# 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 

## Section 7. References

Alpine Geophysics, 2007, “Emissions Inventory Assistance: 2005 Base Year Biogenic and Other (non

- LADCO) State Emissions, Preparation and Delivery of Non

- MRPO Emission Files< March 12 , 2007.

AER, 2004, “Analysis of Recent Regional Haze Data”, Atmospheric and Environmental Research, Inc., August 2004.

Baker, K., “Hot Spot Test (Draft), Lake Michigan Air Directors Consortium, March 23, 2005

Baker, K. 2007a, “Ozone Source Apportionment Results for Receptors in Non

- Attainment Counties in the Great Lakes Region”, Lake Michigan Air Directors Consortium, October 2007.

Baker, K., 2007b, “Source Apportionment Results for PM 2. 5 

## and Regional Haze at Receptors in the Great Lakes Region”, Lake M ichigan Air Directors Consortium, November 2007.

Baker, K. and D. Kenski, 2007, “Diagnostic and Operational Evaluation of 2002 and 2005 8

- Hour Ozone to Support Model Attainment Demonstrations”, Lake Michigan Air Directors Consortium, October 2007.

Blanch ard, C.L., and S. Tanenbaum, 2003, “ VOC and NO x 

## Limitation of Ozone Formation at Monitoring Sites in Illinois, Indiana, Michigan, Missouri, Ohio, and Wisconsin, 1998

- 2002 ”

, Report to LADCO

, February 2003.

.

Blanchard, C.L., and S. Tanenbaum, 2004, “ The Eff ects of Changes in Sulfate, Ammonia, and Nitric Acid on Fine PM Composition at Monitoring Sites in Illinois, Indiana, Michigan, Missouri, Ohio, and Wisconsin, 2000

- 20 02”, Report to LADCO, February 2004

.

Blanchard, C.L., and S. Tanenbaum , 2005 a , “Weekday/W eekend Differences in Ambient Concentrations of Primary and Secondary Air Pollutants in Atlanta, Baltimore, Chicago, Dallas

Fort Worth, Denver, Houston, New York, Phoenix, Washington, and Surrounding Areas”, Prepared for National Renewable Energy Laborator y, NREL Project ES04

- 1, July 30, 2005.

Blanchard, C.L., and S. Tanenbaum, 2005 b , “ Analysis of Data from the Midwest Ammonia Monitoring Project ”, Draft Final Technical Memorandum, March 31, 2005.

Breiman, L., J. Friedman, R. Olshen, and C. Stone, 1984, “C lassification and Regression Trees”, Chapman & Hall (1984).

Camalier, L., Cox, W., Dolwick, P., 2007. The effects of meteorology on ozone in urban areas and their use in assessing ozone trends. Atmosp heric Environment 41, 7127

- 7137

Cox, W.M, and S.

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- 434 (1993).

DeBell, L.J., K. Gebhart, J. Hand, W. Malm, M. Pitchford, B. Schichtel, and W. White, 2006, “Spatial and Seasonal P atterns and Temporal Variability of Haze and its Constituents in the United States, Report IV”, Cooperative Institute for Research in the Atmosphere, November
2006

### 115



## DRI, 2005, “Source Apportionment Analysis of Air Quality Monitoring Data: Phase II”, Desert Research Institute. March 2005

EC/R, Incorporated, 2004, “Fire Emissions Inventory Development for the Midwest Regional Planning Organization”, Final Report, September 30, 2004.

EC/R, Incorporated, 2007, “Reasonable Progress for Class I Areas in the Nor thern Midwest

– Factor Analysis”, Draft Final Technical Memorandum, July 18, 2007.

E.H. Pechan, 2004, “LADCO Nonroad Emissions Inventory Project

– Development of Local Data for Construction and Agricultural Equipment”, Final Report, September 10, 2004

E. H. Pechan, 2005, “Development of Updated Growth and Control Factors for Lake Michigan Air Directors Consortium (LADCO)”, Draft Report, December 29, 2005.

E. H. Pechan, 2007, “Development of 2005 Base Year Growth and Control Factors for Lake Michigan Air D irectors Consortium (LADCO)”, Final Report, September 2007.

Environ, 2004, “LADCO Nonroad Emissions Inventory Project for Locomotive, Commercial Marine, and Recreational Marine Emission Sources, Final Report, December 2004.

Environ, 2007 a , “Modeling Week end and Weekday Ozone in Southeast Michigan”, Draft Final Report, Prepared for National Renewable Energy Laboratory, July 30, 2007.

Environ, 2007 b , “LADCO 2005 Locomotive Emissions”, Draft, February 2007.

Environ, 2007 c , “LADCO 2005 Commercial Marine Em issions”, Draft, March 2, 2007.

Environ, 2008 , “LADCO On

- Road Emission Inventory Development Using CONCEPT MV”, January 2008.

.
Hollander, M. and D. Wolfe, 1973, “Nonparametric Statistical Methods”, John Wiley & Sons, New York (1973).

Hopke, P. K., 2005, Analyses of Midwest PM

- Related Measurements, Report to LADCO

, March 2005.

Kenski, D.M., 2004, Quantifying Transboundary Transport of PM 2. 5 

## : A GIS Analysis, Proc. AWMA 97 th 

## Annual Conf., Indianapolis

, June 2004

.
Kenski, D.M., 2007a, “CART Analysis for O zone Trends and Meteorological Similarity”, May 10,
2007.

Kenski, D.M., 2007 b , “ Impact of Missing Data on Worst Days at Midwest Northern Class 1 Areas ”, Donna Kenski

, Midwest RPO

, March 12, 2007 (revised 6/19/07)

.
Kenski, D.M., 2008a, “Updated Cox Trends Analysis”, February 2008.

Kenski, D.M. 2008 b , “Updated CART Analysis for Ozone through 2007”, January 30, 2008.

### 116



## LADCO, 2005, “Meteorological Modeling Performance Summary for Applications to PM 2. 5 

## /Haze/Ozone Modeling Projects”, February 18, 2005

.

LADCO, 2006a, “Base K/Round 4 Strategy Modeling” Emissions”, May 16, 2006

.
LADCO, 2006b, “Base K/Round 4 Modeling: Summary”, August 31, 2006

.
LADCO, 2007a, “Modeling Protocol: 2002 Basecase Technical Details”, October 17, 2007

LADCO, 2007b, “Modeling Protocol : 2005 Basecase Technical Details”, October 19, 2007

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

- Based Ammonia Emissions Model for Agricultural Sources

– Emission Estimates”

MPCA, 2008, “Technical Support Document for the Minnesota State Implementation Plan for Regional Haze”, Draft, Marc h 4, 2008

.

M RPO , 2001, “Principles for Regional Planning”, March 9, 2001.

MRPO, 2007, “Draft List of Class I Areas Located Within (or Impacted by) Midwest RPO States”, June 26, 2007.

MRPO, 200 8 , “Regional Haze in the Upper Midwest: Summary of Technical Information”, Version 2.2, February 22, 2008

.
Millstein, D.E., R.A. Harley, and S.V. Hering, 2007, “Weekly cycles in fine particulate matter”, Atmospheric Environment (2007), doi:10.1016/j.atmosenv.2007.10.010.

Poirot, R. L.; Wishinski, P. R.; Hopke, P. K.; Polissar, A. V.; Comparative Application of Multiple Receptor Methods To Identify Aerosol Sources in Northern Vermont, Environ. Sci. Technol

., 36 (4); 820

- 820 (2002 ).

Sheesley, R.J., J.J. Schauer, E. Bean, and D. Kenski, Trends in Secondary Organic Aer osol at a Remote Site in Michigan’s Upper Peninsula, Env. Sci. and Tech. 38 (24); 6491

- 6500 (Oct 2004).

### 117



## STI, 2004, “Data Processing and Analysis of Aloft Air Quality Data Collected in the Upper Midwest”, Executive Summary, STI

- 903470

- 2568

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.
STI, 2006 a , “Integration of Results for the Upper Midwest Urban Organics Study”, Final Report, STI

- 903520

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STI, 200 8 , “Data Analysis and Source Apportionment of PM 2. 5 

## in Selected Midwestern Cities”, Final Report, STI

- 907018.03

- 3264

- DRF, Sonoma Technology, Inc., February 2008.

EPA, 1999, Regional Haze Regulations, 64 FR 35714, July 1, 1999

.
EPA, 2003, “Guidance for Estimating Natural Visibility Conditions Under the Regional Haze
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

EPA, 200 6 , “Regulatory Impact Analysis of the Revisions to the National Ambient Air Quality Standards”, September 2006 ( http://www.epa.gov/ttn/ecas/ria.html#ria2 007 )

EPA, 2007 a , "Guidance for on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality Goals for Ozone, PM 2. 5 

## , and Regional Haze”, EPA

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- 002, April 2007.

EPA, 2007b, “Regulatory Impact Analysis of the Proposed Revisio ns to the National Ambient Air Quality Standards”, EPA

- 452/R

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- 008, July 2007 ( h ttp://www.epa.gov/ttn/ecas/ria.html#ria2007 )

EPA, 2007c, Clean Air Fine Particle Implementation Rule, 72 FR 2058 6, April 25, 2007

Zhang, R, T. Ramsey, J. Fadel, J. Arogo, Z. Wang, G. Mansell, and H. Xin, 2005, “An Improved Process

- Based Ammonia Emissions Model for Agricultural Sources

– Model Development”

Web Sites: http://www.ladco.org/tech/emis/basem/canada/index.htm

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

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

http://vista.cira.colostate.edu/views/

### 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)