above for locomotives were used to compute total CMV emission reduction values representing the effects of both existing and proposed emission standards. Table III-9 presents the EPA baseline emissions, Pechan’s revised baseline emissions (computed using EPA’s 0.9 percent annual growth assumption), EPA’s controlled emissions, and the percentage reducti on estimates applied in this analysis. For example, the CMV standards are expected to reduce VOC emissions by 33.9 percent in 2018.

## PECHAN September 2007



## 47 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

## Table III-9. Percentage Reductions A ssociated with Federal CMV Standards

Emissions (short tons)

Year Baseline Revised

Baseline Controlled %

Reduction
Volatile Organic Compounds

2005 17,295

2009 16,870 17,926 16,863 5.9%

2012 16,495 18,414 16,344 11.2%

2018 16,034 19,431 12,851 33.9%

PM-2.5

2005 30,042

2009 27,327 31,138 27,324 12.2%

2012 26,657 31,987 26,582 16.9%

2018 22,553 33,753 19,308 42.8%

PM-10

2005 30,972

2009 28,172 32,102 28,169 12.3%

2012 27,481 32,977 27,403 16.9%

2018 23,251 34,798 19,905 42.8% Oxides of Nitrogen

2005 825,229

2009 781,105 855,341 781,105 8.7%

2012 743,915 878,643 742,453 15.5%

2018 686,966 927,171 591,991 36.2%

Sulfur Dioxide

2005 82,543

2009 46,838 85,555 46,839 45.3%

2012 42,515 87,886 42,515 51.6%

2018 6,054 92,740 5,630 93.9%

Carbon Monoxide

2005 153,499

2009 149,966 159,100 149,966 5.7%

2012 146,227 163,434 146,227 10.5%

2018 140,443 172,461 140,443 18.6%



## For commercial marine diesel engines, the RIA examines expected rule emission benefits for four different engine types/sizes . The total CMV emission benefits in each year were used and applied equally to most of the affected SCCs in the 2005 inventory. However, Pechan applied rule penetration (RP) values to two CMV S CCs based on an ENVIRON table indicating RP values of less than 100 percent for these SCCs (see Table III-10).

## PECHAN September 2007



## 48 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

## Table III-10. Commercial Marine Vessel Rule Penetration Values

Percentage of Engines Affected by Proposed EPA Standards Source Category Code (SCC) Source

Definition Purpose Geographic

Area NOx PM-10 HC CO SOx

River Traffic 100% 100% 100% 100% 100% 2280002023 Push Boats Barge Freight

Lake Traffic 100% 100% 100% 100% 100%

2280002021 Tugs Vessel assist and support

functions Near port 100% 100% 100% 100% 100%

2280003200 Mid-Great

Lakes 85% 81% 86% 86% 77%

2280003100 Deep draft Laker and ocean-going large vessels Near port 81% 71% 87% 83% 63%

2280002022 Ferries River or lake ferrying Regular routes 100% 100% 100% 100% 100%

2280002024 Other

Commercial

Vessels Excursion

boats primarily Near dock 100% 100% 100% 100% 100%

2280002025 Dredges Dredging projects Varies 100% 100% 100% 100% 100%

2280002029 Support

Vessels General work

boats Near port 100% 100% 100% 100% 100%

2280002030,

2280004030 1 Commercial Fishing Market fishing Great Lakes 100% 100% 100% 100% 100%

2280002040,

2280004040 1 Military Coast Guard and Navy Great Lakes 100% 100% 100% 100% 100%

## PECHAN September 2007



## 49 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

# SECTION IV. PREPARATION OF GROWTH AND CONTROL

FILES

This section describes the contents of the grow th and control factor files submitted to LADCO

earlier this month. The first s ubsection discusses the preparation of the point and area source/

MAR factor file and the revised NONROAD model growth file. The fina l subsection describes

the contents of the c ontrol factor files.

Table IV-1 presents the RPO Data Exchange Pr otocol Format for reporting emission growth and

control data. Pechan utilized this format to cr eate growth and control f actor files for LADCO. Because the growth factors ( unlike the control factors) do not differ by pollutant, Pechan developed a separate file contai ning only the point and area source/ MAR growth factors. Pechan revised the growth packet portion of the NONROAD model growth file ( NATION.GRW ) to replace the default model equipment population gr owth rates with growth rates based on more recent/more region-specific information. Two sets of control factor files we re prepared: one for area source/MAR categories and one for point source categories. The point and area source/MAR growth and control files were develope d in fixed field ascii format. The format of the default NONROAD model growth file was re tained in the revised version prepared for LADCO. The following subsections describe the contents of the gr owth and control factor files.



## A. GROWTH FACTORS

Pechan compiled the LADCO region growth factor information into the file $LADCO_2005_GF_Final_RPO.txt . Table IV-2 displays the RPO Data Exchange Protocol Format fields and identifies the fields that were p opulated in this file. Th e file contains separate records for each SCC/state for each year between 2006 and 2018 (population-based growth indicator records are reported by SCC/state/county because population projections were available at the county-level).

Pechan revised the input file used by the NONROAD model ( NATION.GRW ) to reflect historical equipment population changes and to estimate future equipment population changes. In particular, Pechan incorporated LADCO st ate-specific records to the GROWTH packet portion of this file. The fixed field format of the data in this packet is as follows:

Characters Description 1-5 FIPS code (00000 = applies to entire na tion; ss000 = applies to all of state ss) 6-10 subregion code (left blank) 11-15 year of estimate (4-digit year) 17-20 indicator code (alphanumeri c code identified within NONROAD)

26-45 value for indicator

## PECHAN September 2007



## 50 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

## Table IV-1. RPO Data Exchange Protoc ol Format for Growth/Control Data

Field Name Field Description Field Length

RECORD TYPE A code that identifies the type of record (G for growth, C for control) 2

COUNTRY CODE A code that identifies the country (US = United States) 2

STATE PROVINCE TRIBAL CODE The code for the state/province/tribe 4

COUNTY FIPS The FIPS code for the county 3

SIC 4-digit SIC, or 2 digit SIC with remaining digits blank (not zero) 4

SCC EPA source classification code or a fraction of the code 10

SITE ID Unique state/local/tribal ID reported consistently over time 15

EMISSION UNIT ID Unique state/local/tribal ID reported consistently over time 6

EMISSION RELEASE POINT ID State/ local/tribal ID for point /location where emissions are

released to ambient air 6

POLLUTANT CODE Pollutant code 9

PROCESS ID Unique state/local/tribal ID reported consistently over time 6

BASE DATE Date that the control st rategy comes into effect 6

FUTURE DATE Future date that the c ontrol strategy affects 6

PRIMARY CONTROL EQUIPMENT CODE Primary control equipment code 10

BASE DATE CONTROL EFFICIENCY Base year % control efficiency(60% reduction = 60) 6

FUTURE DATE CONTROL EFFICIENCY Future year % control efficiency(60% reduction = 60) 6

FUTURE DATE GROWTH FACTOR Growth factor based on changes in throughput, economic growth (unrelated to controls). This is an absolute growth rate not an annual growth rate. 11

CONTROL TYPE MACT, RACT, LAER, SIPCALL, BART, etc 10

FUTURE DATE CHEMICAL SPECIATION

PROFILE Code matching speciate chemical speciation profile unless in base year 6

ALLOWABLE EMISSIONS CAP Allowable emissions cap units must be in TONS/day 10

MARKET PENETRATION OF NEW

SPECIATION PROFILE Fraction of future year emissions using new speciation profile 6

RESERVED FOR FUTURE USE FIELD 3 (Field used to enter future year control efficiency value where available) 10

RESERVED FOR FUTURE USE FIELD 2 (Field used to enter future year rule effectiveness value where available) 10

RESERVED FOR FUTURE USE FIELD 1 (Field used to enter future year RP value where available) 10

CONTROL DESCRIPTION A text description of the control 80

PRIMARY CONTACT Email address of the primary c ontact/developer of this record 30

## PECHAN September 2007



## 51 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

## Table IV-2. Fields Populated in Growth Factor File

Field Name Populated in Growth Factor File?

RECORD TYPE Yes

COUNTRY CODE Yes

STATE PROVINCE TRIBAL CODE Yes

COUNTY FIPS Yes (with “000” except for population data)

SIC No

SCC Yes

SITE ID No

EMISSION UNIT ID No

EMISSION RELEASE POINT ID No

POLLUTANT CODE No

PROCESS ID No

BASE DATE Yes

FUTURE DATE Yes

PRIMARY CONTROL EQUIPMENT CODE No

BASE DATE CONTROL EFFICIENCY No

FUTURE DATE CONTROL EFFICIENCY No

FUTURE DATE GROWTH FACTOR Yes

CONTROL TYPE No

FUTURE DATE CHEMICAL SPECIATION PROFILE No

ALLOWABLE EMISSIONS CAP No

MARKET PENETRATION OF NE W SPECIATION PROFILE No

RESERVED FOR FUTURE USE FIELD 3 (future year CE) No

RESERVED FOR FUTURE USE FIELD 2 (future year RE) No

RESERVED FOR FUTURE USE FIELD 1 (future year RP) No

CONTROL DESCRIPTION No

PRIMARY CONTACT Yes



## B. CONTROL FACTORS

Pechan compiled control factors for the LADCO states in two sets of ascii f iles: one set for point source controls ( LADCO 2005 Base Year Point Control File.txt ), and the other set for area source/MAR controls ( LADCO 2005 Base Year Area Source and MAR Control File.txt ).

1. Point Source Control Factors

The LADCO 2005 Base Year Point Control File.txt file reports control information at the Process ID-level and for the specif ic date that each control is e xpected to be implemented. Note that the Base Date Control Efficiency field is populated with a zero for every record because Pechan did not have any base year control information other than that reported in the base year inventory supplied by LADCO. LADCO should rely on the control information in the base year inventory to identify the base year level of control. For MA CT standards, the point source control factors are incremental to base year cont rol levels. Because all other point source control

## PECHAN September 2007



## 52 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

## factors represent absolute control levels, L ADCO should subtract any existing 2005 inventory

level of control from the control factor leve l of control to determ ine net reductions for nonMACT controls. Pechan found very few point source records with control information, so LADCO should expect very little control ov erlap between the 2005 inve ntory and the control file. Table IV-3 identifies the RPO Data Exchange Protocol fields that are populated in the point

source control file.

2. Area Source and MAR Control Factors

Pechan compiled the area source and MAR control f actor information into a single ascii file that reports the level of control for each year of in terest (2009, 2012, and 2018). In cases where there is no change in emission reduction after the initia l implementation year, the level of control is repeated for each year. For controls where em ission reductions increase over time (due to increased levels of RP), the level of control in creases for each successive modeling year. Except for the single control for which emission reduc tions are county-specific (Stage II Vehicle Refueling), the area source and MAR c ontrol factor file is expressed at the state-level. In cases

where it was feasible to do so, Pechan populated the 5 th 

## , 4 th 

## , and 3 rd 

## fields from the end of each control factor file (“RESERVED FOR FUTURE USE” in the RPO Data Exchange Protocol Format) with future year CE, RE, and RP values, respectively (the field “FUTURE DATE CONTROL EFFICIENCY” was populated with th e overall percentage emission reduction). Table IV-4 identifies the RPO Data Exchange Prot ocol fields that are pop ulated in this file.

## PECHAN September 2007



## 53 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

## Table IV-3. Fields Populated in Po int Source Control Factor File

RPO Data Exchange Protocol Format Field Name Populated in Point

Source Control

Factor File

RECORD TYPE Yes

COUNTRY CODE Yes

STATE PROVINCE TRIBAL CODE Yes

COUNTY FIPS Yes

SIC Yes

SCC Yes

SITE ID Yes

EMISSION UNIT ID Yes

EMISSION RELEASE POINT ID Yes

POLLUTANT CODE Yes

PROCESS ID Yes

BASE DATE Yes

FUTURE DATE 1 Yes

PRIMARY CONTROL EQUIPMENT CODE No

BASE DATE CONTROL EFFICIENCY 2 Yes

FUTURE DATE CONTROL EFFICIENCY 3 Yes

FUTURE DATE GROWTH FACTOR No

CONTROL TYPE Yes

FUTURE DATE CHEMICAL SPECIATION PROFILE No

ALLOWABLE EMISSIONS CAP No

MARKET PENETRATION OF NE W SPECIATION PROFILE No

RESERVED FOR FUTURE USE FIELD 3 (future year CE) No

RESERVED FOR FUTURE USE FIELD 2 (future year RE) No

RESERVED FOR FUTURE USE FIELD 1 (future year RP) No

CONTROL DESCRIPTION Yes

PRIMARY CONTACT Yes

1 Represents date that control is first implemented



### . 2 All records populated with “0" - LADCO should rely on control information reported in base year inventory.

3 Populated with overall percentage emission reduction.

## PECHAN September 2007



## 54 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

## Table IV-4. Fields Populated in Ar ea Source/MAR Control Factor File

RPO Data Exchange Protocol Format Field Name Populated in Area

Source/MAR Control

Factor File

RECORD TYPE Yes

COUNTRY CODE Yes

STATE PROVINCE TRIBAL CODE Yes

COUNTY FIPS Yes

SIC No

SCC Yes

SITE ID No

EMISSION UNIT ID No

EMISSION RELEASE POINT ID No

POLLUTANT CODE Yes

PROCESS ID No

BASE DATE Yes

FUTURE DATE Yes

PRIMARY CONTROL EQUIPMENT CODE No

BASE DATE CONTROL EFFICIENCY Yes

FUTURE DATE CONTROL EFFICIENCY 1 Yes

FUTURE DATE GROWTH FACTOR No

CONTROL TYPE No

FUTURE DATE CHEMICAL SPECIATION PROFILE No

ALLOWABLE EMISSIONS CAP No

MARKET PENETRATION OF NE W SPECIATION PROFILE No

RESERVED FOR FUTURE USE FIELD 3 (future year CE) Yes 2

RESERVED FOR FUTURE USE FIELD 2 (future year RE) Yes 2

RESERVED FOR FUTURE USE FIELD 1 (future year RP) Yes 2

CONTROL DESCRIPTION Yes

PRIMARY CONTACT Yes

1 Populated with overall percentage emission reduction (product of CE, RE, and RP).

2 Not populated for Federal locomotive standards or Stage II Vehicle Refueling control program.

## PECHAN September 2007



## 55 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

# SECTION V. REFERENCES

BLS, 2007: U.S. Bureau of Labor Statistics, “State Occupational Em ployment Projections, Long-Term,” accessed from http://www.projectionscentr al.com/projections.asp, Office of Employment Projections, Washi ngton, DC. Accessed May 2007.

DOE, 2007a: U.S. Department of Energy, “A nnual Energy Outlook 2007, with Projections through 2030,” DOE/EIA-0383(2007), Energy In formation Administration, Office of Integrated Analysis and Forecasti ng, Washington, DC. February 2007.

DOE, 2007b: U.S. Department of Energy, “State Energy Consumption, Price, and Expenditure Estimates (SEDS),” accessed from http:/ /www.eia.doe.gov/emeu/states/_seds.html, Energy Information Administration, Office of Integrated Analysis and Forecasting, Washington, D.C. Accessed April 2007.

ENVIRON, 2007a: ENVIRON International Corporation, “Draft LADCO 2005 Commercial Marine Emissions,” prepared for Lake Michig an Air Directors Cons ortium, Des Plaines,
IL, March 2, 2007.

ENVIRON, 2007b: ENVIRON International Corporation, “Draft LADCO 2005 Locomotive Emissions,” prepared for Lake Michigan Ai r Directors Consortium, Des Plaines, IL, February 2007.

EPA, 2004: U.S. Environmental Protection Ag ency, “National Emission Inventory—Ammonia Emissions from Animal Husbandry Operati ons, Draft Report,” (no further identifying information available). January 2004.

EPA, 2007a: U.S. Environmental Protection Agency, “Clean Watersheds Needs Survey 2000,”

data for LADCO states accessed from http://www.epa.gov/owm/mtb/cwns/index.htm, Office of Wastewater Management, Washington, DC. Accessed May 2007.

EPA, 2007b: U.S. Environmental Protection Ag ency, “Draft Regulatory Impact Analysis:

Control of Emissions of Air Pollution from Locomotive Engines and Marine Compression-Ignition Engines Less than 30 Lite rs per Cylinder – Chapter 3: Emission Inventory,” Assessment and Standards Di vision, Office of Transportation and Air Quality, EPA420-D-07-001, March 2007.

FAA, 2007: U.S. Federal Aviation Administratio n, “Terminal Area Forecast System,” data for

LADCO states accessed from http://www.apo.data.faa.gov/main/taf.asp , Office of Aviation Policy and Plans, Wa shington, DC. Accessed March 2007.

Freedonia, 2006: The Freedonia Group, Inc., “Solvents to 2010, U.S. Industry Study with Forecasts to 2010 & 2015,” Study #2055, Cleveland, OH, April 2006.

## PECHAN September 2007



## 56 Development of 2005 Base Year Growth and Control Factors for LADCO - Final Report 

## Harnett, 2007: William T. Harnett, “Guidance for Estimating VOC and NO

x 

## Emission Changes
from MACT Standards,” Memorandum to Regional Air Division Directors, U.S. Environmental Protection Agency, Re search Triangle Park, NC, May 11, 2007.

Metra, 2007: Metra, “2007 Metra Program and Budget,” accessed from http://metrarail.com/Budget/2007BudgetBook.pdf , Chicago, IL, May 2007.

Page, 2007: Stephen D. Page, “Emission Re duction Credit for Three Federal Rules for Categories of Consumer and Commercial Pr oducts under Section 183(e) of the Clean Air
Act,” Memorandum to Air Division Directors, U.S. Environmental Protection Agency, Research Triangle Park, NC, May 30, 2007.

Pechan, 2004: E.H. Pechan & Associates, Inc ., “Development of Growth and Control Factors for Lake Michigan Air Directors Consor tium (LADCO) – Final Report,” Durham, NC, prepared for Lake Michigan Air Directors Consortium, Des Plaines, IL, December 15,

2004.

Pechan, 2005: E.H. Pechan & Associates, Inc ., “Development of Updated Growth and Control Factors for Lake Michigan Air Direct ors Consortium (LADCO) – Draft Report,” Durham, NC, prepared for Lake Michigan Ai r Directors Consortium, Des Plaines, IL,
December 29, 2005.