west virginia department of environmental protection

Appendix C

On-Road Mobile Emissions

Inventory Documentation

Promoting a healthy environment.

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BROOKE-HANCOCK-JEFFERSON

TRANSPORTATION STUDY

TECHNICAL MEMORANDUM 2011-1

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 PM2.5 National Ambient Air Quality Standard

Submitted: May 2011

Revised: February 2012

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Production of this 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

The Metropolitan Planning Organization for

Steubenville, Ohio-Weirton, WV Area

124 North Fourth Street, 2nd Floor

Steubenville, OH 43952

740/282-3685

304/797-9666

740/282-1821 FAX

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

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Transportation Air Quality Analysis And Technical Documentation Weirton-Steubenville, WV-OH

For the U.S. EPA 1997 Annual PM2.5 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 Mobile Emission Estimates

For the U.S. EPA 1997 Annual PM2.5 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 Processing ............................................................................................................................... Page 22

Appendix A – Interagency Consultation Documentation

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Transportation Air Quality Analysis And Technical Documentation Weirton-Steubenville, WV-OH

For the U.S. EPA 1997 Annual PM2.5 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 .............................................. Page 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 Area Boundary

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

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

Structure.................................................................................................................. Page 8

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Transportation Air Quality Analysis And Technical Documentation Weirton-Steubenville, WV-OH

For the U.S. EPA 1997 Annual PM2.5 National Ambient Air Quality Standard 

May 2011; Revised February 2012 Page iii

SELECTED ABBREVIATIONS & ACRONYMS

BHJ – Brooke-Hancock-Jefferson Metropolitan 

Planning Commission

CFR – Code of Federal Regulations

FHWA – Federal Highway Administration; 

FTA – Federal Transit Administration; 

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

NOX – Nitrogen Oxides

ODOT – Ohio Department of Transportation

OEPA – Ohio Environmental Protection 

Agency

PM – Particulate Matter

PM2.5 - Particulate Matter with an aerodynamic 

diameter less than 2.5 microns (often referred to 

as Fine Particulate Matter)

PM10 – 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 

Environmental Protection

WVDOT – West Virginia Department of 

Transportation

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REVISION HISTORY

February 24, 2012

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

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

Revise; Table 1; BHJ Region on-Road Mobile Emissions by State; Add Maintenance Year 2025

Revise Page 4; Table 2; BHJ Region on-Road Mobile Emissions by 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

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Transportation Air Quality Analysis And Technical Documentation Weirton-Steubenville, WV-OH

For the U.S. EPA 1997 Annual PM2.5 National Ambient Air Quality Standard 

May 2011; Revised February 2012 Page 1

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 PM2.5 National Ambient Air Quality Standard

INTRODUCTION

This memorandum documents the air quality analyses and underlying planning assumptions performed 

for the Annual PM2.5 on-road mobile source emission inventories for the Weirton-Steubenville, WVOH Metropolitan area State Implementation Plan (SIP). The Ohio Department of Transportation 

(ODOT), Division of Transportation System Development-Modeling and Forecasting Section and the 

Brooke-Hancock-Jefferson Planning Commission (BHJMPC) completed these analyses in coordination 

with the Ohio Environmental Protection Agency (OEPA) and West Virginia Department of 

Environmental Protection (WVDEP). 

The BHJ Region is comprised 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 PM2.5 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 the 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 demand 

model based emission factors as well as the complete air quality analyses for the metropolitan area.

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Transportation Air Quality Analysis And Technical Documentation Weirton-Steubenville, WV-OH

For the U.S. EPA 1997 Annual PM2.5 National Ambient Air Quality Standard 

May 2011; Revised February 2012 Page 2

Figure 1

BHJ GEOGRAPHIC AREA

FINE PARTICULATE MATTER

NON-ATTAINMENT AREA BOUNDARY

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Transportation Air Quality Analysis And Technical Documentation Weirton-Steubenville, WV-OH

For the U.S. EPA 1997 Annual PM2.5 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

(PM2.5), Nitrogen Oxides (NOx), Sulfur Dioxide (SO2) modeled for each county that make up the BHJ 

Region. The Model Years for the demonstration includes the Base Year 2005, Attainment Year 2008 (a 

leap year), Interim Year 2015, and Maintenance Year 2025.

Table 1

BHJ REGION ON-ROAD MOBILE EMISSIONS BY STATE

PM 2.5 Analysis

STATE TOTALS

OHIO

Jefferson County

PM 2.5 NOX SO2 VMT

Year (tons/yr) (tons/yr) (tons/yr) (annual)

2005 73.17 2105.85 18.18 562,071,084

2008 60.97 1758.04 5.65 557,286,295

2015 31.88 870.17 3.93 544,597,475

2022 16.78 374.80 3.56 549,620,540

2025 16.49 322.71 3.56 572,657,778

WEST VIRGINIA

Brooke and Hancock Counties

PM 2.5 NOX SO2 VMT

Year (tons/yr) (tons/yr) (tons/yr) (annual)

2005 38.04 1023.15 13.59 393,012,357

2008 27.93 772.27 4.03 389,998,556

2015 13.50 323.71 2.87 400,179,462

2022 8.82 158.47 2.50 390,998,315

2025 8.44 114.56 2.36 391,153,545

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Transportation Air Quality Analysis And Technical Documentation Weirton-Steubenville, WV-OH

For the U.S. EPA 1997 Annual PM2.5 National Ambient Air Quality Standard 

May 2011; Revised February 2012 Page 4

Table 2

BHJ REGION ON-ROAD MOBILE EMISSIONS BY COUNTY COUNTY TOTALS

Jefferson County

PM 2.5 NOX SO2 VMT

Year (tons/yr) (tons/yr) (tons/yr) (annual)

2005 73.17 2105.85 18.18 562,071,084

2008 60.97 1758.04 5.65 557,286,295

2015 31.88 870.17 3.93 544,597,475

2022 16.78 374.80 3.56 549,620,540

2025 16.49 322.71 3.56 572,657,778

Brooke County

PM 2.5 NOX SO2 VMT

Year (tons/yr) (tons/yr) (tons/yr) (annual)

2005 19.41 540.40 7.01 212,244,639

2008 14.42 412.02 2.11 212,778,859

2015 6.97 174.71 1.51 219,881,355

2022 4.55 86.23 1.33 215,917,336

2025 4.34 62.47 1.26 216,619,184

Hancock County

PM 2.5 NOX SO2 VMT

Year (tons/yr) (tons/yr) (tons/yr) (annual)

2005 18.63 482.75 6.58 180,767,718

2008 13.51 360.25 1.92 177,219,697

2015 6.53 149.00 1.36 180,298,107

2022 4.28 72.24 1.17 175,080,979

2025 4.10 52.09 1.10 174,534,361

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Transportation Air Quality Analysis And Technical Documentation Weirton-Steubenville, WV-OH

For the U.S. EPA 1997 Annual PM2.5 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 emission estimates will be based a single-season approach. The group also decided that since 

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 Outputs 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 comparable 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 extrapolation 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 each respective analysis 

year. 

The 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 2008 – 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 

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Transportation Air Quality Analysis And Technical Documentation Weirton-Steubenville, WV-OH

For the U.S. EPA 1997 Annual PM2.5 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 forecasting 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 metropolitan 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 SteubenvilleWeirton Metropolitan Area highway network employs the following land use variables:

[AREA_TYPE] ≡ Area Type

[AVG_PARK] ≡ Average parking cost

[ENROLL] ≡ School enrollment 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] ≡ Occupied 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, WV-OH non-attainment area compiled 

September 2010.

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Transportation Air Quality Analysis And Technical Documentation Weirton-Steubenville, WV-OH

For the U.S. EPA 1997 Annual PM2.5 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

Brooke, WV Hancock, WV Jefferson, OH

2005 Population 24,696 32,572 69,457 126,725

2005 Households 9,976 13,714 28,472 52,162

2005 Employment 8,351 12,958 23,263 44,572

2008 Population 24,602 31,971 67,643 124,216

2008 Households 9,912 13,441 27,592 50,945

2008 Employment 8,234 10,736 23,676 42,646

2014 Population 24,431 30,965 64,381 119,777

2014 Households 9,813 13,007 26,134 48,954

2014 Employment 8,145 13,047 21,819 43,011

2020 Population 24,253 29,790 60,934 114,977

2020 Households 9,694 12,488 24,532 46,714

2020 Employment 7,770 12,252 21,551 41,573

County Variable by Year Total

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Transportation Air Quality Analysis And Technical Documentation Weirton-Steubenville, WV-OH

For the U.S. EPA 1997 Annual PM2.5 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

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Transportation Air Quality Analysis And Technical Documentation Weirton-Steubenville, WV-OH

For the U.S. EPA 1997 Annual PM2.5 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-2005 and attainment year2008 representing 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 CountyData Manager. The subsequent tables provide the specific inputs that are not using the MOVES default 

values.

Table 4

MOVES INPUTS

MOVES Version 8/26/2010

Scale Custom Domain 

MOVES Modeling Technique Emission Factor Method 

Rates per Distance 

Rates per Vehicle 

Time Span Time Aggregation: Hour 

1 Month representing average annual temperatures 

All hours of day selected 

16 speed bins 

Weekdays only 

Geographic Bounds Brooke, Hancock, and Jefferson counties

Vehicles/Equipment All source types, gasoline and diesel 

Road Type All road types including off-network 

Pollutants and Processes NOX, All PM2.5 categories, SO2

, Total Energy Consumption 

Strategies None 

General Output Units = grams, joules and miles 

Output Emissions Time = hour, Location = custom area, on-road emission rates by road 

type and source use type

Advance Performance None 

RunSpec Parameter Settings 

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Transportation Air Quality Analysis And Technical Documentation Weirton-Steubenville, WV-OH

For the U.S. EPA 1997 Annual PM2.5 National Ambient Air Quality Standard 

May 2011; Revised February 2012 Page 10

Table 4 (continued)

MOVES INPUTS

Source Type Population Combination of local and default data 

Local data (Ohio and West Virginia) from motor vehicle registration 

Default data used for source types 51, 52, 53, 61, and 62 

Future year growth rate based on MPO model Household growth rate

Vehicle Type VMT Combination of local and default data 

HPMSVTypeYear VMT = daily VMT from travel demand model 

monthVMTFraction = default 

dayVMTFraction=default 

hourVMTFraction=local 

I/M Program None

Fuel Formulation Default

Fuel Supply Default 

Metereology Data Local data obtained from NOAA National Climatic Data Center. Data 

will consist of monthly high and low temperatures and daily relative 

humidity for 2002. 

Ramp Fraction Using the base year travel demand model for VHT fractions.

Future fractions will be assumed constant

Road Type Distribution Use ODOT and WV Division of Highways county summary VMT 

categorized by federal functional classes 

Age Distribution Combination of local and default data. 

Local data (Ohio and West Virginia) from motor vehicle registration 

Default data used for source types 41, 42, 43, 51, 52, 53, 61, and 62 

The same age distribution will be used for all analysis years 

Average Speed Distribution Default 

Alternative Fuel Type Default 

County Data Manager Sources 

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

representative of 12 months in 2009. Data entered into a spreadsheet provided by U.S. EPA converted 

the Mobile6 to get the correct data for the MOVES model. Listed in the table below are the average 

annual hourly temperatures and relative humidity distribution profiles for the Weirton-Steubenville, 

WV-OH metropolitan area. 

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Transportation Air Quality Analysis And Technical Documentation Weirton-Steubenville, WV-OH

For the U.S. EPA 1997 Annual PM2.5 National Ambient Air Quality Standard 

May 2011; Revised February 2012 Page 11

Table 5

AVERAGE TEMPERATURE AND RELATIVE HUMIDITY DATA

TIME OF DAY AVG HOURLY 

TEMPERATURE

AVG HOURLY RELATIVE 

HUMIDITY

Midnight 47.20 79.7%

1:00 AM 46.10 67.1%

2:00 AM 45.20 80.7%

3:00 AM 44.60 82.9%

4:00 AM 44.10 83.8%

5:00 AM 43.60 84.1%

6:00 AM 43.10 82.6%

7:00 AM 43.50 79.6%

8:00 AM 45.90 74.8%

9:00 AM 49.60 70.1%

10:00 AM 53.50 65.7%

11:00 AM 56.80 62.0%

NOON 59.70 59.8%

1:00 PM 61.30 58.2%

2:00 PM 61.90 57.4%

3:00 PM 62.00 57.3%

4:00 PM 61.60 58.3%

5:00 PM 60.50 61.2%

6:00 PM 58.60 65.3%

7:00 PM 56.20 69.2%

8:00 PM 53.70 66.1%

9:00 PM 51.60 74.3%

10:00 PM 50.10 75.7%

11:00 PM 48.60 78.1%

RAMP FRACTION

The Base Year Travel Demand Model used the Vehicles Hour of Travel (VHT) fractions to derive the 

Ramp Fractions shown in Table 6. The future year networks also used the base year fractions.

Table 6

RAMP FRACTIONS

roadTypeID roadDesc rampFraction

2 Rural Restricted Access 0.02

4 Urban Restricted Access 0.09

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Transportation Air Quality Analysis And Technical Documentation Weirton-Steubenville, WV-OH

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May 2011; Revised February 2012 Page 12

SOURCE TYPE POPULATION

A combination of local and MOVES default data is the Source Type Population 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 registrations accounted for all 

other Source Type Population needed to run the MOVES model. 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

Jefferson 

County, OH

Brooke/Hancock 

County, WV

11 MotorCycle 6,072 1,673

21 Passenger Car 74,051 21,369

31 Passenger Truck 39,994 17,415

32 Light Commercial Truck 3,033 2,573

41 Intercity Bus 38 15

42 Transit Bus 68 8

43 School Bus 144 99

51 Refuse truck 23 12

52 Single Unit Short-haul Truck 958 739

53 Single Unit Long-haul Truck 299 84

54 Motor Home 1,953 63

61 Combination Short-haul Truck 283 130

62 Combination Long-haul Truck 326 145

sourceTypeID sourceTypeName

sourceTypePopulation

VEHICLE AGE DISTRIBUTION

A grouping of data from Ohio and West Virginia sources along with the MOVES model defaults 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 registrations accounted for all other 

Vehicle Type ID. Table 8 and Table 9 show the Vehicle Age Distribution for Jefferson County, OH and 

Brooke/Hancock County, WV respectively.

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Transportation Air Quality Analysis And Technical Documentation Weirton-Steubenville, WV-OH

For the U.S. EPA 1997 Annual PM2.5 National Ambient Air Quality Standard 

May 2011; Revised February 2012 Page 13

Table 8

VEHICLE AGE DISTRIBUTION FOR JEFFERSON COUNTY, OH

yearid sourcetypeid ageid ageFraction yearid sourcetypeid ageid ageFraction yearid sourcetypeid ageid ageFraction

2005 11 0 0.0019 2005 21 9 0.0527 2005 31 18 0.0209

2005 11 1 0.0200 2005 21 10 0.0572 2005 31 19 0.0179

2005 11 2 0.0516 2005 21 11 0.0613 2005 31 20 0.0125

2005 11 3 0.0785 2005 21 12 0.0537 2005 31 21 0.0116

2005 11 4 0.0850 2005 21 13 0.0542 2005 31 22 0.0091

2005 11 5 0.0871 2005 21 14 0.0532 2005 31 23 0.0075

2005 11 6 0.0622 2005 21 15 0.0540 2005 31 24 0.0052

2005 11 7 0.0655 2005 21 16 0.0450 2005 31 25 0.0040

2005 11 8 0.0597 2005 21 17 0.0414 2005 31 26 0.0026

2005 11 9 0.0464 2005 21 18 0.0343 2005 31 27 0.0009

2005 11 10 0.0392 2005 21 19 0.0324 2005 31 28 0.0011

2005 11 11 0.0309 2005 21 20 0.0236 2005 31 29 0.0006

2005 11 12 0.0207 2005 21 21 0.0211 2005 31 30 0.0062

2005 11 13 0.0186 2005 21 22 0.0144 2005 32 0 0.0100

2005 11 14 0.0186 2005 21 23 0.0102 2005 32 1 0.0275

2005 11 15 0.0156 2005 21 24 0.0086 2005 32 2 0.0925

2005 11 16 0.0137 2005 21 25 0.0065 2005 32 3 0.0975

2005 11 17 0.0109 2005 21 26 0.0050 2005 32 4 0.0450

2005 11 18 0.0116 2005 21 27 0.0025 2005 32 5 0.0450

2005 11 19 0.0100 2005 21 28 0.0017 2005 32 6 0.0375

2005 11 20 0.0088 2005 21 29 0.0015 2005 32 7 0.0350

2005 11 21 0.0107 2005 21 30 0.0408 2005 32 8 0.0575

2005 11 22 0.0093 2005 31 0 0.0053 2005 32 9 0.0375

2005 11 23 0.0139 2005 31 1 0.0218 2005 32 10 0.0475

2005 11 24 0.0244 2005 31 2 0.0454 2005 32 11 0.0425

2005 11 25 0.0179 2005 31 3 0.0526 2005 32 12 0.0375

2005 11 26 0.0139 2005 31 4 0.0664 2005 32 13 0.0425

2005 11 27 0.0165 2005 31 5 0.0729 2005 32 14 0.0300

2005 11 28 0.0183 2005 31 6 0.0645 2005 32 15 0.0725

2005 11 29 0.0142 2005 31 7 0.0606 2005 32 16 0.0375

2005 11 30 0.1044 2005 31 8 0.0688 2005 32 17 0.0250

2005 21 0 0.0044 2005 31 9 0.0523 2005 32 18 0.0150

2005 21 1 0.0193 2005 31 10 0.0641 2005 32 19 0.0300

2005 21 2 0.0305 2005 31 11 0.0542 2005 32 20 0.0175

2005 21 3 0.0407 2005 31 12 0.0548 2005 32 21 0.0175

2005 21 4 0.0393 2005 31 13 0.0477 2005 32 22 0.0250

2005 21 5 0.0468 2005 31 14 0.0462 2005 32 23 0.0200

2005 21 6 0.0437 2005 31 15 0.0503 2005 32 24 0.0025

2005 21 7 0.0464 2005 31 16 0.0418 2005 32 25 0.0175

2005 21 8 0.0536 2005 31 17 0.0302 2005 32 26 0.0075

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page C - 25
Transportation Air Quality Analysis And Technical Documentation Weirton-Steubenville, WV-OH

For the U.S. EPA 1997 Annual PM2.5 National Ambient Air Quality Standard 

May 2011; Revised February 2012 Page 14

Table 8 (continued)

VEHICLE AGE DISTRIBUTION FOR JEFFERSON COUNTY, OH

yearid sourcetypeid ageid ageFraction yearid sourcetypeid ageid ageFraction yearid sourcetypeid ageid ageFraction

2005 32 27 0.0075 2005 42 5 0.1429 2005 43 14 0.0000

2005 32 28 0.0050 2005 42 6 0.1429 2005 43 15 0.0074

2005 32 29 0.0000 2005 42 7 0.0000 2005 43 16 0.0000

2005 32 30 0.0150 2005 42 8 0.0000 2005 43 17 0.0074

2005 41 0 0.0000 2005 42 9 0.0000 2005 43 18 0.0074

2005 41 1 0.0000 2005 42 10 0.0000 2005 43 19 0.0148

2005 41 2 0.0333 2005 42 11 0.0000 2005 43 20 0.0296

2005 41 3 0.1000 2005 42 12 0.0000 2005 43 21 0.0000

2005 41 4 0.0333 2005 42 13 0.0000 2005 43 22 0.0074

2005 41 5 0.0333 2005 42 14 0.0000 2005 43 23 0.0222

2005 41 6 0.1667 2005 42 15 0.0000 2005 43 24 0.0000

2005 41 7 0.0333 2005 42 16 0.0000 2005 43 25 0.0000

2005 41 8 0.0333 2005 42 17 0.0000 2005 43 26 0.0000

2005 41 9 0.0333 2005 42 18 0.0000 2005 43 27 0.0000

2005 41 10 0.1333 2005 42 19 0.1427 2005 43 28 0.0000

2005 41 11 0.1000 2005 42 20 0.0000 2005 43 29 0.0000

2005 41 12 0.0667 2005 42 21 0.0000 2005 43 30 0.0074

2005 41 13 0.0000 2005 42 22 0.0000 2005 51 0 0.0000

2005 41 14 0.1000 2005 42 23 0.0000 2005 51 1 0.0000

2005 41 15 0.0333 2005 42 24 0.0000 2005 51 2 0.0455

2005 41 16 0.0000 2005 42 25 0.0000 2005 51 3 0.0455

2005 41 17 0.0000 2005 42 26 0.0000 2005 51 4 0.2727

2005 41 18 0.0333 2005 42 27 0.0000 2005 51 5 0.0000

2005 41 19 0.0000 2005 42 28 0.0000 2005 51 6 0.0455

2005 41 20 0.0333 2005 42 29 0.0000 2005 51 7 0.0909

2005 41 21 0.0000 2005 42 30 0.0000 2005 51 8 0.1818

2005 41 22 0.0000 2005 43 0 0.0444 2005 51 9 0.0909

2005 41 23 0.0000 2005 43 1 0.1185 2005 51 10 0.0000

2005 41 24 0.0000 2005 43 2 0.0667 2005 51 11 0.0455

2005 41 25 0.0000 2005 43 3 0.0593 2005 51 12 0.0909

2005 41 26 0.0000 2005 43 4 0.0222 2005 51 13 0.0455

2005 41 27 0.0000 2005 43 5 0.0593 2005 51 14 0.0000

2005 41 28 0.0000 2005 43 6 0.0370 2005 51 15 0.0000

2005 41 29 0.0000 2005 43 7 0.0444 2005 51 16 0.0000

2005 41 30 0.0336 2005 43 8 0.0519 2005 51 17 0.0000

2005 42 0 0.0000 2005 43 9 0.0667 2005 51 18 0.0000

2005 42 1 0.1429 2005 43 10 0.0741 2005 51 19 0.0000

2005 42 2 0.2857 2005 43 11 0.0667 2005 51 20 0.0000

2005 42 3 0.0000 2005 43 12 0.0815 2005 51 21 0.0000

2005 42 4 0.1429 2005 43 13 0.1037 2005 51 22 0.0453

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page C - 26
Transportation Air Quality Analysis And Technical Documentation Weirton-Steubenville, WV-OH

For the U.S. EPA 1997 Annual PM2.5 National Ambient Air Quality Standard 

May 2011; Revised February 2012 Page 15

Table 8 (continued)

VEHICLE AGE DISTRIBUTION FOR JEFFERSON COUNTY, OH

yearid sourcetypeid ageid ageFraction yearid sourcetypeid ageid ageFraction yearid sourcetypeid ageid ageFraction

2005 51 23 0.0000 2005 53 1 0.0000 2005 54 10 0.0287

2005 51 24 0.0000 2005 53 2 0.0455 2005 54 11 0.0417

2005 51 25 0.0000 2005 53 3 0.0455 2005 54 12 0.0355

2005 51 26 0.0000 2005 53 4 0.2727 2005 54 13 0.0307

2005 51 27 0.0000 2005 53 5 0.0000 2005 54 14 0.0294

2005 51 28 0.0000 2005 53 6 0.0455 2005 54 15 0.0219

2005 51 29 0.0000 2005 53 7 0.0909 2005 54 16 0.0301

2005 51 30 0.0000 2005 53 8 0.1818 2005 54 17 0.0225

2005 52 0 0.0000 2005 53 9 0.0909 2005 54 18 0.0219

2005 52 1 0.0000 2005 53 10 0.0000 2005 54 19 0.0178

2005 52 2 0.0455 2005 53 11 0.0455 2005 54 20 0.0273

2005 52 3 0.0455 2005 53 12 0.0909 2005 54 21 0.0225

2005 52 4 0.2727 2005 53 13 0.0455 2005 54 22 0.0355

2005 52 5 0.0000 2005 53 14 0.0000 2005 54 23 0.0314

2005 52 6 0.0455 2005 53 15 0.0000 2005 54 24 0.0225

2005 52 7 0.0909 2005 53 16 0.0000 2005 54 25 0.0191

2005 52 8 0.1818 2005 53 17 0.0000 2005 54 26 0.0362

2005 52 9 0.0909 2005 53 18 0.0000 2005 54 27 0.0150

2005 52 10 0.0000 2005 53 19 0.0000 2005 54 28 0.0130

2005 52 11 0.0455 2005 53 20 0.0000 2005 54 29 0.0109

2005 52 12 0.0909 2005 53 21 0.0000 2005 54 30 0.2309

2005 52 13 0.0455 2005 53 22 0.0453 2005 61 0 0.0023

2005 52 14 0.0000 2005 53 23 0.0000 2005 61 1 0.0102

2005 52 15 0.0000 2005 53 24 0.0000 2005 61 2 0.0228

2005 52 16 0.0000 2005 53 25 0.0000 2005 61 3 0.0255

2005 52 17 0.0000 2005 53 26 0.0000 2005 61 4 0.0383

2005 52 18 0.0000 2005 53 27 0.0000 2005 61 5 0.0409

2005 52 19 0.0000 2005 53 28 0.0000 2005 61 6 0.0427

2005 52 20 0.0000 2005 53 29 0.0000 2005 61 7 0.0412

2005 52 21 0.0000 2005 53 30 0.0000 2005 61 8 0.0401

2005 52 22 0.0453 2005 54 0 0.0034 2005 61 9 0.0418

2005 52 23 0.0000 2005 54 1 0.0096 2005 61 10 0.0557

2005 52 24 0.0000 2005 54 2 0.0239 2005 61 11 0.0529

2005 52 25 0.0000 2005 54 3 0.0280 2005 61 12 0.0454

2005 52 26 0.0000 2005 54 4 0.0335 2005 61 13 0.0488

2005 52 27 0.0000 2005 54 5 0.0260 2005 61 14 0.0379

2005 52 28 0.0000 2005 54 6 0.0485 2005 61 15 0.0458

2005 52 29 0.0000 2005 54 7 0.0362 2005 61 16 0.0527

2005 52 30 0.0000 2005 54 8 0.0225 2005 61 17 0.0396

2005 53 0 0.0000 2005 54 9 0.0239 2005 61 18 0.0353

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Transportation Air Quality Analysis And Technical Documentation Weirton-Steubenville, WV-OH

For the U.S. EPA 1997 Annual PM2.5 National Ambient Air Quality Standard 

May 2011; Revised February 2012 Page 16

Table 8 (continued)

VEHICLE AGE DISTRIBUTION FOR JEFFERSON COUNTY, OH

yearid sourcetypeid ageid ageFraction yearid sourcetypeid ageid ageFraction yearid sourcetypeid ageid ageFraction

2005 61 19 0.0352 2005 62 3 0.0470 2005 62 18 0.0221

2005 61 20 0.0283 2005 62 4 0.0359 2005 62 19 0.0221

2005 61 21 0.0340 2005 62 5 0.0331 2005 62 20 0.0083

2005 61 22 0.0314 2005 62 6 0.0249 2005 62 21 0.0166

2005 61 23 0.0224 2005 62 7 0.0276 2005 62 22 0.0166

2005 61 24 0.0242 2005 62 8 0.0331 2005 62 23 0.0166

2005 61 25 0.0157 2005 62 9 0.0663 2005 62 24 0.0083

2005 61 26 0.0147 2005 62 10 0.0884 2005 62 25 0.0083

2005 61 27 0.0077 2005 62 11 0.1050 2005 62 26 0.0110

2005 61 28 0.0068 2005 62 12 0.1243 2005 62 27 0.0028

2005 61 29 0.0059 2005 62 13 0.0746 2005 62 28 0.0028

2005 61 30 0.0538 2005 62 14 0.0635 2005 62 29 0.0000

2005 62 0 0.0028 2005 62 15 0.0414 2005 62 30 0.0109

2005 62 1 0.0028 2005 62 16 0.0442

2005 62 2 0.0028 2005 62 17 0.0359

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Transportation Air Quality Analysis And Technical Documentation Weirton-Steubenville, WV-OH

For the U.S. EPA 1997 Annual PM2.5 National Ambient Air Quality Standard 

May 2011; Revised February 2012 Page 17

Table 9

VEHICLE AGE DISTRIBUTION FOR BROOKE/HANCOCK COUNTY, WV

yearid sourcetypeid ageid ageFraction yearid sourcetypeid ageid ageFraction yearid sourcetypeid ageid ageFraction

2005 11 0 0.0291 2005 21 9 0.0639 2005 31 18 0.0136

2005 11 1 0.0472 2005 21 10 0.0654 2005 31 19 0.0112

2005 11 2 0.0678 2005 21 11 0.0596 2005 31 20 0.0101

2005 11 3 0.0872 2005 21 12 0.0515 2005 31 21 0.0103

2005 11 4 0.0993 2005 21 13 0.0437 2005 31 22 0.0087

2005 11 5 0.0690 2005 21 14 0.0352 2005 31 23 0.0064

2005 11 6 0.0460 2005 21 15 0.0315 2005 31 24 0.0063

2005 11 7 0.0617 2005 21 16 0.0255 2005 31 25 0.0035

2005 11 8 0.0654 2005 21 17 0.0208 2005 31 26 0.0033

2005 11 9 0.0575 2005 21 18 0.0165 2005 31 27 0.0017

2005 11 10 0.0412 2005 21 19 0.0121 2005 31 28 0.0013

2005 11 11 0.0339 2005 21 20 0.0101 2005 31 29 0.0013

2005 11 12 0.0212 2005 21 21 0.0100 2005 31 30 0.0207

2005 11 13 0.0194 2005 21 22 0.0055 2005 32 0 0.0346

2005 11 14 0.0224 2005 21 23 0.0044 2005 32 1 0.0287

2005 11 15 0.0163 2005 21 24 0.0037 2005 32 2 0.0606

2005 11 16 0.0121 2005 21 25 0.0039 2005 32 3 0.0740

2005 11 17 0.0121 2005 21 26 0.0034 2005 32 4 0.0807

2005 11 18 0.0139 2005 21 27 0.0010 2005 32 5 0.0843

2005 11 19 0.0042 2005 21 28 0.0011 2005 32 6 0.0909

2005 11 20 0.0073 2005 21 29 0.0009 2005 32 7 0.0701

2005 11 21 0.0079 2005 21 30 0.0401 2005 32 8 0.0524

2005 11 22 0.0163 2005 31 0 0.0318 2005 32 9 0.0528

2005 11 23 0.0091 2005 31 1 0.0322 2005 32 10 0.0555

2005 11 24 0.0115 2005 31 2 0.0523 2005 32 11 0.0488

2005 11 25 0.0085 2005 31 3 0.0590 2005 32 12 0.0232

2005 11 26 0.0067 2005 31 4 0.0650 2005 32 13 0.0319

2005 11 27 0.0109 2005 31 5 0.0751 2005 32 14 0.0303

2005 11 28 0.0103 2005 31 6 0.0719 2005 32 15 0.0382

2005 11 29 0.0103 2005 31 7 0.0644 2005 32 16 0.0335

2005 11 30 0.0743 2005 31 8 0.0639 2005 32 17 0.0205

2005 21 0 0.0247 2005 31 9 0.0576 2005 32 18 0.0083

2005 21 1 0.0387 2005 31 10 0.0620 2005 32 19 0.0114

2005 21 2 0.0534 2005 31 11 0.0545 2005 32 20 0.0134

2005 21 3 0.0668 2005 31 12 0.0494 2005 32 21 0.0110

2005 21 4 0.0592 2005 31 13 0.0437 2005 32 22 0.0130

2005 21 5 0.0619 2005 31 14 0.0366 2005 32 23 0.0091

2005 21 6 0.0563 2005 31 15 0.0334 2005 32 24 0.0079

2005 21 7 0.0639 2005 31 16 0.0308 2005 32 25 0.0020

2005 21 8 0.0653 2005 31 17 0.0180 2005 32 26 0.0039

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For the U.S. EPA 1997 Annual PM2.5 National Ambient Air Quality Standard 

May 2011; Revised February 2012 Page 18

Table 9 (continued)

VEHICLE AGE DISTRIBUTION FOR BROOKE/HANCOCK COUNTY, WV

yearid sourcetypeid ageid ageFraction yearid sourcetypeid ageid ageFraction yearid sourcetypeid ageid ageFraction

2005 32 27 0.0024 2005 42 5 0.0576 2005 43 14 0.0464

2005 32 28 0.0031 2005 42 6 0.0504 2005 43 15 0.0374

2005 32 29 0.0028 2005 42 7 0.0461 2005 43 16 0.0144

2005 32 30 0.0007 2005 42 8 0.0492 2005 43 17 0.0111

2005 41 0 0.0843 2005 42 9 0.0759 2005 43 18 0.0136

2005 41 1 0.0672 2005 42 10 0.0609 2005 43 19 0.0138

2005 41 2 0.0576 2005 42 11 0.0506 2005 43 20 0.0118

2005 41 3 0.0506 2005 42 12 0.0489 2005 43 21 0.0104

2005 41 4 0.0693 2005 42 13 0.0434 2005 43 22 0.0107

2005 41 5 0.0562 2005 42 14 0.0394 2005 43 23 0.0073

2005 41 6 0.0488 2005 42 15 0.0320 2005 43 24 0.0092

2005 41 7 0.0379 2005 42 16 0.0321 2005 43 25 0.0000

2005 41 8 0.0453 2005 42 17 0.0181 2005 43 26 0.0000

2005 41 9 0.0535 2005 42 18 0.0082 2005 43 27 0.0000

2005 41 10 0.0560 2005 42 19 0.0231 2005 43 28 0.0000

2005 41 11 0.0550 2005 42 20 0.0071 2005 43 29 0.0000

2005 41 12 0.0597 2005 42 21 0.0032 2005 43 30 0.0000

2005 41 13 0.0528 2005 42 22 0.0007 2005 51 0 0.0498

2005 41 14 0.0487 2005 42 23 0.0013 2005 51 1 0.0398

2005 41 15 0.0400 2005 42 24 0.0009 2005 51 2 0.0340

2005 41 16 0.0167 2005 42 25 0.0006 2005 51 3 0.0767

2005 41 17 0.0147 2005 42 26 0.0002 2005 51 4 0.0926

2005 41 18 0.0133 2005 42 27 0.0004 2005 51 5 0.0604

2005 41 19 0.0180 2005 42 28 0.0003 2005 51 6 0.0544

2005 41 20 0.0112 2005 42 29 0.0001 2005 51 7 0.0243

2005 41 21 0.0090 2005 42 30 0.0002 2005 51 8 0.0696

2005 41 22 0.0099 2005 43 0 0.0794 2005 51 9 0.0625

2005 41 23 0.0038 2005 43 1 0.0660 2005 51 10 0.0514

2005 41 24 0.0048 2005 43 2 0.0647 2005 51 11 0.0730

2005 41 25 0.0048 2005 43 3 0.0594 2005 51 12 0.0610

2005 41 26 0.0040 2005 43 4 0.0798 2005 51 13 0.0796

2005 41 27 0.0036 2005 43 5 0.0406 2005 51 14 0.0442

2005 41 28 0.0026 2005 43 6 0.0511 2005 51 15 0.0479

2005 41 29 0.0007 2005 43 7 0.0435 2005 51 16 0.0145

2005 41 30 0.0000 2005 43 8 0.0585 2005 51 17 0.0169

2005 42 0 0.0624 2005 43 9 0.0696 2005 51 18 0.0156

2005 42 1 0.0771 2005 43 10 0.0419 2005 51 19 0.0040

2005 42 2 0.0742 2005 43 11 0.0526 2005 51 20 0.0043

2005 42 3 0.0727 2005 43 12 0.0556 2005 51 21 0.0043

2005 42 4 0.0627 2005 43 13 0.0512 2005 51 22 0.0000

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page C - 30
Transportation Air Quality Analysis And Technical Documentation Weirton-Steubenville, WV-OH

For the U.S. EPA 1997 Annual PM2.5 National Ambient Air Quality Standard 

May 2011; Revised February 2012 Page 19

Table 9 (continued)

VEHICLE AGE DISTRIBUTION FOR BROOKE/HANCOCK COUNTY, WV

yearid sourcetypeid ageid ageFraction yearid sourcetypeid ageid ageFraction yearid sourcetypeid ageid ageFraction

2005 51 23 0.0092 2005 53 1 0.1419 2005 54 10 0.0323

2005 51 24 0.0027 2005 53 2 0.1124 2005 54 11 0.0161

2005 51 25 0.0070 2005 53 3 0.0585 2005 54 12 0.0161

2005 51 26 0.0003 2005 53 4 0.0609 2005 54 13 0.0161

2005 51 27 0.0000 2005 53 5 0.1017 2005 54 14 0.0000

2005 51 28 0.0000 2005 53 6 0.0783 2005 54 15 0.0323

2005 51 29 0.0000 2005 53 7 0.0185 2005 54 16 0.0161

2005 51 30 0.0000 2005 53 8 0.0138 2005 54 17 0.0645

2005 52 0 0.0622 2005 53 9 0.0686 2005 54 18 0.0161

2005 52 1 0.0520 2005 53 10 0.0748 2005 54 19 0.0161

2005 52 2 0.0412 2005 53 11 0.0517 2005 54 20 0.0484

2005 52 3 0.0466 2005 53 12 0.0129 2005 54 21 0.0645

2005 52 4 0.0559 2005 53 13 0.0031 2005 54 22 0.0806

2005 52 5 0.0572 2005 53 14 0.0064 2005 54 23 0.0323

2005 52 6 0.0434 2005 53 15 0.0067 2005 54 24 0.0968

2005 52 7 0.0344 2005 53 16 0.0000 2005 54 25 0.0323

2005 52 8 0.0351 2005 53 17 0.0032 2005 54 26 0.0161

2005 52 9 0.0435 2005 53 18 0.0024 2005 54 27 0.0000

2005 52 10 0.0578 2005 53 19 0.0000 2005 54 28 0.0000

2005 52 11 0.0531 2005 53 20 0.0002 2005 54 29 0.0000

2005 52 12 0.0460 2005 53 21 0.0101 2005 54 30 0.1452

2005 52 13 0.0580 2005 53 22 0.0006 2005 61 0 0.0843

2005 52 14 0.0430 2005 53 23 0.0011 2005 61 1 0.0672

2005 52 15 0.0251 2005 53 24 0.0005 2005 61 2 0.0576

2005 52 16 0.0409 2005 53 25 0.0000 2005 61 3 0.0506

2005 52 17 0.0220 2005 53 26 0.0020 2005 61 4 0.0693

2005 52 18 0.0219 2005 53 27 0.0000 2005 61 5 0.0562

2005 52 19 0.0239 2005 53 28 0.0000 2005 61 6 0.0488

2005 52 20 0.0190 2005 53 29 0.0000 2005 61 7 0.0379

2005 52 21 0.0225 2005 53 30 0.0000 2005 61 8 0.0453

2005 52 22 0.0088 2005 54 0 0.0161 2005 61 9 0.0535

2005 52 23 0.0112 2005 54 1 0.0000 2005 61 10 0.0560

2005 52 24 0.0115 2005 54 2 0.0484 2005 61 11 0.0550

2005 52 25 0.0125 2005 54 3 0.0000 2005 61 12 0.0597

2005 52 26 0.0130 2005 54 4 0.0000 2005 61 13 0.0528

2005 52 27 0.0265 2005 54 5 0.0323 2005 61 14 0.0487

2005 52 28 0.0059 2005 54 6 0.0645 2005 61 15 0.0400

2005 52 29 0.0032 2005 54 7 0.0323 2005 61 16 0.0167

2005 52 30 0.0027 2005 54 8 0.0484 2005 61 17 0.0147

2005 53 0 0.1697 2005 54 9 0.0161 2005 61 18 0.0133

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page C - 31
Transportation Air Quality Analysis And Technical Documentation Weirton-Steubenville, WV-OH

For the U.S. EPA 1997 Annual PM2.5 National Ambient Air Quality Standard 

May 2011; Revised February 2012 Page 20

Table 9 (continued)

VEHICLE AGE DISTRIBUTION FOR BROOKE/HANCOCK COUNTY, WV

yearid sourcetypeid ageid ageFraction yearid sourcetypeid ageid ageFraction yearid sourcetypeid ageid ageFraction

2005 61 19 0.0180 2005 62 3 0.1140 2005 62 18 0.0035

2005 61 20 0.0112 2005 62 4 0.1186 2005 62 19 0.0012

2005 61 21 0.0090 2005 62 5 0.0804 2005 62 20 0.0010

2005 61 22 0.0099 2005 62 6 0.0643 2005 62 21 0.0006

2005 61 23 0.0038 2005 62 7 0.0403 2005 62 22 0.0010

2005 61 24 0.0048 2005 62 8 0.0304 2005 62 23 0.0000

2005 61 25 0.0048 2005 62 9 0.0315 2005 62 24 0.0009

2005 61 26 0.0040 2005 62 10 0.0320 2005 62 25 0.0003

2005 61 27 0.0036 2005 62 11 0.0290 2005 62 26 0.0003

2005 61 28 0.0026 2005 62 12 0.0080 2005 62 27 0.0000

2005 61 29 0.0006 2005 62 13 0.0087 2005 62 28 0.0000

2005 61 30 0.0001 2005 62 14 0.0115 2005 62 29 0.0000

2005 62 0 0.1668 2005 62 15 0.0062 2005 62 30 0.0000

2005 62 1 0.1331 2005 62 16 0.0013

2005 62 2 0.1140 2005 62 17 0.0011

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page C - 32
Transportation Air Quality Analysis And Technical Documentation Weirton-Steubenville, WV-OH

For the U.S. EPA 1997 Annual PM2.5 National Ambient Air Quality Standard 

May 2011; Revised February 2012 Page 21

ROAD TYPE DISTRIBUTION 

The ODOT and WV Division of Highway county summary Vehicle Miles Traveled (VMT) data 

categorized by federal functional class for the three county non-attainment areas is the basis for Road 

Type Distribution Fraction. Table 10 illustrates Road Type Distribution.

Table 10

ROAD TYPE DISTRIBUTION FOR BROOKE-HANCOCK-JEFFERSON

NON-ATTAINMENT AREA

source

TypeID

road

TypeID roadDesc roadTypeVMTFraction

source

TypeID

road

TypeID roadDesc roadTypeVMTFraction

11 1 Off-Network 0 43 4 Urban Restricted Access 0.25

11 2 Rural Restricted Access 0.11 43 5 Urban Unrestricted Access 0.26

11 3 Rural Unrestricted Access 0.38 51 1 Off-Network 0

11 4 Urban Restricted Access 0.25 51 2 Rural Restricted Access 0.11

11 5 Urban Unrestricted Access 0.26 51 3 Rural Unrestricted Access 0.38

21 1 Off-Network 0 51 4 Urban Restricted Access 0.25

21 2 Rural Restricted Access 0.11 51 5 Urban Unrestricted Access 0.26

21 3 Rural Unrestricted Access 0.38 52 1 Off-Network 0

21 4 Urban Restricted Access 0.25 52 2 Rural Restricted Access 0.11

21 5 Urban Unrestricted Access 0.26 52 3 Rural Unrestricted Access 0.38

31 1 Off-Network 0 52 4 Urban Restricted Access 0.25

31 2 Rural Restricted Access 0.11 52 5 Urban Unrestricted Access 0.26

31 3 Rural Unrestricted Access 0.38 53 1 Off-Network 0

31 4 Urban Restricted Access 0.25 53 2 Rural Restricted Access 0.11

31 5 Urban Unrestricted Access 0.26 53 3 Rural Unrestricted Access 0.38

32 1 Off-Network 0 53 4 Urban Restricted Access 0.25

32 2 Rural Restricted Access 0.11 53 5 Urban Unrestricted Access 0.26

32 3 Rural Unrestricted Access 0.38 54 1 Off-Network 0

32 4 Urban Restricted Access 0.25 54 2 Rural Restricted Access 0.11

32 5 Urban Unrestricted Access 0.26 54 3 Rural Unrestricted Access 0.38

41 1 Off-Network 0 54 4 Urban Restricted Access 0.25

41 2 Rural Restricted Access 0.11 54 5 Urban Unrestricted Access 0.26

41 3 Rural Unrestricted Access 0.38 61 1 Off-Network 0

41 4 Urban Restricted Access 0.25 61 2 Rural Restricted Access 0.11

41 5 Urban Unrestricted Access 0.26 61 3 Rural Unrestricted Access 0.38

42 1 Off-Network 0 61 4 Urban Restricted Access 0.25

42 2 Rural Restricted Access 0.11 61 5 Urban Unrestricted Access 0.26

42 3 Rural Unrestricted Access 0.38 62 1 Off-Network 0

42 4 Urban Restricted Access 0.25 62 2 Rural Restricted Access 0.11

42 5 Urban Unrestricted Access 0.26 62 3 Rural Unrestricted Access 0.38

43 1 Off-Network 0 62 4 Urban Restricted Access 0.25

43 2 Rural Restricted Access 0.11 62 5 Urban Unrestricted Access 0.26

43 3 Rural Unrestricted Access 0.38

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page C - 33
Transportation Air Quality Analysis And Technical Documentation Weirton-Steubenville, WV-OH

For the U.S. EPA 1997 Annual PM2.5 National Ambient Air Quality Standard 

May 2011; Revised February 2012 Page 22

POST PROCESSING

Several custom programs created by ODOT were computed the total 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 rewritten 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 link speeds from the travel demand model. Using a link’s volumeto-capacity ratio and link group code, a post-process to the model based on HCM methods estimates the 

link 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 program. The hourly volumes multiplied by the MOVES 

emission factor for the corresponding hour of day, 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 emissions 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 intrazonal 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. 

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page C - 34
APPENDIX A

INTERAGENCY CONSULTATION DOCUMENTATION

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1

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 Moore (Dave.Moore1@dot.state.oh.us)'; 'Sam 

Granato (Sam.Granato@dot.state.oh.us)'; 'Paul Braun (paul.braun@epa.state.oh.us)'; 

'Leigh.Oesterling@fhwa.dot.gov'; 'Martin Kotsch (kotsch.martin@epa.gov)'; 'Patricia 

Morris (morris.patricia@epa.gov)'; Sedosky, 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 PM2.5 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 PM2.5 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 PM2.5, NOx and SO2 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 PM2.5 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 meet

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page C - 49
2

the PM2.5 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 PM2.5 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 PM2.5 nonattainment area had

attained the 1997 annual PM2.5 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 57th

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 57th 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/Pages/PublicNoticeandComment.aspx   

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page C - 50
1

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 Moore (Dave.Moore1@dot.state.oh.us)'; 'Sam 

Granato (Sam.Granato@dot.state.oh.us)'; 'Paul Braun (paul.braun@epa.state.oh.us)'; 

'Leigh.Oesterling@fhwa.dot.gov'; 'Martin Kotsch (kotsch.martin@epa.gov)'; 'Patricia 

Morris (morris.patricia@epa.gov)'; Sedosky, 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 PM2.5 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 PM2.5 NAAQS.

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

emissions of PM2.5, NOx and SO2 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 PM2.5 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 PM2.5 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 Portion of the Steubenville-Weirton, OH-WV 2006 

PM2.5 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 

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page C - 51
2

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 meet the PM2.5 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 incorporation of the finalized 

maintenance plan into the West Virginia SIP. DEP has previously proposed redesignation of the 

area with respect to the separate 1997 PM2.5 NAAQS. The comment period will begin April 13, 

2012 and end on May 15, 2012. 

The area was designated as nonattainment with the 2006 PM2.5 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 accordance with Section 179(c)(1) of the CAA, EPA 

proposed to determine that the Steubenville-Weirton, OH-WV PM2.5 nonattainment area had 

attained the 2006 24-hour PM2.5 NAAQS [76FR61291]. 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 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 57th 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, 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 57th 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/Pages/PublicNoticeandComment.aspx

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page C - 52
west virginia department of environmental protection

Appendix D

LADCO

Documentation

Promoting a healthy environment.

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Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 2
Table of Contents

Appendix D1: Regional A 2.5ir Quality Analyses for Ozone, PM , and Regional Haze: 

Final Technical Support Document. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Page 5

Appendix D2: Development of 2005 Base Year Growth and Control Factor for

Lake Michigan Air Directors Consortium (LADCO) Final Report. . . . . . . . . . . . . Page 177

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 3
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Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 4
Appendix D1:

R 2.5egional Air Quality Analyses for Ozone, PM , and

Regional Haze: Final Technical Support Document

http://www.ladco.org/reports/technical_support_document/tsd/tsd_version_iv_april_25_2008_fin

al.pdf

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 5
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Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 6
Regional Air Quality Analyses for

Ozone, PM2.5, and Regional Haze: 

Final Technical Support Document

April 25, 2008

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

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 7
ii

Table of Contents

Section-Title Page

Executive Summary iii

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 PM2.5 21

2.3 Regional Haze 35

3.0 Air Quality Modeling 46

3.1 Selection of Base Year 46

3.2 Future Years of Interest 46

3.3 Modeling System 47

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 Demonstration for Ozone and PM2.5 71

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 PM2.5 90

5.0 Reasonable Progress Assessment for Regional Haze 93

5.1 Class I Areas Impacted 93

5.2 Future Year Modeling Results 96

5.3 Weight of Evidence Determination for Regional Haze 104

6.0 Summary 111

7.0 References 114

Appendix I Ozone and PM2.5 Modeling Results

Appendix II Ozone Source Apportionment Modeling Results

Appendix III PM2.5Source Apportionment Modeling Results

Appendix IV Haze Source Apportionment Modeling Results

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 8
iii

EXECUTIVE SUMMARY

States in the upper Midwest face a number of air quality challenges. More than 50 counties are 

currently classified as nonattainment for the 8-hour ozone standard and 60 for the fine particle 

(PM2.5) standard (1997 versions). 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 PM2.5 (right)

To support the development of State Implementation Plans (SIPs) for ozone, PM2.5, 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. 

Monitoring 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 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.

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 9
iv

• 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 or industrial centers. 

PM2.5

• Current monitoring data (2005-2007) show 30 sites in violation of the annual PM2.5

standard of 15 ug/m3. Nonattainment sites are characterized by an elevated 

regional background (about 12 – 14 ug/m3) and a significant local (urban) increment 

(about 2 – 3 ug/m3). Historical PM2.5 data show a slight downward trend since 

deployment of the PM2.5 monitoring network in 1999.

• PM2.5 concentrations are also influenced by meteorology, but the relationship is 

more complex and less well understood compared to ozone.

• On an annual average basis, PM2.5 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 22 – 24 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 impairment is dominated by sulfate and nitrate.

Air quality models were applied to support the regional planning efforts. Two base years were 

used in the modeling analyses: 2002 and 2005. Basecase modeling was conducted to evaluate 

model performance (i.e., assess the model's ability to reproduce observed concentrations). This 

exercise was intended to build confidence in the model prior to its use in examining control 

strategies. Model performance for ozone and PM2.5 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 PM2.5 and if 

not, then what additional emission reductions would be necessary for attainment. Based on the 

modeling and other supplemental analyses, the following general conclusions can be made:

• Existing controls are expected to produce significant improvement in ozone and 

PM2.5 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 decision (i.e., how much safeguard to incorporate).

• Modeling suggests that most 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 and northeastern Ohio.

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 10
v

• Modeling suggests that most sites are expected to meet the current PM2.5

standard by the applicable attainment date, except for sites in Detroit, Cleveland, 

and Granite City.

The regional modeling for PM2.5 does not include air quality benefits expected 

from local controls. States are conducting local-scale analyses and will use

these results, in conjunction with the regional-scale modeling, to support their 

attainment demonstrations for PM2.5.

• These findings of residual nonattainment for ozone and PM2.5 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 

PM2.5 design values on the order of 16 - 17 ug/m3). 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 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). If either of these conditions is not met, 

then attainment may be less likely.

• Modeling suggests that the new PM2.5 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 eastern U.S. are expected to be 

greater than (less improved than) the uniform rate of visibility improvement 

values in 2018 based on existing controls, including those in northern Michigan 

and some in the northeastern U.S. Visibility levels in 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. 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.

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 11
1

Section 1.0 Introduction

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

Lake Michigan Directors Consortium (LADCO)1and its contractors to support the development 

of State Implementation Plans (SIPs) for ozone, fine particles (PM2.5 ), 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 emphasized. First, a regional, multi-pollutant approach 

was taken in addressing ozone, PM2.5, and haze for technical reasons (e.g., commonality in

precursors, emission sources, atmospheric 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 35719), and its implementation guidance for ozone (70 FR 71663) and PM2.5

(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 one 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 organized in the following sections. This Introduction provides an overview of 

regulatory requirements and background information on regional planning. Section 2 reviews 

the ambient monitoring data and presents a conceptual model of ozone, PM2.5, and haze for the 

region. Section 3 discusses the air quality modeling analyses, including development of the key 

model inputs (emissions inventory and meteorological data), and basecase model performance 

evaluation. A modeled attainment demonstration for ozone and PM2.5 is presented in Section 4, 

along with relevant data analyses considered as part of the weight-of-evidence determination. 

Section 5 documents the reasonable progress assessment for regional haze, along with

relevant data analyses considered as part of the weight-of-evidence determination. Finally, key 

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, more than 100 counties were designated as nonattainment.

2

 The

designations became effective on June 15, 2004. SIPs for ozone were 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 are 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 nonattainment in the region.

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 12
2

For PM2.5, EPA promulgated designations on December 17, 2004 (70 FR 944, January 5, 2005). 

In the 5-state region, 70 counties were designated as nonattainment.3 The designations became 

effective on April 5, 2005. SIPs for PM2.5 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 April 

2008) and for haze no later than three years after the date on which the Administrator 

promulgated the PM2.5 designations (per the Omnibus Appropriations Act of 2004) (i.e., by 

December 2007). The applicable attainment date for PM2.5 nonattainment areas 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 areas which impairment results from 

manmade air pollution.” There are 156 Class I areas, including two in northern Michigan: Isle 

Royale National Park and Seney National Wildlife Refuge4. EPA’s visibility rule (64 FR 35714, 

July 1, 1999) requires reasonable progress in achieving “natural conditions” by the year 2064. 

As noted above, the first regional haze SIP was 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 was established by the States of Illinois, Indiana, Michigan, and Wisconsin in 1989. The 

four states and EPA signed a Memorandum of Agreement (MOA) that initiated the Lake 

Michigan Ozone Study (LMOS) and identified LADCO as the organization to oversee the 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 purposes 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 federally 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 an MOA that established the MRPO. An 

operating principles document for MRPO, which describe the roles and responsibilities of states, 

tribes, federal agencies, and stakeholders, was issued in March 2001. MRPO has a similar 

purpose as LADCO, but is focused 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 Regional Planning 

Organizations (RPOs) 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 

designated 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.

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 13
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 

collection and analysis a priority. More than $7M in RPO grant funds were used for special 

purpose monitoring, preparing and improving emissions inventories, and conducting air quality 

analyses5. An overview of the technical work is provided below.

Monitoring: Numerous monitoring projects were conducted to supplement on-going state and 

local air pollution monitoring. 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: Baseyear emissions inventories were prepared for 2002 and 2005. States provided 

point source and area source emissions data, and MOBILE6 input files and mobile 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 strategy 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 PM2.5 and moderate ozone nonattainment areas), 2012 (planning

to address a 2013 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 ozone, PM2.5, and regional haze problems) and supplemental information for 

the attainment demonstration. Given uncertainties in emissions inventories and modeling, 

especially for PM2.5, 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 strategies. The analyses were 

conducted in accordance with EPA’s modeling guidelines (EPA, 2007a). The PM/haze 

modeling covers the full calendar year (2002 and 2005) for an eastern U.S. 36 km domain, while 

the ozone modeling focuses on the summer period (2002 and 2005) for a Midwest 12 km 

subdomain. The same model (CAMx) was used for ozone, PM2.5, and regional haze.

 

5

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

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 14
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 precursors), PM2.5 (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).

Analyses of the data were conducted to produce a conceptual model, which is a qualitative 

summary of the physical, chemical, and meteorological processes that control the formation and 

distribution of pollutants in a given region. This section reviews the relevant data analyses and 

describes our understanding of ozone, PM2.5, and regional haze with respect to current 

conditions, data variability (spatial, temporal, and chemical), influence of meteorology (including 

transport patterns), precursor sensitivity, and source culpability.

2.1 Ozone

In 1979, EPA adopted an ozone standard of 0.12 ppm, averaged over a 1-hour period. This 

standard is attained when the number of days per calendar year with maximum hourly average 

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 4th highest daily 1-hour value over a 3-year 

period) less than 0.12 ppm.

In 1997, EPA tightened the ozone standard to 0.08 ppm, averaged over an 8-hour period6. The 

standard is attained 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 ppm (or 85 ppb). 

Current Conditions: 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 represent sites with design values above 

the standard. Currently, there are 19 sites in violation of the 8-hour ozone NAAQS in the 5-state 

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

Table 1 provides the 4th-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 tightened 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 over an 8-

hour period.

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Figure 1. 8-hour ozone design values (2005-2007)

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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

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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 sunlight, as well as precursor concentrations (see, for 

example, Figure 2). 

Figure 2. Number of hot days and 8-hour “exceedance” days 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 slow-moving high pressure system to the east of the 

region. The relative importance of various meteorological factors is discussed later in this 

section.

Transport of ozone (and its precursors) is a significant factor and occurs on several spatial 

scales. Regionally, over 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. This

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 elevated regional ozone concentrations (June 23 – 25, 2005)

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

hour standard

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Locally, emissions from urban areas add to the regional background leading to ozone 

concentration hot spots downwind. Depending on the synoptic wind patterns (and local landlake breezes), different downwind areas are affected (see, for example, Figure 4).

Figure 4. Examples of recent high ozone days in the Lake Michigan area

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 background 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 ozone measurements over Lake Michigan (left) and along upwind boundary 

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

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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 estimate the impact of these source regions, two simple 

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. Composite 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 Indiana) 

as being upwind on these high ozone days.

Figure 6 Back trajectory analysis showing upwind areas associated with high ozone

concentrations

Second, to assess the impact from Chicago/NW Indiana, Blanchard (2005a) compared ozone 

concentrations 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 ozone 

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 contributes about 10-15 ppb to downwind ozone levels.

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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 affecting 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 high 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. 

• Downwind 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.

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Data Variability: Since 1980, considerable progress has been made to meet the previous 1-

hour ozone standard. Figure 8 shows the decline in both the 1-hour and 8-hour design values 

for the 5-state LADCO region over the last 25 years. 

Figure 8 Ozone design value trends in 5-State region

The trend is more dramatic for the higher ozone sites in the 5-state region (see Figure 9). This 

plot shows a pronounced downward trend in the design value since the 2001-2003 period, due, 

in part, to the very low 4th high values in 2004.

 

Figure 9. Trend in ozone design values and 4th high values for higher ozone sites in region

The improvement 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

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Figure 10. Ozone design value maps for 1995-1997, 2000-2002, and 2005-2007

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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 model 

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 Method: This method uses a statistical model 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 (Kenski, 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 4th high ozone values. By holding the 

meteorological effects 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 4th high ozone concentrations for several 

monitors 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 examine 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 documented in Camalier et al., 

2007. The seasonal average trends 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.

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Chiwaukee, WI Sheboygan, WI

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

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

Indianapolis, IN

Figure 11a. Trends in meteorologically 

adjusted 4th high 8-hour ozone 

concentrations for seven Midwestern sites

(1997 – 2007)

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Chiwaukee, WI Sheboygan, WI

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

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

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

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

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

Indianapolis, IN

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

Figure 11b. Trends in seasonal 8-hour ozone 

concentrations for seven Midwestern sites

(1997 – 2007)

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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 with current 8-

hour design values close to or above the standard (Kenski, 2008b). The CART model searches 

through 60 meteorological variables to determine which are most efficient in predicting ozone. 

Although the exact selection of predictive variables changes from site to site, the most common 

predictors were temperature, wind direction, and relative humidity. Only occasionally were

upper air variables, transport time or distance, lake breeze, or other variables significant. (Note, 

the ozone and meteorological data for the CART analysis are the same as used in the EPA/Cox 

analysis.)

For each monitor, regression trees were developed that classify each summer day (MaySeptember) by its meteorological conditions. Similar days 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 Figure 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 nonmeteorological influences. Trends over the 13-year period at most sites were found to be 

declining, with the exception of Detroit which showed fairly flat trends. Comparison 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, 2008b). This metric reflects the variability from the 13-year average in the number of 

days in the higher ozone concentration nodes (see Figure 13). Examination of these plots 

indicates:

• 2002 and 2005 were 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 factors 

(i.e., emission reductions).

In conclusion, all 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 meteorological variability is accounted for. The decreases are 

present whether seasonal average ozone, peak values (annual 4th highs), or a subset of high 

days with similar meteorology are considered. The consistency in results across models is a 

good indication that these trends reflect impacts of emission control programs.

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Chiwaukee, WI Sheboygan, WI

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

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

Indianapolis, IN

Figure 12. Trends for higher ozone CART 

groups (average ozone > 65 ppb) for seven 

Midwestern sites (1995 – 2007)

Note: line represents linear best fit

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Figure 13. Ozone conduciveness index (and 

number of high ozone days) for seven 

Midwestern site (1995 – 2007)

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Precursor Sensitivity: Ozone is 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/NOx ratios, typical of rural environments (with low 

NOx), ozone tends to be more responsive to reductions in NOx. Conversely, in areas with low 

VOC/NOx ratios, typical of urban environments (with high NOx), ozone tends to be more 

responsive to VOC reductions. 

An analysis of VOC and NOx-limitation was conducted with the ozone MAPPER program, which 

is based on the Smog Production (SP) algorithm (Blanchard, et al., 2003). 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 14. As can be 

seen, ozone is usually VOC-limited in cities and NOx-limited in rural areas. (Data from aircraft 

measurements suggest that ozone is usually NOx-limited over Lake Michigan and away from 

urban centers on days when ozone in the urban centers is VOC-limited.) The highest ozone 

days were found to be NOx-limited. 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 14. Mean afternoon extent of reaction (1998 – 2002)

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Blanchard (2004 and 2005a) examined weekend-weekday differences in ozone and NOx in the 

Midwest. All urban areas in these two studies exhibited substantially lower (40-60%) weekend 

concentrations of NOx compared to weekday concentrations. Despite lower weekend NOx

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 15). This small but counterproductive change in local ozone concentrations 

suggests that local urban-scale NOx reductions alone may not be very effective. 

Figure 15. Weekday/weekend differences in 8-hour ozone – number of sites with weekend 

increase (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 (2005a) looked at the effect 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 reductions.

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 weekend 

changes in ozone precursor emissions cause both increases and decreases in Southeast 

Michigan ozone, depending upon location and time:

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• 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-sensitive 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 Michigan 

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 

PM2.5 impacts).

2.2 PM2.5

In 1997, EPA adopted the PM2.5 standards of 15 ug/m

3

 (annual average) and 65 ug/m3 (24-hour 

average). The annual standard is attained if the 3-year average of the annual average PM2.5

concentration is less than or equal to the level of the standard. The daily standard is attained if 

the 98th percentile of 24-hour PM2.5 concentrations in a year, averaged over three years, is less 

than or equal to the level of the standard.

In 2006, EPA revised the PM2.5 standards to 15 ug/m3 (annual average) and 35 ug/m3 (24-hour 

average). 

Current Conditions: Maps of annual and 24-hour PM2.5 design values for the 3-year period 

2005-2007 are shown in Figure 16. The “hotter” colors represent higher concentrations, where 

red dots represent sites with design values above the annual standard. Currently, there are 30

sites in violation of the annual PM2.5 standard.

Table 2 provides the annual PM2.5 concentrations and associated design values since 2003 for 

several high monitoring sites throughout the region.

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Figure 16. PM2.5 design values - annual average (top) and 24-hour average (bottom) (2005-2007)

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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

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When EPA initially set the 24-hour standard at 65 μg/m3, it also adopted the following 

concentration ranges for its Air Quality Index (AQI) scale:

Good < 15 ug/m3

Moderate 15-40 μg/m3

Unhealthy for Sensitive Groups (USG) 40-65 μg/m3

Unhealthy 65-150 μg/m3

Figure 17 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. Moderate and USG levels can occur any time of the year. 

Figure 17. Percent of days in AQI categories for PM2.5 (2002-2004)

Data Variability: PM2.5 concentrations vary spatially, temporally, and chemically in the region. 

This variability is discussed further below.

On an annual basis, PM2.5 exhibits a distinct and consistent spatial pattern. As seen in Figure 

16, across the Midwest, annual concentrations follow a gradient from low values (5-6 μg/m3) in 

northern and western areas (Minnesota and northern Wisconsin) to high values (17-18 μg/m3) in 

Ohio and along the Ohio River. In addition, concentrations in urban areas are higher than in 

upwind rural areas, indicating that local urban sources add a significant increment of 2-3 μg/m3

to the regional background of 12 - 14 μg/m3 (see Figure 18). 

Figure 18. Regional (lighter shading) v. local components (darker shading) of annual average PM2.5 

concentrations

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Because monitoring for PM2.5 only began in earnest in 1999, after promulgation of the PM2.5

standard, limited data are available to assess trends. Time series based on federal reference 

method (FRM) PM2.5-mass data show a downward trend in each state (see Figure 19)

7

.

Figure 19. PM2.5 trends in annual average (top) and daily concentrations (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).

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A statistical analysis of PM2.5 trends was performed using the nonparametric Theil test for slope 

(Hollander and Wolfe, 1973). Trends were generally consistent around the region, for both PM 

mass and for the individual components of mass. Figure 20 shows trends for PM2.5 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 show 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 decrease for this set of sites is -0.24 ug/m3/year. 

Figure 20. Annual trends in PM2.5 mass (1999 – 2006)

Seasonal trends show mostly similar patterns (Figure 21). Trends were downward at most sites 

and seasons, with overall seasonal averages varying between -0.15 to -0.56 ug/m3/year. The 

strongest and most significant decreases took place during the winter quarter (January - March). 

No statistically significant increasing trends were observed.

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Figure 21. Seasonal trends in PM2.5 mass (1999 – 2006)

PM2.5 shows a slight variation from weekday to weekend, as seen in Figure 22. Although most 

cities have slightly lower concentrations on the weekend, the difference is usually less than 1 

μg/m3. There is a more pronounced weekday/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 22 Day-of-week variability in PM2.5 (2002-2004)

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In the Midwest, PM2.5 is made up 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 23. Spatial map of PM2.5 chemical composition in the Midwest (2002-2003)

The three major components vary spatially (Figure 23), including notable urban and rural 

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

account for much of the annual variability in PM2.5 mass noted above.

Figure 24. Average regional (lighter shading) v. local (darker shading) of PM2.5 chemical species

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Figure 25 Seasonal and spatial variability in PM2.5 components

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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 formed 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 fertilizer 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 concentrations 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 summer. Like nitrate, organic carbon has 

both regional and local components. 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 

from biogenic sources.

Precursor Sensitivity: Data from the Midwest ammonia monitoring network were analyzed with 

thermodynamic equilibrium models to assess the effect of changes in precursor gas 

concentrations on PM2.5 concentrations (Blanchard, 2005b). These analyses indicate that 

particle formation responds in varying degrees to reductions in sulfate, nitric acid, and ammonia. 

Based on Figure 26, which shows PM2.5 concentrations as a function of sulfate, nitric acid 

(HNO3), and ammonia (NH3), several key findings should be noted: 

• PM2.5 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 PM2.5 mass decreases.

• PM2.5 mass is also sensitive to reductions in nitric acid and ammonia. The greatest PM2.5

decrease in response to nitric acid reductions occurs during the winter, when nitrate is a

significant fraction of PM2.5.

• Under conditions with lower sulfate levels (i.e., proxy of future year conditions), PM2.5 is 

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

• Ammonia 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) NOx lead to reductions, albeit nonproportional reductions, in PM-nitrate (Blanchard, 2004). This result is consistent with analyses 

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

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Figure 26. Predicted mean PM fine mass concentrations at Bondville, IL (top) and Detroit (Allen Park), MI 

(bottom) as functions of changes in sulfate, nitric acid (HNO3), and ammonia (NH3)

Note: starting at the baseline values (represented by the red star), either moving downward (reductions in nitric 

acid) or moving leftward (reductions in sulfate or ammonia) results in lower PM2.5 values

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Meteorology: PM2.5 concentrations are not as strongly influenced by meteorology 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 PM2.5. In the case of PM,

the reason is two-fold: (1) stagnation and limited mixing under these conditions cause PM2.5 to 

build up, usually over several days, and (2) these conditions generally promote higher 

conversion of important precursors (SO2 to SO4) and higher emissions of some precursors, 

especially biogenic carbon. Wind direction is another strong determinant of PM2.5; air 

transported from polluted source regions has higher concentrations.

Unlike ozone, PM2.5 has occasional winter episodes. Conditions are similar to those for summer 

episodes, in that stationary high pressure and (seasonally) warm temperatures are usually 

factors. Winter episodes are also fueled by high humidity and low mixing heights. 

PM2.5 chemical species show noticeable transport influences. Trajectory 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 27

shows results from an ensemble trajectory analysis of 17 rural eastern IMPROVE sites. 

Figure 27. Sulfate and nitrate source regions based on ensemble trajectory analysis

When these results are considered together with analyses of precursor sensitivity (e.g., Figure 

26), 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 Midwest, but that nitric acid reductions are 

more effective when the probable reductions in future sulfate levels are considered. 

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Source Culpability: Three source apportionment studies were performed using speciated PM2.5

monitoring data and statistical analysis methods (Hopke, 2005, STI, 2006, and STI, 2008). 

Figure 28 summarizes the source contributions from these studies. The studies show that a

large portion of PM2.5 mass consists of secondary, regional impacts, which cannot be attributed 

to individual facilities or sources (e.g., secondary sulfate, secondary nitrate, and secondary 

organic aerosols). Nevertheless, wind analyses (e.g., Figure 27) provide information on likely 

source regions. Regional- or national-scale control programs may be the most effective way to 

deal with these impacts. EPA's CAIR, for example, will provide for substantial reductions in 

SO2 emissions over the eastern half of the U.S., which will reduce sulfate (and PM2.5) 

concentrations and improve visibility levels.

The studies also show that a smaller, yet significant portion of PM2.5 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 statistical analysis methods 

help to identify local sources and quantify their impact. This information is valuable to states 

wishing to develop control programs to address local impacts. A combination of 

national/regional-scale and local-scale emission reductions 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 sources, and secondary organic aerosols. Additional sampling and analysis 

is underway in Cleveland and Detroit to provide further information on sources of organic 

carbon.

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0

1

2

3

4

5

6

7

Coal

Combustion

Secondary

Nitrate

Mobile

Industrial

Soil

Burning

ug/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

Law ndale

Springfield

Tikhon

Craig

Taft

Figure 28. Major Source Contributions in the Midwest based on Hopke, 2005 (upper left), STI, 2006 (upper right), and STI, 2008 (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 by STI 2006, and OM and diesel by STI 2008)

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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 

impairment results from manmade air pollution”. To implement this provision, in 1999, EPA 

adopted regulations to address regional haze visibility impairment (USEPA, 1999). EPA’s rule 

requires states to “make reasonable progress toward meeting the national goal”. Specifically, 

states must establish reasonable progress goals, which provide for improved visibility on the 

most impaired (20% worst) days sufficient to achieve natural 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, which is expressed in terms of visual range, light 

extinction (1/Mm), or deciviews (dv), depends not just on the total PM2.5 mass concentration, but 

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

Current Conditions: A map of the average light extinction values for the most impaired (20% 

worst) visibility days for the 5-year baseline period (2000-2004) is shown in Figure 29. 

Figure 29. Baseline Visibility Levels for 20% Worst Days (2000 – 2004), units: Mm-1

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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. The new IMPROVE equation was used to calculate updated baseline values. 

Second, due 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” data 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 these days to be excluded 

from the baseline calculations. However, the light extinction due to the remaining measured 

species is significant (i.e., above the 80th percentile). It makes sense to include these days in 

the baseline calculations, because they are largely dominated 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 in the baseline calculation

results in a small, but measurable, effect on the baseline values (i.e., values 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 19.86 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.

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Table 4. Summary of visibility metrics (deciviews) for northern Class I areas

Old IMPROVE Equation (Cite: VIEWS, November 2005)

20% Worst Days

2000 2001 2002 2003 2004

Baseline

Value

2018

URI Value

Natural 

Conditions

Voyageurs 18.50 18.00 19.00 19.20 17.60 18.46 16.74 11.09

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

Natural

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

URI 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 21.97 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 Kenski, 2007 adjustment for missing days

 URI = uniform rate of improvement

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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 EPA 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 updated 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 PM2.5 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 composition 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 PM2.5 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 PM2.5 mass and chemical species for rural IMPROVE (and IMPROVE-protocol) sites 

in the eastern U.S. showed a high degree of correlation between PM2.5-mass, sulfate, and 

nitrate levels (see Figure 31). The Class I sites in northern Michigan and northern Minnesota, in 

particular, are highly correlated for PM2.5 mass, sulfates, and organic carbon mass (AER, 2004).

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Figure 31. Correlations among IMPROVE (and IMPROVE-protocol) monitoring sites in Eastern U.S.

Long-term trends at Boundary Waters (the only regional site with a sufficient data record) show 

significant decreases in total PM2.5 (-0.005 ug/year) and SO4 (-0.04 ug/year) and an increase in 

NO3 (+0.01 ug/year). These PM2.5 and SO4 trends are generally consistent with long-term 

trends at other IMPROVE sites in the eastern U.S., which have shown widespread decreases in 

SO4 and PM2.5 (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. Poor visibility days occur 

throughout the year, as indicated in Figure 32. (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 33 (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, suggesting the importance of different sources and meteorological conditions at different 

times of the year.

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Figure 32. Daily light extinction values for 20% worst days at Boundary Waters (2000 – 2004)

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Figure 33. Monthly average light extinction values for northern Class I areas

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Precursor Sensitivity: Results from two analyses using thermodynamic equilibrium models 

provide information on the effect of changes in precursor concentrations on PM2.5

concentrations (and, in turn, visibility levels) in the northern Class I areas. First, a preliminary 

analysis using data collected at Seney indicated that PM2.5 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, 2005b). Figure 34 shows PM2.5 concentrations 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 PM2.5 concentrations. 

Thus, reductions in sulfate, nitric acid, and ammonia will lower PM2.5 concentrations and 

improve visibility in the northern Class I areas.

Figure 34. Predicted PM2.5 mass concentrations at Great River Bluffs, MN as functions of changes 

in sulfate, nitric acid, and ammonia

Meteorology and Transport: The role of meteorology in haze is complex. Wind speed and wind 

direction govern the movement of air masses from polluted areas to the cleaner 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 and 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 understand 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 35. The orange 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. 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. 

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Figure 35. Composite back trajectories for 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, 2008). Based on this 

information, the most important contributing states are Michigan, Minnesota, and Wisconsin, as 

well as Missouri, North Dakota, Iowa, Indiana and Illinois (see, for example, Figure 35 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 from 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 using monitoring data from Boundary Waters and 

statistical analysis methods (DRI, 2005). The study shows that a large portion of PM2.5 mass 

consists of secondary, regional impacts, which cannot be attributed to individual facilities or 

sources (e.g., secondary sulfate, secondary nitrate, and secondary organic aerosols). Industrial 

sources contribute about 3-4% and mobile sources about 4-7% to PM2.5 mass. 

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

2004). As seen in Figure 36, the highest PM2.5 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 because 

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.

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Figure 36. Monthly concentrations of PM2.5 species (top), and secondary and biogenic-related 

organic carbon species in Seney (bottom)

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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 PM2.5 chemical speciation 

data were examined for days with high organic carbon and elemental carbon concentrations, 

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 wildfires 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 carbon in the northern Class I is largely 

uncontrollable.

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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 deciding 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 candidate control programs.

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

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

and LADCO, 2007b. 

This section reviews the development and evaluation of the modeling system used for the multipollutant analyses. Application of the modeling system (i.e., attainment demonstration for ozone 

and PM2.5, and reasonable progress assessment for haze) is covered in the following sections.

3.1 Selection of Base Year

Two base years 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 year. 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 2002 was more severe 

compared to 2005. 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 for ozone and PM2.5, and reasonable progress 

goals for regional haze, several future years are of interest:

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

2009 Planning year for ozone moderate nonattainment areas and PM2.5 nonattainment 

areas (attainment date 2010)

2012 Planning year for ozone moderate nonattainment areas and PM2.5 nonattainment 

areas, with 3-year extension (attainment date 2013)

2018 First milestone year for regional haze planning

 

8

According to USEPA’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, 2007c). 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 attainment year should be modeled 

for attainment planning purposes.

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Detailed emissions inventories were developed for 2009 and 2018. To support modeling for 

other future years, less rigorous emissions processing was conducted (e.g., 2012 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 nested grids, process analysis, source apportionment, and plume-ingrid), 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 with 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 was used for this modeling. A subset of the RPO domain was 

used for the LADCO modeling. For PM2.5 and haze, the large eastern U.S. grid at 36 km (see 

box on right side of Figure 36) was used. A PM2.5 sensitivity run was also performed for this 

domain at 12 km. For ozone, the smaller grid at 12 km (see shaded portion of the box on the 

right side of Figure 37) was used for most model runs. An ozone sensitivity run was also 

performed with a 4km sub-grid over the Lake Michigan area 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 37. Modeling grids – RPO domain (left) and LADCO modeling domain (right)

12 km

36 km

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3.5 Model Inputs: Meteorology

Meteorological inputs were derived using the Fifth-Generation NCAR/Penn State Meteorological 

Model (MM5) – version 3.6.3 for the years 2001–2003, and version 3.7 for the year 2005. The 

MM5 modeling domains are consistent with the National RPO grid projections (see Figure 38). 

Figure 38. MM5 modeling 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 non-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 2005.

Model performance was assessed quantitatively with the METSTAT tool from Environ. The 

metrics used to quantify model performance include mean observation, mean prediction, bias, 

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

calculated for several sub-regions of the modeling domain (Figure 39) and represent hourly 

spatial averages of multiple monitor locations. Additional analysis of rainfall is done on a 

monthly basis.

Figure 39. Sub-domains used for model performance for 2001-2003 (left) and 2005 (right)

A summary of the performance evaluation results for the meteorological modeling is provided 

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

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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 model performance. 

Figure 40. Daily temperature bias for 2002 (left) and 2005 (right) with hotter colors 

(yellow/orange/red) representing overestimates and cooler colors (blues) representing 

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 Fields: The wind 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-predicted 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 good agreement spatially and in

terms of magnitude in the winter, fall, and early spring months. There are, however, large overpredictions of rainfall in the late spring and summer months (see Figure 41). These overpredictions are seen spatially and in magnitude 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 deposition in the photochemical model. CAMx does not explicitly use the convective and 

non-convective rainfall output by MM5, but estimates wet scavenging by hydrometeors using 

cloud, ice, snow, and rain water mixing ratios output by MM5. Nevertheless, this could have an 

effect on model performance for PM2.5, as discussed in Section 3.7, and may warrant further 

attention.

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Figure 41. Comparison of observed (left column) and modeled (right column) monthly rainfall for 

July 2002 (top) and July 2005 (bottom)

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3.6 Model Inputs: Emissions

Emission 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 provided in two summary reports (LADCO, 2006a and LADCO, 2008a) 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 [commercial marine, aircraft, and 

railroads]), emissions were prepared using data supplied by the LADCO States and other 

RPOs.

Base Year Emissions: State and source sector emission summaries for 2002 (Base K) and 

2005 (Base M) are compared in Figure 42. Additional detail is provided in Tables 6a (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 42. Base K and Base M emissions for 5-state LADCO region by state (top) and source 

sector (bottom), units: tons per summer weekday

A summary of the base year emissions by sector for the LADCO States is provided below.

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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 539 727 723

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Heat 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)

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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 (weekday, 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 planning 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 (Environ, 2008). CONCEPT was run with meteorological data for a July and 

January weekday, Saturday, and Sunday (July 15 – 17 and January 16 – 18). A spatial plot of 

emissions is provided in Figure 43.

VOC Emissions NOx Emissions

Figure 43. Motor vehicle 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, updated local data for railroads and commercial 

marine were prepared by a contractor (Environ, 2007b, 2007c). Table 7 compares the Base M

2005 and Base K 2002 emissions. Compared to 2002, the new 2005 emissions reflect 

substantially lower commercial marine emissions and lower locomotive NOx emissions.

Table 7. Locomotive and commercial marine emissions 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 ---- ----

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Area Sources: 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 2005, 

special attention was given to two source categories: industrial adhesive and sealant solvents 

(which were dropped from the inventory to avoid double-counting) and outdoor wood boilers

(which were added to the inventory).

Point Sources: 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, the 

annual and summer season emissions were temporalized for modeling purposes using profiles 

prepared by Scott Edick (Michigan DEQ) based on CEM data. 

 

Biogenics: For Base M, a contractor (Alpine) provided an updated version of the 

CONCEPT/MEGAN biogenics model. Compared to the previous (EMS/BIOME) emissions, 

there is more regional isoprene using MEGAN compared to the BIOME estimates used for Base 

K (see Figure 44). Also, with the secondary organic aerosol updates to the CAMx air quality 

model, Base M includes emissions for monoterpenes and sesquiterpenes, which are precursors of secondary PM2.5 organic carbon mass.

Figure 44. Isoprene emissions for Base M (left) v. Base K (right)

Ammonia: For Base M, the 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 45. A spatial plot of emissions is provided in 

Figure 46, 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 45. Average daily ammonia emissions for Midwest States by month (2005) - (units: average 

daily emissions – tons per day)

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Figure 46. 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 parameters) 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 47. Additional plots 

and emission reports are available on the LADCO website 

(http://www.ladco.org/tech/emis/basem/canada/index.htm). 

Figure 47. Canadian point source emissions for SO2 (left) and NOx (right)

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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 agricultural burns. Projected emissions 

were also developed for 2010 and 2018 assuming “no smoke management” and “optimal smoke 

management” scenarios. An early model sensitivity run showed very little difference in modeled 

PM2.5 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 emission 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.)

Source sector emission summaries for the base years and future years are shown in Figure 48. 

Additional detail is provided in Tables 6a 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 48. Base year and future year emissions for 5-State LADCO Region (TPD, July weekday)

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.

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Table 8. Motor Vehicle Emissions Produced by CONCEPT Modeling (July weekday – tons per day)

Year State Sum of CO Sum of TOG Sum of NOx 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 1,650,128,709.9

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 4,619.2 423.7 693.5 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 30.2 288,042,232.1

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

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For EGUs, future year emissions were based on IPM2.1.9 modeling completed by the RPOs in 

July 2005 Base K and IPM3.0 completed by EPA in February 2007 for Base M. Several CAIR 

scenarios were assumed:

Base K

1a: IPM2.1.9, with 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 assumed 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 IPM3.0, 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 States, future year emission files were based on data 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 information 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

• Inspection - maintenance programs, including IL’s vehicle emissions tests (NE IL), IN’s vehicle 

emissions testing program (NW IN), OH’s E-check program (NE OH), and WI’s vehicle inspection 

program (SE WI) – note: a special emissions modeling run was done for the Cincinnati/Dayton area to 

reflect the removal of the state’s E-check program and inclusion of low RVP gasoline

• Reformulated 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 Spark 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

• Aerosol coatings

• Portable fuel containers

Power Plants

• Title IV (Phases I and II)

• NOx SIP Call

• Clean Air Interstate Rule

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Other Point Sources

• VOC 2-, 4-, 7-, and 10-year MACT standards

• Combustion turbine MACT

Other controls included in the modeling include: consent decrees (refineries, ethanol plants, and 

ALCOA)9, NOx RACT in Illinois and Ohio10, 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 additional 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 Modeling 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 model performance evaluation 

focused on the magnitude, spatial pattern, and temporal of modeled and measured 

concentrations. This exercise was intended to assess whether, and to what degree, confidence 

in the model is warranted (and to assess whether model improvements are necessary).

Model performance was assessed by comparing modeled and monitored concentrations. 

Graphical (e.g., side-by-side spatial plots, time series plots, and scatter plots) and statistical 

analyses were conducted. No rigid acceptance/rejection criteria were used for this study. 

Instead, the statistical guidelines recommended by EPA and other modeling studies (e.g., 

modeling by the other RPOs) were used to assess the reasonableness of the results. The 

model performance results presented here describe how well the model replicates observed 

ozone and PM2.5 concentrations after a series of iterative improvements to model inputs.

Ozone: Spatial plots are provided for high ozone periods in June 2002 and June 2005 (see 

Figures 49a and 49b). The plots 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-counting 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.

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Figure 49a. Modeled (top) v. monitored (bottom) 8-hour ozone concentrations: June 20 – 25, 2002

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Figure 49b Modeled (top) v. monitored (bottom) 8-hour ozone concentrations: June 23– 28 2005

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Standard model performance statistics were generated 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 generally within 30% (see Figure 50). 

Figure 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

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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 52). 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 52. 4 km grids for Lake Michigan region and Detroit-Cleveland region

Hourly time series plots were prepared for several monitors (see Figure 53). The results are 

similar at 12 km and 4 km, with some site-by-site and day-by-day differences.

Figure 53. Ozone time series plots for 12 km and 4 km modeling (June 17-29, 2002)

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An additional diagnostic analysis was performed to assess the response of the modeling system 

to 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 95th 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 the modeled and 

monitored ozone concentration changes (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 strategies.

Figure 54. Comparison of change in predicted and observed ozone concentrations (2002 v. 2005) 

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PM2.5: Time series plots of the monthly average mean bias and annual fractional bias for Base 

M and Base K are shown in Figure 55. 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 carbon 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 55. PM2.5 Model performance - monthly average mean bias and annual fractional bias for 

Base M (left column) and Base K (right column)

Base M Base K

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Two analyses were undertaken to understand sulfate model performance for 2005:

• Assess Meteorological Influences: The MM5 model performance evaluation showed that 

rainfall is over-predicted by MM5 over most of the domain during the summer months 

(LADCO, 2007c). Because CAMx does not explicitly use the rainfall output by MM5, this 

may or may not result in over-prediction sulfate wet deposition (and under-prediction 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 56)

showed a noticeable difference from the basecase (i.e., higher sulfate concentrations), 

and suggests 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 basecase 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 performance (see purple line in Figure 56)

showed little difference from the basecase.

Figure 56. Monthly sulfate bias for Base M (MRPO EGU) v. two sensitivity analyses (Note: positive 

values indicate over-prediction, negative values 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 values were only slightly different from the 

current base strategy run. In general, the future year values (without wet deposition) 

were a little higher (+0.15 ug/m3 or less) in the Ohio Valley and a little lower (-.10 ug/m3

of less) in the Great Lakes region. 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 results are not expected to change very much.

Time series plots of daily sulfate, nitrate, elemental carbon, and organic carbon concentrations 

for three Midwestern locations are presented in Figures 57 (2002) and 58 (2005). These results 

are consistent with 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

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 SULFATE NITRATE ORGANIC CARBON

Seney

Detroit

Chicago

Figure 57. Time series of sulfate, nitrate, and organic carbon at three Midwest sites for 2005

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 SULFATE NITRATE ORGANIC CARBON

Seney

Detroit

Chicago

Figure 58. Time series of sulfate, nitrate, and organic carbon at three Midwest sites for 2005

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In summary, model performance for ozone and PM2.5 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 patterns of modeled 

concentrations are consistent with monitored data

• Model accurately simulates the change in monitored ozone concentrations due to 

reductions in precursor emissions.

PM2.5

• Good agreement in the magnitude of fine particle mass, but some species 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: 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 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 these 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, consistent 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 attainment modeling is supplemented by additional information to provide a 

weight of evidence determination.

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Section 4.0 Attainment Demonstration for Ozone and PM2./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 PM2.5

and if not, then what additional emission reductions would be necessary for attainment. 

Traditionally, attainment demonstrations involved a “bright line” test in which a single modeled 

value was compared to the ambient standard. To provide a more robust assessment of 

expected future year air quality, EPA’s modeling guidelines call for consideration of 

supplemental information. This section summarizes the results of the primary (guideline) 

modeling analysis and a weight of evidence determination 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 effectiveness of existing and possible 

additional control programs. The model was used in a relative sense to project future year 

values, which are then compared to the standard to determine attainment/nonattainment. 

Specifically, the modeling test consists of the following steps:

(1) Calculate base year design values: For ozone and PM2.5, 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 2005-200711

(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 values: For each grid cell (with a monitor), the 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 2002 and 2005 base year design values for ozone and PM2.5 is provided in 

Figure 59. 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.

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Figure 59. 2002 v. 2005 base year design values for ozone (top) and PM2.5 (bottom)

2002 2005

Statistical 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 41

Peak Value 19.3 ug/m3 17.7 ug/m3

Ave Exceedance Amount 1.2 ug/m3 0.9 ug/m3

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Ozone results are provided for those grid cells with ozone monitors. The RRF calculation 

considers 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.) PM2.5 results are provided for those grid cells with 

FRM (PM2.5-mass) monitors. Spatial mapping was performed to extrapolate PM2.5-speciation 

data from STN and IMPROVE sites to FRM sites. RRF values for PM2.5 were derived as a 

function of quarter and chemical species.

Additional, hot-spot modeling will be performed by the states for certain PM2.5 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 Gaussian 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 the 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 PM2.5 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 PM2.5 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 10 (PM2.5), and spatial maps of the 

Base M future year concentrations are provided in Figures 60-62.

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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

2008 2009 2012

Table 9. Summary of Ozone Modeling Results

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County 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

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 2005 (observed) 2009 

2012 2018

Figure 60. Observed base year and projected future year design values for ozone – Base M

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 2005 (observed) 2009 

2012 2018

Figure 61. Observed base year and projected future year design values for PM2.5 (annual average)–Base M

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 2005 (observed) 2009 

2012 2018

Figure 62. Observed base year and projected future year design values for PM2.5 (24-hr average)-Base M

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The number of monitors with design values 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/m3)

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 26 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 Section 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 PM2.5 concentrations (i.e., about 0.1 ug/m

3

 for PM2.5 and 0.1-0.2 ppb for ozone). 

Second, EPA suggested adjustments 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 PM2.5 concentrations (on the 

order of 0.1 – 0.2 ug/m3), but do not produce any new residual nonattainment areas.

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The limited 4 km ozone modeling (based on Base K) performed by LADCO included a future 

year analysis for 2009. The figure below shows the 2009 values with 12 km and 4 km grid 

spacing for the LADCO modeling and similar modeling conducted by a stakeholder group

(Midwest 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 year (2009) values for Lake Michigan area (top) and Detroit-Cleveland region

(bottom)

These 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 results for northwestern Indiana/northeastern Illinois should be viewed with 

caution (i.e., probably 1 – 4 ppb too high).

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In summary, the ozone modeling provides the following information for the nonattainment areas 

in the region (see Table 12):

Table 12. Ozone Nonattainment Areas in the LADCO Region (as of December 31, 2007)

Area Name Category

Number of

Counties

Attainment 

Deadline

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 planning 

year. Rather, 2005 – 2007 air quality data are available to determine attainment.

Basic (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 Indianapolis

• 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 PM2.5 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 75 ppb. With respect to the new lower 24-hour 

PM2.5 standard, the modeling shows 13 sites in 2012 and 10 in 2018 with design values greater 

than 35 ug/m3.

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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 supplemental 

analyses are recommended:

• additional modeling

• analyses of trends in ambient air quality and emissions, and

• observational models and diagnostic analyses

Furthermore, according to EPA’s guidelines, if the future year modeled values are “close” to the 

standard (i.e., 82 – 87 ppb for ozone and 14.5 – 15.5 ug/m3 for PM2.5), 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 attainment. 

A WOE determination for ozone and PM2.5 is provided in the following 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):

 Ozone 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 Determination for Ozone

The WOE determination for ozone consists of the primary 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 in Section 4.1. Key findings 

from this modeling include:

• Base M regional modeling shows attainment by 2008 and 2009 at all sites, except 

Holland (MI), and attainment at all sites by 2012.

• Base K modeling results reflect generally higher future year values, and show more 

sites in nonattainment 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 modeling also shows that the proposed lower 8-hour standard will not be 

met at many sites, even by 2018, with existing controls.

Additional Modeling: Four additional 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 of these analyses is described below.

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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 estimates of future year ozone concentrations were calculated based 

on model-based RRFs and appropriate monitor-based concentrations for each year between 

2001 and 2007. RRF values for 2001, 2003, 2004, 2006, and 2007 were derived based on the 

2002 and 2005 modeling results. Monitor-based concentrations reflect 4th high values, design 

values, or average of three design values centered on the year in question. The probability of 

attainment was determined as the percentage of these seven estimates below the standard. 

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 redone with different types of assumptions for 

calculating base year design values (i.e., using the 3-year period centered on base year, and 

using the highest 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 and “acceptable” model performance). The 

results for several high concentration sites are presented in Tables 13a and 13b 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 associated 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 monitoring network 

just meets or 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 

predicted to be greater than the NAAQS. 

Based on examination of the spatial plots in Figures 49a and 49b, the most notable areas of 

high modeled ozone concentrations are over the Great Lakes. Over-water monitoring, however, 

is not required by EPA12. A cursory analysis of unmonitored areas for ozone was performed 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 observed values (in monitored grid 

cells). A spatial map of the estimated 2009 values is provided in Figure 64. 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 of high ozone throughout the region.

 

12 Air quality measurements over Lake Michigan were collected by LADCO previously to understand 

ozone transport in the area (see, for example, Figure 5). Due to cut-backs in USEPA funding, however, 

these measurements were discontinued in 2003.

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Table 13a. Primary and Additional Ozone Modeling Results – Lake Michigan and Cleveland Areas (2009)

2009 Modeling Results Lake Michigan Area Cleveland 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 guidance-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-2002baseyear)

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 95.0

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 Projected 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-year 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 81.4 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

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Table 13b. Primary and Additional Ozone Modeling Results – Cincinnati, Columbus, St. Louis, Indianapolis, and Detroit (2009)

2009 Modeling Results Cincinnati Area Columbus St. Louis Area Indianapolis Area Detroit Area

Wilmington Lebanon Sycamore NewAlbany W. Alton OrchardFarm Noblesville Fortville New Haven

390271002 39165007 390610006 390490029 291831002 291831004 180571001 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) 0.885 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 three 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 86.3 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 Year 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

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Figure 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, 2007b). 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 standard 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 Trends: EPA’s modeling guidelines note that while air quality models are generally 

the most 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 trends should first be 

normalized to account for year-to-year variations in meteorological conditions (EPA, 2002). 

Meterologically-adjusted 4th high 8-hour ozone concentrations were derived using the 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 trends in precursor 

emissions. Both VOC and NOx emissions affect ozone concentrations. Given that observationbased 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 considered 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 values 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 Cleveland are expected to be close to the standard).

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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 65. Estimates of Future Year Ozone Concentrations – Lake Michigan Area (Sheboygan and Holland), Cincinnati (Sycamore), and 

Cleveland (Ashtabula)

Note: Primary (guideline) modeling values (Base K and Base M results) are represented by large red diamonds, additional modeling 

values by small black circles, and trends-based values by small pink squares

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Observational Models and Diagnostic Analyses: The observation-based modeling (i.e., 

MAPPER) is presented in Section 3. The key findings from this modeling are that most urban 

areas are VOC-limited and rural areas are NOx-limited.

The primary diagnostic analysis is source apportionment modeling with CAMx to provide more 

quantitative information 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 66) and 6 source sectors (EGU point, non-EGU point, on-road, off-road, 

area, and biogenic sources) at ozone monitoring sites in the region.

 

Figure 66. Source regions (left) and key monitoring sites (right) for ozone modeling analysis

Modeling results for 2009 (Base M) and 2012 (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 contributions from NOx emissions are shown 

in blue, and from VOC emissions in green.)

The sector-level results (see, for example, Figure 67) show that on-road and nonroad NOx 

emissions generally have the largest contributions 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 67. Source-sector results for Holland (left) and Ashtabula (right) monitors – 2009 (Base M)

Kenosha County

Cook County

Indianapolis

Holland

St. Louis

Detroit

Cincinnati

Door County

Cleveland

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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

nonattainment 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 68. Source-region results for Holland (left) and Ashtabula (right) monitors – 2009 (Base M)

Summary: Air quality modeling and other supplemental analyses were performed to estimate 

future year ozone concentrations. Based on this information, the following general conclusions 

can be made:

• Existing (“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 of 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 current 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 the 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 weather 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 hand, if either of these conditions is not met, 

then attainment may be less likely.

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4.3 Weight-of-Evidence Determination for PM2.5

The WOE determination for PM2.5 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 regional modeling shows attainment by 2009 at all sites, except Detroit, 

Cleveland, and Granite City, and attainment at all sites by 2012, except for Detroit 

and Granite City.

The regional modeling for PM2.5 does not reflect any air quality benefit expected 

from local controls. States are conducting local-scale analyses and will use these 

results, in conjunction with the regional-scale modeling, to support their attainment 

demonstrations for PM2.5

• Base K modeling results reflect generally higher future year values, and show more 

sites in nonattainment in 2009 and 2012 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 PM2.5 should reflect a weight-of-evidence approach, 

with consideration of monitoring based information.

• Base M modeling also shows that the new PM2.5 24-hour standard will not be met at 

many sites, even by 2018, with existing controls.

Additional Modeling: EPA’s latest regional PM2.5 modeling was considered as corroborative 

information. This modeling was performed as part of the September 2006 revision to the PM2.5

standard (USEPA, 2006). EPA applied the CMAQ model with 2001 meteorology to estimate 

PM2.5 levels in 2015 and 2020 first with national rules in effect or proposed, and then with 

additional controls to attain the current standard (15 ug/m3 annual/65 ug/m3 daily). Additional 

analyses were performed to evaluate strategies for attaining more stringent standards in 2020 

(15/35, and 14/35). Baseline (2015) PM2.5 levels were predicted to be above the current 

standard in four counties in the LADCO region: Madison County, IL at 15.2 ug/m3, Wayne 

County, MI at 17.4, Cuyahoga County, OH at 15.4, and Scioto County, OH at 15.6. These 

results are consistent with LADCO’s Base K modeling for 2012/2018, which is not surprising 

given that EPA’s modeling and LADCO’s Base K modeling have a similar base year (2001 v. 

2002).

Observational Models and Diagnostic Analyses: The observation-based modeling (i.e., 

application of thermodynamic equilibrium models) is presented in Section 3. The key findings 

from this modeling are that PM2.5 mass is sensitive to reductions in sulfate, nitric acid, and 

ammonia concentrations. 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 PM2.5 mass decreases. Under conditions 

with lower sulfate levels (i.e., proxy of future year conditions), PM2.5 is more sensitive to 

reductions in nitric acid compared to reductions in ammonia.

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The primary diagnostic analysis is source apportionment modeling with CAMx to provide more 

quantitative information on source region (and source sector) impacts (Baker, 2007b). 

Specifically, the model estimated the impact of 18 geographic source regions (which are 

identified in Figure 69) and 6 source sectors (EGU point, non-EGU point, on-road, off-road, 

area, and biogenic sources) at PM2.5 monitoring sites in the region.

 

Figure 69. Source regions (left) and key monitoring sites (right) for PM2.5 modeling analysis

Modeling results for 2012 (Base K) and 2018 (Base M) are provided in Appendix III 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 sector-level results (see, for example, Figure 70) show that EGU sulfate, 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 Cleveland (right) monitors – 2018 (Base M)

Cook County

Steubenville

Granite City

Detroit

Cincinnati

Cleveland

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The source region results (see, for example, 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 Ohio), 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 were performed to estimate 

future year PM2.5 concentrations. Based on this information, the following general conclusions 

can be made:

• Existing (“on the books”) controls are expected to produce significant 

improvement in PM2.5 air quality.

• The choice of the 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, PM2.5 concentrations are not as strongly influenced by 

meteorology as ozone.

• Most sites are expected to meet the current PM2.5 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 16 – 17 ug/m3). 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 appropriate 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.

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Section 5. Reasonable Progress Assessment for Regional Haze

Air 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 subject to such requirements

• uniform rate of visibility improvement needed to attain natural visibility conditions 

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 state to “address regional haze in each mandatory Class I 

Federal area located within the 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 information 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). The 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

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Table 14. Draft List of 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 Wilderness 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-Glades 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)

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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) VISTAS Areas of Influence

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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, Voyageurs, Isle Royale, and 

Seney (see Section 2.3);

• use of the new IMPROVE light extinction equation; and

• use of EPA default values for natural conditions, based on the new IMPROVE 

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 15 (2002 base year) and Table 16 (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 improvement 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 the other four factors, in setting reasonable progress goals. 

An assessment of the five factors was performed for LADCO and the State of Minnesota by a 

contractor (EC/R, 2007). Specifically, ECR examined reductions in SO2 and NOx emissions 

from EGUs and industrial, commercial and institutional (ICI) boilers; NOx emissions from mobile 

sources and reciprocating engines and turbines; and ammonia emissions from agricultural 

operations. 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 Compliance): The average cost effectiveness values (in terms of $M 

per ton) are provided in Table 16. 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.

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Voyageurs Boundary Waters 

Isle Royale Seney

 

Mammoth Cave Upper Buffalo 

Figure 73. Visibility modeling results for Class I areas in eastern U.S.

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Mingo Shenandoah

Dolly Sods Bringantine 

Lye Brook Acadia

Figure 73 (cont.) Visibility modeling results for Class I areas in eastern U.S.

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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)

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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)

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Table 17. Estimated Cost Effectiveness for Potential Control 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 more 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

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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 believed 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 additional 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 equipment. 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 74, along with the costeffectiveness expressed in $M per deciview improvement). These results show that 

although EGU and ICI boiler controls have higher cost-per-deciview values (compared 

to some of the other measures), their visibility impacts are larger.

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Visibility Improvement (dv)

0.00

0.25

0.50

0.75

1.00

SO2NOx NH3 SO2NOx NH3 SO2 NOx NH3 SO2 NOx NH3 SO2NOx 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 SO2NOx NH3 SO2 NOx NH3 SO2 NOxNH3 SO2 NOx NH3

Strategy 1 Strategy 2

EGUs ICI Boilers Rec.Eng. Mobile Ag

Figure 74. Results of ECR analysis of reasonable progress factors – visibility improvement (Factor 

5) is on top, and cost effectiveness (Factor 1) is on bottom

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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 areas 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 modeling 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 to differences 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.

Additional Modeling: Two 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 receptor 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 used to calculate the baseline, given that 

the Base M modeling reflects a 2005 base year. The results of this alternative analysis (see 

Table 18) are generally consistent with the primary modeling (see Table 16).

Second, Minnesota’s modeling reflects a 2002 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 northern 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 

results at the IMPROVE monitor. This variability in visibility levels should be kept in mind when 

reviewing the values presented in Tables 15, 16, and 18, which reflect results at the IMPROVE 

monitor locations.

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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)

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Observational Models and Diagnostic Analyses: The observation-based modeling (i.e., 

application of thermodynamic equilibrium models) is presented in Section 3. The key findings 

from this modeling are that PM2.5 mass is sensitive to reductions in sulfate, nitric acid, and 

ammonia concentrations. 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 PM2.5 mass decreases and visibility 

improves. Under conditions with lower sulfate levels (i.e., proxy of future year conditions), PM2.5

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

As discussed in Section 2, thermodynamic equilibrium modeling based on data collected at 

Seney indicates that PM2.5 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 PM2.5 concentrations and improve visibility levels in the 

northern Class I areas.

Trajectory analyses for the 20% worst visibility days for the four northern Class I areas are 

provided in Figure 75. (Note, this figure is similar to Figure 34, but the trajectory results for each 

Class I area are displayed separately here.) The orange 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 regions located to the south, and good air days with transport from Canada. 

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Seney Isle Royale

Boundary Waters Voyageurs

Figure 75. Trajectory analysis results for northern Class I areas on 20% worst visibility days

The primary diagnostic analysis is source apportionment modeling with CAMx to provide more 

quantitative information on source region (and source sector) impacts (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 76) and 6 source se ctors (EGU point, non-EGU point, on-road, off-road, 

area, and ammonia sources) at visibility/haze monitoring sites in the eastern U.S.

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Figure 76. 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 IV 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 region and sector-level contributions in 

terms of absolute modeled values.

The sector-level results (see, for example, Figure 77) show that EGU sulfate, non-EGU-sulfate, 

and ammonia emissions generally have the largest contributions at the key monitor locations. 

The source group contributions vary by receptor location due to emissions inventory differences. 

Figure 77. Source-sector results for Seney (left) and Boundary Waters (right) – 2018 (Base M)

The source region results (see, for example, Figure 78) show that emissions from a number of 

nearby states contribute to regional haze levels.

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Figure 78. Source-region results for Seney (left) and Boundary Waters (right) – 2018 (Base M)

Table 19 provides a summary of the estimated state-level culpabilities 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 general conclusions can be 

made:

• Existing (“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 uniform rate of visibility improvement 

values in 2018, including those in northern Michigan and some in the 

northeastern U.S. 

• Visibility levels in 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.

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Table 19. State Culpabilities Based on PSAT Modeling and Trajectory Analyses

Boundary Waters Seney

LADCO -

Round 4 

PSAT

LADCO -

Round 5 

PSAT

MPCAPSATCENRAP -

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%

Illinois 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%

CENRAPWRAP 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

MPCAPSATCENRAP -

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%

CENRAPWRAP 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%

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Section 6. Summary

To support the development of SIPs for ozone, PM2.5, and regional haze in the States 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 base 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 summary of the physical, chemical, and meteorological 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 or industrial centers

PM2.5

• Current monitoring data show 30 sites in violation of the annual PM2.5 standard of 15 

ug/m3. Nonattainment sites are characterized by an elevated regional background 

(about 12 – 14 ug/m3) and a significant local (urban) increment (about 2 – 3 ug/m3). 

Historical PM2.5 data show a slight downward trend since deployment of the PM2.5

monitoring network in 1999.

• PM2.5 concentrations are also influenced by meteorology, but the relationship is more 

complex and less well understood compared to ozone.

• On an annual average basis, PM2.5 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 22 – 24 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 quality models were applied to support the regional planning efforts. Two base years were 

used in the modeling analyses: 2002 and 2005. EPA’s modeling guidance recommends using 

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2002 as the baseline inventory year, but also allows for use of an alternative baseline 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 2002 was more severe compared to 2005. Examination of multiple base 

years provides for a more complete technical assessment. Both sets of model runs are 

discussed in this document. 

Basecase modeling was conducted to evaluate model performance (i.e., assess the model's 

ability to reproduce the observed concentrations). This exercise was intended to assess 

whether, and to degree, confidence in the model is warranted (and to assess whether model 

improvements are necessary). Model performance for ozone and PM2.5 was 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 patterns of modeled 

concentrations are consistent with monitored data

• Model accurately simulates the change in monitored ozone concentrations due to 

reductions in precursor emissions.

PM2.5

• 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 meteorological 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 would be sufficient to provide for attainment of the standards for ozone and PM2.5 and if 

not, then what additional emission reductions would be necessary for attainment. Traditionally, 

attainment demonstrations involved a “bright line” test in which a single 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, 

according to EPA’s modeling guidance, if the future year modeled values are “close” to the 

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standard (i.e., 82 – 87 ppb for ozone and 14.5 – 15.5 ug/m3 for PM2.5 ), then the results of the 

primary modeling should be reviewed along with the supplemental information in a “weight of 

evidence” (WOE) assessment of whether each area is likely to achieve timely attainment. Key 

findings of the WOE determination include:

• Existing controls are expected to produce significant improvement in ozone and 

PM2.5 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 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 are expected to meet the current PM2.5 standard by the applicable 

attainment date, except for sites in Detroit, Cleveland, and Granite City.

The regional modeling for PM2.5 does not reflect air quality benefits expected 

from local controls. States are conducting local-scale analyses and will use

these results, in conjunction with the regional-scale modeling, to support their 

attainment demonstrations for PM2.5.

• These findings of residual nonattainment for ozone and PM2.5 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 

PM2.5 design values on the order of 16 - 17 ug/m3). 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 likely) 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.

• The new PM2.5 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 eastern U.S. are expected to be 

greater than (less improved than) the uniform rate of visibility improvement 

values in 2018 based on existing controls, including those in northern Michigan 

and some in the northeastern U.S. Visibility levels in 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.

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Section 7. References

Alpine Geophysics, 2007, “Emissions Inventory Assistance: 2005 Base Year Biogenic and 

Other (non-LADCO) State Emissions, Preparation and Delivery of Non-MRPO Emission Files< 

March 12, 2007.

AER, 2004, “Analysis of Recent Regional Haze Data”, Atmospheric and Environmental 

Research, Inc., August 2004.

Baker, K., “Hot Spot Test (Draft), Lake Michigan Air Directors Consortium, March 23, 2005

Baker, K. 2007a, “Ozone Source Apportionment Results for Receptors in Non-Attainment 

Counties in the Great Lakes Region”, Lake Michigan Air Directors Consortium, October 2007.

Baker, K., 2007b, “Source Apportionment Results for PM2.5 and Regional Haze at Receptors in 

the Great Lakes Region”, Lake Michigan Air Directors Consortium, November 2007.

Baker, K. and D. Kenski, 2007, “Diagnostic and Operational Evaluation of 2002 and 2005 8-

Hour Ozone to Support Model Attainment Demonstrations”, Lake Michigan Air Directors 

Consortium, October 2007.

Blanchard, C.L., and S. Tanenbaum, 2003, “VOC and NOx Limitation of Ozone Formation at 

Monitoring Sites in Illinois, Indiana, Michigan, Missouri, Ohio, and Wisconsin, 1998-2002”, 

Report to LADCO, February 2003..

Blanchard, C.L., and S. Tanenbaum, 2004, “The Effects of Changes in Sulfate, Ammonia, and 

Nitric Acid on Fine PM Composition at Monitoring Sites in Illinois, Indiana, Michigan, Missouri, 

Ohio, and Wisconsin, 2000-2002”, Report to LADCO, February 2004.

Blanchard, C.L., and S. Tanenbaum, 2005a, “Weekday/Weekend Differences in Ambient 

Concentrations of Primary and Secondary Air Pollutants in Atlanta, Baltimore, Chicago, DallasFort Worth, Denver, Houston, New York, Phoenix, Washington, and Surrounding Areas”, 

Prepared for National Renewable Energy Laboratory, NREL Project ES04-1, July 30, 2005.

Blanchard, C.L., and S. Tanenbaum, 2005b, “Analysis of Data from the Midwest Ammonia 

Monitoring Project”, Draft Final Technical Memorandum, March 31, 2005.

Breiman, L., J. Friedman, R. Olshen, and C. Stone, 1984, “Classification and Regression 

Trees”, Chapman & Hall (1984).

Camalier, L., Cox, W., Dolwick, P., 2007. The effects of meteorology on ozone in urban areas 

and their use in assessing ozone trends. Atmospheric Environment 41, 7127-7137

Cox, W.M, and S.-H. Chu, 1993, Meteorologically Adjusted Ozone Trends in Urban Areas: A 

Probabililistic Approach, Atmospheric Environment 27B(4):425-434 (1993).

DeBell, L.J., K. Gebhart, J. Hand, W. Malm, M. Pitchford, B. Schichtel, and W. White, 2006, 

“Spatial and Seasonal Patterns and Temporal Variability of Haze and its Constituents in the 

United States, Report IV”, Cooperative Institute for Research in the Atmosphere, November 

2006

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DRI, 2005, “Source Apportionment Analysis of Air Quality Monitoring Data: Phase II”, Desert

Research Institute. March 2005

EC/R, Incorporated, 2004, “Fire Emissions Inventory Development for the Midwest Regional 

Planning Organization”, Final Report, September 30, 2004.

EC/R, Incorporated, 2007, “Reasonable Progress for Class I Areas in the Northern Midwest –

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 Directors 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, 2007a, “Modeling Weekend and Weekday Ozone in Southeast Michigan”, Draft Final 

Report, Prepared for National Renewable Energy Laboratory, July 30, 2007.

Environ, 2007b, “LADCO 2005 Locomotive Emissions”, Draft, February 2007.

Environ, 2007c, “LADCO 2005 Commercial Marine Emissions”, Draft, March 2, 2007.

Environ, 2008, “LADCO On-Road Emission Inventory Development Using CONCEPT MV”, 

January 2008..

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 PM2.5: A GIS Analysis, Proc. 

AWMA 97th Annual Conf., Indianapolis, June 2004.

Kenski, D.M., 2007a, “CART Analysis for Ozone Trends and Meteorological Similarity”, May 10, 

2007.

Kenski, D.M., 2007b, “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. 2008b, “Updated CART Analysis for Ozone through 2007”, January 30, 2008.

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LADCO, 2005, “Meteorological Modeling Performance Summary for Applications to 

PM2.5/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

LADCO, 2007b, “Modeling Protocol: 2005 Basecase Technical Details”, October 19, 2007

LADCO, 2007c, “An Evaluation of an Annual 2005 MM5 Simulation to Support Photochemical 

and Emissions Modeling Applications”, January 10, 2007.

LADCO, 2007d, “Base M/Round 5 Modeling: Summary (DRAFT), October 10, 2007

LADCO, 2008a, “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-wide 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. Ramsey, H. Xin, Y. Liang, and J. Arogo, 2005, “An 

Improved Process-Based Ammonia Emissions Model for Agricultural Sources – Emission 

Estimates”

MPCA, 2008, “Technical Support Document for the Minnesota State Implementation Plan for 

Regional Haze”, Draft, March 4, 2008.

MRPO, 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.

MRPO, 2008, “Regional Haze in the Upper Midwest: Summary of Technical Information”, 

Version 2.2, February 22, 2008.

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.

Poirot, R. L.; Wishinski, P. R.; Hopke, P. K.; Polissar, A. V.; Comparative Application of Multiple 

Receptor Methods To Identify Aerosol Sources in Northern Vermont, Environ. Sci. Technol.,

36(4); 820-820 (2002).

Sheesley, R.J., J.J. Schauer, E. Bean, and D. Kenski, Trends in Secondary Organic Aerosol at 

a Remote Site in Michigan’s Upper Peninsula, Env. Sci. and Tech. 38(24); 6491-6500 (Oct 

2004).

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STI, 2004, “Data Processing and Analysis of Aloft Air Quality Data Collected in the Upper 

Midwest”, Executive Summary, STI-903470-2568-ES3, Sonoma Technology, Inc, August 5, 

2004.

STI, 2006a, “Integration of Results for the Upper Midwest Urban Organics Study”, Final Report, 

STI-903520-2942-FR, Sonoma Technology, Inc. March 31, 2006.

STI, 2008, “Data Analysis and Source Apportionment of PM2.5 in Selected Midwestern Cities”, 

Final Report, STI-907018.03-3264-DRF, Sonoma Technology, Inc., February 2008.

EPA, 1999, Regional Haze Regulations, 64 FR 35714, July 1, 1999.

EPA, 2003, “Guidance for Estimating Natural Visibility Conditions Under the Regional Haze 

Rule”, EPA-454/B-03-005, September 2003.

EPA, 2002, “Recommended Approach for Performing Mid-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

EPA, 2006, “Regulatory Impact Analysis of the Revisions to the National Ambient Air Quality 

Standards”, September 2006 (http://www.epa.gov/ttn/ecas/ria.html#ria2007)

EPA, 2007a, "Guidance for on the Use of Models and Other Analyses for Demonstrating 

Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze”, EPA-454/B07-002, April 

2007. 

EPA, 2007b, “Regulatory Impact Analysis of the Proposed Revisions to the National Ambient Air 

Quality Standards”, EPA-452/R-07-008, July 2007

(http://www.epa.gov/ttn/ecas/ria.html#ria2007)

EPA, 2007c, Clean Air Fine Particle Implementation Rule, 72 FR 20586, April 25, 2007

Zhang, R, T. Ramsey, J. Fadel, J. Arogo, Z. Wang, G. Mansell, and H. Xin, 2005, “An Improved 

Process-Based Ammonia Emissions Model for Agricultural Sources – Model Development”

Web Sites:

http://www.ladco.org/tech/emis/basem/canada/index.htm

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/

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APPENDIX I

Ozone and PM2.5 Modeling Results

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Key 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 - OTB

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Key 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 - OTB 2009 - Will Do

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 131
Key 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) 2012 - OTB 2018 - OTB

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 132
2005 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 Results

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 133
2005 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 Results

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 134
2005 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

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 135
24-Hour PM2.5 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 Results

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 136
Site 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)

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 137
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 0.8888

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 138
Site 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 0.8564

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 139
Site 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 0.8172

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Site 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 0.8475

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Site 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

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119

APPENDIX II

Ozone Source Apportionment Modeling Results

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120

APPENDIX III

PM2.5 Source Apportionment Modeling Results

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Chicago (Cicero), Illinois

2005 (Round 5)

2012 (Round 4)

2018 (Round 5)

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Clark County, Indiana

2005 (Round 5)

2012 (Round 4)

2018 (Round 5)

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Dearborn, Michigan

2005 (Round 5)

2012 (Round 4)

2018 (Round 5)

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Cincinnati, Ohio

2005 (Round 5)

2012 (Round 4)

2018 (Round 5)

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Cleveland, Ohio

2005 (Round 5)

2012 (Round 4)

2018 (Round 5)

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Steubenville, Ohio

2005 (Round 5)

2012 (Round 4)

2018 (Round 5)

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121

APPENDIX IV

Haze Source Apportionment Modeling Results

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Boundary Waters, Minnesota

2005 (Round 5)

2018 (Round 4)

2018 (Round 5)

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Voyageurs, Minnesota

2005 (Round 5)

2018 (Round 4)

2018 (Round 5)

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Seney, Michigan

2005 (Round 5)

2018 (Round 4)

2018 (Round 5)

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Isle Royale, Michigan

2005 (Round 5)

2018 (Round 4)

2018 (Round 5)

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Shenandoah, Virginia

2005 (Round 5)

2018 (Round 4)

2018 (Round 5)

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Mammoth Cave, Kentucky

2005 (Round 5)

2018 (Round 4)

2018 (Round 5)

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Lye Brook, Vermont

2005 (Round 5)

2018 (Round 4)

2018 (Round 5)

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Appendix D2:

Development of 2005 Base Year Growth and Control

Factor for Lake Michigan Air Directors Consortium

(LADCO) Final Report

http://www.ladco.org/reports/rpo/emissions/ladco_2005_base_yr_growth_and_controls_report_fi

nal.pdf

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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) 

FINAL REPORT 

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

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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. NOx SIP Call................................................................................................................. 27 

2. MACT Standards .......................................................................................................... 27 

3. Consent Decrees............................................................................................................ 27 

4. On-the-Books (OTB) Control Additions ...................................................................... 35 

5. Best Available Retrofit 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 

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PECHAN September 2007 

 iv Development of 2005 Base Year Growth and 

Control Factors for LADCO – Final Report 

TABLES 

Table II-1. Priority Emission Activity Area Source/MAR Categories........................................... 4 

Table II-2. Growth Indicators for Priority Area Source/MAR Categories ..................................... 7 

Table II-3. Priority Emission Activity Point Source Categories................................................... 15 

Table II-4. Growth Indicators for Priority Point Source Categories............................................. 19 

Table II-5. Priority Emission Activity NONROAD Model Source Categories............................ 24 

Table II-6. Growth Indicators for Priority NONROAD Model Source Categories...................... 25 

Table III-1. RICE Engines in Illinois Affected by NOx SIP Call ................................................ 28 

Table III-2. Post-2005 MACT Standards and Expected VOC, NOx, 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-Hour Ozone Nonattainment Area 35 

Table III-5. BART Eligible Non-EGU Sources........................................................................... 36 

Table III-6. Residential Wood Combustion NSPS Emission Reductions ................................... 43 

Table III-7. Locomotive Emissions Reported in EPA Draft RIA................................................ 45 

Table III-8. Percentage Reductions Associated with Federal Locomotive Standards................. 46 

Table III-9. Percentage Reductions Associated with Federal CMV Standards........................... 47 

Table III-10. Commercial Marine Vessel Rule Penetration Values ............................................ 48 

Table IV-1. RPO Data Exchange Protocol Format for Growth/Control Data.............................. 50 

Table IV-2. Fields Populated in Growth Factor File .................................................................... 51 

Table IV-3. Fields Populated in Point Source Control Factor File............................................... 53 

Table IV-4. Fields Populated in Area Source/MAR Control Factor File ..................................... 54 

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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 NOx burner 

MACT maximum achievable 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 

NOx 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 available control technology 

RE rule effectiveness 

REMI Regional Economic Models, Inc. 

RIA Regulatory Impact Analysis 

RICE reciprocating internal combustion engines 

ROG reactive organic gases 

RP rule penetration 

RPO Regional Planning Organization 

SCC source classification code 

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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 

SO2 sulfur dioxide 

VOC volatile organic compound 

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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 anthropogenic emissions for the purpose of assessing 

progress for air quality goals, including goals related 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 data for all non-electric 

generating unit (EGU) point, area, and nonroad source 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 and derivation of these files (Pechan, 2004). Revised files were 

later provided to LADCO in March 2005. 

In September 2005, LADCO contracted with Pechan to conduct the following two tasks to 

develop updated growth and control 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 technology (MACT) standard control efficiency 

assumptions; 

 (c) Residential wood combustion unit lifetime; and 

 (d) Regional Planning Organization (RPO) inventories. 

 Task 2: Develop non-EGAS default-based 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 information than that used in the earlier study. As 

with the earlier study, this effort involved the preparation of emission activity growth and control 

information relative to a 2002 base year inventory 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 demonstrations). Because the incremental level 

of effort required to develop emission activity growth factors for each year over the 2003-2018 

period was nominal, Pechan prepared non-EGU point and area and nonroad source growth 

factors for each year over this entire period. 

For the current study, LADCO requested that Pechan develop growth and control 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 2009, 2012, and 2018. As with the previous studies, 

Pechan provided updated point/area source and marine, aircraft, and railroad (MAR) category 

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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 period. 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 development of the emission activity growth data; 

• Section III, which discusses how the updated 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 references consulted in preparing this report. 

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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 resources: (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 provide state-level growth rates, the DOE energy 

forecasts provide regional or national growth rates (e.g., the same growth rate is applied to each 

LADCO state because each of these states is included in the DOE’s East North Central division). 

Instead of relying on REMI’s population forecasts, Pechan developed growth factors from 

county-level population projections available from each LADCO region state. 

LADCO requested that Pechan review the growth indicators applied to particular source 

classification codes (SCCs) in the 2005 base year inventory. For these “priority” source 

categories, Pechan evaluated alternative growth methodologies and data sources before selecting 

a forecasting approach. The balance of this section 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 Protection Agency (EPA) SCC documentation and 

emission estimation guidance to identify the emissions activity (throughput) data associated with 

each SCC. Pechan then investigated the availability of LADCO state-specific projections for 

these data. 

Table II-1 presents the descriptions and emissions activity for each priority area source/MAR 

category. The last column in this table identifies each category’s growth indicator assignment 

under the previous Pechan forecast effort (Pechan, 2005). Table II-2 displays the assigned 

growth indicator for each priority area/MAR source category and any alternative indicators that 

were considered. This table also presents the percentage growth rates for the assigned indicators 

over two forecast periods: 2005-2009 and 2005-2018. 

In addition to population data from the LADCO states and REMI employment data, the 

following information sources supplied 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). 

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 PECHAN 

 4 Development of 2005 Base Year 

for LADCO - Final Report 

Table II-1. Priority Emission Activity Area Source/MAR Categories 

POLLUTANT SCC DESC2 DESC3 DESC4 EMISSIONS ACTIVITY CURR

NH3 2805047100 Agriculture Production 

- Livestock 

Swine production - deeppit house operations (unspecified animal 

Confinement Annual average number of swine REMI 

NH3 2805039200 Agriculture Production 

- Livestock 

Swine production - operations with lagoons (unspecified animal 

Manure handling and storage 

Annual average number of swine REMI 

NH3 2805047300 Agriculture Production 

- Livestock 

Swine production - deeppit house operations (unspecified animal 

Land application of manure 

Annual average number of swine REMI 

NH3 2805001100 Agriculture Production 

- Livestock 

Beef cattle - finishing operations on feedlots (drylots) 

Confinement Annual average number of beef 

cattle 

REMI 

NH3 2805039100 Agriculture Production 

- Livestock 

Swine production - operations with lagoons (unspecified animal 

Confinement Annual average number of swine REMI 

NH3 2805003100 Agriculture Production 

- Livestock 

Beef cattle - finishing operations on pasture/range 

Confinement Annual average number of beef 

cattle 

REMI 

NH3 2805001300 Agriculture Production 

- Livestock 

Beef cattle - finishing operations on feedlots (drylots) 

Land application of manure 

Annual average number of beef cattle 

REMI 

NH3 2630020000 Wastewater Treatment Public Owned Total Processed Volume of wastewater processed REMI NH3 2805007100 Agriculture Production 

- Livestock 

Poultry production - layers with dry manure management system 

Confinement Annual average number of poultry Regre

sector 

NH3 2805021300 Agriculture Production 

- Livestock 

Dairy cattle - scrape dairy Land application of 

manure 

Annual average number of dairy cattle 

Regreexplan

NH3 2805030000 Agriculture Production 

- Livestock 

Poultry Waste Emissions Not Elsewhere 

Classified (see also 28-05-007 -008-009) 

Annual average number of poultry REMI 

NH3 2805021200 Agriculture Production 

- Livestock 

Dairy cattle - scrape dairy Manure handling and 

storage 

Annual average number of dairy cattle 

Regreexplan

NH3 2104008070 Residential Wood Outdoor Boiler Amount of wood burned 

NOX 2285002006 Railroad Equipment Diesel Line Haul Locomotives: 

Class I Operations 

Amount of diesel consumed by Class I line-haul locomotives 

AEO Frelativ

NOX 2280002023 Marine Vessels 

Commercial 

Diesel Push Boats Amount of diesel consumed by 

commercial push boats 

AEO SrelativTransp

NOX 2102006001 Industrial Natural Gas All Boiler Types Volume of natural gas burned by 

industrial area source boilers 

AEO I

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
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 5 Development of 2005 Base Year 

for LADCO - Final Report 

Table II-1 (continued) 

POLLUTANT SCC DESC2 DESC3 DESC4 EMISSIONS ACTIVITY CU

NOX 2275020000 Aircraft Commercial Aircraft Total: All Types Number of commercial aircraft 

landing-takeoff cycles 

Feitin(LT

NOX 2285002010 Railroad Equipment Diesel Yard Locomotives Amount of diesel consumed by 

yard locomotives 

AEfor ou

NOX 2104006000 Residential Natural Gas Total: All Combustor 

Types 

Volume of residential natural gas consumed 

AE

NOX 2285002009 Railroad Equipment Diesel Line Haul 

Locomotives: Commuter Lines 

Amount of diesel consumed by commuter locomotives 

AEad

NOX 2280003200 Marine Vessels Commercial Residual Underway emissions Amount of residual oil 

consumed by CMV during underway operations 

AEfor Tr

NOX 2102006000 Industrial Natural Gas Total: Boilers and IC 

Engines 

Volume of natural gas burned by industrial area source boilers and IC engines 

AE

NOX 2104008070 Residential Wood Outdoor Boiler Amount of wood burned 

ROG 2461850000 Miscellaneous Non-industrial: 

Commercial 

Pesticide Application: Agricultural 

All Processes (Not listed for this SCC) Re

"P

ROG 2461020000 Miscellaneous Non-industrial: 

Commercial 

Asphalt Application: All Processes 

Total: All Solvent Types 

Amount of solvent used Re

"POp

ROG 2401200000 Surface Coating Other Special Purpose 

Coatings 

Total: All Solvent Types 

Amount of solvent used Po

ROG 2401001000 Surface Coating Architectural Coatings Total: All Solvent 

Types 

Amount of solvent used Re

levso

ROG 2460100000 Miscellaneous Non-industrial: 

Consumer and Commercial 

All Personal Care Products Total: All Solvent 

Types 

Amount of solvent used Po

sta

ROG 2501011010 Petroleum and Petroleum 

Product Storage 

Portable Gas Cans Residential Volume of gasoline stored RE

ROG 2501060100 Petroleum and Petroleum 

Product Storage 

Gasoline Service Stations Stage 2: Total Volume of gasoline pumped by 

stations 

ReEx

ROG 2460800000 Miscellaneous Non-industrial: 

Consumer and Commercial 

All FIFRA Related Products Total: All Solvent 

Types 

Amount of solvent used Re

for va

ROG 2501060050 Petroleum and Petroleum 

Product Storage 

Gasoline Service Stations Stage 1: Total Volume of gasoline pumped 

into stations 

RE

ROG 2460400000 Miscellaneous Non-industrial: 

Consumer and Commercial 

All Automotive Aftermarket Products 

Total: All Solvent Types 

Amount of solvent used Po

sta

ROG 2425000000 Graphic Arts All Processes Total: All Solvent 

Types 

Amount of solvent used RE

ou

ROG 2401005000 Surface Coating Auto Refinishing: SIC 7532 Total: All Solvent 

Types 

Amount of solvent used RE

Se

ROG 2460500000 Miscellaneous Non-industrial: 

Consumer and Commercial 

All Coatings and Related Products 

Total: All Solvent Types 

Amount of solvent used Po

sta

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
 PECHAN 

 6 Development of 2005 Base Year 

for LADCO - Final Report 

Table II-1 (continued) 

POLLUTANT SCC DESC2 DESC3 DESC4 EMISSIONS ACTIVITY CU

ROG 2460200000 Miscellaneous Non-industrial: 

Consumer and Commercial 

All Household Products Total: All Solvent 

Types 

Amount of solvent used Po

sta

ROG 2415020000 Degreasing Fabricated Metal Products 

(SIC 34): All Processes 

Total: All Solvent Types 

Amount of solvent used RE

em

ROG 2415025000 Degreasing Industrial Machinery and 

Equipment (SIC 35): All Processes 

Total: All Solvent Types 

Amount of solvent used RE

Eq

ROG 2460600000 Miscellaneous Non-industrial: 

Consumer and Commercial 

All Adhesives and Sealants Total: All Solvent 

Types 

Amount of solvent used Po

sta

ROG 2420000370 Dry Cleaning All Processes Special Naphthas Amount of special naphthas 

used 

SCSCLa

ROG 2630010000 Wastewater Treatment Industrial Total Processed Volume of wastewater 

processed 

Prde

ROG 2501060101 Petroleum and Petroleum 

Product Storage 

Gasoline Service Stations Stage 2: 

Displacement Loss/Uncontrolled 

Volume of gasoline pumped via uncontrolled 

ReEx

ROG 2415360000 Degreasing Auto Repair Services (SIC 

75): Cold Cleaning 

Total: All Solvent Types 

Amount of solvent used RE

Se

ROG 2401002000 Surface Coating Architectural Coatings - 

Solvent-based 

Total: All Solvent Types 

Amount of solvent used RE

ROG 2401003000 Surface Coating Architectural Coatings - 

Water-based 

Total: All Solvent Types 

Amount of solvent used RE

ROG 2104008070 Residential Wood Outdoor Boiler Amount of wood burned 

SO2 2601020000 On-site Incineration Commercial/Institutional Total Amount of material burned RESO2 2102004000 Industrial Distillate Oil Total: Boilers and IC 

Engines 

Amount of distillate oil burned by area source industrial boilers/IC engines 

AE

SO2 2103004000 Commercial/Institutional Distillate Oil Total: Boilers and IC 

Engines 

Amount of distillate oil burned by area source commercial boilers/IC engines 

No da

SO2 2275020000 Aircraft Commercial Aircraft Total: All Types See NOX entry FA

sta

SO2 2102005000 Industrial Residual Oil Total: All Boiler 

Types 

Amount of residual oil burned by area source industrial boilers 

AE

SO2 2102002000 Industrial Bituminous/Subbituminous 

Coal 

Total: All Boiler Types 

Amount of bit/sub coal burned by area source industrial boilers 

AE

SO2 2285002006 Railroad Equipment Diesel Line Haul 

Locomotives: Class I Operations 

See NOX entry AE

for 

SO2 2104008070 Residential Wood Outdoor Boiler Amount of wood burned 

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
 PECHAN 

 7 Development of 2005 Base Year 

for LADCO - Final Report 

Table II-2. Growth Indicators for Priority Area Source/MAR Categories 

Total % Change 

Pollutant SCC 

2005-

2009 

2005-

2018 Growth Indicator Basis Alternatives Considered 

NH3 2805047100 -1.7 6.2 Interpolated SCC/state-level animal count projections from EPA NH3 

inventory of Animal Husbandry Operations 

 

NH3 2805039200 -2.3 4.4 Interpolated SCC/state-level animal count projections from EPA NH3 

inventory of Animal Husbandry Operations 

 

NH3 2805047300 -1.6 6.4 Interpolated SCC/state-level animal count projections from EPA NH3 

inventory of Animal Husbandry Operations 

 

NH3 2805001100 5.2 16.7 Interpolated SCC/state-level animal count projections from EPA NH3 

inventory of Animal Husbandry Operations 

 

NH3 2805039100 -1.6 6.4 Interpolated SCC/state-level animal count projections from EPA NH3 

inventory of Animal Husbandry Operations 

 

NH3 2805003100 3.2 3.5 Interpolated SCC/state-level animal count projections from EPA NH3 

inventory of Animal Husbandry Operations 

 

NH3 2805001300 5.2 16.7 Interpolated SCC/state-level animal count projections from EPA NH3 

inventory of Animal Husbandry Operations 

 

NH3 2630020000 2.8 9.5 Municipal design flow forecasts from Drinking Water Infrastructure Needs 

Survey 

NH3 2805007100 -4.5 8.3 Interpolated SCC/state-level animal count projections from EPA NH3 

inventory of Animal Husbandry Operations 

 

NH3 2805021300 -10.2 -39.0 Interpolated SCC/state-level animal count projections from EPA NH3 

inventory of Animal Husbandry Operations 

 

NH3 2805030000 -5.3 6.8 Interpolated SCC/state-level animal count projections from EPA NH3 

inventory of Animal Husbandry Operations 

 

NH3 2805021200 -10.2 -39.0 Interpolated SCC/state-level animal count projections from EPA NH3 

inventory of Animal Husbandry Operations 

 

NH3 2104008070 78.0 84.3 Extrapolation of national 1999-2004 trend in OWB sales (exponential 

growth) thru 2006; linear growth thru 2008; 2009+ based on rural population growth rate 

NOX 2285002006 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = -1.4% per year; 

forecast = 1.4% per year 

NOX 2280002023 4.3 9.9 AEO national Domestic Shipping sector distillate projections adjusted for 

LADCO region growth in REMI Water Transportation sector output relative to nation 

1998-2004 fuel consumption data barge traffic on regional rivers indisimilar annual growth rate (1.0%) 

NOX 2102006001 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = -0.01% per yea

AEO forecast = 1.4% per year 

NOX 2275020000 0.0 0.0 No growth due to contradictory historic and forecast trends (Federal 

Aviation Administration commercial aircraft landing and take-offs data) 

FAA 1990-2005 = -0.01% per yearforecast = 1.7% per year 

NOX 2285002010 0.0 0.0 No growth due to contradictory historic and forecast trend (historic trend 

based on 1996-2002 regional Switch and Terminal Services employment) 

1996-2004 = -1.6% per year employment decrease; AEO foreca1.4% per year 

NOX 2104006000 1.7 2.8 AEO residential natural gas consumption forecast DOE 1990-2004 = 0.5% annual; A

forecast = 0.2% 

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
 PECHAN 

 8 Development of 2005 Base Year 

for LADCO - Final Report 

Table II-2 (continued) 

Total % Change 

Pollutant SCC 

2005-

2009 

2005-

2018 Growth Indicator Basis Alternatives Cons

NOX 2285002009 7.4 26.1 Annual growth rate (1.8%) from 2005-2009 diesel fuel consumption projections 

reported in Marta 2007 budget 

NOX 2280003200 0.4 1.3 1995-2005 Great Lakes region ton-miles trend (0.1% annual growth) AEO forecasts nat

Shipping residual o+1.1% per year 

NOX 2102006000 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = 

AEO forecast = 1.4

NOX 2104008070 78.0 84.3 Extrapolation of national 1999-2004 trend in OWB sales (exponential growth) thru 

2006; linear growth thru 2008; 2009+ based on rural population growth rate 

ROG 2461850000 4.8 16.1 Regional employment projections for "Pesticide Handlers, Sprayers" combined with 

projected solvent content change from Freedonia's "Solvents to 2010-Agricultural Chemical Market for Solvents" (-0.4% per year) 

 

ROG 2461020000 -1.9 -6.0 No employment growth assumption due to contradictory historic and forecast trends in 

"Paving, Surfacing, & Tamping Operators" employment, combined with projected solvent content change from Freedonia's "Solvents to 2010-Asphalt Production Market for Solvents" (-0.5% per year) 

 

ROG 2401200000 -6.5 -16.1 Population forecast combined with projected change in paint solvent content from 

Freedonia's "Solvents to 2010-Paints/Coatings Market for Solvents" (-1.9% per year) 

 

ROG 2401001000 -9.9 -9.3 Regression with Population forecast as explanatory variable combined with Freedonia 

projected change in proportion of total Architectural coatings that are solvent-based (-

2.0% per year) 

 

ROG 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) 

 

ROG 2501011010 0.2 0.3 Regression equation with Gas and Oil Expenditures as explanatory variable ROG 2501060100 0.2 0.3 Regression equation with Gas and Oil Expenditures as explanatory variable ROG 2460800000 -10.5 -15.6 Regression equation with Population as explanatory variable ROG 2501060050 0.2 0.3 Regression equation with Gas and Oil Expenditures as explanatory variable ROG 2460400000 0.1 3.3 Population forecast combined with projected change in solvent use/vehicle from 

Freedonia's "Solvents to 2010-Transportation Markets for Solvents" (-0.4% per year) 

 

ROG 2425000000 0.0 0.0 No employment growth assumption due to contradictory historic and forecast trends 

for "Printing Machine Operators" employment, and no projected change in solvent 

content of ink from Freedonia's "Solvents to 2010-Printing Ink Market for Solvents" 

ROG 2401005000 -12.9 -38.9 Employment projections for "Automotive Body and Related Repairers" combined w/ 

change in proportion of automotive coatings that are solvent-based from Freedonia's 

"Automotive Coatings, Adhesives & Sealants-Automotive Coatings Demand by Formulation and Substrate" (-4.3% per year) 

 

ROG 2460500000 -6.5 -16.1 Population forecast combined with projected change in paint solvent content from 

Freedonia's "Solvents to 2010-Paints/Coatings Market for Solvents" (-1.9% per year) 

 

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
 PECHAN 

 9 Development of 2005 Base Year 

for LADCO - Final Report 

Table II-2 (continued) 

Total % Change 

Pollutant SCC 

2005-

2009 

2005-

2018 Growth Indicator Basis Alternatives Cons

ROG 2460200000 0.6 6.8 Population forecast combined with projected change in cleaning product solvent 

content from Freedonia's "Solvents to 2010-Cleaning Product Market for Solvents" (-

0.1% per year) 

 

ROG 2415020000 -15.0 -35.9 Fabricated Metals sector employment forecast combined with projected change in 

solvent use from Freedonia's "Solvents to 2010-Metal Processing Market for Solvents" (-3.7% per year) 

 

ROG 2415025000 0.2 -11.4 Machinery and Computer Equipment sector employment forecast combined with 

projected change in solvent use from Freedonia's "Solvents to 2010-Metal Processing 

Market for Solvents" (-3.7% per year) 

 

ROG 2460600000 -10.0 -24.4 Population forecast combined with projected change in solvent content from 

Freedonia's "Solvents to 2010-Adhesives and Sealants Market for Solvents" (-2.7% 

per year) 

 

ROG 2420000370 -1.6 -0.4 Regional employment projections for "Laundry and Dry Cleaning Workers" (+0.7% per 

year) combined with projected solvent content from Freedonia's "Solvents to 2010-Dry 

Cleaning and Other Markets for Solvents" (-0.7%) 

ROG 2630010000 4.1 13.8 Growth rate from regional industrial wastewater flow design forecast from Drinking 

Water Infrastructure Needs Survey 

ROG 2501060101 0.2 0.3 Regression equation with Gas and Oil Expenditures as explanatory variable ROG 2415360000 2.4 10.3 Regional employment projections for "Automotive Service Technicians and 

Mechanics" 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 Freedonia 

projected change in proportion of total Architectural coatings that are solvent-based (-2.0% per year) 

 

ROG 2401003000 3.6 12.3 Regression equation with Population as explanatory variable ROG 2104008070 78.0 84.3 Extrapolation of national 1999-2004 trend in OWB sales (exponential growth) thru 

2006; linear growth thru 2008; 2009+ based on rural population growth rate 

SO2 2601020000 7.2 15.0 Commercial sector employment forecast SO2 2102004000 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = 

AEO forecast = -0.

SO2 2103004000 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = 

AEO forecast = 0.8

SO2 2275020000 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = 

AEO forecast = +1

SO2 2102005000 -49.4 -49.6 AEO forecast for industrial sector residual oil consumption DOE 1990-2004 = 

AEO forecast = -5.

SO2 2102002000 2.9 -0.6 AEO forecast for other industrial coal combustion DOE 1990-2004 = 

AEO forecast = <-0

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
 PECHAN 

 10 Development of 2005 Base Year 

for LADCO - Final Report 

Table II-2 (continued) 

Total % Change 

Pollutant SCC 

2005-

2009 

2005-

2018 Growth Indicator Basis Alternatives Cons

SO2 2285002006 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = 

AEO forecast = +1

SO2 2104008070 78.0 84.3 Extrapolation of national 1999-2004 trend in OWB sales (exponential growth) thru 

2006; linear growth thru 2008; 2009+ based on rural population growth rate 

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
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–Push Boats: historical (1998-2004) fuel 

consumption for barge traffic on rivers 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 itinerant aircraft operations (FAA, 2007); 

• Diesel Line Haul Locomotives–Commuter Lines: Metra diesel fuel 

expenditure/price projections (Metra, 2007); 

• Pesticide Application–Agricultural: LADCO region projected number of “pesticide 

handlers, sprayers, and applicators, vegetation” (BLS, 2007); 

• Commercial Asphalt Application–All Processes: LADCO region projected number 

of “paving, surfacing, and tamping operators” (BLS, 2007); 

• Graphic Arts –All Processes: LADCO 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 Naphthas: LADCO region projected number of 

“Laundry and Dry Cleaning Workers” (BLS, 2007); 

• Wastewater Treatment-Industrial: LADCO region projected wastewater treatment 

industrial design flow (EPA, 2007a); and 

• Degreasing–Auto Repair Services (SIC 75): Cold Cleaning: LADCO region 

projected number of “Automotive Service Technicians and Mechanics” (BLS, 2007). 

Many of the above are solvent use categories for 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 available regional historical (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 categories whenever historical trend data were 

readily available (e.g., occupational employment data). In selecting from alternative data 

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PECHAN September 2007 

 12 Development of 2005 Base Year Growth and 

Control Factors for LADCO - Final Report 

sources/trend data, the general decision-making hierarchy was as follows, listed in order of 

preference: 

1. If the forecast and historical trends were in the same direction, Pechan relied on forecast 

data (an exception was made, however, in cases 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 directions (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 Combustion from Outdoor Wood Boilers (SCC 2104008070) was not 

originally identified as a source category for growth indicator review because 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 homeowners seek ways to avoid recent 

large increases in natural gas and home heating oil prices. This source 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 historical and forecast OWB data exist to assist in identifying future 

trends in LADCO region OWB use: state-specific sales from nine manufacturers obtained by 

EPA from nine manufacturers, and national sales data obtained by the New York Attorney 

General’s Office via subpoena of 21 manufacturers. These sales data are for 1999-2004. 

Because of the much greater manufacturer coverage for the national data, and because the state 

estimates indicate that the majority of recent OWBs sales have occurred in the LADCO states, 

Pechan focused the historical trend analysis on the national data. These 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 phenomenon. 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 distillate oil prices rose 61 and 87 percent, 

respectively). Because DOE data indicate that natural gas accounts for the majority of 

residential energy consumption in the LADCO States, and increases in residential natural gas 

prices continued through 2006 (the average annual price for residential natural gas increased 28 

percent between 2004 and 2006), Pechan forecast the national number of OWBs through 2006 

via extrapolation of the 1999-2004 national OWB trend. 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 natural gas prices for the East North 

Central region (which includes 5 of the 6 LADCO region States) to identify whether recent 

increases are expected to continue. The AEO 2007 projects the average 2007 price for residential 

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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 than in 2006, and forecasts 

continued price decreases through the 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 2008, and the fact that distillate oil prices are 

forecast to increase 6.1 percent between 2006 and 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 specifically 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 significantly restrain OWB 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-2008 year OWB growth to more closely trend with 

population growth in these areas. To approximate this growth, Pechan compiled 1990 and 2000 

total and rural area population data for the LADCO region. These data indicate that rural area 

population grew at approximately 60 percent of the rate of total population 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 

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 197
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 challenging 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 historical growth rates, and additional 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 yielded 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 categories, including the description and emissions 

activity associated with each category. The last column in this table identifies each category’s 

growth indicator assignment under the previous growth and control factor contract. Table II-4 

presents the assigned growth indicator for each priority point source category and identifies any 

alternative growth indicators that were considered. Pechan first considered the use of historical 

throughput data from LADCO state point source inventories to identify recent trends that 

provided sufficient support for extrapolation. As mentioned above, for energy consumption 

sectors, Pechan compared regional historical (1990-2004) energy consumption data to AEO 2007

forecast data to determine if the forecast growth rates appear suspect relative to historical trends. 

In selecting from alternative growth indicator data sources, the general decision-making 

hierarchy was as follows, listed in order of preference: 

(1) If throughput data were available for states representing a majority of emissions for a 

given category, and these data indicated a consistent trend, the historical throughput trend 

was extended thru 2009, and held constant thereafter (two reasons for not extending the 

trend throughput the entire forecast period are that throughput data are only available for 

a three or a six-year period, and in some cases the historical throughput decrease was so 

large that it would eventually 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 trends were in different directions (e.g., forecast trend is for an 

increase in activity, but historical trend was a decrease), Pechan applied a no growth assumption. 

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 PECHAN 

 15 Development of 2005 Base Year 

for LADCO - Final Report 

Table II-3. Priority Emission Activity Point Source Categories 

POLLUTANT SCC DESC2 DESC3 DESC4 EMISSIONS ACTIVITY CUR

NH3 10200601 Industrial Natural Gas > 100 Million Btu/hr Volume of natural gas burned in 

industrial pt source boilers of >100 MMBtu 

No generg

NH3 30102599 Chemical 

Manufacturing 

Cellulosic Fiber Production Other Not Classified Amount of cellulosic fiber produced Avg o

Plast

NH3 10200602 Industrial Natural Gas 10-100 Million Btu/hr Volume of natural gas burned in 

industrial pt source boilers of 10-100 MMBtu 

No g

NH3 30199999 Chemical 

Manufacturing 

Other Not Classified Specify in Comments Field Amount of (unknown) chemical 

products produced 

Avg oChem

NH3 10200204 Industrial Bituminous/Subbituminous 

Coal 

Spreader Stoker Amount of bituminous coal burned in 

spreader stoker industrial pt source boilers 

AEO 

NOX 20200202 Industrial Natural Gas Reciprocating Volume of natural gas burned in 

industrial pt source reciprocating engines 

No g

NOX 30500606 Mineral Products Cement Manufacturing (Dry 

Process) 

Kilns Amount of cement produced via dry 

process 

LADCgrow

NOX 30600201 Petroleum 

Industry 

Catalytic Cracking Units Fluid Catalytic Cracking Unit Amount of fresh feed processed via 

fluid catalytic cracking units (FCCU) 

AEO (natio

NOX 30600104 Petroleum 

Industry 

Process Heaters Gas-fired Volume of gas burned in petroleum 

industry pt source process heaters 

AEO 

NOX 10200202 Industrial Bituminous/Subbituminous 

Coal 

Pulverized Coal: Dry Bottom Amount of bituminous coal burned in 

dry bottom industrial pt source boilers 

AEO 

NOX 10200601 Industrial Natural Gas > 100 Million Btu/hr Volume of natural gas burned in 

industrial pt source boilers of >100 MMBtu 

No g

NOX 30300304 Primary Metal 

Production 

By-product Coke Manufacturing 

Quenching Amount of coal charged to 

manufacture coke 

REMSteel 

NOX 10200217 Industrial Bituminous/Subbituminous 

Coal 

Atmospheric Fluidized Bed Combustion: Bubbling Bed (Bituminous 

Amount of bituminous coal burned in bubbling bed industrial pt source boilers 

AEO 

NOX 10200707 Industrial Process Gas Coke Oven Gas Volume of process gas burned in coke 

ovens 

AEO proje

NOX 10200602 Industrial Natural Gas 10-100 Million Btu/hr Volume of natural gas burned in 

industrial pt source boilers of 10-100 MMBtu 

No g

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
 PECHAN 

 16 Development of 2005 Base Year 

for LADCO - Final Report 

Table II-3 (continued) 

POLLUTANT SCC DESC2 DESC3 DESC4 EMISSIONS ACTIVITY CURREN

NOX 20200254 Industrial Natural Gas 4-cycle Lean Burn Volume of natural gas burned 

in industrial sector 4-cycle lean burn IC engines 

No grow

NOX 20100102 Electric Generation Distillate Oil (Diesel) Reciprocating Amount of distillate oil burned 

in reciprocating engines for electricity 

AEO Ele

NOX 20200201 Industrial Natural Gas Turbine Volume of natural gas burned 

in industrial sector turbines 

No grow

NOX 39000689 In-process Fuel Use Natural Gas General Volume of industrial process 

pt source natural gas burned 

No grow

NOX 10200201 Industrial Bituminous/Subbituminous 

Coal 

Pulverized Coal: Wet Bottom 

Amount of bituminous coal burned in wet bottom industrial pt source boilers 

AEO Ind

NOX 10200701 Industrial Process Gas Petroleum Refinery Gas Volume of petroleum refinery 

(still) gas burned 

No grow

ROG 30100399 Chemical Manufacturing Ammonia Production Other Not Classified Amount of ammonia produced REMI ouROG 30201916 Food and Agriculture Vegetable Oil Processing Oil Extraction Amount of extractor feed cake 

produced 

REMI ouand Oils 

ROG 40500511 Printing/Publishing General Gravure: 2754 Amount of solvent in ink used 

by pt sources 

REMI ouBusiness 

ROG 30199999 Chemical Manufacturing Other Not Classified Specify in Comments 

Field 

Amount of (unknown) chemical products produced 

Avg of RChemica

ROG 30125099 Chemical Manufacturing Methanol/Alcohol 

Production 

Other Not Classified Amount of methanol/alcohol 

produced 

REMI ou

ROG 40201301 Surface Coating 

Operations 

Paper Coating Coating Operation Amount of solvent in coating 

used by pt sources 

No growemission

ROG 40200101 Surface Coating 

Operations 

Surface Coating Application - General 

Paint: Solvent-base Amount of coating mix applied 

by pt sources 

No growemission

ROG 30102599 Chemical Manufacturing Cellulosic Fiber Production Other Not Classified Amount of cellulosic fiber 

produced 

Avg of RPlastics, 

ROG 30500201 Mineral Products Asphalt Concrete Rotary Dryer: 

Conventional Plant (see 3-05-002-50 to -53 for 

Amount of hot mix asphalt produced by pt sources 

Avg of RMisc. Pe

ROG 30201906 Food and Agriculture Vegetable Oil Processing Corn Oil: General Amount of extractor feed cake 

produced 

REMI ouand Oils 

ROG 30201919 Food and Agriculture Vegetable Oil Processing Fugitive Leaks Amount of extractor feed cake 

produced 

REMI ouand Oils 

ROG 40200110 Surface Coating 

Operations 

Surface Coating Application - General 

Paint: Solvent-base Amount of solvent-based 

coatings applied by pt sources 

Historica

ROG 40201899 Surface Coating 

Operations 

Metal Coil Coating Other Not Classified Amount of solvent in coating 

used by pt sources 

REMI ouDrawing 

ROG 40388801 Petroleum Product 

Storage at Refineries 

Fugitive Emissions Specify in Comments 

Field 

Petroleum product storage capacity at refineries 

AEO Ref(national

ROG 40200701 Surface Coating 

Operations 

Surface Coating Application - General 

Adhesive Application Amount of adhesive coatings 

applied by pt sources 

REMI ou

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
 PECHAN 

 17 Development of 2005 Base Year 

for LADCO - Final Report 

Table II-3 (continued) 

POLLUTANT SCC DESC2 DESC3 DESC4 EMISSIONS ACTIVITY CURREN

SO2 30600201 Petroleum Industry Catalytic Cracking Units Fluid Catalytic Cracking 

Unit 

Amount of fresh feed processed via FCCU 

AEO Ref(national

SO2 10200202 Industrial Bituminous/Subbituminous 

Coal 

Pulverized Coal: Dry Bottom 

Amount of bituminous coal burned in dry bottom industrial pt source boilers 

AEO Ind

SO2 30600805 Petroleum Industry Fugitive Emissions Miscellaneous: 

Sampling/Non-Asphalt Blowing/Purging/etc. 

Barrels of refinery feed processed 

AEO Ref(national

SO2 30199999 Chemical Manufacturing Other Not Classified Specify in Comments 

Field 

Amount of (unknown) chemical products produced 

Avg of RChemica

SO2 10200217 Industrial Bituminous/Subbituminous 

Coal 

Atmospheric Fluidized Bed Combustion: Bubbling Bed (Bituminous 

Amount of bituminous coal burned in bubbling bed industrial pt source boilers 

AEO Ind

SO2 10200201 Industrial Bituminous/Subbituminous 

Coal 

Pulverized Coal: Wet Bottom 

Amount of bituminous coal burned in wet bottom industrial pt source boilers 

AEO Ind

SO2 10200225 Industrial Bituminous/Subbituminous 

Coal 

Traveling Grate (Overfeed) Stoker (Subbituminous Coal) 

Amount of subbituminous coal burned in overfeed stoker industrial pt source boilers 

AEO Ind

SO2 30500606 Mineral Products Cement Manufacturing (Dry 

Process) 

Kilns Amount of cement produced 

via dry process 

LADCO growth 

SO2 30600401 Petroleum Industry Blowdown Systems Blowdown System with 

Vapor Recovery System with Flaring 

Barrels of refinery feed processed 

AEO Ref(national

SO2 10200204 Industrial Bituminous/Subbituminous 

Coal 

Spreader Stoker Amount of bituminous coal 

burned in spreader stoker industrial pt source boilers 

AEO Ind

SO2 10300217 Commercial/Institutional Bituminous/Subbituminous 

Coal 

Atmospheric Fluidized Bed Combustion: Bubbling Bed (Bitumin.) 

Amount of bituminous coal burned in bubbling bed commercial pt source boilers 

AEO Co

SO2 39000701 In-process Fuel Use Process Gas Coke Oven or Blast 

Furnace 

Volume of coke oven or blast furnace gas burned 

AEO Meprojectio

SO2 30103201 Chemical Manufacturing Elemental Sulfur Production Mod. Claus: 2 Stage w/o 

Control (92-95% Removal) 

Amount of 100% sulfur produced 

REMI ou

SO2 10300225 Commercial/Institutional Bituminous/Subbituminous 

Coal 

Traveling Grate (Overfeed) Stoker (Subbituminous Coal) 

Amount of subbituminous coal burned in overfeed stoker commercial pt source boilers 

AEO Co

SO2 10300209 Commercial/Institutional Bituminous/Subbituminous 

Coal 

Spreader Stoker (Bituminous Coal) 

Amount of bituminous coal burned in spreader stoker commercial pt source boilers 

AEO Co

SO2 10200401 Industrial Residual Oil Grade 6 Oil Amount of residual oil (No. 6) 

burned in industrial pt source boilers 

No grow

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
 PECHAN 

 18 Development of 2005 Base Year 

for LADCO - Final Report 

Table II-3 (continued) 

POLLUTANT SCC DESC2 DESC3 DESC4 EMISSIONS ACTIVITY CURREN

EMISSION TREND ANALYSIS CATEGORIES NOT LISTED ABOVE WITH CONSISTENT THROUGHPUT TRENDS 

VOC 40202201 Petroleum and Solvent 

Evaporation 

Surface Coating Operations Plastic Parts: Coating 

Operation 

Amount of solvent used in coating applied 

Historica

NOX 39000699 In Process Fuel Use Natural Gas General Amount of nat gas used in 

industrial processes 

No grow

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
 PECHAN 

 19 Development of 2005 Base Year 

for LADCO - Final Report 

Table II-4. Growth Indicators for Priority Point Source Categories 

Total % Change 

Pollutant SCC 

Emissions 

Priority 

Category 

2005-

2009 

2005-

2018 Growth Indicator Basis Alternatives Considered 

NH3 10200601 NOx -11.5 -11.5 Historic throughput trend from 3 states (-3.0% per 

year) extended thru 2009; post-2009 held constant. 

DOE 1990-2004 = -0.01% per year; AEforecast = +1.4% per year 

NH3 30102599 4.9 18.2 Avg of REMI employment & output growth factors for 

Plastics, Materials, & Synthetics sector 

 

NH3 10200602 NOx -12.2 -12.2 Historic throughput trend from 4 states (-3.2% per 

year) extended thru 2009; post-2009 held constant. 

DOE 1990-2004 = -0.01% per year; AEforecast = +1.4% per year 

NH3 30199999 8.9 25.6 Avg of REMI employment & output growth factors for 

Chemicals sector 

 

NH3 10200204 NOx 2.9 -0.6 AEO forecast for other industrial coal combustion DOE 1990-2004 = -1.5% per year; AE

forecast = <-0.1% per year 

NOX 20200202 NOx 0.0 0.0 No growth due to contradictory historic and forecast 

trends 

DOE 1990-2004 = -0.01% per year; AEforecast = +1.4% per year 

NOX 30500606 8.2 29.4 LADCO region historical cement production growth 

rate (+2.0% per year) 

NOX 30600201 SO2 0.4 0.4 AEO refinery distillation projections for Petroleum 

Administration District (PAD) II, which includes all LADCO states plus additional surrounding states 

Similar 1990-2005 data also includes states not in LADCO region and shows very small growth rate 

NOX 30600104 5.9 20.6 1991-2002 Midwest Census region Refining sector 

natural gas consumption growth rate (+1.5% per year) 

AEO National Refining sector natural gconsumption forecast is +2.7% per yea

NOX 10200202 NOx 2.9 -0.6 AEO forecast for other industrial coal combustion DOE 1990-2004 = -1.5% per year; AE

forecast = <-0.1% per year 

NOX 10200601 NOx -11.5 -11.5 Historic throughput trend from 3 states (-3.0% per 

year) extended thru 2009; post-2009 held constant. 

DOE 1990-2004 = -0.01% per year; AEforecast = +1.4% per year 

NOX 30300304 -6.2 -19.8 AEO forecast for metallurgical coal consumption DOE 1990-2004 = -3.0% per year; AE

forecast = -1.7% per year 

NOX 10200217 2.9 -0.6 AEO forecast for other industrial coal combustion DOE 1990-2004 = -1.5% per year; AE

forecast = <-0.1% per year 

NOX 10200707 -6.2 -19.8 AEO forecast for metallurgical coal consumption DOE 1990-2004 = -3.0% per year; AE

forecast = -1.7% per year 

NOX 10200602 NOx -12.2 -12.2 Historic throughput trend from 4 states (-3.2% per 

year) extended thru 2009; post-2009 held constant. 

DOE 1990-2004 = -0.01% per year; AEforecast = +1.4% per year 

NOX 20200254 0.0 0.0 No growth due to contradictory historic and forecast 

trends 

DOE 1990-2004 = -0.01% per year; AEforecast = +1.4% per year 

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
 PECHAN 

 20 Development of 2005 Base Year 

for LADCO - Final Report 

Table II-4 (continued) 

Total % Change 

Pollutant SCC 

Emissions 

Priority 

Category 

2005-

2009 

2005-

2018 Growth Indicator Basis Alternatives Considered 

NOX 20100102 1.7 5.5 DOE 1990-2004 historic trend (+0.4%) AEO forecast = +5.0% per 

2009, but near equivalent from 2009 to 2018 

NOX 20200201 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = -0.01% 

AEO forecast = +1.4% per 

NOX 39000689 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = -0.01% 

AEO forecast = +1.4% per 

NOX 10200201 SO2 2.9 -0.6 AEO forecast for other industrial coal combustion DOE 1990-2004 = -1.5% p

AEO forecast = <-0.1% pe

NOX 10200701 -1.3 -4.1 DOE 1990-2004 historic trend (-0.3% per year) AEO forecast = 2009 (-1.0

year) and 2018 (-0.1% per 

ROG 30100399 -19.7 -28.6 Freedonia's "Chemical Catalysts to 2009-Ammonia Catalyst 

Demand" - national projections adjusted for relative state growth in REMI output for Agricultural Chemicals sector 

 

ROG 30201916 2.2 11.9 Avg of REMI employment & output growth factors for Grain Mill 

Products and Fats and Oils sector 

 

ROG 40500511 0.0 0.0 No growth due to contradictory historic trend versus forecast trend 

in regional employment for "Printing Machine Operators" 

ROG 30199999 8.9 25.6 Avg of REMI employment & output growth factors for Chemicals 

sector 

 

ROG 30125099 1.1 5.3 Freedonia's "Chemical Catalysts to 2009-Alcohols Catalyst Demand 

by Application" - national projections adjusted for relative state 

growth in REMI output for Industrial Chemicals sector 

 

ROG 40201301 0 0 No growth based on consistent historic LADCO emissions trend ROG 40200101 VOC -6.4 -21.4 Regional employment projections for "Coating, Painting, and 

Spraying Machine Operators, and Tenders" adjusted for solvent content of paints and coatings from Freedonia's "Solvents to 2010-Paints and Coatings Market for Solvents" (-1.9% per year) 

 

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
 PECHAN 

 21 Development of 2005 Base Year 

for LADCO - Final Report 

Table II-4 (continued) 

Total % Change 

Pollutant SCC 

Emissions 

Priority 

Category 

2005-

2009 

2005-

2018 Growth Indicator Basis Alternatives Considered 

ROG 30102599 4.9 18.2 Avg of REMI employment & output growth factors for Plastics, 

Materials, & Synthetics sector 

 

ROG 30500201 8.3 21.5 Avg of REMI employment & output growth factors for Misc. 

Petroleum and Coal Products sector 

 

ROG 30201906 2.2 11.9 Avg of REMI employment & output growth factors for Grain Mill 

Products and Fats and Oils sector 

 

ROG 30201919 2.2 11.9 Avg of REMI employment & output growth factors for Grain Mill 

Products and Fats and Oils sector 

 

ROG 40200110 VOC -6.4 -21.4 Regional employment projections for "Coating, Painting, and 

Spraying Machine Operators, and Tenders" adjusted for solvent content of paints and coatings from Freedonia's "Solvents to 2010-Paints and Coatings Market for Solvents" (-1.9% per year) 

 

ROG 40201899 6.9 26.0 Freedonia's "Protective Coatings to 2009-Demand for Coil 

Coatings" - national projections adjusted for relative state growth in REMI output for Nonferrous Rolling and Drawing sector, adjusted for projected solvent content information for paints and coatings 

from "Solvents to 2010-Paints/Coatings Market for Solvents" (-1.9% 

per year) 

 

ROG 40388801 0.4 0.4 AEO refinery distillation projections for Petroleum Administration 

District (PAD) II, which includes all LADCO states plus additional surrounding states 

1990-2005 data also inclunot in LADCO region and similar very small growth r

ROG 40200701 -1.6 -1.0 Freedonia's "Solvents to 2010-Adhesives & Sealants Market for 

Solvents" national projections, adjusted for relative state growth in REMI output in Total Manufacturing sector 

SO2 30600201 SO2 0.4 0.4 AEO refinery distillation projections for Petroleum Administration 

District (PAD) II, which includes all LADCO states plus additional surrounding states 

1990-2005 data also inclunot in LADCO region and similar very small growth r

SO2 10200202 NOx 2.9 -0.6 AEO forecast for other industrial coal combustion DOE 1990-2004 = -1.5% p

AEO forecast = <-0.1% pe

SO2 30600805 SO2 0.4 0.4 AEO refinery distillation projections for Petroleum Administration 

District (PAD) II, which includes all LADCO states plus additional surrounding states 

1990-2005 data also inclunot in LADCO region and similar very small growth r

SO2 30199999 8.9 25.6 Avg of REMI employment & output GFs for Chemicals sector SO2 10200217 2.9 -0.6 AEO forecast for other industrial coal combustion DOE 1990-2004 = -1.5% p

AEO forecast = <-0.1% pe

SO2 10200201 SO2 2.9 -0.6 AEO forecast for other industrial coal combustion DOE 1990-2004 = -1.5% p

AEO forecast = <-0.1% pe

SO2 10200225 2.9 -0.6 AEO forecast for other industrial coal combustion DOE 1990-2004 = -1.5% p

AEO forecast = <-0.1% pe

SO2 30500606 8.2 29.4 LADCO region historical cement production growth rate (+2.0% per 

year) 

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
 PECHAN 

 22 Development of 2005 Base Year 

for LADCO - Final Report 

Table II-4 (continued) 

Total % Change 

Pollutant SCC 

Emissions 

Priority 

Category 

2005-

2009 

2005-

2018 Growth Indicator Basis Alternatives Considered 

SO2 30600401 0.4 0.4 AEO refinery distillation projections for Petroleum Administration 

District (PAD) II, which includes all LADCO states plus additional surrounding states 

1990-2005 data also inclunot in LADCO region and similar very small growth r

SO2 10200204 NOx 2.9 -0.6 AEO forecast for other industrial coal combustion DOE 1990-2004 = -1.5% p

AEO forecast = <-0.1% pe

SO2 10300217 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = -1.2% p

AEO forecast = +0.0% per 

SO2 39000701 -6.2 -19.8 AEO forecast for metallurgical coal consumption DOE 1990-2004 = -3.0% p

AEO forecast = -1.7% per 

SO2 30103201 2.5 8.2 1996-2005 recovered elemental sulfur production growth rate 

(+0.6% per year) for IL + MI + MN + OH 

SO2 10300225 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = -1.2% p

AEO forecast = +0.0% per 

SO2 10300209 0.0 0.0 No growth due to contradictory historic and forecast trends DOE 1990-2004 = -1.2% p

AEO forecast = +0.0% per 

SO2 10200401 SO2 -49.4 -49.6 AEO forecast for industrial residual oil consumption (-5.1% per 

year) 

DOE 1990-2004 = -6.6% p

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. 

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
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 reviewed the complete list of point SCCs in the 

LADCO base year inventory to identify the priority category growth indicators that could be 

applied to non-priority point source categories. This step yielded priority category growth 

indicator assignments for an additional 539 point source 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 categories 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 basis for the NONROAD growth rates. For these 

other three categories, NONROAD relies on national equipment population forecasts prepared 

by a relevant trade association (see Table II-5 for details). 

Table II-6 reports this study’s growth indicator assignments for priority NONROAD model 

source categories. Given the acknowledged shortcomings of the NONROAD growth rates (use 

of 1989-1996 national equipment populations to project future equipment populations in each 

region of the country), the growth factor improvements generally reflect the use of regional/statelevel forecast data that are expected to correlate with use of the equipment (i.e., regional 

occupational employment projections, state-level economic sector employment forecasts, or 

state-level landing/take-off projections). Table II-6 also displays any alternative growth 

indicators that were considered. For the three categories for which NONROAD relies on 

forecasts rather than historical 1989-1996 trends, Pechan compiled available recent historical 

equipment population estimates. This information was used to revise the current national 

forecast approach to reflect more recent information, 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 indicated that the category’s fuel-specific 

growth rate had traditionally been similar to the overall sector’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 model 

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 207
 PECHAN 

 24 Development of 2005 Base Year 

for LADCO - Final Report 

Table II-5. Priority Emission Activity NONROAD Model Source Categories 

SCC DESCRIPTION SUMMARY EMISSIONS ACTIVITY CURRENT GROWTH BASIS 

2260001030 2-Stroke ATV Population of 2-stroke gasoline ATVs NONROAD (Motorcycle Industry Council national 2-stro2265001030 4-Stroke ATV Population of 4-stroke gasoline ATVs NONROAD (Motorcycle Industry Council national 4-stro2260001010 2-Stroke Offroad Motorcycles Population of 2-stroke gasoline offroad motorcycles NONROAD (Motorcycle Industry Council national off-hi

projections) 

2265001010 4-Stroke Offroad Motorcycles Population of 4-stroke gasoline offroad motorcycles NONROAD (Motorcycle Industry Council national off-hi

projections) 

2267006000 LPG Light Commercial Population of light commercial LPG-fueled 

equipment 

NONROAD (national 1989-1996 LPG light commercial 

2270004000 Diesel Lawn & Garden 

Equipment 

Population of lawn & garden diesel-fueled equipment 

NONROAD (national 1989-1996 diesel lawn & garden e

2270008000 Diesel Airport Service 

Equipment 

Population of airport service diesel-fueled equipment 

NONROAD (national 1989-1996 diesel airport service e

2267008000 LPG Airport Service Equipment Population of airport service LPG-fueled equipment NONROAD (national 1989-1996 total airport service eq2268008000 CNG Airport Service Equipment Population of airport service CNG-fueled equipment NONROAD (national 1989-1996 total airport service eq2260001020 2-Stroke Snowmobiles Population of 2-stroke gasoline snowmobiles NONROAD national growth (see below) with state adju

income forecasts 

2265001020 4-Stroke Snowmobiles Population of 4-stroke gasoline snowmobiles NONROAD (International Snowmobile Manufacturers A

population projections) 

2260007000 2-Stroke Logging Equipment Population of logging 2-stroke gasoline equipment NONROAD (national 1989-1996 gasoline logging equip2265007000 4-Stroke Logging Equipment Population of logging 4-stroke gasoline equipment NONROAD (national 1989-1996 gasoline logging equip2270006000 Diesel Light Commercial Population of light commercial diesel-fueled 

equipment 

NONROAD (national 1989-1996 diesel light commercia

2268006000 CNG Light Commercial Population of light commercial CNG-fueled 

equipment 

NONROAD (national 1989-1996 CNG light commercial 

2267007000 LPG Logging Equipment Population of logging LPG-fueled equipment NONROAD (national 1989-1996 logging equipment pop2268007000 CNG Logging Equipment Population of logging CNG-fueled equipment NONROAD (national 1989-1996 logging equipment pop2285002000 Diesel Railway Maintenance Population of railway maintenance diesel-fueled 

equipment 

NONROAD (national 1989-1996 diesel railway maintenrate) 

2260006000 2-Stroke Light Commercial Population of light commercial 2-stroke gasoline 

equipment 

NONROAD (national 1989-1996 gasoline light commer

2265006000 4-Stroke Light Commercial Population of light commercial 4-stroke gasoline 

equipment 

NONROAD (national 1989-1996 gasoline light commer

2270002000 Diesel Construction Equipment Population of construction diesel-fueled equipment NONROAD national growth with state adjustment base

forecasts 

2270003000 Diesel Industrial Equipment Population of industrial diesel-fueled equipment NONROAD (national 1989-1996 diesel industrial equip2267003000 LPG Industrial Equipment Population of industrial LPG-fueled equipment NONROAD (national 1989-1996 LPG industrial equipm2270001000 Diesel Recreational Vehicles Population of diesel recreational vehicles NONROAD (national 1989-1996 diesel recreational equ2282020000 Diesel Recreational Marine Population of diesel recreational marine vessels NONROAD (national 1989-1996 diesel recreational equ

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
 PECHAN 

 25 Development of 2005 Base Year 

for LADCO - Final Report 

Table II-6. Growth Indicators for Priority NONROAD Model Source Categories 

Total % Change 

SCC 

2005-

2009 

2005-

2018 Growth Indicator Basis Alternatives Considered C

2260001030 10.8 39.7 50% higher growth rate than overall LADCO region 

"Recreational Vehicle Service Technicians" employment projections, which is 1.7%/yr, based on premise that ATV market is less mature than market for other recreation vehicles 

Available ATV/OHV registration data for MI+MN+WI indicate avg. 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 ATV sales (not population) of +3.8% per year for 2000-2005; 2005-2006 sales = -4.2% 

Dabpu2

2265001030 10.8 39.7 50% higher growth rate than overall LADCO region 

"Recreational Vehicle Service Technicians" employment projections, which is 1.7%/yr, based on premise that ATV market is less mature than market for other recreation vehicles 

Available ATV/OHV registration data for MI+MN+WI indicate avg. 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 ATV sales (not population) of +3.8% per year for 2000-2005; 2005-2006 sales = -4.2% 

Dabpu2

2260001010 5.1 17.7 LADCO region employment projections for "Motorcycle 

Mechanics" 

Unable to compile regional registration trends; national offroad motorcycle sales for 2000-2006 = +2.3%, but 2001-2006 = -1.6% 

Err

2265001010 5.1 17.7 LADCO region employment projections for "Motorcycle 

Mechanics" 

Unable to compile regional registration trends; national offroad motorcycle sales for 2000-2006 = +2.3%, but 2001-2006 = -1.6% 

Err

2267006000 19.8 57.5 NONROAD (national 1989-1996 LPG light commercial 

equipment population growth rate) 

REMI Commercial sector employment forecast for LADCO region (2005-2009=+1.8%yr; 2005-2018 = +1.1%/yr) 

Dhdo

2270004000 5.8 20.1 LADCO region employment projections for "Landscaping 

and Groundskeeping Workers" 

2270008000 -0.7 18.0 State-level FAA itinerant air carrier + air taxi landing and 

take-off (LTO) forecast (updated as of December 2006) 

2267008000 -0.7 18.0 State-level FAA itinerant air carrier + air taxi landing and 

take-off (LTO) forecast (updated as of December 2006) 

2268008000 -0.7 18.0 State-level FAA itinerant air carrier + air taxi landing and 

take-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" employment projections, which is 1.7%/yr, based on premise that snowmobile market is more mature than market for other recreation vehicles 

Annual growth in snowmobile registrations for states representing 92% of 2006 LADCO region registrations: 2000-2006 = +0.0% 

Nbms

2265001020 3.5 11.9 50% lower growth rate than overall LADCO region 

"Recreational Vehicle Service Technicians" employment projections, which is 1.7%/yr, based on premise that snowmobile market is more mature than market for other recreation vehicles 

Annual growth in snowmobile registrations for states representing 92% of 2006 LADCO region registrations: 2000-2006 = +0.0% 

Nbms

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
 PECHAN 

 26 Development of 2005 Base Year 

for LADCO - Final Report 

Table II-6 (continued) 

Total % Change

SCC 

2005-

2009 

2005-

2018 Growth Indicator Basis Alternatives Considered 

2260007000 2.2 7.3 2265007000 2.2 7.3 LADCO region employment projections for "Logging 

Equipment Operators" 

2270006000 7.2 15.0 REMI Commercial sector employment forecast for 

LADCO region 

2268006000 7.2 15.0 REMI Commercial sector employment forecast for 

LADCO region 

2267007000 2.2 7.3 LADCO region employment projections for "Logging 

Equipment Operators" 

2268007000 2.2 7.3 LADCO region employment projections for "Logging 

Equipment Operators" 

2285002000 12.2 36.7 NONROAD (national 1989-1996 diesel railway 

maintenance equipment population growth rate) 

LADCO region employment projections for "Rail-Track Laying & Maintenance Equipment Operators" (-1.6%/yr) 

2260006000 7.2 15.0 REMI Commercial sector employment forecast for 

LADCO region 

2265006000 7.2 15.0 REMI Commercial sector employment forecast for 

LADCO region 

2270002000 4.7 16.2 LADCO region employment projections for "Operating 

Engineers and Other Construction Equipment Operators" 

2270003000 0.1 0.2 LADCO region employment projections for "Industrial 

Machinery Mechanics" 

2267003000 0.1 0.2 LADCO region employment projections for "Industrial 

Machinery Mechanics" 

2270001000 10.4 31.5 NONROAD (national 1989-1996 diesel recreational 

equipment population growth rate) 

LADCO region employment projections for "Recreational Vehicle Service Technicians" (+1.7%/yr) 

2282020000 10.4 31.5 NONROAD (national 1989-1996 diesel recreational 

equipment population growth rate) 

Two options: 1996-2005 LADCO region recreational boat registration growth rate (0.8%/yr) and LADCO region employment projections (+1.5%/yr) for "Motorboat Mechanics" 

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
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. NOx SIP Call 

All states in the LADCO region affected by the NOx (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 Illinois recommended that an 82 percent NOx 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 compliance date. Table III-1 lists these engines and 

the associated NOx control efficiencies applied in the emission projections. This requirement is 

expected to affect NOx emissions in all projection years. 

2. MACT Standards 

Table III-2 summarizes the control factors used to estimate the post-2005 effects of MACT 

emission standards on volatile organic compounds (VOC), NOx, and PM emissions in the 

projection years. The information 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 criteria pollutant emission reductions according 

to the draft EPA guidance were not included in Table III-2. Table III-2 was circulated to the 

states for review, and Wisconsin provided its 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 efficiencies by source (Pechan, 2005) and pollutant were 

merged with the LADCO state 2005 point source file and control factors assigned accordingly. 

The 2005 point source control efficiencies (CEs) for sulfur dioxide (SO2) and NOx 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 MACTEC revisions/additions from their earlier 

report to the control factor file (MACTEC incorporated cases and settlements control factors for 

refineries that were not evaluated for the 812 study) 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 decrees. These sources all have post-2005 control 

factors applied in the analysis. There are two refineries in the study 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. 

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 211
PECHAN 

2007  28 Development of 2005 Base Year G

for LADCO - Final Report 

Table III-1. RICE Engines in Illinois Affected by NOx SIP Call 

Id Number Device Process Device Description Pollutant

% 

Reduction 

027807AAC 0003 01 ENGINES 09-ENG AND 10-ENG NOX 59 041804AAC 0009 01 ENGINE 1213 NOX 82 041804AAC 0010 01 ENGINE 1214 NOX 82 041804AAC 0011 01 ENGINE 1215 NOX 82 041804AAC 0012 01 ENGINE 1216 NOX 82 041804AAC 0013 01 ENGINE 1217 NOX 82 

073816AAA 0001 01 WORTHINGTON MLV-10 COMPRESSOR & GAS FIRED ENGINE #12 NOX 82 073816AAA 0004 01 CLARK TCV-10 COMPRESSOR & GAS FIRED ENGINE ENGINE #9 NOX 82 073816AAA 0012 01 WORTHINGTON MLV-10 COMPRESSOR AND GAS FIRED ENGINE NO. 13 NOX 82 073816AAA 0013 01 WORTHINGTON MLV-10 COMPRESSOR AND GAS FIRED ENGINE NO. 14 NOX 82 073816AAA 0014 01 WORTHINGTON MLV-10 COMPRESSOR AND GAS FIRED ENGINE NO. 15 NOX 82 073816AAA 0015 01 WORTHINGTON MLV-14 ENGINE #10 NOX 82 085809AAA 0010 01 3 CLARK COMPRESSORS NOX 82 

093802AAF 0003 01 ENGINE E-1008 NOX 82 

113817AAA 0002 01 ENGINE EC21 NOX 82 113817AAA 0003 01 ENGINE IC11 NOX 82 113821AAA 0002 01 ENTERPRISE RECIP COMP EC-21 4000 MP EF 3.3.2-1 NOX 82 113821AAA 0005 01 COOPER COMPRESSOR CC22 EF 3.3.2-1 4000 HP NOX 82 149820AAB 0002 01 2 RECIPROCATING ENGINES (1013 - 1014) NOX 59 149820AAB 0003 01 3 RECIPROCATING ENGINES (1015 - 1017) NOX 82 167801AAA 0001 01 ENGINES 1116 AND 1117 NOX 82 167801AAA 0003 01 1-COOPER RECIPROCATING ENGINE, 4000HP, 1115 NOX 82 167801AAA 0008 01 ENGINES 1118 AND 1119 NOX 59 

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
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2007  29 Development of 2005 Base Year G

for LADCO - Final Report 

Table III-2. Post-2005 MACT Standards and Expected VOC, NOx, and PM Reduct

MACT Standard – Source Category

Code of Federal 

Regulations 

Subpart

Compliance 

Date (existing 

sources)

VOC 

(% Reduction)

NOx

(% Reduction)

Total PM 

(% Reduction) Affected SCCs

M

C

Asphalt Processing and Asphalt Roofing 

Manufacture

LLLLL 5/1/2006 85 30505001, 30500101, 

30500102, 30505010, 

30601101

0

Auto and Light Duty Trucks IIII 4/26/2007 40 40201601 to 40201632; 

40201699

0

Coke Ovens: Pushing, Quenching and Battery Stacks

CCCCC 4/14/2006 0 30300304; 30300303 0

Fabric Printing, Coating & Dyeing OOOO 5/29/2006 60 40201101 to 40201199; 

40201201; 40201210

0

Integrated Iron and Steel FFFFF 5/20/2006 (5) 20 30301501 to 30301596 0Iron and Steel Foundries EEEEE 4/22/2007 5 304003XX, 304007XX 0Lime Manufacturing AAAAA 1/5/2007 23 305016XX 0Metal Can KKKK 11/13/2006 70 40201702; 40201703 to 

40201799

0

Metal Furniture RRRR 5/23/2006 0 402020XX Misc. Coating Manufacturing HHHHH 12/11/2006 64 402026XX 1Misc. Metal Parts and Products MMMM 1/2/2007 0 402025XX Misc. Organic Chemical Production and Processes (MON)

FFFF 11/10/2006 66 645200XX; 30113001 to 

30113007; 684300XX; 

30101005 to 30101099; 

68445001; 68445010; 68445013; 68445020; 68445022; 68445101; 

68445201; 30110002 to 

30110099; 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; 

1

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
PECHAN 

2007 

Table III-2 (continued) 

 30 Development of 2005 Base Year G

for LADCO - Final Report 

MACT Standard – Source Category

Code of Federal 

Regulations 

Subpart

Compliance 

Date (existing 

sources)

VOC 

(% Reduction)

NOx

(% Reduction)

Total PM 

(% Reduction) Affected SCCs

M

C

64610201 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; 

65135001 

Organic Liquids Distribution EEEE 2/3/2007 70 40300102, 40300104, 

40300106, 40300107, 

40301010-40301021

0

Plastic Parts PPPP 4/19/2007 0 402022XX Plywood and Composite Wood Products

DDDD 10/1/2007 54 307007XX; 30700921 to 

30700971; 30701001 to 30701057; 30700602 to 

30700661

1

Refractory Products Manufacturing SSSSS 4/17/2006 81 0 0Reinforced Plastic Composites Production

WWWW 4/21/2006 39 0 1

Site Remediation GGGGG 10/8/2006 50 0 504001XX; 50400201, 

50400202; 504002XX; 

504100XX; 504101XX; 504102XX; 504103XX; 504102XX; 504103XX; 

04104XX; 504105XX; 

504106XX; 504107XX; 

50480001; 50482001; 50482002; 50482599; 

50480004

0

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
PECHAN 

Table III-2 (continued) 

 31 Development of 2005 Base Year G

for LADCO - Final Report 

MACT Standard – Source Category

Code of Federal 

Regulations 

Subpart

Compliance 

Date (existing 

sources)

VOC 

(% Reduction)

NOx

(% Reduction)

Total PM 

(% Reduction) Affected SCCs

M

C

Stationary Combustion Turbines YYYY 3/5/2007 13 17 20100101, 20100201, 

20200101, 20200103, 20200201, 20200203, 20200901, 20300102, 

20300202, 20300203

0

Taconite Iron Ore Processing RRRRR 10/30/2006 0 0 62 32302371 to 32302399 0

Wood Building Products QQQQ 5/28/2006 63 0 40202101 to 40202199 0

**Based on organic hazardous air pollutant (HAP) emission reductions Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
PECHAN 

2007  32 Development of 2005 Base Year G

for LADCO - Final Report 

Table III-3. LADCO State Non-EGU Point Sources Affected by Consent Decree Requi

and Other On-the-Books Controls 

Identification Codes FCCU Requirements Heater/Bo

State County Facility* Company Location State SO2 NOx SO2 

18 089 00003 BP Amoco Whiting IN FCU 500: Install wet gas 

scrubber; FCU 600: Use SO2 adsorbing catalyst 

additive and/or hydrotreatment.

FCU 600: Install SCR; FCU 500: Low NOx combustion 

promoter and NOx

adsorbing catalyst additive

Elimination of oil burning and restricting H2S in 

refinery fuel gas

39 095 0448010246 BP Amoco Toledo OH SO2 catalyst additive Install SNCR system Elimination of oil burning 

and restricting H2S in 

refinery fuel gas

17 197 197090AAI CITGO Global 

Refinery

Lemont IL New wet gas scrubber Low NOx combustion 

promoter (20 ppmvd limit)

Comply with NSPS Subparts A and J for fuel gas combustion devices. Eliminate fuel oil burning

17 119 119090AAA Conoco Philips 

Global Refinery

Roxanna (Wood River)

IL Install new wet gas 

scrubber (25 ppmvd or lower)

FCCU 1: Scrubberbased NOx emission 

reduction technology to achieve 20 ppmvd

Subject to NSPS Subpar

A and J for fuel gas 

combustion devices

17 119 119090AAA Conoco Philips 

Global Refinery

Hartford (Wood River)

IL Install new wet gas 

scrubber (25 ppmvd or lower)

FCCU 2: Enhanced SNCR

Subject to NSPS Subpar

A and J for fuel gas 

combustion devices

17 197 197800AAA Exxon-Mobil 

Refinery

Joliet IL Install new wet gas 

scrubber (25 ppmvd or lower)

Install and operate an SCR system

Accept NSPS Subpart J 

applicability for heaters and boilers and reduce oeliminate fuel oil firing

17 033 033808AAB Marathon 

Ashland Refinery

Robinson IL Existing wet gas 

scrubber

Catalyst additive Accept NSPS Subpart J 

applicability for heaters and boilers and reduce oeliminate fuel oil firing

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
PECHAN 

2007 

Table III-3 (continued) 

 33 Development of 2005 Base Year G

for LADCO - Final Report 

Identification Codes FCCU Requirements Heater/Bo

State County Facility* Company Location State SO2 NOx SO2 

26 163 A9831 Marathon 

Ashland Refinery

Detroit MI SO2 catalyst additive Catalyst additive Accept NSPS Subpart J 

applicability for heaters aboilers and reduce or eliminate fuel oil firing 

27 163 2716300003 Marathon 

Ashland Refinery

St Paul Park MN New wet gas scrubber 

on unit 1; catalyst additive on other unit 

Catalyst additive Accept NSPS Subpart J 

applicability for heaters aboilers and reduce or eliminate fuel oil firing 

39 151 1576000301 Marathon 

Ashland Refinery

Canton OH SO2 catalyst additive Catalyst additive Accept NSPS Subpart J 

applicability for heaters aboilers and reduce or eliminate fuel oil firing 

39 095 0448010246 Sunoco 

Petroleum Refinery 

Toledo OH Install new wet gas 

scrubber to meet 25 ppmvd SO2

Install SCR systems or alternate technology to meet 20 ppmvd 

Accept NSPS Subpart J 

applicability and reduce oeliminate fuel oil burning 

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
PECHAN 

2007 

Table III-3 (continued) 

 34 Development of 2005 Base Year G

for LADCO - Final Report 

Identification Codes 

State County Facility* Company Location State Notes 

17 115 115015AAE ADM Decatur IL Settlement agreement 17 143 143065AJE ADM Peoria IL Settlement agreement 17 001 001815AAF ADM Quincy IL Settlement agreement 18 173 00002 Alcoa Warrick Units 1,2,3 IN Settlement agreement 

*Facility identification codes are those used in the 2002 point source files. 

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
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 for Severstal (iron and steel), US Steel, and 

Marathon refinery. For Severstal, the key information provided indicated that NOx emissions 

after the summer 2007 would be reduced at the blast furnace B and C stoves via a low NOx

burner (LNB) installation. A 50 percent NOx control factor was applied with 2007 

implementation year based on information in the 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 installed 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 applied. For the Marathon refinery in Michigan, 

the Michigan DEQ estimated that catalyst additives applied to the FCCU would reduce NOx 

emissions by 25-50 percent and SO2 emissions by 60-80 percent. The midpoint of each range 

was used to estimate post-2005 control factors for this 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 facilities/sources in the 

future year control factor file. This information was developed from the OTB updated control 

factor file provided by LADCO from the 2002 base year projections. The compliance date 

information in this file was used to eliminate controls that had compliance dates of 2005 or 

earlier. Ohio EPA provided information about the expected effects of NOx Reasonably 

Available Control Technology (RACT) 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 equipment to meet the requirements, and the estimated 

NOx control percentages. 

Table III-4. Ohio RACT Rule Summary Cleveland/Akron 8-Hour Ozone 

Nonattainment Area 

Source Category 

Unit Size 

(MMBtu/hour) NOx Control 

Estimated NOx 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 for the states in the LADCO study region. In 

instances where criteria air pollutant control percentages (for SO2 and NOx) are listed in this 

table, those control percentages were applied in estimating 2018 emissions. 

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 219
PECHAN 

2007  36 Development of 2005 Base Year G

for LADCO - Final Report 

Table III-5. BART Eligible Non-EGU Sources 

State State ID Source Name County 

County 

ID Source ID BART Emission Unit ID Description Stack 

ILLINOIS 17 Conoco Phillips Madison 11D 119090AAA ILLINOIS 17 Exxon Mobil Will 197 197800AAA ILLINOIS 17 CITGO Will 197 197090AAI ILLINOIS 17 National Steel – 

Granite City 

Madison 119 119813AAI 

INDIANA 18 AGC DIVISIONALCOA POWER 

GENERATING 

 Warrick 173 2 Boiler #2 Dry Bottom, pulverized 

coal-fired boiler 

241-2

 Boiler #3 Dry Bottom pulverized 

coal-fired boiler 

242 

 Boiler #4 Dry Bottom, pulverized 

coal-fired boiler 

243 

INDIANA 18 Alcoa Inc. – Warrick Warrick 173 7 105m.1, 10 POTLINE #3. ROOMS 

105 AND 106 gtc 

105M 

 107M, 108M POTLINE #4. ROOMS 

107 AND 108 GTC 

107M 

 109M,110M POTLINE #5, ROOMS 

109 AND 110, A-398 

109M 

 111M,112M, POTLINE #6  130m.1,104 potline #2, Rooms 103 

and 104, A-398 

103m

 134.63 HDC FURNACE 

COMLEXES 

1EH 

 134.71 OFFLINES #2 134.7INDIANA 18 ESSROC CEMENT 

CORP. (Speed) 

 Clark 19 8 EU20 Kiln #1 

 EU21 Kiln #2 INDIANA 18 GE PLASTICS MT. 

VERNON INC. 

 Posey 129 2 08-706 CO AND ORGANIC 

SULFIDE STREAM FROM PHOSGENE FED 

08-70

707 

 09-001 B&W NATURAL GAS 

AND OIL FIRED BOILER 

09-00

 09-001 Riley Boiler 12-00 12-001 Hot Oil Heater  09-002 LASKER BOILER 09-00 09-002 ERIE BOILER 09-00Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
PECHAN 

2007 

Table III-5 (continued) 

 37 Development of 2005 Base Year G

for LADCO - Final Report 

State State ID Source Name County 

County 

ID Source ID BART Emission Unit ID Description Stack 

INDIANA 18 ISG-BURNS 

HARBOR (Formerly Beth. Steel) 

 Porter 127 1 460-01 #7 Boiler 4 

 46002 #8 Boiler 5  460-03 #9 Boiler 6  460-04 #10 Boiler 7  460-05 Boiler #11 8  460-06 #12 Boiler 9  512-06 #1 COKE BATTERY 

PUSHING 

11 

 512-08 #1 Coke Battery 

Underfire 

13 

 512-14 #2 COKE BATTERY 

PUSHING 

12 

 512-16 #2 COKE BATTERY 

UNDERFIRE STACK 

14 

 520 BLAST FURNACE 

FUGITIVES 

 

 520-04 SINTER WINDBOX 

STACK 

25 

 520-18 BLAST FURNACE D 

CASTHOUSE EMISSIONS 

33 

 520-18 C BLAST FURNACE 

STOVES 

31 

 520-19 BLAST FURNACE D 

STOVES 

34 

 520-19 BLAST FURNACE C 

CASTHOUSE 

33 

 534 STEELMAKING 

FUGITIVES 

 

 534-01 STEELMAKING HMD 

STATION #1 

57 

 534-02 STEELMAKING HMD 

#2 

59 

 534-10 STEELMAKING 

VESSELS #1 & #2 

62 

 534-11 STEELMAKING 

VESSELS 

64 

 534-23 STEELMAKING FM 

BOILER 

65 

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
PECHAN 

Table III-5 (continued) 

 38 Development of 2005 Base Year G

for LADCO - Final Report 

State State ID Source Name County 

County 

ID Source ID BART Emission Unit ID Description Stack 

 595-24 CASTER #1 80  670-05 HOT STRIP FURNACE 

#1 

90 

 670-07 HOT STRIP #3 

FURNACE 

92 

 670-07 HOT STRIP 91  673-14 160" OKATE MILL 

FURNACE #1 

112 

 673-15 160" PLATE MILL 

FURNACE #2 

113 

 673-16.17 160" PLATE MILL 

FURNACES 4&5 

110 

 673-18.19 160" PLATE MILL 

FURNACES 6&7 

111 

 673-20 160" PLATE MILL 

FURNACE #8 

114 

 674.26,27 110" PLATE MILL 

FURNACES #1 

122 

MICHIGAN 26 Lafarge Midwest Inc. Alpena 7 B1477 Kilns #1-#5 MICHIGAN 26 Stone Container Corp. Ontonagon 131 A5754 Riley Boiler  Paper Machine #2 MICHIGAN 26 Tilden Mining Co Marquette 103 B4885 Pelletizing Line #1, includes 

kiln, furnace, cooler, dryer 

 Boiler #2  Primary crusher MICHIGAN 26 Empire Iron Mining Marquette 103 B1827 Pelletizing Lines #1 - #3 furnace  Boilers #1 - #3  Primary crusher MICHIGAN 26 St. Mary’s Cement 

(CEMEX) 

Charlevoix 29 B1559 Kiln and pre-calciner 

MICHIGAN 26 New Page Paper 

(Escanaba) 

Delta 41 A0884 Boiler #8 

 Boiler #9  Recovery furnace  Lime kiln MINNESOTA 27 Ipsat Inland St. Louis 137 2713700062 MINNESOTA 27 EVTAC-Fairlane St. Louis 137 2713700113 MINNESOTA 27 National Steel 

(Keewatin) 

St. Louis 137 2713700063 

MINNESOTA 27 Hibbing Taconite St. Louis 137 2713700061 MINNESOTA 27 USS Minntac St. Louis 137 2713700005 MINNESOTA 27 Northshore Mining Lake 75 2707500003 Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
PECHAN 

Table III-5 (continued) 

 39 Development of 2005 Base Year G

for LADCO - Final Report 

State State ID Source Name County 

County 

ID Source ID BART Emission Unit ID Description Stack 

N. DAKOTA 38 Great River Energy – 

Coal Creek 

McLean 55 17 

N. DAKOTA 38 Basin Electric Power – 

Leland Olds 

Mercer 57 1 

N. DAKOTA 38 Great River Energy – 

Stanton 

Mercer 57 4 

N. DAKOTA 38 Minnkota Power – MR 

Young 

Oliver 65 1 

OHIO 39 Mead Paper Division Ross 67 671010028 WISCONSIN 55 Georgia-Pacific 

Consumer Products (Formerly Fort James) 

Brown 9 405032870 Boiler B26 stoker (coal, tire and 

other fuels), 350 mmBtu/hr 

S10 

 Boiler B27 cyclone, 615 mmBtu/hr S10 Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
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 determine 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 reduction credits using EPA guidance to states for 

estimating the benefits of three Federal rules 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 maintenance (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 equivalent value estimated by EPA because the aerosol 

coatings rule is a subset of the Coatings and related products category represented by SCC 

2460500000. 

For AIM coatings, EPA estimates that the amended Federal AIM rule will achieve a reduction of 

31 percent from the post-1998 Federal rule baseline of 3.6 pounds per capita. This is a creditable 

reduction of 1.1 pounds per capita. AIM coating emission reductions are applied to the 

following SCCs in the base year LADCO inventory 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 categories (SCCs 2460*) in each analysis year. 

b. Portable Fuel Containers 

For portable fuel containers (PFCs), while there are state-by-state differences in likely rule 

adoption dates, all state’s control factors are based on the EPA mobile source air toxics (MSAT) 

rule requirements. EPA adopted emission standards 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. 

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 224
PECHAN September 2007 

 41 Development of 2005 Base Year Growth and 

Control Factors for LADCO - Final Report 

cans) under the consumer products authority of the Clean Air Act. Starting with containers 

manufactured in 2009, the standard limits evaporation 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 estimated assuming that the new rule affects PFC 

sales starting during 2009, and that each PFC that meets the MSAT standard has 75 percent 

lower emissions than the PFC being replaced. 

To account for the fact that growth in the portable fuel container 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: 

 QN = emissions in projection year 

 Qo = emissions in base year 

 Ri = annual retirement rate 

 Fe = emission factor ratio for existing sources (1.0) 

 GN = projection year growth factor (projection year activity/base year activity) 

 Fn = emission factor ratio for new sources relative to existing sources 

 t = number of years between base year (2002) and projection year 

The first term in the equation represents new source growth and controls, the second term 

accounts for retirement and controls for existing 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 first year, with 10 percent retired in each additional 

forecast year), Pechan replaced the (1-Ri)

t

 terms in this equation with the appropriate proportion 

of containers retired between the base year and 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 emission reduction of 26.4 percent was computed for 

Illinois. Pechan then back-calculated the appropriate rule penetration (RP) value for each 

forecast year based on the overall emissions reduction, the 75 percent CE value, and an rule 

effectiveness (RE) of 100 percent (e.g., the calculated RP for Illinois for 2012 is 35.2 percent). 

 

3 Note that to simplify the analysis Pechan assumed that all post-2005 new container growth would be affected by 

the MSAT rule (due to low growth rates, this assumption does not have a significant impact on the overall emission 

reduction estimates of this rule). 

[ ] ( ) () () ⎭

⎬

⎫

⎩

⎨

⎧ ⎥⎦

⎤ ⎢⎣⎡ ⎥⎦⎤ ⎢⎣⎡ = − n + − e + − − Fn t

i F R ti F R N Qo G NQ 1 1 1 1

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PECHAN September 2007 

 42 Development of 2005 Base Year Growth and 

Control Factors for LADCO - Final Report 

c. Residential Wood Heating (Woodstoves and Fireplace Inserts) 

Pechan developed control factors 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 control factors were developed using an annual 

2 percent retirement rate for wood stoves/fireplaces along with pre- and post-NSPS wood stove 

and fireplace emission factors. SCCs for "controlled" 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 penetration percentages modeled. 

d. Stage II Vehicle Refueling 

Pechan developed updated (2005 base year) Stage II vehicle refueling control factors via 

MOBILE runs for the LADCO states. Onroad refueling control factors were calculated based on 

the percentage difference between the projection year (2009, 2012, and 2018) MOBILE6 

refueling emission factors and the 2005 MOBILE6 refueling emission factors. 

MOBILE6 emission factors were calculated at January and July temperature and fuel conditions. 

July emission factors were used as the surrogate 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 modeled were the January and July average daily monthly maximum and 

minimum temperatures for each state (i.e., Illinois, Indiana, Michigan, Minnesota, Ohio, and 

Wisconsin) based on 30-year average temperature 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, reformulated gasoline, oxygenated fuel, gasoline sulfur, 

and Stage II control programs for each of the states mentioned above. Fuel data and Stage II 

control program information for each state and corresponding projection year were based on 

EPA’s National Mobile Inventory Model (NMIM) County Database (version NDC20060201). 

Data extracted from NMIM’s County Database for these input parameters were based on January 

and July values. 

Stage II control programs for IL, IN, and WI began in 1998 with a phase-in year of one year and 

with a percent efficiency value of 86.0 percent for LDGVs and LDGTs in the program. 

Similarly, the HDGVs in the program have 86.0 percent 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 first weighted according to the default MOBILE6 

VMT mix to determine the weighted average refueling emission factor for 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). 

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PECHAN 

2007  43 Development of 2005 Base Year G

for LADCO - Final Report 

Table III-6. Residential Wood Combustion NSPS Emission Reductions 

(percentage values) 

2009 2012 

SCC SCC Description Pollutant Reduction

Control 

Efficiency 

Rule 

Penetration Reduction 

Control 

Efficiency

Rule 

Penetration Reduc

2104008001 Total Fireplaces CO 2.9 55.0 5.3 4.9 55.0 8.9 2104008010 Total Woodstoves CO 3.1 55.0 5.6 5.2 55.0 9.5 2104008000 Total Fireplaces & Woodstoves CO 3.0 55.0 5.5 5.1 55.0 9.3 2104008001 Total Fireplaces NOX 1.9 28.6 6.6 3.3 28.6 11.5 2104008010 Total Woodstoves NOX 2.0 28.6 7.0 3.4 28.6 11.9 2104008000 Total Fireplaces & Woodstoves NOX 2.0 28.6 7.0 3.3 28.6 11.5 2104008001 Total Fireplaces PM10-PRI 2.3 35.9 6.4 4.0 35.9 11.1 2104008010 Total Woodstoves PM10-PRI 2.5 35.9 7.0 4.2 35.9 11.7 2104008000 Total Fireplaces & Woodstoves PM10-PRI 2.4 35.9 6.7 4.1 35.9 11.4 2104008001 Total Fireplaces PM25-PRI 2.3 35.9 6.4 4.0 35.9 11.1 2104008010 Total Woodstoves PM25-PRI 2.5 35.9 7.0 4.2 35.9 11.7 2104008000 Total Fireplaces & Woodstoves PM25-PRI 2.4 35.9 6.7 4.1 35.9 11.4 2104008001 Total Fireplaces VOC 5.9 77.4 7.6 9.8 77.4 12.7 2104008010 Total Woodstoves VOC 5.6 77.4 7.2 9.4 77.4 12.1 2104008000 Total Fireplaces & Woodstoves VOC 5.4 77.4 7.0 9.2 77.4 11.9 

Note: Rule effectiveness (RE) of 100 percent for each SCC/year.Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan 
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 areas, the overall control 

efficiency for refueling, due to fleet turnover, was calculated based on the percentage difference 

between the 2005 and corresponding projection year emission factors. These control efficiencies 

were then assigned to individual counties, based 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 (CMVs) (EPA, 2007b). These are the two off-road 

source categories that are addressed in this report. Base year emissions (2005) information for 

these two source categories was developed 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 chapter presents EPA’s analysis of the emissions impact of the 

proposed rule for three source categories affected: commercial marine diesel engines, 

recreational marine diesel engines, 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 recreational 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 Analysis (RIA) chapter was used to develop 2009, 

2012 and 2018 estimates of baseline and post-control emissions by pollutant by locomotive 

usage. This information is summarized in Table III-7. The RIA examined 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. 

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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 standards 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 standards. 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 estimate 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 the controlled emissions 

reported in EPA’s draft RIA. Table III-8 shows how the revised baseline and percent reduction 

for each modeling year. For example, for large line haul railroads, the baseline 2009 

uncontrolled VOC emissions are estimated to be 46,014 tons nationally. Controlled emissions of 

42,008 tons represent an 8.7 percent VOC reduction from this uncontrolled emission estimate 

(46,014 – 42,008 = 4,010; 4,010/46,014 * 100 = 8.7). 

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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 described 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 reduction estimates applied in this analysis. For 

example, the CMV standards are expected to reduce VOC emissions by 33.9 percent in 2018. 

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PECHAN September 2007 

 47 Development of 2005 Base Year Growth and 

Control Factors for LADCO - Final Report 

Table III-9. Percentage Reductions Associated 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 SCCs based on an ENVIRON table indicating RP 

values of less than 100 percent for these SCCs (see Table III-10). 

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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 

2280002023 Push Boats Barge Freight River Traffic 100% 100% 100% 100% 100%

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, 

22800040301

Commercial 

Fishing 

Market 

fishing 

Great Lakes 100% 100% 100% 100% 100%

2280002040, 

22800040401

Military Coast Guard 

and Navy 

Great Lakes 100% 100% 100% 100% 100%

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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 growth and control factor files submitted to LADCO 

earlier this month. The first subsection discusses the preparation of the point and area source/

MAR factor file and the revised NONROAD model growth file. The final subsection describes 

the contents of the control factor files. 

Table IV-1 presents the RPO Data Exchange Protocol Format for reporting emission growth and 

control data. Pechan utilized this format to create growth and control factor files for LADCO. 

Because the growth factors (unlike the control factors) do not differ by pollutant, Pechan 

developed a separate file containing 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 growth rates with growth rates based on more 

recent/more region-specific information. Two sets of control factor files were prepared: one for 

area source/MAR categories and one for point source categories. The point and area 

source/MAR growth and control files were developed in fixed field ascii format. The format of 

the default NONROAD model growth file was retained in the revised version prepared for 

LADCO. The following subsections describe the contents of the growth 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 populated in this file. The 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 state-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 nation; 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 (alphanumeric code identified within NONROAD) 

26-45 value for indicator 

 

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PECHAN September 2007 

 50 Development of 2005 Base Year Growth and 

Control Factors for LADCO - Final Report 

Table IV-1. RPO Data Exchange Protocol 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 strategy comes into effect 6

FUTURE DATE Future date that the control 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 contact/developer of this record 30

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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 NEW 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 files: 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 specific date that each control is expected 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 MACT standards, the point source 

control factors are incremental to base year control levels. Because all other point source control 

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PECHAN September 2007 

 52 Development of 2005 Base Year Growth and 

Control Factors for LADCO - Final Report 

factors represent absolute control levels, LADCO should subtract any existing 2005 inventory 

level of control from the control factor level of control to determine net reductions for nonMACT controls. Pechan found very few point source records with control information, so 

LADCO should expect very little control overlap between the 2005 inventory 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 factor information into a single ascii file that 

reports the level of control for each year of interest (2009, 2012, and 2018). In cases where there 

is no change in emission reduction after the initial implementation year, the level of control is 

repeated for each year. For controls where emission reductions increase over time (due to 

increased levels of RP), the level of control increases for each successive modeling year. Except 

for the single control for which emission reductions are county-specific (Stage II Vehicle 

Refueling), the area source and MAR control factor file is expressed at the state-level. In cases 

where it was feasible to do so, Pechan populated the 5th, 4th, and 3rd 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 the overall percentage emission reduction). 

Table IV-4 identifies the RPO Data Exchange Protocol fields that are populated in this file. 

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PECHAN September 2007 

 53 Development of 2005 Base Year Growth and 

Control Factors for LADCO - Final Report 

Table IV-3. Fields Populated in Point 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 DATE1 Yes 

PRIMARY CONTROL EQUIPMENT CODE No 

BASE DATE CONTROL EFFICIENCY2 Yes 

FUTURE DATE CONTROL EFFICIENCY3 Yes 

FUTURE DATE GROWTH FACTOR No 

CONTROL TYPE Yes 

FUTURE DATE CHEMICAL SPECIATION PROFILE No 

ALLOWABLE EMISSIONS CAP No 

MARKET PENETRATION OF NEW 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. 

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PECHAN September 2007 

 54 Development of 2005 Base Year Growth and 

Control Factors for LADCO - Final Report 

Table IV-4. Fields Populated in Area 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 EFFICIENCY1 Yes 

FUTURE DATE GROWTH FACTOR No 

CONTROL TYPE No 

FUTURE DATE CHEMICAL SPECIATION PROFILE No 

ALLOWABLE EMISSIONS CAP No 

MARKET PENETRATION OF NEW SPECIATION PROFILE No 

RESERVED FOR FUTURE USE FIELD 3 (future year CE) Yes2

RESERVED FOR FUTURE USE FIELD 2 (future year RE) Yes2

RESERVED FOR FUTURE USE FIELD 1 (future year RP) Yes2

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.

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 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 Employment Projections, 

Long-Term,” accessed from http://www.projectionscentral.com/projections.asp, Office of 

Employment Projections, Washington, DC. Accessed May 2007. 

DOE, 2007a: U.S. Department of Energy, “Annual Energy Outlook 2007, with Projections 

through 2030,” DOE/EIA-0383(2007), Energy Information Administration, Office of 

Integrated Analysis and Forecasting, 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 Michigan Air Directors Consortium, Des Plaines, 

IL, March 2, 2007. 

ENVIRON, 2007b: ENVIRON International Corporation, “Draft LADCO 2005 Locomotive 

Emissions,” prepared for Lake Michigan Air Directors Consortium, Des Plaines, IL, 

February 2007. 

EPA, 2004: U.S. Environmental Protection Agency, “National Emission Inventory—Ammonia 

Emissions from Animal Husbandry Operations, 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 Agency, “Draft Regulatory Impact Analysis: 

Control of Emissions of Air Pollution from Locomotive Engines and Marine 

Compression-Ignition Engines Less than 30 Liters per Cylinder – Chapter 3: Emission 

Inventory,” Assessment and Standards Division, Office of Transportation and Air 

Quality, EPA420-D-07-001, March 2007. 

FAA, 2007: U.S. Federal Aviation Administration, “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, Washington, 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. 

Weirton, WV 2006 PM2.5 Redesignation Request & Maintenance Plan Page D - 239
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 NOx Emission Changes 

from MACT Standards,” Memorandum to Regional Air Division Directors, U.S. 

Environmental Protection Agency, Research 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 Reduction Credit for Three Federal Rules for 

Categories of Consumer and Commercial Products 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 Consortium (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 Directors Consortium (LADCO) – Draft Report,” 

Durham, NC, prepared for Lake Michigan Air Directors Consortium, Des Plaines, IL, 

December 29, 2005. 

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