# APPENDIX H

Lake Michigan Air Directors Consortium

(LADCO) Emission Estimates Technical Support

Document

## F - 1 This page left intentionally blank.

# Regional Air Quality Analyses for

Ozone, PM 2. 5 

# , and Regional Haze:

F inal Technical Support Document

April 25 , 2008

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

### ii 

# Table of Contents

Section

- Title Pa ge

Executive Summary i i i

1

. 0 Introduction 1

1

. 1 SIP Requirements 1

1

. 2 Organization 2

1

. 3 Technical Work: Overview 3

2

. 0 Ambient Data Analyses 4

2

. 1 Ozone 4

2

. 2 PM 2. 5 

### 2 1

2

. 3 Regional Haze 35

3

. 0 Air Quality Model ing 4 6

3

. 1 Selection of Base Year 46

3

. 2 Future Years of Interest 46

3

. 3 Modeling System 4 7

3

. 4 Domain/Grid Resolution 47

3

. 5 Model Inputs: Meteorology 48

3

. 6 Model Inputs: Emissions 51

3

. 7 Base Year Modeling Results 60

4

. 0 Attainment De monstration for Ozone and PM 2.5 

### 7 1

4

. 1 Future Year Modeling Results 71

4

. 2 Supplemental Analyses 82

4

. 3 Weight of Evidence Determination for Ozone 82

4

. 4 Weight of Evidence Determination for PM 2. 5 

### 90

5

. 0 Reasonable Progress Assessment for Regional Haze 9 3

5

. 1 Class I Areas Impacted 93

5

. 2 Future Year Modeling Results 96

5

. 3 Weight of Evidence Determination for Regional Haze 1 04

6

. 0 Summary 1 11

7

. 0 References 1 1 4

Appendix I Ozone and PM 2.5 

### Modeling Results

Appendix II Ozone Source Apportio nment Modeling Results

Appendix III PM 2.5 

### Source Apportionment Modeling Results

Appendix IV Haze Source Apportionment Modeling Results

### iii 

### EXECUTIVE SUMMARY

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

cu rrently classified as nonattainment for the 8

- hour ozone standard and 60 for the f ine particle

( PM 2.5 

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

below. In addition, visibility impairment due to regional haze is a problem in the larger national

parks and wilderness areas (i.e., Class I areas). There are 156 Class I areas in the U.S.,

including two in northern Michigan

.

Figure i

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

### (right )



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

### , and

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

analyses were conducted by the Lake Michigan Air Directors Consortium (LADCO ), its member

states, and various contractors

. The analyses include preparation of regional emissions

inventories and meteorological data, evaluation and application of regional chemical transport

models, and collection and analysis of ambient monitoring data.

M onitoring data were analyzed to produce a conceptual understanding of the air quality

problems. Key findings of the analyses include:

Ozone

• Current monitoring data (2005

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

- hour

ozone standard of 85 parts per billion (ppb) . Historical ozone data show a steady

downward trend over the past 15 years, especially since 2001

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

• Ozone concentrations are strongly influenced by meteorological con ditions, with

more high ozone days and higher ozone levels during summers with above normal

temperatures.

### iv



### • Inter

- and intra

- regional t ransport of ozone and ozone precursors aff ects many

portions of the five states , and is the principal cause of nonattainme nt in some areas

far from population or industrial centers

.

PM 2.5



### • Current monitoring data (200 5

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

### standard of 15 ug/m 3



### . N onattainment sites are characterized by an elevated

regional background ( about 12

– 14 ug/m 3 

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

( about 2

– 3 ug/m 3 

### ). Historical PM 2.5 

### data show a slight downward trend since

deployment of the PM 2.5 

### monitoring network in 1999.

• PM 2.5 

### concentrations are also influenced by meteorology , but the re lationship is

more complex and less well understood compared to ozone.

• O n an annual average basis, PM 2.5 

### chemical composition consists mostly of sulfate,

nitrate, and organic carbon in similar proportions.

Haze

• Current monitoring data (2000

- 2004) show visibility levels in the Class I areas in

northern Michigan are on the order of 2 2

– 2 4 deciviews . The goal of EPA’s visibility

program is to achieve natural conditions, which is about 12 deciviews for these

Class I areas, by the year 2064.

• Visibility im pairment is dominated by sulfate and nitrate.

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

. Two base years were

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

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

exercise wa s intended to build confidence in the model prior to its use in examining co ntrol

strategies. M odel performance for ozone and PM 2.5 

### was found to be generally acceptable.

Future year strategy modeling was conducted to determine whether existing (“on the books”)

controls would be sufficient to provide for attainment of the standards for ozone and PM 2.5 

### and if

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

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



## PM 2.5 

### concentrations and visibility levels

.

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

difference between the base years of 2002 and 2005 is meteorology. 2002 was

more ozone conducive than 2005. The choice of which base year to use as the

basis for the SIP is a policy decisio n (i.e., how much safeguard to incorporate).

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

- hour ozone standard by the applicable attainment date, except for sites in western Michigan

and, possibly, in eastern Wisconsin a nd northeast ern Ohio.

### v



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

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

and Granite City

.

T he regiona l modeling for PM 2.5 

### does not include air quality benefit s expected

from local controls. States are conducting local

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

- scale modeling, to support their

attainment demonstrations for PM 2.5



### .

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

### are supported by

current (2005

– 2007) monitoring data which show significant nonattainment in

the region (e.g., peak ozone design values on the order of 90

– 93 ppb, and peak

PM 2.5 

### d esign values on the order of 16

- 17 ug/m 3 

### ). It is unlikely that sufficient

emission reductions will occur in the next couple of years to provide for

attainment at all sites.

• Attainment at most sites by the applicable attainment date is dependent on actual

future year meteorology (e.g., if the weather conditions a re consistent with [or

less severe than] 2005, then attainment is likely) and actual future year

emissions (e.g., if the emission reductions associated with the existing controls

are achieved, then attainment is likely). I f either of these conditions is n ot met,
then attainment may be less likely.

• M ode ling s uggests that the new PM 2.5 

### 24

- hour standard and the new lower

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

controls.

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

greater than

(less improved than) the uniform rate of visibility improvement

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

and s ome in the northeastern U.S. Visibility levels in m any other Cla ss I areas in

the eastern U.S. are expected to be less than (more improved than) the uniform

rate of visibility improvement value s in 2018. These results, along with

information on the costs of compliance, time necessary for compliance, energy

and non air

quality environmental impacts of compliance, and remaining useful

life of existing sources, should be considered by the states in setting reasonable

progress goals for regional haze.

### 1 

### Section 1. 0 Introduction

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

Lake Michigan Directors Consortium (LADCO) 1 

### and its contractors to support the development

of State Implementation Plans (SIPs) for ozone, fine particles ( PM 2.5 

### ), and regional haze in the

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

. The analyses include preparation of

regional emissions inventories and meteorological modeling data for two base years (2002 and

2005), evaluation and application of regional chemical transport models, and analysis of

ambient monitoring data.

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

- pollutant approach

was taken in addressing ozone, PM 2.5 

### , and haze f or technical reasons (e.g., common ality in

precursors, emission sources, atmosphe ric processes, transport influences, and geographic

areas of concern), and practical reasons (e.g., more efficient use of program resources).

Furthermore, EPA has consistently encouraged multi

- pollutant planning in its rule for the haze

program (64 FR 357 19), and its implementation guidance for ozone (70 FR 71663) and PM 2.5 

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

- of

- evidence approach was taken in considering the results of

the various analyses (i.e., two sets of modeling results

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

2005 base year

-- and ambient data analyses) in order to provide a more robust assessment of

expected future year air quality.

The report is orga nized in the following sections. This I ntroduction p rovides an overview of

regulatory requirements and background information on regional planning . Section 2 r eviews

the ambient monitoring data and presents a conceptual model of ozone, PM 2.5 

### , and haze for the

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

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

### is presented in Section 4,

along with relevant data analyses considered as part of the weight

- of

- evidence de termination

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

, a long with

relevant data analyses considered as part of the weight

- of

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

1.1 SIP Requirements

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

the 5

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

. 2 

### The

designations became effective on June 15, 2004. SIPs for ozone we re due no later than three

years from the effective date of the nonattainment designations (i.e., by June 2007). The

attainment date for ozone varies as a function of nonattainment classification. For the region,

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

nonattainment areas), or June 2010 (moderate nonattainment areas).

1 

### A sub

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

for the regional haze activities of the multi

- state organization.

2 

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

as n onattainment in the region.

### 2



### For PM 2.5 

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

In the 5

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

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

### are due no later than three years from the effective

date of the nonattainment designations (per section 172(b) of the Clean Air Act) (i.e., by A pril

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

promulgate d the PM 2.5 

### designations (per the Omnibus Appropriations Act of 2004) (i.e., by

December 2007). The applicable attainment date for PM 2.5 

### nonattainment ar eas is five years

from the date of the nonattainment designation (i.e., by April 2010).

For haze, the Clean Air Act sets “as a national goal the prevention of any future, and the

remedying of any existing, impairment of visibility in Class I ar eas which impairment results from

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

Royale National Park and Seney National Wildlife Refuge 4



### . EPA’s visibility rule (64 FR 35714,

July 1, 1999) require s reasonabl e progress in achieving “natural conditions” by the year 2064.

As noted above, the first regional haze SIP wa s due in December 2007 and must address the

initial 10

- year implementation period (i.e., reasonable progress by the year 2018). SIP

requirements (pursuant to 40 CFR 51.308(d)) include setting reasonable progress goals,

determining baseline conditions, determining natural conditions, providing a long

- term control

strategy, providing a monitoring strategy (air quality and emissions), and establishing BART

emissions limitations and associated compliance schedule.

1.2 Organization

LADCO wa s established by the States of Illinois, Indiana, Michigan, and Wisconsin in 1989. T he

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

Michigan Ozone Study (LMOS) and identified LADCO as the organization to oversee th e study.

Additional MOAs were signed by the States in 1991 (to establish the Lake Michigan Ozone

Control Program)

, January 2000 (to broaden LADCO’s responsibilities) , and June 2004 (to

update LADCO’s mission and reaffirm the commitment to regional planning). In March 2004,
Ohio joined LADCO. LADCO consists of a Board of Directors (i.e., the State Air Directors), a

technical staff, and various workgroups. The main p urpose s of LADCO are to provide technical

assessments for and assistance to its member states

, and to provide a forum for its member

states to discuss regional air quality issues.

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

tribes in Michigan and Wisconsin

, EPA

, and Federal Land Managers (i.e., National Park

Service, U.S. Fish & Wildlife Agency, and U.S. Forest Service )

. In October 2000, the States of

Illinois, Indiana, Michigan, Ohio, and Wisconsin signed a n MOA that established the MRPO. An

o perating principles document for M RPO, which describe the roles and responsibilities of states,

tribes, federal agencies, and stakeholders, w as issued in March 2001

. M RPO has a similar

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

Class I areas located inside the borders of the five states, and the impact of emissions from the

five states on visibility impairment due to regional haze in the Federal Class I areas located

outside the borders of the five states. MRPO works cooperatively with the R egional Planning

Organizations (R POs ) representing other parts of the country. The RPOs sponsored several

3 

### USEPA subsequently adjusted the final designations, which resulted in 63 counties in the region being

designate d as nonattainment (70 FR 19844, April 15, 2005)

.

4 

### Although Rainbow Lake in northern Wisconsin is also a Class I area, the visibility rule does not apply

because the Federal Land Manager determined that visibility is not an air quality related value there

.

### 3



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

matters.

1.3 Technical Work: Overview

To ensure the reliability and effectiveness of its planning process, LADCO has made data

collec tion and analysis a priority. M ore than $ 7 M in RPO grant funds were used for special

purpose monitor ing, preparing and improving emissions inventories, and conducting air quality

analyses 5 

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

Monitoring: Numerous monitoring projects were conducted to supplement on

- going state and

local air pollution mo nitoring. These projects include rural monitoring (e.g., comprehensive

sampling in the Seney National Wildlife Refuge and in Bondville, IL ) ; urban monitoring (e.g.,

continuation of the St. Louis Supersite); aloft (aircraft) measurements; regional ammonia

monitoring; and organic speciation sampling in Seney, Bondville, and five urban areas.

Emissions: B aseyear emissions inventor ies were prepared for 2002 and 2005 . States provided

point source and area source emissions data , and MOBILE6 input files and mob ile source

activity data. LADCO and its contractors developed the emissions data for other source

categories (e.g., select nonroad sources, ammonia, fires, and biogenics) and processed the

data for input into an air quality model. To support control stra tegy modeling, future year

inventories were prepared. The future years of interest include 2008 (planning year to address

the 2009 attainment year for basic ozone nonattainment ares), 2009 ( planning year to address

the 2010 attainment year for P M 2.5 

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

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

Air Quality Analyses: The weight

- of

- evidence approach relies on data analysis and modeling.

Air quality data analyses were used to provide both a conceptual model (i.e., a qualitative

description of the o zone

, PM 2.5 

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

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

, especially for PM 2. 5 

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

Modeling includes baseyear analyses for 2002 and 2005 to evaluate model performance and

future year strategy analyses to assess candidate control stra tegies. The analyses wer e

conducted in accordance with EPA’s modeling guidelines ( EPA, 2007a)

. The PM/haze

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

the ozone modeling focus es on the summer period (200 2 and 2005 ) for a Midwest 12 km

subdomain. The same model (CAMx) was used for ozone, PM 2.5 

### , and regional haze

.

5 

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

### 4 

### Section 2.0 Ambient Data Analyses

An extensive network of air quality monitors in the 5

- state region provides data for ozone (and

its precurs ors)

, PM 2. 5 

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

data are used to determine attainment/nonattainment designations, support SIP development,

and provide air quality information to public (see, for example, www.airnow.gov ).

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

summar y of the physical, chemical

, and m eteorological processes that control the formation and

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

describes our understanding of ozone, PM 2.5, 

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

co nd i tions

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

, i nfluence of meteorology ( includin g

t ransport patterns )

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

2.1 Ozone

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

- hour period. This

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

- year period

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

### highest daily 1

- hour value over a 3

- year period) less than 0.12 ppm.

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

- hour period 6



### . T he

standard is attain ed if the 3

- year average of the 4th

- highest daily maximum 8

- hour average

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

than 0.08 pp m (or 85 ppb)

.

Current C onditions : A map of the 8

- hour ozone design values at each monitoring site in the

region for the 3

- year period 2005

- 2007 is shown in Figure 1. The “hotter” colors represent

higher concentrations , where yellow and orange dots re present sites w ith design values above

the standard

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

- hour oz one NAAQS in the 5

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

Table 1 provides the 4 th



### - highest daily 8

- hour ozone values and the associated design values

since 2001 for several high monitoring sites throughout the region.

6 

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

- hour ozone standard to increase public health

protection and prevent environmental damage from ground

- level ozone. USEPA set the primary (health)

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

- hour period.

### 5 Figure 1

. 8

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

- 2007 )

### Key Sites

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

Lake Michigan Area



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

Racine 92 111 82 69 95 71 77 95 87 82 78 81

Milwaukee-Bayside 93 99 92 73 93 73 83 94 88 86 79 83

Harrington Beach 102 93 99 72 94 72 84 98 88 88 79 83

Manitowoc 97 83 92 74 95 78 85 90 83 87 82 86

Sheboygan 102 105 93 78 97 83 88 100 92 89 86 89

Kewaunee 90 92 97 73 88 76 85 93 87 86 79 83

Door County 95 95 93 78 101 79 92 94 88 90 86 90

Hammond 90 101 81 67 87 75 77 90 83 78 76 79

Whiting 64 88 81 88 77 85

Michigan City 90 107 82 70 84 75 73 93 86 78 76 77

Ogden Dunes 

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

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

Jenison 86 93 91 69 86 83 88 90 84 82 79 85

Muskegon 95 96 94 70 90 90 86 95 86 84 83 88

Indianapolis Area



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

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

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

Detroit Area



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

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

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

Cleveland Area



### Ashtabula (Conneaut) 97 103 99 81 93 86 92 99 94 91 86 90

Notre Dame (Geauga) 99 115 97 75 88 70 68 103 95 86 77 75

Eastlake (Lake) 89 104 92 79 97 83 74 95 91 89 86 84

Akron (Summit) 98 103 89 77 89 77 91 96 89 85 81 85

Cincinnati Area



### Wilmington (Clinton) 93 99 96 78 83 81 82 96 91 85 80 82

Sycamore (Hamilton) 88 100 93 76 89 81 90 93 89 86 82 86

Hamilton (Butler) 83 100 94 75 86 79 91 92 89 85 80 85

Middleton (Butler) 87 98 83 76 88 76 91 89 85 82 80 85

Lebanon ( Warren) 85 98 95 81 92 86 88 92 91 89 86 88

Columbus Area



### London (Madison) 84 97 90 75 81 76 83 90 87 82 77 80

New Albany (Franklin) 90 103 94 78 92 82 87 95 91 88 84 87

Franklin (Franklin) 83 99 84 73 86 79 79 88 85 81 79 81

Ohio Other Areas

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

St. Louis Area

W. Alton (MO) 85 99 91 77 89 91 89 91 89 85 85 89

Orchard (MO) 88 98 90 76 92 92 83 92 88 86 86 89

Sunset Hills (MO) 88 98 88 70 89 80 89 91 85 82 79 86

Arnold (MO) 86 93 82 70 92 79 87 87 81 81 80 86

Margaretta (MO) 80 98 90 72 91 76 91 89 86 84 79 86

Maryland Heights (MO) 88 84 94 88 4th High 8-hour Value Design Values Table 1. Ozone Data for Select Sites in 5-State Region

### 7



### Meteorology

and Transport

: Most pollutants exhibit some dependence on meteorological

factors, especially wind direction, because that governs which sources are upwind and thus

most influential on a given sample. Ozone is even more dependent, since its production is

driven by high temperatures and sunligh t

, as well as precursor concentrations (see, for

example, Figure 2 )

.

Figure 2. Number of h ot d ays and 8

- h our “ e xceedance” d ays in 5

- state region



### Qualitatively, ozone episodes in the region are associated with hot weather, clear skies

(sometimes hazy ), low wind speeds, high solar radiation, and southerly to southwesterly winds.

These conditions are often a result of a s low

- moving high pressure system to the east of the

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

section.

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

scales. Regionally, o ver a multi

- day period, somewhat stagnant summertime conditions can

lead to the build

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

. Th is

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

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

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

– 25, 2005)

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

- hour standard

### 8



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

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

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

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

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

- hour standard



### Aloft (aircraft) measurements in the Lake Michigan area also provide evidence of elevated

regional backgro und concentrations and “plumes” from urban areas. For one example summer

day (August 20, 2003

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

of 80

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

- 150 ppb (STI, 2004)

.

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

(right)

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

### 9



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

- state
region

– i.e., western Michigan. To understand the source regions likely impacting high ozone

concentrations in western Michigan and e stimate the impact of these source regions , two s imple

transport

- related analyses were performed

.

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

- hour

peak > 80 ppb) during the period 2002

- 2006 in western Michigan to characterize general

transport patterns. Comp osite trajectory plots for all high ozone days based on data from three

sites (Cass County, Holland, and Muskegon) are provided in Figure 6. The plots point back to

areas located to the south

- southwest (especially, northeastern Illinois and northwestern I ndiana)

as being upwind on these high ozone days.

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

c oncentrations



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

concentrat ions upwind (Braidwood, IL), within Chicago (ten sites in the City), and downwind

(Holland and Muskegon) for days in 1999

– 2002 with southwesterly winds

- i.e., transport

towards western Michigan. Figure 7 shows the distribution of daily peak 8

- hour ozon e concentrations by day

- of

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

between day

- of

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

Indiana contribute s about 10

- 15 ppb to downwind ozone levels.

### 10

Figure 7 . Mean day

- of

- week peak 8

- hour ozone concentrations at sites upwind, within, and

downwind of Chicago, 1999

– 2002 (southwesterly wind days)



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

• 

### Ozone transport is a problem af fecting many portions of the eastern U.S. The Lake

Michigan area (and other areas in the LADCO region ) both receive high levels of

incoming (transported) ozone and ozone precursors from upwind source areas on many

hot summer days, and contribute to the hi gh levels of ozone and ozone precursors

affecting downwind receptor areas.

• 

### The presence of a large body of water (i.e., Lake Michigan) influences for the formation

and transport of ozone in the Lake Michigan area . Depending on large

- scale synoptic

winds and local

- scale lake breezes, different parts of the area experience high ozone

concentrations. For example, under southerly flow, high ozone can occur in eastern

Wisconsin, and under southwesterly flow, high ozone can occur in western Michigan.

• 

### Downwi nd shoreline areas around Lake Michigan are affected by both regional transport

of ozone and subregional transport from major cities in the Lake Michigan area

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

even as far north as the Upper Peninsula) are impacted by high levels of incoming

(transported) ozone.

### 11



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

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

- hour a nd 8

- hour design values

for the 5

- state LADCO region over the last 25 years.

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

- State r egion



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

- state region (see Figure 9 ) . This

plot shows a pronoun ced downward trend in the design value since the 2001

- 2003 period , due,

in part, to the very low 4 th 

### high values in 2004.

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

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



### The imp rovement in ozone concentrations is also seen in the decrease in the number of sites

measuring nonattainment over the past 15 years in the Lake Michigan area (see Figure 10). 75 85 95 105

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

65 75 85 95 105 115

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

## Design Values

4 th 

### High Values

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

- 1 997, 2000

- 2002, and 2 00 5

- 200 7

### 13



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

- to

- year variations in meteorology

can make it difficult to assess trends in ozone air quality. Two approaches were considered to

adjust ozone trends for meteorological influences : an air quality

- meteorology statistical m odel

developed by EPA (i.e., Cox method), and statistical grouping of meteorological variables

performed by LADCO (i.e., Classification and Regression Trees, or CART).

Cox M ethod

: This method uses a statistical m odel to ‘remove’ the annual effect of meteorology

on ozone (Cox and Chu, 1993 ). A regression model was fit to the 1997

- 2007 data to relate daily

peak 8

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

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

temperature, mid

- day average relative humidity, morning and afternoon wind speed and wind

direction. The model is then used to predict 4 th 

### high ozone values. By holding the

meteorological e ffects constant, the long term trend can be examined independently of

meteorology. Presumably

, any trend reflects changes in emissions of ozone precursors.

Figure 11a shows the meteorologically

- adjusted 4 th 

### high ozone concentrations for several

monitor s near major urban areas in the region . The plots indicate a general downward trend

since the late 1990s for most cities, indicating that recent emission reductions have had a

positive effect in improving ozone air quality.

A similar model was run to e xamine meteorologically adjusted trends in seasonal average

ozone. This model incorporates more meteorological variables, including rain and long

- distance

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

2007. The seasonal average trend s are shown in Figure 11b. Trends determined by seasonal

model for the same set of sites examined above are consistent with those developed by the 4 th



### high model

.

### 14 Chiwaukee, WI Sheboygan, WI

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

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

Indianapolis, IN

Figure 11 a

. Trends in meteorologically

adjusted 4 th 

### high 8

- hour ozone

concentrations for seven Mid western sites

(199 7

– 200 7 )

### 15 Chiwaukee, WI Sheboygan, WI

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

Adjusted for Weather

Unadjusted for Weather

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

Adjusted for Weather

Unadjusted for Weather



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

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

Adjusted for Weather

Unadjusted for Weather

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

Adjusted for Weather

Unadjusted for Weather



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

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

Adjusted for Weather

Unadjusted for Weather

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

Adjusted for Weather

Unadjusted for Weather



### Indianapolis, IN

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

Adjusted for Weather

Unadjusted for Weather 

### Figure 11b

. Trends in seasonal 8

- hour ozone

concentrations for seven Mid western sites

(199 7

– 200 7 )

### 16



### CART : Classification and Regression Tree (CART) analysis is another statistical technique

which partitions data sets into similar groups (Breiman et al., 1984). CART analysis was

performed using data for the period 1995

- 2007 for 22 selected ozone monitors w ith current 8

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

. T he CART model searches

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

Although the exact sel ection of predictive variables changes from site to site, the most common predictors we re temperature, wind direction, and relative humidity. Only occasionally were upper air variables, transport time or distance, lake breeze, or other variables s ignifica nt. (Note,

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

analysis.)

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

September) by its meteorological conditions. Similar d ays are assigned to nodes, which are

equivalent to branches of the regression tree. Ozone time series for the higher concentration

nodes are plotted for select sites in Fig ure 12 . By grouping days with similar meteorology, the

influence of meteorological variability on the trend in ozone concentrations is partially removed;

the remaining trend is presumed to be due to trends in precursor emissions or other non

meteorological influences. Trends over the 13

- year period at most sites were found to be

declin ing, with the exception of Detroit which showed fairly flat trends. C omparison of the

average of the high concentration node values for 2001

- 2003 v. 2005

- 2007 showed an

improvement of about 5 ppb across all sites (even Detroit)

.

The effect of meteorology was further examined by using an ozone conduciveness index

(Kenski, 2008 b ) . This metric reflects the variability from the 1 3

- year average in the number of

days in the higher ozone concentration nodes (see Figure 13)

. Examination of these plots

indicates :

• 

### 2002 and 2005 w

ere both above normal

, with 2002 tending to be more severe ; and

• 

### 2001

- 2003 and 2005

- 2007 were both above normal, with no clear pattern in which

period was more severe (i.e., ozone conduciveness values were similar at most sites,

2001

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

- 2007 values were higher at a few sites)

.

Given the similarity in ozone conduciveness between 2001

- 2003 and 2005

- 2007, the

improvement in ozone levels noted above is presumed to be due to non

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

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

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

1997

- 2007 period, even when meteoro logical variability is accounted for. The decreases are

present whether seasonal average ozone, peak values (annual 4 th 

### highs), or a subset of high

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

good indicatio n that these trends reflect impacts of emission control programs

.

### 17 Chiwaukee, WI Sheboygan, WI

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

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

Indianapolis, IN

Figure 12

. Trends for higher o zone C ART

groups (average ozone > 6 5 ppb) for seven

Midwestern sites (199 5

– 20 0 7 )

Note: line represents linear best fit

### 18 Figure 13 . Ozone c onduciveness i ndex (and

number of high ozone days) f or s even

M idwestern site (199 5

– 200 7 )

### 19



### Pr ecursor S ensitivity : O zone i s formed from the reactions of hydrocarbons and nitrogen oxides

under meteorological conditions that are conducive to such reactions (i.e., warm temperatures

and strong sunlight )

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

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

VOC/NO x

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

responsive to VOC reductions.

An analysis of V OC and NO

x 

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

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

, et al., 200 3 ) . The “Extent of

Reaction” parameter in the SP algorithm provides an indication of VOC and NOx sensitivity:

Extent Range Precursor Sensitivity

< 0 .6 VOC

- sensitive

0. 6

– 0.8 Transitional

> 0.8 NOx

- sensitive



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

seen, ozone is usually VOC

- l imited in cities and NOx

- limited in rural areas. ( Data from aircraft

measurements suggest that ozone is usually NO

x 

### - limited over Lake Michigan and away from

urban centers on days when ozone in the urban centers is VOC

- limited. ) T he highest ozone

days we re found t o be NO

x 

### - limite d. This analysis suggests that a NOx reduction strategy would

be effective in reducing ozone levels. Examination of day

- of

- week concentrations, however,

raises some question about the effectiveness of NOx reductions.

Figure 1 4 . Mean afternoon extent of reaction (1998

– 2002)

### 20



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

- weekday differences in ozone and NO

x 

### in the

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

- 60%) weekend

concentrations of NO

x 

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

x 

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

higher concentrations of ozone

, although the increase was generally not statistically significant

( see Figure 1 5 ). This small but counterproductive change in local ozone concentrations

suggests that local urban

- scale NO

x 

### reductions alone may not be very effective.

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

- h our o zone

– number of sites with weekend

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



### Two additional analyses, however, demonstrate the positive effect of NOx emission reductions

on downwind ozone concentrations. First, Blanchard (2005 a ) looked at the effe ct of changes in

precursor emissions in Chicago on downwind ozone levels in western Michigan. For the

transport days of interest (i.e., southwesterly flow during the summers of 1999

– 2002), mean

NOx concentrations in Chicago are about 50% lower and mean ozone concentrations at the

(downwind) western Michigan sites are about 1.5

– 5.2 ppb (3

– 8 %) lower on Sunday

compared to Wednesday. This degree of change in downwind ozone levels suggests a

positive, albeit non

- linear response to urban area emission re ductions.

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

- of

- week emissions in

southeastern Michigan on downwind ozone levels. This modeling study found that w eekend

changes in ozone precursor emissions cause both increases and decrease s in Southeast

Michigan ozone, depending upon location and time :

### 21

• 

### Weekend increases in 8

- hour maximum ozone occur in and immediately downwind of

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

- sensitive areas).

• 

### Weekend decreases in 8

- hour maximum ozone occur outside and downwind of the

Detroit urban area (i.e., in NOx

- sen sitive areas).

• 

### At the location of the peak 8

- hour ozone downwind of Detroit, ozone was lower on

weekends than weekdays.

• 

### Ozone benefits (reductions) due to weekend emission changes in Southeast Michiga n

can be transported downwind for hundreds of miles.

• 

### Southeast Michigan benefits from lower ozone transported into the region on Saturday

through Monday because of weekend emission changes in upwind areas.

In summary, these analyses suggest that urban VOC reductions and regional (urban and rural)

NOx reductions will be effective in lowering ozone concentrations. Local NOx reductions can

lead to local ozone increases (i.e., NOx disbenefits), but this effect does not appear to pose a

problem with respect to attainment of the standard. It should also be noted that urban VOC and

regional NOx reductions are likely to have multi

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

P M 2. 5 

### impacts).

2.2 P M 2.5 

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

### standard s of 15 ug/ m 3 

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

- hour

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

- year average of the annual average PM 2. 5 

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

the 98th percentile of 24

- h our PM 2. 5 

### concentrations in a year, averaged over three years, is less

than or equal to the level of the standard

.

In 2006, EPA revised the PM 2. 5 

### standard s to 15 ug/m 3 

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

### (24

- hour

average).

Current C onditions : Maps of annual and 24

- hour PM 2. 5 

### design values for the 3

- year period

200 5

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

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

. Currently, there are 3 0

sites in vio lation of the annual PM 2. 5 

### standard.

Table 2 provides the annual PM 2. 5 

### concentrations and associated design values since 200 3 for

several high monitoring sites throughout the region.

### 22

Figure 1 6

. PM 2.5 

### d esign v alues

- a nnual a verage (top) and 24

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

- 2007)2005 BY 2002 BY

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

w/ 2007 Average

Chicago - Washington HS Cook 170310022 15.6 14.2 16.9 13.2 15.7 15.6 14.8 15.3 15.2 15.9

Chicago - Mayfair Cook 170310052 15.9 15.3 17.0 14.5 15.5 16.1 15.6 15.7 15.8 17.1

Chicago - Springfield Cook 170310057 15.6 13.8 16.7 13.5 15.1 15.4 14.7 15.1 15.0 15.6

Chicago - Lawndale Cook 170310076 14.8 14.2 16.6 13.5 14.3 15.2 14.8 14.8 14.9 15.6

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

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

Cicero Cook 170316005 16.8 15.2 16.3 14.3 14.8 16.1 15.3 15.1 15.5 16.4

Granite City Madison 171191007 17.5 15.4 18.2 16.3 15.1 17.0 16.6 16.5 16.7 17.3

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

Jeffersonville Clark 180190005 15.8 15.1 18.5 15.0 16.5 16.5 16.2 16.7 16.4 17.2

Jasper Dubois 180372001 15.7 14.4 16.9 13.5 14.4 15.7 14.9 14.9 15.2 15.5

Gary Lake 180890031 16.8 13.3 14.5 16.8 15.1 14.9 15.6

Indy - Washington Park Marion 180970078 15.5 14.3 16.4 14.1 15.8 15.4 14.9 15.4 15.3 16.2

Indy - W 18th Street Marion 180970081 16.2 15.0 17.9 14.2 16.1 16.4 15.7 16.1 16.0

Indy - Michigan Street Marion 180970083 16.3 15.0 17.5 14.1 15.9 16.3 15.5 15.8 15.9 16.6

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

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

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

Dearborn Wayne 261630033 19.2 16.8 18.6 16.1 16.9 18.2 17.2 17.2 17.5 19.3

Wyandotte Wayne 261630036 16.3 13.7 16.4 12.9 13.4 15.5 14.3 14.2 14.7 16.6

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

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

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

Cleveland-St. Tikhon Cuyahoga 390350038 17.6 17.5 19.2 14.9 16.2 18.1 17.2 16.8 17.4 18.4

Cleveland-Broadway Cuyahoga 390350045 16.4 15.3 19.3 14.0 15.3 17.0 16.2 16.2 16.5 16.7

Cleveland-E14 & Orange Cuyahoga 390350060 17.2 16.4 19.4 15.0 15.9 17.7 16.9 16.8 17.1 17.6

Newburg Hts - Harvard Ave Cuyahoga 390350065 15.6 15.2 18.6 13.1 15.8 16.5 15.6 15.8 16.0 16.2

Columbus - Fairgrounds Franklin 390490024 16.4 15.0 16.4 13.6 14.6 15.9 15.0 14.9 15.3 16.5

Columbus - Ann Street Franklin 390490025 15.3 14.6 16.4 13.6 14.7 15.4 14.9 14.9 15.1 16.0

Columbus - Maple Canyon Franklin 390490081 14.9 13.6 14.6 12.9 13.1 14.4 13.7 13.5 13.9 16.0

Cincinnati - Seymour Hamilton 390610014 17.0 15.9 19.8 15.5 16.5 17.6 17.1 17.3 17.3 17.7

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

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

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

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

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

Steubenville Jefferson 390810016 17.7 15.9 16.4 13.8 16.2 16.7 15.4 15.5 15.8 17.7

Mingo Junction Jefferson 390811001 17.3 16.2 18.1 14.6 15.6 17.2 16.3 16.1 16.5 17.5

Ironton Lawrence 390870010 14.3 13.7 17.0 14.4 15.0 15.0 15.0 15.4 15.2 15.7

Dayton Montgomery 391130032 15.9 14.5 17.4 13.6 15.6 15.9 15.2 15.5 15.5 15.9

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

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

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

Akron - Brittain Summit 391530017 15.4 15.0 16.4 13.5 14.4 15.6 15.0 14.8 15.1 16.4

Akron - W. Exchange Summit 391530023 14.2 13.9 15.7 12.8 13.7 14.6 14.1 14.1 14.3 15.6 Annual Average Conc. Design Values 

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

### 24

When 

### EPA initially set the 2 4

- hour s tandard at 65 μg/m 3



### , it also adopted the following

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

Good < 1 5 ug/m 3



### Moderate 15

- 40 μg/m 3



### Unhealthy for Sensitive Groups (USG) 40

- 65 μg/m 3



### Unhealthy 65

- 150 μg/m 3



### Fig ure 1 7 shows the frequency of these AQI categories for major metropolitan areas in the

region. Daily average concentrations are often in the moderate range and occasionally in the

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

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

### (2002

- 2004)



### Data Variability : PM 2. 5 

### concentrations vary spatially, temporally, and chemically in the region.

This variability is discussed furt her below.

On an annual basis, PM 2.5 

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

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

- 6 μg/m 3 

### ) in

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

- 18 μg/m 3 

### ) in

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

upwind rural areas, indicating that local urban sources add a significant increment of 2

- 3 μg/m 3



### to the regional backgro und of 12

- 14 μg/m 3 

### (see Figure 1 8 ).

Figure 1 8

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

### concentrations

### 25



### Because monitoring for PM 2. 5 

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

### standar d

, limited data are available to assess trends

. T ime series based on federal reference

method (FRM) PM 2. 5 

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



### .

Figure 19

. PM 2. 5 

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

7 

### Despite the general downward trend since 1999, all states experienced an increase during 2005.

Further analyses are underway to understand this increase (e.g., examination of meteorological and

emissions effects).

### 26



### A statistical analysis of PM 2. 5 

### trends was performed using the n onparametric Theil test for slope

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

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

### based on

FRM data at sites with six or more years of data since 1999. The size and direction of each

arrow shows the size and direction of the trend for each site; solid arrows sh ow statistically

significant trends and open arrows show trends that are not significant. Region

- wide decreases

are widespread and consistent; all sites had decreasing concentration trends ( 13 of the 38 were

statistically significant ) . The average decrea se for this set of sites is

- 0.24 ug/m 3 

### /year.

Figure 2 0

. Annual t rends in PM 2. 5 

### m ass ( 1999

– 2006 )



### Seasonal trends show mostly similar patterns (Fig ure 2 1 ). Trends were downward at most sites

and seasons, with overall seasonal averages varying b etween

- 0.15 to

- 0.56 ug/m 3 

### /year. The

strongest and most significant decreases took place during the winter quarter (Jan uary

- March ).

No statistically significant increasing trends were observed.

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

### m ass ( 1999

– 2006 )



### PM 2.5 

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

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

μg/m 3 

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

strongly source

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

than urban monitors.

Figure 2 2 Day

- of

- w eek v ariability in PM 2. 5 

### (2002

- 2004)

### 28



### In the Midwest, PM 2.5 

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

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

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

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

### chemical composition in the Midwest (2002

- 2003)



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

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

account for much of th e annual variability in PM 2.5 

### mass noted above.

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

### che mical species

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

### c omponents

### 30



### Ammonium sulfate peaks in the summer and is highest in the southern and eastern parts of the

Midwest, closest to the Ohio River Valley. Sulfate is primarily a regional pollutant;

concentrations are similar in rural and urban areas and highly correlated over large distances. It

is for med when sulfuric acid (an oxidation product of sulfur dioxide) and ammonia react in the

atmosphere, especially in cloud droplets. Coal combustion is the primary source of sulfur

dioxide; ammonia is emitted primarily from animal husbandry operations and f ertilizer use.

Ammonium nitrate has almost the opposite spatial and seasonal pattern, with the highest

concentrations occurring in the winter and in the northern parts of the region. Nitrate seems to

have both regional and local sources, because urban co ncentrations are higher than rural

upwind concentrations. Ammonium nitrate forms when nitric acid reacts with ammonia, a

process that is enhanced when temperatures are low and humidity is high. Nitric acid is a

product of the oxidation of nitric oxide, a pollutant that is emitted by combustion processes.

Organic carbon is more consistent from season to season and city to city , although

concentrations are generally slightly higher in the summe r . Like nitrate, organic carbon has

both regional and local co mponents. Particulate organic carbon can be emitted directly from

cars and other fuel combustion sources or formed in a secondary process as volatile organic

gases react and condense. In rural areas, summer organic carbon has significant contributions

fr om biogenic sources.

Precursor S ensitivity:

Data from the Midwest ammonia monitoring network were analyzed with

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

concentrations on PM 2.5 

### concentrations (Blanchard, 200 5 b ). These analyses indicate that

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

. Based on Figure 2 6 , which shows PM 2.5 

### concentrations as a function of sulfate, nitric acid

(HNO 3), and ammonia (NH3), several k ey findings should be noted:

• 

### P M 2. 5 

### mass is sensitive to reductions in sulfate at all times of the year and all parts of the

region . Even though sulfate reductions cause more ammonia to be available to form

ammonium nitrate ( PM

- nitrate increases slightly when sulfate is reduced ) , this increase

is generally offset by the sulfate reductions, such that PM 2.5 

### mass decreases.

• 

### PM 2. 5 

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

### decrease in response to nitric acid reductio ns occurs during the winter , when nitrate is a

significant fraction of PM 2. 5



### .

• 

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

### is

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

• 

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

Examination of weekend/weekday difference in PM

- nitrate and NOx concentrations in the

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

propo rtional reductions, in PM

- nitrate (Blanchard, 200 4 )

. This result is consistent with analyses

of continuous PM

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

### 31

Figure 2 6

. Predicted mean PM fine mass con centrations at Bondville, IL (top) and Detroit (Allen Park), MI

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

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

acid) or moving leftward (reductions in sulfate or ammonia) results in lower PM 2.5 

### values

### 32



### Meteorology : PM 2 .5 

### concentrations are not as strongly influenced by meteorolog y as ozone, but

the two pollutants share some similar meteorological dependencies. In the summer, conditions

that are conducive to ozone (hot temperatures, stagnant air masses

, and low wind speeds due

to stationary high pressure systems) also frequently give rise to high PM 2.5 

### . In the case of PM

, the reason is two

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

### to

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

conversion of important precursors (SO 2 

### to SO 4 

### ) and higher emissions of some precursors,

especially biogenic ca rbon. Wind direction is another strong determinant of PM 2.5 

### ; air

transported from polluted source regions has higher concentrations.

Unlike ozone, PM 2.5 

### has occasional winter episodes. Conditions are similar to those for summer

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

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

PM 2. 5 

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

demonstrated that high PM

- sulfate is associated with air masses that traveled through the

sulfate

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

, 2004). Likewise, high PM

- nitrate

is associated with air masses that traveled through the ammonia

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

Figure 2 7

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



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

2 6 )

, one possible conclusion is that ammonia control in the Midwest could be effective at

reducing nitrate concentrations. The thermodynamic equilibrium modeling shows that ammonia

reductions would reduce PM concentrations in the Mid west, but that nitric acid reductions are

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

### 33



### Source Culpability :

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

### monitoring data and statistical analysis methods (Hopke, 2005, STI, 2006, and STI, 200 8 ).

Figure 2 8 summarizes the source contributions from these studies. The studies show that a

large portion of PM 2. 5 

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

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

organic aerosols). Nevertheless, wind analyses (e.g., Figure 2 7 ) provide information on likely

source regions. Regional

- or national

- scale control programs may be the m ost effective way to

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

SO2 emissions over the eastern half of the U.S., which will reduce sulfate (and PM 2. 5 

### )

concentrations and improve visibility levels.

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

### mass is due to emissions

from nearby (local) sources. Local (urban) excesses occur in many urban areas for organic and

elemental carbon, crustal matter, and, in some cases, sulfate. The stat istical analysis methods

h elp to identify local sources and quantify their impact. This information is valuable to s tates

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

national/regional

- scale and local

- scale emission redu ctions may be necessary to provide for

attainment.

The carbon sources are not easily identified in complex urban environments. LADCO’s Urban

Organics Study (STI, 2006) identified four major sources of organic carbon: mobile sources,

burning, industrial s ources, and secondary organic aerosols. Additional sampling and analysis

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

carbon.

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

Cincinnati (Dec 03-Mar 05)

Indianapolis (Jan 02-Mar 05)

Allen Park (2002-2004)

Dearborn (Jun 02-May 05)

Bondville (2002-2004)

St. Louis (2004)

Granite City (2002-2004)

Tikhon (Jan 03-Jun05)

Orange (Apr 01-Mar 05)

Lorain (Feb 02-Dec04)

0 2 4 6 8 10

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

Dearborn

La w ndale

Springfield

Tikhon

Craig

Taft



### Figure 2 8

. Major Source Contributions in the Midwest based on Hopke

, 2005 (upper left), STI, 2006 (upper right), and STI, 200 8 (lower left)

(Note: the labeling of similar source types varies between studies

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

Hopke

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

### 35



### 2.3 Haze

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

, and the

remedying of any existing, impairment of visibility in mandatory Class I Federal areas which

imp airment re sults from manmade air pollution” . To impl ement this provision, in 1999, EPA

adopted regulations to address regional haze visibility impairment (USEPA, 1999)

. EPA’s rule

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

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

most impaired (20% worst) days sufficient to achieve natu ral conditions by the year 2064, and

for no degradation on the least impaired (20% best) days

.

The primary cause of impaired visibility in the Class I areas is pollution by fine particles that

scatter light. The degree of impairment, w hich is expressed in terms of visual range, light

extinction (1/Mm)

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

### mass concentration, but

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

Current C onditions : A map of the average l ight extinction values for the most impaired (20%

worst) visibility days for the 5

- year baseline period ( 2000

- 2004 ) is shown in Figure 2 9

.

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

– 2004) , units: Mm - 1

### 36



### Initially, the baseline (2000

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

the 20% worst and 20% best days for each year, as reported on the VIEWS website:

http://vista.cira.colostate.edu/views/Web/IMPROVE/SummaryData.aspx . These values were

calculated using the original IMPROVE equation for reconstructed light extinction

.

Three changes were made to the baseline calculations to produce a new set of values. First,

the reconstructed light extinction equation was revised by the IMPROVE Steering Committee in

2005

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

Second, d ue to sampler problems, the 2002

- 2004 data for Boundary Waters were invalid for

certain chemical species. (Note, sulfate and nitrate data were valid.) A “substituted” dat a set

was developed by using values from Voyageurs for the invalid species



## .

Third, LADCO identified a number of days during 2000

- 2004 where data capture at the Class I

monitors was incomplete ( Kenski, 2007b ). The missing data cause the se days to be exclu ded

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

species is significant ( i.e., above the 80 th 

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

the baseline calculations, because they are largely dominate d by anthropogenic sources. ( Only

one of these days is driven by high organic carbon, which might indicate non

- anthropogenic

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

results in a s mall

, but measurab le

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

to 0.8 dv )

.

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

Average Worst Day

DV, per RHR Average Worst Day DV,

with Missing Data Days Difference

BOWA 19.59 19.8 6 0.27

ISLE 20.74 21.59 0.85

SENE 24.16 24.38 0.22

VOYA 19.27 19.48 0.21



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

and northern Minnesota are presented in Table 4

. The updated baseline values reflect the most

current, complete understanding of visibility impairing effects and, as such, will be used for SIP

planning purposes.

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

Old IMPROVE Equation (Cite: VIEWS, November 2005)

20% Worst Days

2000 2001 2002 2003 2004 Baseline

Value 2018

UR I Value Natural

Conditions

Voyageurs 18.50 18.00 19.00 19.20 17.60 18.46 16.74 11.0 9

BWCA 19.85 19.99 19.68 19.73 17.65 19.38 17.47 11.21

Isle Royale 20.00 22.00 20.80 19.50 19.10 20.28 18.17 11.22

Seney 22.60 24.90 24.00 23.80 22.60 23.58 20.73 11.37

20% Best Days

2000 2001 2002 2003 2004 Baseline

Value Natur al

Conditions

Voyageurs 6.30 6.20 6.70 7.00 5.40 6.32 3.41

BWCA 5.90 6.52 6.93 6.67 5.61 6.33 3.53

Isle Royale 5.70 6.40 6.40 6.30 5.30 6.02 3.54

Seney 5.80 6.10 7.30 7.50 5.80 6.50 3.69

New IMPROVE Equation (Cite: VIEWS, March 2006)

20% Worst Days

2000 2001 2002 2003 2004 Baseline

Value 2018

UR I Value Natural

Conditions

Voyageurs 19.55 18.57 20.14 20.25 18.87 19.48 17.74 12.05

BWCA 20.20 20.04 20.76 20.13 18.18 19.86 17.94 11.61

Isle Royale 20.53 23.07 2 1.97 22.35 20.02 21.59 19.43 12.36

Seney 22.94 25.91 25.38 24.48 23.15 24.37 21.64 12.65

20% Best Days

2000 2001 2002 2003 2004 Baseline

Value Natural

Conditions

Voyageurs 7.01 7.12 7.53 7.68 6.37 7.14 4.26

BWCA 6.00 6.92 7.00 6 .45 5.77 6.43 3.42

Isle Royale 6.49 7.16 7.07 6.99 6.12 6.77 3.72

Seney 6.50 6.78 7.82 8.01 6.58 7.14 3.73

Notes: (1) BWCA values for 2002

- 2004 reflect "substituted" data.

(2) New IMPROVE equation values include Ken s ki, 200 7 adjustment for missing days

UR I

= uniform rate of improvement

### 38



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

natural conditions values for each Class I area were taken directly from EP A guidance (EPA,

2003). These values were calculated using the original IMPROVE equation. This equation was

revised by the IMPROVE Steering Committee in 2005, and the new IMPROVE equation was

used to calculate updated natural conditions values. The upda ted values are reported on the

VIEWS website.

A summary of the initial and updated natural conditions values are presented in Table 4 . The

updated natural conditions values (based on the new IMPROVE equation) will be used for SIP

planning purposes.

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

### chemical species

are ammonium sulfate, ammonium nitrate, and organic carbon. The contribution of these

species on the 20% best and 20% worst visibility days (based on 2000

– 2004 data) is provided

in Figure 30 . For the 20% worst visibility days, the contributions are: sulfate = 35

- 55%, nitrate =

25

- 30%, and organic carbon = 12

- 22%. Although the chemical c omposition is similar , sulfate

increases in importance from west to east and concentrations are highest at Seney ( the

easternmost site )

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

extinction than to PM 2. 5 

### mass because of their hygroscopic properties.

20% Best Days 20% Worst Days



### 0 20 40 60 80 100 120

VOYA BOWA ISRO Seney VOYA BOWA ISRO Seney sea salt

soil

elem. carbon

coarse mass

organic carbon

nitrate

sulfate

Figure 30 . Chemical composition of light extinction for 20% best visibility days (left) and 20%

worst visibility days (right) in terms of Mm - 1



### Analysis of PM 2. 5 

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

- protocol) sites

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

### - mass , sulfate, and

nitrate levels (see Figure 3 1 ). The Class I sites in northern Michigan and northern Minnesota , in

particular, are highly correlated for PM 2. 5 

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

.

### 39

Figure 3 1

. Correlations a mong IMPROVE (and IMPROVE

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



### Lo ng

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

significant decreases in total PM 2. 5 

### (

- 0.005 ug/year) and SO4 (

- 0.04 ug/year) and an increase in

NO3 (+0.01 ug/year). These PM 2. 5 

### and SO4 trends are generally consistent with long

- term

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

SO4 and PM 2. 5 

### (DeBell, et al, 2006 ). Detecting changes in nitrate has been hampered by

uncertainties in the IMPROVE data for particular years and

, thus

, this estimate should be

considered tentative.

Haze in the Midwest Class I areas has no strong seasonal pattern. P oor visibility days occur

throughout the year, as indicated in Fig ure 3 2

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

, such as Shenandoah National Park in Virginia, show a strong tendency for the worst air quality

days to occur in the summer months. ) This figure and Figure 3 3 (which presents the monthly

average light extinction values based on all sampling days) also show that sulfate and organic

carbon concentrations are higher in the summer, and nitrate concentrations are higher in the

winter, sugge sting the importance of different sources and meteorological conditions at different

times of the year.

### 40

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

– 2004)

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

### 42



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

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

analysis using data collected at Seney indicate d that PM 2.5 

### there is most sensitive to reductions

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

, 2004).

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

for a site in Minnesota

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

northern Class I areas (Blanchard, 2005 b )

. F igure 34 shows PM 2.5 

### co ncentrations as a function

of sulfate, nitric acid ( HNO3 ) , and ammonia ( NH3 ) . Reductions in sulfate (i.e., movement to the

left of baseline value [represented by the red star]), as well as reductions in nitric acid (i.e.,

movement downward) and NH3 (i.e., movement to the left), result in lower PM 2.5 

### concentrations.

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

### concentrations and

improve visibility in the northern Class I areas.

Figure 3 4 . Predicted PM 2.5 

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

in sulfate, nitric acid, and ammonia



### Meteorology and T

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

direction govern the movement of air masses from polluted areas to the cl eaner wilderness

areas. As noted above, increasing humidity increases the efficiency with which sulfate and

nitrate aerosols scatter light. Temperature and humidity together govern whether ammonium

nitrate can form from its precursor gases, nitric acid a nd ammonia. Temperature and sunlight

also play an indirect role in emissions of biogenic organic species that condense to form

particulate organic matter; emissions increase in the summer daylight hours.

Trajectory analyses were performed to understan d transport patterns for the 20% worst and

20% best visibility days. The composite results for the four northern Class I areas are provided

in Figure 3 5

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

areas are where the ai r is least likely to come from . As can be seen, bad air days are generally

associated with transport from regions located to the south, and good air days with transport

from Canada.

### 43

Figure 3 5 . Composite back trajectories f or light extinction

- 20% best visibility days (left) and

20% worst visibility days (right) ( 2000

– 2005 )



### Source Culpability : Air quality data analyses (including the trajectory analyses above) and

dispersion modeling were used to provide information on source region and source sector

contributions to regional haze in the northern Class I areas (see MRPO, 200 8 ). Based on this

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

well as Missouri, North Dakota, Iow a, Indiana and Illinois (see, for example, Figure 3 5 above).

The most important contributing pollutants and source sectors are SO2 emissions from

electrical generating units (EGUs) and certain non

- EGUs, which lead to sulfate formation, and

NOx emissions f rom a variety of source types (e.g., motor vehicles), which lead to nitrate

formation. Ammonia emissions from livestock waste and fertilizer applications are also

important, especially for nitrate formation.

A source apportionment study was performed usi ng monitoring data from Boundary Waters and

statistical analysis methods (DRI, 2005). The study shows that a large portion of PM 2.5 

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

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

sources contribute about 3

- 4% and mobile sources about 4

- 7 % to PM 2.5 

### mass.

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

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

### concentrations occurred during the summer,

with organic carbon being the dominant species. The higher summer organic carbon

concentrations were attributed mostly to secondary organic aerosols of biogenic origin b ec ause of the lack of primary emission markers, and concentrations of know biogenic

- related species

(e.g., pinonic acid

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

### 44

Figure 3 6 . Monthly concentrations of PM 2.5 

### species (top )

, and secondary and b iogenic

- related

organic carbon species in Seney (bottom )

### 45



### Although the Seney study showed that biomass burning was a relatively small contributor to

organic carbon on an annual average basis, episodic impacts are apparent (see, for example,

high organic carbon days in Figure 32). To assess further whether burning is a significant

contributor to visibility impairment in the northern Class I areas, the PM 2.5 

### chemical speciation

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

which are indicative of biomass burning impacts. Only a handful of such days were identified:

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

Site 2000 2001 2002 2003 2004

Voyageurs

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

Jun 28

Jul 19

Boundary Waters

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

Jul 19

Isle Royale

--- --- Jun 1 Aug 25

---

Jun 28

Seney

--- --- Jun 28

--- ---



### Back trajectories on these days point mostly to wildfi res in Canada. Elimination of these high

organic carbon concentration days has a small effect in lowering the baseline visibility levels in

the northern Class I areas (i.e., Minnesota Class I areas change by about 0.3 deciviews and

Michigan Class I areas change by less than 0.2 deciviews). This suggests that fire activity,

although significant on a few days, is on average a relatively small contributor to visibility

impairment in the northern Class I areas.

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

uncontrollable.

### 46 

### Section 3.0 Air Quality Modeling

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

planning efforts

. Used properly, models can assist policy makers in dec iding which control

programs are most effective in improving air quality, and meeting specific goals and objectives.

For example, models can be used to conduct “what if” analyses, which provide information for

policy makers on the effectiveness of candida te control programs.

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

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

and LADCO, 2007b.

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

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

and PM 2. 5 

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

3.1 Selection of Base Year

Two base year s were used in the modeling analyses: 2002 and 2005. EPA’s modeling

guidance recommends using 2002 as the baseline inventory year, but also allows for use of an

alternative baseline inventory year, especially a more recent ye ar. Initially , LADCO conducted

modeling with a 2002 base year (i.e., Base K/Round 4 modeling, which was completed in 2006)

. A decision was subsequently made to conduct modeling with a 2005 base year (i.e., Base

M/Round 5, which was completed in 2007). As discussed in the previous section, 2002 and

2005 both had above normal ozone conducive conditions, although 200 2 was more severe compared to 200 5 . Examination of multiple base years provides for a more complete technical

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

3.2 Future Years of Interest

To address the multiple attainment requirements f or ozone and PM 2.5 

### , and reasonable progress

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

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



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

### nonattainment

areas (attainment date 2010)

2012 Planning year for ozone moderate nonattainment areas and PM 2.5 

### nonattainment

areas , with 3

- yea r extension (attainment date 2013)

2018 First milestone year for regional haze planning

8 

### According to USE PA’ s ozone implementation rule (USEPA, 2005), emission reductions needed for

attainment must be implemented by the beginning of the ozone season immediately preceding the area’s

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

– i.e., emission reductions

should be in place by the beginning of the year preceding the attainment date (USEPA, 2007 c ). The logic

for requiring emissions reductions by the year (or season) immediately preceding the attainment year

follows from language in the Clean Air Act , and the ability for an area to receive up to two 1

- year

extensions. Therefore, emissions in the year preceding the attainment year should be at a level that is

consistent with attainment. It also follows that the year preceding the att ainment year should be modeled

for attainment planning purposes.

### 47



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

other future years, less rigorous emissions processing w as conducted (e.g., 20 12 emissions

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

3.3 Modeling System

The air quality analyses were conducted with the CAMx model, with emissions and meteorology

generated using EMS (and CONCEPT ) and MM5, respectively. The selection of CAMx as the

primary model is based on several factors: performance, operator considerations (e.g., ease of

application and resource requirements), technical support and documentation, model

extensions (e.g., 2

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

- in

grid), and model science

. CAMx model set

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

Base M (2005) Base K (2002)

• CAMx v4.50 * CAMx 4.30

• CB05 gas phase chemistry * CB

- IV wi th updated gas

- phase chemistry

• SOA chemistry updates * No SOA chemistry updates

• AERMOD dry deposition scheme * Wesley

- based dry deposition

• ISORROPIA inorganic chemistry • ISORROPIA inorganic chemistry

• SOAP organic chemistry • SOAP organic chemistry

• RADM aqueous phase chemistry • RADM aqueous phase chemistry

• PPM horizontal transport • PPM horizontal transport



### 3. 4 Domain/Grid Resolution

The National RPO grid projection w as used for this modeling. A subset of the RPO domain was

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

### and haze , the large eastern U.S. grid at 36 km (see

box on right side of Figure 3 6 ) was used . A PM 2. 5 

### sensitivity run was also performed for this

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

right side of Figure 3 7 ) w as used for most model runs . An ozone sensitivity run was also

performed with a 4km sub

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

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

higher resolution in the boundary layer.

Figure 3 7

. Modeling grids

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

# 12 km

36 km

### 48



### 3.5 Model Inputs : Meteorology

Meteorological inputs w ere derived using the Fifth

- Generation NCAR/Penn State Meteorological

Model (MM5)

– version 3.6.3 for the year s 2001

– 2003, and version 3.7 for the year 2 005

. The

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

.

Figure 3 8 . MM5 m odeling domain for 2001

- 2003 (left) and 2005 (right)



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

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

EPA and LADCO. The 36 km n on

- summer portion of the annual 2003 simulation was conducted

by Wisconsin DNR. The annual 2005 36/12 km (and summer season 4 km) MM5 modeling was

completed by Alpine Geophysics

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

summer season of 200 5.

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

metrics used to quantify model performance include mean observat ion, mean prediction, bias

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

calculated for several sub

- region s of the modeling domain (Figure 3 9 ) and represent hourly

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

monthly basis.

Figure 3 9 . Sub

- domains used for m odel performance for 2001

- 2003 (left) and 2005 (right)



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

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

### 49



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

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

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

.

Figure 40

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

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

underestimates

Note: months are represented from left to right (January to December) and days are represented

from top to bottom (1 to 30(31)

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

corner is December 31



### Wind Field

s : The win d fields are generally good . Wind speed bias is less than 0.5 m/sec and

wind speed error is consistently between 1.0 and 1.5 m/sec. Wind direction error is generally

within 15

- 30 degrees.

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

- pr edicted in the late spring and

summer months, and mixing ratio error is highest during this period. There is little bias and

error during the cooler months when there is less moisture in the air.

Rainfall: The modeled and observed rainfall totals show go od agreement spatially and in

terms of magnitude in the winter, fall, and early spring months. There are , however, large over

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

. These over

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

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

MM5. This over

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

- prediction

of wet dep

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

non

- convective rainfall output by MM5, but estimates wet scavenging by hydromete o rs using

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

effect on model performance for PM 2. 5 

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

attention.

### 50

Figure 4 1

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

July 2002 (top) and July 2005 (bottom)

### 51



### 3.6 Model Inputs: Emissions

E mission inventories were prepared for two base years: 2002 (Base K) and 2005 (Base M), and

several future years: 2008, 2009, 2012, and 2018. Further details of the emission inventories

are provide d in two summary reports (LADCO, 2006 a and LADCO, 200 8 a ) and the following

pages of the LADCO web site:

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

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

For on

- road, nonroad, ammonia, and biogenic sources, emissions were estimated by models.

For the other sectors (point sources, area sources, and MAR [comme rcial marine, air craft, and

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

RPOs

.

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

2005 ( Base M) are compared in Figure 4 2

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

– tons per day) and 6b (EGUs

– tons per year)

.

VOC NOx SO2

0 2000 4000 6000 8000 10000 12000

2002

(K) 2005

(M) 2002

(K) 2005

(M) 2002

(K) 2005

(M) 

### WI

OH

MI

IN

IL

0 2000 4000 6000 8000 10000 12000

2002

(K) 2005

(M) 2002

(K) 2005

(M) 2002

(K) 2005

(M) Area

Nonroad+MAR

Onroad

Non-EGU

EGU



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

- s tate LADCO r egion by state (top) and source

sector (bottom) , u nits: tons per summer weekday



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

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

Nonroad

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

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

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

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

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

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

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

MAR

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

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

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

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

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

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

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

OtherArea

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

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

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

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

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

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

U.S. Total 17876 21093 18638 18683 20512 24300 3856 4899 4100 4220 4418 5357 2075 2947 2062 2559 2189 2709 2735 2621 2570

On-Road

IL 446 341 314 268 260 197 151 890 748 578 528 474 300 201 9 4 3 13 10 6

IN 405 282 237 235 193 150 138 703 541 425 402 313 187 173 11 3 2 9 7 2

MI 522 351 335 269 303 217 163 926 722 680 501 619 385 204 14 4 3 12 9 3

OH 574 680 365 424 340 238 242 1035 934 609 693 512 270 274 18 4 4 16 12 4

WI 238 175 144 119 117 88 68 481 457 303 322 226 118 138 9 2 2 8 6 2

5-State Total 2185 1829 1395 1315 1213 890 762 4035 3402 2595 2446 2144 1260 990 61 17 14 58 44 17

U.S. Total 14263 7825 23499 13170

EGU

IL 9 7 8 6 8 9 7 712 305 227 275 244 231 224 1310 1158 944 958 789 810 869 13 34 77

IN 6 6 6 6 7 6 6 830 393 406 370 424 283 255 2499 2614 1267 1033 1263 1048 1036 16 73 74

MI 12 6 11 4 11 12 4 448 393 218 242 219 247 243 1103 1251 1022 667 1031 1058 725 15 25 29

OH 5 4 6 5 7 7 6 1139 408 330 280 322 271 285 3131 3405 1463 1326 994 701 983 28 94 80

WI 3 5 3 2 4 4 3 293 213 146 165 139 147 177 602 545 512 460 492 500 435 0 22 25

5-State Total 35 28 34 23 37 38 26 3422 1712 1327 1332 1348 1179 1184 8645 8973 5208 4444 4569 4117 4048 72 248 285

U.S. Total 214 140 195 124 197 215 138 14371 10316 7746 7274 7721 7007 6095 31839 34545 20163 16903 17629 14727 14133 685 1131 1571

Non-EGU

IL 313 221 286 218 305 350 258 356 330 334 218 338 343 235 373 423 251 335 257 249 346 16 17 19

IN 150 130 160 137 170 199 167 238 179 212 175 216 225 178 292 218 270 216 274 290 180 35 36 44

MI 123 116 115 119 122 139 140 216 240 208 242 214 229 271 162 158 166 148 171 185 163 20 21 25

OH 77 84 75 87 79 90 104 177 175 157 166 160 167 178 240 289 231 288 210 216 293 27 28 33

WI 88 84 97 87 104 120 106 98 97 91 93 92 94 81 163 156 154 152 155 156 85 0 0.1 0.1

5-State Total 751 635 733 648 780 898 775 1085 1021 1002 894 1020 1058 943 1230 1244 1072 1139 1067 1096 1067 98 102 121

U.S. Total 4087 3877 4409 4700 5378 6446 6730 6129 6435 6952 5759 5630 6093 6340 6970 1444 1777

IL 1681 1576 1470 1353 1432 1434 1217 2621 2010 1671 1572 1545 1287 1029 1725 1656 1212 1337 1059 1072 1251 119 155 189

IN 1045 1009 867 901 843 853 826 2134 1453 1339 1250 1248 989 819 2966 2902 1690 1294 1691 1492 1256 81 133 131

MI 1530 1546 1291 1311 1239 1139 1134 1958 1730 1429 1314 1349 1118 946 1356 1495 1260 865 1271 1312 927 183 190 190

OH 1432 1735 1165 1323 1137 1062 1082 2831 2048 1478 1619 1342 1001 1074 3416 3761 1732 1650 1240 953 1304 121 195 166

WI 1005 821 909 705 878 862 630 1128 1019 747 800 647 520 551 800 750 687 635 667 675 540 35 54 47

5-State Total 6693 6687 5702 5593 5529 5350 4889 10672 8260 6664 6555 6131 4915 4419 10263 10564 6581 5781 5928 5504 5280 539 727 723Heat Input (MMBTU/year) Scenario SO2 (tons/year) SO2 (lb/MMBTU) NOx (tons/year) NOx (lb/MMBTU)

IL 980,197,198 2001 - 2003 (average) 362,417 0.74 173,296 0.35

IPM 2.1.9 241,000 73,000

1,310,188,544 IPM3.0 (base) 277,337 0.423 70,378 0.107

IPM3.0 - will do 140,296 0.214 62,990 0.096

IPM3.0 - may do 140,296 0.214 62,990 0.096

IN 1,266,957,401 2001 - 2003 (average) 793,067 1.25 285,848 0.45

IPM 2.1.9 377,000 95,000

1,509,616,931 IPM3.0 (base) 361,835 0.479 90,913 0.120

IPM3.0 - will do 417,000 0.552 94,000 0.125

IPM3.0 - may do 417,000 0.552 94,000 0.125

MI 756,148,700 2001 - 2003 (average) 346,959 0.92 132,995 0.35

IPM 2.1.9 399,000 100,000

1,009,140,047 IPM3.0 (base) 244,151 0.484 79,962 0.158

IPM3.0 - will do 244,151 0.484 79,962 0.158

IPM3.0 - may do 244,151 0.484 79,962 0.158

OH 1,306,296,589 2001 - 2003 (average) 1,144,484 1.75 353,255 0.54

IPM 2.1.9 216,000 84,000

1,628,081,545 IPM3.0 (base) 316,883 0.389 96,103 0.118

IPM3.0 - will do 348,000 101,000

IPM3.0 - may do 348,000 101,000

WI 495,475,007 2001 - 2003 (average) 191,137 0.77 90,703 0.36

IPM 2.1.9 155,000 46,000

675,863,447 IPM3.0 (base) 127,930 0.379 56,526 0.167

IPM3.0 - will do 150,340 0.445 55,019 0.163

IPM3.0 - may do 62,439 0.185 46,154 0.137

IA 390,791,671 2001 - 2003 (average) 131,080 0.67 77,935 0.40

IPM 2.1.9 147,000 51,000

534,824,314 IPM3.0 (base) 115,938 0.434 59,994 0.224

IPM3.0 - will do 115,938 0.434 59,994 0.224

IPM3.0 - may do 100,762 0.377 58,748 0.220

MN 401,344,495 2001 - 2003 (average) 101,605 0.50 85,955 0.42

IPM 2.1.9 86,000 42,000

447,645,758 IPM3.0 (base) 61,739 0.276 41,550 0.186

IPM3.0 - will do 54,315 0.243 49,488 0.221

IPM3.0 - may do 51,290 0.229 39,085 0.175

MO 759,902,542 2001 - 2003 (average) 241,375 0.63 143,116 0.37

IPM 2.1.9 281,000 78,000

893,454,905 IPM3.0 (base) 243,684 0.545 72,950 0.163

IPM3.0 - will do 237,600 0.532 72,950 0.163

IPM3.0 - may do 237,600 0.532 72,950 0.163

ND 339,952,821 2001 - 2003 (average) 145,096 0.85 76,788 0.45

IPM 2.1.9 109,000 72,000

342,685,501 IPM3.0 (base) 41,149 0.240 44,164 0.258

IPM3.0 - will do 56,175 0.328 58,850 0.343

IPM3.0 - may do 56,175 0.328 58,850 0.343

SD 39,768,357 2001 - 2003 (average) 12,545 0.63 15,852 0.80

IPM 2.1.9 12,000 15,000

44,856,223 IPM3.0 (base) 4,464 0.199 2,548 0.114

IPM3.0 - will do 4,464 0.199 2,548 0.114

IPM3.0 - may do 4,464 0.199 2,548 0.114 

### Table 6b. EGU Emissions for Midwest States (2018)

### 54



### On

- road Sources : For 2002, EMS was run by LADCO using VMT and MOBILE6 inputs supplied

by the LADCO States. EMS was run to generate 36 days (weekda y, Saturday, Sunday for each

month) at 36 km, and 9 days (weekday, Saturday, Sunday for June

– August) at 12 km. For

2005

, CONCEPT was run by a contractor (Environ) using transportation data (e.g., VMT and

vehicle speeds) supplied by the state and local p lanning agencies in the LADCO States and

Minnesota for 24 networks. These data were first processed with T3 (Travel Demand Modeling

[TDM] Transformation Tool) to provide input files for CONCEPT to calculate link

- specific, hourly

emission estimates (Envir o n, 2008 ) . CONCEPT was run with meteorological data for a July and

January weekday, Saturday, and Sunday (July 15

– 17 and January 16

– 18). A sp atial plot of

emissions is provided in Figure 4 3

.

VOC Emissions NOx Emissions

Figure 4 3

. M otor v ehicle emissions for VOC (left) and NOx (right) for a July weekday (2005)



### Off

- road Sources : For 2002 and 2005, NMIM and NMIM2005, respectively, were run by

Wisconsin DNR. Additional off

- road sectors (i.e., commercial marine, aircraft, and railroads

[MAR] ) were handled separately. Local data for agricultural equipment, construction equipment,

commercial marine, recreational marine, and railroads were prepared by contractors (Environ

, 2004, and E.H. Pechan , 2004 ). For Base M, u pdated local data for railro ads and commercial

marine w ere prepared by a contractor (Environ , 2007 b, 2007 c )

. Table 7 compares the Base M

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

substantially lower commercial marine emissions and lower locomo tive NOx emissions.

Table 7 . Locomotive and c ommercial m arine e missions for the five LADCO States ( 2002 v. 2005)

Railroads (TPY) Commercial Marine (TPY)

2002 2005 2002 2005

VOC 7,890 7,625 1,562 828

CO 20,121 20,017 8,823 6,727

NOx 182,226 145, 132 64,441 42,336

PM 5,049 4,845 3,113 1,413

SO2 12,274 12,173 25,929 8,637

NH3 86 85

---- ----

### 55



### Area Sources : For 2002 and 2005, E MS was run by LADCO using data supplied by the LADCO

States to produce weekday, Saturday, and Sunday emissions for e ach month. For 2005,

s pecial attention was given to two source categories: industrial adhesi ve and sealant solvents

(which were dropped from the inventory to avoid double

- counting) and outdoor wood boilers

(which were added to the inventory)

.

Point Sourc es : For 2002 and 2005, EMS was run by LADCO using data supplied by the LADCO

States to produce weekday, Saturday, and Sunday emissions for each month. For EGUs, t he

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

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

.

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

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

t here is more regional isoprene using MEGA N compared to the BIOME estimates used for Base

K (see Figure 4 4 ). Also, with the secondary organic aerosol updates to the CAMx air quality

model, Base M includes emissions for monoterpenes and sesquiterpenes, which are pre

cursors of secondary PM 2. 5 

### organ ic carbon mass

.

Figure 4 4 . Isoprene emissions for Base M (left) v. Base K (right)



### Ammonia : For Base M, t he CMU

- based 2002 (Base K) ammonia emissions were projected to

2005 using growth factors from the Round 4 emissions modeling. These emissions were then

adjusted by applying temporal factors by month based on the process

- based ammonia

emissions model

(Zhang, et al, 2005, and Mansell, et al, 2005) . A plot of average daily

emissions by state and month is provided in Figure 4 5

. A spatial plot of emissi ons is provided in

Figure 4 6 , which shows high emissions densities in the central U.S.

0 300 600 900 1200 1500 1800

Jan Fed March Apr May Jun Jul Aug Sept Oct Nov Dec

Illinois Indiana Iowa Michigan

Minnesota Ohio Wisconsin



### Figure 4 5 . Average daily ammonia emissions for Midwest States by month (2005)

- (units: average

daily emissions

– tons per day)

### 56 Figure 4 6

. Ammonia emissions for a July weekday (2005)

– 12 km modeling domain



### Canadian Emissions: For Base M, Scott Edick (Michigan DEQ) processed the 2005 Canadian

National Pollutant Release Inventory , Version 1.0 (NPRI). Specifically, a subset of the NPRI

data (emissions and stack par ameters) relevant to the air quality modeling were reformatted

. The resulting emissions represent a significant improvement in the base year emissions.

A spatial plot of point source SO2 and NOx emissions is provided in Figure 4 7

. Additional plots

and emission reports are available on the LADCO website

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

Figure 4 7

. Canadian point source emissions for SO2 (left) and NOx (rig ht)

### 57



### Fires: For Base K, a contractor (EC/R , 2004 ) developed a 2001, 2002, and 2003 fire emissions

inventory for eight Midwest States (five LADCO states plus Iowa, Minnesota, and Missouri),

including emissions from wild fires, prescribed fires, and agricultu ral burns. Projected emissions

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

management” scenarios. A n early mode l sensitivity run showed very little difference in modeled

PM 2. 5 

### concentrations. Consequently, the fire emissions were not included in subsequent

modeling runs (i.e., they were not in the Base K or Base M modeling inventories)

.

Future Year Emissions : Complete e mission inventories were developed for several future years:

Base K

– 2009 , 2012, and 2018

, and Base M

– 2009 and 2018

. In addition, 2008 (Base K and

Base M) and 2012 (Base M) proxy inventories were estimated based on the 2009 and 2018

data. (Note, the EGU emissions for the Base M 2012 inventory were based on EPA’s IPM3.0

modeling.)

S ource se ctor emission summaries for the base years and future years are shown in Figure 4 8

. Additional detail is provided in Table s 6 a and 6b

.

VOC NOx SO2

0 3000 6000 9000 12000

2002 2005 2009 2012 2015 2018 2002 2005 2009 2012 2015 2018 2002 2005 2009 2012 2015 2018 Area

On-road

Nonroad

Non-EGU

EGU



### Figure 4 8 . Base year and future year emissions for 5

- State LADCO Region (TPD, July wee kday)



### For on

- road, and nonroad, the future year emissions were estimated by models (i.e.,

EMS/ CONCEPT and NMIM , respectively). One adjustment was made to the 2009 and 2018

motor vehicle emission files prepared by Environ with CONCEPT. To reflect newer

transportation modeling conducted by CATS for the Chicago area, emissions were increased by

9% in 2009 and 2018. The 2005 base year and adjusted 2009 and 2018 motor vehicle

emissions are provided in Table 8

.

### 58 

### Table 8 . Motor Vehicle Emissions Produced by C ONCEPT Modeling (July weekday

– ton s per day )

Year State Sum of CO Sum of TOG Sum of NO x Sum of PM2.5 Sum of SO2 Sum of NH3 Sum of VMT

2005 IL 3,684.3 341.5 748.2 12.9 9.6 35.9 344,087,819.6

IN 3,384.9 282.0 541.1 8.9 11.1 25.7 245,537,231.9

MI 4,210 .3 351.9 722.0 12.4 13.9 35.3 340,834,025.9

MN 2,569.1 218.7 380.5 6.3 7.6 17.7 170,024,599.7

OH 6,113.4 679.8 933.6 16.2 18.8 36.5 360,521,068.6

WI 2,206.0 175.1 457.5 7.8 9.2 19.7 189,123,964.3

Total 22,168.0 2,049.0 3,782.9 64.5 70.2 170.8 1,65 0,128,709.9

2009 IL 2,824.4 268.0 527.8 10.1 4.2 38.9 372,132,591.1

IN 2,839.5 234.9 401.9 6.7 2.8 26.1 249,817,026.3

MI 3,172.0 269.2 500.9 9.2 4.0 37.1 356,347,010.5

MN 2,256.8 206.3 307.5 5.1 2.3 21.5 204,443,017.8

OH 4,619.2 423.7 69 3.5 11.8 4.7 39.5 387,428,127.2

WI 1,673.4 119.4 322.1 5.7 2.3 20.6 197,729,964.9

Total 17,385.3 1,521.5 2,753.6 48.7 20.3 183.6 1,767,897,737.8

2018 IL 2,084.7 151.5 200.7 6.3 3.7 43.1 413,887,887.3

IN 2,217.3 138.4 173.0 4.4 2.6 30.2 28 8,042,232.1

MI 2,434.3 163.5 204.1 5.9 3.6 40.5 388,128,431.8

MN 1,799.6 123.1 137.1 3.6 2.2 24.9 237,022,213.7

OH 3,361.5 242.5 274.1 6.8 4.0 43.1 421,694,093.4

WI 1,255.5 68.4 138.5 3.9 2.0 22.2 218,277,167.5

Total 13,152.9 887.5 1,127.5 30.8 18.1 203.9 1,967,052,025.8

### 59



### For EGUs

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

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

scenarios were assumed:

Base K

1a: IPM2.1.9, wi th full trading and banking

1b: IPM2.1.9, with restricted trading (compliance with state

- specific emission budgets) and full trading
1d: IPM2.1.9, with restricted trading (compliance with state

- specific emission budgets)

Base M

5a: EPA’s IPM3.0 was assum ed as the future year base for EGUs.

5b: EPA’s IPM3.0, with several “will do” adjustments identified by the States. These adjustments should

reflect a legally binding commitment (e.g., signed contract, consent decree, or operating permit).

5c: EPA’s IPM 3.0, with several “may do” adjustments identified by the States. These adjustments reflect

less rigorous criteria, but should still be some type of public reality (e.g., BART determination or press

announcement).



### For other sectors (area, MAR, and non

- EGU point sources)

, the future year emissions for the

LADCO States were derived by applying growth and control factors to the base year inventory.

These factors were developed by a contractor (E.H. Pechan , 2005 and E.H. Pechan, 2007 ).

For the non

- LADCO Sta tes, future year emission files were based on dat a from other RPOs

.

Growth factors were based initially on EGAS (version 5.0), and were subsequently modified (for

select, priority categories) by examining emissions activity data. Due to a lack of informa tion on

future year conditions, the biogenic VOC and NOx emissions, and all Canadian emissions were

assumed to remain the constant between the base year and future years.

A “base” control scenario was prepared for each future year based on the following “ on the

books” controls :

On

- Highway Mobile Sources

• Federal Motor Vehicle Emission Control Program, low

- sulfur gasoline and ultra

- low sulfur diesel fuel

• I nspection

- m aintenance programs, including IL’s vehicle emissions tests (NE IL), IN’s vehicle

emissi ons testing program (NW IN), OH’s E

- check program (NE OH), and WI’ s vehicle inspection

program (SE WI)

– note: a speci al emissions modeling run was done for the Cincinnati/Dayton area to

reflect the removal of the state’s E

- check p rogram and inclusion of l ow RVP gasoline

• Refo rmulated gasoline , including in Chicago

- Gary,

- Lake County, IL,IN; and Milwaukee, Racine, WI

Off

- Highway Mobile Sources

• Federal control programs incorporated into NONROAD model (e.g., nonroad diesel rule), plus the

evaporative Large Spa rk Ignition and Recreational Vehicle standards

• Heavy

- duty diesel (2007) engine standard/Low sulfur fuel

• Federal railroad/locomotive standards

• Federal commercial marine vessel engine standards

Area Sources (Base M only)

• Consumer solvents

• AIM coatings

• Aeros ol coatings

• Portable fuel containers

Power Plants

• Title IV (Phases I and II)

• NOx SIP Call

• Clean Air Interstate Rule

### 60 Other Point Sources

• VOC 2

- , 4

- , 7

- , and 10

- year MACT standards

• C ombustion turbine MACT



### Other controls included in the modeling include: c onsent decrees (refineries, ethanol plants, and

ALCOA) 9



### , NOx RACT in Illinois and Ohio 10 

### , and BART for a few non

- EGU sources in Indiana

and Wisconsin

.

For Base K, several additional control scenarios were considered:

Scenario 2

– “base” controls plus ad ditional controls recommended in LADCO White

Papers for stationary and mobile sources

Scenario 3

– Scenario 2 plus additional White Papers for stationary and mobile sources

Scenario 4

– “base” controls plus additional candidate control measures under

discussion by State Commissioners

Scenario 5

– “base” controls plus additional candidate control measures identified by the

LADCO Project Team

3. 7 Basecase Mo d el ing Results

The purpose of the basecase modeling is to evaluate model performance (i.e., assess the

model's ability to reproduce the observed concentrations )

. The m odel performance evaluation

focus ed on the magnitude, spatial pattern, and temporal of modeled and measured

concentrations. This exercise wa s intended to assess whether, and to w hat degree, confidence

in the model is warranted (and to assess whether model improvements are necessary)

.

Model performance w as assess ed by comparing modeled and monitored concentrations.

Graphical (e.g., side

- by

- side spatial plots, time series plots, a nd scatter plots) and statistical

analyses were conducted

. No rigid acceptance/rejection criteria w ere used for this study.

Instead, the statisti cal guidelines recommended by EPA and other modeling studies (e.g.,

modeling by the other RPOs) w ere used to assess the re asonableness of the results

. The

model performance results presented here describe how well the model replicates observed

ozone and PM 2. 5 

### concentrations after a series of iterative improvements to model inputs.

Ozone : Spatial plots are provi ded for high ozone period s in June 2002 and June 2005 (see

Figure s 4 9a and 49b ). The plot s show that the model is doing a reasonable job of reproducing

the magnitude, day

- to

- day variation, and spatial pattern of ozone concentrations. There is a

tendency, however, to underestimate the magnitude of regional ozone levels. This is more

apparent with the 2002 modeling; the regional concentrations in the 2005 modeling agree better

with observations due to model and inventory improvements.

9 

### E.H. Pechan’s original control file included control factors for three sources in Wayne County, MI.

These control factors were not applied in the regional

- scale modeling to avoid double

- co unting with the

State’s local

- scale analysis for PM2.5

10 

### NOx RACT in Wisconsin is included in the 2005 basecase ( and EGU “will do” scenario ). NOx RACT in

Indiana was not included in the modeling inventory.

### 61

Figure 4 9a . Modeled (top) v. m onitored (bottom) 8

- h our o zone c oncentrations: June 20

– 25, 2002

### 62

Figure 4 9 b Modeled (top) v. m onitored (bottom) 8

- h our o zone c oncentrations: June 23

– 28 2005

### 63

Standard model performance statistics were g enerated for the entire 12 km domain, and by day

and by monitoring site. The domain

- wide mean normalized bias for the 2005 base year is

similar to that for the 2002 base year and is ge nerally within 30% (see Figure 50 ).

Fig ure 50 . Mean bias for summer 2005 (Base M) and summer 2002 (Base K)



### Station

- average metrics (over the entire summer) are shown in Figure 51 . The bias results

further demonstrate the model’s tendency to underestimate absolute ozone concentrations.

Figure 51 . Mean bias (left) and gross error (right) for summer 2005

### 64

A limited 4 km ozone analysis was performed by LADCO to address the effect of grid spacing.

For this modeling, 4 km grids were placed over Lake Michigan and the Detroit

- Cleveland area

(see Figure 5 2 ). Model inputs included 4 km emissions developed by LADCO (consistent with

Base K/Round 4) and the 4 km meteorology developed by Alpine Geophysics.

Figure 5 2

. 4 km grids for Lake Michigan region and Detroit

- Cleveland region



### Hourly time series plots were prepared for several monitors (see Figure 5 3 ). The results are

similar at 12 km and 4 km, with some site

- by

- site and day

- by

- day differences.

Figure 5 3 . Ozone time series plots for 12 km and 4 km modeling (June 17

- 29, 2002)

### 65

An additional diag nostic analysis was performed to assess the response of the modeling system

t o changes in emissions (Baker and Kenski, 2007) . Specifically, the 2002

- to

- 2005 change in

observed ozone concentrations was compared to the change in modeled ozone concentrations

based on the 95 th 

### percentile (and above) concentration values for each monitor

. This analysis

was also done with the inclusion of model performance criteria which eliminated poorly

performing days (i.e., error > 35%). The results show good agreement in t he modeled and

monitored ozone concentration change s (e.g., ozone improves by about 9

- 10 ppb between

2002 and 2005 according to the model and the measurements)

– see Figure 54

. This provides

further support for using the model to develop ozone control str ategies.

Figure 5 4 . Comparison of change in predicted and observed ozone concentrations (2002 v. 2005)

### 66

PM 2. 5 

### : Time series plots of the monthly average mean bias and annual fractional bias for Base

M and Base K are shown in Figure 5 5 . As can be seen , Base M model performance for most

species is fair (i.e., close to “no bias” throughout most of the year), with two main exceptions.

First, the Base M and Base K results for organic carbon are poor, suggesting the need for more

work on primary organic ca rbon emissions. Second, the Base M results for sulfate, while

acceptable (i.e., bias values are within 35%) , are not as good as the Base K results (e.g.,

noticeable underprediction during the summer months)

.

Figure 5 5 . PM 2. 5 

### Model p erformance

- m onthly a verage m ean b ias and a nnual f ractional b ias for

Base M (left column) and Base K (right column) 

## Base K

Base M

### 67

Two analyses were undertaken to understand sulfate model performance for 2005 :

• Assess Meteorological Influences: The MM5 model p erformance evaluation showed that

rainfall is over

- predicted by MM5 over most of the domain during the summer months

(LADCO, 2007 c ). Because CAMx does not explicitly use the rainfall output by MM5, this

may or may not result in over

- predict ion sulfate wet deposition (and under

- predict ion of

sulfate concentrations) . A sensitivity run was performed with no wet deposition for July,

August, and September . The resulting model performance (see green line in Figure 5 6)

showed a noticeable difference from the ba secase (i.e., higher sulfate concentrations),

and suggest s that further evaluation of MM5 precipitation fields may be warranted.

• Assess Emissions Influences: The major contributor to sulfate concentrations in the

region is SO2 emitted from EGUs. The base case modeling inventory for EGUs is based

on annual emissions, which were allocated to a typical weekday, Saturday, and Sunday

by month using CEM

- based temporal profiles. A sensitivity run was performed using

day

- specific emissions . The resulting model p erformance (see purple line in Figure 56 )

showed lit tle difference from the basecase.

Figure 5 6 . Monthly sulfate bias for Base M (MRPO EGU) v. two sensitivity analyses (Note: positive

values indicate over

- prediction, negative v alues indicate under

- prediction)



### To assess the effect of the wet deposition issue on future year modeled values, another

sensitivity run was conducted with no wet deposition in Quarters 2

- 3 for the base year

(2005) and 2018. The resulting future year val ues were only slightly different from the

current base strategy run . In general, t he future year values (without wet deposition)

were a little higher (+ 0.1 5 ug/m 3 

### or less ) in the Ohio Valley and a little lower (

- .1 0 ug/m 3



### of less) in the Great Lakes regio n. This sensitivity run provides a bound for sulfate wet

deposition issue in terms of the attainment test , given that having no wet deposition is

unrealistic. The results suggest that even with an improved wet deposition treatment,

the Base M strategy re sults are not expected to change very much

.

Time series plots of daily sulfate, nitrate, elemental carbon, and organic carbon concentrations

for three Mi dwest ern locations are presented in Figure s 5 7 (2002) and 5 8 (2005) . These results

are consistent wit h the model performance statistics (i.e., good agreement for sulfates and

nitrates and poor agreement [large underprediction] for organic carbon). Monthly Sulfate Bias (ug/m3)

-3 -2 -1 0 1 2 3 4 5

1 2 3 4 5 6 7 8 9 10 11 12

MRPO EGU MOG daily EGU No Wet Deposition

### 68 

## SULFATE NITRATE ORGANIC CARBON

Seney

Detroit

Chicago

Figure 5 7 . Time series of sulfate, nitrate, and organic carbon at three Midwest sites for 2005

### 69 

## SULFATE NITRATE ORGANIC CARBON

Seney

Detroit

Chicago

Figure 5 8 . Time series of sulfate, nit rate, and organic carbon at three Midwest sit es for 2005

### 70

In summary, model performance for ozone and PM 2. 5 

### is generally acceptable and can be

characterized as follows:

Ozone

• Good agreement between modeled and monitored concentration for higher

concentration levels (> 60 ppb)

– i.e., bias within 30 %

• Regional modeled concentrations appear to be underestimated in the 2002 base

year, but show better agreement (with monitored data) in the 2005 base year due to

model and inventory improvements.

• Day

- to

- day and hour

- to

- hour variation in and spatial patte rns of modeled

concentrations are consistent with monitored data

• Model accurately simulates the change in monitored ozone concentrations due to

reductions in precursor emissions.

PM 2. 5



### • Good agreement in the magnitude of fine particle mass, but some spec ies are

overestimated and some are underestimated (during periods of the year when it is

important)

• Sulfates: good agreement in the 2002 base year, but underestimated in

the summer in the 2005 base year due probably to meteorological factors

• Nitrates: slig htly overestimated in the winter in the 2002 base year, but

good a greement in the 2005 base year as a result of model and inventory

improvements

• Organic Carbon: grossly underestimated in the 2002 and 2005 base

years due likely to missing primary organic ca rbon emissions and,

possibly, other factors (e.g., grid resolution and model chemistry).

• Temporal variation and spatial patterns of modeled concentrations are consistent

with monitored data

Several observations should be noted on the implications of thes e model performance findings

on the attainment modeling presented in the following section. First, it has been demonstrated

that model performance overall is acceptable and, thus, the model can be used for air quality

planning purposes. Second, consisten t with EPA guidance, the model is used in a relative

sense to project future year values. EPA suggests that this approach “should reduce some of

the uncertainty attendant with using absolute model predictions alone” ( EPA, 2007a)

. Furthermore, the attainm ent modeling is supplemented by additional information to provide a

weight of evidence determination.

### 71 Section 4.0 Attainment Demonstration for Ozone and PM 2./ 5



### Air quality modeling and other information were used to determine whether existing (“on the

books”) controls would be sufficient to provide for attainment of the NAAQS for ozone and PM 2.5 

### and if not, then what additional emission reductions would be necessary for attainment

. Traditionally, attainment demonstrations involved a “bright line” t est in which a single modeled

value was compared to the ambient standard. To provide a more robust assessment of

expecte d future year air quality, EPA’ s modeling guidelines call for consideration of

supplemental information. This section summarizes the r esults of the primary (guideline)

modeling analysis and a weight of evidence de termination based on the modeling results and

other supplemental analyses

.

4.1 Future Year Modeling Results

The purpose of the future year modeling is to assess the effectiven ess of existing and possible

additional control programs. T he model was used in a relative sens e to project future year

values, which are then compared to the standard to determine attainment / nonattainment.

Specifically, t he modeling test consists of the following steps:

( 1 ) Calculate base year design values : F or ozone and PM 2.5 

### , the base year design

values were derived by averaging the three 3

- year periods centered on the

emissions base year:

2002 base year: 2000

- 2002, 2001

- 2003, and 2002

- 2004

2005 base year: 2003

- 2005, 2004

- 2006, and 2005

- 2007 11



### ( 2 ) Estimate the expected change in air quality: For each grid cell, a relative

reduction factor (RRF) is calculated by taking the ratio of the future year and

baseline modeling results.

( 3 ) Calculate future year valu es: For each grid cell (with a monitor), t he RRFs are

multiplied by the base year design values to project the future year values

( 4 ) Assess attainment: Future year values are compared to the NAAQS to assess

attainment or nonattainment.

A comparison of the 2 002 and 2005 base year design values for ozone and PM 2. 5 

### is provided in

Figure 5 9 . In general, the figure shows that the 2005 base year design values are much lower

than the 2002 base year design values, especially for ozone.

11 

### A handful of source

- oriented PM2.5 monitors in Illinois and Indiana were excluded from the annual

attainment test, because these monitors are not to be used to judging attainment of the annual standard.

### 72 Figure 5 9 . 2002 v. 2005 base year design values for ozone (top) and PM 2. 5 

### (bottom) 

## 2002 2005

Statis tical Summary

# Sites > NAAQS 93 9

Peak Value 99.0 ppb 90.0 ppb

Ave Exceedance Amount 7 ppb 2 ppb



## 2002 2005

Statistical Summary

# Sites > NAAQS 58 4 1

Peak Value 19.3 ug/m 3 

### 17.7 ug /m 3



### Ave Exceedance Amount 1.2 ug/m 3 

### 0.9 ug/m 3

### 73

O zone results are pr ovided for those grid cells with ozone monitors. The RRF calculation

consider s all nearby grid cells (i.e., 3x3 for 12 km modeling ) and a threshold of 85 ppb. (If there

were less than 10 days above this value , then the threshold was lowered until either there were

10 days or the threshold reached 70 ppb.) PM 2.5 

### results are provid ed for those grid cells with

FRM ( PM 2.5 

### - m ass) monitors. Spatial mapping w as performed to extrapolate PM 2.5 

### - speciation

data from STN and IMPROVE sites to FRM sites. RRF values for PM 2.5 

### were derived as a

function of quarter and chemical species.

Additional, hot

- spot modeling will be performed b y the states for certain PM 2.5 

### nonattainment

areas (e.g., Detroit, Cleveland, and Granite City) to address primary emissions from local point

sources which may not be adequately accounted for by the regional grid modeling. This

modeling will consist of Ga ussian dispersion modeling (e.g., AERMOD) performed in

accordance with EPA’s modeling guidance (see Section 5.3 of the April 2007 guidance

document). Further analyses will need to be undertaken to determine how to best combine the

regional modeling and th e hot

- spot modeling. This could mean some adjustment to the model

results presented in this document to reflect better the regional component.

The ozone and PM 2. 5 

### modeling results are provided in Appendix I for select monitors (high

concentration sites) in the 5

- state region for the following future years of interest: 2008 (ozone

only), 2009, 2012, and 2018. (Note, RRF values for ozone, and for PM 2. 5 

### by season and

chemical species are also included in Appendix I for key monitoring sites.) A summary of the

modeling results is provided in Table 9 (ozone) and Table 1 0 (PM 2. 5 

### ) , and s patial maps of the

Base M future year concent r ations are provided in Figures 60

- 6 2

.Key Sites 2018

Round 5 Round 4 Round 5 Round 4 Round 5 Round 4 Round 5

Lake Michigan Area

Chiwaukee 550590019 82.0 93.0 82.3 92.0 80.9 90.3 76.2

Racine 551010017 77.6 85.9 77.5 84.9 76.1 82.9 71.2

Milwaukee-Bayside 550190085 79.6 85.4 79.8 84.9 78.0 82.3 72.7

Harrington Beach 550890009 80.0 86.7 80.1 85.4 78.3 82.9 72.5

Manitowoc 550710007 81.3 80.3 80.8 78.9 78.6 76.3 72.5

Sheboygan 551170006 84.4 90.0 84.0 88.9 81.8 86.4 75.4

Kewaunee 550610002 78.9 82.5 78.1 81.0 75.9 79.1 69.9

Door County 550290004 84.8 83.6 83.9 81.8 81.5 79.3 74.7

Hammond 180892008 75.4 86.9 75.4 86.6 74.6 86.3 71.6

Whiting 180890030 77.0 77.0 76.2 73.1

Michigan City 180910005 74.2 87.4 73.9 86.5 72.5 85.4 68.1

Ogden Dunes 181270020 75.7 82.3 75.6 82.8 74.5 82.0 70.8

Holland 260050003 85.6 84.9 85.3 83.4 82.8 81.0 76.1

Jenison 261390005 77.9 78.7 77.1 77.6 74.5 75.5 68.7

Muskegon 261210039 80.8 82.7 80.5 81.5 78.0 79.4 71.9

Indianapolis Area

Noblesville 189571001 78.0 85.2 78.1 83.7 75.6 82.0 68.7

Fortville 180590003 73.9 85.1 73.9 83.8 71.4 82.1 65.1

Fort B. Harrison 180970050 74.8 84.8 75.1 83.7 73.2 82.4 69.1

Detroit Area

New Haven 260990009 82.7 86.3 81.4 85.3 80.2 83.5 76.1

Warren 260991003 82.5 84.3 81.3 83.3 80.7 81.9 77.6

Port Huron 261470005 79.0 80.5 77.5 79.1 75.5 77.0 70.9

Cleveland Area

Ashtabula 390071001 84.9 84.7 83.4 82.7 81.0 80.2 75.1

Geauga 390550004 75.7 90.3 74.7 88.8 72.7 86.2 67.3

Eastlake 390850003 82.8 84.2 81.9 82.8 80.5 80.6 76.2

Akron 391530020 79.3 83.0 78.1 81.4 75.6 78.5 68.7

Cincinnati Area

Wilmington 390271002 77.8 84.8 77.5 83.5 74.9 81.1 68.3

Sycamore 390610006 81.7 85.4 81.9 84.7 80.3 82.9 74.6

Lebanon 391650007 83.6 80.1 83.0 79.0 80.7 77.0 74.2

Columbus Area

London 390970007 75.4 79.9 75.0 78.4 72.6 76.5 66.3

New Albany 390490029 82.4 84.1 81.8 82.6 79.6 80.2 73.0

Franklin 290490028 77.0 77.7 75.9 76.5 74.1 74.7 69.0

St. Louis Area

W. Alton (MO) 291831002 82.4 86.1 81.0 85.2 78.6 84.0 74.9

Orchard (MO) 291831004 83.3 83.3 82.0 82.2 80.0 80.4 76.2

Sunset Hills (MO) 291890004 79.5 82.8 78.7 81.9 77.1 80.6 73.9

Arnold (MO) 290990012 78.7 78.4 77.2 77.4 75.6 75.8 72.0

Margaretta (MO) 295100086 79.8 84.0 79.3 83.4 77.9 82.5 74.4

Maryland Heights (MO) 291890014 84.5 83.4 81.7 78.1 2009 2012 2008 Table 9. Summary of Ozone Modeling ResultsCounty Site ID Site Round 5 Round4 Round 5 Round4 Round 5 Round4

Cook 170310022 Chicago - Washington HS 14.1 14.8 14.0 14.6 13.9 14.4

Cook 170310052 Chicago - Mayfair 14.4 15.8 14.2 15.5 13.9 15.0

Cook 170310057 Chicago - Springfield 13.9 14.5 13.8 14.3 13.7 14.1

Cook 170310076 Chicago - Lawndale 13.8 14.5 13.7 14.3 13.6 14.1

Cook 170312001 Blue Island 13.7 14.5 13.6 14.3 13.4 14.1

Cook 170313301 Summit 14.2 14.8 14.0 14.6 13.9 14.4

Cook 170316005 Cicero 14.4 15.3 14.3 15.1 14.2 14.9

Madison 171191007 Granite City 15.1 16.0 14.9 15.8 14.3 15.5

St. Clair 171630010 E. St. Louis 14.1 14.9 13.9 14.7 13.4 14.5

Clark 180190005 Jeffersonville 13.8 15.5 13.7 15.0 13.4 14.4

Dubois 180372001 Jasper 12.4 13.8 12.2 13.5 11.8 13.0

Lake 180890031 Gary 13.0 12.8 12.4

Marion 180970078 Indy-Washington Park 12.8 14.5 12.6 14.2 12.0 13.7

Marion 180970083 Indy- Michigan Street 13.4 14.8 13.1 14.9 12.6 14.0

Wayne 261630001 Allen Park 13.0 14.5 12.8 14.1 12.4 13.3

Wayne 261630015 Southwest HS 14.2 15.8 13.9 15.3 13.5 14.4

Wayne 261630016 Linwood 13.1 14.1 12.8 13.7 12.5 13.0

Wayne 261630033 Dearborn 15.8 17.7 15.5 17.1 15.1 16.1

Wayne 261630036 Wyandotte 13.1 15.1 12.8 14.7 12.5 13.9

Butler 390170003 Middleton 13.5 14.2 13.2 13.7 12.8 13.1

Butler 390170016 Fairfield 13.1 13.5 12.9 12.9 12.5 12.2

Cuyahoga 390350027 Cleveland-28th Street 13.5 14.4 13.2 13.8 12.7 12.9

Cuyahoga 390350038 Cleveland-St. Tikhon 15.2 16.1 14.8 15.4 14.3 14.4

Cuyahoga 390350045 Cleveland-Broadway 14.4 14.6 14.0 14.0 13.5 13.1

Cuyahoga 390350060 Cleveland-GT Craig 15.0 15.3 14.6 14.7 14.1 13.7

Cuyahoga 390350065 Newburg Hts - Harvard Ave 14.0 14.1 13.6 13.5 13.1 12.6

Franklin 390490024 Columbus - Fairgrounds 12.9 14.6 12.6 14.0 12.0 13.0

Franklin 390490025 Columbus - Ann Street 12.7 14.1 12.4 13.5 11.9 12.5

Franklin 390490081 Columbus - Maple Canyon 11.7 14.0 11.4 13.4 10.9 12.5

Hamilton 390610014 Cincinnati - Seymour 14.5 15.5 14.3 14.8 13.8 14.0

Hamilton 390610040 Cincinnati - Taft Ave 12.8 13.6 12.6 13.0 12.2 12.3

Hamilton 390610042 Cincinnati - 8th Ave 14.0 14.6 13.8 14.0 13.4 13.2

Hamilton 390610043 Sharonville 12.9 13.6 12.7 13.0 12.3 12.2

Hamilton 390617001 Norwood 13.4 14.2 13.2 13.6 12.8 12.8

Hamilton 390618001 St. Bernard 14.7 15.2 14.4 14.6 14.0 13.8

Jefferson 390810016 Steubenville 12.8 16.3 12.5 15.9 12.7 16.2

Jefferson 390811001 Mingo Junction 13.5 15.5 13.2 15.0 13.4 15.3

Lawrence 390870010 Ironton 12.8 14.2 12.5 13.7 12.3 13.2

Montgomery 391130032 Dayton 13.2 13.7 12.9 13.2 12.4 12.3

Scioto 391450013 New Boston 12.1 15.4 11.9 14.8 11.6 14.2

Stark 391510017 Canton - Dueber 14.0 15.0 13.6 14.3 13.3 13.6

Stark 391510020 Canton - Market 12.6 13.6 12.3 13.0 11.9 12.2

Summit 391530017 Akron - Brittain 13.0 14.4 12.7 13.6 12.3 12.9

Summit 391530023 Akron - W. Exchange 12.3 13.6 12.0 13.0 11.5 12.2 2009 2012 2018 Table 10. Summary of PM2.5 Modeling Results

### 76 2005 (observed) 2009

2012 2018

Figure 60 . Observed base year and projected future year design values for ozone

– Base M

### 77 2005 (observed) 2009

2012 2018

Figure 61 . Observed base year and projected future year design values for PM 2. 5 

### (annual average)

– Base M

### 78 2005 (observed) 2009

2012 2018

Figure 6 2 . Observed base year and projected future year design values for PM 2. 5 

### (24

- hr average)

- Base M

### 79

The number of monitors with design va lues above the standard are as follows:

Table 11 . Number of sites above standard

Ozone (8 hour: 85 ppb)

State 2002 2005 2009 2012 2018

BaseK Base M BaseK Base M BaseK Base M BaseK Base M

IL 3 0 0 0 0 0 0 0

IN 22 0 0 0 0 0 0 0

MI 15 3 1 1 0 0 0 0

OH 40 4 1 0 1 0 0 0

WI 13 2 4 0 3 0 1 0

Total 93 9 6 1 4 0 1 0

PM2.5 (Annual: 15 ug/m 3 

### )

State 2002 2005 2009 2012 2018

BaseK Base M BaseK Base M BaseK Base M BaseK Base M

IL 11 7 3 1 3 0 2 0

IN 10 6 1 0 1 0 0 0

MI 6 2 3 1 2 1 0 0

OH 31 2 6 7 1 4 0 1 1

WI 0 0 0 0 0 0 2 0

Total 58 41 14 3 10 1 5 1



### The modeling results above reflect the “base” controls identified in Sect ion 3.6 , with EGU

emissions based on IPM modeling (i.e., Round 4

– IPM2.1.9, and Round 5

– IPM3.0). In

addition, two sets of alternative future year EGU emissions were examined in Round 5. First,

alternative control assumptions were provided for several facilities by the states (i.e., “will do”

and “may do” scenarios). In general, these scenarios produced a small change in future year

ozone and PM 2. 5 

### concentrations (i.e., about 0.1 ug/ m 3 

### for PM 2. 5 

### and 0. 1

- 0.2 ppb for ozone).

Second, EPA suggested adjust ments to the 2010 IPM emissions to reflect 2009 conditions. The

revised (2009) SO2 emissions represent a 5

- 6% increase in domainwide SO2 emissions. The

increased SO2 emissions result in slightly greater annual average PM 2. 5 

### concentrations (on the

order o f 0.1

– 0.2 ug/m 3 

### ) , but do not produce any new residual nonattainment areas.

### 80

The limited 4 km ozone modeling (based on Base K) performed by LADCO included a future

year analysis for 2009. The figure below show s the 2009 values with 12 km and 4 km grid

sp acing for the LADCO modeling and similar modeling conducted by a stakeholder group

(M idwest Ozone Group ).

70 75 80 85 90 95

Cook Lake

LaPorte Porter Allegan Muskegon Kenosha

Milwaukee Ozaukee Racine

Sheboygan

MOG-12km MOG-4km LADCO-12km LADCO-4km

70 75 80 85 90 95

New Haven Warren Geauga Eastlake Portage Akron

MOG-12km MOG-4km LADCO-12km LADCO-4km



### Figure 63 . Future y ear (2009) v alues for Lake Michigan area (top) and D etroit

- Cleveland r egion

(bottom)



### T hese results show that the 12 km and 4 km values are similar, with the most notable changes

in northwestern Indiana and northeastern Illinois (e.g., 4 km values are as much as 4 ppb lower

than 12 km values). The differences in the southern part of the Lake Michigan area are

plausible, given the tight emissions gradient there (i.e., finer grid resolution appears to provide

more appropriate representation).

In light of these findings, 12 km grid spacing can continue to be used for ozone modeling, but

the Base K/Round 4 resu lts for northwester n Indiana /northeastern Illinois should be viewed with

caution (i.e., p robably 1

– 4 ppb too high).

### 81

In summary, the ozone modeling provide s the following information for the nonattainment areas

in the region (see Table 1 2 ):

Table 1 2 . Ozone Nonattain ment Ar eas in the LADCO Region (as of December 31 , 2007)

Area Name Category Number of

C ounties Attainment

D eadline

Detroit

- Ann Arbor, MI Marginal 8 2007

Chicago

- Gary

- Lake County, IL

- IN Moderate 10 2010

Cleveland

- Akron

- Lorain, OH Moderate 8 2010

Milwaukee

- Racine, WI Moderate 6 2010

Sheboygan, WI Moderate 1 2010

St Louis, MO

- IL Moderate 4 2010

Allegan Co, MI Subpart 1 1 2009

Cincinnati

- Hamilton, OH

- KY

- IN Subpart 1 6 2009

Columbus, OH Subpart 1 6 2009

Door Co, WI Subpart 1 1 2009

Kewaunee Co, WI Subpart 1 1 2009

Manitowoc Co, WI Subpart 1 1 2009

53



### Marginal Areas (2007 attainment date): No modeling was conducted for the 2006 SIP p lanning

year. Rather, 2005

– 2007 air quality data are available to determine attainment.

Basi c (Subpart 1) Areas (2009 attainment date): The modeling results for the 2008 SIP planning

year show :

• Base K: all areas in attainment, except Cincinnati and Ind ianapolis

• Base M: all areas in attainment, except Holland (Allegan County)

Moderate Areas (2010 attainment date): The modeling results for the 2009 SIP planning year

show:

• Base K: all areas still in nonattainment

• Base M: all areas in attainment

The PM 2. 5 

### modeling results show:

• Base K: all areas in attainment, except for Chicago, Cincinnati, Cleveland, Detroit,

Granite City (IL), Louisville, Portsmouth (OH), and Steubenville

• Base M: all areas in attainment, except for Cleveland, Detroit, and Granite City (IL)

With respect to the new lower 8

- hour ozone standard, the modeling about 30 sites in 2012 and

5 sites in 2018 with design values greater than 7 5 ppb. With respect to the new lower 24

- hour

PM 2. 5 

### standard, the modeling shows 13 sites in 2012 and 10 in 2018 with design values greater

t han 35 ug/m 3



### .

### 82

4.2 Supplemental Analyses

EPA’s modeling guidelines recommend that attainment demonstrations consist of a primary

(guideline) modeling analysis and supplemental analyses. Three basic types of sup plemental

an alyses are recommend ed:

• additional modeling

• analyses of trends in ambient air quality and emissions, and

• observational models and diagnostic analyses

Furthermore, according to EPA’s guid elines , if the future year model ed values are “close” to the

standar d (i.e., 82

– 87 ppb for ozone and 14.5

– 15.5 ug/m 3 

### for PM 2. 5 

### ) , then the results of the

primary modeling should be reviewed along with the supplemental information in a “weight of

evidence” assessment of whether each area is likely to achieve timely attai nment.

A WOE determination for ozone and PM 2. 5 

### is provided in the follow ing sections . Special

attention is given to the following areas with future year modeled values that exceed or are

“close” to the ambient standard (see Appendix I) :

Ozo ne PM2.5

Lake Michigan area Chicago, IL

Cleveland, OH Cleveland, OH

Cincinnati, OH Cincinnati, OH

Granite City, IL

Detroit, MI



### 4.3 Weight

- of

- Evidence De termination for Ozone

The WOE determination for ozone consists of the prim ary modeling and other supplemental

analyses (some of which were discussed in Section 2)

. A summary of this information is

provided below.

Primary ( Guideline

) Modeling : The guideline modeling is presented i n Section 4.1. Key findings

from th is modeling include:

• B ase M regional modeling shows attainment by 2008 and 2009 at all sites, except

Holland (MI), and attainment at all sites by 2012.

• B ase K modeling results reflect generally higher future year values, and show more

sites in nonattainm ent compared to the Base M modeling. The difference in the two

modeling analyses is due mostly to lower base year design values in Base M.



## • Base K and Base M modeling analyses are considered “SIP quality”, so the

attainment demonstration for ozone should reflect a weight

- of

- evidence approach,

with consideration of monitoring based information.

• Base M mode ling also shows that the proposed lower 8

- hour standard wi ll not be

met at many sites , even by 2018, with existing controls.

A dditional

Modeling : Four a dditional modeling analyses were considered: (1) re

- examination of

the primary modeling to estimate attainment probabilities, (2) remodeling with different

assumptions, ( 3 ) an unmonitored area analysis, and ( 4 ) EPA’s latest regional ozone modeling.

Each o f these analyses is described below.

### 83

First, the primary modeling results (which were initially processed using EPA’s attainment test)

were re

- examined to estimate the probability of attaining the ozone standard (Lopez, 2007, and

LADCO, 2008b) . Seven estim ates of future year ozone concentrations were calculated based

on model

- based RRFs and appropriate monitor

- based concentrations for each year between

2001 and 2007. R RF values for 2001, 2003, 2004, 2006, and 2007 were derived based on the

2002 and 2005 mo deling results . M onitor

- based concentrations reflect 4 th 

### high values, design

values, or average of three design values centered on the year in question . The probability of

attainment was de termined as the percentage of these seven estimates below the sta ndard.

The results indicate that sites in the Lake Michigan area (Chiwaukee, Sheboygan, Holland,

Muskegon), Cleveland (Ashtabula), and St. Louis (W Alton) have a fairly low probability of

attainment by 2009 (i.e., about 50% or less).

Second , the primary modeling analysis was re done with different types of assump tions for

calculating base year design values (i.e., using the 3

- year period centered on base year , and

using the h ighest 3

- year period that includes the base year ), and for calculating RRFs (i.e.,

using all days with base year modeled value > 70 ppb , and using all days with base year

modeled value > 85 ppb, with at least 10 days an d “acceptable” model performance )

. The

results for several high concentration sites are presented in Table s 1 3 a and 13 b for 2009. The

different modeling assumptions produce eight estimates of future year ozone concentrations.

The highest estimates are associated with base year design values representing the 3

- year average for 2001

- 2003, and the lowest estimates are asso ciated with base year design values

representing the 3

- year average 2004

- 2006. The different RRF approaches produce little

change in future year ozone concentrations. This suggests that future year concentration

estimates are most sensitive to the choice of the base year and the methodology used to derive

the base year design values.

Third

, EPA’s modeling guidelines recommend that an “unmonitored area analysis” be included

as a supplemental analysis , particularly in nonattainment areas where the monitori ng network

just meets o r minimally exceeds the size of the network required to report data to EPA’s Air

Quality System . The purpose of this analysis is to identify areas where future year values are

predicte d to be greater than the NAAQS.

Based on exam ination of the spatial plots in Figures 4 9a and 49b , the most notable areas of

high modeled ozone concentrations are over the Great Lakes. Over

- water monitoring, however,

is not required by EPA 12



### . A cursory analysis of unmonitored areas for ozone was perf ormed by

LADCO using an earlier version of the 2002 base year modeling (i.e, Base I) (Baker, 2005).

Base year and future year “observed” values were derived for unmonitored grid cells using the

absolute modeled concentrations (in all grid cells) and the o bserved values (in monitored grid

cells). A spatial map of the estimated 2009 values is provided in Figure 6 4 . As can be seen,

there are very few (over land) grid cells where additional monitors may be desirable

. This

indicates that the current modeling analysis , which focuses on monitored locations, is

addressing areas o f high ozone throughout the region.

12 

### Air quality measurements over Lake Michigan were collected by LADCO previously to understand

oz one transport in the area (see, for example, Figure 5). Due to cut

- backs in USEPA funding, however,

these measurements were discontinued in 2003.

### 84 Table 1 3 a. Primary and Additional Ozone Modeling Results

– Lake Michigan and Cleveland Areas (2009)

2009 Modeling Results Lake Michigan Area C leveland Area

Chiwaukee Harr.Beach Sheboygan DoorCounty Holland Hammond MichiganCity Ashtabula Geauga Eastlake

550590019 550890009 551170006 550290004 260050003 180892008 180910005 390071001 390550004 390850003

Attainment Test

(based on EPA guidan ce

- 2002 baseyear)

Base Year Design Value
(average of three 3

- year periods) 98.3 93.0 97.0 91.0 94.0 88.3 90.3 95.7 99.0 92.7

RRF (all days > 85 ppb, or at least 10 days) 0.935 0.918 0.916 0.899 0.888 0.980 0.958 0.865 0.897 0.894

Future Year Design Value 91.9 85.4 88.9 81.8 83.5 86.5 86.5 82.8 88.8 82.9

Attainment Test

(based on EPA guidance

- 2005 baseyear)

Base Year Design Value
(average of three 3

- year periods) 84.7 83.3 88.0 88.7 90.0 77.7 77.0 89.0 79.3 86.3

RRF (all days > 85 ppb, or at least 10 days) 0.972 0.961 0.955 0.946 0.948 0.971 0.960 0.937 0.942 0.949

Future Year Design Value 82.3 80.1 84.0 83.9 85.3 75.4 73.9 83.4 74.7 81.9

Weight of Evidence

(alternative approaches

- 2002bas eyear)

Alt 1

- Base Year Des. Value

(3

- year period centered on 2002) 101.0 98.0 100.0 94.0 97.0 90.0 93.0 99.0 103.0 95.0

Alt 2

- Base Year Des. Value
(Highest 3

- year period including 2002 ) 101.0 98.0 100.0 94.0 97.0 92.0 93.0 99.0 103 9 5.0

RRF (all days > 85 ppb, or at least 10 days) 0.935 0.918 0.916 0.899 0.888 0.980 0.958 0.865 0.897 0.894

Alt 1

- Future Year Projected Value 94.4 90.0 91.6 84.5 86.1 88.2 89.1 85.6 92.4 84.9

Alt 2

- Future Year Projected Value 94. 4 90.0 91.6 84.5 86.1 90.2 89.1 85.6 92.4 84.9

Alt 1

- RRF (all days > 70 ppb) 0.933 0.918 0.912 0.907 0.893 0.969 0.947 0.876 0.907 0.900

Alt 1

- Future Year Projected Value 94.2 90.0 91.2 85.3 86.6 87.2 88.1 86.7 93.4 85.5

Alt 2

- Future Year Pro

jected Value 94.2 90.0 91.2 85.3 86.6 89.1 88.1 86.7 93.4 85.5

Alt 2

- RRF (all days > 85 ppb, or at least 10

days; with acceptable model performance) 0.945 0.904 0.910 0.904 0.887 0.976 0.964 0.866 0.896 0.894

Alt 1

- Future Year Projected Value 95 .4 88.6 91.0 85.0 86.0 87.8 89.7 85.7 92.3 84.9

Alt 2

- Future Year Projected Value 95.4 88.6 91.0 85.0 86.0 89.8 89.7 85.7 92.3 84.9

Weight of Evidence

(alternative approaches

- 2005baseyear)

Alt 1

- Base Year Des. Value

(3

- y ear period centered on 2005) 83.0 79.0 86.0 86.0 88.0 76.0 76.0 86.0 77.0 86.0

Alt 2

- Base Year Des. Value
(Highest 3

- year period including 2005) 86.0 88.0 89.0 90.0 93.0 79.0 78.0 91.0 86.0 89.0

Alt 1

- Future Year Projected Value 80.7 75.9 82.1 8 1.4 83.4 73.8 73.0 80.6 72.5 81.6

Alt 2

- Future Year Projected Value 83.6 84.6 85.0 85.1 88.2 76.7 74.9 85.3 81.0 84.5

### 85

Table 1 3 b. Primary and Additional Ozone Modeling Results

– Cincinnati, Columbus, St. Louis, Indianapolis, and Detroit (2009)

2009 M odeling Results Cincinnati Area Columbus St. Louis Area Indianapolis Area Detroit Area

Wilmington Lebanon Sycamore New Albany W. Alton OrchardFarm Noblesville Fortville New Haven

390271002 39165007 390610006 390490029 291831002 291831004 1 80571001 18059003 260990009

Attainment Test

(based on EPA guidance

- 2002 baseyear)

Base Year Design Value
(average of three 3

- year periods) 94.3 90.7 90.7 94.0 90.0 90.0 93.7 91.3 92.3

RRF (all days > 85 ppb, or at least 10 days) 0.8 85 0.908 0.938 0.888 0.947 0.914 0.894 0.918 0.924

Future Year Design Value 83.5 82.4 85.1 83.5 85.2 82.3 83.8 83.8 85.3

Attainment Test

(based on EPA guidance

- 2005 baseyear)

Base Year Design Value
(average of thre e 3

- year periods) 82.3 87.7 84.3 86.3 86.3 87.0 83.3 78.7 86.0

RRF (all days > 85 ppb, or at least 10 days) 0.941 0.947 0.967 0.947 0.938 0.942 0.945 0.947 0.947

Future Year Design Value 77.4 83.1 81.5 81.7 80.9 82.0 78.7 74.5 81.4

Weight of Evidence

(alternative approaches

- 2002baseyear)

Alt 1

- Base Year Des. Value

(3

- year period centered on 2002) 96.0 92.0 93.0 95.0 91.0 92.0 96.0 94.0 97.0

Alt 2

- Base Year Des. Value
(Highest 3

- year period including 2002 ) 96.0 92.0 93.0 96.0 91.0 92.0 96.0 94.0 97.0

RRF (all days > 85 ppb, or at least 10 days) 0.885 0.908 0.938 0.888 0.947 0.914 0.894 0.918 0.924

Alt 1

- Future Year Projected Value 85.0 83.5 87.2 84.4 86.2 84.1 85.8 8 6.3 89.6

Alt 2

- Future Year Projected Value 85.0 83.5 87.2 85.2 86.2 84.1 85.8 86.3 89.6

Alt 1

- RRF (all days > 70 ppb) 0.885 0.914 0.940 0.901 0.945 0.911 0.912 0.907 0.918

Alt 1

- Future Year Projected Value 85.0 84.1 87.4 85.6 86.0 83 .8 87.6 85.3 89.0

Alt 2

- Future Year Projected Value 85.0 84.1 87.4 86.5 86.0 83.8 87.6 85.3 89.0

Alt 2

- RRF (all days > 85 ppb, or at least 10 days;

with acceptable model performance) 0.880 0.911 0.940 0.886 0.951 0.913 0.894 0.916 0.935

Alt 1

- Future Year Projected Value 84.5 83.8 87.4 84.2 86.5 84.0 85.8 86.1 90.7

Alt 2

- Future Year Projected Value 84.5 83.8 87.4 85.1 86.5 84.0 85.8 86.1 90.7

Weight of Evidence

(alternative approaches

- 2005baseyear)

Alt 1

- Base Year Des. Value

(3

- year period centered on 2005) 80.0 86.0 81.0 84.0 85.0 86.0 80.0 76.0 82.0

Alt 2

- Base Year Des. Value
(Highest 3

- year period including 2005) 85.0 89.0 86.0 88.0 89.0 89.0 87.0 81.0 90.0

Alt 1

- Future Yea r Projected Value 75.3 81.4 78.3 79.5 79.7 81.0 75.6 72.0 77.7

Alt 2

- Future Year Projected Value 80.0 84.3 83.2 83.3 83.5 83.8 82.2 76.7 85.2

### 86 Fig ure 64. Estimated Future Year Values (unmonitored grid cells)



### Finally, EPA’s latest regional ozone modeling was considered as corroborative information

. This modeling was performed as part of the June 2007 proposal to revise the ozone standard

(EPA, 2007 b ). EPA applied the CMAQ model with 2001 meteorology to first estimate ozone

levels in 2020 based on the current standard and national rules in effect or proposed (i.e., the

baseline), and then to evaluate strategies for attaining a more stringent (70 ppb) primary

standard. Baseline (2020) ozone levels were predicted to be below the current stan dard in 481

of the 491 counties with ozone monitors . Of the 10 counties predicted to be above the

standard, there is one county in the LADCO region (i.e., Kenosha County, WI at 86 ppb ) . This

result is consistent with LADCO’s Base K modeling for 2018 (i.e ., Kenosha County, WI at 86.7

ppb), which is not surprising given that EPA’s modeling and LADCO’s Base K modeling have a

similar base year (2001 v. 2002).

Analysis of T

rends : EPA’s modeling guid elines note that while air quality models are generally

the m ost appropriate tools for assessing the expected impacts of a change in emissions, it may

also be possible to extrapolate future trends based on measured historical trends of air quality

and emissions. To do so, USEPA’s guidance suggests that ambient tren ds should first be

normalized to account for year

- to

- year variations in meteorological conditions ( EPA, 2002)

. Meterologically

- adjusted 4 th 

### high 8

- hour ozone con centrations were derived using th e air quality

– meteorological regression model developed by EPA (i.e., Cox method

– see Section 2.1 ).

The historical trend in these met

- adjusted ozone concentrations were extrapolated to estimate

future year ozone concentrations based on historical and projected trend s in precursor

emissions. Both VOC and NOx em issions affect ozone concentrations. Given that observation

based methods show that urban areas in the region are generally VOC

- limited and rural areas

in the region are NOx

- limited (see Section 2.1) , urban VOC emissions and regional NOx

emissions are con sidered important. The trends in urban VOC and regional NOx emissions

were calculated to produce appropriate weighting factors.

The resulting 2009 and 2012 ozone values are provided in Figure 65, along with the primary

and alternative modeling ozone va lues for key sites in the Lake Michigan, Cleveland, and

Cincinnati areas. The results reflect a fairly wide scatter, but, on balance, the supplemental

information is supportive of the primary modeling results (i.e., sites in the Lake Michigan area

and Cle veland are expected to be close to the standard).

### 87 Sheboygan

75 80 85 90 95 100

2002 2005 2009 2012 

### Holland

75 80 85 90 95 100

2002 2005 2009 2012



### Sycamore

75 80 85 90 95 100

2002 2005 2009 2012 

### Ashtabula

75 80 85 90 95 100

2002 2005 2009 2012



### Figure 6 5 . Estimates of Future Year Ozone Concentrations

– Lake Michigan Area (Sheboygan and Holland), Cincinnati (Sycamore ), and

Cleveland (Ashtabula)

Note: Primary (guideline) modeling valu e s (Base K and Base M results) are represented by large red diamond s , additional modeling

values by small black circles, and trends

- based value s by small pink squares

### 88



### Observation al Models and Diagnostic Analyses: The observation

- based modeling (i.e.,

MAPPE R) is presented in Section 3. The key findings from this modeling are that most urban

areas are VOC

- limited and rural areas are NOx

- li mited.

The primary diagnostic analysis is source apportionment modeling with CAMx t o provide more

quantitative informati on on source region (and source sector) impacts (Baker, 2007a)

. Specifically, the model estimated the impact of 18 geographic source regions (which are

identified in Figure 6 6 ) and 6 source sectors (EGU point, non

- EGU point, on

- road, off

- road,

area, and b iogenic sources) at o zone monitoring site s in the region.

Figure 6 6 . Source regions (left) and key monitoring sites (right) for ozone modeling analysis



### Modeling results for 2009 (Base M ) and 20 12 ( Base K ) are provided in Appendix II for several

key monitoring sites. For each monitoring site, there are two graphs: one showing sector

- level

contributions, and one showing source region and sector

- level contributions in terms of

percentages. (Note, in the sector

- level graph, the contribution s from NOx emissions are shown

in blue, and from VOC emissions in green. )

The sector

- level results (see, for example, Figure 6 7 ) show that on

- road and nonroad NOx

emissions generally have the largest contributio ns at the key monitor locations (> 15% each).

EGU and non

- EGU NOx emissions are also important contributors (> 10% each). The source

group contributions vary by receptor location due to emissions inventory differences.

Figure 6 7 . Source

- sector res ults for Holland (left) and Ashtabula (right) monitors

– 20 09 (Base M) Kenosha County

Cook County

Indianapolis

Holland

St. Louis Detroit

Cincinn ati Door County

Cleveland

### 89



### The source region results (see, for example, Figure 68) show that while nearby areas generally

have the highest impacts (e.g., the northeastern IL/northwestern IN/southeastern WI

nonatt ainment area contributes 25

- 35 % to high sites in the Lake Michigan area, and Cleveland

nonattainment counties contribute 20

- 25 % to high sites in northeastern Ohio)

, there is an even

larger regional impact (i.e., contribution from other states).

Figure 6 8 . Source

- region results for Holland (left) and Ashtabula (right) monitors

– 20 09 (Base M)



### Summary : Air quality modeling and other supplemental analyses were performed to estimate

future year ozone concentrations. Based on this information, the followi ng general conclusions

can be made:

• E xisting (“on the books”) controls are expected to produce significant

improvement in ozone air quality

.

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

difference between the base years o f 2002 and 2005 is meteorology. As noted

above, 2002 was more ozone conducive than 2005. The choice of which base

year to use as the basis for the SIP is a policy decision (i.e., how much safeguard

to incorporate).

• Most sites are expected to meet the cu rrent 8

- hour standard by the applicable

attainment date, except, for sites in western Michigan and, possibly, in eastern

Wisconsin and northeastern Ohio.

• Current monitoring data show significant nonattainment in these areas (e.g.,

peak design values on th e order of 90

– 93 ppb). It is not clear whether sufficient

emission reductions will occur in the next couple of years to provide for

attainment.

• Attainment by the applicable attainment date is dependent on actual future year

meteorology (e.g., if the we ather conditions are consistent with [or less severe

than] 2005, then attainment is likely) and actual future year emissions (e.g., if the

emission reductions associated with the existing controls are achieved, then

attainment is likely). On the other han d, if either of these conditions is not met,

then attainment may be less likely.

### 90



### 4.3 Weight

- of

- Evidence Determination for PM 2.5 

### The WOE determination for PM 2. 5 

### consists of the primary modeling and other supplemental

analyses. A summary of this informati on is provided below.

Primary ( Guideline

) Modeling : The results of the guideline modeling are presented in Section

4.1. Key findings from this modeling include:

• Base M regional modeling shows attainment by 2009 at all sites, except Detroit,

Cleveland, a nd Granite City, and attainment at all sites by 2012, except for Detroit

and Granite City.

T he regiona l modeling for PM 2. 5 

### does not reflect any air quality benefit expected

from local controls. States are conducting local

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

- scale modeling, to support their attainment

demonstrations for PM 2. 5



### • Base K modeling results reflect generally higher future year values, and show more

sites in nonattainment in 2009 and 2012 compared to the Bas e M modeling. The

difference in the two modeling analyses is due mostly t o lower base year design

values in Base M.



## • Base K and Base M modeling analyses are considered “SIP quality”, so the

attainment demonstration for PM 2. 5 

### should reflect a weight

- of

- evi dence approach,

with consideration of monitoring based information.

• Base M mode ling also shows that the new PM 2. 5 

### 24

- hour standard wi ll not be met at
many sites , even by 2018, with existing controls.

A dditional Modeling : EPA’s latest regional PM 2. 5 

### model ing was considered as corroborative

information . This modeling was performed as part of the September 2006 revision to the PM 2. 5 

### standard ( USEPA, 2006)

. EPA applied the CMAQ model with 2001 meteorology to estimate

PM 2. 5 

### levels in 20 15 and 2020 first with national rules in effect or proposed , and then with

additional controls to attain the current standard (15 ug/m 3 

### annual/65 ug/m 3 

### daily). Additional

analyses were performed to evaluate strategies for attaining more stringent standards in 2020

(15/35, and 14/35) . Baseline (20 15 ) PM 2. 5 

### levels were predicted to be above the current

standard in four counties in the LADCO region : Madison County, IL at 15.2 ug/m 3 

### , Wayne

County, MI at 17.4, Cuyahoga County, OH at 15.4 , and Scioto County, OH at 15.6. These

resul ts are consistent with LADCO’s Base K modeling for 2012/ 2018, whic h is not surprising

given that EPA’s modeling and LADCO’s Base K modeling have a similar base year (2001 v.

2002).

Observation al Models and Diagnostic Analyses : The observation

- based modeli ng (i.e.,

application of thermodynamic equilibrium models) is presented in Section 3. The key findings

from this modeling are that PM 2. 5 

### mass is sensitive to reductions in sulfate , nitric acid, and

ammonia concentrations . Even though sulfate reductions c ause more ammonia to be available

to form ammonium nitrate ( PM

- nitrate increases slightly when sulfate is reduced ) , this increase

is generally offset by the sulfate reductions, such that PM 2.5 

### mass decreases. Under conditions

with lower sulfate levels (i. e., proxy of future year conditions), PM 2.5 

### is more sensitive to

reductions in nitric acid compared to reductions in ammonia

.

### 91



### The primary diagnostic analysis is source apportionment modeling with CAMx t o provide more

quantitative information on source reg ion (and source sector) impacts (Baker, 2007b)

. Specifically, the model estimated the impact of 18 geographic source regions (which are

identified in Figure 6 9 ) and 6 source sectors (EGU point, non

- EGU point, on

- road, off

- road,

area, and biogenic sources) at PM 2. 5 

### monitoring site s in the region.

Figure 6 9 . Source regions (left) and key monitoring sites (right) for PM 2. 5 

### modeling analysis



### Modeling results for 20 12 (Base K) and 201 8 (Base M) are provided in Appendix I I I for several

key monitoring sites. For each monitoring site, there are two graphs: one showing sector

- level

contributions, and one showing source region and sector

- level contributions in terms of absolute

modeled values.

The sect or

- level res ults (see, for example, Figure 70 ) show that EGU sulf ate, non

- EGU

- sulfate,

and area organic carbon emissions generally have the largest contributions at the key monitor

locations (> 15% each). Ammonia emissions are also important contributors (> 10%). The

source group contributions vary by receptor location due to emissions inventory differences.

Figure 70 . Source

- sector results for Detroit (left) and Cle v eland (right) monitors

– 2018 (Base M) Cook County

Steubenville

Granite City Detroit

Cincinnati Cleveland

### 92



### The source region results (see, for examp le, Figure 71) show that while nearby areas generally

have the highest impacts (e.g., Detroit nonattainment counties contribute 40% to high sites in

southeastern Michigan, and Cleveland nonattainment counties contribute 35% to high sites in

northeastern Oh io), there is an even larger regional impact (i.e., contribution from other states).

Figure 71 . Source

- region results for Detroit (left) and Cleveland (right) monitors

– 2018 (Base M)



### Summary : Air quality modeling and other supplemental analyses wer e performed to estimate

future year PM 2. 5 

### concentrations. Based on this information, the following general conclusions

can be made:

• E xisting (“on the books”) controls are expected to produce significant

improvement in PM 2. 5 

### air quality.

• The choice of th e base year affects the future year model projections. It is not

clear how much of this is attributable to differences in meteorology, because, as

noted in Section 3, PM 2 .5 

### concentrations are not as strongly influenced by

meteorolog

y as ozone

.

• Most sites are expected to meet the current PM 2. 5 

### standard by the applicable

attainment date, except for sites in Detroit, Cleveland, and Granite City

.

• Current monitoring data show significant nonattainment in these areas (e.g.,

peak design values on the order of 1 6

– 17 ug/m 3 

### ). It is not clear whether

sufficient emission reductions will occur in the next couple of years to provide for

attainment. States are conducting local

- scale analyses for Detroit, Cleveland,

and Granite City , in particular, to identify approp riate additional local controls.

• Attainment by the applicable attainment date is dependent ( possibly) on actual

future year meteorology and (more likely ) on actual future year emissions (e.g., if

the emission reductions associated with the “on the books” controls are

achieved, then attainment is likely). On the other hand, if either of these

conditions is not met

(especially, with respect to emissions) , then attainment may

be less likely.

### 93 

### Section 5. Reasonable Progress Assessment for Regional Haze

Ai r quality modeling and other information were used to assess the improvement in visibility that

would be provided by existing (“on the books”) controls and possible additional control

programs. In determining reasonable progress for regional haze, Section 169A of the Clean Air

Act and EPA’s visibility rule requires states to consider five factors :

• costs of compliance

• time necessary for compliance

• energy and non

- air quality environmental impacts of compliance

• remaining useful life of any existing source su bject to such requirements

• uniform rate of visibility improvement needed to attain natural visibility condit ions

by 2064

The uniform rate of visibility improvement requirement can be depicted graphically in the form of

a “glide path” (see Figure 72 ).

Figure 72 . Visibility “glide paths” for northern Class I areas (units: deciviews)



### 5.1 Class I Areas Impacted

EPA’s visibility

rule requires a s tate to “address regional haze in each mandatory Class I

Federal area located within t he State and in each mandatory Class I Federal area located

outside the State which may be affected by emissions from within the State.” (40 CFR Part

51.308(d)) To meet this requirement, technical analyses conducted by the RPOs were

consulted to obtain i nformation on areas of influence and culpability for Class I areas in the

eastern U.S. (MRPO, 2007). A summary of this information is provided in Table 1 (MRPO,

2007). Th e table shows that every LADCO State impacts multiple Class I areas in the eastern

U .S

. 5

10 15 20 25

2004 2010 2020 2030 2040 2050 2060 2064

Voyageurs Bounday Waters

Isle Royale Seney

Voyageurs Boundry Waters



## 20% best visibility days 20% worst visibility days Baseline

Conditions

Baseline

Conditions Natural

Conditions

### 94 Table 1 4 . Draft List o f Class I Areas Impacted by LADCO States

AREA NAME IL IN MI OH WI

81.401 Alabama.

Sipsey Wilderness Area (1) (1)

81.404 Arkansas.

Caney Creek Wilderness Area (2), ( 4 ) (2), ( 4 ) (2), ( 4 )

Upper Buffalo Wi lderness Area (1), ( 2 ),( 4 ),( 5 ) ( 2) , ( 4 ) (2), ( 4 ) (2)

81.408 Georgia.

Cohotta Wilderness Area

Okefenokee Wilderness Area

Wolf Island Wilderness Area

81.411 Kentucky.

Mammoth Cave NP (1), (2), ( 5 ) (1), ( 2 ), ( 5 ) (1 ), (2) (1), ( 2 ), ( 5 )

81.412 Louisiana.

Breton Wilderness Area

81.413 Maine.

Acadia National Park (3) (3) (3) ( 3 )

Moosehorn Wilderness Area. (3) (3) (3) ( 3 )

81.414 Michigan.

Isle Royale NP. ( 1 ), ( 2 ) (1), ( 2 ) (1), ( 2 ) ( 1 ), ( 2 )

Seney Wilderness Area (1), (2) (1), ( 2 ) (1), (2) (1), (2) (1), ( 2 )

81.415 Minnesota.

Boundary Waters Canoe Area Wilderness (2) (2) (2) (1), (2)

Voyageurs NP (2) (2) ( 1 ), ( 2 )

81.416 Missouri.

Hercules

- Gl ades Wilderness Area ( 2 ), ( 4 ), ( 5 ) ( 2 ), ( 4 ), ( 5 ) (2), ( 4 ) (2)

Mingo Wilderness Area ( 2 ), ( 4 ), ( 5 ) ( 2 ), ( 4 ), ( 5 ) (2) ( 2 ), ( 4 ) (2)

81.419 New Hampshire.

Great Gulf Wilderness Area (3) ( 3 ) (3) (1), ( 3 )

Pres

. Range

- Dry River Wilderness Area.

81.42 New Jersey.

Brigantine Wilderness Area (3) (3) (1), (3) (1), ( 3 )

### 95

81.422 North Carolina.

Great Smoky Mountains NP{1} (1) (1) (1)

Joyce Kilmer

- Slickrock Wilderness Area{2}

Linville Gorge Wilderness Area.

Shining Rock Wilderness Area.

Swanquarter Wilderness Area

81.426 South Carolina.

Cape Romain Wilderness

81.428 Tennessee.

Great Smoky Mountains NP{1}. (1) (1) (1)

Joyce Kilmer

- Slickrock Wilderness{2}

81.431 Vermont.

Lye Brook Wilderness (2), ( 3 ) (2), ( 3 ) (2), ( 3 ) (1), (2), ( 3 )

81.433 Virginia.

James River Face Wilderness. (2) (2) (2) ( 2 ), ( 5 )

Shenandoah NP (2), (3) (1), ( 2 ), ( 3 ) (2), (3) (1),( 2 ),( 3 ),( 5 )

81.435 West Virginia.

Dolly Sods/Otter Creek Wilderness. (2), (3) (1), ( 2 ), ( 3 ) (1), (2), (3) (1),( 2 ),( 3 ),( 5 )



### Key

(1) MRPO Back Trajectory Analyses

(2) MRPO PSAT Modeling

(3) MANE

- VU Contribution Assessment

(4) Missouri

- Arkansas Contribution Assessment

(5) VI STAS Areas of Influence

### 96



### 5. 2 Future Year Modeling Results

For regional haze, the calculation of future year conditions assumed:

• baseline concentrations based on 2000

- 2004 IMPROVE data, with updated

(subsitituted) data for Mingo, Boundary Waters, Voyage urs, Isle Royale, and

Seney (see Section 2.3 ) ;

• u se of the new IMPROVE light extinction equation ; and

• use of EPA default values for natural conditions, based on the new IM PROVE

light extinction equation.

The uniform rate of visibility improvement values for the 2018 planning year were derived (for

the 20% worst visibility days) based on a straight line between baseline concentration value

(plotted in the year 2004

-- end year of the 5

- year baseline period) and natural condition value

(plotted in the year 2064

-- date for achieving natural conditions). Plots of these “glide paths”

with the Base M modeling results are presented in Figure 73 for Class I areas in the eastern

U.S. A tabular summary of measured baseline and modeled future year deciview values for

these Class I areas are provided in Table 1 5 (2002 base year) and Table 1 6 (2005 base year) 13



### .

The haze results show that several Class I areas in the eastern U.S. are expected to be greater

than (less improved than) the uniform rate of visibility impr ovement values (in 2018), including

those in northern Michigan and several in the northeastern U.S. Many other Class I areas in the

eastern U.S. are expected to be less than (more improved than) the uniform rate of visibility

improvement values (in 2018). As noted above, states should consider these results, along with

information on th e other four factors , in setting reasonable progress goals.

A n assessment of the f ive factors was performed for LADCO and the State of Minnesota by a

contractor (EC/R, 2 007). Sp ecifically, ECR examined reductions in S O 2 and NO x emissions

from EGUs and i ndustrial, commercial and institutional (ICI) boilers; NO x emissions from mobile

sources and reciprocating engines and turbines; and a mmonia emissions from agricultural

op erations. The impacts of “on the books” controls were also examined to provide a frame of

reference for assessing the impacts of the additional control measures.

The results of ECR’s analysis of the five factors are summarized below:

Factor 1 (Cost of C ompliance): The average cost effectiveness values (in terms of $M

per ton ) are provided in Table 1 6 . For comparison, cost

- effectiveness estimates

previously provided for “on the books” controls include:

CAIR SO2: $700

- $1,200, NOx: $1,400

– $2.600 ($/ T)

BART SO2: $300

- $963, NOx: $248

- $1,770

MACT SO2: $1,500, NOx: $7,600

Most of the cost

- effectiveness values for the additional controls are within the range of

cost

- effectiveness values for “on the books” controls.

13 

### Model results reflect the grid cell where the IMPROVE monitor is located.

### 97 Voyageurs Bou ndary Waters

Isle Royale Seney

Mammoth Cave Upper Buffalo

Figure 73 . Visibility modeling results for Class I areas in eastern U.S.

### 98 Mingo Shenandoah

Dolly Sods Bringantine

Lye Brook Acadia

Figure 73 (cont.) Visibility modeling results for Class I areas in eastern U.S

.Worst 20% 2018 2009 2012 2018 2018 2018

Site Baseline URP OTB OTB OTB EGU2

(5-state region) EGU2

(12-state region)

BOWA1 19.86 17.70 19.05 19.01 18.94 18.40 17.72

VOYA2 19.48 17.56 19.14 19.19 19.18 18.94 18.38

SENE1 24.38 21.35 22.98 22.71 22.38 21.26 20.63

ISLE1 21.59 19.21 20.46 20.28 20.04 19.09 18.64

HEGL1 26.75 22.76 24.73 24.34 23.85 23.01 22.04

MING1 28.15 24.08 25.18 24.67 24.01 22.53 21.45

CACR1 26.36 22.55 24.01 23.55 22.99 22.43 21.57

UPBU1 26.27 22.47 24.02 23.58 23.06 22.31 21.38

MACA1 31.37 26.14 28.06 27.03 25.52 24.27 22.57

DOSO1 29.04 24.23 24.86 23.59 22.42 21.60 20.15

SHEN1 29.31 24.67 24.06 22.79 21.57 20.43 19.42

JARI1 29.12 24.48 24.81 23.79 22.42 21.59 20.88

BRIG1 29.01 24.68 25.87 25.25 24.39 23.91 23.45

LYBR1 24.45 21.16 21.80 21.32 20.69 20.18 19.79

Best 20% 2018 2009 2012 2018 2018 2018

Site Baseline URP OTB OTB OTB EGU2

(5-state region) EGU2

(12-state region)

BOWA1 6.42 6.42 6.71 6.73 6.87 6.83 6.81

VOYA2 7.09 7.09 7.21 7.25 7.34 7.31 7.26

SENE1 7.14 7.14 7.19 7.19 7.23 7.06 6.91

ISLE1 6.75 6.75 6.57 6.51 6.47 6.20 6.06

HEGL1 12.84 12.84 12.61 12.62 12.61 12.43 12.02

MING1 14.46 14.46 13.96 13.93 13.94 13.74 13.33

CACR1 11.24 11.24 10.91 10.92 10.90 10.75 10.42

UPBU1 11.71 11.71 11.47 11.46 11.42 11.28 11.01

MACA1 16.51 16.51 16.06 15.91 15.54 15.18 14.75

DOSO1 12.28 12.28 11.72 11.45 11.19 10.93 10.67

SHEN1 10.93 10.93 9.73 9.53 9.17 9.05 8.90

JARI1 14.21 14.21 13.56 13.33 12.97 12.65 12.46

BRIG1 14.33 14.33 13.74 13.69 13.47 13.32 13.21

LYBR1 6.36 6.36 6.12 6.05 5.96 5.88 5.82 

### Table 15. Haze Results - Round 4 (Based on 2000-2004)Worst 20% 2018 2009 2012 2018 2018

Site Baseline URP OTB OTB OTB OTB+Will DO

BOWA1 19.86 17.94 18.45 18.33 17.94 17.92

VOYA2 19.48 17.75 18.20 18.07 17.63 17.66

SENE1 24.38 21.64 23.10 23.04 22.59 22.42

ISLE1 21.59 19.43 20.52 20.43 20.09 20.13

ISLE9 21.59 19.43 20.33 20.22 19.84 19.82

HEGL1 26.75 23.13 24.72 24.69 24.22 24.17

MING1 28.15 24.27 25.88 25.68 24.74 24.83

CACR1 26.36 22.91 23.39 23.29 22.44 22.40

UPBU1 26.27 22.82 23.34 23.27 22.59 22.55

MACA1 31.37 26.64 27.11 27.01 26.10 26.15

DOSO1 29.05 24.69 24.00 23.90 23.00 23.04

SHEN1 29.31 25.12 24.99 24.87 23.92 23.95

JARI1 29.12 24.91 25.17 25.01 24.06 24.12

BRIG1 29.01 25.05 25.79 25.72 25.21 25.22

LYBR1 24.45 21.48 22.04 21.86 21.14 21.14

ACAD1 22.89 20.45 21.72 21.72 21.49 21.49

Best 20% 2018 2009 2012 2018 2018

Site Baseline Max OTB OTB OTB OTB+Will DO

BOWA1 6.42 6.42 6.21 6.19 6.14 6.12

VOYA2 7.09 7.09 6.86 6.83 6.75 6.76

SENE1 7.14 7.14 7.57 7.58 7.71 7.78

ISLE1 6.75 6.75 6.62 6.59 6.60 6.62

ISLE9 6.75 6.75 6.56 6.55 6.52 6.50

HEGL1 12.84 12.84 12.51 12.32 11.66 11.64

MING1 14.46 14.46 14.07 13.89 13.28 13.29

CACR1 11.24 11.24 10.88 10.85 10.52 10.52

UPBU1 11.71 11.71 11.13 11.08 10.73 10.74

MACA1 16.51 16.51 15.76 15.69 15.25 15.25

DOSO1 12.28 12.28 11.25 11.23 11.00 11.01

SHEN1 10.93 10.93 10.13 10.11 9.91 9.91

JARI1 14.21 14.21 13.38 13.38 13.14 13.14

BRIG1 14.33 14.33 14.15 14.08 13.92 13.92

LYBR1 6.37 6.37 6.25 6.23 6.14 6.15

ACAD1 8.78 8.78 8.86 8.86 8.82 8.82



### Table 16. Haze Results - Round 5.1 (Based on 2000-2004)

### 101 Table 1 7 . Estimated Cost Effectiveness for Potential C ontrol Measures

Average Cost effectiveness ($/ton)

Emission category Control strategy Region SO2 NOX NH3

EGU EGU1 3

- State 1,540 2,037

9

- State 1,743 1,782

EGU2 3

- State 1,775 3,016

9

- State 1,952 2,984

ICI boilers ICI1 3

- State 2,992 2,537

9

- State 2,275 1,899

ICI Workgroup 3

- State 2,731 3,814

9

- State 2,743 2,311

3

- State 538 Reciprocating engines
emitting 100 tons/year or

more 9

- State 506 Reciprocating engines

and turbines

3

- State 754 Turbines emitting 100
tons/year or m ore 9

- State 754

3

- State 1,286 Reciprocating engines
emitting 10 tons/year or more 9

- State 1,023

3

- State 800 Turbines emitting 10
tons/year or more 9

- State 819

10% reduction 3

- State 31

- 2,700 Agricultural sources

9

- State 31

- 2,700

15% reduction 3

- State 31

- 2,700

9

- State 31

- 2,700

Mobile sources Low

- NOX Reflash 3

- State 241

9

- State 241

MCDI 3

- State 10,697

9

- State 2,408

Anti

- Idling 3

- State (430)

- 1,700

9

- State (430)

- 1, 700

Cetane Additive Program 3

- State 4,119

9

- State 4,119

Cement Plants Process Modification Michigan

-

Conversion to dry kiln Michigan 9,848

LoToxTM Michigan 1,399

Glass Manufacturing LNB Wisconsin 1,041

Oxy

- firing Wisconsin 2,833

Electric boost Wisconsin 3,426

SCR Wisconsin 1,054

SNCR Wisconsin 1,094

Lime Manufacturing Mid

- kiln firing Wisconsin 688

LNB Wisconsin 837

SNCR Wisconsin 1,210

SCR Wisconsin 5,037

FGD Wisconsin 128

- 4,828

Oil Refinery LNB Wisconsin 3,288

SNCR Wisconsin 4,260

SCR Wisconsin 17,997

LNB+FGR Wisconsin 4,768

ULNB Wisconsin 2,242

FGD Wisconsin 1,078

### 102



### Factor 2 (Time Necessary for Compliance): All of the control measures can be

implemented by 2018. Thus, this factor can be easily addressed.

Factor 3 (Energy and Non

- Air Quality Environmental Impacts): The energy and other

environmental impacts are belie ved to be manageable. For example, the increased

energy demand from add

- on control equipment is less than 1% of the total electricity

and steam production in the region, and solid waste disposal and wastewater treatment

costs are less than 5% of the total operating costs of the pollution control equipment. It

should also be noted that the SO2 and NOx controls would have beneficial

environmental impacts (e.g., reduced acid deposition and nitrogen deposition).

Factor 4 (Remaining Useful Life): The addition al control measures are intended to be

market

- based strategies applied over a broad geographic region. It is not expected that

the control requirements will be applied to units that will be retired prior to the

amortization period for the control equipmen t. Thus, this factor can be easily addressed.

Factor 5 (Visibility Impacts ): The estimated incremental improvement in 2018 visibility

levels for the additional measures is shown in Figure 7 4 , along with the cost

effectiveness expressed in $M per deciview improvement). These results show that

a lthough EGU and ICI boiler controls have higher cost

- per

- deciview values (compared

to some of the other measures), their visibility impacts are larger

.

### 103 

# Visibility Improvement (dv)

0.00 0.25 0.50 0.75 1.00

SO2 NOx NH3 SO2 NOx NH3 SO2 NOx NH3 SO2 NOx NH3 SO2 NOx NH3

Strategy 1 Strategy 2 

## EGUs ICI Boilers Rec.Eng. Mobile Ag



# Cost Effectiveness ($M/dv)

0 1000 2000 3000 4000 5000 6000

SO2 NOx NH3 SO2 NOx NH3 SO2 NOx NH3 SO2 NOx NH3 SO2 NOx NH3

Strategy 1 Strategy 2 

## EGUs ICI Boilers Rec.Eng. Mobile Ag

Figure 74. Results of ECR analysis of reasonable p rogress factors

– visibility improvement (Factor

5) is on top, and cost effectiveness (Factor 1) is on bottom

### 104



### 5. 3 Weight

- of

- Evidence Determination for Haze

The WOE determination for haze consists of the primary modeling and other supplemental

analyses. A summary of this information is provided below.

Primary ( Guideline

) Modeling : The results of the guideline modeling are presented in Section

4.1. Key findings from this modeling include:

• Base M modeling results show that the northern Minnesota Class I ar eas are close

to the glide path, whereas the northern Michigan Class I areas are above the glide

path in 2018

. Other sites in the eastern U.S. are close to (or below) the glide path,

except for Mingo (MO), Brigantine (NJ), and Acadia (ME).

• Base K modelin g results show that the northern Minnesota and northern Michigan

Class I areas are above the glide path in 2018. Other sites in the eastern U.S. are

close to (or below) the glide path.

• The difference in the two modeling analyses is due mostly t o differ ences in future

year emission projections , especially for EGUs (e.g., use of IPM2.1.9 v. IPM3.0).



## • Base K and Base M modeling analyses are considered “SIP quality”, so the

attainment demonstration for haze should reflect a weight

- of

- evidence approach,

with consideration of monitoring based information.

A dditional Modeling : T wo additional modeling analyses were considered: (1) the primary

modeling redone with different baseline values, and (2) modeling by the State of Minnesota

which looked at different rec eptor locations in the northern Class I areas (MPCA, 2008) . Each

of these analyses is described below.

First, the primary modeling analysis (Base M) was revised using an alternative baseline value.

Specifically, the data for the period 2000

- 2005 were us ed to calculate the baseline, given that

the Base M modeling reflects a 2005 base year. The results of this alternative analysis (see

Table 1 8 ) are generally consistent with the primary modeling (see Table 1 6 ).

Second, Minnesota’s modeling reflects a 200 2 base year and much of the data developed by

LADCO for its modeling. (Note, Minnesota conducted modeling for LADCO’s domain at 36 km,

and for a statewide domain at 12 km.) The purpose of the 12 km modeling was to address local

scale impacts on the north ern Class I areas at several locations, not just the location of the

IMPROVE monitor. Results for the Boundary Waters on the 20% worst days range from 18.3

– 19.0 dv, with an average value of 18.7 dv, which is consistent with Minnesota’s 36 km modeling

re sults at the IMPROVE monitor. This variability in visibility levels should be kept in mind when

reviewing the values presented in Tables 1 5 , 1 6

, and 1 8

, which reflect results a t the IMPROVE

monitor locations.Worst 20% 2009 2012 2018 2018

Site Baseline URP OTB OTB OTB OTB+Will DO

BOWA1 20.10 18.12 18.63 18.51 18.12 18.09

VOYA2 19.62 17.86 18.27 18.15 17.70 17.72

SENE1 24.77 21.94 23.44 23.39 22.94 22.77

ISLE1 21.95 19.71 20.84 20.76 20.41 20.44

ISLE9 21.95 19.71 20.65 20.55 20.15 20.13

HEGL1 27.45 23.67 25.30 25.27 24.79 24.73

MING1 28.92 24.86 25.88 25.68 24.74 24.83

CACR1 27.05 23.44 23.88 23.78 22.92 22.86

UPBU1 26.97 23.36 23.92 23.85 23.14 23.09

MACA1 31.76 26.93 27.42 27.32 26.39 26.44

DOSO1 29.36 24.92 24.20 24.11 23.19 23.23

SHEN1 29.45 25.23 25.06 24.94 23.98 24.01

JARI1 29.40 25.13 25.32 25.17 24.22 24.28

BRIG1 29.12 25.14 25.84 25.77 25.26 25.26

LYBR1 24.71 21.69 22.22 22.06 21.36 21.36

ACAD1 22.91 20.47 21.72 21.72 21.49 21.49

Best 20% 2009 2012 2018 2018

Site Baseline URP OTB OTB OTB OTB+Will DO

BOWA1 6.40 6.40 6.20 6.17 6.13 6.10

VOYA2 7.05 7.05 6.82 6.78 6.71 6.71

SENE1 7.20 7.20 7.60 7.61 7.73 7.80

ISLE1 6.80 6.80 6.67 6.64 6.65 6.66

ISLE9 6.80 6.80 6.62 6.61 6.57 6.55

HEGL1 13.04 13.04 12.71 12.51 11.85 11.82

MING1 14.68 14.68 14.07 13.89 13.28 13.29

CACR1 11.62 11.62 11.24 11.20 10.86 10.86

UPBU1 11.99 11.99 11.41 11.36 11.01 11.02

MACA1 16.64 16.64 15.88 15.82 15.37 15.38

DOSO1 12.24 12.24 11.21 11.19 10.96 10.97

SHEN1 10.85 10.85 10.04 10.02 9.82 9.83

JARI1 14.35 14.35 13.51 13.51 13.27 13.27

BRIG1 14.36 14.36 14.17 14.10 13.94 13.94

LYBR1 6.21 6.21 6.11 6.09 6.01 6.01

ACAD1 8.57 8.57 8.67 8.66 8.62 8.62



### Table 18. Haze Results - Round 5.1 (Based on 2000-2005)

### 106



### Observation al Models and Diagnostic Analyse s : The observation

- based modeling (i.e.,

application of thermodynamic equilibrium models) is presented in Section 3. The key findings

from this modeling are that PM 2. 5 

### mass is sensitive to reductions in sulfate, nitric acid, and

ammonia concentrations . E ven though sulfate reductions cause more ammonia to be available

to form ammonium nitrate ( PM

- nitrate increases slightly when sulfate is reduced ) , this increase

is generally offset by the sulfate reductions, such that PM 2. 5 

### mass decreases and visibility

im proves

. Under conditions with lower sulfate levels (i.e., proxy of future year c onditions), PM 2.5 

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

As discussed in Section 2, t hermodynamic equilibrium modeling based on dat a collected at

Seney indicates that PM 2. 5 

### there is most sensitive to reductions in sulfate, but also responsive to

reductions in nitric acid (Blanchard

, 2004). An analysis using data from the Midwest ammonia

monitoring network for a site in Minnesota (i.e ., Great River Bluffs, which is the closest ammonia

monitoring site to the northern Class I areas) suggested that reductions in sulfate, nitric acid,

and ammonia concentrations will lower PM 2. 5 

### concentrations and improve visibility levels in the

northern C lass I areas

.

Trajectory analyses for the 20% worst visibility days for the four northern Class I areas are provided in Figure 7 5 . (Note, this figure is similar to Figure 3 4 , but the trajectory results for each

Class I area are displayed separately here

. ) The o range areas are where the air is most likely

to come from, and the green areas are where the air is least likely to come from . Darker

shading represents higher frequency. As can be seen, bad air days are generally associated

with transport from r egions located to the south, and good air days with transport from Canada.

### 107 

### Seney Isle Royale

Boundary Waters Voyageurs

Figure 7 5 . Trajectory analysis results for northern Class I areas on 20% worst visibility days



### The primary diagnostic analysis is source apportionment modeling with CAMx t o provide more

quantitative information on source region (and source sector) im pacts (Baker, 2007b)

. Specifically , the CAMx model was applied to provide source contribution information.

Specifically, the model estimated the impact of 18 geographic source regions (which are

identified in Figure 7 6 ) and 6 source se ctors (EGU point, n on

- EGU point, on

- road, off

- road,

area, and ammonia sources) at visibility/haze monitoring site s in the eastern U.S.

### 108

Figure 7 6 . Source regions (left) and key monitoring sites (right) for haze modeling analysis



### Modeling results for 2018 (Base K and Base M) are provided in Appendix I V for several key

monitoring sites ( Class I areas). For each monitoring site, there are two graphs: one showing

sector

- level contributions, and one showing source r egion and sector

- level contributions in

terms of absolute modeled values.

The sector

- level results (see, for example, Figure 7 7 ) show that EGU sulfate, non

- EGU

- sulfate,

and ammonia emissions generally have the largest contributions at the key monitor loca tions.

The source group contributions vary by receptor location due to emissions inventory differences.

Figure 7 7 . Source

- sector results for Seney (left) and Boundary Waters (right)

– 2018 (Base M)



### The source region results (see, for example, Fi gure 7 8 ) show that emissions from a number of

nearby states contribute to regional haze levels.

### 109

Figure 7 8 . Source

- region results for Seney (left) and Boundary Waters (right)

– 2018 (Base M)



### Table 19 provides a summary of the estimated state

- level culp abilities based on the LADCO

back trajectory analyses and the PSAT analyses for 2018.

Summary : Air quality modeling and other supplemental analyses were performed to estimate

future year

visibility levels

. Based on this information, the following genera l conclusions can be

made:

• E xisting (“on the books”) controls are expected to improve visibility levels in the

northern Class I areas

.

• Visibility levels in a few Class I areas in the eastern U.S. are expected to be

greater than (less improved than) the u niform rate of visibility improvement

value s in 2018, including those in northern Michigan and s ome in the

northeastern U.S.

• Visibility levels in m any other Class I areas in the eastern U.S. are expected to

be less than

(more improved than) the uniform rate of visibility improvement

value s in 2018.

### 110 Table 19 . State Culpabilities Based on PSAT Modeling and Trajectory Analyses

Boundary Waters Seney

LADCO

- Round 4

PSAT LADCO

-

Round 5

PSAT MPCA

PSAT CENRAP

-

PSAT LADCO

-

Traj. Analysis LADCO

- Round 4

PSAT LADCO

-

Round 5

PSAT CENRAP

-

PSAT LADCO

-

Traj. Analysis

Michigan 3.4% 4.8% 3.0% 1.9% 0.7% 13.8% 18.1% 14.7%

Minnesota 30.5% 23.5% 28.0% 30.6% 37.6% 4.8% 1.6% 3.8%

Wisconsin 10.4% 10.9% 10.0% 6.4% 10.6% 12.6% 10.9% 8.4%

Ill inois 5.2% 5.1% 6.0% 3.5% 2.7% 13.0% 14.3% 7.4%

Indiana 2.9% 3.9% 3.0% 1.8% 1.2% 9.6% 11.6% 2.2%

Iowa 7.6% 8.3% 8.0% 2.5% 7.4% 6.2% 3.8% 5.7%

Missouri 5.2% 3.4% 6.0% 2.1% 3.3% 6.5% 4.8% 3.2%

N. Dakota 5.7% 1.1% 6.0% 4.6% 5.9% 1.5% 0.1% 0.6%

Canada 1.9% 2.7% 3.0% 12.5% 15.1% 2.1% 1.2% 11.1%

CENRAP

WRAP 10.9% 13.5% 4.2% 10.1% 13.1% 10.0% 7.0%

83.6% 77.2% 73.0% 70.2% 94.6% 83.3% 76.4% 64.1%



### Voyageurs Isle Royale

LADCO

- Round 4

PSAT LADCO

-

Round 5

PSAT MPCA

PSAT CENRAP

-

PSAT LADCO

-

Traj. Analysis LADCO

- Round 4

PSAT LADCO

-

Round 5

PSAT CENRAP

-

PSAT LADCO

-

Traj. Analysis

Michigan 2.0% 4.9% 2.0% 1.0% 1.6% 12.7% 13.4%

Minnesota 35.0% 20.2% 31.0% 31.5% 36.9% 14.1% 9.5%

Wisconsin 6.3% 7.9% 6.0% 3.7% 9.7% 16.3% 14.7%

Illinois 3.0% 7.1% 3.0% 1.8% 1.2% 7.0% 8.7%

Indiana 1.6% 4.6% 2.0% 0.8% 5.6% 5.2%

Iowa 7.4% 7.1% 7.0% 2.4% 10.2% 6.9% 8.3%

Missouri 4.3% 4.0% 4.0% 1.6% 0.3% 3.9% 4.6%

N. Dakota 10.3% 1.7% 13.0% 6.1% 7.1% 3.6% 0.3 %

Canada 2.7% 3.3% 5.0% 17.2% 13.3% 2.2% 1.7%

CENRAP

WRAP 10.2% 13.7% 6.1% 16.5% 12.5% 12.6%

82.7% 74.5% 73.0% 72.2% 96.8% 84.9% 79.0%

### 111 

### Section 6. Summary

T o support the development of SIPs for ozone, PM 2.5 

### , and regional haze in the Sta tes of Illinois,

Indiana, Michigan, Ohio, and Wisconsin, technical analyses were conducted by LADCO, its

member states, and various contractors

. The analyses include preparation of regional

emissions inventories and meteorological modeling data for two ba se years, evaluation and

application of regional chemical transport models, and review of ambient monitoring data.

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

qualitative summar y

of the physical, chemical

, and m e teorological processes that control the

formation and distribution of pollutants in a given region. Key findings of the analyses include:

Ozone

• Current monitoring data show about 20 sites in violation of the 8

- hour ozone

standard of 85 ppb . Historical ozone data show a steady downward trend over the

past 15 years, especially since 2001

- 2003, due likely to federal and state emission

control programs.

• Ozone concentrations are strongly influenced by meteorological conditions, with

more high ozone days and higher ozone levels during summers with above normal

temperatures.

• Inter

- and intra

- regional transport of ozone and ozone precursors affects many

portions of the five states , and is the principal cause of nonattainment in some areas

far from population o r industrial centers

PM 2.5



### • Current monitoring data show 3 0 sites in violation of the annual PM 2.5 

### standard of 15

ug/m 3 

### . Nonattainment sites are characterized by an elevated regional background

( about 12

– 14 ug/m 3 

### ) and a significant local (urban) incre ment ( about 2

– 3 ug/m 3 

### ).

Historical PM 2.5 

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

### monitoring network in 1999.

• PM 2.5 

### concentrations are also influenced by meteorology, but the relationship is more

complex and less well understood compared to ozone.

• On an annual average basis, PM 2.5 

### chemical composition consists of mostly sulfate,

nitrate, and organic carbon in similar proportions.

Haze

• Current monitoring data show visibility levels in the Class I areas in northern

Michigan are on the order of 2 2

– 2 4 deciviews. The goal of EPA’s visibility program

is to achieve natural conditions, which is on the order of 12 deciviews for these

Class I areas, by the year 2064.

• Visibility impairment is dominated by sulfate and nitrate.

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

used in the modeli ng analyses: 2002 and 2005. EPA’s modeling guidance recommends using

### 11 2



### 2002 as the baseline inventory year, but also allows for use of an alternative b aseline inventory

year, especially a more recent year. Initially, LADCO conducted modeling with a 2002 base

year (i.e., Base K modeling, which was completed in 2006). A decision was subsequently made

to conduct modeling with a 2005 base year (i.e., Base M, which was completed in 2007).

Statistical analyses showed that 2002 and 2005 both had above normal ozone

- conducive

conditions, although 200 2 was more severe compared to 200 5 . Examination of multiple base

years provides for a more complete technical as sessment. Both sets of model runs are

discussed in this document.

B asecase modeling was conducted to evaluate model performance (i.e., assess the model's

ability to reproduce the observed concentrations ) . This exercise wa s intended to assess whether, a nd to degree, confidence in the model is warranted (and to assess whether model

improvements are necessary) . M odel performance for ozone and PM 2.5 

### was generally

acceptable and can be characterized as follows:

Ozone

• Good agreement between modeled and mon itored concentration for higher

concentration levels (> 60 ppb)

– i.e., bias within 30%

• Regional modeled concentrations appear to be underestimated in the 2002 base

year, but show better agreement (with monitored data) in the 2005 base year due to

model a nd inventory improvements

.

• Day

- to

- day and hour

- to

- hour variation in and spatial patterns of modeled

concentrations are consistent with monitored data

• Model accurately simulates the change in monitored ozone concentrations due to

reductions in precursor e missions.

PM 2. 5



### • Good agreement in the magnitude of fine particle mass, but some species are

overestimated and some are underestimated

• Sulfates: good agreement in the 2002 base year, but underestimated in

the summer in the 2005 base year due probably to m eteorological factors

• Nitrates: slightly overestimated in the winter in the 2002 base year, but

good agreement in the 2005 base year as a result of model and inventory

improvements

• Organic Carbon: grossly underestimated in the 2002 and 2005 base

years due likely to missing primary organic carbon emissions

• Temporal variation and spatial patterns of modeled concentrations are consistent

with monitored data

Future year strategy modeling was conducted to determine whether existing (“on the books”)

controls wo uld be sufficient to provide for attainment of the standards for ozone and PM 2.5 

### and if

not, then what additional emission reductions would be necessary for attainment. Traditionally,

attainment demonstrations involved a “bright line” test in which a sing le modeled value (based

on EPA guidance) was compared to the ambient standard. To provide a more robust

assessment of expected future year air quality, other information was considered. Furthermore,

a ccording to EPA’s modeling guidance, if the future yea r model ed values are “close” to the

### 113



### standard (i.e., 82

– 87 ppb for ozone and 14.5

– 15.5 ug/m 3 

### for PM 2.5 

### ) , then the results of the

primary modeling should be reviewed along with the supplemental information in a “weight of

evidence” (WOE) assessment of w hether each area is likely to achieve timely attainment. Key

findings of the WOE determination include:

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

PM 2.5 

### concentrations and visibility levels.

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

difference between the base years of 2002 and 2005 is meteorology. 2002 was

more ozone conducive than 2005. The choice of which base year to use as the

basis for the SIP is a policy decision (i.e., how much safeguard to incorporate).

• Most sites are expected to meet the current 8

- hour standard by the applicable

attainment date, except for sites in western Michigan and, possibly, in eastern

Wisconsin and northeastern Ohio.

• Most sites a re expected to meet the current PM 2.5 

### standard by the applicable

attainment date, except for sites in Detroit, Cleveland, and Granite City

.

T he regiona l modeling for PM 2.5 

### does not reflect air quality benefit s expected

from local controls. States are conducting local

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

- scale modeling, to support their

attainment demonstrations for PM 2.5



### .

• These findings of residual nonattainment for ozone and PM 2.5 

### are supported by

current (2005

– 2007) monitoring dat a which show significant nonattainment in

the region (e.g., peak ozone design values on the order of 90

– 93 ppb, and peak

PM 2.5 

### d esign values on the order of 16

- 17 ug/m 3 

### ). It is unlikely that sufficient

emission reductions will occur in the next few of years to provide for attainment at

all sites.

• Attainment at most sites by the applicable attainment date is dependent on actual

future year meteorology (e.g., if the weather conditions are consistent with [or

less severe than] 2005, then attainment is li kely) and actual future year

emissions (e.g., if the emission reductions associated with the existing controls

are achieved, then attainment is likely). If either of these conditions is not met,

then attainment may be less likely.

• T he new PM 2.5 

### 24

- hour s tandard and the new lower ozone standard will not be

met at several sites , even by 2018, with existing controls.

• Visibility levels in a few Class I areas in the eastern U.S. are expected to be

greater than

(less improved than) the uniform rate of visibili ty improvement

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

and s ome in the northeastern U.S. Visibility levels in m any other Class I areas in

the eastern U.S. are expected to be less than (more improved than) the unifor m rate of visibility improvement value s in 2018.

### 114 

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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

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Environ, 2004, “LADCO Nonroad Emissions Inventory Project for Locomotive, Commercial

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Environ, 2007 a , “Modeling Week end and Weekday Ozone in Southeast Michigan”, Draft Final

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Environ, 2007 b , “LADCO 2005 Locomotive Emissions”, Draft, February 2007.

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Environ, 2008 , “LADCO On

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January 2008.

.

Hollander, M. and D. Wolfe, 1973, “Nonparametric Statistical Methods”, John Wiley & Sons,

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Hopke, P. K., 2005, Analyses of Midwest PM

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, March

2005.

Kenski, D.M., 2004, Quantifying Transboundary Transport of PM 2. 5 

### : A GIS Analysis, Proc.

AWMA 97 th 

### Annual Conf., Indianapolis

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.

Kenski, D.M., 2007a, “CART Analysis for O zone Trends and Meteorological Similarity”, May 10,

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Kenski, D.M., 2007 b , “ Impact of Missing Data on Worst Days at Midwest Northern Class 1

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, Midwest RPO

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.

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### 116



### LADCO, 2005, “Meteorological Modeling Performance Summary for Applications to

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### /Haze/Ozone Modeling Projects”, February 18, 2005

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LADCO, 2006a, “Base K/Round 4 Strategy Modeling” Emissions”, May 16, 2006

.

LADCO, 2006b, “Base K/Round 4 Modeling: Summary”, August 31, 2006

.

LADCO, 2007a, “Modeling Protocol: 2002 Basecase Technical Details”, October 17, 2007

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LADCO, 2007 c , “An Evaluation of an Annual 2005 MM5 Simulation to Support Photochemical

and Emissions Modeling Applications”, January 10, 2007

.

LADCO, 2007 d , “Base M/Round 5 Modeling: Summary (DRAFT), O ctober 10, 2007

LADCO, 2008 a , “Base M Strategy Modeling: Emissions (Revised)”, February 27, 2008

LADCO, 2008b, “ Critique: Modeled Attainment Probability (i.e., Bob Lopez’ analysis) ”, January

10, 2008

Lopez, 2007, “Assessing the Site

- Specific and Region

- w ide Probability of Attainment Level

Ozone Air Quality under varying seasonal conditions using the results of 2002 (Round 4) and

2005 (Round 5) Regional Modeling”, Draft 2, Bob Lopez

, WDNR

, December 12 , 2007

Mansell, G, Z. Wang, R. Zhang, J. Fadel, T. Rams ey, H. Xin, Y. Liang, and J. Arogo, 2005, “An

Improved Process

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– Emission

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MPCA, 2008, “Technical Support Document for the Minnesota State Implementation Plan for

Regional Haze”, Draft, Marc h 4, 2008

.

M RPO , 2001, “Principles for Regional Planning”, March 9, 2001.

MRPO, 2007, “Draft List of Class I Areas Located Within (or Impacted by) Midwest RPO

States”, June 26, 2007.

MRPO, 200 8 , “Regional Haze in the Upper Midwest: Summary of Technical Information”,

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.

Millstein, D.E., R.A. Harley, and S.V. Hering, 2007, “Weekly cycles in fine particulate matter”,

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### STI, 2004, “Data Processing and Analysis of Aloft Air Quality Data Collected in the Upper

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.

STI, 2006 a , “Integration of Results for the Upper Midwest Urban Organics Study”, Final Report,

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### in Selected Midwestern Cities”,

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.

EPA, 2003, “Guidance for Estimating Natural Visibility Conditions Under the Regional Haze

R ule”, 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

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Attainment of Air Quality Goals for Ozone, PM 2. 5 

### , and Regional Haze”, EPA

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- Based Ammonia Emissions Model for Agricultural Sources

– Model Development”

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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/

### 118 

## APPENDIX I

Ozone and PM 2. 5 

## Modeling ResultsKey Sites 2005 BY 2002 BY

'03 '04 '05 '06 '07 '03-'05 '04-'06 '05-'07 Average Average RRF Round 5

Lake Michigan Area Lake Michigan Area

Chiwaukee 550590019 88 78 93 79 85 86 83 85 84.7 98.3 0.968 82.0 Chiwaukee

Racine 551010017 82 69 95 71 77 82 78 81 80.3 91.7 0.966 77.6 Racine

Milwaukee-Bayside 550190085 92 73 93 73 83 86 79 83 82.7 91.0 0.963 79.6 Milwaukee-Bayside

Harrington Beach 550890009 99 72 94 72 84 88 79 83 83.3 93.0 0.960 80.0 Harrington Beach

Manitowoc 550710007 92 74 95 78 85 87 82 86 85.0 87.0 0.957 81.3 Manitowoc

Sheboygan 551170006 93 78 97 83 88 89 86 89 88.0 97.0 0.959 84.4 Sheboygan

Kewaunee 550610002 97 73 88 76 85 86 79 83 82.7 89.3 0.954 78.9 Kewaunee

Door County 550290004 93 78 101 79 92 90 86 90 88.7 91.0 0.956 84.8 Door County

Hammond 180892008 81 67 87 75 77 78 76 79 77.7 88.3 0.971 75.4 Hammond

Whiting 180890030 64 88 81 88 76 77 85 79.3 0.971 77.0 Whiting

Michigan City 180910005 82 70 84 75 73 78 76 77 77.0 90.3 0.964 74.2 Michigan City

Ogden Dunes 181270020 77 69 90 70 84 78 76 81 78.3 86.3 0.967 75.7 Ogden Dunes

Holland 260050003 96 79 94 91 94 89 88 93 90.0 94.0 0.951 85.6 Holland

Jenison 261390005 91 69 86 83 88 82 79 85 82.0 86.0 0.950 77.9 Jenison

Muskegon 261210039 94 70 90 90 86 84 83 88 85.0 90.0 0.951 80.8 Muskegon

Indianapolis Area Indianapolis Area

Noblesville 189571001 101 75 87 77 84 87 79 82 82.7 93.7 0.944 78.0 Noblesville

Fortville 180590003 92 72 80 75 81 81 75 78 78.0 91.3 0.948 73.9 Fortville

Fort B. Harrison 180970050 91 73 80 76 83 81 76 79 78.7 90.0 0.951 74.8 Fort B. Harrison

Detroit Area Detroit Area

New Haven 260990009 102 81 88 78 93 90 82 86 86.0 92.3 0.962 82.7 New Haven

Warren 260991003 101 71 89 78 91 87 79 86 84.0 90.0 0.982 82.5 Warren

Port Huron 261470005 87 74 88 78 89 83 80 85 82.7 88.0 0.956 79.0 Port Huron

Cleveland Area Cleveland Area

Ashtabula 390071001 99 81 93 86 92 91 86 90 89.0 95.7 0.954 84.9 Ashtabula

Geauga 390550004 97 75 88 70 68 86 77 75 79.3 99.0 0.954 75.7 Geauga

Eastlake 390850003 92 79 97 83 74 89 86 84 86.3 92.7 0.959 82.8 Eastlake

Akron 391530020 89 77 89 77 91 85 81 85 83.7 93.3 0.948 79.3

Cincinnati Area Cincinnati Area

Wilmington 390271002 96 78 83 81 82 85 80 82 82.3 94.3 0.945 77.8 Wilmington

Sycamore 390610006 93 76 89 81 90 86 82 86 84.7 90.3 0.965 81.7 Sycamore

Lebanon 391650007 95 81 92 86 88 89 86 88 87.7 87.0 0.954 83.6 Lebanon

Columbus Area Columbus Area

London 390970007 90 75 81 76 83 82 77 80 79.7 88.7 0.946 75.4 London

New Albany 390490029 94 78 92 82 87 88 84 87 86.3 93.0 0.954 82.4 New Albany

Franklin 290490028 84 73 86 79 79 81 79 81 80.3 86.0 0.958 77.0 Franklin

St. Louis Area St. Louis Area

W. Alton (MO) 291831002 91 77 89 91 89 85 85 89 86.3 90.0 0.954 82.4 W. Alton (MO)

Orchard (MO) 291831004 90 76 92 92 83 86 86 89 87.0 90.0 0.958 83.3 Orchard (MO)

Sunset Hills (MO) 291890004 88 70 89 80 89 82 79 86 82.3 88.3 0.966 79.5 Sunset Hills (MO)

Arnold (MO) 290990012 82 70 92 79 87 81 80 86 82.3 84.7 0.956 78.7 Arnold (MO)

Margaretta (MO) 295100086 90 72 91 76 91 84 79 86 83.0 87.7 0.962 79.8 Margaretta (MO)

Maryland Heights (MO) 291890014 88 84 94 88 86 88 87.3 0.967 84.5 Maryland Heights (MO) 4th High 8-hour Value Des. Values (truncated) 2008 - OTBKey Sites 2005 BY 2002 BY

'03 '04 '05 '06 '07 '03-'05 '04-'06 '05-'07 Average Average RRF Round 5 Round 4 RRF Round 5

Lake Michigan Area Lake Michigan Area

Chiwaukee 550590019 88 78 93 79 85 86 83 85 84.7 98.3 0.972 82.3 92.0 0.971 82.2 Chiwaukee

Racine 551010017 82 69 95 71 77 82 78 81 80.3 91.7 0.965 77.5 84.9 0.964 77.4 Racine

Milwaukee-Bayside 550190085 92 73 93 73 83 86 79 83 82.7 91.0 0.965 79.8 84.9 0.964 79.7 Milwaukee-Bayside

Harrington Beach 550890009 99 72 94 72 84 88 79 83 83.3 93.0 0.961 80.1 85.4 0.960 80.0 Harrington Beach

Manitowoc 550710007 92 74 95 78 85 87 82 86 85.0 87.0 0.951 80.8 78.9 0.949 80.7 Manitowoc

Sheboygan 551170006 93 78 97 83 88 89 86 89 88.0 97.0 0.955 84.0 88.9 0.953 83.9 Sheboygan

Kewaunee 550610002 97 73 88 76 85 86 79 83 82.7 89.3 0.945 78.1 81.0 0.943 78.0 Kewaunee

Door County 550290004 93 78 101 79 92 90 86 90 88.7 91.0 0.946 83.9 81.8 0.945 83.8 Door County

Hammond 180892008 81 67 87 75 77 78 76 79 77.7 88.3 0.971 75.4 86.6 0.970 75.3 Hammond

Whiting 180890030 64 88 81 88 76 77 85 79.3 0.971 77.0 0.970 77.0 Whiting

Michigan City 180910005 82 70 84 75 73 78 76 77 77.0 90.3 0.960 73.9 86.5 0.959 73.8 Michigan City

Ogden Dunes 181270020 77 69 90 70 84 78 76 81 78.3 86.3 0.965 75.6 82.8 0.964 75.5 Ogden Dunes

Holland 260050003 96 79 94 91 94 89 88 93 90.0 94.0 0.948 85.3 83.4 0.947 85.2 Holland

Jenison 261390005 91 69 86 83 88 82 79 85 82.0 86.0 0.940 77.1 77.6 0.939 77.0 Jenison

Muskegon 261210039 94 70 90 90 86 84 83 88 85.0 90.0 0.947 80.5 81.5 0.945 80.3 Muskegon

Indianapolis Area Indianapolis Area

Noblesville 189571001 101 75 87 77 84 87 79 82 82.7 93.7 0.945 78.1 83.7 0.946 78.2 Noblesville

Fortville 180590003 92 72 80 75 81 81 75 78 78.0 91.3 0.947 73.9 83.8 0.948 73.9 Fortville

Fort B. Harrison 180970050 91 73 80 76 83 81 76 79 78.7 90.0 0.955 75.1 83.7 0.956 75.2 Fort B. Harrison

Detroit Area Detroit Area

New Haven 260990009 102 81 88 78 93 90 82 86 86.0 92.3 0.947 81.4 85.3 0.947 81.4 New Haven

Warren 260991003 101 71 89 78 91 87 79 86 84.0 90.0 0.968 81.3 83.3 0.969 81.4 Warren

Port Huron 261470005 87 74 88 78 89 83 80 85 82.7 88.0 0.937 77.5 79.1 0.938 77.5 Port Huron

Cleveland Area Cleveland Area

Ashtabula 390071001 99 81 93 86 92 91 86 90 89.0 95.7 0.937 83.4 82.7 0.941 83.7 Ashtabula

Geauga 390550004 97 75 88 70 68 86 77 75 79.3 99.0 0.942 74.7 88.8 0.945 75.0 Geauga

Eastlake 390850003 92 79 97 83 74 89 86 84 86.3 92.7 0.949 81.9 82.8 0.954 82.4 Eastlake

Akron 391530020 89 77 89 77 91 85 81 85 83.7 93.3 0.934 78.1 81.4 0.935 78.2

Cincinnati Area Cincinnati Area

Wilmington 390271002 96 78 83 81 82 85 80 82 82.3 94.3 0.941 77.5 83.5 0.942 77.6 Wilmington

Sycamore 390610006 93 76 89 81 90 86 82 86 84.7 90.3 0.967 81.9 84.7 0.968 82.0 Sycamore

Lebanon 391650007 95 81 92 86 88 89 86 88 87.7 87.0 0.947 83.0 79.0 0.948 83.1 Lebanon

Columbus Area Columbus Area

London 390970007 90 75 81 76 83 82 77 80 79.7 88.7 0.941 75.0 78.4 0.942 75.0 London

New Albany 390490029 94 78 92 82 87 88 84 87 86.3 93.0 0.947 81.8 82.6 0.948 81.8 New Albany

Franklin 290490028 84 73 86 79 79 81 79 81 80.3 86.0 0.945 75.9 76.5 0.948 76.2 Franklin

St. Louis Area St. Louis Area

W. Alton (MO) 291831002 91 77 89 91 89 85 85 89 86.3 90.0 0.938 81.0 85.2 0.932 80.5 W. Alton (MO)

Orchard (MO) 291831004 90 76 92 92 83 86 86 89 87.0 90.0 0.942 82.0 82.2 0.939 81.7 Orchard (MO)

Sunset Hills (MO) 291890004 88 70 89 80 89 82 79 86 82.3 88.3 0.956 78.7 81.9 0.954 78.5 Sunset Hills (MO)

Arnold (MO) 290990012 82 70 92 79 87 81 80 86 82.3 84.7 0.938 77.2 77.4 0.937 77.1 Arnold (MO)

Margaretta (MO) 295100086 90 72 91 76 91 84 79 86 83.0 87.7 0.955 79.3 83.4 0.955 79.3 Margaretta (MO)

Maryland Heights (MO) 291890014 88 84 94 88 86 88 87.3 0.955 83.4 0.954 83.3 Maryland Heights (MO) 4th High 8-hour Value Des. Values (truncated) 2009 - Will Do 2009 - OTBKey Sites 2005 BY 2002 BY

'03 '04 '05 '06 '07 '03-'05 '04-'06 '05-'07 Average Average RRF Round 5 Round 4 RRF Round 5

Lake Michigan Area Lake Michigan Area

Chiwaukee 550590019 88 78 93 79 85 86 83 85 84.7 98.3 0.956 80.9 90.3 0.900 76.2 Chiwaukee

Racine 551010017 82 69 95 71 77 82 78 81 80.3 91.7 0.947 76.1 82.9 0.886 71.2 Racine

Milwaukee-Bayside 550190085 92 73 93 73 83 86 79 83 82.7 91.0 0.944 78.0 82.3 0.880 72.7 Milwaukee-Bayside

Harrington Beach 550890009 99 72 94 72 84 88 79 83 83.3 93.0 0.939 78.3 82.9 0.870 72.5 Harrington Beach

Manitowoc 550710007 92 74 95 78 85 87 82 86 85.0 87.0 0.925 78.6 76.3 0.853 72.5 Manitowoc

Sheboygan 551170006 93 78 97 83 88 89 86 89 88.0 97.0 0.930 81.8 86.4 0.857 75.4 Sheboygan

Kewaunee 550610002 97 73 88 76 85 86 79 83 82.7 89.3 0.918 75.9 79.1 0.845 69.9 Kewaunee

Door County 550290004 93 78 101 79 92 90 86 90 88.7 91.0 0.919 81.5 79.3 0.843 74.7 Door County

Hammond 180892008 81 67 87 75 77 78 76 79 77.7 88.3 0.960 74.6 86.3 0.922 71.6 Hammond

Whiting 180890030 64 88 81 88 76 77 85 79.3 0.960 76.2 0.922 73.1 Whiting

Michigan City 180910005 82 70 84 75 73 78 76 77 77.0 90.3 0.942 72.5 85.4 0.884 68.1 Michigan City

Ogden Dunes 181270020 77 69 90 70 84 78 76 81 78.3 86.3 0.951 74.5 82.0 0.904 70.8 Ogden Dunes

Holland 260050003 96 79 94 91 94 89 88 93 90.0 94.0 0.920 82.8 81.0 0.846 76.1 Holland

Jenison 261390005 91 69 86 83 88 82 79 85 82.0 86.0 0.909 74.5 75.5 0.838 68.7 Jenison

Muskegon 261210039 94 70 90 90 86 84 83 88 85.0 90.0 0.918 78.0 79.4 0.846 71.9 Muskegon

Indianapolis Area Indianapolis Area

Noblesville 189571001 101 75 87 77 84 87 79 82 82.7 93.7 0.914 75.6 82.0 0.831 68.7 Noblesville

Fortville 180590003 92 72 80 75 81 81 75 78 78.0 91.3 0.916 71.4 82.1 0.835 65.1 Fortville

Fort B. Harrison 180970050 91 73 80 76 83 81 76 79 78.7 90.0 0.931 73.2 82.4 0.879 69.1 Fort B. Harrison

Detroit Area Detroit Area

New Haven 260990009 102 81 88 78 93 90 82 86 86.0 92.3 0.932 80.2 83.5 0.885 76.1 New Haven

Warren 260991003 101 71 89 78 91 87 79 86 84.0 90.0 0.961 80.7 81.9 0.924 77.6 Warren

Port Huron 261470005 87 74 88 78 89 83 80 85 82.7 88.0 0.913 75.5 77.0 0.858 70.9 Port Huron

Cleveland Area Cleveland Area

Ashtabula 390071001 99 81 93 86 92 91 86 90 89.0 95.7 0.910 81.0 80.2 0.844 75.1 Ashtabula

Geauga 390550004 97 75 88 70 68 86 77 75 79.3 99.0 0.916 72.7 86.2 0.848 67.3 Geauga

Eastlake 390850003 92 79 97 83 74 89 86 84 86.3 92.7 0.932 80.5 80.6 0.883 76.2 Eastlake

Akron 391530020 89 77 89 77 91 85 81 85 83.7 93.3 0.903 75.6 78.5 0.821 68.7 Akron

Cincinnati Area Cincinnati Area

Wilmington 390271002 96 78 83 81 82 85 80 82 82.3 94.3 0.910 74.9 81.1 0.830 68.3 Wilmington

Sycamore 390610006 93 76 89 81 90 86 82 86 84.7 90.3 0.948 80.3 82.9 0.881 74.6 Sycamore

Lebanon 391650007 95 81 92 86 88 89 86 88 87.7 87.0 0.921 80.7 77.0 0.846 74.2 Lebanon

Columbus Area Columbus Area

London 390970007 90 75 81 76 83 82 77 80 79.7 88.7 0.911 72.6 76.5 0.832 66.3 London

New Albany 390490029 94 78 92 82 87 88 84 87 86.3 93.0 0.922 79.6 80.2 0.845 73.0 New Albany

Franklin 290490028 84 73 86 79 79 81 79 81 80.3 86.0 0.923 74.1 74.7 0.859 69.0 Franklin

St. Louis Area St. Louis Area

W. Alton (MO) 291831002 91 77 89 91 89 85 85 89 86.3 90.0 0.911 78.6 84.0 0.868 74.9 W. Alton (MO)

Orchard (MO) 291831004 90 76 92 92 83 86 86 89 87.0 90.0 0.919 80.0 80.4 0.876 76.2 Orchard (MO)

Sunset Hills (MO) 291890004 88 70 89 80 89 82 79 86 82.3 88.3 0.937 77.1 80.6 0.897 73.9 Sunset Hills (MO)

Arnold (MO) 290990012 82 70 92 79 87 81 80 86 82.3 84.7 0.918 75.6 75.8 0.874 72.0 Arnold (MO)

Margaretta (MO) 295100086 90 72 91 76 91 84 79 86 83.0 87.7 0.939 77.9 82.5 0.896 74.4 Margaretta (MO)

Maryland Heights (MO) 291890014 88 84 94 88 86 88 87.3 0.936 81.7 0.894 78.1 Maryland Heights (MO) 4th High 8-hour Value Des. Values (truncated) 2018 - OTB 2012 - OTB2005 BY 2002 BY

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

w/ 2007 Average

Round 5 Round4 Key Site

Chicago - Washington HS Cook 170310022 15.6 14.2 16.9 13.2 15.7 15.6 14.8 15.3 15.2 15.9 14.1 14.8 Chicago - Washington HS

Chicago - Mayfair Cook 170310052 15.9 15.3 17.0 14.5 15.5 16.1 15.6 15.7 15.8 17.1 14.4 15.8 Chicago - Mayfair

Chicago - Springfield Cook 170310057 15.6 13.8 16.7 13.5 15.1 15.4 14.7 15.1 15.0 15.6 13.9 14.5 Chicago - Springfield

Chicago - Lawndale Cook 170310076 14.8 14.2 16.6 13.5 14.3 15.2 14.8 14.8 14.9 15.6 13.8 14.5 Chicago - Lawndale

Blue Island Cook 170312001 14.9 14.1 16.4 13.2 14.3 15.1 14.6 14.6 14.8 15.6 13.7 14.5 Blue Island

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

Cicero Cook 170316005 16.8 15.2 16.3 14.3 14.8 16.1 15.3 15.1 15.5 16.4 14.4 15.3 Cicero

Granite City Madison 171191007 17.5 15.4 18.2 16.3 15.1 17.0 16.6 16.5 16.7 17.3 15.1 16.0 Granite City

E. St. Louis St. Clair 171630010 14.9 14.7 17.1 14.5 15.6 15.6 15.4 15.7 15.6 16.2 14.1 14.9 E. St. Louis

Jeffersonville Clark 180190005 15.8 15.1 18.5 15.0 16.5 16.5 16.2 16.7 16.4 17.2 13.8 15.5 Jeffersonville

Jasper Dubois 180372001 15.7 14.4 16.9 13.5 14.4 15.7 14.9 14.9 15.2 15.5 12.4 13.8 Jasper

Gary Lake 180890031 16.8 13.3 14.5 16.8 15.1 14.9 15.6 13.0 Gary

Indy-Washington Park Marion 180970078 15.5 14.3 16.4 14.1 15.8 15.4 14.9 15.4 15.3 16.2 12.8 14.5 Indy-Washington Park

Indy-W 18th Street Marion 180970081 16.2 15.0 17.9 14.2 16.1 16.4 15.7 16.1 16.0 13.4 Indy-W 18th Street

Indy- Michigan Street Marion 180970083 16.3 15.0 17.5 14.1 15.9 16.3 15.5 15.8 15.9 16.6 13.4 14.8 Indy- Michigan Street

Allen Park Wayne 261630001 15.2 14.2 15.9 13.2 12.8 15.1 14.4 14.0 14.5 15.8 13.0 14.5 Allen Park

Southwest HS Wayne 261630015 16.6 15.4 17.2 14.7 14.5 16.4 15.8 15.5 15.9 17.3 14.2 15.8 Southwest HS

Linwood Wayne 261630016 15.8 13.7 16.0 13.0 13.9 15.2 14.2 14.3 14.6 15.5 13.1 14.1 Linwood

Dearborn Wayne 261630033 19.2 16.8 18.6 16.1 16.9 18.2 17.2 17.2 17.5 19.3 15.8 17.7 Dearborn

Wyandotte Wayne 261630036 16.3 13.7 16.4 12.9 13.4 15.5 14.3 14.2 14.7 16.6 13.1 15.1 Wyandotte

Middleton Butler 390170003 17.2 14.1 19.0 14.1 15.4 16.8 15.7 16.2 16.2 16.5 13.5 14.2 Middleton

Fairfield Butler 390170016 15.8 14.7 17.9 14.0 14.9 16.1 15.5 15.6 15.8 15.9 13.1 13.5 Fairfield

Cleveland-28th Street Cuyahoga 390350027 15.4 15.6 17.3 13.0 14.5 16.1 15.3 14.9 15.4 16.5 13.5 14.4 Cleveland-28th Street

Cleveland-St. Tikhon Cuyahoga 390350038 17.6 17.5 19.2 14.9 16.2 18.1 17.2 16.8 17.4 18.4 15.2 16.1 Cleveland-St. Tikhon

Cleveland-Broadway Cuyahoga 390350045 16.4 15.3 19.3 14.1 15.3 17.0 16.2 16.2 16.5 16.7 14.4 14.6 Cleveland-Broadway

Cleveland-E14 & Orange Cuyahoga 390350060 17.2 16.4 19.4 15.0 15.9 17.7 16.9 16.8 17.1 17.6 15.0 15.3 Cleveland-E14 & Orange

Newburg Hts - Harvard Ave Cuyahoga 390350065 15.6 15.2 18.6 13.1 15.8 16.5 15.6 15.8 16.0 16.2 14.0 14.1 Newburg Hts - Harvard Ave

Columbus - Fairgrounds Franklin 390490024 16.4 15.0 16.4 13.6 14.6 15.9 15.0 14.9 15.3 16.5 12.9 14.6 Columbus - Fairgrounds

Columbus - Ann Street Franklin 390490025 15.3 14.6 16.5 13.8 14.7 15.5 15.0 15.0 15.1 16.0 12.7 14.1 Columbus - Ann Street

Columbus - Maple Canyon Franklin 390490081 14.9 13.6 14.6 12.9 13.1 14.4 13.7 13.5 13.9 16.0 11.7 14.0 Columbus - Maple Canyon

Cincinnati - Seymour Hamilton 390610014 17.0 15.9 19.8 15.5 16.5 17.6 17.1 17.3 17.3 17.7 14.5 15.5 Cincinnati - Seymour

Cincinnati - Taft Ave Hamilton 390610040 15.5 14.6 17.5 13.6 15.1 15.9 15.2 15.4 15.5 15.7 12.8 13.6 Cincinnati - Taft Ave

Cincinnati - 8th Ave Hamilton 390610042 16.7 16.0 19.1 14.9 15.9 17.3 16.7 16.6 16.9 17.3 14.0 14.6 Cincinnati - 8th Ave

Sharonville Hamilton 390610043 15.7 14.9 16.9 14.5 14.8 15.8 15.4 15.4 15.6 16.0 12.9 13.6 Sharonville

Norwood Hamilton 390617001 16.0 15.3 18.4 14.4 15.1 16.6 16.0 16.0 16.2 16.3 13.4 14.2 Norwood

St. Bernard Hamilton 390618001 17.3 16.4 20.0 15.9 16.1 17.9 17.4 17.3 17.6 17.3 14.7 15.2 St. Bernard

Steubenville Jefferson 390810016 17.7 15.9 16.4 13.8 16.2 16.7 15.4 15.5 15.8 17.7 12.8 16.3 Steubenville

Mingo Junction Jefferson 390811001 17.3 16.2 18.1 14.6 15.6 17.2 16.3 16.1 16.5 17.5 13.5 15.5 Mingo Junction

Ironton Lawrence 390870010 14.3 13.7 17.0 14.4 15.0 15.0 15.0 15.5 15.2 15.7 12.8 14.2 Ironton

Dayton Montgomery 391130032 15.9 14.5 17.4 13.6 15.6 15.9 15.2 15.5 15.5 15.9 13.2 13.7 Dayton

New Boston Scioto 391450013 14.7 13.0 16.2 14.3 14.0 14.6 14.5 14.8 14.7 17.1 12.1 15.4 New Boston

Canton - Dueber Stark 391510017 16.8 15.6 17.8 14.6 15.9 16.7 16.0 16.1 16.3 17.3 14.0 15.0 Canton - Dueber

Canton - Market Stark 391510020 15.0 14.1 16.6 11.9 14.4 15.2 14.2 14.3 14.6 15.7 12.6 13.6 Canton - Market

Akron - Brittain Summit 391530017 15.4 15.0 16.4 13.5 14.4 15.6 15.0 14.8 15.1 16.4 13.0 14.4 Akron - Brittain

Akron - W. Exchange Summit 391530023 14.2 13.9 15.7 12.8 13.7 14.6 14.1 14.1 14.3 15.6 12.3 13.6 Akron - W. Exchange Annual Average Conc. Design Values 2009 Modeling Results2005 BY 2002 BY

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

w/ 2007 Average

Round 5 Round4 Key Site

Chicago - Washington HS Cook 170310022 15.6 14.2 16.9 13.2 15.7 15.6 14.8 15.3 15.2 15.9 14.0 14.6 Chicago - Washington HS

Chicago - Mayfair Cook 170310052 15.9 15.3 17.0 14.5 15.5 16.1 15.6 15.7 15.8 17.1 14.2 15.5 Chicago - Mayfair

Chicago - Springfield Cook 170310057 15.6 13.8 16.7 13.5 15.1 15.4 14.7 15.1 15.0 15.6 13.8 14.3 Chicago - Springfield

Chicago - Lawndale Cook 170310076 14.8 14.2 16.6 13.5 14.3 15.2 14.8 14.8 14.9 15.6 13.7 14.3 Chicago - Lawndale

Blue Island Cook 170312001 14.9 14.1 16.4 13.2 14.3 15.1 14.6 14.6 14.8 15.6 13.6 14.3 Blue Island

Summit Cook 170313301 15.6 14.2 16.9 13.8 14.8 15.6 15.0 15.2 15.2 16.0 14.0 14.6 Summit

Cicero Cook 170316005 16.8 15.2 16.3 14.3 14.8 16.1 15.3 15.1 15.5 16.4 14.3 15.1 Cicero

Granite City Madison 171191007 17.5 15.4 18.2 16.3 15.1 17.0 16.6 16.5 16.7 17.3 14.9 15.8 Granite City

E. St. Louis St. Clair 171630010 14.9 14.7 17.1 14.5 15.6 15.6 15.4 15.7 15.6 16.2 13.9 14.7 E. St. Louis

Jeffersonville Clark 180190005 15.8 15.1 18.5 15.0 16.5 16.5 16.2 16.7 16.4 17.2 13.7 15.0 Jeffersonville

Jasper Dubois 180372001 15.7 14.4 16.9 13.5 14.4 15.7 14.9 14.9 15.2 15.5 12.2 13.5 Jasper

Gary Lake 180890031 16.8 13.3 14.5 16.8 15.1 14.9 15.6 12.8 Gary

Indy-Washington Park Marion 180970078 15.5 14.3 16.4 14.1 15.8 15.4 14.9 15.4 15.3 16.2 12.6 14.2 Indy-Washington Park

Indy-W 18th Street Marion 180970081 16.2 15.0 17.9 14.2 16.1 16.4 15.7 16.1 16.0 13.2 Indy-W 18th Street

Indy- Michigan Street Marion 180970083 16.3 15.0 17.5 14.1 15.9 16.3 15.5 15.8 15.9 16.6 13.1 14.9 Indy- Michigan Street

Allen Park Wayne 261630001 15.2 14.2 15.9 13.2 12.8 15.1 14.4 14.0 14.5 15.8 12.8 14.1 Allen Park

Southwest HS Wayne 261630015 16.6 15.4 17.2 14.7 14.5 16.4 15.8 15.5 15.9 17.3 13.9 15.3 Southwest HS

Linwood Wayne 261630016 15.8 13.7 16.0 13.0 13.9 15.2 14.2 14.3 14.6 15.5 12.8 13.7 Linwood

Dearborn Wayne 261630033 19.2 16.8 18.6 16.1 16.9 18.2 17.2 17.2 17.5 19.3 15.5 17.1 Dearborn

Wyandotte Wayne 261630036 16.3 13.7 16.4 12.9 13.4 15.5 14.3 14.2 14.7 16.6 12.8 14.7 Wyandotte

Middleton Butler 390170003 17.2 14.1 19.0 14.1 15.4 16.8 15.7 16.2 16.2 16.5 13.2 13.7 Middleton

Fairfield Butler 390170016 15.8 14.7 17.9 14.0 14.9 16.1 15.5 15.6 15.8 15.9 12.9 12.9 Fairfield

Cleveland-28th Street Cuyahoga 390350027 15.4 15.6 17.3 13.0 14.5 16.1 15.3 14.9 15.4 16.5 13.2 13.8 Cleveland-28th Street

Cleveland-St. Tikhon Cuyahoga 390350038 17.6 17.5 19.2 14.9 16.2 18.1 17.2 16.8 17.4 18.4 14.8 15.4 Cleveland-St. Tikhon

Cleveland-Broadway Cuyahoga 390350045 16.4 15.3 19.3 14.1 15.3 17.0 16.2 16.2 16.5 16.7 14.0 14.0 Cleveland-Broadway

Cleveland-E14 & Orange Cuyahoga 390350060 17.2 16.4 19.4 15.0 15.9 17.7 16.9 16.8 17.1 17.6 14.6 14.7 Cleveland-E14 & Orange

Newburg Hts - Harvard Ave Cuyahoga 390350065 15.6 15.2 18.6 13.1 15.8 16.5 15.6 15.8 16.0 16.2 13.6 13.5 Newburg Hts - Harvard Ave

Columbus - Fairgrounds Franklin 390490024 16.4 15.0 16.4 13.6 14.6 15.9 15.0 14.9 15.3 16.5 12.6 14.0 Columbus - Fairgrounds

Columbus - Ann Street Franklin 390490025 15.3 14.6 16.5 13.8 14.7 15.5 15.0 15.0 15.1 16.0 12.4 13.5 Columbus - Ann Street

Columbus - Maple Canyon Franklin 390490081 14.9 13.6 14.6 12.9 13.1 14.4 13.7 13.5 13.9 16.0 11.4 13.4 Columbus - Maple Canyon

Cincinnati - Seymour Hamilton 390610014 17.0 15.9 19.8 15.5 16.5 17.6 17.1 17.3 17.3 17.7 14.3 14.8 Cincinnati - Seymour

Cincinnati - Taft Ave Hamilton 390610040 15.5 14.6 17.5 13.6 15.1 15.9 15.2 15.4 15.5 15.7 12.6 13.0 Cincinnati - Taft Ave

Cincinnati - 8th Ave Hamilton 390610042 16.7 16.0 19.1 14.9 15.9 17.3 16.7 16.6 16.9 17.3 13.8 14.0 Cincinnati - 8th Ave

Sharonville Hamilton 390610043 15.7 14.9 16.9 14.5 14.8 15.8 15.4 15.4 15.6 16.0 12.7 13.0 Sharonville

Norwood Hamilton 390617001 16.0 15.3 18.4 14.4 15.1 16.6 16.0 16.0 16.2 16.3 13.2 13.6 Norwood

St. Bernard Hamilton 390618001 17.3 16.4 20.0 15.9 16.1 17.9 17.4 17.3 17.6 17.3 14.4 14.6 St. Bernard

Steubenville Jefferson 390810016 17.7 15.9 16.4 13.8 16.2 16.7 15.4 15.5 15.8 17.7 12.5 15.9 Steubenville

Mingo Junction Jefferson 390811001 17.3 16.2 18.1 14.6 15.6 17.2 16.3 16.1 16.5 17.5 13.2 15.0 Mingo Junction

Ironton Lawrence 390870010 14.3 13.7 17.0 14.4 15.0 15.0 15.0 15.5 15.2 15.7 12.5 13.7 Ironton

Dayton Montgomery 391130032 15.9 14.5 17.4 13.6 15.6 15.9 15.2 15.5 15.5 15.9 12.9 13.2 Dayton

New Boston Scioto 391450013 14.7 13.0 16.2 14.3 14.0 14.6 14.5 14.8 14.7 17.1 11.9 14.8 New Boston

Canton - Dueber Stark 391510017 16.8 15.6 17.8 14.6 15.9 16.7 16.0 16.1 16.3 17.3 13.6 14.3 Canton - Dueber

Canton - Market Stark 391510020 15.0 14.1 16.6 11.9 14.4 15.2 14.2 14.3 14.6 15.7 12.3 13.0 Canton - Market

Akron - Brittain Summit 391530017 15.4 15.0 16.4 13.5 14.4 15.6 15.0 14.8 15.1 16.4 12.7 13.6 Akron - Brittain

Akron - W. Exchange Summit 391530023 14.2 13.9 15.7 12.8 13.7 14.6 14.1 14.1 14.3 15.6 12.0 13.0 Akron - W. Exchange Annual Average Conc. Design Values 2012 Modeling Results2005 BY 2002 BY

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

w/ 2007 Average Round 5

OTB Round 5

Will Do Round4 Key Site

Chicago - Washington HS Cook 170310022 15.6 14.2 16.9 13.2 15.7 15.6 14.8 15.3 15.2 15.9 13.9 13.8 14.4 Chicago - Washington HS

Chicago - Mayfair Cook 170310052 15.9 15.3 17.0 14.5 15.5 16.1 15.6 15.7 15.8 17.1 13.9 13.8 15.0 Chicago - Mayfair

Chicago - Springfield Cook 170310057 15.6 13.8 16.7 13.5 15.1 15.4 14.7 15.1 15.0 15.6 13.7 13.5 14.1 Chicago - Springfield

Chicago - Lawndale Cook 170310076 14.8 14.2 16.6 13.5 14.3 15.2 14.8 14.8 14.9 15.6 13.6 13.4 14.1 Chicago - Lawndale

Blue Island Cook 170312001 14.9 14.1 16.4 13.2 14.3 15.1 14.6 14.6 14.8 15.6 13.4 13.3 14.1 Blue Island

Summit Cook 170313301 15.6 14.2 16.9 13.8 14.8 15.6 15.0 15.2 15.2 16.0 13.9 13.8 14.4 Summit

Cicero Cook 170316005 16.8 15.2 16.3 14.3 14.8 16.1 15.3 15.1 15.5 16.4 14.2 14.0 14.9 Cicero

Granite City Madison 171191007 17.5 15.4 18.2 16.3 15.1 17.0 16.6 16.5 16.7 17.3 14.3 14.2 15.5 Granite City

E. St. Louis St. Clair 171630010 14.9 14.7 17.1 14.5 15.6 15.6 15.4 15.7 15.6 16.2 13.4 13.3 14.5 E. St. Louis

Jeffersonville Clark 180190005 15.8 15.1 18.5 15.0 16.5 16.5 16.2 16.7 16.4 17.2 13.4 13.4 14.4 Jeffersonville

Jasper Dubois 180372001 15.7 14.4 16.9 13.5 14.4 15.7 14.9 14.9 15.2 15.5 11.8 11.9 13.0 Jasper

Gary Lake 180890031 16.8 13.3 14.5 16.8 15.1 14.9 15.6 12.4 12.4 Gary

Indy- Washington Park Marion 180970078 15.5 14.3 16.4 14.1 15.8 15.4 14.9 15.4 15.3 16.2 12.0 12.1 13.7 Indy- Washington Park

Indy- W 18th Street Marion 180970081 16.2 15.0 17.9 14.2 16.1 16.4 15.7 16.1 16.0 12.6 12.7 Indy- W 18th Street

Indy- Michigan Street Marion 180970083 16.3 15.0 17.5 14.1 15.9 16.3 15.5 15.8 15.9 16.6 12.6 12.6 14.0 Indy- Michigan Street

Allen Park Wayne 261630001 15.2 14.2 15.9 13.2 12.8 15.1 14.4 14.0 14.5 15.8 12.4 12.4 13.3 Allen Park

Southwest HS Wayne 261630015 16.6 15.4 17.2 14.7 14.5 16.4 15.8 15.5 15.9 17.3 13.5 13.5 14.4 Southwest HS

Linwood Wayne 261630016 15.8 13.7 16.0 13.0 13.9 15.2 14.2 14.3 14.6 15.5 12.5 12.5 13.0 Linwood

Dearborn Wayne 261630033 19.2 16.8 18.6 16.1 16.9 18.2 17.2 17.2 17.5 19.3 15.1 15.1 16.1 Dearborn

Wyandotte Wayne 261630036 16.3 13.7 16.4 12.9 13.4 15.5 14.3 14.2 14.7 16.6 12.5 12.5 13.9 Wyandotte

Middleton Butler 390170003 17.2 14.1 19.0 14.1 15.4 16.8 15.7 16.2 16.2 16.5 12.8 12.8 13.1 Middleton

Fairfield Butler 390170016 15.8 14.7 17.9 14.0 14.9 16.1 15.5 15.6 15.8 15.9 12.5 12.6 12.2 Fairfield

Cleveland-28th Street Cuyahoga 390350027 15.4 15.6 17.3 13.0 14.5 16.1 15.3 14.9 15.4 16.5 12.7 12.9 12.9 Cleveland-28th Street

Cleveland-St. Tikhon Cuyahoga 390350038 17.6 17.5 19.2 14.9 16.2 18.1 17.2 16.8 17.4 18.4 14.3 14.5 14.4 Cleveland-St. Tikhon

Cleveland-Broadway Cuyahoga 390350045 16.4 15.3 19.3 14.1 15.3 17.0 16.2 16.2 16.5 16.7 13.5 13.7 13.1 Cleveland-Broadway

Cleveland-E14 & Orange Cuyahoga 390350060 17.2 16.4 19.4 15.0 15.9 17.7 16.9 16.8 17.1 17.6 14.1 14.2 13.7 Cleveland-E14 & Orange

Newburg Hts - Harvard Ave Cuyahoga 390350065 15.6 15.2 18.6 13.1 15.8 16.5 15.6 15.8 16.0 16.2 13.1 13.3 12.6 Newburg Hts - Harvard Ave

Columbus - Fairgrounds Franklin 390490024 16.4 15.0 16.4 13.6 14.6 15.9 15.0 14.9 15.3 16.5 12.0 12.1 13.0 Columbus - Fairgrounds

Columbus - Ann Street Franklin 390490025 15.3 14.6 16.5 13.8 14.7 15.5 15.0 15.0 15.1 16.0 11.9 11.9 12.5 Columbus - Ann Street

Columbus - Maple Canyon Franklin 390490081 14.9 13.6 14.6 12.9 13.1 14.4 13.7 13.5 13.9 16.0 10.9 11.0 12.5 Columbus - Maple Canyon

Cincinnati - Seymour Hamilton 390610014 17.0 15.9 19.8 15.5 16.5 17.6 17.1 17.3 17.3 17.7 13.8 13.9 14.0 Cincinnati - Seymour

Cincinnati - Taft Ave Hamilton 390610040 15.5 14.6 17.5 13.6 15.1 15.9 15.2 15.4 15.5 15.7 12.2 12.3 12.3 Cincinnati - Taft Ave

Cincinnati - 8th Ave Hamilton 390610042 16.7 16.0 19.1 14.9 15.9 17.3 16.7 16.6 16.9 17.3 13.4 13.4 13.2 Cincinnati - 8th Ave

Sharonville Hamilton 390610043 15.7 14.9 16.9 14.5 14.8 15.8 15.4 15.4 15.6 16.0 12.3 12.4 12.2 Sharonville

Norwood Hamilton 390617001 16.0 15.3 18.4 14.4 15.1 16.6 16.0 16.0 16.2 16.3 12.8 12.8 12.8 Norwood

St. Bernard Hamilton 390618001 17.3 16.4 20.0 15.9 16.1 17.9 17.4 17.3 17.6 17.3 14.0 14.1 13.8 St. Bernard

Steubenville Jefferson 390810016 17.7 15.9 16.4 13.8 16.2 16.7 15.4 15.5 15.8 17.7 12.7 12.7 16.2 Steubenville

Mingo Junction Jefferson 390811001 17.3 16.2 18.1 14.6 15.6 17.2 16.3 16.1 16.5 17.5 13.4 13.4 15.3 Mingo Junction

Ironton Lawrence 390870010 14.3 13.7 17.0 14.4 15.0 15.0 15.0 15.5 15.2 15.7 12.3 12.3 13.2 Ironton

Dayton Montgomery 391130032 15.9 14.5 17.4 13.6 15.6 15.9 15.2 15.5 15.5 15.9 12.4 12.5 12.3 Dayton

New Boston Scioto 391450013 14.7 13.0 16.2 14.3 14.0 14.6 14.5 14.8 14.7 17.1 11.6 11.6 14.2 New Boston

Canton - Dueber Stark 391510017 16.8 15.6 17.8 14.6 15.9 16.7 16.0 16.1 16.3 17.3 13.3 13.3 13.6 Canton - Dueber

Canton - Market Stark 391510020 15.0 14.1 16.6 11.9 14.4 15.2 14.2 14.3 14.6 15.7 11.9 12.0 12.2 Canton - Market

Akron - Brittain Summit 391530017 15.4 15.0 16.4 13.5 14.4 15.6 15.0 14.8 15.1 16.4 12.3 12.3 12.9 Akron - Brittain

Akron - W. Exchange Summit 391530023 14.2 13.9 15.7 12.8 13.7 14.6 14.1 14.1 14.3 15.6 11.5 11.6 12.2 Akron - W. Exchange Annual Average Conc. Design Values 2018 Modeling Results

## 24-Hour PM 2.5 Base Year

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

w/ 2007 2009 2012 2018 Key Site

Chicago - Washington HS Cook 170310022 37.7 32.5 45.7 27.0 35.7 38.6 35.1 36.1 36.6 36 36 35 Chicago - Washington HS

Chicago - Mayfair Cook 170310052 37.3 38.8 48.3 31.6 39.4 41.5 39.6 39.8 40.3 36 36 36 Chicago - Mayfair

Chicago - Springfield Cook 170310057 36.4 33.1 46.5 27.7 38.9 38.7 35.8 37.7 37.4 32 32 31 Chicago - Springfield

Chicago - Lawndale Cook 170310076 32.6 39.7 45.1 29.0 37.2 39.1 37.9 37.1 38.1 35 35 34 Chicago - Lawndale

McCook Cook 170311016 43.0 39 39 38 McCook

Blue Island Cook 170312001 39.6 38.5 43.8 28.1 35.1 40.6 36.8 35.7 37.7 34 34 33 Blue Island

Schiller Park Cook 170313103 40.7 50.3 30.0 36.6 45.5 40.3 39.0 41.6 39 39 39 Schiller Park

Summit Cook 170313301 38.4 42.4 49.1 27.4 36.7 43.3 39.6 37.7 40.2 38 38 37 Summit

Maywood Cook 170316005 38.5 42.5 44.6 29.2 36.9 41.9 38.8 36.9 39.2 38 38 37 Maywood

Granite City Madison 171191007 40.8 35.4 44.1 36.3 36.0 40.1 38.6 38.8 39.2 33 33 32 Granite City

E. St. Louis St. Clair 171630010 32.6 30.2 39.6 29.2 33.1 34.1 33.0 34.0 33.7 28 28 28 E. St. Louis

Jeffersonville Clark 180190005 28.4 45.5 35.9 43.3 37.0 36.6 41.6 38.4 29 31 31 Jeffersonville

Jasper Dubois 180372001 39.5 30.0 41.2 31.6 39.5 36.9 34.3 37.4 36.2 28 29 28 Jasper

Gary - IITRI Lake 180890022 39.0 34 34 35 Gary - IITRI

Gary - Burr School Lake 180890026 39.0 33 34 32 Gary - Burr School

Gary Lake 180890031 38.7 27.1 36.2 38.7 32.9 34.0 35.2 24 24 27 Gary

Indy-West Street Marion 180970043 38.0 33 33 33 Indy-West Street

Indy-English Avenue Marion 180970066 38.0 32 32 32 Indy-English Avenue

Indy-Washington Park Marion 180970078 39.3 31.0 42.5 31.7 37.6 37.6 35.1 37.3 36.6 31 31 32 Indy-Washington Park

Indy-W 18th Street Marion 180970081 36.2 31.9 45.7 34.8 38.4 37.9 37.5 39.6 38.3 31 31 31 Indy-W 18th Street

Indy- Michigan Street Marion 180970083 36.7 31.3 40.3 33.5 37.2 36.1 35.0 37.0 36.0 28 28 29 Indy- Michigan Street

Luna Pier Monroe 261150005 34.7 35.0 49.3 32.6 32.2 39.7 39.0 38.0 38.9 32 32 31 Luna Pier

Oak Park Oakland 261250001 36.6 32.5 52.2 33.0 35.3 40.4 39.2 40.2 39.9 36 36 35 Oak Park

Port Huron St. Clair 261470005 37.2 32.2 47.6 37.9 36.3 39.0 39.2 40.6 39.6 34 34 33 Port Huron

Ypsilanti Washtenaw 261610008 38.8 31.5 52.1 31.3 34.5 40.8 38.3 39.3 39.5 35 35 34 Ypsilanti

Allen Park Wayne 261630001 40.5 36.9 43.0 34.1 35.9 40.1 38.0 37.7 38.6 35 34 33 Allen Park

Southwest HS Wayne 261630015 33.6 36.0 49.7 36.2 34.0 39.8 40.6 40.0 40.1 35 35 33 Southwest HS

Linwood Wayne 261630016 46.2 38.3 51.8 36.9 34.8 45.4 42.3 41.2 43.0 39 39 38 Linwood

E 7 Mile Wayne 261630019 37.1 35.0 52.3 36.2 33.0 41.5 41.2 40.5 41.0 38 38 37 E 7 Mile

Dearborn Wayne 261630033 42.8 39.4 50.2 43.1 36.6 44.1 44.2 43.3 43.9 40 40 39 Dearborn

Wyandotte Wayne 261630036 34.8 32.3 46.7 33.2 28.6 37.9 37.4 36.2 37.2 35 35 34 Wyandotte

Newberry Wayne 261630038 36.8 57.5 28.6 33.4 39.1 39.8 42.7 38 37 36 Newberry

FIA Wayne 261630039 43.9 32.4 34.8 37.0 39.7 33 33 31 FIA

Middleton Butler 390170003 38.6 37.2 47.6 30.2 37.1 41.1 38.3 38.3 39.3 28 28 27 Middleton

Fairfield Butler 390170016 34.8 32.2 43.4 35.2 34.5 36.8 36.9 37.7 37.1 27 28 27 Fairfield

Butler 390170017 34.6 34.3 44.9 37.9 39.6 40.8 29 29 28

Cleveland-28th Street Cuyahoga 390350027 41.3 40.9 35.7 31.5 39.0 39.3 36.0 35.4 36.9 32 32 31 Cleveland-28th Street

Cleveland-St. Tikhon Cuyahoga 390350038 47.3 42.5 51.2 36.1 39.7 44.9 47.0 42.3 44.2 36 35 34 Cleveland-St. Tikhon

Cleveland-Broadway Cuyahoga 390350045 42.2 36.1 46.2 29.5 37.0 41.5 37.3 37.6 38.8 31 30 29 Cleveland-Broadway

Cleveland-GT Craig Cuyahoga 390350060 45.5 42.2 49.5 31.0 38.7 45.7 40.9 39.7 42.1 37 37 35 Cleveland-GT Craig

Newburg Hts - Harvard Ave Cuyahoga 390350065 39.1 36.1 47.9 27.8 39.1 41.0 37.3 38.3 38.9 31 30 30 Newburg Hts - Harvard Ave

Columbus - Fairgrounds Franklin 390490024 39.2 35.1 45.0 34.0 34.2 39.8 38.0 37.7 38.5 33 32 31 Columbus - Fairgrounds

Columbus - Ann Street Franklin 390490025 37.0 35.5 44.9 34.0 35.5 39.1 38.1 38.1 38.5 31 31 30 Columbus - Ann Street

Cincinnait Hamilton 390610006 45.0 33.3 34.7 37.7 40.6 27 28 27 Cincinnait

Cincinnati - Seymour Hamilton 390610014 37.8 42.0 38.5 35.2 38.1 39.4 38.6 37.3 38.4 26 25 24 Cincinnati - Seymour

Cincinnati - Taft Ave Hamilton 390610040 31.9 30.5 45.8 32.8 34.7 36.1 36.4 37.8 36.7 24 24 23 Cincinnati - Taft Ave

Cincinnati - 8th Ave Hamilton 390610042 33.8 31.9 44.4 34.5 35.9 36.7 36.9 38.3 37.3 28 28 27 Cincinnati - 8th Ave

Sharonville Hamilton 390610043 37.3 31.4 39.9 34.9 34.0 36.2 35.4 36.3 36.0 28 28 27 Sharonville

Norwood Hamilton 390617001 37.1 34.6 47.1 34.0 33.7 39.6 38.6 38.3 38.8 30 30 29 Norwood

St. Bernard Hamilton 390618001 35.8 33.9 51.4 36.1 35.4 40.4 40.5 41.0 40.6 30 30 29 St. Bernard

Steubenville Jefferson 390810016 39.6 43.8 43.8 32.1 43.5 42.4 39.9 39.8 40.7 29 28 28 Steubenville

Mingo Junction Jefferson 390811001 40.9 51.5 44.2 32.9 35.4 45.5 42.9 37.5 42.0 30 30 30 Mingo Junction

Dayton Montgomery 391130032 42.7 32.5 45.0 30.3 36.9 40.1 35.9 37.4 37.8 30 30 30 Dayton

Canton - Dueber Stark 391510017 34.2 36.3 47.6 32.2 33.4 39.4 38.7 37.7 38.6 28 28 27 Canton - Dueber

Akron - Brittain Summit 391530017 36.9 36.9 45.2 31.5 33.3 39.7 37.9 36.7 38.1 30 30 29 Akron - Brittain

Green Bay - Est High Brown 550090005 33.5 32.3 41.5 36.9 37.1 35.8 36.9 38.5 37.1 35 34 32 Green Bay - Est High

Madison Dane 550250047 32.0 31.9 40.1 33.4 44.3 34.7 35.1 39.3 36.4 32 31 29 Madison

Milwaukee-Health Center Milwaukee 550790010 33.2 38.4 38.7 40.7 40.6 36.8 39.3 40.0 38.7 35 34 33 Milwaukee-Health Center

Milwaukee-SER Hdqs Milwaukee 550790026 29.6 28.7 41.5 42.6 39.8 33.3 37.6 41.3 37.4 34 34 33 Milwaukee-SER Hdqs

Milwaukee-Virginia FS Milwaukee 550790043 39.2 41.4 37.1 44.0 38 39.2 40.8 39.7 39.9 36 36 36 Milwaukee-Virginia FS

Milwaukee- Fire Dept Hdqs Milwaukee 550790099 33.7 38.9 37.1 38.3 40.7 36.6 38.1 38.7 37.8 33 32 32 Milwaukee- Fire Dept Hdqs

Waukesha Waukesha 551330027 29.1 38.4 41.1 28.2 33.8 36.2 35.9 34.4 35.5 31 31 29 Waukesha 98th Percentile (24-hour) Design Values Round 5 Modeling ResultsSite ID State County Season Species Species Comp. of Ave.

FRM (fraction) Species RRF

1703100521 IL Cook winter so4 0.1772 0.9342

1703100521 IL Cook winter no3 0.3099 1.0128

1703100521 IL Cook winter ocm 0.2147 0.9942

1703100521 IL Cook winter ec 0.0372 0.888

1703100521 IL Cook winter soil 0.0242 1.1674

1703100521 IL Cook winter nh4 0.1421 0.97

1703100521 IL Cook winter pbw 0.0947 0.9678

1703100521 IL Cook spring so4 0.32 0.8018

1703100521 IL Cook spring no3 0.0609 0.9385

1703100521 IL Cook spring ocm 0.2742 1.0629

1703100521 IL Cook spring ec 0.0501 0.8712

1703100521 IL Cook spring soil 0.0505 1.1796

1703100521 IL Cook spring nh4 0.1203 0.8619

1703100521 IL Cook spring pbw 0.0984 0.8492

1703100521 IL Cook summer so4 0.3089 0.725

1703100521 IL Cook summer no3 0 1.0124

1703100521 IL Cook summer ocm 0.1599 1.069

1703100521 IL Cook summer ec 0.0351 0.8683

1703100521 IL Cook summer soil 0.0318 1.204

1703100521 IL Cook summer nh4 0.0932 0.7354

1703100521 IL Cook summer pbw 0.094 0.7217

1703100521 IL Cook fall so4 0.1872 0.9151

1703100521 IL Cook fall no3 0.1628 0.9408

1703100521 IL Cook fall ocm 0.2389 1.0091

1703100521 IL Cook fall ec 0.0403 0.8623

1703100521 IL Cook fall soil 0.0284 1.1443

1703100521 IL Cook fall nh4 0.1062 0.9247

1703100521 IL Cook fall pbw 0.0614 0.9233

1711910071 IL Madison winter so4 0.213 0.9195

1711910071 IL Madison winter no3 0.2705 1.0306

1711910071 IL Madison winter ocm 0.2093 0.9289

1711910071 IL Madison winter ec 0.0434 0.9083

1711910071 IL Madison winter soil 0.0306 1.1782

1711910071 IL Madison winter nh4 0.1528 0.9513

1711910071 IL Madison winter pbw 0.0804 0.9243

1711910071 IL Madison spring so4 0.3194 0.7717

1711910071 IL Madison spring no3 0.0189 0.8611

1711910071 IL Madison spring ocm 0.2455 1.1103

1711910071 IL Madison spring ec 0.0564 1.0046

1711910071 IL Madison spring soil 0.0459 1.2252

1711910071 IL Madison spring nh4 0.1121 0.7894

1711910071 IL Madison spring pbw 0.1085 0.7783

1711910071 IL Madison summer so4 0.313 0.705

1711910071 IL Madison summer no3 0 0.884

1711910071 IL Madison summer ocm 0.153 1.1546

1711910071 IL Madison summer ec 0.0345 1.0513

1711910071 IL Madison summer soil 0.0302 1.2532

1711910071 IL Madison summer nh4 0.102 0.7409

1711910071 IL Madison summer pbw 0.1096 0.7133

1711910071 IL Madison fall so4 0.2058 0.9037

1711910071 IL Madison fall no3 0.1308 0.9426

1711910071 IL Madison fall ocm 0.259 1.0233

1711910071 IL Madison fall ec 0.0563 0.9248

1711910071 IL Madison fall soil 0.0549 1.1412

1711910071 IL Madison fall nh4 0.1073 0.9185

1711910071 IL Madison fall pbw 0.0655 0.918



### PM2.5 RRFs by Species and Season (2009)Site ID State County Season Species Species Comp. of Ave.

FRM (fraction) Species RRF

1803720011 IN Dubois winter so4 0.2669 0.8833

1803720011 IN Dubois winter no3 0.2548 0.9526

1803720011 IN Dubois winter ocm 0.1747 0.9374

1803720011 IN Dubois winter ec 0.0313 0.9319

1803720011 IN Dubois winter soil 0.0192 1.1349

1803720011 IN Dubois winter nh4 0.1646 0.9069

1803720011 IN Dubois winter pbw 0.0885 0.9006

1803720011 IN Dubois spring so4 0.4141 0.6808

1803720011 IN Dubois spring no3 0.0022 0.8106

1803720011 IN Dubois spring ocm 0.178 0.9997

1803720011 IN Dubois spring ec 0.0324 0.9083

1803720011 IN Dubois spring soil 0.0218 1.1284

1803720011 IN Dubois spring nh4 0.1432 0.7075

1803720011 IN Dubois spring pbw 0.1556 0.6916

1803720011 IN Dubois summer so4 0.3687 0.644

1803720011 IN Dubois summer no3 0 0.8029

1803720011 IN Dubois summer ocm 0.1174 1.0136

1803720011 IN Dubois summer ec 0.0207 0.913

1803720011 IN Dubois summer soil 0.0213 1.1988

1803720011 IN Dubois summer nh4 0.1168 0.6789

1803720011 IN Dubois summer pbw 0.1246 0.6613

1803720011 IN Dubois fall so4 0.2964 0.8232

1803720011 IN Dubois fall no3 0.138 0.8797

1803720011 IN Dubois fall ocm 0.2116 0.9861

1803720011 IN Dubois fall ec 0.0437 0.9019

1803720011 IN Dubois fall soil 0.03 1.1387

1803720011 IN Dubois fall nh4 0.1449 0.8444

1803720011 IN Dubois fall pbw 0.0941 0.8558

1809700811 IN Marion winter so4 0.2358 0.9192

1809700811 IN Marion winter no3 0.2729 0.9769

1809700811 IN Marion winter ocm 0.1851 0.9546

1809700811 IN Marion winter ec 0.0385 0.8647

1809700811 IN Marion winter soil 0.0239 1.0835

1809700811 IN Marion winter nh4 0.1561 0.9446

1809700811 IN Marion winter pbw 0.0877 0.944

1809700811 IN Marion spring so4 0.3745 0.6868

1809700811 IN Marion spring no3 0.0167 0.8082

1809700811 IN Marion spring ocm 0.2034 0.9881

1809700811 IN Marion spring ec 0.0447 0.8547

1809700811 IN Marion spring soil 0.0376 1.0625

1809700811 IN Marion spring nh4 0.1313 0.7182

1809700811 IN Marion spring pbw 0.1309 0.7056

1809700811 IN Marion summer so4 0.3582 0.6529

1809700811 IN Marion summer no3 0 0.8099

1809700811 IN Marion summer ocm 0.1231 1.0043

1809700811 IN Marion summer ec 0.03 0.8444

1809700811 IN Marion summer soil 0.0253 1.0918

1809700811 IN Marion summer nh4 0.1114 0.6854

1809700811 IN Marion summer pbw 0.1163 0.6674

1809700811 IN Marion fall so4 0.2751 0.8538

1809700811 IN Marion fall no3 0.149 0.9452

1809700811 IN Marion fall ocm 0.223 0.9648

1809700811 IN Marion fall ec 0.0525 0.8412

1809700811 IN Marion fall soil 0.0358 1.089

1809700811 IN Marion fall nh4 0.1378 0.8905

1809700811 IN Marion fall pbw 0.0865 0.8888Site ID State County Season Species Species Comp. of Ave.

FRM (fraction) Species RRF

2616300331 MI Wayne winter so4 0.1587 0.9206

2616300331 MI Wayne winter no3 0.2394 0.9813

2616300331 MI Wayne winter ocm 0.3193 1.0781

2616300331 MI Wayne winter ec 0.0383 0.9279

2616300331 MI Wayne winter soil 0.0541 1.0206

2616300331 MI Wayne winter nh4 0.1188 0.9518

2616300331 MI Wayne winter pbw 0.0714 0.9566

2616300331 MI Wayne spring so4 0.3383 0.7398

2616300331 MI Wayne spring no3 0.0259 0.8787

2616300331 MI Wayne spring ocm 0.3543 1.0234

2616300331 MI Wayne spring ec 0.0504 0.8671

2616300331 MI Wayne spring soil 0.0915 1.0153

2616300331 MI Wayne spring nh4 0.1191 0.7818

2616300331 MI Wayne spring pbw 0.1126 0.7619

2616300331 MI Wayne summer so4 0.3311 0.6681

2616300331 MI Wayne summer no3 0 0.8431

2616300331 MI Wayne summer ocm 0.2297 1.0029

2616300331 MI Wayne summer ec 0.0362 0.8332

2616300331 MI Wayne summer soil 0.061 1.0177

2616300331 MI Wayne summer nh4 0.1027 0.6974

2616300331 MI Wayne summer pbw 0.1073 0.6754

2616300331 MI Wayne fall so4 0.1898 0.854

2616300331 MI Wayne fall no3 0.1075 0.9367

2616300331 MI Wayne fall ocm 0.3689 1.0607

2616300331 MI Wayne fall ec 0.0546 0.8862

2616300331 MI Wayne fall soil 0.1676 1.0317

2616300331 MI Wayne fall nh4 0.0866 0.8919

2616300331 MI Wayne fall pbw 0.0553 0.8821

3903500381 OH Cuyahoga winter so4 0.2117 0.8993

3903500381 OH Cuyahoga winter no3 0.2665 0.9856

3903500381 OH Cuyahoga winter ocm 0.2048 0.9716

3903500381 OH Cuyahoga winter ec 0.0413 0.8903

3903500381 OH Cuyahoga winter soil 0.0465 1.0959

3903500381 OH Cuyahoga winter nh4 0.1459 0.9416

3903500381 OH Cuyahoga winter pbw 0.0832 0.9541

3903500381 OH Cuyahoga spring so4 0.3334 0.7145

3903500381 OH Cuyahoga spring no3 0.0374 0.8393

3903500381 OH Cuyahoga spring ocm 0.2068 1.0899

3903500381 OH Cuyahoga spring ec 0.052 0.9362

3903500381 OH Cuyahoga spring soil 0.0697 1.0601

3903500381 OH Cuyahoga spring nh4 0.1256 0.7666

3903500381 OH Cuyahoga spring pbw 0.115 0.7761

3903500381 OH Cuyahoga summer so4 0.3241 0.6303

3903500381 OH Cuyahoga summer no3 0 0.89

3903500381 OH Cuyahoga summer ocm 0.1306 1.0998

3903500381 OH Cuyahoga summer ec 0.0419 0.9354

3903500381 OH Cuyahoga summer soil 0.0583 1.0906

3903500381 OH Cuyahoga summer nh4 0.1074 0.7038

3903500381 OH Cuyahoga summer pbw 0.1183 0.6674

3903500381 OH Cuyahoga fall so4 0.2055 0.8193

3903500381 OH Cuyahoga fall no3 0.1275 0.9189

3903500381 OH Cuyahoga fall ocm 0.2234 1.0245

3903500381 OH Cuyahoga fall ec 0.0499 0.8913

3903500381 OH Cuyahoga fall soil 0.0675 1.0927

3903500381 OH Cuyahoga fall nh4 0.1034 0.8615

3903500381 OH Cuyahoga fall pbw 0.0637 0.8564Site ID State County Season Species Species Comp. of Ave.

FRM (fraction) Species RRF

3904900241 OH Franklin winter so4 0.2555 0.8622

3904900241 OH Franklin winter no3 0.2373 1.0002

3904900241 OH Franklin winter ocm 0.2082 0.974

3904900241 OH Franklin winter ec 0.0375 0.8537

3904900241 OH Franklin winter soil 0.0259 1.0844

3904900241 OH Franklin winter nh4 0.1495 0.9261

3904900241 OH Franklin winter pbw 0.0861 0.9274

3904900241 OH Franklin spring so4 0.3754 0.6615

3904900241 OH Franklin spring no3 0.0176 0.8436

3904900241 OH Franklin spring ocm 0.2069 1.062

3904900241 OH Franklin spring ec 0.0405 0.8678

3904900241 OH Franklin spring soil 0.0371 1.0551

3904900241 OH Franklin spring nh4 0.1296 0.7212

3904900241 OH Franklin spring pbw 0.128 0.6992

3904900241 OH Franklin summer so4 0.3703 0.622

3904900241 OH Franklin summer no3 0 0.9056

3904900241 OH Franklin summer ocm 0.1343 1.0654

3904900241 OH Franklin summer ec 0.0311 0.8565

3904900241 OH Franklin summer soil 0.0267 1.0667

3904900241 OH Franklin summer nh4 0.1142 0.7021

3904900241 OH Franklin summer pbw 0.1186 0.6614

3904900241 OH Franklin fall so4 0.2692 0.8119

3904900241 OH Franklin fall no3 0.1186 0.9099

3904900241 OH Franklin fall ocm 0.2489 1.019

3904900241 OH Franklin fall ec 0.0533 0.8371

3904900241 OH Franklin fall soil 0.0423 1.0924

3904900241 OH Franklin fall nh4 0.1217 0.8539

3904900241 OH Franklin fall pbw 0.0821 0.8519

3906100141 OH Hamilton winter so4 0.2685 0.8104

3906100141 OH Hamilton winter no3 0.2378 1.0886

3906100141 OH Hamilton winter ocm 0.19 0.961

3906100141 OH Hamilton winter ec 0.035 0.8969

3906100141 OH Hamilton winter soil 0.0229 1.4146

3906100141 OH Hamilton winter nh4 0.1583 0.9077

3906100141 OH Hamilton winter pbw 0.0874 0.8687

3906100141 OH Hamilton spring so4 0.3583 0.6331

3906100141 OH Hamilton spring no3 0.0025 1.0155

3906100141 OH Hamilton spring ocm 0.1986 1.0798

3906100141 OH Hamilton spring ec 0.0466 0.9228

3906100141 OH Hamilton spring soil 0.0289 1.3785

3906100141 OH Hamilton spring nh4 0.1215 0.6968

3906100141 OH Hamilton spring pbw 0.128 0.6307

3906100141 OH Hamilton summer so4 0.3722 0.577

3906100141 OH Hamilton summer no3 0 1.0923

3906100141 OH Hamilton summer ocm 0.121 1.082

3906100141 OH Hamilton summer ec 0.0309 0.9099

3906100141 OH Hamilton summer soil 0.0199 1.537

3906100141 OH Hamilton summer nh4 0.1178 0.6441

3906100141 OH Hamilton summer pbw 0.1261 0.5734

3906100141 OH Hamilton fall so4 0.2608 0.7754

3906100141 OH Hamilton fall no3 0.1184 0.9857

3906100141 OH Hamilton fall ocm 0.213 1.0235

3906100141 OH Hamilton fall ec 0.0512 0.8876

3906100141 OH Hamilton fall soil 0.0328 1.4007

3906100141 OH Hamilton fall nh4 0.1254 0.846

3906100141 OH Hamilton fall pbw 0.0828 0.8172Site ID State County Season Species Species Comp. of Ave.

FRM (fraction) Species RRF

3908110011 OH Jefferson winter so4 0.2367 0.8217

3908110011 OH Jefferson winter no3 0.1709 1.0522

3908110011 OH Jefferson winter ocm 0.3288 0.8819

3908110011 OH Jefferson winter ec 0.0435 0.9091

3908110011 OH Jefferson winter soil 0.0272 0.4368

3908110011 OH Jefferson winter nh4 0.1199 0.8904

3908110011 OH Jefferson winter pbw 0.073 0.8583

3908110011 OH Jefferson spring so4 0.3508 0.6666

3908110011 OH Jefferson spring no3 0.0154 0.9156

3908110011 OH Jefferson spring ocm 0.3078 0.9995

3908110011 OH Jefferson spring ec 0.0395 0.9853

3908110011 OH Jefferson spring soil 0.0407 0.4844

3908110011 OH Jefferson spring nh4 0.114 0.7054

3908110011 OH Jefferson spring pbw 0.1095 0.6713

3908110011 OH Jefferson summer so4 0.3779 0.6156

3908110011 OH Jefferson summer no3 0 1.0837

3908110011 OH Jefferson summer ocm 0.2098 1.0145

3908110011 OH Jefferson summer ec 0.0308 0.9689

3908110011 OH Jefferson summer soil 0.0323 0.3632

3908110011 OH Jefferson summer nh4 0.1065 0.6428

3908110011 OH Jefferson summer pbw 0.1007 0.625

3908110011 OH Jefferson fall so4 0.2315 0.7694

3908110011 OH Jefferson fall no3 0.0702 1.0302

3908110011 OH Jefferson fall ocm 0.372 0.9312

3908110011 OH Jefferson fall ec 0.051 0.9086

3908110011 OH Jefferson fall soil 0.0344 0.4555

3908110011 OH Jefferson fall nh4 0.0859 0.8284

3908110011 OH Jefferson fall pbw 0.0629 0.7951

3911300321 OH Montgomer winter so4 0.2613 0.8598

3911300321 OH Montgomer winter no3 0.2407 1.029

3911300321 OH Montgomer winter ocm 0.1954 0.9442

3911300321 OH Montgomer winter ec 0.036 0.8746

3911300321 OH Montgomer winter soil 0.0259 1.1295

3911300321 OH Montgomer winter nh4 0.1531 0.9304

3911300321 OH Montgomer winter pbw 0.0876 0.9205

3911300321 OH Montgomer spring so4 0.3659 0.6606

3911300321 OH Montgomer spring no3 0.0163 0.8639

3911300321 OH Montgomer spring ocm 0.1895 1.0976

3911300321 OH Montgomer spring ec 0.0442 0.9417

3911300321 OH Montgomer spring soil 0.0253 1.0873

3911300321 OH Montgomer spring nh4 0.1313 0.7149

3911300321 OH Montgomer spring pbw 0.1326 0.6839

3911300321 OH Montgomer summer so4 0.375 0.6234

3911300321 OH Montgomer summer no3 0 0.9474

3911300321 OH Montgomer summer ocm 0.128 1.1047

3911300321 OH Montgomer summer ec 0.029 0.9496

3911300321 OH Montgomer summer soil 0.0205 1.1299

3911300321 OH Montgomer summer nh4 0.1114 0.6931

3911300321 OH Montgomer summer pbw 0.1114 0.6482

3911300321 OH Montgomer fall so4 0.3062 0.8033

3911300321 OH Montgomer fall no3 0.1012 0.9634

3911300321 OH Montgomer fall ocm 0.2221 1.0158

3911300321 OH Montgomer fall ec 0.0514 0.877

3911300321 OH Montgomer fall soil 0.028 1.1391

3911300321 OH Montgomer fall nh4 0.1352 0.8625

3911300321 OH Montgomer fall pbw 0.0982 0.8475Site ID State County Season Species Species Comp. of Ave.

FRM (fraction) Species RRF

3915100171 OH Stark winter so4 0.2362 0.8558

3915100171 OH Stark winter no3 0.2234 1.0222

3915100171 OH Stark winter ocm 0.2478 0.9255

3915100171 OH Stark winter ec 0.0414 0.8866

3915100171 OH Stark winter soil 0.0334 1.099

3915100171 OH Stark winter nh4 0.1376 0.925

3915100171 OH Stark winter pbw 0.0802 0.9155

3915100171 OH Stark spring so4 0.3581 0.6834

3915100171 OH Stark spring no3 0.0236 0.855

3915100171 OH Stark spring ocm 0.221 1.0892

3915100171 OH Stark spring ec 0.0501 1.0017

3915100171 OH Stark spring soil 0.058 1.0528

3915100171 OH Stark spring nh4 0.1288 0.7264

3915100171 OH Stark spring pbw 0.1256 0.7009

3915100171 OH Stark summer so4 0.3621 0.6277

3915100171 OH Stark summer no3 0 0.8203

3915100171 OH Stark summer ocm 0.1483 1.0984

3915100171 OH Stark summer ec 0.0403 1.016

3915100171 OH Stark summer soil 0.037 1.0781

3915100171 OH Stark summer nh4 0.1157 0.6739

3915100171 OH Stark summer pbw 0.124 0.651

3915100171 OH Stark fall so4 0.2293 0.8041

3915100171 OH Stark fall no3 0.1262 0.9363

3915100171 OH Stark fall ocm 0.2722 1.0226

3915100171 OH Stark fall ec 0.0545 0.9202

3915100171 OH Stark fall soil 0.0461 1.0959

3915100171 OH Stark fall nh4 0.1105 0.8549

3915100171 OH Stark fall pbw 0.0706 0.8428

3915300171 OH Summit winter so4 0.2511 0.8771

3915300171 OH Summit winter no3 0.2376 1.0052

3915300171 OH Summit winter ocm 0.2185 0.9429

3915300171 OH Summit winter ec 0.0334 0.8677

3915300171 OH Summit winter soil 0.0255 1.0835

3915300171 OH Summit winter nh4 0.1489 0.9374

3915300171 OH Summit winter pbw 0.0851 0.945

3915300171 OH Summit spring so4 0.387 0.7046

3915300171 OH Summit spring no3 0.0072 0.8466

3915300171 OH Summit spring ocm 0.1901 1.0967

3915300171 OH Summit spring ec 0.035 0.9482

3915300171 OH Summit spring soil 0.0304 1.0524

3915300171 OH Summit spring nh4 0.1294 0.7521

3915300171 OH Summit spring pbw 0.1342 0.7384

3915300171 OH Summit summer so4 0.3694 0.6378

3915300171 OH Summit summer no3 0 0.8587

3915300171 OH Summit summer ocm 0.1417 1.1077

3915300171 OH Summit summer ec 0.0332 0.9506

3915300171 OH Summit summer soil 0.0198 1.0744

3915300171 OH Summit summer nh4 0.1121 0.6961

3915300171 OH Summit summer pbw 0.1146 0.6691

3915300171 OH Summit fall so4 0.2443 0.8074

3915300171 OH Summit fall no3 0.1175 0.9392

3915300171 OH Summit fall ocm 0.2636 1.0252

3915300171 OH Summit fall ec 0.0623 0.8883

3915300171 OH Summit fall soil 0.0494 1.086

3915300171 OH Summit fall nh4 0.109 0.8622

3915300171 OH Summit fall pbw 0.0723 0.8506

### 119 

## APPENDIX I I

Ozone Source Appor tionment Modeling Results

### 120 

## APPENDIX I II

PM 2.5 

## Source Apportionment Modeling Results

## Chicago ( Cicero ) , Illinois

2005 (Round 5)

2012 (Round 4)

2018 (Round 5)

## Clark County, Indiana

2005 (Round 5)

2012 (Round 4)

2018 (Round 5)

## Dearborn , Mi chigan

2005 (Round 5)

2012 (Round 4)

2018 (Round 5)

## Cincinnati, Oh io

2005 (Round 5)

2012 (Round 4)

2018 (Round 5)

## Cleveland, Ohio

2005 (Round 5)

2012 (Round 4)

2018 (Round 5)

## Steubenville, Ohio

2005 (Round 5)

2012 (Round 4)

2018 (Round 5)

### 121 

## APPENDIX I V

Haze Source Apportionment Modeling Results

## Boundary Waters, Minnesota

2005 (Round 5)

201 8 (Round 4)

2018 (Round 5)

## Voyageurs, Minnesota

2005 (Round 5)

201 8 (Round 4)

2018 (Round 5)

## Seney, Michigan

2005 (Round 5)

201 8 (Round 4)

2018 (Round 5)

## Isle Royale, Mich igan

2005 (Round 5)

201 8 (Round 4)

2018 (Round 5)

## Shenandoah, Virginia

2005 (Round 5)

2018 (Round 4)

2018 (Round 5)

## Mammoth Cave, Kentucky

2005 (Round 5)

2018 (Round 4)

2018 (Round 5)

## Lye Brook, Vermont

2005 (Round 5)

2018 (Round 4)

2018 (Round 5)

### 1



## Regional Air Quality Analyses for Ozone, PM2.5, and Regional Haze:

Final Technical Support Document ( Supplement ), September 12, 2008

The purpose of this paper is to summarize a new modeling analysis performed by the Lake

Michigan Air Dir ectors Consorti um (LADCO) to address the effect of the recent court decision

vacat ing EPA’s Clean Air Interstate Rule (CAIR). This new modeling is intended to supplem ent
the LADCO Technical Support Document (“ Regional Air Quality Analyses for Ozone, PM2.5,

and Regional Haze: Final Technical Support Document”, April 25, 2008) , which summarizes the

air quality analyses conducted by LADCO and its contractors to support the development of

State Implementation Plans for ozone, PM2.5, and regional haze in the States of Illinoi s, Indiana, Michigan, Ohio, and Wisconsin

.

Compared to the previous LADCO modeling (Round 5.1), the new modeling shows similar

results for ozone, but much more nonattainment for PM2.5 and higher visibility levels for

regional haze. Specifically, the new modeling shows:

Ozone : Attainment of the 0.08 ppm standard by 2009 everywhere in the region, except

Holland, MI , and n onattainment of the 0.075 ppm standard through at least 2018.

PM2.5 : Widespread nonattainment of annual (15 ug/m 3 

### ) and daily (35 ug/m 3 

### ) standards.

Haze : Higher visibility levels on the 20% worst visibility days in 2018 in Class I areas in

the eastern U.S., resulting in most areas being above the glide path.

Background : On July 11, 2008, the U.S. Court of Appeals for D.C. Circuit v acated EPA’s CAIR

rule (cite). The reductions in NOx and SO2 emissions associated with this rule were a key part

of the LADCO States’ attainment demonstrations for ozone and PM2.5 and the reasonable

progress determinations for regional haze . LADCO’s prev ious modeling (Round 5.1) relied on

EGU emission projections from EPA’s IPM3.0 analysis, which assumed implementation of

Phases I and II of CAIR. For this new modeling, alternative EGU emission projections were

developed , which did not rely on CAIR (or IP M).

Model Set

- Up : The new modeling was performed consistent with LADCO’s previous modeling

(Round 5.1):

Model Version: CAMx v4.50beta_deposition

Future Years: 2009, 2012, 2018

Runs: (a) Ozone : Summer 2005 meteorology with 12 km grids

(b) PM2.5 an d haze : F ull year 2005 meteorology with 36 km grids



### Emission Scenarios: The new modeling assumed the same set of “on the books” controls as

in LADCO’s previous modeling (Round 5.1) for all sectors, except EGUs. In light of the CAIR

decision, three new E GU scenarios were prepared:

### 2

Scenario A: 2007 CEM

- based emissions w ere projected for all states in the modeling domain

based on EIA growth rates by state (NERC region) and fuel type. The assumed growth rates for

the Midwest States were: MAIN (IL, IA, MO, WI): 8.8% (2007

- 2018); ECAR (IN, KY, MI, OH):

13.5% (2007

- 2018); and MAPP (MN): 15.1% (2007

- 2018). No control w as applied. The annual

emissions w ere temporalized based on profiles derived from 2004

- 2006 CEM data. (Note, these

are the same temporal prof iles used in R ound 5.1 .)

Scenario B. Scenario A emissions for the LADCO States and select neighboring states (e.g.,

MN, IA, MO, KY, TN, and WV) w ere adjusted by applying legally enforceable controls (i.e.,

emission reductions required by a Consent Decree , state rule, or permit). Only those legally

enforceable controls identified (and justified) by the States w ere applied. The States also

supplied the appropriate control factors. A table summarizing the Scenario B con trols is provided

in Appendix I.

Sc enario C. For the years 2009 and 2012, Scenario A emissions for all states w ere adjusted by

applying all planned SO2 and NOx controls based on the July 10 CAMD list (i.e., 9 0 % reduction

for scrubbers, 9 5 % reduction for SCRs). Because the July 10 CAMD list only includes controls

generally out to 2011, additional SO2 and NOx controls for the year 2018 w ere assumed for all

BART

- eligible EGUs in the five LADCO State plus MN, IA, MO, KY, TN, and MO list (i.e., 9 0 %

reduction for scrubbers, 9 5 % reduction for SCRs )

. 1 

### A ll Scenario B controls we re included in

Scenario C. A table summarizing the Scenario C controls is provided in Appendix II.



### Table 1 and Figure 1 provide a summary of the 5

- state regional NOx and SO2 emissions for

each scenario and future year. (No te, the CAIR emissions included here are based on EPA’s

IPM3.0 modeling.) Several comments on the emissions should be noted:

Summer NOx

• There is ll ittle difference between the three alternative scenarios and CAIR . T his

suggests that summer ozone concen trations for the alternative scenarios are

likely to be similar to those predicted with CAIR (i.e., Round 5.1).

Annual NOx:

• There is a s ignificant change in emissions between scenarios , mostly during the

non

- summer months.

• Scenario B reflects application of NOx controls in several states (e.g., IL,OH, WI).

• Because there are relatively few SCRs (in the LADCO States) on the CAMD list,

Scenario C results in only a small emissions decrease compared to Scenario B

.

• Assumed BART controls result in a significant em issions decrease.

Annual SO2

• There is a s ignificant change in emissions between scenarios

.

• Scenario B reflects application of SO2 controls in several states (e.g., I L,OH, WI).

• Because there are several FGD s (in the LADCO States) on the CAMD list,

Scenario C results in a large emissions decrease compared to Scenario B

.

• Assumed BART controls result in a significant emissions decrease (i.e., even

lower emissions than t he IPM

- estimated CAIR emissions).

1 

### A subsequent analysis was conducted with the following inventory changes: (a) 95% reduction for

scrubbers, 90% redcuction for SCRs (consistent with EPA’s default assumptions for IPM), and (b)

revisions provided for a few plants in Indiana and Minnesota. The changes resulted in a relatively small

difference in the regioinal NOx and SO2 emissions (e.g., about a 2% NOx increase and about a 1

- 2%

decrease in SO2). To assess the impact of the chan ges, PM2.5 modleing was conducted with the new

Scenario B and Scenario C emissions for 2012. The modeling showed little change in the predicted

PM2.5 concentrations.

### 3 

### Figure 1. Regional NOx and SO2 Emissions

Summe r NO x Emi ss ions

0 500 1,000 1,500 2,000 2,500 3,000

2005 2007 200

9A 200 9B 200 9C

200 9 C AIR 201 2A 201 2B 201 2C

201 2 C AIR 201 8A 201 8B 201 8C

201 8BART

201 8 C AIR



### Annu al NO x Emi ss ions

0 200,000 400,000 600,000 800,000 1,000,000

2005 2007 200

9A 200 9B 200 9C

200 9 C AIR 201 2A 201 2B 201 2C

201 2 C AIR 201 8A 201 8B 201 8C

201 8BART

201 8 C AIR



### Annu al SO2 Emi ss ions

0 500,000 1,000,000 1,500,000 2,000,000 2,500,000 3,000,000

2005 2007 200

9A 200 9B 200 9C

200 9 C AIR 201 2A 201 2B 201 2C

201 2 C AIR 201 8A 201 8B 201 8C

201 8BART

201 8 C AIR

### 4 

### Table 1. Regional NOx and SO2 Emissions



# Summer NOx Emissions (TPD)

2005 2007 2009 A 2009 B 2009 C 2010

CAIR 2012 A 2012 B 2012 C 2012

CAIR 2018 A 2018 B 2018 C 2018 C

BART 2018

CAIR

IL 305 305 311 311 311 275 340 236 236 266 333 227 227 219 224

I N 393 393 376 376 374 384 393 393 390 368 410 386 383 292 264

MI 393 393 350 350 350 242 366 366 366 229 377 377 377 260 243

OH 408 408 395 355 335 285 423 351 351 290 431 366 366 230 290

WI 413 413 167 160 160 238 184 170 170 177 183 168 168 168 17 7

1,912 1,912 1,599 1,552 1,530 1,424 1,706 1,516 1,513 1,330 1,734 1,524 1,521 1,169 1,198



# Annual NOx Emissions (TPY)

2005 2007 2009 A 2009 B 2009 C 2010

CAIR 2012 A 2012 B 2012 C 2012

CAIR 2018 A 2018 B 2018 C 2018 C

BART 2018

CAIR

IL 126,786 121,006 124,917 124,917 124,917 83,224 137,438 81,989 81,989 82,248 135,983 79,771 79,771 63,590 69,958

IN 214,727 203,493 203,776 203,776 201,947 133,188 212,790 212,790 210,877 125,541 221,950 212,805 210,81 0 177,027 90,415

MI 120,332 112,484 112,478 112,478 112,478 83,117 117,621 117,621 117,621 77,897 122,447 122,447 122,447 89,444 79,543

OH 255,554 240,351 240,016 173,071 164,911 94,346 251,065 172,514 172,514 97,679 261,644 179,737 179,737 125,762 95, 678

WI 71,414 54,582 56,540 54,065 54,065 53,032 62,266 57,759 57,759 56,480 61,812 56,952 56,952 56,952 56,158

788,812 731,917 737,727 668,307 658,317 446,908 781,179 642,673 640,760 439,845 803,837 651,712 649,717 512,774 391,752



# Annual SO2 Emissions (TPY)

2005 2007 2009 A 2009 B 2009 C 2010

CAIR 2012 A 2012 B 2012 C 2012

CAIR 2018 A 2018 B 2018 C 2018 C

BART 2018

CAIR

IL 326,598 273,467 281,028 281,028 281,028 295,516 309,209 196,238 194,746 267,1 10 305,364 106,638 105,152 82,351 275,716

IN 866,964 722,301 721,252 721,252 619,486 374,335 754,323 754,323 558,567 379,144 786,551 764,065 559,945 426,695 359,915

MI 350,694 343,487 343,140 343,140 315,326 227,296 358,879 358,879 301,062 233,204 373, 964 373,964 313,677 178,680 242,853

OH 1,100,510 960,820 959,466 959,466 693,438 427,145 1,003,633 897,099 572,807 370,532 1,045,945 819,770 481,623 333,740 315,560

WI 181,426 137,562 142,007 142,007 133,738 139,181 156,659 144,818 133,592 139,203 155, 818 144,027 132,849 77,214 127,073

2,826,192 2,437,638 2,446,892 2,446,892 2,043,017 1,463,473 2,582,703 2,351,356 1,760,775 1,389,192 2,667,641 2,208,463 1,593,245 1,098,679 1,321,116

### 5



### Modeling Results: Several tables summarizing the modeling result s are provided:

Table 2

- future year ozone and PM2.5 concentrations for key monitors in the

LADCO region

Table 3

- number of monitoring sites greater than the National Ambient Air Quality

Standards (NNAQS)

Table 4

–

visibility levels fo r Class I areas in the eastern U.S.

Note, given that Scenario B and BART controls were only applied in an 11

- state Midwest

region, the validity of the results for other Class I areas in the eastern U.S. may be

questionable. The Scenario C controls, on the other hand, cover all states and are, thus,

likely valid in other Class I areas.

Spatial plots of the future year ozone and PM2.5 concentrations are provided in Figures 2

– 4.

Based on these results, the following key findings should be noted:

Ozone

• There is little change from the previous LADCO modeling (Round 5.1 with CAIR)

• The modeling shows a ttainment of the 0.08 ppm (85 ppb) standard by 2009,

except Holland. (Note, Holland does meet this standard by 2012.)

• The modeling shows nonattainm ent of the 0.075 ppm (75 ppb) standard through

2018.

PM2.5

- Annual

• There is a significant change from the previous LADCO modeling (Round 5.1

with CAIR)

• The modeling shows extensive non atta inment of the annual standard.

PM2.5

- Daily

• There is a signif icant change from the previous LADCO modeling (Round 5.1

with CAIR)

• Th e modeling shows extensive nonattainment of the daily standard.

Haze

• There is a significant change from the previous LADCO modeling (Round 5.1

with CAIR)

• T he modeling shows higher vis ibility levels in 2018 for the 20% worst visibility

days (average about 0.5 deciviews for the northern Class I areas ). The resulting

visibility levels in the northern Class I areas (except Voyageurs) a re above t he

glide path.2005

Round 5

with CAIR Round 5

with CAIR Round 5

with CAIR

Site Site ID Base Year Scen. A Scen. B Scen.C Scen. A Scen. B Scen.C Scen. A Scen. B Scen.C Scen.C-BART

Lake Michigan Area

Chiwaukee 550590019 84.7 82.2 82.2 82.0 82.3 81.1 80.8 80.6 80.9 77.2 77.2 77.0 76.0 76.2

Racine 551010017 80.3 77.8 77.8 77.5 77.5 76.6 76.2 76.1 76.1 72.9 72.3 72.1 71.1 71.2

Milwaukee-Bayside 550890085 82.7 79.9 79.9 79.7 79.8 78.5 78.0 78.0 78.0 74.3 73.6 73.4 72.4 72.7

Harrington Beach 550890009 83.3 80.1 80.1 79.9 80.1 78.6 78.1 78.0 78.3 73.9 73.2 73.1 72.2 72.5

Manitowoc 550710007 85.0 80.8 80.8 80.7 80.8 79.0 78.5 78.4 78.6 73.9 73.2 73.1 72.0 72.5

Sheboygan 551170006 88.0 84.1 84.0 83.9 84.0 82.2 81.7 81.5 81.8 76.9 76.0 75.9 74.8 75.4

Kewaunee 550610002 82.7 78.2 78.2 78.0 78.1 76.4 75.9 75.7 75.9 71.3 70.7 70.5 69.4 69.9

Door County 550290004 88.7 84.1 84.1 83.9 83.9 82.0 81.4 81.3 81.5 76.5 75.6 75.5 74.2 74.7

Hammond 180892008 77.7 76.2 76.2 76.0 75.4 75.6 75.3 75.2 74.6 73.2 72.7 72.6 71.7 71.6

Whiting 180890030 79.3 77.8 77.8 77.7 77.0 77.2 76.9 76.8 76.2 74.8 74.3 74.2 73.2 73.1

Michigan City 180910005 77.0 74.5 74.5 74.3 73.9 73.3 72.9 72.8 72.5 69.7 69.2 69.1 68.1 68.1

Ogden Dunes 181270020 78.3 76.3 76.3 76.2 75.6 75.5 75.1 75.0 74.5 72.9 72.3 72.1 71.2 70.8

Holland 260050003 90.0 85.7 85.7 85.5 85.3 83.5 83.1 82.9 82.8 78.2 77.5 77.3 76.0 76.1

Jenison 261390005 82.0 76.8 76.8 76.7 76.0 75.1 74.6 74.5 74.5 70.2 69.6 69.5 67.9 68.7

Muskegon 261210039 85.0 80.6 80.6 80.5 80.5 78.6 78.2 78.1 78.0 73.5 72.8 72.8 71.5 71.9

Indianapolis Area

Noblesville 189571001 82.7 78.3 78.3 78.1 78.1 76.1 75.9 75.7 75.6 70.2 69.9 69.8 68.9 68.7

Fortville 180590003 78.0 74.1 74.1 73.9 73.9 71.9 71.8 71.7 71.4 66.7 66.5 66.3 65.4 65.1

Fort B. Harrison 180970050 78.7 75.4 75.3 75.2 75.1 73.8 73.6 73.6 73.2 70.6 70.3 70.2 69.3 69.1

Detroit Area

New Haven 260990009 86.0 82.4 82.3 82.1 81.4 81.4 81.2 81.1 80.2 78.1 77.8 77.7 76.5 76.1

Warren 260991003 84.0 82.4 82.3 82.2 81.3 82.1 81.8 81.7 80.7 79.7 79.4 79.3 78.0 77.6

Port Huron 261470005 82.7 78.2 78.2 78.1 77.5 76.5 76.3 76.2 75.5 72.6 72.5 72.3 70.9 70.9

Cleveland Area

Ashtabula 390071001 89.0 84.2 84.1 83.9 83.4 82.0 81.8 81.6 81.0 76.8 76.5 76.4 74.8 75.1

Geauga 390550004 79.3 75.8 75.8 75.6 74.7 74.0 73.8 73.7 72.7 69.5 69.2 69.1 67.6 67.3

Eastlake 390850003 86.3 83.1 83.1 82.9 81.9 81.8 81.6 81.5 80.5 78.2 78.0 77.8 76.5 76.2

Akron 391530020 83.7 79.1 79.1 79.0 78.1 76.9 76.7 76.6 75.6 70.9 70.6 70.4 68.7 68.7

Cincinnati Area

Wilmington 390271002 82.3 77.3 77.4 77.1 77.5 75.3 75.2 74.8 74.9 70.1 69.9 69.5 67.1 68.3

Sycamore 390610006 84.7 81.5 81.4 81.1 81.9 80.4 80.2 79.8 80.3 76.4 76.0 75.7 73.5 74.6

Lebanon 391650007 87.7 82.8 82.8 82.4 83.0 80.8 80.7 80.3 80.7 75.4 75.1 74.8 72.6 74.2

Columbus Area

London 390970007 79.7 75.0 75.0 74.8 75.0 73.0 72.8 72.7 72.6 68.1 67.8 67.6 65.9 66.3

New Albany 390490029 86.3 82.1 82.1 81.9 81.8 80.2 80.0 79.9 79.6 74.7 74.3 74.2 73.3 73.0

Franklin 290490028 80.3 76.7 76.6 76.5 75.9 75.1 74.9 74.8 74.1 70.5 70.2 70.1 70.2 69.0

St. Louis Area

W. Alton (MO) 291831002 86.3 81.1 81.2 81.1 81.0 80.0 79.9 79.9 78.6 76.9 76.8 76.7 74.2 74.9

Orchard (MO) 291831004 87.0 82.1 82.1 82.0 82.0 80.9 80.8 80.7 80.0 77.7 77.6 77.4 75.2 76.2

Sunset Hills (MO) 291890004 82.3 79.2 79.2 79.1 78.7 78.3 78.1 78.1 77.1 75.3 75.2 75.1 73.0 73.9

Arnold (MO) 290990012 82.3 77.8 77.8 77.7 77.2 76.7 76.6 76.5 75.6 73.6 73.4 73.4 71.3 72.0

Margaretta (MO) 295100086 83.0 79.8 79.8 79.7 79.3 78.8 78.7 78.6 77.9 75.7 75.6 75.5 73.7 74.4

Maryland Heights (MO) 291890014 87.3 85.4 85.4 85.3 84.0 84.3 84.1 84.0 81.7 81.1 80.9 80.8 78.4 78.1 Round 5 without CAIR Round 5 without CAIR Round 5 without CAIR 

# Table 2a. Ozone Modeling Results

2009 2012 20182005

Round 5

with CAIR Round 5

with CAIR Round 5

with CAIR

Site Site ID Base Year Scen. A Scen. B Scen.C Scen. A Scen. B Scen.C Scen. A Scen. B Scen.C Scen.C-BART

Illinois

Chicago - Washington HS 170310022 15.2 14.9 14.8 14.5 14.1 14.8 14.7 14.2 14.0 15.0 14.6 14.2 13.7 13.9

Chicago - Mayfair 170310052 15.8 15.1 15.1 14.8 14.4 15.1 14.9 14.5 14.2 15.1 14.7 14.3 13.7 13.9

Chicago - Springfield 170310057 15.0 14.6 14.6 14.3 13.9 14.6 14.4 14.0 13.8 14.8 14.4 14.0 13.4 13.7

Chicago - Lawndale 170310076 14.9 14.5 14.5 14.2 13.8 14.5 14.3 13.9 13.7 14.7 14.3 13.9 13.3 13.6

Blue Island 170312001 14.8 14.4 14.4 14.0 13.7 14.4 14.2 13.8 13.6 14.5 14.1 13.7 13.2 13.4

Summit 170313301 15.2 14.9 14.9 14.6 14.2 14.9 14.7 14.3 14.0 15.0 14.6 14.3 13.7 13.9

Cicero 170316005 15.5 15.1 15.1 14.8 14.4 15.1 14.9 14.5 14.3 15.2 14.9 14.4 13.9 14.2

Granite City 171191007 16.7 16.3 16.2 15.9 15.1 16.1 16.0 15.3 14.9 15.9 15.6 14.9 14.2 14.3

E. St. Louis 171630010 15.6 15.2 15.2 14.8 14.1 15.0 14.9 14.3 13.9 14.9 14.6 14.0 13.3 13.4

Indiana

Jeffersonville 180190005 16.4 15.8 15.7 14.8 13.8 15.8 15.6 14.5 13.7 16.0 15.5 14.3 13.7 13.4

Jasper 180372001 15.2 14.3 14.2 13.4 12.4 14.2 14.0 13.0 12.2 14.3 13.9 12.8 12.1 11.8

Gary 180890031 15.6 13.9 13.9 13.5 13.0 13.8 13.6 13.1 12.8 13.7 13.4 12.9 12.3 12.4

Indy-Washington Park 180970078 15.3 14.4 14.4 13.6 12.8 14.3 14.2 13.2 12.6 14.3 13.9 12.9 12.2 12.0

Indy-W 18th Street 180970081 16.0 15.1 15.1 14.3 15.0 14.9 13.9 15.0 14.6 13.5 12.8

Indy- Michigan Street 180970083 15.9 15.0 15.0 14.2 13.4 14.9 14.8 13.8 13.1 14.9 14.5 13.5 12.8 12.6

Michigan

Allen Park 261630001 14.5 11.0 14.0 13.5 13.0 14.0 13.8 13.2 12.8 13.9 13.6 13.0 12.4 12.4

Southwest HS 261630015 15.9 15.3 15.3 14.8 14.2 15.2 15.0 14.4 13.9 15.1 14.8 14.1 13.5 13.5

Linwood 261630016 14.6 14.1 14.1 13.6 13.1 14.0 13.9 13.3 12.8 13.9 13.6 13.0 12.5 12.5

Dearborn 261630033 17.5 17.0 17.0 16.4 15.8 16.9 16.7 16.0 15.5 16.8 16.4 15.7 15.1 15.1

Wyandotte 261630036 14.7 14.2 14.1 13.6 13.1 14.1 13.9 13.3 12.8 14.0 13.7 13.0 12.4 12.5

Ohio

Middletown - Bonita 390170003 16.2 15.3 15.2 14.3 13.5 15.2 15.0 13.9 13.2 15.2 14.8 13.7 13.0 12.8

Fairfield 390170016 15.8 15.1 15.0 14.1 13.1 15.1 14.9 13.7 12.9 15.2 14.7 13.5 12.8 12.5

Cleveland-28th Street 390350027 15.4 14.9 14.9 14.3 13.5 14.7 14.5 13.9 13.2 14.6 14.2 13.5 12.8 12.7

Cleveland-St. Tikhon 390350038 17.4 16.7 16.7 16.0 15.2 16.5 16.3 15.6 14.8 16.3 16.0 15.2 14.4 14.3

Cleveland-Broadway 390350045 16.5 15.9 15.8 15.2 14.4 15.6 15.5 14.8 14.0 15.5 15.1 14.4 13.6 13.5

Cleveland-GT Craig 390350060 17.1 16.5 16.4 15.8 15.0 16.3 16.1 15.4 14.6 16.1 15.7 15.0 14.2 14.1

Newburg Hts - Harvard Ave 390350065 16.0 15.4 15.3 14.7 14.0 15.2 15.0 14.3 13.6 15.1 14.7 14.0 13.2 13.1

Columbus - Fairgrounds 390490024 15.3 14.6 14.5 13.7 12.9 14.4 14.1 13.2 12.6 14.2 13.8 12.8 12.2 12.0

Columbus - Ann Street 390490025 15.1 14.4 14.3 13.5 12.7 14.2 13.9 13.1 12.4 14.1 13.6 12.6 12.0 11.9

Cincinnati - Seymour 390610014 17.3 16.6 16.5 15.5 14.5 16.5 16.3 15.1 14.3 16.6 16.2 14.9 14.2 13.8

Cincinnati - Taft Ave 390610040 15.5 14.8 14.7 13.8 12.8 14.8 14.6 13.4 12.6 14.9 14.5 13.2 12.5 12.2

Cincinnati - 8th Ave 390610042 16.9 12.0 16.1 15.0 14.0 16.1 15.9 14.7 13.8 16.2 15.7 14.4 13.7 13.4

Sharonville 390610043 15.6 14.9 14.8 13.9 12.9 14.9 14.7 13.5 12.7 14.9 14.5 13.3 12.6 12.3

Norwood 390617001 16.2 15.5 15.4 14.4 13.4 15.4 15.2 14.0 13.2 15.5 15.1 13.8 13.1 12.8

St. Bernard 390618001 17.6 16.8 16.7 15.7 14.7 16.7 16.5 15.3 14.4 16.8 16.4 15.1 14.3 14.0

Steubenville 390810016 15.8 14.5 14.4 13.5 12.8 14.3 14.2 13.1 12.5 14.8 14.5 13.3 12.9 12.7

Mingo Junction 390811001 16.5 15.2 15.2 14.3 13.5 15.0 14.9 13.8 13.2 15.6 15.2 14.0 13.6 13.4

Ironton 390870010 15.2 14.8 14.6 13.6 12.8 14.6 14.4 13.2 12.5 14.8 14.1 12.8 12.4 12.3

Dayton 391130032 15.5 14.9 14.8 14.0 13.2 14.8 14.6 13.6 12.9 14.8 14.3 13.3 12.6 12.4

New Boston 391450013 14.7 12.0 14.0 13.0 12.1 14.1 13.8 12.5 11.9 14.2 13.6 12.2 11.7 11.6

Canton - Dueber 391510017 16.3 15.7 15.6 14.8 14.0 15.5 15.3 14.4 13.6 15.4 14.9 14.0 13.3 13.3

Canton - Market 391510020 14.6 11.0 14.1 13.3 12.6 13.9 13.7 12.9 12.3 13.9 13.5 12.6 12.0 11.9

Akron - Brittain 391530017 15.1 14.6 14.5 13.8 13.0 14.4 14.2 13.4 12.7 14.3 13.8 13.0 12.3 12.3

Akron - W. Exchange 391530023 14.3 13.7 13.7 13.0 12.3 13.6 13.3 12.6 12.0 13.4 13.0 12.2 11.6 11.5 Round 5 without CAIR Round 5 without CAIR Round 5 without CAIR

2009 2018 2012 

# Table 2b. PM 2.5 

# Modeling Results (Annual)2005

Round 5

with CAIR Round 5

with CAIR Round 5

with CAIR

Key Site County Site ID Base Year Scen. A Scen. B Scen.C Scen. A Scen. B Scen.C Scen. A Scen. B Scen.C Scen. C - BART

Illinois

Chicago - Washington HS Cook 170310022 36.6 36 36 36 36 36 36 37 36 37 36 37 37 35

Chicago - Mayfair Cook 170310052 40.3 37 37 37 36 37 36 37 36 38 37 37 37 36

Chicago - Springfield Cook 170310057 37.4 34 34 33 32 35 34 33 32 36 34 33 33 31

Chicago - Lawndale Cook 170310076 38.1 35 35 35 35 36 35 36 35 36 35 36 36 34

McCook Cook 170311016 43.0 39 39 39 39 40 39 40 39 40 40 41 40 38

Blue Island Cook 170312001 37.7 35 35 35 34 36 35 36 34 36 35 36 36 33

Schiller Park Cook 170313103 41.6 40 40 40 39 40 40 40 39 41 40 40 39 39

Summit Cook 170313301 40.2 38 38 39 38 39 38 39 38 39 38 39 39 37

Maywood Cook 170316005 39.2 38 38 38 38 38 38 39 38 39 38 39 39 37

Granite City Madison 171191007 39.2 36 36 35 33 36 35 34 33 36 35 35 33 32

E. St. Louis St. Clair 171630010 33.7 31 31 30 28 31 30 29 28 31 30 30 29 28

Indiana

Jeffersonville Clark 180190005 38.4 35 33 31 29 35 34 32 31 37 35 34 33 31

Jasper Dubois 180372001 36.2 32 32 30 28 32 32 30 29 33 31 31 30 28

Gary - IITRI Lake 180890022 39.0 35 35 35 34 35 34 35 34 36 36 36 35 35

Gary - Burr School Lake 180890026 39.0 34 34 34 33 34 34 35 34 34 34 34 34 32

Gary Lake 180890031 35.2 29 28 26 24 28 28 24 24 29 28 27 27 27

Indy-West Street Marion 180970043 38.0 34 34 33 33 35 35 34 33 36 35 34 34 33

Indy-English Avenue Marion 180970066 38.0 34 34 32 32 35 34 33 32 35 34 33 33 32

Indy-Washington Park Marion 180970078 36.6 33 33 32 31 33 33 32 31 34 33 32 32 32

Indy-W 18th Street Marion 180970081 38.3 33 33 31 31 33 33 32 31 34 33 32 32 31

Indy- Michigan Street Marion 180970083 36.0 32 32 29 28 32 31 29 28 32 31 29 29 29

Michigan

Luna Pier Monroe 261150005 38.9 34 34 32 32 34 34 32 32 34 33 32 31 31

Oak Park Oakland 261250001 39.9 38 38 37 36 38 37 37 36 38 37 37 36 35

Port Huron St. Clair 261470005 39.6 36 35 35 34 35 35 35 34 35 35 34 33 33

Ypsilanti Washtenaw 261610008 39.5 37 37 36 35 37 36 36 35 37 36 36 35 34

Allen Park Wayne 261630001 38.6 36 36 36 35 36 35 35 34 36 35 35 34 33

Southwest HS Wayne 261630015 40.1 36 36 36 35 36 35 35 35 36 35 35 34 33

Linwood Wayne 261630016 43.0 40 40 40 39 40 40 40 39 40 39 39 39 38

E 7 Mile Wayne 261630019 41.0 39 39 39 38 39 39 39 38 39 38 38 38 37

Dearborn Wayne 261630033 43.9 41 41 41 40 41 41 41 40 41 40 40 40 39

Wyandotte Wayne 261630036 37.2 36 36 36 35 35 35 35 35 35 35 35 35 34

Newberry Wayne 261630038 42.7 39 39 39 38 39 38 38 37 39 38 38 37 36

FIA Wayne 261630039 39.7 35 34 34 33 35 34 34 33 35 34 33 33 31

Ohio

Middleton Butler 390170003 39.3 33 32 29 28 33 33 29 28 34 32 29 28 27

Fairfield Butler 390170016 37.1 32 31 29 27 31 30 28 28 32 30 29 28 27

Butler 390170017 40.8 33 32 30 29 33 33 30 29 33 32 30 29 28

Cleveland-28th Street Cuyahoga 390350027 36.9 34 34 33 32 34 33 33 32 34 33 33 31 31

Cleveland-St. Tikhon Cuyahoga 390350038 44.2 40 40 37 36 40 39 36 35 40 38 36 35 34

Cleveland-Broadway Cuyahoga 390350045 38.8 35 35 33 31 35 34 32 30 35 34 31 29 29

Cleveland-GT Craig Cuyahoga 390350060 42.1 39 39 38 37 39 38 38 37 39 38 37 36 35

Newburg Hts - Harvard Ave Cuyahoga 390350065 38.9 35 35 33 31 35 34 32 30 36 35 32 31 30

Columbus - Fairgrounds Franklin 390490024 38.5 34 34 33 33 34 33 32 32 34 34 33 32 31

Columbus - Ann Street Franklin 390490025 38.5 34 33 31 31 33 33 31 31 34 33 31 31 30

Cincinnait Hamilton 390610006 40.6 33 33 30 27 33 32 29 28 34 32 29 28 27 Round 5 without CAIR Round 5 without CAIR Round 5 without CAIR

2009 2012 2018 

# Table 2c. PM 2.5 

# Modeling Results (Daily)2005

Round 5

with CAIR Round 5

with CAIR Round 5

with CAIR

Key Site County Site ID Base Year Scen. A Scen. B Scen.C Scen. A Scen. B Scen.C Scen. A Scen. B Scen.C Scen. C - BART Round 5 without CAIR Round 5 without CAIR Round 5 without CAIR

2009 2012 2018 

# Table 2c. PM 2.5 

# Modeling Results (Daily)

Cincinnati - Seymour Hamilton 390610014 38.4 33 33 28 26 33 32 27 25 33 31 29 25 24

Cincinnati - Taft Ave Hamilton 390610040 36.7 31 30 26 24 31 30 26 24 32 29 26 24 23

Cincinnati - 8th Ave Hamilton 390610042 37.3 32 32 30 28 32 31 29 28 33 31 29 28 27

Sharonville Hamilton 390610043 36.0 32 31 30 28 32 31 29 28 32 31 29 28 27

Norwood Hamilton 390617001 38.8 34 33 32 30 33 33 31 30 34 33 31 30 29

St. Bernard Hamilton 390618001 40.6 35 35 32 30 35 34 31 30 35 33 32 31 29

Steubenville Jefferson 390810016 40.7 36 35 32 29 35 34 30 28 37 35 31 29 28

Mingo Junction Jefferson 390811001 42.0 37 37 33 30 37 36 32 30 38 36 32 30 30

Dayton Montgomery 391130032 37.8 34 33 31 30 33 33 31 30 34 33 31 31 30

Canton - Dueber Stark 391510017 38.6 33 32 30 28 33 31 30 28 33 30 29 28 27

Akron - Brittain Summit 391530017 38.1 33 33 31 30 33 32 31 30 33 32 30 29 29

Wisconsin

Green Bay - Est High Brown 550090005 37.1 35 34 35 35 34 35 35 34 33 33 33 32 32

Madison Dane 550250047 36.4 33 33 32 32 33 32 32 31 32 31 30 29 29

Milwaukee-Health Center Milwaukee 550790010 38.7 35 35 35 35 35 35 35 34 35 34 34 34 33

Milwaukee-SER Hdqs Milwaukee 550790026 37.4 34 34 34 34 34 34 34 34 34 34 34 34 33

Milwaukee-Virginia FS Milwaukee 550790043 39.9 37 37 37 36 37 36 37 36 36 36 37 36 36

Milwaukee- Fire Dept Hdqs Milwaukee 550790099 37.8 34 34 33 33 34 33 33 32 34 33 33 33 32

Waukesha Waukesha 551330027 35.5 32 32 32 31 32 32 32 31 32 31 31 30 29

### 10 

### Table 3. Mo deling Res ults: Number of Sites > NAAQS

Ozone (85 ppb) Round 5 without CAIR Round 5 w/ CAIR

2009 Baseyear Scen. A Scen. B Scen. C Scen. C

- BART

IL 0 0 0 0

---- 0

IN 0 0 0 0

---- 0

MI 3 1 1 1

---- 1

OH 4 0 0 0

---- 0

WI 2 0 0 0

---- 0

Total 9 1 1 1 1

2012

IL 0 0 0 0

---- 0

IN 0 0 0 0

---- 0

MI 3 0 0 0

---- 0

OH 4 0 0 0

---- 0

WI 2 0 0 0

---- 0

Total 9 0 0 0 0

2018

IL 0 0 0 0 0 0

IN 0 0 0 0 0 0

MI 3 0 0 0 0 0

OH 4 0 0 0 0 0

WI 2 0 0 0 0 0

Total 9 0 0 0 0 0

O zone (75 ppb) Round 5 without CAIR Round5 w/ CAIR

2009 Baseyear Scen. A Scen. B Scen. C Scen. C

- BART

IL 12 6 6 6

---- 4

IN 26 10 9 8

---- 5

MI 21 12 12 12

---- 12

OH 45 27 25 24

---- 21

WI 12 10 10 10

---- 10

Total 116 65 62 60

---- 52

20 12

IL 12 3 3 3

---- 1

IN 26 5 4 4

---- 3

MI 21 9 8 8

---- 6

OH 45 18 14 12

---- 11

WI 12 10 9 9

---- 9

Total 116 45 38 36 30

2018

IL 12 0 0 0 0 0

IN 26 0 0 0 0 0

MI 21 3 3 3 3 3

OH 45 3 3 2 1 1

WI 12 3 2 1 1 1

Total 116 9 8 6 5 5

### 11

PM2.5

- Annual Round 5 without CAIR Round 5 w/ CAIR

2009 Baseyear Scen. A Scen. B Scen. C Scen. C

- BART

IL 7 4 4 1

---- 1

IN 6 2 2 0

---- 0

MI 2 2 2 1

---- 1

OH 26 1 3 12 5

---- 1

WI 0 0 0 0

---- 0

Total 41 21 20 7 3

2012

IL 7 3 1 1

---- 0

IN 6 1 1 0

---- 0

MI 2 2 1 1

---- 1

OH 26 12 9 4

---- 0

WI 0 0 0 0

---- 0

Total 41 18 12 6 1

2018

IL 7 3 1 0 0 0

IN 6 1 1 0 0 0

MI 2 2 1 1 1 1

OH 26 13 8 2 0 0

WI 0 0 0 0 0 0

Total 41 19 11 3 1 1

PM2.5

- Daily

Round 5 without CAIR Round 5 w/ CAIR

2009 Baseyear Scen. A Scen. B Scen. C Scen. C

- BART

IL 16 7 7 6

---- 6

IN 13 0 0 0

---- 0

MI 14 10 9 9

---- 5

OH 31 4 3 2

---- 2

WI 8 1 1 1

---- 1

Total 82 2 2 20 18

---- 14

2012

IL 16 9 6 8

---- 6

IN 13 0 0 0

---- 0

MI 14 8 6 6

---- 5

OH 31 3 3 2

---- 1

WI 8 1 1 1

---- 1

Total 82 21 16 1 7 13

2018

IL 16 10 6 8 8 5

IN 13 4 1 1 0 0

MI 14 8 6 6 5 4

OH 31 5 3 2 1 0

WI 8 1 1 1 1 1

Total 82 2 8 17 18 1 5 10

### 12 

### Ta ble 4 . Mo deling Results: Future Year Visibility Levels

Worst 20% 2018

Round 5 without CAIR Round 5 w/ CAIR

Site Baseline

(2000

- 2004) 2018 URP Scen. A Scen. B Scen. C Scen. C

BART

BOWA1 19.86 17.94 19.09 18.87 18.54 18.02 17.94

VOYA2 19.48 17.75 18.60 18.44 18.17 17.77 17.63

SENE1 24.38 21.64 24.02 23.58 23.03 22.38 22.59

ISLE1 21.59 19.43 21.05 20.86 20.62 20.22 20.09

ISLE9 21.59 19.43 20.83 20.58 20.38 19.84 19.84

HEGL1 26.75 23.13 26.24 25.83 24.87 24.23 24.22

MI NG1 28.15 24.27 27.51 26.98 25.81 24.93 24.74

CACR1 26.36 22.91 25.32 24.80 23.57 22.97 22.44

UPBU1 26.27 22.82 25.31 24.79 23.50 22.79 22.59

MACA1 31.37 26.64 30.11 29.08 27.06 26.24 26.10

DOSO1 29.05 24.69 27.88 26.96 24.36 23.74 23.00

SHEN1 29 .31 25.12 28.38 27.65 25.24 24.69 23.92

JARI1 29.12 24.91 28.06 27.21 25.00 24.48 24.06

BRIG1 29.01 25.05 28.10 28.07 26.57 26.25 25.21

LYBR1 24.45 21.48 24.06 23.86 22.58 22.30 21.14

ACAD1 22.89 20.45 22.88 22.76 22.31 22.16 21.49

Best 20% 2018

Round 5 without CAIR Round 5 w/ CAIR

Site Baseline

(2000

- 2004) 2018 Max Scen. A Scen. B Scen. C Scen. C

BART

BOWA1 6.42 6.42 6.20 6.17 6.16 6.12 6.14

VOYA2 7.09 7.09 6.87 6.83 6.81 6.78 6.75

SENE1 7.14 7.14 7.80 7.78 7. 81 7.77 7.71

ISLE1 6.75 6.75 6.77 6.76 6.72 6.67 6.60

ISLE9 6.75 6.75 6.63 6.61 6.58 6.53 6.52

HEGL1 12.84 12.84 12.17 12.20 12.07 11.63 11.66

MING1 14.46 14.46 13.78 13.77 13.70 13.37 13.28

CACR1 11.24 11.24 10.94 10.99 10.97 10.78 10. 52

UPBU1 11.71 11.71 11.18 11.23 11.18 10.96 10.73

MACA1 16.51 16.51 16.32 16.21 15.76 15.34 15.25

DOSO1 12.28 12.28 12.02 11.84 11.27 11.03 11.00

SHEN1 10.93 10.93 10.98 10.91 10.25 10.16 9.91

JARI1 14.21 14.21 14.19 13.98 13.42 13.21 13.14

BRI G1 14.33 14.33 14.32 14.46 14.22 14.17 13.92

LYBR1 6.37 6.37 6.39 6.38 6.31 6.28 6.14

ACAD1 8.78 8.78 8.97 8.96 8.90 8.89 8.82

### 13 

### Figure 2. Ozone Modeling Results

2009 Round 5

–

S cen

. A Round 5

– Scen. B Round 5

– Scen. C Round 5

- CAIR

2012 Round 5

–

Scen. A Round 5

– Scen. B Round 5

– Scen. C Round 5

- CAIR

2018 Round 5

–

Scen. A Round 5

– Scen. B Round 5

– Scen. C Round 5

- CAIR

### 14 

### Figure 3. PM2.5 Annual Modeling Results

2009 Round 5

– Scen. A Round 5

– S cen. B Round 5

– Scen. C Round 5

- CAIR

2012 Round 5

– Scen. A Round 5

– Scen. B Round 5

– Scen. C Round 5

- CAIR

2018 Round 5

– Scen. A Round 5

– Scen. B Round 5

– Scen. C Round 5

- CAIR

### 15 

### Figure 4 . PM2.5 Daily Modeling Results

2009 Round 5

– Scen. A Round 5

– Scen. B Round 5

– Scen. C Round 5

- CAIR

2012 Round 5

– Scen. A Round 5

– Scen. B Round 5

– Scen. C Round 5

- CAIR

2018 Round 5

– Scen. A Round 5

– Scen. B Round 5

– Scen. C Round 5

- CAIR

# Appendix I

Scenario B (Legally Enforceable) ControlsNOx

– 2009

Point Source Grown and Controlled Emissions by facility for NOX r6s1b_2009 Base Year = 2002

Future Year = 2009

STID=17 CYID=57 fcid=057801AAA name=AES DUCK CREEK

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 57 057801AAA 0001 0001 01 10100202 NOX 0.8147 0.8416 0.8416 0.00 0.00 SCR SCR added by LADCO

STID=17 CYID=143 fcid=143805AAG name=AES ED EDWARDS STATION

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 143 143805AAG 0001 0001 01 10100202 NOX 3.0515 3.1522 3.1522 0.00 0.00 lnb LNB added by LADCO

17 143 143805AAG 0001 0003 01 10100202 NOX 6.9419 7.1708 7.1708 0.00 0.00 lnb LNB added by LADCO

17 143 143805AAG 0002 0004 01 10100202 NOX 2.1310 2.2013 2.2013 0.00 0.00 lnb LNB added by LADCO

---- -------- -------- ---------- fcid 12.1244 12.5243 12.5243

cyid 12.1244 12.5243 12.5243

stid 12.9392 13.3659 13.3659

STID=39 CYID=1 fcid=0701000007 name="DP&L, J.M. STUART GENERATING STATION"

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrlde s

39 1 0701000007 R1 B001 B001P1 10100202 NOX 6.9860 6.9756 2.3252 0.85 0.95 SCR SCR added by LADCO

39 1 0701000007 R2 B002 B002P1 10100202 NO X 3.6327 3.6273 1.2091 0.85 0.95 SCR SCR added by LADCO

39 1 0701000007 R3 B003 B003P1 10100202 NOX 5.0133 5.0058 1.6686 0.85 0.95 SCR SCR added by LADCO

39 1 0701000007 R4 B004 B004P1 10100202 NOX 7.8493 7.8376 2.6125 0.85 0.95 SCR SCR added by LADCO

---- -------- -------- ---------- fcid 23.4814 23.4464 7.8155

cyi d 23.4814 23.4464 7.8155

STID=39 CYID=167 fcid=0684000000 name=MUSKINGUM RIVER POWER PLANT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control E F Control EF ctrltype ctrldes

39 167 0684000000 R1 B001 B001P1 10200501 NOX 0.0017 0.0017 0.0001 0.00 0.95 SCR SCR added by LADCO

39 167 0684000000 R2 B002 B002P1 10100201 NOX 5.8167 5.8080 0.2904 0.00 0.95 SCR SCR added by LADCO

39 167 0684000000 R2 B002 B002P2 10100501 NOX 0.0000 0.0000 0.0000 0.00 0.95 SCR SCR added by LADCO

39 167 0684000000 R3 B003 B003P1 10100201 NOX 7.9017 7.8899 0.3945 0.00 0.95 SCR SCR added by LADCO

39 167 0684000000 R3 B003 B003P2 10100501 NOX 0.0000 0.0000 0.0000 0.00 0.95 SCR SCR added by LADCO

39 167 0684000000 R4 B004 B004P1 10100203 NOX 7.8775 7.8657 0.3933 0.00 0.95 SCR SCR added by LADCO

39 167 0684000000 R4 B004 B004P2 10100501 NOX 0.0000 0.0000 0.0000 0.00 0.95 SCR SCR added by LADCO

39 167 0684000000 R6 B006 B006P1 10100202 NOX 3.8586 3.8528 0.1926 0.00 0.95 SCR SCR added by LADCO

39 167 0684000000 R6 B006 B006P2 10100501 NOX 0.0000 0.0000 0.0000 0.00 0.95 SCR SCR added by LADCO

---- -------- -------- ---------- f cid 25.4561 25.4182 1.2709

cyid 25.4561 25.4182 1.2709

stid 48.9375 48.8646 9.0864

STID=55 CYID=79 fcid=241007800 name=WIS ELECTRIC POWER VALLEY STATION

Base Yr Grown Controlled Base Year Future YearSTID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

55 79 241007800 S11 B21 01 10100202 NOX 2.7972 2.8 895 1.6470 0.00 0.43 SCR SCR added by LADCO

55 79 241007800 S11 B22 01 10100202 NOX 2.9073 3.0032 1.7118 0.00 0.43 SCR SCR added by LADCO

55 79 241007800 S12 B23 01 10100202 NOX 2.3270 2.4038 1.2740 0.00 0.47 SCR SCR added by LADCO

55 79 241007800 S12 B24 01 1010020 2 NOX 2.3427 2.4199 1.2826 0.00 0.47 SCR Scrubber added by LADCO

---- -------- -------- ---------- fcid 10.3742 10.7164 5.9154

cyid 10.3742 10.7164 5.9154

STID=55 CYID=117 fcid=460033090 name=WP & L Alliant Energy

- Edgewater Gen Station

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

55 117 460033090 S1 1 B23 01 10100203 NOX 1.6197 1.6731 1.0038 0.00 0.40 SCR SCR added by LADCO

55 117 460033090 S11 B24 01 10100203 NOX 4.1072 4.2426 3. 4789 0.00 0.18 SCR SCR added by LADCO

55 117 460033090 S12 B25 01 10100221 NOX 5.6804 5.8677 4.9876 0.00 0.15 SCR SCR added by LADCO

- --- -------- -------- ---------- fcid 11.4072 11.7834 9.4703

cyid 11.4072 11.7834 9.4703

stid 21.7814 22.4997 15.3857

======== ======== ==========

83.6581 84.7302 37.8380NOx

- 2012

Point Source Grown and Controlled Emissions by facility for N OX r6s1b_2012 Base Year = 2002

Future Year = 2012

STID=17 CYID=33 fcid=0338 01AAA name=AMEREN ENERGY GENERATING CO

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc poli d Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 33 033801AAA 0005 0005 01 10100202 NOX 1.642 1.871 0.9357 0.00 0.500 SCR SCR added by LADCO

17 33 033801AAA 0006 0006 01 10100202 NOX 2.116 2.413 1.2063 0.00 0.500 SCR SCR added by LADCO

---- -------- -------- ---------- fcid 3.758 4.284 2.1420

cyid 3.758 4.284 2.1420

STID=17 CYID=57 fcid=057801AAA name=AES DUCK CREEK

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 57 057801AAA 0001 0001 01 10100202 NOX 0.815 0.929 0.9288 0.00 0.000 SCR SCR added by LADCO

STID=17 CYID=79 fcid=079808AAA name=AMER EN ENERGY GENERATING CO

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 79 079808AAA 0003 0003 01 10100202 NOX 6.735 7.678 7.6780 0.00 0.000 SCR SCR added by LADCO

17 79 079808AAA 0012 0013 01 10100501 NOX 5.936 5.378 5.3781 0.00 0.000 SCR SCR added by LADCO

---- -------- -------- ---------- fcid 12.671 13.056 13.0561

cyid 12.671 13.056 13.0561

STID=17 CYID=97 fcid=097190AAC name=MIDWEST GENERATION LLC

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctr ltype ctrldes

17 97 097190AAC 0016 0031 02 10100401 NOX 0.000 0.000 0.0000 0.00 0.999 SHUTDOWN SCR added by LADCO

STID=17 CYID=137 fcid=137805AAA name=AMEREN ENE RGY GENERATING CO

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons /Day Tons/Day Control EF Control EF ctrltype ctrldes

17 137 137805AAA 0003 0003 01 10100202 NOX 5.356 6.106 6.1058 0.00 0.000 LNB LNB added by LADCO

STID=17 CYID=143 fcid=143805AAG name=AES ED EDWARDS STATION

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 143 143805AAG 0001 0001 01 10100202 NOX 3.052 3.479 3.4789 0.00 0.000 lnb LNB added by LADCO

17 143 143805AAG 0001 0003 01 10100202 NOX 6.942 7.914 7.9141 0.00 0.000 lnb LNB added by LADCO

17 143 14

3805AAG 0002 0004 01 10100202 NOX 2.131 2.429 2.4294 0.00 0.000 lnb LNB added by LADCO

---- -------- - ------- ---------- fcid 12.124 13.822 13.8224

cyid 12.124 13.822 13.8224STID=17 CYID=167 fcid=167120AAO name=CITY WATER LIGHT & POWER

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 167 167120AAO 0010 0012 01 10100203 NOX 6.527 7.441 0.0074 0.00 0.999 SHUTDOWN SHUTDOWN added by LADCO

17 167 167120AAO 0010 0013 01 10100203 NOX 2.646 3.017 0.0030 0.00 0.999 SHUTDOWN SHUTDOWN added by LADCO

---- -------- -------- ---------- fcid 9.173 10.458 0.0105

cyid 9.173 10.458 0.0 105

STID=17 CYID=179 fcid=179801AAA name=MIDWEST GENERATION LLC

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 179 179801AAA 0018 0029 01 10 100203 NOX 22.429 25.570 1.2785 0.00 0.950 SCR SCR added by LADCO

17 179 179801AAA 0018 0031 01 10100203 NOX 38.993 44.454 2.2227 0.00 0.950 SCR SCR added by LADCO

---- -------- -------- ---------- fcid 61.422 7 0.024 3.5012

cyid 61.422 70.024 3.5012

STID=17 CYID=197 fcid=197809AAO name=MIDWEST GENERATION LLC

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 197 197809AAO 0032 0033 02 10100604 NOX 0.000 0.000 0.0000 0.00 0.800 SCR SCR added by LADCO

STID=17 CYID=197 fcid=197810AAK name=MIDWEST GENERATION LLC

Base Yr Grown Controlled Base Year F uture Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 197 197810AAK 0011 0016 02 10100222 NOX 5.731 6.534 3.9203 0.00 0.400 SCR SCR added by LADCO

17 197 197810AAK 0011 0016 03 10100501 NOX 0.000 0.000 0.0000 0.00 0.400 SCR SCR added by LADCO

17 197 197810AAK 0013 0010 02 10100223 NOX 8.598 9.802 0.0098 0.00 0.999 SHUTDOWN SCR added by LADCO

17 197 197810AAK 0013 0010 03 10100501 NOX 0.000 0.000 0.0000 0.00 0.999 SHUTDOWN SCR added by LADCO

17 197 197810AAK 0007 0012 02 10100223 NOX 10.974 12.511 0.01 25 0.00 0.999 SHUTDOWN SCR added by LADCO

17 197 197810AAK 0007 0012 03 10100501 NOX 0.000 0.000 0.0000 0.00 0.999 SHUTDOWN SCR added by LADCO

-- -- -------- -------- ---------- fcid 25.303 28.847 3.9426

cyid 25.303 28.847 3.9426

stid 130.622 147.527 43.5096

STID=27 CYID=61 fcid=2706100004 name=Minnesota Power Inc

- Boswell Energy Ctr

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

27 61 2706100004 SV003 EU003 001 10100226 NOX 13.661 14.142 2.8284 0.00 0.800 SCR SCR added by LADCO

27 61 2706100004 SV003 EU00 3 002 10100501 NOX 0.000 0.000 0.0000 0.00 0.800 SCR SCR added by LADCO

---- -------- -------- ---------- fci d 13.661 14.142 2.8284

cyid 13.661 14.142 2.8284

STID=27 CYID=109 fcid=2710900011 name=Rochester Public Utilities

- Silver Lake

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes27 1 09 2710900011 SV003 EU004 001 10100202 NOX 2.079 2.152 1.2911 0.00 0.400 SNCR SCR added by LADCO

---- ---- ---- -------- ---------- stid 15.739 16.294 4.1195

STID=39 CYID=1 fcid=0701000007 name="DP&L, J.M. STUART GENERATING STATION"

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid T ons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

39 1 0701000007 R1 B001 B001P1 10100202 NOX 6.986 7.296 2.4319 0.85 0.950 SCR SCR added by LADCO

39 1 0701000007 R2 B002 B002P1 10100202 NOX 3.633 3.794 1.2646 0.85 0.950 SCR SCR added by LADCO

39 1 0701000007 R3 B003 B003P 1 10100202 NOX 5.013 5.235 1.7452 0.85 0.950 SCR SCR added by LADCO

39 1 0701000007 R4 B004 B004P1 10100202 NOX 7.849 8.197 2.7324 0.85 0.950 SCR SCR added by LADCO

---- -------- -------- ---------- fcid 23.481 24.522 8.1740

cyid 23.481 24.522 8.1740

STID=39 CYID=31 fcid=0616000000 name=CONESVILLE POWER PLANT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid s tkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

39 31 0616000000 R4 B004 B004P1 10100212 NOX 20.852 21.776 1.0888 0.00 0.950 SCR SCR added by LADCO

STID=39 CYID=167 fcid=0684000000 name=MUSKINGUM RIVER POWER PLANT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

39 167 0684000000 R1 B001 B001P1 10200501 NOX 0.002 0.002 0.0001 0.00 0.950 SCR SCR added by LADCO

39 167 0684000000 R2 B002 B002P1 10100201 NOX 5.817 6.074 0.3037 0.00 0.950 SCR SCR added by LADCO

39 167 0684000000 R2 B002 B002P2 10100501 NOX 0.000 0.000 0.0000 0.00 0.950 SCR SCR added by LADCO

39 167 068400

0000 R3 B003 B003P1 10100201 NOX 7.902 8.252 0.4126 0.00 0.950 SCR SCR added by LADCO

39 167 0684000000 R3 B003 B003P2 10100501 NOX 0.000 0. 000 0.0000 0.00 0.950 SCR SCR added by LADCO

39 167 0684000000 R4 B004 B004P1 10100203 NOX 7.877 8.227 0.4113 0.00 0.950 SCR SCR added by LADCO

39 167 0684000000 R4 B004 B004P2 10100501 NOX 0.000 0.000 0.0000 0.00 0.950 SCR SCR added by LADCO

39 167 0684000000 R6 B006 B006P1 1010 0202 NOX 3.859 4.030 0.2015 0.00 0.950 SCR SCR added by LADCO

39 167 0684000000 R6 B006 B006P2 10100501 NOX 0.000 0.000 0.0000 0.00 0. 950 SCR SCR added by LADCO

---- -------- -------- ---------- fcid 25.456 26. 584 1.3292

cyid 25.456 26.584 1.3292

stid 69.789 72.882 10.5920

STID=55 CYID=79 fcid=241007690 name=WIS ELECTRIC POWER OAK CR EEK STATION

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

55 79 241007690 S13 B25 01 10100202 NOX 4.755 5.421 3.0898 0.00 0.430 SCR SCR added by LADCO

55 79 241007690 S13 B26 01 10100202 NOX 3.277 3.736 2.2045 0.00 0.410 SCR SCR added by LADCO

55 79 241007690 S14 B27 01 10100212 NOX 3.333 3.800 2.8499 0.00 0.250 SCR SCR added by LADCO

55 79 241007690 S14 B28 01 10100212 NOX 3.384 3.857 2.9316 0.00 0.240 SC R SCR added by LADCO

---- -------- -------- ---------- fcid 14.749 16.814 11. 0757

STID=55 CYID=79 fcid=241007800 name=WIS ELECTRIC POWER VALLEY STATIONBase Yr Grown Controll ed Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

55 79 241007800 S11 B21 0 1 10100202 NOX 2.797 3.189 1.8177 0.00 0.430 SCR SCR added by LADCO

55 79 241007800 S11 B22 01 10100202 NOX 2.907 3.314 1.8893 0.0 0 0.430 SCR SCR added by LADCO

55 79 241007800 S12 B23 01 10100202 NOX 2.327 2.653 1.4061 0.00 0.470 SCR SCR added by LADCO

55 79 241007800 S12 B24 01 10100202 NOX 2.343 2.671 1.4155 0.00 0.470 SCR Scrubber added by LADCO

---- -------- -------- ---------- fcid 10.374 11.827 6.5285

cyid 25.123 28.641 17.6042

STID=55 CYID=117 fcid=460033090 name=WP & L Alliant Energy

- Edgewater Gen Station

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrl type ctrldes

55 117 460033090 S11 B23 01 10100203 NOX 1.620 1.846 1.1079 0.00 0.400 SCR SCR added by LADCO

55 117 460033090 S11 B24 0 1 10100203 NOX 4.107 4.682 3.8395 0.00 0.180 SCR SCR added by LADCO

55 117 460033090 S12 B25 01 10100221 NOX 5.680 6.476 5.5045 0.0 0 0.150 SCR SCR added by LADCO

---- -------- -------- ---------- fcid 11 .407 13.005 10.4519

cyid 11.407 13.005 10.4519

stid 36.530 41.646 28.0562

======== ======== ==========

252.681 278.349 86.2773NOx 20 18

Point Source Grown and Controlled Emissions by facility for NOX r6s1b_2018 Base Year = 2002

Future Year = 2018

STID=17 CYID=31 fcid=031600AIN name=MIDWEST GENERATION LLC

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 31 031600AIN 0010 0013 01 10100226 NOX 2.283 2. 592 1.5550 0.00 0.400 SCR SCR added by LADCO

17 31 031600AIN 0010 0013 02 10100601 NOX 0.000 0.000 0.0000 0.00 0.400 SCR SCR added by LADCO

17 31 031600AIN 0012 0016 01 10100226 NOX 3.991 4.531 2.7184 0.00 0.400 SCR SCR added by LADCO

17 31 031600AIN 0012 0016 02 1010 0601 NOX 0.000 0.000 0.0000 0.00 0.400 SCR SCR added by LADCO

---- -------- -------- ---------- fcid 6.274 7.122 4.2734

cyid 6.274 7. 122 4.2734

STID=17 CYID=33 fcid=033801AAA name=AMEREN ENERGY GENERATING CO

Base Yr Grown Co ntrolled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 33 033801AAA 0005 0005 01 10100202 NOX 1.642 1.863 0.9317 0.00 0.500 SCR SCR added by LADCO

17 33 033801AAA 0006 0006 01 10100202 NOX 2.116 2.402 1.2012 0.00 0.500 SCR SCR added by LADCO

---- -------- -------- ---------- fcid 3.758 4.266 2.1329

cyid 3.758 4.266 2.1329

STID=17 CYID=57 fcid=057801AAA name=AES DUCK CREEK

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 57 057801AAA 0001 0001 01 10100202 NOX 0.815 0.925 0.924 9 0.00 0.000 SCR SCR added by LADCO

STID=17 CYID=79 fcid=079808AAA name=AMEREN ENERGY GENERATING CO

Base Yr Grown Controlled Ba se Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 79 079808AAA 0003 0003 01 10100202 NOX 6.735 7.645 7.6453 0.00 0.000 SCR SCR added by LADCO

17 79 079808AAA 0012 0013 01 10100501 NOX 5.936 3.984 3.9838 0.00 0.000 SCR SCR added by LADCO

---- -------- -------- ---------- fcid 12.671 11.629 11.6291

cyid 12.671 11.629 11.6291

STID=17 CYID=97 fcid=097190AAC name=MIDWEST GENERATION LLC

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 97 097190AAC 0016 0031 02 10100401 NOX 0.000 0.000 0.0000 0.00 0.999 SHUTDOWN SCR added by LADCO

STID=17 CYID=137 fcid=137805AAA name=AMEREN ENERGY GENERATING CO

Base Yr Grown Controlled Base Ye ar Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 137 137805AAA 0003 0003 01 10100 202 NOX 5.356 6.080 6.0798 0.00 0.000 LNB LNB added by LADCOSTID=17 CYID=143 fcid=143805AAG name=AES ED EDWARDS STATION

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 143 143805AAG 0001 0001 01 10100202 NOX 3.052 3.464 3.4641 0.00 0.000 lnb LNB added by LADCO

17 143 143805AAG 0001 0003 01 10100202 NOX 6.942 7.880 7.8804 0.00 0.000 lnb LNB added by LADCO

17 143 143805AAG 0002 0004 01 10100202 NOX 2.131 2.419 2.4191 0.00 0.000 lnb LNB added by LADCO

---- -------- -------- ---------- fcid 12.124 13.764 13.7636

cyid 12.124 13.764 13.7636

STID=17 CYID= 167 fcid=167120AAO name=CITY WATER LIGHT & POWER

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 167 167120AAO 0010 0012 01 10100203 NOX 6.527 7.410 0.0074 0.00 0.999 SHUTDOWN SHUTDOWN added by LADCO

17 167 167120AAO 0010 0013 01 10100203 NOX 2.646 3.004 0.0030 0.00 0.999 SHUTDOWN SHUTDOWN added by LADCO

---- -------- -------- ---------- fcid 9.173 10.414 0.0104

cyid 9.173 10.414 0.0104

STID=17 CYID=179 fcid=179801AAA name=MIDWEST GENERATION LLC

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Contro l EF Control EF ctrltype ctrldes

17 179 179801AAA 0018 0029 01 10100203 NOX 22.429 25.462 1.2731 0.00 0.950 SCR SCR added by LADCO

17 179 179801 AAA 0018 0031 01 10100203 NOX 38.993 44.265 2.2132 0.00 0.950 SCR SCR added by LADCO

---- -------- ----- --- ---------- fcid 61.422 69.726 3.4863

cyid 61.422 69.726 3.4863

STID=17 CYID=197 fcid=197809AAO name=MIDWEST GENERATION LLC

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 197 197809AAO 0032 0033 02 10100604 NOX 0.000 0.000 0.0000 0.00 0.800 SCR SCR added by LADCO

STID=17 CYID=197 fcid=197810AAK name=MIDWEST GENERATION LLC

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 197 197810AAK 0011 0016 02 10100222 NOX 5.731 6.506 3.9036 0.00 0.400 SCR SCR added by LADCO

17 197 197810AAK 0 011 0016 03 10100501 NOX 0.000 0.000 0.0000 0.00 0.400 SCR SCR added by LADCO

17 197 197810AAK 0013 0010 02 10100223 NOX 8.598 9.760 0.0098 0.00 0.999 SHUTDOWN SCR added by LADCO

17 197 197810AAK 0013 0010 03 10100501 NOX 0.000 0.000 0.0000 0.00 0.999 SHUTDOWN SCR added by LADCO

17 197 197810AAK 0007 0012 02 10100223 NOX 10.974 12.458 0.0125 0.00 0.999 SHUTDOWN SCR added by LADCO

17 197 197810AAK 0007 0012 03 10100501 NOX 0.000 0.000 0.0000 0.00 0.999 SHUTDOWN SCR added by LADCO

---- -------- -------- ---------- fcid 25.303 28.724 3.9258

cyid 25.303 28.724 3.9258

stid 136.896 152.649 46.2263STID=18 CYID=147 fcid=00020 name=INDIANA MICHIGAN POWER

- ROCKPORT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

18 147 00020 1 001 01 10100222 NOX 23.226 25.291 1.2646 0.00 0.950 SCR SCR added by LADCO

18 147 00020 1 001 02 10100501 NOX 0.000 0.000 0.0000 0.00 0.950 SCR SCR added by LADCO

---- -------- -------- ---------- fcid 23.226 25.291 1.2646

cyid 23.226 25.291 1.2646

stid 23.226 25.291 1.2646

STID=27 CYID=61 fcid=2706100004 name=Minnesota Power Inc

- Boswell Energy Ctr

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

27 61 2706100004 SV003 EU003 001 10100226 NOX 13.661 15.733 3.1466 0.00 0.800 SCR SCR added by LADCO

27 61 2706100004 SV003 EU003 002 10100501 NOX 0.000 0.000 0.0000 0.00 0.800 SCR SCR added by LADCO

---- -------- -------- ---------- fcid 13.661 15.733 3.1466

cyid 13.661 15.733 3.1466

STID=27 CYID=109 fcid=2710900011 name=Rochester Public Utilities

- Silver Lake

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

27 109 271090

0011 SV003 EU004 001 10100202 NOX 2.079 2.394 1.4363 0.00 0.400 SNCR SCR added by LADCO

---- -------- ----- --- ---------- stid 15.739 18.127 4.5830

STID=39 CYID=1 fcid=0701000007 name="DP&L, J.M. STUART GENERATING STATION"

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day To ns/Day Tons/Day Control EF Control EF ctrltype ctrldes

39 1 0701000007 R1 B001 B001P1 10100202 NOX 6.986 7.607 2.5358 0.85 0.950 SCR SCR added by LAD CO

39 1 0701000007 R2 B002 B002P1 10100202 NOX 3.633 3.956 1.3186 0.85 0.950 SCR SCR added by LADCO

39 1 0701000007 R3 B003 B003P1 10100202 NOX 5.013 5.459 1.8197 0.85 0.950 SCR SCR added by LADCO

39 1 0701000007 R4 B004 B004P1 10100202 NOX 7.849 8.547 2.8491 0.85 0.950 SCR SCR added by LADCO

---- -------- -------- ---------- fcid 23.481 25.570 8.5232

cyid 23.481 25.570 8.5232

STID=39 CYID=31 fcid=0616000000 name=CONESVILLE POWER PLANT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

39 31 0616000000 R4 B004 B004P1 10100212 NOX 20.852 22.706 1.1353 0.00 0.950 SCR SCR added by LADCO

STID=39 CYID=167 fcid=0684000000 name=MUSKINGUM RIVER POWER PLANT

Base Yr Grown Controlled Base Year F uture Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

39 167 0684000000 R1 B001 B001P1 10200501 NOX 0.002 0.002 0.0001 0.00 0.950 SCR SCR added by LADCO39 167 0684000000 R2 B002 B002P1 10100201 NOX 5.817 6.334 0.3167 0.00 0.950 SCR SCR added by LADCO

39 167 0684000000 R2 B002 B002P2 10100501 NOX 0.000 0.000 0.0000 0.00 0.950 SCR SCR added by LADCO

39 167 0684000000 R3 B003 B003P1 10100201 NOX 7.902 8.604 0.4302 0.00 0.950 SCR SCR added by LADCO

39 167 0684000000 R3 B003 B003P2 10100501 NOX 0.000 0.000 0.00 00 0.00 0.950 SCR SCR added by LADCO

39 167 0684000000 R4 B004 B004P1 10100203 NOX 7.877 8.578 0.4289 0.00 0.950 SCR SCR added by LADCO

39 167 0684000000 R4 B004 B004P2 10100501 NOX 0.000 0.000 0.0000 0.00 0.950 SCR SCR added by LADCO

39 167 0684000000 R6 B006 B006P1 10100202 NOX 3.859 4.202 0.2101 0.00 0.950 SCR SCR added by LADCO

39 167 0684000000 R6 B006 B006P2 10100501 NOX 0.000 0.000 0.0000 0.00 0.950 SCR SCR added by LADCO

---- -------- -------- ---------- fcid 25.456 27.720 1.38 60
cyid 25.456 27.720 1.3860

st id 69.789 75.996 11.0445

STID=54 CYID=39 fcid=0006 name=APPALACHIAN POWER

- KANAWHA RIVER PLANT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

54 39 0006 012 001 99 10100202 NOX 4.829 5.258 2.6291 0.00 0.500 SCR Scrubber added by LADCO

54 39 0006 012 002 99 10100202 NOX 4.921 5.359 2.6794 0.00 0.500 SCR Scrubber added by LADCO

---- -------- -------- ---------- fcid 9.750 10.617 5.3085

cyid 9.750 10.617 5.3085

stid 9.750 10.617 5.3085

STID=55 CYID=79 fcid=241007690 name=WIS ELECTRIC POWER OAK CREEK STATION

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

55 79 241007690 S13 B25 01 10 100202 NOX 4.755 5.398 3.0766 0.00 0.430 SCR SCR added by LADCO

55 79 241007690 S13 B26 01 10100202 NOX 3.277 3.720 2.1951 0.00 0.410 SCR SCR added by LADCO

55 79 241007690 S14 B27 01 10100212 NOX 3.333 3.784 2.8378 0.00 0.250 SCR SCR added by LADCO

55 79 241007690 S14 B28 01 10100212 NOX 3.384 3.841 2.9191 0.00 0.240 SCR SCR added by LADCO

---- -------- -------- ---------- fcid 14.749 16.743 11.0285

STID=55 CYID=79 fcid=241007800 name=WIS ELECTRIC POWER VALL EY STATION

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

55 79 241007800 S11 B21 01 10100202 NOX 2.797 3.175 1.4289 0.00 0.550 SCR SCR added by LADCO

55 79 241007800 S11 B22 01 10100202 NOX 2.907 3.300 1.4852 0.00 0.550 SCR SCR added by LADCO

55 79 241007800 S12 B23 01 10100202 NOX 2.327 2.642 1.1887 0.00 0.550 SCR SCR added by LADCO

55 79 241007800 S12 B24 01 10100202 NOX 2.343 2.659 1.1967 0.00 0.550 SCR SCR added by LADCO

---- -------- -------- ---------- fcid 10.374 11.777 5.2 995

cyid 25.123 28.519 16.3281

STID=55 CYID=117 fcid=460033090 name=WP & L Alliant Energy

- Edgewater Gen Station

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

55 117 460033090 S11 B23 01 10100203 NOX 1.620 1.839 1.103 2 0.00 0.400 SCR SCR added by LADCO55 117 460033090 S11 B24 01 10100203 NOX 4.107 4.662 3.8232 0.00 0.180 SCR SCR added by LADCO

5 5 117 460033090 S12 B25 01 10100221 NOX 5.680 6.448 5.4811 0.00 0.150 SCR SCR added by LADCO

---- -------- -------- ---------- fcid 11.407 12.949 10.4074

cyid 11.407 12.949 10.4074

stid 36.530 41.469 26.735 5

======== ======== ==========

291.931 324.149 95.1624SO2

– 2009

Point Source Grown and Controlled Emissions by facility for SO2 r6s1b_2009 1

Base Year = 2002

Future Year = 2009

STID=19 CYID=115 fcid=58

- 07

- 001 name=MIDAMERICAN ENERGY CO.

- LOUISA STATION

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Cont rol EF Control EF ctrltype ctrldes

19 115 58

- 07

- 001 117487 147281 99 10100222 SO2 33.664 34.774 3.4774 0.0 0.90 SCRUBBER Scrubber added by LADCO

STID=21 CYID=161 fc

id=2116100009 name=EAST KY POWER COOP

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

21 161 2116100009 1 001 99 10100202 SO2 42.166 42.103 4.2103 0.0 0.90 SCRUBBER Scrubber added by LADCO

21 161 2116100009 2 002 99 10100212 SO2 55.385 55.303 5.5303 0.0 0.90 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 97.551 97.406 9.7406

cyid 97.551 97.406 9.7406

stid 9 7.551 97.406 9.7406

STID=27 CYID=141 fcid=2714100004 name=NSP

- Sherburne Generating Plant

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

27 141 271410

0004 SV001 EU001 001 10100222 SO2 16.765 16.987 3.6401 0.3 0.85 SCRUBBER Scrubber added by LADCO

27 141 2714100004 SV001 EU002 001 10100222 SO2 22.549 22. 848 4.8959 0.3 0.85 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 39.314 39.834 8.5360

cyid 39.314 39.834 8.5360

stid 39.314 39.834 8.5360

STID=54 CYID=51 fcid= 0005 name=OHIO POWER

- MITCHELL PLANT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

54 51 0005 012 001 99 10100202 SO2 17.775 17.748 1.7748 0.0 0.90 SCRUBBER Scrubber added by LADCO

54 51 0005 012 002 99 10100202 SO2 5.689 5.680 0.5680 0.0 0.90 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 23.463 23.428 2.3428

cyid 23.463 23.428 2.3428

STID=54 CYID=53 fcid=0009 name=APPALACHIAN POWER

- MOUNTAINEER PLANT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

54 53 0009 001 001 99 10100202 SO2 11.196 11.179 1.1179 0.0 0.90 SCRUBBER Scrubber added by LADCO

STID=54 CYID=79 fcid= 0006 name=APPALACHIAN POWER

- JOHN E AMOS PLANT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes54 79 0006 012 001 99 10100202 SO2 79.635 79.516 7.9516 0.0 0.90 SCRUBBER Scrubber added by LADCO

54 79 0006 003 003 99 10100202 SO2 139.377 139.169 13.9169 0.0 0.90 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 219.012 218.685 21.8685

cyid 219.012 218.685 21.8685

stid 253.671 253.293 25.3293

======== ======== ==========

424.200 425.307 47.0832SO2

– 2012

Point Source Grown and Controlled Emissions by facility for SO2 r6s1b_2012 1

Base Year = 2002

Future Year = 2012

STID=17 CYID=31 fcid=031600AMI name=MIDWEST GENERATION L LC

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day To ns/Day Control EF Control EF ctrltype ctrldes

17 31 031600AMI 0007 0010 01 10100226 SO2 16.13 18.39 1.839 0.0 0.900 SCRUBBER Scrubber added by LADCO

ST ID=17 CYID=97 fcid=097190AAC name=MIDWEST GENERATION LLC

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 97 097190AAC 0018 0033 01 10100226 SO2 24.14 27.52 2.752 0.0 0.900 SCRUBBER Scrubber added by LADCO

17 97 097190AAC 0021 0036 01 10100226 SO2 19.23 21.92 2.192 0.0 0.900 SCRUBBER Scrubber added by LADCO

17 97 09719

0AAC 0016 0031 01 10100203 SO2 4.59 5.24 0.005 0.0 0.999 SHUTDOWN Scrubber added by LADCO

---- -------- -------- ---------- fcid 47.96 54.68 4.950

cyid 47.96 54.68 4.950

STID=17 CYID=125 fcid=125804AAB name=DYNEGY MIDWEST GENERATION INC

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 125 125804AAB 0019 0023 01 10100202 SO2 22.34 25.47 3.821 0.0 0.850 SCRUBBER Scrubber added by LADCO

STID=17 CYID=127 fcid=127855AAC name=ELECTRIC ENERGY INC

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/D ay Control EF Control EF ctrltype ctrldes

17 127 127855AAC 0001 0001 01 10100222 SO2 11.83 13.48 13.482 0.0 0.000 LNB LNB added by LADCO

17 127 127855AAC 0001 0002 01 10100222 SO2 11.48 13.09 13.085 0.0 0.000 LNB LNB added by LADCO

17 127 127855AAC 0002 0003 01 10100222 SO2 10.25 11.68 11.680 0.0 0.000 LNB LNB added by LADCO

17 127 127855AAC 0002 0004 01 10100222 SO2 12.04 13.73 13.731 0.0 0.000 LNB LNB added by LADCO

17 127 127855AAC 0003 0006 01 10100222 SO2 12.68 14.46 14.456 0.0 0.000 LNB LNB added by LADCO

---- -------- -------- ---------- fcid 58.27 66.43 66.435

cyid 58.27 66.43 66.435

STID=17 CYID=135 fcid=135803AAA name=AMEREN ENERGY GENERATING C O

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Ton s/Day Control EF Control EF ctrltype ctrldes

17 135 135803AAA 0001 0001 01 10100203 SO2 32.99 37.61 3.761 0.0 0.900 SCRUBBER Scrubber added by LADCO

17 135 135803AAA 0001 0003 01 10100203 SO2 72.92 83.13 8.313 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 105.91 120.74 12.074

cyid 105.91 120.74 12.074STID=17 CYID=157 fcid=157851AAA name=DYNEGY MIDWEST GENERATION INC

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 157 157851AAA 0001 0001 01 10100203 SO2 25.14 28.66 4.299 0.0 0.850 SCRUBBER Scrubber added by LADCO

17 157 157851AAA 0002 00 02 01 10100203 SO2 25.79 29.41 4.411 0.0 0.850 SCRUBBER Scrubber added by LADCO

17 157 157851AAA 0013 0013 01 10100202 SO2 27.79 31.68 4.752 0.0 0.850 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 78.72 89.75 13.462

cyid 78.72 89.75 13.462

STID=17 CYID=167 fcid=167120AAO name=CITY WATER LIGHT & POWER

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 167 167120AAO 0010 0012 01 10100203 SO2 4 4.20 50.39 0.050 0.0 0.999 SHUTDOWN Scrubber added by LADCO

17 167 167120AAO 0010 0013 01 10100203 SO2 16.40 18.70 0.019 0.0 0.999 SHUTDOWN Scrubber added by LADCO

---- -------- -------- ---------- fcid 60.61 69.10 0.06 9

cyid 60.61 69.10 0.069

STID=17 CYID=179 fcid=179801AAA name=MIDWEST GENERATION LLC

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid p rid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 179 179801AAA 0018 0029 01 10100203 SO2 25.35 28.90 2.890 0.0 0.900 SCRUBBER Scrubber added by LADCO

17 179 179801AAA 0018 0031 01 10100203 SO2 41.57 47.39 4.739 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 66.91 76.29 7.629

cyid 66.91 76.29 7.629

STID=17 CYID=197 fcid=197810AAK name=MIDWEST GENERATION LLC

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Ton s/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 197 197810AAK 0013 0010 03 10100501 SO2 0.00 0.00 0.000 0.0 0.999 SHUTDOWN Scrubber added by LADCO

17 197 197810AAK 0007 0012 02 10100223 SO2 15.33 17.48 0.017 0.0 0.999 SHUTDOWN Scrubber added by LADCO

17 197 197810AAK 0007 0012 03 101 00501 SO2 0.00 0.00 0.000 0.0 0.999 SHUTDOWN Scrubber added by LADCO

---- -------- -------- ---------- fcid 15.33 17.48 0.017

cyid 15.33 17.48 0.017

stid 472.19 538.32 110.295

STID=19 CYID=115 fcid=58

- 07

- 001 name=MIDAMERICAN ENERGY CO.

- LOUISA STATION

Base Yr Grown Controlled Base Year Future Year

STID CYI D fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

19 115 58

- 07

- 001 117487 147281 99 10100222 SO2 33.66 38.38 3.838 0.0 0.900 SCRUBBER Scrubber added by LADCO

STID=21 CYID=161 fcid=2116100009 name=EAST KY POWER COOP

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes21 161 2116100009 1 001 99 10100202 SO2 42.17 44.03 4.403 0.0 0.900 SCRUBBER Scrubber added by LADCO

21 161 2116100009 2 002 99 10100212 SO2 55.39 57.84 5.784 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 97.55 101.87 10.187

cyid 97.55 101.87 10.187

stid 97.55 101.87 10.187

STID=27 CYID=61 fcid=27 06100004 name=Minnesota Power Inc

- Boswell Energy Ctr

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

27 61 2706100004 SV003 EU003 001 10100226 SO2 33.99 35.19 15.081 0.3 0.700 SCRUBBER Scrubber added by LADCO

27 61 2706100004 SV003 EU003 002 10100501 SO2 0.00 0.00 0.000 0.3 0.700 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 33.99 35.19 15.081

cyid 33.99 35.19 15.081

STID=27 CYID=109 fcid=2710900011 name=Rochester Pu blic Utilities

- Silver Lake

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid T ons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

27 109 2710900011 SV003 EU004 001 10100202 SO2 7.86 8.13 1.220 0.0 0.850 SCRUBBER S crubber added by LADCO

STID=27 CYID=141 fcid=2714100004 name=NSP

- Sherburne Generating Plant

Base Yr Grown Controlled Base Year Future Year

STID CYID f

cid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

27 141 2714100004 SV001 EU001 001 10100222 SO2 16.76 17.36 3.719 0.3 0.850 SCRUBBER Scrubber added by LADCO

27 141 2714100004 SV001 EU002 001 10100222 SO2 22.55 23.34 5.002 0.3 0.850 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 39.31 40.70 8.721

cyid 39.31 40.70 8.721

stid 81.16 84.02 25.023

STID=39 CYID=13 fcid=0607130015 name=R. E. BURGER PLANT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Cont rol EF ctrltype ctrldes

39 13 0607130015 R6 B011 B011P1 10100202 SO2 29.83 31.15 3.115 0.0 0.900 SCRUBBER Scrubber added by LADCO

39 13 0607130015 R7 B012 B012P1 10100202 SO2 34.77 36.31 3.631 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 64.60 67.46 6.746

cyid 64.60 67.46 6.746

STID=39 CYID=31 fcid=0616000000 name=CONESVILLE POWER PLANT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

39 31 0616000000 R4 B004 B004P1 10100212 SO2 316.00 330.00 33.000 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- stid 380.60 397.46 39.746

STID=47 CYID=1 fcid=0009 name=TVA BULL RUN FOSSIL PLANTBase Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Da y Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

47 1 0009 S

- 1 001 99 10100212 SO2 130.81 133.01 13.301 0.0 0.900 SCRUBBER Scrubbe r added by LADCO

STID=47 CYID=73 fcid=0007 name=TVA JOHN SEVIER FOSSIL PLANT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stk id dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

47 73 0007 S

- 1A 001 99 10100212 SO2 20.15 20.49 2.0 49 0.0 0.900 SCRUBBER Scrubber added by LADCO

47 73 0007 S

- 1B 002 99 10100212 SO2 20.25 20.59 2.059 0.0 0.900 SCRUBBER Scrubber added by LADCO

47 73 0007 S

- 2A 003 99 10100212 SO2 19.62 19.95 1.995 0.0 0.900 SCRUBBER Scrubber added by LADCO

47 73 0007 S

- 2B 004 99 10100212 SO2 18.93 19.25 1.925 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 78.95 80.28 8.028

cyid 78.95 80.2 8 8.028

STID=47 CYID=85 fcid=0011 name=TVA JOHNSONVILLE FOSSIL PLANT

Base Yr Grown Contr olled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

47 85 0011 S1

- 01 001 99 10100212 SO2 17.06 17.35 1.735 0.0 0.900 SCRUBBER Scrubber added by LADCO

47 85 0011 S1

- 04 004 99 10100212 SO2 19.85 20.18 2.018 0.0 0.900 SCRUBBER Scrubber added by LADCO

47 85 0011 S1

- 05 005 99 10100212 SO2 24.11 24.52 2.452 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 61.02 62.04 6.204

cyid 61.02 62.04 6.204

STID=47 CYID=145 fcid=0013 name =TVA KINGSTON FOSSIL PLANT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Ton s/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

47 145 0013 S

- 1 001 99 10100202 SO2 12.68 12.89 1.289 0.0 0.900 SCRUBBER Scr ubber added by LADCO

47 145 0013 S

- 1 002 99 10100202 SO2 14.00 14.24 1.424 0.0 0.900 SCRUBBER Scrubber added by LADCO

47 145 0013 S

- 1 003 9 9 10100202 SO2 13.80 14.04 1.404 0.0 0.900 SCRUBBER Scrubber added by LADCO

47 145 0013 S

- 1 004 99 10100202 SO2 12.24 12.44 1.244 0.0 0.900 SCRUBBER Scrubber added by LADCO

47 145 0013 S

- 1 005 99 10100202 SO2 19.57 19.90 1.990 0.0 0.900 SCRUBBER Scrubber added by LADCO

47 145 0013 S

- 2 006 99 10100202 SO2 18.92 19.24 1.924 0.0 0.900 SCRUBBER Scrubber added by LADCO

47 145 0013 S

- 2 007 99 10100202 SO2 21.30 21.66 2.166 0.0 0.900 SCRUBBER Scrubber added by LADCO

47 145 0013 S

- 2 008 99 10100202 SO2 18.54 18.85 1.885 0.0 0.900 SCRUBBE R Scrubber added by LADCO

47 145 0013 S

- 2 009 99 10100202 SO2 20.72 21.07 2.107 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 151.77 154.33 15.433

cyid 151.77 154.33 15.433

STID=47 CYID=165 fcid=0025 name=TVA GALLATIN FOSSIL PLANT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Cont rol EF Control EF ctrltype ctrldes

47 165 0025 S

- 01 001 99 10100212 SO2 13.91 14.14 1.414 0.0 0.900 SCRUBBER Scrubber added by LADCO

47 165 00

25 S

- 01 002 99 10100212 SO2 14.87 15.12 1.512 0.0 0.900 SCRUBBER Scrubber added by LADCO

47 165 0025 S

- 02 003 99 10100212 SO2 16.33 16.60 1.660 0.0 0.900 SCRUBBER Scrubber added by LADCO

47 165 0025 S

- 02 004 99 10100212 SO2 20.39 20.73 2.073 0.0 0.900 SCRUBBER Sc rubber added by LADCO

---- -------- -------- ----------fcid 65.49 66.59 6.659

cyid 65.49 66.59 6.659

stid 488.04 496.25 49.625

STID=54 CYID=51 fcid=0005 name=OHIO POWER

- MITCHELL PLANT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

54 51 0005 012 001 99 10100202 SO2 17.77 18.56 1.856 0.0 0.900 SCRUBBER Scrubber added by LADCO

54 51 0005 012 002 99 10100202 SO2 5.69 5.94 0.594 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- -------- --- ----- ---------- fcid 23.46 24.50 2.450

cyid 23.46 24.50 2.450

STID=54 CYID=53 fcid=0009 name=APPALACHIAN POWER

- MOUNTAINEER PLANT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

54 53 0009 001 001 99 10100202 SO2 11.20 11.69 1.169 0.0 0.900 SCRUBBER Scrubber added by LADCO

STID=54 CYID=79 fcid=0006 name=APPALACHIAN POWER

- JOHN E A MOS PLANT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

54 79 0006 012 001 99 10100202 SO2 79.63 83.16 8.316 0.0 0.900 SCRUBBER Scrubber added by LA DCO

54 79 0006 012 002 99 10100202 SO2 100.33 104.78 10.478 0.0 0.900 SCRUBBER Scrubber added by LADCO

54 79 0006 003 003 99 10100202 SO2 139.38 145.55 14.555 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 319.35 333.50 33.350
cyid 319.35 3 33.50 33.350

stid 354.00 369.69 36.969

STID=55 CYID=79 fcid=241007690 name=WIS ELECTRIC POWER OAK CREEK STATION

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

55 79 241007690 S13 B25 01 10100202 SO2 12.75 14.5 4 3.490 0.0 0.760 SCRUBBER Scrubber added by LADCO

55 79 241007690 S13 B26 01 10100202 SO2 8.68 9.89 2.473 0.0 0.750 SCRUBBER Scrubber add ed by LADCO

55 79 241007690 S14 B27 01 10100212 SO2 10.97 12.51 2.876 0.0 0.770 SCRUBBER Scrubber added by LADCO

55 79 241007690 S14 B28 01 10100212 SO2 11.28 12.86 2.958 0.0 0.770 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 43.68 49.80 11.797

cyid 43.6 8 49.80 11.797

stid 43.68 49.80 11.797

======== ======== ==========

1950.90 2075.80 287.480SO2

– 2018

Point Source Grown and Controlled Emissions by facility for SO2 r6s1b_2018 1

Base Year = 2002

Future Year = 2018

STID=17 CYID=31 fcid=031600AIN name=MIDWEST GENERATION LLC

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 31 031600AIN 0010 0013 01 10100226 SO2 10.92 12.39 1.239 0.0 0.900 SCRUBBER Scrubber added by LADCO

17 31 031600AIN 0012 0016 01 10100226 SO2 17.69 20.08 2.008 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 28.61 32.48 3.248

STID=17 CYID=31 fcid=031600AMI name=MIDWEST GENERATION LLC

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 31 031600AMI 0007 0010 01 10100226 SO2 16.13 18.31 1.831 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- cyid 44.74 50.79 5.079

STID=17 CYID =79 fcid=079808AAA name=AMEREN ENERGY GENERATING CO

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 79 079808AAA 0003 0003 01 10100202 SO2 36.35 41.27 4.127 0.0 0. 900 SCRUBBER Scrubber added by LADCO

17 79 079808AAA 0012 0013 01 10100501 SO2 28.99 19.46 1.946 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 65.34 60.72 6.072

cyid 65.34 60.72 6.072

STID=17 CYID=97 fcid=097190AAC name=MIDWEST GENERATIO N LLC

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 97 097190AAC 0018 0033 01 10100226 SO2 24.14 27.40 2.740 0.0 0.900 SCRUBBER Scrubber added by LADCO

17 97 097190AAC 0021 0036 01 10100226 SO2 19.23 21.83 2.183 0.0 0.900 SCRUBBER Scrubber added by LADCO

17 97 097190AAC 0016 0031 01 10100203 SO2 4.59 5.22 0.005 0.0 0.999 SHUTDOWN Scrubber added by LADCO

---- -------- -------- ---------- fcid 47.96 54.45 4.928

cyid 47.96 54.4 5 4.928

STID=17 CYID=125 fcid=125804AAB name=DYNEGY MIDWEST GENERATION INC

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 125 125804AAB 0019 0023 01 10100202 SO2 22.34 25.36 3.805 0.0 0.850 SCRUBBER Scrubber added by LADCO

STID=17 CYID=127 fcid=127855AAC name=ELECTRIC ENERGY INC

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes17 127 127855AAC 0002 0003 01 10100222 SO2 10.25 11.63 11.630 0.0 0.000 LNB LNB added by LADCO

17 127 127855AAC 0002 0004 01 10100222 SO2 12.04 13.67 13.673 0.0 0.000 LNB LNB added by LADCO

17 127 127855AAC 0001 0001 01 10100222 SO2 11.83 13.42 1.342 0.0 0.900 SCRUBBER Scrubber added by LADCO

17 127 127855AAC 0001 0002 01 10100222 SO2 11.48 13.03 1.303 0.0 0.900 SCRUBBER Scrubber added by LADCO

17 127 1 27855AAC 0003 0005 01 10100222 SO2 11.72 13.31 1.331 0.0 0.900 SCRUBBER Scrubber added by LADCO

17 127 127855AAC 0003 0006 01 10100222 SO2 12.68 14.39 1.439 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 70.00 79.46 30.719

cyid 70.00 79.46 30.719

STID=17 CYID=135 fcid=135803AAA name=AMEREN ENERGY GENERATING CO

Base Yr Grown Controlled Bas e Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 135 135803AAA 0001 0001 01 10100203 SO2 32.99 37.45 3.745 0.0 0.900 SCRUBBER Scrubber added by LADCO

17 135 135803AAA 0001 0003 01 10100203 SO2 72.92 82.77 8.277 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 10 5.91 120.22 12.022

cyid 105.91 120.22 12.022

STID=17 CYID=143 fcid=143805AAG name=AES ED EDWARDS STATION

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 143 143805AAG 0002 0004 01 10100202 SO2 15.28 17.34 1.734 0.0 0.900 SCRUBBER Scrubber added by LADCO

STID=17 CYID=157 fcid=157851AAA name=DYNEGY MIDWEST GENERATION INC

Base Yr Grown C ontrolled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 157 157851AAA 0001 0001 01 10100203 SO2 25.14 28.54 4.281 0.0 0.850 SCRUBBER Scrubber added by LADCO

17 157 157851AAA 0002 0002 01 10100203 SO2 25.79 29.28 4.3 92 0.0 0.850 SCRUBBER Scrubber added by LADCO

17 157 157851AAA 0013 0013 01 10100202 SO2 27.79 31.54 4.732 0.0 0.850 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 78.72 89.36 13.404

cyid 78.72 89.36 13.404

STID=17 CYID=167 fcid=16712 0AAO name=CITY WATER LIGHT & POWER

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc poli d Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 167 167120AAO 0010 0012 01 10100203 SO2 44.20 50.18 0.050 0.0 0.999 SHUTDOW N Scrubber added by LADCO

17 167 167120AAO 0010 0013 01 10100203 SO2 16.40 18.62 0.019 0.0 0.999 SHUTDOWN Scrubber added by LADCO

---- -------- -------- ---------- fcid 60.61 68.80 0.069

cyid 60.61 68.80 0.069

STID=17 CYID=179 fcid=179801AAA name=MIDWEST GENERATION LLC

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Co ntrol EF Control EF ctrltype ctrldes

17 179 179801AAA 0018 0029 01 10100203 SO2 25.35 28.77 2.877 0.0 0.900 SCRUBBER Scrubber added by LADCO

17 179 179801AAA 0018 0031 01 10100203 SO2 41.57 47.19 4.719 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- ------- - -------- ----------fcid 66.91 75.96 7.596

cyid 66.91 75.96 7.596

STID=17 CYID=197 fcid=197809AAO name=MIDWEST GENERATION LLC

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 197 197809AAO 0006 0009 01 10100203 SO2 15.89 18.04 1.804 0.0 0.900 SCRUBBER Scrubber added by LADCO

17 197 197809AAO 0016 0031 01 1 0100202 SO2 27.43 31.13 3.113 0.0 0.900 SCRUBBER Scrubber added by LADCO

17 197 197809AAO 0017 0033 01 10100202 SO2 23.13 26.26 2.626 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 66.45 75.44 7.544

STID=17 CYID=197 fcid=197810AAK name=MIDWEST GENERATION LLC

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

17 197 197810AAK 0009 0014 02 10100222 SO2 11.64 13.21 1.321 0.0 0.900 SCRUBBER Scrubber added by LADCO

17 197 197810AAK 0011 0016 02 10100222 SO2 25.67 29.14 2.914 0.0 0.900 SCRUBBER Scrubber added by LADCO

17 197 197810AAK 0013 0010 03 10100501 SO2 0.00 0.00 0.000 0.0 0.999 SHUTDOWN Scrubber added by LADCO

17 197 197810AAK 0007 0012 02 10100223 SO2 15.33 17.40 0.017 0.0 0.999 SHUTDOWN Scrubber added by LADCO

17 197 197810AAK 0007 0012 03 101005 01 SO2 0.00 0.00 0.000 0.0 0.999 SHUTDOWN Scrubber added by LADCO

---- -------- -------- ---------- fcid 52.64 59.75 4.252

cyid 119.09 135.19 11.796

stid 696.90 777.66 97.225

STID=18 CYID=147 fcid=00020 name=INDIANA MICHIGAN POWER

- ROCKPORT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

18 147 00020 1 001 01 10100222 SO2 66.42 72.32 7.232 0.0 0.900 SCRUBBER Scrubber added by LADCO

18 147 00020 1 001 02 10100501 SO2 0.00 0.00 0.000 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 66.42 72.32 7.232

cyid 66.42 72.32 7.232

stid 66.42 72.32 7.232

STID=19 CYID=115 fcid=58

- 07

- 001 name=MIDAMERICAN ENERGY CO.

- LOUISA STATION

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

19 115 58

- 07

- 001 117487 147281 99 10100222 SO2 33.66 38.22 3.822 0.0 0.900 SCRUBBER Scrubber added by LADCO

STID=21 CYID=127 fcid =2112700003 name=KENTUCKY POWER CO

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc poli d Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

21 127 2112700003 2 002 99 10100202 SO2 104.52 113.82 11.382 0.0 0.900 SCRUBBE R Scrubber added by LADCO

STID=21 CYID=161 fcid=2116100009 name=EAST KY POWER COOP

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes21 161 2116100009 1 001 99 10100202 SO2 42.17 45.92 4.592 0.0 0.900 SCRUBBER Scrubber added by LADCO

21 161 2116100009 2 002 99 10100212 SO2 55.39 60.31 6.031 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 97.55 106.23 10.623

cyid 97.55 106.23 10.623

stid 202.07 220.04 22.004

STID=27 CYID=61 fcid=2706100004 name=Minnesota Power Inc

- Boswell Energy Ctr

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Cont rol EF Control EF ctrltype ctrldes

27 61 2706100004 SV003 EU003 001 10100226 SO2 33.99 39.15 16.778 0.3 0.700 SCRUBBER Scrubber added by LADCO

27 61 27

06100004 SV003 EU003 002 10100501 SO2 0.00 0.00 0.000 0.3 0.700 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 33.99 39.15 16.778

cyid 33.99 39.15 16.778

STID=27 CYID=109 fcid=2710900011 name=Rochester Public Utilities

- Silver Lake

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF c trltype ctrldes

27 109 2710900011 SV003 EU004 001 10100202 SO2 7.86 9.05 1.357 0.0 0.850 SCRUBBER Scrubber added by LADCO

STID=27 CYID=141 fcid=2714100004 name=NSP

- Sherburne Generating Plant

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid To ns/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

27 141 2714100004 SV001 EU001 001 10100222 SO2 16.76 19.31 4.138 0.3 0.850 SCRUBBER Sc rubber added by LADCO

27 141 2714100004 SV001 EU002 001 10100222 SO2 22.55 25.97 5.565 0.3 0.850 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 39.31 45.28 9.703

cyid 39.31 45.28 9.703

stid 81.16 93.48 27.838

STID=39 CYID=13 fcid=0607130015 name=R. E. BURGER PLANT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

39 13 0607130015 R6 B011 B011P1 10100202 SO2 29.83 32.48 3.248 0.0 0.900 SCRUBBER Scrubber added by LADCO

39 13 0607130015 R7 B012 B012P1 10100202 SO2 34.77 37. 86 3.786 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 64.60 70.34 7.034

cyid 64.60 70.34 7.034

STID=39 CYID=31 fcid=0616000000 name=CONESVILLE POWER PLANT

Base Yr Grown Controlled Base Year Futur e Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

39 31 0616000000 R4 B004 B004P1 10100212 SO2 316.00 344.11 34.411 0.0 0.900 SCRUBBER Scrubber added by LADCO

STID=39 CYID=167 fcid=0684000000 name=MUSKINGUM RIVER POWER PLANT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes39 167 0684000000 R2 B002 B002P1 10100201 SO2 65.07 70.85 7.085 0.0 0.900 SCRUBBER Scrubber added by LADCO

39 167 0684000000 R2 B002 B002P2 10100501 SO2 0.00 0.00 0.000 0.0 0.900 SCRUBBER Scrubber added by LADCO

39 167 0684000000 R3 B003 B003P1 10100201 SO2 94.58 103.00 10.300 0.0 0.900 SCRUBBE R Scrubber added by LADCO

39 167 0684000000 R3 B003 B003P2 10100501 SO2 0.00 0.00 0.000 0.0 0.900 SCRUBBER Scrubber added by LADCO

39 167 0684000000 R4 B00 4 B004P1 10100203 SO2 81.64 88.90 8.890 0.0 0.900 SCRUBBER Scrubber added by LADCO

39 167 0684000000 R4 B004 B004P2 10100501 SO2 0.00 0.00 0.000 0.0 0.900 SCRUBBER Scrubber added by LADCO

39 167 0684000000 R5 B005 B005P1 10100203 SO2 97.22 105.87 10.587 0.0 0.900 SCRUBBER Scrubber added by LADCO

3 9 167 0684000000 R5 B005 B005P2 10100501 SO2 0.00 0.00 0.000 0.0 0.900 SCRUBBER Scrubber added by LADCO

39 167 0684000000 R6 B006 B006P1 10100202 SO 2 113.96 124.10 12.410 0.0 0.900 SCRUBBER Scrubber added by LADCO

39 167 0684000000 R6 B006 B006P2 10100501 SO2 0.00 0.00 0.000 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 452.48 492.72 49.272

cyid 452.48 492.72 49.272

stid 833.08 907.16 90.716

STID=47 CYID=1 fcid=0009 name=TVA BULL RUN FOSSIL PLANT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

47 1 0009 S

- 1 001 99 10100212 SO2 130.81 136.82 13.682 0.0 0.900 SCRUBBER Scrubber added by LADCO

STID=47 CY ID=73 fcid=0007 name=TVA JOHN SEVIER FOSSIL PLANT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

47 73 0007 S

- 1A 001 99 10100212 SO2 20.15 21.07 2.107 0.0 0.90 0 SCRUBBER Scrubber added by LADCO

47 73 0007 S

- 1B 002 99 10100212 SO2 20.25 21.18 2.118 0.0 0.900 SCRUBBER Scrubber added by LADCO

47 73 0007 S

- 2A 003 99 10100212 SO2 19.62 20.52 2.052 0.0 0.900 SCRUBBER Scrubber added by LADCO

47 73 0007 S

- 2B 004 99 10100212 SO2 18.93 19.8 0 1.980 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 78.95 82.57 8.257

cyid 78.95 82.57 8.257

STID=47 CYID=85 fcid=0011 name=TVA JOHNSONVILLE FOSSIL PLANT

Base Yr Grown Controlled Base Year Futur e Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

47 85 0011 S1

- 01 001 99 10100212 SO2 17.06 17.84 1.784 0.0 0.900 SCRUBBER Scrubber added by LADCO

47 85 0011 S1

- 04 004 99 10100212 SO2 19.85 20.76 2.076 0.0 0.900 SCRUBBER Scrubber added by LADCO

47 85 0011 S1

- 05 005 99 10100212 SO2 24.11 25.22 2.522 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 61.02 63.82 6.382

cyid 61.02 63.82 6.382

STID=47 CYID=145 fcid=0013 name=TVA KINGSTON FOSSIL PLAN T

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Ton s/Day Control EF Control EF ctrltype ctrldes

47 145 0013 S

- 1 001 99 10100202 SO2 12.68 13.26 1.326 0.0 0.900 SCRUBBER Scrubber added by LADCO

47 145 0013 S

- 1 002 99 10100202 SO2 14.00 14.65 1.465 0.0 0.900 SCRUBBER Scrubber added by LADCO47 145 0013 S

- 1 003 99 10100202 SO2 13.80 14.44 1.444 0.0 0.900 SCRUBBER Scrubber added by LADCO

47 145 0013 S

- 1 004 99 10100202 SO2 12.24 12.80 1.280 0.0 0.900 SCRUBBER Scrubber added by LADCO

47 145 0013 S

- 1 005 99 10100202 SO2 19.57 20.47 2.047 0.0 0.900 SCRUBBER Scrubber added by LADCO

47 145 0013 S

- 2 006 99 10100202 SO2 18.92 19.79 1.979 0.0 0.900 SCRUBBER Scrubber added by LADCO

47 145 0013 S

- 2 007 99 10100202 SO2 21.30 22.28 2.228 0.0 0.900 SCRUBBER Scrubber added by LADCO

47 145 0013 S

- 2 008 99 10100202 SO2 18.54 19.39 1.939 0.0 0.900 SCRUBBER Scrubber added by LA DCO

47 145 0013 S

- 2 009 99 10100202 SO2 20.72 21.68 2.168 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 151.77 158.75 15.875

cyid 151.77 158.75 15.875

STID=47 CYID=165 fcid=0025 name=TVA GALLATIN FOSSIL PLANT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

47 165 0025 S

- 01 001 99 10100212 SO2 13.91 14.54 1.454 0.0 0.900 SCRUBBER Scrubber added by LADCO

47 165 0025 S

- 01 002 99 10100212 SO2 14.87 15.56 1.556 0.0 0.900 SCRUBBER Scrubber added by LADCO

47 165 0025 S

- 02 003 99 10100212 SO2 16.33 17.08 1.708 0.0 0.900 SCRUBBER Scrubber added by LADCO

47 165 0025 S

- 02 004 99 10100212 SO2 20.39 21.32 2.132 0.0 0.900 SCRUBBER Scrubber added by LADCO

--- - -------- -------- ---------- fcid 65.49 68.50 6.850

cyid 65.49 68.50 6.850

stid 488.04 510.46 51.046

STID=54 CYID=39 fcid=0006 name=APPALACHIAN POWER

- KANAWHA RIVER PLANT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

54 39 0006 012 001 99 10100202 SO2 19.45 21.18 10.591 0.0 0.500 SCRUBBER Scrubber added by LADCO

54 39 0006 012 002 99 10100202 SO2 20.94 22.80 11.399 0.0 0.500 SCRUBBER Scrubber added by LADCO

---- -------- -------- --- ------- fcid 40.39 43.98 21.990

cyid 40.39 43.98 21.990

STID=54 CYID=51 fcid=0005 name=OHIO POWER

- MITCHELL PLANT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

54 51 0005 012 001 99 10100202 SO2 17.77 19.36 1.936 0.0 0.900 SCRUBBER Scrubber added by LADCO

54 51 0005 012 002 99 10100202 SO2 5.69 6.19 0.619 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 23.46 25.55 2.555

STID=54 CYID=51 fcid=0006 name=OHIO POWER

- KAMMER PLANT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

54 51 0006 013 001 99 10100203 SO2 47.06 51.25 5.125 0.0 0.900 SCRUBBER Scrubber added by LADCO

54 51 0006 013 002 99 10100203 SO2 47.66 51.90 5.190 0.0 0.900 SCRUBBER Scrubber added by LADCO

54 51 0006 013 003 99 10100203 SO2 41.94 45.67 4.567 0.0 0.900 SCRUBBER Scrubber added by LADCO---- -------- -------- ----------

fcid 136.67 148.82 14.882

cyid 160.13 174.37 17.437

STID=54 CYID=53 fcid=0001 name=APPALACHIAN POWER CO.

- PHILIP SPORN PLANT

Base Yr Grown Controlled Base Year Future Year

STID C YID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

54 53 0001 014 001 99 10100202 SO2 18.6 5 20.31 2.031 0.0 0.900 SCRUBBER Scrubber added by LADCO

54 53 0001 014 002 99 10100202 SO2 15.87 17.28 1.728 0.0 0.900 SCRUBBER Scrubber added by LADCO

54 53 0001 014 003 99 10100202 SO2 21.46 23.36 2.336 0.0 0.900 SCRUBBER Scrubber added by LADCO

54 53 0001 014 004 99 10100202 SO2 20.53 22.36 2.236 0.0 0.900 SCRUBBER Scrubber added by LADCO

54 53 0001 005 005 99 10100202 SO2 46.82 50.98 5.098 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 123.33 134.30 13.430

STID=54 CYID=53 fcid=0009 name=APPALACHIAN POWER

- MOUNTAINEER PLANT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

54 53 0009 001 001 99 10100202 SO2 11.20 12.19 1.219 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- --- ----- -------- ---------- cyid 134.53 146.49 14.649

STID=54 CYID=79 fcid=0006 name=APPALACH IAN POWER

- JOHN E AMOS PLANT

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

54 79 0006 012 001 99 10100202 SO2 79.63 86.72 8.672 0.0 0.900 SCRUBBER Scrubber added by LADCO

54 79 0006 012 002 99 10100202 SO2 100.33 109.26 10.926 0.0 0.900 SCRUBBER Scrubber added by LADCO

54 79 0006 003 003 99 10100202 SO2 139.38 151.77 15.177 0.0 0.900 SCRUBBER Scrubber added by LADCO

---- -------- -------- ---------- fcid 319.35 347.75 34.775

cyid 319.35 347.75 34.775

stid 654.39 712.59 88.851

STID=55 CYID=79 fcid=241007690 name=WIS ELECTRIC POWER OAK CREEK STATION

Base Yr Grown Controlled Base Year Future Year

STID CYID fcid stkid dvid prid scc polid Tons/Day Tons/Day Tons/Day Control EF Control EF ctrltype ctrldes

55 79 241007690 S13 B25 01 10100202 SO2 12.75 14.48 3.475 0.0 0.760 SCRUBBER Scrubber added by LADCO

55 79 241007690 S13 B26 01 10100202 SO2 8.68 9.85 2.462 0.0 0.750 SCR UBBER Scrubber added by LADCO

55 79 241007690 S14 B27 01 10100212 SO2 10.97 12.45 2.864 0.0 0.770 SCRUBBER Scrubber added by LADCO

55 79 241007690 S14 B28 01 10100212 SO2 11.28 12.81 2.945 0.0 0.770 SCRUBBER Scrubber added by LADCO

---- -------- -------- ------ ---- fcid 43.68 49.59 11.746

cyid 43.68 49.59 11.746

stid 43.68 49.59 11.746

======== ======== ==========

3099.41 3 381.52 400.481

# Appendix II

Scenario C Controls (CAMD List)

### 1 

## NOx Controls (SCRs , 2007

– 2013) )

Plant Name UniqueID_Final State Name County Capacity

MW On Line

Year SCR

Online

Year

Chesterfield 3797_B_4 Virginia Chesterfield 166 1960 2013

Chesterfield 3797_B_5 Virginia Chesterfield 310 1964 2012

Scherer 6257_B_3 Georgia Monroe 875 1987 2011

Chesterfield 3797_B_6 Virginia Chesterfield 658 1969 2011

Sandow No 4 6648_B_4 Texas Milam 545 1981 2011

Beech Hollow Power Project 82704_B_1 Pennsylvania Washington 272 2011 2011

Longview Power 82702_B_1 West Virginia Monongalia 695 201 1 2011

Cliffside 2721_B_6 North Carolina Cleveland 800 2011 2011

AES Westover 2526_B_11 New York Broome 22 1943 2010

AES Westover 2526_B_12 New York Broome 22 1943 2010

AES Westover 2526_B_13 New York Broome 84 1951 2010

Iatan 2 6065_B_2 Missouri Plat te 850 2010 2010

Southwest 6195_B_2 Missouri Greene 300 2010 2010

Trimble Station (LGE) 6071_B_2 Kentucky Trimble 732 2010 2010

Elm Road Generating Station 56068_B_2 Wisconsin Milwaukee 615 2010 2010

Clay Boswell 1893_B_3 Minnesota Itasca 350 1973 2009

Asheville 2706_B_2 North Carolina Buncombe 184 1971 2009

Conesville 2840_B_4 Ohio Coshocton 780 1973 2009

Marshall 2727_B_3 North Carolina Catawba 657 1969 2009

St Johns River Power Park 207_B_1 Florida Duval 626 1987 2009

Ghent 1356_B_2 Kentucky Car roll 469 1977 2009

Chalk Point LLC 1571_B_1 Maryland Prince George's 341 1964 2009

Chalk Point LLC 1571_B_2 Maryland Prince George's 342 1965 2009

San Juan 2451_B_2 New Mexico San Juan 320 1973 2009

Big Bend 645_B_BB01 Florida Hillsborough 411 1970 200 9

Big Bend 645_B_BB02 Florida Hillsborough 391 1973 2009

Big Bend 645_B_BB03 Florida Hillsborough 414 1976 2009

Nebraska City Unit 2 6096_B_2 Nebraska Otoe 663 2009 2009

Cross 130_B_4 South Carolina Berkeley 652 2009 2009

Springerville 8223_B_4 Arizon a Apache 400 2009 2009

Sandow 5 82010_B_5 Texas Milam 600 2009 2009

Oak Grove 82011_B_1 Texas Robertson 800 2009 2009

Oak Grove 82011_B_2 Texas Robertson 800 2009 2009

TS Power Plant 82013_B_1 Nevada Eureka 200 2009 2009

Plum Point Energy 82014_B_1 Ar kansas Mississippi 665 2009 2009

Comanche 470_B_3 Colorado Pueblo 750 2009 2009

Elm Road Generating Station 56068_B_1 Wisconsin Milwaukee 615 2009 2009

Two Elk Generating Station 55360_B_1 Wyoming Campbell 300 2009 2009

J K Spruce 7097_B_BLR2 Texas Bex ar 750 2009 2009

Dallman 963_B_34 Illinois Sangamon 200 2009 2009

AES Greenidge LLC 2527_B_4 New York Yates 27 1950 2008

AES Greenidge LLC 2527_B_5 New York Yates 27 1950 2008

AES Greenidge LLC 2527_B_6 New York Yates 106 1953 2008

Charles R Lowman 56 _B_2 Alabama Washington 238 1979 2008

Charles R Lowman 56_B_3 Alabama Washington 238 1980 2008

Barry 3_B_5 Alabama Mobile 750 1971 2008

St Johns River Power Park 207_B_2 Florida Duval 626 1988 2008

Morgantown Generating Plant 1573_B_2 Maryland Charles 620 1971 2008

### 2

Bailly 995_B_7 Indiana Porter 160 1962 2008

San Juan 2451_B_1 New Mexico San Juan 322 1976 2008

San Juan 2451_B_3 New Mexico San Juan 495 1979 2008

Weston 4078_B_4 Wisconsin Marathon 519 2008 2008

AES Deepwater 10670_B_AAB001 Texas Harri s 140 1986 2007

La Cygne 1241_B_1 Kansas Linn 724 1973 2007

Morgantown Generating Plant 1573_B_1 Maryland Charles 624 1970 2007

PSEG Hudson Generating Station 2403_B_2 New Jersey Hudson 583 1967 2007

San Juan 2451_B_4 New Mexico San Juan 506 1982 2007

Big Bend 645_B_BB04 Florida Hillsborough 457 1985 2007

Cross 130_B_3 South Carolina Berkeley 620 2007 2007

Wygen II 55479_B_4 Wyoming Campbell 90 2007 2007

Council Bluffs 1082_B_4 Iowa Pottawattamie 790 2007 2007



## SO2 Controls (FGDs, 2007

– 2012)

Plan t Name UniqueID_Final State Name County Capacity

MW On Line

Year Scrubber

Online

Year

James H Miller Jr 6002_B_1 Alabama Jefferson 684 1978 2011

James H Miller Jr 6002_B_2 Alabama Jefferson 687 1985 2011

James H Miller Jr 6002_B_3 Alabama Jefferson 687 1989 2011

James H Miller Jr 6002_B_4 Alabama Jefferson 688 1991 2011

Cape Fear 2708_B_5 North Carolina Chatham 143 1956 2011

Baldwin Energy Complex 889_B_1 Illinois Randolph 624 1970 2011

Baldwin Energy Complex 889_B_2 Illinois Randolph 629 1973 2011

Baldwin Energy Complex 889_B_3 Illinois Randolph 629 1975 2011

Scherer 6257_B_3 Georgia Monroe 875 1987 2011

Milton R Young 2823_B_B1 North Dakota Oliver 250 1970 2011

W H Sammis 2866_B_6 Ohio Jefferson 630 1969 2011

W H Sammis 2866_B_7 Ohio Jefferson 630 1971 2011

PSEG Hudson Generating Station 2403_B_2 New Jersey Hudson 583 1967 2011

John Sevier 3405_B_1 Tennessee Hawkins 176 1955 2011

John Sevier 3405_B_2 Tennessee Hawkins 176 1955 2011

John Sevier 3405_B_3 Tennessee Hawkins 176 1956 2011

John S evier 3405_B_4 Tennessee Hawkins 176 1957 2011

Beech Hollow Power Project 82704_B_1 Pennsylvania Washington 272 2011 2011

Longview Power 82702_B_1 West Virginia Monongalia 695 2011 2011

Cliffside 2721_B_6 North Carolina Cleveland 800 2011 2011

AES Gree nidge LLC 2527_B_4 New York Yates 27 1950 2010

AES Greenidge LLC 2527_B_5 New York Yates 27 1950 2010

Barry 3_B_5 Alabama Mobile 750 1971 2010

E C Gaston 26_B_5 Alabama Shelby 861 1974 2010

Warrick 6705_B_4 Indiana Warrick 300 1970 2010

Coffeen 861_B_ 01 Illinois Montgomery 340 1965 2010

Coffeen 861_B_02 Illinois Montgomery 560 1972 2010

Cardinal 2828_B_3 Ohio Jefferson 630 1977 2010

Brandon Shores 602_B_1 Maryland Anne Arundel 643 1984 2010

Brandon Shores 602_B_2 Maryland Anne Arundel 643 1991 2010

Monroe 1733_B_4 Michigan Monroe 775 1974 2010

Cliffside 2721_B_5 North Carolina Cleveland 550 1972 2010

Crystal River 628_B_4 Florida Citrus 720 1982 2010

Bowen 703_B_1BLR Georgia Bartow 713 1971 2010

### 3

Crist 641_B_6 Florida Escambia 302 1970 2010

Cri st 641_B_7 Florida Escambia 477 1973 2010

Clifty Creek 983_B_1 Indiana Jefferson 217 1955 2010

Clifty Creek 983_B_2 Indiana Jefferson 217 1955 2010

Clifty Creek 983_B_3 Indiana Jefferson 217 1955 2010

Clifty Creek 983_B_4 Indiana Jefferson 217 1955 201 0

Clifty Creek 983_B_5 Indiana Jefferson 217 1955 2010

Clifty Creek 983_B_6 Indiana Jefferson 217 1956 2010

Chalk Point LLC 1571_B_1 Maryland Prince George's 341 1964 2010

Chalk Point LLC 1571_B_2 Maryland Prince George's 342 1965 2010

Dickerson 1572_ B_1 Maryland Montgomery 182 1959 2010

Dickerson 1572_B_2 Maryland Montgomery 182 1960 2010

Dickerson 1572_B_3 Maryland Montgomery 182 1962 2010

R E Burger 2864_B_7 Ohio Belmont 156 1955 2010

R E Burger 2864_B_8 Ohio Belmont 156 1955 2010

Kyger Creek 2 876_B_1 Ohio Gallia 217 1955 2010

Kyger Creek 2876_B_2 Ohio Gallia 217 1955 2010

Kyger Creek 2876_B_3 Ohio Gallia 217 1955 2010

Kyger Creek 2876_B_4 Ohio Gallia 217 1955 2010

Kyger Creek 2876_B_5 Ohio Gallia 217 1955 2010

Cheswick 8226_B_1 Pennsylvani a Allegheny 580 1970 2010

PSEG Mercer Generating Station 2408_B_1 New Jersey Mercer 315 1960 2010

PSEG Mercer Generating Station 2408_B_2 New Jersey Mercer 310 1961 2010

Silver Lake 2008_B_4 Minnesota Olmsted 61 1969 2010

Kingston 3407_B_1 Tennessee Ro ane 135 1954 2010

Kingston 3407_B_2 Tennessee Roane 135 1954 2010

Kingston 3407_B_3 Tennessee Roane 135 1954 2010

Kingston 3407_B_4 Tennessee Roane 135 1954 2010

Kingston 3407_B_5 Tennessee Roane 177 1955 2010

Kingston 3407_B_6 Tennessee Roane 177 195 5 2010

Kingston 3407_B_7 Tennessee Roane 177 1955 2010

Kingston 3407_B_8 Tennessee Roane 177 1955 2010

Kingston 3407_B_9 Tennessee Roane 178 1955 2010

Sioux 2107_B_1 Missouri St. Charles 497 1967 2010

Sioux 2107_B_2 Missouri St. Charles 497 1968 2010

Chesterfield 3797_B_5 Virginia Chesterfield 310 1964 2010

Yorktown 3809_B_1 Virginia York 159 1957 2010

AES Westover 2526_B_11 New York Broome 22 1943 2010

AES Westover 2526_B_12 New York Broome 22 1943 2010

AES Westover 2526_B_13 New York Broome 84 1 951 2010

Iatan 2 6065_B_2 Missouri Platte 850 2010 2010

Southwest 6195_B_2 Missouri Greene 300 2010 2010

Trimble Station (LGE) 6071_B_2 Kentucky Trimble 732 2010 2010

Elm Road Generating Station 56068_B_2 Wisconsin Milwaukee 615 2010 2010

Cholla 113_B _3 Arizona Navajo 271 1980 2009

Mayo 6250_B_1A North Carolina Person 362 1983 2009

Mayo 6250_B_1B North Carolina Person 362 1983 2009

Conesville 2840_B_4 Ohio Coshocton 780 1973 2009

G G Allen 2718_B_1 North Carolina Gaston 162 1957 2009

G G Allen 271 8_B_2 North Carolina Gaston 162 1957 2009

G G Allen 2718_B_3 North Carolina Gaston 260 1959 2009

### 4

G G Allen 2718_B_4 North Carolina Gaston 275 1960 2009

G G Allen 2718_B_5 North Carolina Gaston 265 1961 2009

H L Spurlock 6041_B_1 Kentucky Mason 315 1977 2009

Crystal River 628_B_5 Florida Citrus 717 1984 2009

Deerhaven Generating Station 663_B_B2 Florida Alachua 228 1981 2009

Bowen 703_B_2BLR Georgia Bartow 718 1972 2009

Wansley 6052_B_2 Georgia Heard 892 1978 2009

E W Brown 1355_B_1 Kentucky Mercer 94 1957 2009

E W Brown 1355_B_2 Kentucky Mercer 160 1963 2009

E W Brown 1355_B_3 Kentucky Mercer 422 1971 2009

Ghent 1356_B_2 Kentucky Carroll 469 1977 2009

Fayette Power Project 6179_B_1 Texas Fayette 598 1979 2009

Fayette Power Project 6179_B_2 Texa s Fayette 598 1980 2009

Morgantown Generating Plant 1573_B_1 Maryland Charles 624 1970 2009

Morgantown Generating Plant 1573_B_2 Maryland Charles 620 1971 2009

PPL Brunner Island 3140_B_1 Pennsylvania York 321 1961 2009

PPL Brunner Island 3140_B_2 Penn sylvania York 378 1965 2009

Keystone 3136_B_1 Pennsylvania Armstrong 850 1967 2009

Keystone 3136_B_2 Pennsylvania Armstrong 850 1968 2009

Bull Run 3396_B_1 Tennessee Anderson 881 1967 2009

Bay Shore 2878_B_4 Ohio Lucas 215 1968 2009

Hatfields Ferry Po wer Station 3179_B_1 Pennsylvania Greene 530 1969 2009

Hatfields Ferry Power Station 3179_B_2 Pennsylvania Greene 530 1970 2009

Hatfields Ferry Power Station 3179_B_3 Pennsylvania Greene 530 1971 2009

Nebraska City Unit 2 6096_B_2 Nebraska Otoe 663 2009 2009

Cross 130_B_4 South Carolina Berkeley 652 2009 2009

Springerville 8223_B_4 Arizona Apache 400 2009 2009

Sandow 5 82010_B_5 Texas Milam 600 2009 2009

Oak Grove 82011_B_1 Texas Robertson 800 2009 2009

Oak Grove 82011_B_2 Texas Robertson 800 2009 2 009

TS Power Plant 82013_B_1 Nevada Eureka 200 2009 2009

Plum Point Energy 82014_B_1 Arkansas Mississippi 665 2009 2009

Comanche 470_B_3 Colorado Pueblo 750 2009 2009

Elm Road Generating Station 56068_B_1 Wisconsin Milwaukee 615 2009 2009

Two Elk Gene rating Station 55360_B_1 Wyoming Campbell 300 2009 2009

J K Spruce 7097_B_BLR2 Texas Bexar 750 2009 2009

Dallman 963_B_34 Illinois Sangamon 200 2009 2009

Charles R Lowman 56_B_1 Alabama Washington 86 1969 2008

John E Amos 3935_B_1 West Virginia Putnam 800 1971 2008

John E Amos 3935_B_2 West Virginia Putnam 800 1972 2008

Cholla 113_B_4 Arizona Navajo 380 1981 2008

Roxboro 2712_B_1 North Carolina Person 369 1966 2008

Roxboro 2712_B_3A North Carolina Person 341 1973 2008

Roxboro 2712_B_3B North Caroli na Person 341 1973 2008

Miami Fort 2832_B_7 Ohio Hamilton 500 1975 2008

Miami Fort 2832_B_8 Ohio Hamilton 500 1978 2008

Cogentrix Virginia Leasing Corp 10071_B_2A Virginia Portsmouth 19 1988 2008

Cogentrix Virginia Leasing Corp 10071_B_2B Virginia Port smouth 19 1988 2008

Cogentrix Virginia Leasing Corp 10071_B_2C Virginia Portsmouth 19 1988 2008

J M Stuart 2850_B_1 Ohio Adams 585 1971 2008

J M Stuart 2850_B_2 Ohio Adams 597 1970 2008

### 5

J M Stuart 2850_B_3 Ohio Adams 597 1972 2008

J M Stuart 2850_B_4 Ohio Adams 597 1974 2008

Monroe 1733_B_3 Michigan Monroe 795 1973 2008

Belews Creek 8042_B_1 North Carolina Stokes 1,115 1974 2008

Belews Creek 8042_B_2 North Carolina Stokes 1,115 1975 2008

Bowen 703_B_3BLR Georgia Bartow 902 1974 2008

Bowen 703_B_4B LR Georgia Bartow 929 1975 2008

Hammond 708_B_1 Georgia Floyd 112 1954 2008

Hammond 708_B_2 Georgia Floyd 112 1954 2008

Hammond 708_B_3 Georgia Floyd 112 1955 2008

Hammond 708_B_4 Georgia Floyd 510 1970 2008

Wansley 6052_B_1 Georgia Heard 891 1976 200 8

Harding Street 990_B_70 Indiana Marion 435 1973 2008

Cogentrix Hopewell 10377_B_1A Virginia Hopewell (city) 18 1987 2008

Cogentrix Hopewell 10377_B_1B Virginia Hopewell (city) 18 1987 2008

Cogentrix Hopewell 10377_B_1C Virginia Hopewell (city) 18 198 7 2008

Ghent 1356_B_4 Kentucky Carroll 478 1984 2008

Council Bluffs 1082_B_3 Iowa Pottawattamie 690 1978 2008

PPL Brunner Island 3140_B_3 Pennsylvania York 749 1969 2008

PPL Montour 3149_B_1 Pennsylvania Montour 774 1972 2008

PPL Montour 3149_B_2 Penn sylvania Montour 766 1973 2008

Comanche 470_B_1 Colorado Pueblo 366 1973 2008

Comanche 470_B_2 Colorado Pueblo 370 1975 2008

Cayuga 1001_B_2 Indiana VermilIon 473 1972 2008

Winyah 6249_B_1 South Carolina Georgetown 295 1975 2008

Winyah 6249_B_2 South Carolina Georgetown 295 1977 2008

Winyah 6249_B_3 South Carolina Georgetown 295 1980 2008

Chesterfield 3797_B_6 Virginia Chesterfield 658 1969 2008

Brayton Point 1619_B_1 Massachusetts Bristo 243 1963 2008

Brayton Point 1619_B_2 Massachusetts Bristo 24 4 1964 2008

Weston 4078_B_4 Wisconsin Marathon 519 2008 2008

Gorgas 8_B_10 Alabama Walker 690 1972 2007

Gorgas 8_B_8 Alabama Walker 165 1956 2007

Gorgas 8_B_9 Alabama Walker 175 1958 2007

John E Amos 3935_B_3 West Virginia Putnam 1,300 1973 2007

Moun taineer 6264_B_1 West Virginia Mason 1,300 1980 2007

Cardinal 2828_B_1 Ohio Jefferson 600 1967 2007

Cardinal 2828_B_2 Ohio Jefferson 600 1967 2007

Roxboro 2712_B_2 North Carolina Person 639 1968 2007

Roxboro 2712_B_4A North Carolina Person 343 1980 200 7

Roxboro 2712_B_4B North Carolina Person 343 1980 2007

Cogentrix Virginia Leasing Corp 10071_B_1A Virginia Portsmouth 19 1988 2007

Cogentrix Virginia Leasing Corp 10071_B_1B Virginia Portsmouth 19 1988 2007

Cogentrix Virginia Leasing Corp 10071_B_1C V irginia Portsmouth 19 1988 2007

Killen Station 6031_B_2 Ohio Adams 615 1982 2007

Marshall 2727_B_2 North Carolina Catawba 378 1966 2007

Marshall 2727_B_3 North Carolina Catawba 657 1969 2007

Cogentrix Hopewell 10377_B_2A Virginia Hopewell (city) 18 198 7 2007

Cogentrix Hopewell 10377_B_2B Virginia Hopewell (city) 18 1987 2007

Cogentrix Hopewell 10377_B_2C Virginia Hopewell (city) 18 1987 2007

Ghent 1356_B_3 Kentucky Carroll 478 1981 2007

### 6

Louisa 6664_B_101 Iowa Louisa 700 1983 2007

Allen S King 1915_ B_1 Minnesota Washington 571 1968 2007

Mitchell 3948_B_1 West Virginia Marshall 800 1971 2007

Gibson 6113_B_1 Indiana Gibson 630 1975 2007

Gibson 6113_B_2 Indiana Gibson 628 1975 2007

Winyah 6249_B_4 South Carolina Georgetown 270 1981 2007

Pleasant Pr airie 6170_B_2 Wisconsin Kenosha 617 1985 2007

Cross 130_B_3 South Carolina Berkeley 620 2007 2007

Wygen II 55479_B_4 Wyoming Campbell 90 2007 2007

Council Bluffs 1082_B_4 Iowa Pottawattamie 790 2007 2007



## Assumed BART Facilities and Units

State County Fac ID Facilit y Name Unit ID

MI Bay B2840 CE

- KARN/WEADOCK EU00036

MI Bay B2840 CE

- KARN/WEADOCK EU00037

MI Eaton B4001 LAN. BW&L ERICKSON EU00007

MI Houghton B6553 UP POWER CO / PORTAGE EU00008

MI Huron B2815 DTE

- HARBOR BEACH EU00009

MI Ingham B2647 LAN. BW&L Eckert RG00023

MI Ingham B2647 LAN. BW&L Eckert RG00023

MI Ingham B2647 LAN. BW&L Eckert RG00023

MI Ingham B2647 LAN. BW&L Moores Park RG00021

MI Marquette B4261 WE

- ENERGIES EU00029

MI Marquette B4261 WE

- ENERGIES EU00030

MI Marq uette B4261 WE

- ENERGIES EU00031

MI Marquette B4261 WE

- ENERGIES EU00032

MI Marquette B4261 WE

- ENERGIES EU00033

MI Monroe B2816 DTE

- MONROE EU00062

MI Monroe B2816 DTE

- MONROE EU00068

MI Monroe B2816 DTE

- MONROE EU00063

MI Monroe B2816 DTE

- MONROE EU00064

MI Ottawa B2835 CE

– CAMPBELL EU00062

MI Ottawa B2835 CE

– CAMPBELL EU00061

MI Saint Clair B2796 DTE

- ST. CLAIR / BELLE RIVER EU00111

MI Saint Clair B6145 DTE

– GREENWOOD EU00009

MI Wayne B2132 WYANDOTTE EU00036

MI Wayne B2185 DET ROIT PLD, MISTERSKY EU00014

MI Wayne B2811 DTE

– TRENTON EU00035

OH Lake 0243160009 CEI., EASTLAKE PLANT B005

OH 0247030013 Orion Power Midwest B012

OH 0285010188 Dept of Public Utilities, City of Orrville B001

OH 0285010188 Dept of Public Utilities, City of Orrville B004

OH 0448020006 Toledo Edison Co., Bay Shore B003

OH 0448020006 Toledo Edison Co., Bay Shor e B004

OH 0616000000 Conesville Power Plant B003

OH 0616000000 Conesville Power Plant B004

OH 0616000000 Conesville Power Pl ant B007

OH 0641050002 Cardinal Power Pl ant B001

OH 0641050002 Cardinal Power Pl ant B002

### 7

OH 0641050002 Cardinal Power Plant B003

OH 0641050002 Cardinal Power Pl ant B004

OH 0641050002 Cardinal Power Pl ant B008

OH 0641050002 Cardinal Power P l ant B009

OH 0641050002 Cardinal Power Plant B009

OH Jefferson 0641160017 W. H. SAMMIS PLANT B011

OH Jefferson 0641160017 W. H. SAMMIS PLANT B012

OH Jefferson 0641160017 W. H. SAMMIS PLANT B013

OH 0684000000 Muskingum River Power Plant B006

OH Ada ms 0701000007 DP&L, J.M. Stuart Generating Station B001

OH Adams 0701000007 DP&L, J.M. Stuart Generating Station B002

OH Adams 0701000007 DP&L, J.M. Stuart Generating Station B003

OH Adams 0701000007 DP&L, J.M. Stuart Generating Station B004

OH 070100 0060 DP&L, Killen Station B001

OH 1409040243 City of Hamilton Dept of Public Utilities B002

OH 1409040243 City of Hamilton Dept of Public Utilities B008

OH 1409040243 City of Hamilton Dept of Public Utilities B009

OH 1413100008 CG&E W. C. BECKJORD B005

OH 1413100008 CG&E W. C. BECKJORD B006

OH 1431350093 CG&E MIAMI FORT STATION B015

IL Peoria 856 Ameren

– Edwards 2

IL Sangamon 963 CWLP

– Dallman 31

IL Sangamon 963 CWLP

– Dallman 32

IL Christian 876 Dominion

– Kincaid 1

IL Christian 87 6 Dominion

– Kincaid 2

WI COLUMBIA 111003090 Alliant En ergy

- Columbia Generating B20

WI COLUMBIA 111003090 Alliant En ergy

- Columbia Generating B21

WI COLUMBIA 111003090 Alliant En ergy

- Columbia Generating B22

WI GRANT 122014530 Alliant Energy, Nelso n Dewey B22 (unit 2)

WI MILWAUKEE 241007690 We Energies

- Oak Creek Station B26 (Unit 6)

WI MILWAUKEE 241007690 We Energies

- Oak Creek Station B27 (Unit 7)

WI MILWAUKEE 241007690 We Energies

- Oak Creek Station B28

WI MILWAUKEE 241007800 We Energies

- Valley Station B21

WI MILWAUKEE 241007800 We Energies

- Valley Station B23

WI MILWAUKEE 241007800 We Energies

- Valley Station B24

WI BRO WN 405031990 WI Public Service Corp

- JP Pulliam B27 (unit 8)

WI SHEBOYGAN 460033090 WP & L Allian t Energy

– Edgewater B24

W I BUFFALO 606034110 Dairyland Power Coop Alma Station

(J.P. Madgett boilers) B25 (+B26)

WI BUFFALO 606034110 Dairyland Power Coop Alma Station B27

WI VERNON 663020930 Dairyland Power Coop Genoa Station B20

WI VERNON 663020930 Dairyland Power Coop Genoa Station B25

IN Porter 995 Bailly 7

IN Porter 995 Bailly 8

IN Vermillion 1001 Cayuga 1

IN Vermillion 1001 Cayuga 2

IN Montgomery 1024 Crawfordsville 6

IN Warrick 1012 Culley 2

### 8

IN Warrick 1012 Culley 3

IN Gibson 6113 Gibson 1

IN Gibson 6113 Gi bson 2

IN Cass 1032 Logansport 6

IN Sullivan 6213 Merom 1

IN Sullivan 6213 Merom 2

IN LaPorte 997 Michigan City 12

IN Lake 996 Mitchell 11

IN Pike 994 Petersburg 1

IN Pike 994 Petersburg 2

IN Pike 994 Petersburg 3

IN Pike 1043 Ratts 1

IN Pike 104 3 Ratts 2

IN Wayne 7335 RPL 2

IN Jasper 6085 Schahfer 14

IN Jasper 6085 Schahfer 15

IN Lake 981 Stateline 4

IN Marion 990 Stout 70

IN Dearborn 988 Tanners Creek 4

IN Vigo 1010 Wabash River 6

IN Warrick 6705 Warrick 4

IA 07

- 02

- 005 Cedar Fal ls Utilities Unit #7 (EU10.1A)

IA 88

- 01

- 004 Central Iowa Power Cooperative

(CIPCO)

– Summit Lake Station Comb Turbines (EU

1 /1G , EU2 /2 G)

IA 70

- 08

- 003 Central Iowa Power Cooperative

(CIPCO)

– Fair Station Unit # 2 (EU 2 &

EU 2G)

IA 85

- 01

- 006 City of Am es

- Steam Electric Plant Boiler #7 (EU 2)

IA 29

- 01

- 013 Interstate Power & Light

- Burlington Main Plant Boiler.

IA 03

- 03

- 001 Interstate Power & Light

- Lansing Boiler #4. Sixteen

units in total.

IA 23

- 01

- 014 Interstate Power & Light

- ML Kapp Boiler #2. Six units

in total.

IA 57

- 01

- 042 Interstate Power & Light

- Prairie Creek Boiler #4. Fourteen

units in total.

IA 78

- 01

- 026 MidAmerican Energy Co

- Council Bluffs Boiler #3 (EU003)

IA 97

- 04

- 010 MidAmerican Energy Co

- Neal North Boilers #1

- 3

(EU0 01

- EU003)

IA 97

- 04

- 011 MidAmerican Energy Co

- Neal South Boiler #4 (EU003)

IA 70

- 01

- 011 Muscatine Power and Water Boiler #8

IA 63

- 02

- 005 Pella Municipal Power Plant Boilers #6

- 8

MN 2709900001 Austin Utilities NE Power Station EU001

MN 27 13700027 Hibbing Public Utilities EU003

MN 2703100001 MN Power, Taconite Harbor EU003

MN 2706100004 MN Power, Boswell Energy Center EU003

MN 2701500010 New Ulm Public Utilities EU003

- Boiler 4

MN 2711100002 Otter Tail Power Hoot Lake EU003

MN 27 10900011 Rochester Public Utilities, Silver Lake EU003

MN 2710900011 Rochester Public Utilities, Silver Lake EU004

MN 2713700028 Virginia Public Utilities EU003

- Boiler 9

MN 2714100004 Xcel Energy, Sherco EU001, EU002

MN 2716300005 Xcel Energy, Allen S King EU001

- Boiler 1

### 9

MN 2705300015 Xcel Energy, Riverside EU003

- Boiler 8

MO 290710003 Ameren

- Labadie B1, B2, B3, B4

MO 291830001 Ameren

- Sioux B1, B2

MO 290990016 Ameren

- Rush Island B1, B2

MO 290950031 Auila

- Sibley B3

- 5C

MO 291430004 Assoc. Electric

- New Madrid B1(EP

- 01), B2

(EP

- 02)

MO 290770039 City Utilities Springfield

- Southwest B1 (E09)

MO 290770005 City Utilities Springfield

- James River EO7, EO8

MO 290970001 Empire Distric Electric

- Asbury B7

MO 29083 0001 KC Power and Light

- Montrose EP08

MO 290210004 Aqula

- Lake Road EP06

MO 291750001 Assoc. Electric

- Thomas Hill EP01, EP02

MO 290950021 Trigen

- Kansas City B1A

MO 290190002 City of Columbia Municipal Power Plant EP02

MO 291950010 Marshall Munipal Utilities EP05

MO 290950050 Independence Power & Light

- Blue Valley B3 (EP05)

WV 3943 Fort Martin

WV 6004 Pleasants

WV 3948 Mitchell

WV 3935 Amos

WV 6264 Mountaineer

WV 3944 Harrison

TN 3396 TVA Bull Run

TN 3399 TVA Cumberland

KY 1363 Cane Run

KY 1364 Mill Creek

KY 6041 Spurlock

KY 1384 John Sherman Cooper

KY 1353 Big Sandy

KY 1356 Ghent

KY 1355 Brown

KY 1374 Owensboro Municipal

KY 1372 Henderson Municipal

KY 1378 Paradise

KY 1361 Coleman

KY 1382 Reid/Henderson 2

KY 6639 Green