Regional Air Quality Analyses for

Ozone, PM2.5, and Regional Haze: 

Final Technical Support Document

April 25, 2008

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

Table of Contents

Section-Title Page

Executive Summary iii

1.0 Introduction 1

1.1 SIP Requirements 1

1.2 Organization 2

1.3 Technical Work: Overview 3

2.0 Ambient Data Analyses 4

2.1 Ozone 4

2.2 PM2.5 21

2.3 Regional Haze 35

3.0 Air Quality Modeling 46

3.1 Selection of Base Year 46

3.2 Future Years of Interest 46

3.3 Modeling System 47

3.4 Domain/Grid Resolution 47

3.5 Model Inputs: Meteorology 48

3.6 Model Inputs: Emissions 51

3.7 Base Year Modeling Results 60

4.0 Attainment Demonstration for Ozone and PM2.5 71

4.1 Future Year Modeling Results 71

4.2 Supplemental Analyses 82

4.3 Weight of Evidence Determination for Ozone 82

4.4 Weight of Evidence Determination for PM2.5 90

5.0 Reasonable Progress Assessment for Regional Haze 93

5.1 Class I Areas Impacted 93

5.2 Future Year Modeling Results 96

5.3 Weight of Evidence Determination for Regional Haze 104

6.0 Summary 111

7.0 References 114

Appendix I Ozone and PM2.5 Modeling Results

Appendix II Ozone Source Apportionment Modeling Results

Appendix III PM2.5Source Apportionment Modeling Results

Appendix IV Haze Source Apportionment Modeling Results
iii

EXECUTIVE SUMMARY

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

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

(PM2.5) standard (1997 versions). A map of these nonattainment areas is provided in the figure 

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

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

including two in northern Michigan.

Figure i. Current nonattainment counties for ozone (left) and PM2.5 (right)

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

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

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

states, and various contractors. The analyses include preparation of regional emissions 

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

models, and collection and analysis of ambient monitoring data. 

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

problems. Key findings of the analyses include:

Ozone

• Current monitoring data (2005-2007) show about 20 sites in violation of the 8-hour 

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

downward trend over the past 15 years, especially since 2001-2003, due likely to 

federal and state emission control programs.

• Ozone concentrations are strongly influenced by meteorological conditions, with 

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

temperatures.
iv

• Inter- and intra-regional transport of ozone and ozone precursors affects many 

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

far from population or industrial centers. 

PM2.5

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

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

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

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

deployment of the PM2.5 monitoring network in 1999.

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

more complex and less well understood compared to ozone.

• On an annual average basis, PM2.5 chemical composition consists mostly of sulfate, 

nitrate, and organic carbon in similar proportions.

Haze

• Current monitoring data (2000-2004) show visibility levels in the Class I areas in 

northern Michigan are on the order of 22 – 24 deciviews. The goal of EPA’s visibility 

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

Class I areas, by the year 2064.

• Visibility impairment is dominated by sulfate and nitrate.

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

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

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

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

strategies. Model performance for ozone and PM2.5 was found to be generally acceptable.

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

controls would be sufficient to provide for attainment of the standards for ozone and PM2.5 and if 

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

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

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

PM2.5 concentrations and visibility levels.

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

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

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

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

• Modeling suggests that most sites are expected to meet the current 8-hour ozone 

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

and, possibly, in eastern Wisconsin and northeastern Ohio.
v

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

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

and Granite City.

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

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

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

attainment demonstrations for PM2.5.

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

current (2005 – 2007) monitoring data which show significant nonattainment in 

the region (e.g., peak ozone design values on the order of 90 – 93 ppb, and peak 

PM2.5 design values on the order of 16 - 17 ug/m3). It is unlikely that sufficient 

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

attainment at all sites.

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

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

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

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

are achieved, then attainment is likely). If either of these conditions is not met, 

then attainment may be less likely.

• Modeling suggests that the new PM2.5 24-hour standard and the new lower 

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

controls.

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

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

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

and some in the northeastern U.S. Visibility levels in many other Class I areas in 

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

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

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

and non air quality environmental impacts of compliance, and remaining useful 

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

progress goals for regional haze.
1

Section 1.0 Introduction

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

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

of State Implementation Plans (SIPs) for ozone, fine particles (PM2.5 ), and regional haze in the 

States of Illinois, Indiana, Michigan, Ohio, and Wisconsin. The analyses include preparation of 

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

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

ambient monitoring data. 

Two aspects of the analyses should be emphasized. First, a regional, multi-pollutant approach 

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

precursors, emission sources, atmospheric processes, transport influences, and geographic 

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

Furthermore, EPA has consistently encouraged multi-pollutant planning in its rule for the haze 

program (64 FR 35719), and its implementation guidance for ozone (70 FR 71663) and PM2.5

(72 FR 20609). Second, a weight-of-evidence approach was taken in considering the results of 

the various analyses (i.e., two sets of modeling results -- one for a 2002 base year and one for a 

2005 base year -- and ambient data analyses) in order to provide a more robust assessment of 

expected future year air quality.

The report is organized in the following sections. This Introduction provides an overview of 

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

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

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

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

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

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

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

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

study findings are reviewed and summarized in Section 6.

1.1 SIP Requirements

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

the 5-state region, more than 100 counties were designated as nonattainment.

2

 The

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

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

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

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

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

 

1

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

for the regional haze activities of the multi-state organization.

2

 Based on more recent air quality data, many counties in Indiana, Michigan, and Ohio were 

subsequently redesignated as attainment. As of December 31, 2007, there are 53 counties designated 

as nonattainment in the region.
2

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

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

effective on April 5, 2005. SIPs for PM2.5 are due no later than three years from the effective 

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

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

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

December 2007). The applicable attainment date for PM2.5 nonattainment areas is five years 

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

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

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

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

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

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

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

initial 10-year implementation period (i.e., reasonable progress by the year 2018). SIP 

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

determining baseline conditions, determining natural conditions, providing a long-term control 

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

emissions limitations and associated compliance schedule. 

1.2 Organization

LADCO was established by the States of Illinois, Indiana, Michigan, and Wisconsin in 1989. The 

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

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

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

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

update LADCO’s mission and reaffirm the commitment to regional planning). In March 2004, 

Ohio joined LADCO. LADCO consists of a Board of Directors (i.e., the State Air Directors), a 

technical staff, and various workgroups. The main purposes of LADCO are to provide technical 

assessments for and assistance to its member states, and to provide a forum for its member 

states to discuss regional air quality issues. 

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

tribes in Michigan and Wisconsin, EPA, and Federal Land Managers (i.e., National Park

Service, U.S. Fish & Wildlife Agency, and U.S. Forest Service). In October 2000, the States of 

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

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

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

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

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

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

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

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

 

3

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

designated as nonattainment (70 FR 19844, April 15, 2005).

4

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

because the Federal Land Manager determined that visibility is not an air quality related value there.
3

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

matters.

1.3 Technical Work: Overview

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

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

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

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

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

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

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

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

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

Emissions: Baseyear emissions inventories were prepared for 2002 and 2005. States provided 

point source and area source emissions data, and MOBILE6 input files and mobile source 

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

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

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

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

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

the 2010 attainment year for PM2.5 and moderate ozone nonattainment areas), 2012 (planning

to address a 2013 alternative attainment date), and 2018 (first milestone year for regional haze).

Air Quality Analyses: The weight-of-evidence approach relies on data analysis and modeling. 

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

description of the ozone, PM2.5, and regional haze problems) and supplemental information for 

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

especially for PM2.5, these data analyses are a necessary part of the overall technical support.

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

future year strategy analyses to assess candidate control strategies. The analyses were 

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

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

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

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

 

5

 Since 1999, MRPO has received almost $10M in RPO grant funds from USEPA.
4

Section 2.0 Ambient Data Analyses

An extensive network of air quality monitors in the 5-state region provides data for ozone (and 

its precursors), PM2.5 (both total mass and individual chemical species), and visibility. These 

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

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

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

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

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

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

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

transport patterns), precursor sensitivity, and source culpability.

2.1 Ozone

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

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

concentrations above 0.12 ppm is equal to or less than 1.0, averaged over a 3-year period, 

which generally reflects a design value (i.e., the 4th highest daily 1-hour value over a 3-year 

period) less than 0.12 ppm.

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

standard is attained if the 3-year average of the 4th-highest daily maximum 8-hour average 

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

than 0.08 ppm (or 85 ppb). 

Current Conditions: A map of the 8-hour ozone design values at each monitoring site in the 

region for the 3-year period 2005-2007 is shown in Figure 1. The “hotter” colors represent 

higher concentrations, where yellow and orange dots represent sites with design values above 

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

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

Table 1 provides the 4th-highest daily 8-hour ozone values and the associated design values 

since 2001 for several high monitoring sites throughout the region.

 

6

 On March 12, 2008, USEPA further tightened the 8-hour ozone standard to increase public health 

protection and prevent environmental damage from ground-level ozone. USEPA set the primary (health) 

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

hour period.
5

Figure 1. 8-hour ozone design values (2005-2007)
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 sunlight, as well as precursor concentrations (see, for 

example, Figure 2). 

Figure 2. Number of hot days and 8-hour “exceedance” days in 5-state region

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

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

These conditions are often a result of a slow-moving high pressure system to the east of the 

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

section.

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

scales. Regionally, over a multi-day period, somewhat stagnant summertime conditions can 

lead to the build-up in ozone and ozone precursor concentrations over a large spatial area. This

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

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

Figure 3. Example of elevated regional ozone concentrations (June 23 – 25, 2005)

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

hour standard
8

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

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

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

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

hour standard

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

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

day (August 20, 2003 – see Figure 5), the incoming background ozone levels were on the order 

of 80 – 100 ppb and the downwind ozone levels over Lake Michigan were on the order of 100 -

150 ppb (STI, 2004).

Figure 5. Aircraft ozone measurements over Lake Michigan (left) and along upwind boundary 

(right) – August 20, 2003 (Note: aircraft measurements reflect instantaneous values)
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 estimate the impact of these source regions, two simple 

transport-related analyses were performed.

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

peak > 80 ppb) during the period 2002-2006 in western Michigan to characterize general

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

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

areas located to the south-southwest (especially, northeastern Illinois and northwestern Indiana) 

as being upwind on these high ozone days.

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

concentrations

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

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

(Holland and Muskegon) for days in 1999 – 2002 with southwesterly winds - i.e., transport 

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

concentrations by day-of-week, with a line connecting the mean values. The difference 

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

Indiana contributes about 10-15 ppb to downwind ozone levels.
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 affecting many portions of the eastern U.S. The Lake 

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

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

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

affecting downwind receptor areas.

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

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

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

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

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

• Downwind shoreline areas around Lake Michigan are affected by both regional transport 

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

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

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

(transported) ozone.
11

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

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

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

Figure 8 Ozone design value trends in 5-State region

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

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

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

 

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

The improvement in ozone concentrations is also seen in the decrease in the number of sites

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

75

85

95

105

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

65

75

85

95

105

115

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

Design Values

4

th High Values
12

 

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

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

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

Cox Method: This method uses a statistical model to ‘remove’ the annual effect of meteorology 

on ozone (Cox and Chu, 1993). A regression model was fit to the 1997-2007 data to relate daily 

peak 8-hour ozone concentrations to six daily meteorological variables plus seasonal and 

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

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

direction. The model is then used to predict 4th high ozone values. By holding the 

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

meteorology. Presumably, any trend reflects changes in emissions of ozone precursors. 

Figure 11a shows the meteorologically-adjusted 4th high ozone concentrations for several 

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

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

positive effect in improving ozone air quality. 

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

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

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

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

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

th

high model.
14

Chiwaukee, WI Sheboygan, WI

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

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

Indianapolis, IN

Figure 11a. Trends in meteorologically 

adjusted 4th high 8-hour ozone 

concentrations for seven Midwestern sites

(1997 – 2007)
15

Chiwaukee, WI Sheboygan, WI

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

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

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

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

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

Indianapolis, IN

1998 2000 2002 2004 2006

20

40

60

80

Seasonal Average Ozone (ppb)

Adjusted for Weather

Unadjusted for Weather

Figure 11b. Trends in seasonal 8-hour ozone 

concentrations for seven Midwestern sites

(1997 – 2007)
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 with current 8-

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

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

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

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

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

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

analysis.)

For each monitor, regression trees were developed that classify each summer day (MaySeptember) by its meteorological conditions. Similar days are assigned to nodes, which are 

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

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

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

the remaining trend is presumed to be due to trends in precursor emissions or other nonmeteorological influences. Trends over the 13-year period at most sites were found to be 

declining, with the exception of Detroit which showed fairly flat trends. Comparison of the 

average of the high concentration node values for 2001-2003 v. 2005-2007 showed an 

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

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

(Kenski, 2008b). This metric reflects the variability from the 13-year average in the number of 

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

indicates:

• 2002 and 2005 were both above normal, with 2002 tending to be more severe; and

• 2001-2003 and 2005-2007 were both above normal, with no clear pattern in which 

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

2001-2003 values were higher at a few sites, and 2005-2007 values were higher at a 

few sites).

Given the similarity in ozone conduciveness between 2001-2003 and 2005-2007, the 

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

(i.e., emission reductions).

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

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

1997-2007 period, even when meteorological variability is accounted for. The decreases are 

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

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

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

groups (average ozone > 65 ppb) for seven 

Midwestern sites (1995 – 2007)

Note: line represents linear best fit
18

Figure 13. Ozone conduciveness index (and 

number of high ozone days) for seven 

Midwestern site (1995 – 2007)
19

Precursor Sensitivity: Ozone is formed from the reactions of hydrocarbons and nitrogen oxides 

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

and strong sunlight). In areas with high VOC/NOx ratios, typical of rural environments (with low 

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

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

responsive to VOC reductions. 

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

is based on the Smog Production (SP) algorithm (Blanchard, et al., 2003). The “Extent of 

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

Extent Range Precursor Sensitivity

< 0.6 VOC-sensitive

0.6 – 0.8 Transitional

> 0.8 NOx-sensitive

A map of the Extent of Reaction values for high ozone days is provided in Figure 14. As can be 

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

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

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

days were found to be NOx-limited. This analysis suggests that a NOx reduction strategy would 

be effective in reducing ozone levels. Examination of day-of-week concentrations, however,

raises some question about the effectiveness of NOx reductions.

Figure 14. Mean afternoon extent of reaction (1998 – 2002)
20

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

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

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

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

higher concentrations of ozone, although the increase was generally not statistically significant 

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

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

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

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

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

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

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

transport days of interest (i.e., southwesterly flow during the summers of 1999 – 2002), mean 

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

(downwind) western Michigan sites are about 1.5 – 5.2 ppb (3 – 8 %) lower on Sunday 

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

positive, albeit non-linear response to urban area emission reductions.

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

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

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

Michigan ozone, depending upon location and time:
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-sensitive areas).

• At the location of the peak 8-hour ozone downwind of Detroit, ozone was lower on 

weekends than weekdays.

• Ozone benefits (reductions) due to weekend emission changes in Southeast Michigan 

can be transported downwind for hundreds of miles.

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

through Monday because of weekend emission changes in upwind areas.

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

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

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

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

regional NOx reductions are likely to have multi-pollutant benefits (e.g., both lower ozone and 

PM2.5 impacts).

2.2 PM2.5

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

3

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

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

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

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

than or equal to the level of the standard.

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

average). 

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

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

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

sites in violation of the annual PM2.5 standard.

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

several high monitoring sites throughout the region.
22

Figure 16. PM2.5 design values - annual average (top) and 24-hour average (bottom) (2005-2007)
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 24-hour standard at 65 μg/m3, it also adopted the following 

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

Good < 15 ug/m3

Moderate 15-40 μg/m3

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

Unhealthy 65-150 μg/m3

Figure 17 shows the frequency of these AQI categories for major metropolitan areas in the 

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

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

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

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

This variability is discussed further below.

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

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

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

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

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

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

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

concentrations
25

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

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

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

7

.

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

 

7

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

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

emissions effects).
26

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

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

mass and for the individual components of mass. Figure 20 shows trends for PM2.5 based on 

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

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

significant trends and open arrows show trends that are not significant. Region-wide decreases 

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

statistically significant). The average decrease for this set of sites is -0.24 ug/m3/year. 

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

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

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

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

No statistically significant increasing trends were observed.
27

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

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

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

μg/m3. There is a more pronounced weekday/weekend difference at monitoring sites that are 

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

than urban monitors.

Figure 22 Day-of-week variability in PM2.5 (2002-2004)
28

In the Midwest, PM2.5 is made up of mostly ammonium sulfate, ammonium nitrate, and organic 

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

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

Figure 23. Spatial map of PM2.5 chemical composition in the Midwest (2002-2003)

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

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

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

Figure 24. Average regional (lighter shading) v. local (darker shading) of PM2.5 chemical species
29

Figure 25 Seasonal and spatial variability in PM2.5 components
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 formed when sulfuric acid (an oxidation product of sulfur dioxide) and ammonia react in the 

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

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

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

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

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

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

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

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

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

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

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

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

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

from biogenic sources.

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

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

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

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

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

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

• PM2.5 mass is sensitive to reductions in sulfate at all times of the year and all parts of the 

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

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

is generally offset by the sulfate reductions, such that PM2.5 mass decreases.

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

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

significant fraction of PM2.5.

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

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

• Ammonia becomes more limiting as one moves from west to east across the region.

Examination of weekend/weekday difference in PM-nitrate and NOx concentrations in the 

Midwest demonstrate that reductions in local (urban) NOx lead to reductions, albeit nonproportional reductions, in PM-nitrate (Blanchard, 2004). This result is consistent with analyses 

of continuous PM-nitrate from several US cities, including St. Louis (Millstein, et al, 2007). 
31

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

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

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

acid) or moving leftward (reductions in sulfate or ammonia) results in lower PM2.5 values
32

Meteorology: PM2.5 concentrations are not as strongly influenced by meteorology as ozone, but 

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

that are conducive to ozone (hot temperatures, stagnant air masses, and low wind speeds due 

to stationary high pressure systems) also frequently give rise to high PM2.5. In the case of PM,

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

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

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

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

transported from polluted source regions has higher concentrations.

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

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

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

PM2.5 chemical species show noticeable transport influences. Trajectory analyses have 

demonstrated that high PM-sulfate is associated with air masses that traveled through the 

sulfate-rich Ohio River Valley (Poirot, et al, 2002 and Kenski, 2004). Likewise, high PM-nitrate 

is associated with air masses that traveled through the ammonia-rich Midwest. Figure 27

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

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

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

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

reducing nitrate concentrations. The thermodynamic equilibrium modeling shows that ammonia 

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

more effective when the probable reductions in future sulfate levels are considered. 
33

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

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

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

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

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

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

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

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

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

concentrations and improve visibility levels.

The studies also show that a smaller, yet significant portion of PM2.5 mass is due to emissions 

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

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

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

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

national/regional-scale and local-scale emission reductions may be necessary to provide for 

attainment.

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

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

burning, industrial sources, and secondary organic aerosols. Additional sampling and analysis 

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

carbon.
34

0

1

2

3

4

5

6

7

Coal

Combustion

Secondary

Nitrate

Mobile

Industrial

Soil

Burning

ug/m3

Northbrook (Jan 03-Mar 05)

Cincinnati (Dec 03-Mar 05)

Indianapolis (Jan 02-Mar 05)

Allen Park (2002-2004)

Dearborn (Jun 02-May 05)

Bondville (2002-2004)

St. Louis (2004)

Granite City (2002-2004)

Tikhon (Jan 03-Jun05)

Orange (Apr 01-Mar 05)

Lorain (Feb 02-Dec04)

 

0

2

4

6

8

10

sulfate

nitrate

OM

diesel

soil

steel

ind. zinc

ind. copper

mixed ind.

Allen Park

Dearborn

Law ndale

Springfield

Tikhon

Craig

Taft

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

(Note: the labeling of similar source types varies between studies – e.g., organic carbon/mobile sources are named gasoline and diesel by 

Hopke, mobile by STI 2006, and OM and diesel by STI 2008)
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 

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

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

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

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

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

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

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

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

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

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

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

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

Figure 29. Baseline Visibility Levels for 20% Worst Days (2000 – 2004), units: Mm-1
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. The new IMPROVE equation was used to calculate updated baseline values. 

Second, due to sampler problems, the 2002-2004 data for Boundary Waters were invalid for 

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

was developed by using values from Voyageurs for the invalid species.

Third, LADCO identified a number of days during 2000-2004 where data capture at the Class I

monitors was incomplete (Kenski, 2007b). The missing data cause these days to be excluded 

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

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

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

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

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

results in a small, but measurable, effect on the baseline values (i.e., values increase from 0.2 

to 0.8 dv).

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

Average Worst Day 

DV, per RHR

Average Worst Day DV,

with Missing Data Days

Difference

BOWA 19.59 19.86 0.27

ISLE 20.74 21.59 0.85

SENE 24.16 24.38 0.22

VOYA 19.27 19.48 0.21

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

and northern Minnesota are presented in Table 4. The updated baseline values reflect the most 

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

planning purposes.
37

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

Old IMPROVE Equation (Cite: VIEWS, November 2005)

20% Worst Days

2000 2001 2002 2003 2004

Baseline

Value

2018

URI Value

Natural 

Conditions

Voyageurs 18.50 18.00 19.00 19.20 17.60 18.46 16.74 11.09

BWCA 19.85 19.99 19.68 19.73 17.65 19.38 17.47 11.21

Isle Royale 20.00 22.00 20.80 19.50 19.10 20.28 18.17 11.22

Seney 22.60 24.90 24.00 23.80 22.60 23.58 20.73 11.37

20% Best Days

2000 2001 2002 2003 2004

Baseline

Value

Natural

Conditions

Voyageurs 6.30 6.20 6.70 7.00 5.40 6.32 3.41

BWCA 5.90 6.52 6.93 6.67 5.61 6.33 3.53

Isle Royale 5.70 6.40 6.40 6.30 5.30 6.02 3.54

Seney 5.80 6.10 7.30 7.50 5.80 6.50 3.69

New IMPROVE Equation (Cite: VIEWS, March 2006)

20% Worst Days

2000 2001 2002 2003 2004

Baseline

Value

2018

URI Value

Natural

Conditions

Voyageurs 19.55 18.57 20.14 20.25 18.87 19.48 17.74 12.05

BWCA 20.20 20.04 20.76 20.13 18.18 19.86 17.94 11.61

Isle Royale 20.53 23.07 21.97 22.35 20.02 21.59 19.43 12.36

Seney 22.94 25.91 25.38 24.48 23.15 24.37 21.64 12.65

20% Best Days

2000 2001 2002 2003 2004

Baseline

Value

Natural

Conditions

Voyageurs 7.01 7.12 7.53 7.68 6.37 7.14 4.26

BWCA 6.00 6.92 7.00 6.45 5.77 6.43 3.42

Isle Royale 6.49 7.16 7.07 6.99 6.12 6.77 3.72

Seney 6.50 6.78 7.82 8.01 6.58 7.14 3.73

Notes: (1) BWCA values for 2002 - 2004 reflect "substituted" data.

 (2) New IMPROVE equation values include Kenski, 2007 adjustment for missing days

 URI = uniform rate of improvement
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 EPA guidance (EPA, 

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

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

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

VIEWS website.

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

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

planning purposes.

Data Variability: For the four northern Class I areas, the most important PM2.5 chemical species 

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

species on the 20% best and 20% worst visibility days (based on 2000 – 2004 data) is provided 

in Figure 30. For the 20% worst visibility days, the contributions are: sulfate = 35-55%, nitrate = 

25-30%, and organic carbon = 12-22%. Although the chemical composition is similar, sulfate 

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

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

extinction than to PM2.5 mass because of their hygroscopic properties.

20% Best Days 20% Worst Days 

0

20

40

60

80

100

120

VOYA BOWA ISRO Seney VOYA BOWA ISRO Seney

sea salt

soil

elem. carbon

coarse mass

organic carbon

nitrate

sulfate

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

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

Analysis of PM2.5 mass and chemical species for rural IMPROVE (and IMPROVE-protocol) sites 

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

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

particular, are highly correlated for PM2.5 mass, sulfates, and organic carbon mass (AER, 2004).
39

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

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

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

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

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

SO4 and PM2.5 (DeBell, et al, 2006). Detecting changes in nitrate has been hampered by 

uncertainties in the IMPROVE data for particular years and, thus, this estimate should be 

considered tentative. 

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

throughout the year, as indicated in Figure 32. (Note, in contrast, other parts of the country, 

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

days to occur in the summer months.) This figure and Figure 33 (which presents the monthly 

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

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

winter, suggesting the importance of different sources and meteorological conditions at different 

times of the year.
40

Figure 32. Daily light extinction values for 20% worst days at Boundary Waters (2000 – 2004)
41

Figure 33. Monthly average light extinction values for northern Class I areas
42

Precursor Sensitivity: Results from two analyses using thermodynamic equilibrium models 

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

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

analysis using data collected at Seney indicated that PM2.5 there is most sensitive to reductions 

in sulfate, but is also sensitive to reductions in nitric acid (Blanchard, 2004). 

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

for a site in Minnesota -- Great River Bluffs, which is the closest ammonia monitoring site to the 

northern Class I areas (Blanchard, 2005b). Figure 34 shows PM2.5 concentrations as a function 

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

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

movement downward) and NH3 (i.e., movement to the left), result in lower PM2.5 concentrations. 

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

improve visibility in the northern Class I areas.

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

in sulfate, nitric acid, and ammonia

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

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

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

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

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

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

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

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

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

in Figure 35. The orange areas are where the air is most likely to come from, and the green 

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

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

from Canada. 
43

Figure 35. Composite back trajectories for light extinction- 20% best visibility days (left) and 

20% worst visibility days (right) (2000 – 2005)

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

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

contributions to regional haze in the northern Class I areas (see MRPO, 2008). Based on this 

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

well as Missouri, North Dakota, Iowa, Indiana and Illinois (see, for example, Figure 35 above). 

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

electrical generating units (EGUs) and certain non-EGUs, which lead to sulfate formation, and 

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

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

important, especially for nitrate formation.

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

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

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

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

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

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

2004). As seen in Figure 36, the highest PM2.5 concentrations occurred during the summer, 

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

concentrations were attributed mostly to secondary organic aerosols of biogenic origin because 

of the lack of primary emission markers, and concentrations of know biogenic-related species 

(e.g., pinonic acid – see Figure 36) were also high during the summer.
44

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

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

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

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

Site 2000 2001 2002 2003 2004

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

Jun 28

Jul 19

Boundary Waters --- --- Jun 28 Aug 25 Jul 17

Jul 19

Isle Royale --- --- Jun 1 Aug 25 ---

Jun 28

Seney --- --- Jun 28 --- ---

Back trajectories on these days point mostly to wildfires in Canada. Elimination of these high 

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

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

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

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

impairment in the northern Class I areas.

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

uncontrollable.
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 deciding which control 

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

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

policy makers on the effectiveness of candidate control programs.

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

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

and LADCO, 2007b. 

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

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

3.1 Selection of Base Year

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

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

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

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

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

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

2005 both had above normal ozone conducive conditions, although 2002 was more severe 

compared to 2005. Examination of multiple base years provides for a more complete technical 

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

3.2 Future Years of Interest

To address the multiple attainment requirements for ozone and PM2.5, and reasonable progress 

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

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

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

areas (attainment date 2010)

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

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

2018 First milestone year for regional haze planning

 

8

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

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

attainment date. The PM2.5 implementation rule contains similar provisions – i.e., emission reductions 

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

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

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

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

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

for attainment planning purposes.
47

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

other future years, less rigorous emissions processing was conducted (e.g., 2012 emissions 

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

3.3 Modeling System

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

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

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

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

extensions (e.g., 2-way nested grids, process analysis, source apportionment, and plume-ingrid), and model science. CAMx model set-up for Base M and Base K is summarized below:

Base M (2005) Base K (2002)

• CAMx v4.50 * CAMx 4.30

• CB05 gas phase chemistry * CB-IV with updated gas-phase chemistry

• SOA chemistry updates * No SOA chemistry updates

• AERMOD dry deposition scheme * Wesley-based dry deposition

• ISORROPIA inorganic chemistry • ISORROPIA inorganic chemistry

• SOAP organic chemistry • SOAP organic chemistry

• RADM aqueous phase chemistry • RADM aqueous phase chemistry

• PPM horizontal transport • PPM horizontal transport

3.4 Domain/Grid Resolution

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

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

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

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

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

performed with a 4km sub-grid over the Lake Michigan area and Detroit/Cleveland.

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

higher resolution in the boundary layer. 

Figure 37. Modeling grids – RPO domain (left) and LADCO modeling domain (right)

12 km

36 km
48

3.5 Model Inputs: Meteorology

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

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

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

Figure 38. MM5 modeling domain for 2001-2003 (left) and 2005 (right)

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

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

EPA and LADCO. The 36 km non-summer portion of the annual 2003 simulation was conducted 

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

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

summer season of 2005.

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

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

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

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

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

monthly basis.

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

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

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

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

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

underestimates

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

from top to bottom (1 to 30(31) – i.e., upper left hand corner is January 1 and lower right hand 

corner is December 31

Wind Fields: The wind fields are generally good. Wind speed bias is less than 0.5 m/sec and 

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

within 15-30 degrees.

Mixing Ratio: The mixing ratio (a measure of humidity) is over-predicted in the late spring and 

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

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

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

terms of magnitude in the winter, fall, and early spring months. There are, however, large overpredictions of rainfall in the late spring and summer months (see Figure 41). These overpredictions are seen spatially and in magnitude over the entire domain, particularly in the 

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

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

of wet deposition in the photochemical model. CAMx does not explicitly use the convective and 

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

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

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

attention.
50

Figure 41. 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

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

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

are provided in two summary reports (LADCO, 2006a and LADCO, 2008a) and the following 

pages of the LADCO web site:

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

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

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

For the other sectors (point sources, area sources, and MAR [commercial marine, aircraft, and 

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

RPOs.

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

2005 (Base M) are compared in Figure 42. Additional detail is provided in Tables 6a (all sectors 

– tons per day) and 6b (EGUs – tons per year). 

VOC NOx SO2

 

0

2000

4000

6000

8000

10000

12000

2002

(K)

2005

(M)

2002

(K)

2005

(M)

2002

(K)

2005

(M)

WI

OH

MI

IN

IL

0

2000

4000

6000

8000

10000

12000

2002

(K)

2005

(M)

2002

(K)

2005

(M)

2002

(K)

2005

(M)

Area

Nonroad+MAR

Onroad

Non-EGU

EGU

Figure 42. Base K and Base M emissions for 5-state LADCO region by state (top) and source 

sector (bottom), units: tons per summer weekday

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

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

Nonroad

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

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

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

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

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

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

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

MAR

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

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

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

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

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

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

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

OtherArea

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

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

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

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

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

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

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

On-Road

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

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

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

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

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

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

U.S. Total 14263 7825 23499 13170

EGU

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

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

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

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

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

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

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

Non-EGU

IL 313 221 286 218 305 350 258 356 330 334 218 338 343 235 373 423 251 335 257 249 346 16 17 19

IN 150 130 160 137 170 199 167 238 179 212 175 216 225 178 292 218 270 216 274 290 180 35 36 44

MI 123 116 115 119 122 139 140 216 240 208 242 214 229 271 162 158 166 148 171 185 163 20 21 25

OH 77 84 75 87 79 90 104 177 175 157 166 160 167 178 240 289 231 288 210 216 293 27 28 33

WI 88 84 97 87 104 120 106 98 97 91 93 92 94 81 163 156 154 152 155 156 85 0 0.1 0.1

5-State Total 751 635 733 648 780 898 775 1085 1021 1002 894 1020 1058 943 1230 1244 1072 1139 1067 1096 1067 98 102 121

U.S. Total 4087 3877 4409 4700 5378 6446 6730 6129 6435 6952 5759 5630 6093 6340 6970 1444 1777

IL 1681 1576 1470 1353 1432 1434 1217 2621 2010 1671 1572 1545 1287 1029 1725 1656 1212 1337 1059 1072 1251 119 155 189

IN 1045 1009 867 901 843 853 826 2134 1453 1339 1250 1248 989 819 2966 2902 1690 1294 1691 1492 1256 81 133 131

MI 1530 1546 1291 1311 1239 1139 1134 1958 1730 1429 1314 1349 1118 946 1356 1495 1260 865 1271 1312 927 183 190 190

OH 1432 1735 1165 1323 1137 1062 1082 2831 2048 1478 1619 1342 1001 1074 3416 3761 1732 1650 1240 953 1304 121 195 166

WI 1005 821 909 705 878 862 630 1128 1019 747 800 647 520 551 800 750 687 635 667 675 540 35 54 47

5-State Total 6693 6687 5702 5593 5529 5350 4889 10672 8260 6664 6555 6131 4915 4419 10263 10564 6581 5781 5928 5504 5280 539 727 723
Heat Input (MMBTU/year) Scenario SO2 (tons/year) SO2 (lb/MMBTU) NOx (tons/year) NOx (lb/MMBTU)

IL 980,197,198 2001 - 2003 (average) 362,417 0.74 173,296 0.35

IPM 2.1.9 241,000 73,000

1,310,188,544 IPM3.0 (base) 277,337 0.423 70,378 0.107

IPM3.0 - will do 140,296 0.214 62,990 0.096

IPM3.0 - may do 140,296 0.214 62,990 0.096

IN 1,266,957,401 2001 - 2003 (average) 793,067 1.25 285,848 0.45

IPM 2.1.9 377,000 95,000

1,509,616,931 IPM3.0 (base) 361,835 0.479 90,913 0.120

IPM3.0 - will do 417,000 0.552 94,000 0.125

IPM3.0 - may do 417,000 0.552 94,000 0.125

MI 756,148,700 2001 - 2003 (average) 346,959 0.92 132,995 0.35

IPM 2.1.9 399,000 100,000

1,009,140,047 IPM3.0 (base) 244,151 0.484 79,962 0.158

IPM3.0 - will do 244,151 0.484 79,962 0.158

IPM3.0 - may do 244,151 0.484 79,962 0.158

OH 1,306,296,589 2001 - 2003 (average) 1,144,484 1.75 353,255 0.54

IPM 2.1.9 216,000 84,000

1,628,081,545 IPM3.0 (base) 316,883 0.389 96,103 0.118

IPM3.0 - will do 348,000 101,000

IPM3.0 - may do 348,000 101,000

WI 495,475,007 2001 - 2003 (average) 191,137 0.77 90,703 0.36

IPM 2.1.9 155,000 46,000

675,863,447 IPM3.0 (base) 127,930 0.379 56,526 0.167

IPM3.0 - will do 150,340 0.445 55,019 0.163

IPM3.0 - may do 62,439 0.185 46,154 0.137

IA 390,791,671 2001 - 2003 (average) 131,080 0.67 77,935 0.40

IPM 2.1.9 147,000 51,000

534,824,314 IPM3.0 (base) 115,938 0.434 59,994 0.224

IPM3.0 - will do 115,938 0.434 59,994 0.224

IPM3.0 - may do 100,762 0.377 58,748 0.220

MN 401,344,495 2001 - 2003 (average) 101,605 0.50 85,955 0.42

IPM 2.1.9 86,000 42,000

447,645,758 IPM3.0 (base) 61,739 0.276 41,550 0.186

IPM3.0 - will do 54,315 0.243 49,488 0.221

IPM3.0 - may do 51,290 0.229 39,085 0.175

MO 759,902,542 2001 - 2003 (average) 241,375 0.63 143,116 0.37

IPM 2.1.9 281,000 78,000

893,454,905 IPM3.0 (base) 243,684 0.545 72,950 0.163

IPM3.0 - will do 237,600 0.532 72,950 0.163

IPM3.0 - may do 237,600 0.532 72,950 0.163

ND 339,952,821 2001 - 2003 (average) 145,096 0.85 76,788 0.45

IPM 2.1.9 109,000 72,000

342,685,501 IPM3.0 (base) 41,149 0.240 44,164 0.258

IPM3.0 - will do 56,175 0.328 58,850 0.343

IPM3.0 - may do 56,175 0.328 58,850 0.343

SD 39,768,357 2001 - 2003 (average) 12,545 0.63 15,852 0.80

IPM 2.1.9 12,000 15,000

44,856,223 IPM3.0 (base) 4,464 0.199 2,548 0.114

IPM3.0 - will do 4,464 0.199 2,548 0.114

IPM3.0 - may do 4,464 0.199 2,548 0.114

Table 6b. EGU Emissions for Midwest States (2018)
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 (weekday, Saturday, Sunday for each 

month) at 36 km, and 9 days (weekday, Saturday, Sunday for June – August) at 12 km. For 

2005, CONCEPT was run by a contractor (Environ) using transportation data (e.g., VMT and 

vehicle speeds) supplied by the state and local planning agencies in the LADCO States and 

Minnesota for 24 networks. These data were first processed with T3 (Travel Demand Modeling 

[TDM] Transformation Tool) to provide input files for CONCEPT to calculate link-specific, hourly 

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

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

emissions is provided in Figure 43.

VOC Emissions NOx Emissions

Figure 43. Motor vehicle emissions for VOC (left) and NOx (right) for a July weekday (2005)

Off-road Sources: For 2002 and 2005, NMIM and NMIM2005, respectively, were run by 

Wisconsin DNR. Additional off-road sectors (i.e., commercial marine, aircraft, and railroads 

[MAR]) were handled separately. Local data for agricultural equipment, construction equipment, 

commercial marine, recreational marine, and railroads were prepared by contractors (Environ, 

2004, and E.H. Pechan, 2004). For Base M, updated local data for railroads and commercial 

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

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

substantially lower commercial marine emissions and lower locomotive NOx emissions.

Table 7. Locomotive and commercial marine emissions for the five LADCO States (2002 v. 2005)

Railroads (TPY) Commercial Marine (TPY)

2002 2005 2002 2005

VOC 7,890 7,625 1,562 828

CO 20,121 20,017 8,823 6,727

NOx 182,226 145,132 64,441 42,336

PM 5,049 4,845 3,113 1,413

SO2 12,274 12,173 25,929 8,637

NH3 86 85 ---- ----
55

Area Sources: For 2002 and 2005, EMS was run by LADCO using data supplied by the LADCO 

States to produce weekday, Saturday, and Sunday emissions for each month. For 2005, 

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

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

(which were added to the inventory).

Point Sources: For 2002 and 2005, EMS was run by LADCO using data supplied by the LADCO 

States to produce weekday, Saturday, and Sunday emissions for each month. For EGUs, the 

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

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

 

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

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

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

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

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

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

Ammonia: For Base M, the CMU-based 2002 (Base K) ammonia emissions were projected to 

2005 using growth factors from the Round 4 emissions modeling. These emissions were then 

adjusted by applying temporal factors by month based on the process-based ammonia 

emissions model (Zhang, et al, 2005, and Mansell, et al, 2005). A plot of average daily 

emissions by state and month is provided in Figure 45. A spatial plot of emissions is provided in 

Figure 46, which shows high emissions densities in the central U.S.

0

300

600

900

1200

1500

1800

Jan Fed March Apr May Jun Jul Aug Sept Oct Nov Dec

Illinois Indiana Iowa Michigan

Minnesota Ohio Wisconsin

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

daily emissions – tons per day)
56

Figure 46. Ammonia emissions for a July weekday (2005) – 12 km modeling domain

Canadian Emissions: For Base M, Scott Edick (Michigan DEQ) processed the 2005 Canadian 

National Pollutant Release Inventory, Version 1.0 (NPRI). Specifically, a subset of the NPRI 

data (emissions and stack parameters) relevant to the air quality modeling were reformatted. 

The resulting emissions represent a significant improvement in the base year emissions. 

A spatial plot of point source SO2 and NOx emissions is provided in Figure 47. Additional plots 

and emission reports are available on the LADCO website 

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

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

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

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

PM2.5 concentrations. Consequently, the fire emissions were not included in subsequent 

modeling runs (i.e., they were not in the Base K or Base M modeling inventories).

Future Year Emissions: Complete emission inventories were developed for several future years: 

Base K – 2009, 2012, and 2018, and Base M – 2009 and 2018. In addition, 2008 (Base K and 

Base M) and 2012 (Base M) proxy inventories were estimated based on the 2009 and 2018 

data. (Note, the EGU emissions for the Base M 2012 inventory were based on EPA’s IPM3.0 

modeling.)

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

Additional detail is provided in Tables 6a and 6b. 

VOC NOx SO2

0

3000

6000

9000

12000

2002 2005 2009 2012 2015 2018 2002 2005 2009 2012 2015 2018 2002 2005 2009 2012 2015 2018

Area

On-road

Nonroad

Non-EGU

EGU

Figure 48. Base year and future year emissions for 5-State LADCO Region (TPD, July weekday)

For on-road, and nonroad, the future year emissions were estimated by models (i.e., 

EMS/CONCEPT and NMIM, respectively). One adjustment was made to the 2009 and 2018 

motor vehicle emission files prepared by Environ with CONCEPT. To reflect newer 

transportation modeling conducted by CATS for the Chicago area, emissions were increased by 

9% in 2009 and 2018. The 2005 base year and adjusted 2009 and 2018 motor vehicle 

emissions are provided in Table 8.
58

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

Year State Sum of CO Sum of TOG Sum of NOx Sum of PM2.5 Sum of SO2 Sum of NH3 Sum of VMT

2005 IL 3,684.3 341.5 748.2 12.9 9.6 35.9 344,087,819.6

IN 3,384.9 282.0 541.1 8.9 11.1 25.7 245,537,231.9

MI 4,210.3 351.9 722.0 12.4 13.9 35.3 340,834,025.9

MN 2,569.1 218.7 380.5 6.3 7.6 17.7 170,024,599.7

OH 6,113.4 679.8 933.6 16.2 18.8 36.5 360,521,068.6

WI 2,206.0 175.1 457.5 7.8 9.2 19.7 189,123,964.3

 Total 22,168.0 2,049.0 3,782.9 64.5 70.2 170.8 1,650,128,709.9

2009 IL 2,824.4 268.0 527.8 10.1 4.2 38.9 372,132,591.1

IN 2,839.5 234.9 401.9 6.7 2.8 26.1 249,817,026.3

MI 3,172.0 269.2 500.9 9.2 4.0 37.1 356,347,010.5

MN 2,256.8 206.3 307.5 5.1 2.3 21.5 204,443,017.8

OH 4,619.2 423.7 693.5 11.8 4.7 39.5 387,428,127.2

WI 1,673.4 119.4 322.1 5.7 2.3 20.6 197,729,964.9

 Total 17,385.3 1,521.5 2,753.6 48.7 20.3 183.6 1,767,897,737.8

2018 IL 2,084.7 151.5 200.7 6.3 3.7 43.1 413,887,887.3

IN 2,217.3 138.4 173.0 4.4 2.6 30.2 288,042,232.1

MI 2,434.3 163.5 204.1 5.9 3.6 40.5 388,128,431.8

MN 1,799.6 123.1 137.1 3.6 2.2 24.9 237,022,213.7

OH 3,361.5 242.5 274.1 6.8 4.0 43.1 421,694,093.4

WI 1,255.5 68.4 138.5 3.9 2.0 22.2 218,277,167.5

 Total 13,152.9 887.5 1,127.5 30.8 18.1 203.9 1,967,052,025.8
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 completed by EPA in February 2007 for Base M. Several CAIR 

scenarios were assumed:

Base K

1a: IPM2.1.9, with full trading and banking

1b: IPM2.1.9, with restricted trading (compliance with state-specific emission budgets) and full trading

1d: IPM2.1.9, with restricted trading (compliance with state-specific emission budgets)

Base M

5a: EPA’s IPM3.0 was assumed as the future year base for EGUs.

5b: EPA’s IPM3.0, with several “will do” adjustments identified by the States. These adjustments should 

reflect a legally binding commitment (e.g., signed contract, consent decree, or operating permit). 

5c: EPA’s IPM3.0, with several “may do” adjustments identified by the States. These adjustments reflect 

less rigorous criteria, but should still be some type of public reality (e.g., BART determination or press 

announcement).

For other sectors (area, MAR, and non-EGU point sources), the future year emissions for the 

LADCO States were derived by applying growth and control factors to the base year inventory. 

These factors were developed by a contractor (E.H. Pechan, 2005 and E.H. Pechan, 2007). 

For the non-LADCO States, future year emission files were based on data from other RPOs.

Growth factors were based initially on EGAS (version 5.0), and were subsequently modified (for 

select, priority categories) by examining emissions activity data. Due to a lack of information on 

future year conditions, the biogenic VOC and NOx emissions, and all Canadian emissions were 

assumed to remain the constant between the base year and future years.

A “base” control scenario was prepared for each future year based on the following “on the 

books” controls:

On-Highway Mobile Sources

• Federal Motor Vehicle Emission Control Program, low-sulfur gasoline and ultra-low sulfur diesel fuel

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

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

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

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

• Reformulated gasoline, including in Chicago-Gary,-Lake County, IL,IN; and Milwaukee, Racine, WI

Off-Highway Mobile Sources

• Federal control programs incorporated into NONROAD model (e.g., nonroad diesel rule), plus the 

evaporative Large Spark Ignition and Recreational Vehicle standards

• Heavy-duty diesel (2007) engine standard/Low sulfur fuel

• Federal railroad/locomotive standards

• Federal commercial marine vessel engine standards

Area Sources (Base M only)

• Consumer solvents

• AIM coatings

• Aerosol coatings

• Portable fuel containers

Power Plants

• Title IV (Phases I and II)

• NOx SIP Call

• Clean Air Interstate Rule
60

Other Point Sources

• VOC 2-, 4-, 7-, and 10-year MACT standards

• Combustion turbine MACT

Other controls included in the modeling include: consent decrees (refineries, ethanol plants, and 

ALCOA)9, NOx RACT in Illinois and Ohio10, and BART for a few non-EGU sources in Indiana 

and Wisconsin.

For Base K, several additional control scenarios were considered:

Scenario 2 – “base” controls plus additional controls recommended in LADCO White 

Papers for stationary and mobile sources

Scenario 3 – Scenario 2 plus additional White Papers for stationary and mobile sources

Scenario 4 – “base” controls plus additional candidate control measures under

discussion by State Commissioners

Scenario 5 – “base” controls plus additional candidate control measures identified by the 

LADCO Project Team

3.7 Basecase Modeling Results

The purpose of the basecase modeling is to evaluate model performance (i.e., assess the 

model's ability to reproduce the observed concentrations). The model performance evaluation 

focused on the magnitude, spatial pattern, and temporal of modeled and measured 

concentrations. This exercise was intended to assess whether, and to what degree, confidence 

in the model is warranted (and to assess whether model improvements are necessary).

Model performance was assessed by comparing modeled and monitored concentrations. 

Graphical (e.g., side-by-side spatial plots, time series plots, and scatter plots) and statistical 

analyses were conducted. No rigid acceptance/rejection criteria were used for this study. 

Instead, the statistical guidelines recommended by EPA and other modeling studies (e.g., 

modeling by the other RPOs) were used to assess the reasonableness of the results. The 

model performance results presented here describe how well the model replicates observed 

ozone and PM2.5 concentrations after a series of iterative improvements to model inputs.

Ozone: Spatial plots are provided for high ozone periods in June 2002 and June 2005 (see 

Figures 49a and 49b). The plots show that the model is doing a reasonable job of reproducing 

the magnitude, day-to-day variation, and spatial pattern of ozone concentrations. There is a 

tendency, however, to underestimate the magnitude of regional ozone levels. This is more 

apparent with the 2002 modeling; the regional concentrations in the 2005 modeling agree better 

with observations due to model and inventory improvements.

 

9

 E.H. Pechan’s original control file included control factors for three sources in Wayne County, MI. 

These control factors were not applied in the regional-scale modeling to avoid double-counting with the 

State’s local-scale analysis for PM2.5 

10 NOx RACT in Wisconsin is included in the 2005 basecase (and EGU “will do” scenario). NOx RACT in 

Indiana was not included in the modeling inventory.
61

 

 

Figure 49a. Modeled (top) v. monitored (bottom) 8-hour ozone concentrations: June 20 – 25, 2002
62

 

 

Figure 49b Modeled (top) v. monitored (bottom) 8-hour ozone concentrations: June 23– 28 2005
63

Standard model performance statistics were generated for the entire 12 km domain, and by day 

and by monitoring site. The domain-wide mean normalized bias for the 2005 base year is 

similar to that for the 2002 base year and is generally within 30% (see Figure 50). 

Figure 50. Mean bias for summer 2005 (Base M) and summer 2002 (Base K)

Station-average metrics (over the entire summer) are shown in Figure 51. The bias results 

further demonstrate the model’s tendency to underestimate absolute ozone concentrations.

Figure 51. Mean bias (left) and gross error (right) for summer 2005
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 52). Model inputs included 4 km emissions developed by LADCO (consistent with 

Base K/Round 4) and the 4 km meteorology developed by Alpine Geophysics. 

Figure 52. 4 km grids for Lake Michigan region and Detroit-Cleveland region

Hourly time series plots were prepared for several monitors (see Figure 53). The results are 

similar at 12 km and 4 km, with some site-by-site and day-by-day differences.

Figure 53. Ozone time series plots for 12 km and 4 km modeling (June 17-29, 2002)
65

An additional diagnostic analysis was performed to assess the response of the modeling system 

to changes in emissions (Baker and Kenski, 2007). Specifically, the 2002-to-2005 change in 

observed ozone concentrations was compared to the change in modeled ozone concentrations

based on the 95th percentile(and above) concentration values for each monitor. This analysis 

was also done with the inclusion of model performance criteria which eliminated poorly 

performing days (i.e., error > 35%). The results show good agreement in the modeled and 

monitored ozone concentration changes (e.g., ozone improves by about 9-10 ppb between 

2002 and 2005 according to the model and the measurements) – see Figure 54. This provides 

further support for using the model to develop ozone control strategies.

Figure 54. Comparison of change in predicted and observed ozone concentrations (2002 v. 2005) 
66

PM2.5: Time series plots of the monthly average mean bias and annual fractional bias for Base 

M and Base K are shown in Figure 55. As can be seen, Base M model performance for most 

species is fair (i.e., close to “no bias” throughout most of the year), with two main exceptions. 

First, the Base M and Base K results for organic carbon are poor, suggesting the need for more 

work on primary organic carbon emissions. Second, the Base M results for sulfate, while 

acceptable (i.e., bias values are within 35%), are not as good as the Base K results (e.g., 

noticeable underprediction during the summer months). 

Figure 55. PM2.5 Model performance - monthly average mean bias and annual fractional bias for 

Base M (left column) and Base K (right column)

Base M Base K
67

Two analyses were undertaken to understand sulfate model performance for 2005:

• Assess Meteorological Influences: The MM5 model performance evaluation showed that 

rainfall is over-predicted by MM5 over most of the domain during the summer months 

(LADCO, 2007c). Because CAMx does not explicitly use the rainfall output by MM5, this 

may or may not result in over-prediction sulfate wet deposition (and under-prediction of 

sulfate concentrations). A sensitivity run was performed with no wet deposition for July, 

August, and September. The resulting model performance (see green line in Figure 56)

showed a noticeable difference from the basecase (i.e., higher sulfate concentrations), 

and suggests that further evaluation of MM5 precipitation fields may be warranted.

• Assess Emissions Influences: The major contributor to sulfate concentrations in the 

region is SO2 emitted from EGUs. The basecase modeling inventory for EGUs is based 

on annual emissions, which were allocated to a typical weekday, Saturday, and Sunday 

by month using CEM-based temporal profiles. A sensitivity run was performed using 

day-specific emissions. The resulting model performance (see purple line in Figure 56)

showed little difference from the basecase.

Figure 56. Monthly sulfate bias for Base M (MRPO EGU) v. two sensitivity analyses (Note: positive 

values indicate over-prediction, negative values indicate under-prediction)

To assess the effect of the wet deposition issue on future year modeled values, another 

sensitivity run was conducted with no wet deposition in Quarters 2-3 for the base year 

(2005) and 2018. The resulting future year values were only slightly different from the 

current base strategy run. In general, the future year values (without wet deposition) 

were a little higher (+0.15 ug/m3 or less) in the Ohio Valley and a little lower (-.10 ug/m3

of less) in the Great Lakes region. This sensitivity run provides a bound for sulfate wet 

deposition issue in terms of the attainment test, given that having no wet deposition is 

unrealistic. The results suggest that even with an improved wet deposition treatment, 

the Base M strategy results are not expected to change very much.

Time series plots of daily sulfate, nitrate, elemental carbon, and organic carbon concentrations 

for three Midwestern locations are presented in Figures 57 (2002) and 58 (2005). These results 

are consistent with the model performance statistics (i.e., good agreement for sulfates and 

nitrates and poor agreement [large underprediction] for organic carbon).

Monthly Sulfate Bias (ug/m3)

-3

-2

-1

0

1

2

3

4

5

1 2 3 4 5 6 7 8 9 10 11 12

MRPO EGU MOG daily EGU No Wet Deposition
68

 SULFATE NITRATE ORGANIC CARBON

Seney

Detroit

Chicago

Figure 57. Time series of sulfate, nitrate, and organic carbon at three Midwest sites for 2005
69

 SULFATE NITRATE ORGANIC CARBON

Seney

Detroit

Chicago

Figure 58. Time series of sulfate, nitrate, and organic carbon at three Midwest sites for 2005
70

In summary, model performance for ozone and PM2.5 is generally acceptable and can be 

characterized as follows:

Ozone

• Good agreement between modeled and monitored concentration for higher 

concentration levels (> 60 ppb) – i.e., bias within 30%

• Regional modeled concentrations appear to be underestimated in the 2002 base 

year, but show better agreement (with monitored data) in the 2005 base year due to 

model and inventory improvements.

• Day-to-day and hour-to-hour variation in and spatial patterns of modeled 

concentrations are consistent with monitored data

• Model accurately simulates the change in monitored ozone concentrations due to 

reductions in precursor emissions.

PM2.5

• Good agreement in the magnitude of fine particle mass, but some species are 

overestimated and some are underestimated (during periods of the year when it is 

important)

• Sulfates: good agreement in the 2002 base year, but underestimated in 

the summer in the 2005 base year due probably to meteorological factors

• Nitrates: slightly overestimated in the winter in the 2002 base year, but 

good agreement in the 2005 base year as a result of model and inventory 

improvements

• Organic Carbon: grossly underestimated in the 2002 and 2005 base 

years due likely to missing primary organic carbon emissions and, 

possibly, other factors (e.g., grid resolution and model chemistry).

• Temporal variation and spatial patterns of modeled concentrations are consistent 

with monitored data

Several observations should be noted on the implications of these model performance findings 

on the attainment modeling presented in the following section. First, it has been demonstrated 

that model performance overall is acceptable and, thus, the model can be used for air quality 

planning purposes. Second, consistent with EPA guidance, the model is used in a relative 

sense to project future year values. EPA suggests that this approach “should reduce some of 

the uncertainty attendant with using absolute model predictions alone” (EPA, 2007a). 

Furthermore, the attainment modeling is supplemented by additional information to provide a 

weight of evidence determination.
71

Section 4.0 Attainment Demonstration for Ozone and PM2./5

Air quality modeling and other information were used to determine whether existing (“on the 

books”) controls would be sufficient to provide for attainment of the NAAQS for ozone and PM2.5

and if not, then what additional emission reductions would be necessary for attainment. 

Traditionally, attainment demonstrations involved a “bright line” test in which a single modeled 

value was compared to the ambient standard. To provide a more robust assessment of 

expected future year air quality, EPA’s modeling guidelines call for consideration of 

supplemental information. This section summarizes the results of the primary (guideline) 

modeling analysis and a weight of evidence determination based on the modeling results and 

other supplemental analyses.

4.1 Future Year Modeling Results

The purpose of the future year modeling is to assess the effectiveness of existing and possible 

additional control programs. The model was used in a relative sense to project future year 

values, which are then compared to the standard to determine attainment/nonattainment. 

Specifically, the modeling test consists of the following steps:

(1) Calculate base year design values: For ozone and PM2.5, the base year design 

values were derived by averaging the three 3-year periods centered on the 

emissions base year:

2002 base year: 2000-2002, 2001-2003, and 2002-2004

2005 base year: 2003-2005, 2004-2006, and 2005-200711

(2) Estimate the expected change in air quality: For each grid cell, a relative 

reduction factor (RRF) is calculated by taking the ratio of the future year and 

baseline modeling results. 

(3) Calculate future year values: For each grid cell (with a monitor), the RRFs are 

multiplied by the base year design values to project the future year values

(4) Assess attainment: Future year values are compared to the NAAQS to assess 

attainment or nonattainment.

A comparison of the 2002 and 2005 base year design values for ozone and PM2.5 is provided in 

Figure 59. In general, the figure shows that the 2005 base year design values are much lower 

than the 2002 base year design values, especially for ozone.

 

11 A handful of source-oriented PM2.5 monitors in Illinois and Indiana were excluded from the annual 

attainment test, because these monitors are not to be used to judging attainment of the annual standard.
72

Figure 59. 2002 v. 2005 base year design values for ozone (top) and PM2.5 (bottom)

2002 2005

Statistical Summary

# Sites > NAAQS 93 9

Peak Value 99.0 ppb 90.0 ppb

Ave Exceedance Amount 7 ppb 2 ppb

2002 2005

Statistical Summary

# Sites > NAAQS 58 41

Peak Value 19.3 ug/m3 17.7 ug/m3

Ave Exceedance Amount 1.2 ug/m3 0.9 ug/m3
73

Ozone results are provided for those grid cells with ozone monitors. The RRF calculation 

considers all nearby grid cells (i.e., 3x3 for 12 km modeling) and a threshold of 85 ppb. (If there 

were less than 10 days above this value, then the threshold was lowered until either there were 

10 days or the threshold reached 70 ppb.) PM2.5 results are provided for those grid cells with 

FRM (PM2.5-mass) monitors. Spatial mapping was performed to extrapolate PM2.5-speciation 

data from STN and IMPROVE sites to FRM sites. RRF values for PM2.5 were derived as a 

function of quarter and chemical species.

Additional, hot-spot modeling will be performed by the states for certain PM2.5 nonattainment 

areas (e.g., Detroit, Cleveland, and Granite City) to address primary emissions from local point 

sources which may not be adequately accounted for by the regional grid modeling. This 

modeling will consist of Gaussian dispersion modeling (e.g., AERMOD) performed in 

accordance with EPA’s modeling guidance (see Section 5.3 of the April 2007 guidance 

document). Further analyses will need to be undertaken to determine how to best combine the 

regional modeling and the hot-spot modeling. This could mean some adjustment to the model 

results presented in this document to reflect better the regional component. 

The ozone and PM2.5 modeling results are provided in Appendix I for select monitors (high 

concentration sites) in the 5-state region for the following future years of interest: 2008 (ozone 

only), 2009, 2012, and 2018. (Note, RRF values for ozone, and for PM2.5 by season and 

chemical species are also included in Appendix I for key monitoring sites.) A summary of the 

modeling results is provided in Table 9 (ozone) and Table 10 (PM2.5), and spatial maps of the 

Base M future year concentrations are provided in Figures 60-62.
Key Sites 2018

Round 5 Round 4 Round 5 Round 4 Round 5 Round 4 Round 5

Lake Michigan Area

Chiwaukee 550590019 82.0 93.0 82.3 92.0 80.9 90.3 76.2

Racine 551010017 77.6 85.9 77.5 84.9 76.1 82.9 71.2

Milwaukee-Bayside 550190085 79.6 85.4 79.8 84.9 78.0 82.3 72.7

Harrington Beach 550890009 80.0 86.7 80.1 85.4 78.3 82.9 72.5

Manitowoc 550710007 81.3 80.3 80.8 78.9 78.6 76.3 72.5

Sheboygan 551170006 84.4 90.0 84.0 88.9 81.8 86.4 75.4

Kewaunee 550610002 78.9 82.5 78.1 81.0 75.9 79.1 69.9

Door County 550290004 84.8 83.6 83.9 81.8 81.5 79.3 74.7

Hammond 180892008 75.4 86.9 75.4 86.6 74.6 86.3 71.6

Whiting 180890030 77.0 77.0 76.2 73.1

Michigan City 180910005 74.2 87.4 73.9 86.5 72.5 85.4 68.1

Ogden Dunes 181270020 75.7 82.3 75.6 82.8 74.5 82.0 70.8

Holland 260050003 85.6 84.9 85.3 83.4 82.8 81.0 76.1

Jenison 261390005 77.9 78.7 77.1 77.6 74.5 75.5 68.7

Muskegon 261210039 80.8 82.7 80.5 81.5 78.0 79.4 71.9

Indianapolis Area

Noblesville 189571001 78.0 85.2 78.1 83.7 75.6 82.0 68.7

Fortville 180590003 73.9 85.1 73.9 83.8 71.4 82.1 65.1

Fort B. Harrison 180970050 74.8 84.8 75.1 83.7 73.2 82.4 69.1

Detroit Area

New Haven 260990009 82.7 86.3 81.4 85.3 80.2 83.5 76.1

Warren 260991003 82.5 84.3 81.3 83.3 80.7 81.9 77.6

Port Huron 261470005 79.0 80.5 77.5 79.1 75.5 77.0 70.9

Cleveland Area

Ashtabula 390071001 84.9 84.7 83.4 82.7 81.0 80.2 75.1

Geauga 390550004 75.7 90.3 74.7 88.8 72.7 86.2 67.3

Eastlake 390850003 82.8 84.2 81.9 82.8 80.5 80.6 76.2

Akron 391530020 79.3 83.0 78.1 81.4 75.6 78.5 68.7

Cincinnati Area

Wilmington 390271002 77.8 84.8 77.5 83.5 74.9 81.1 68.3

Sycamore 390610006 81.7 85.4 81.9 84.7 80.3 82.9 74.6

Lebanon 391650007 83.6 80.1 83.0 79.0 80.7 77.0 74.2

Columbus Area

London 390970007 75.4 79.9 75.0 78.4 72.6 76.5 66.3

New Albany 390490029 82.4 84.1 81.8 82.6 79.6 80.2 73.0

Franklin 290490028 77.0 77.7 75.9 76.5 74.1 74.7 69.0

St. Louis Area

W. Alton (MO) 291831002 82.4 86.1 81.0 85.2 78.6 84.0 74.9

Orchard (MO) 291831004 83.3 83.3 82.0 82.2 80.0 80.4 76.2

Sunset Hills (MO) 291890004 79.5 82.8 78.7 81.9 77.1 80.6 73.9

Arnold (MO) 290990012 78.7 78.4 77.2 77.4 75.6 75.8 72.0

Margaretta (MO) 295100086 79.8 84.0 79.3 83.4 77.9 82.5 74.4

Maryland Heights (MO) 291890014 84.5 83.4 81.7 78.1

2008 2009 2012

Table 9. Summary of Ozone Modeling Results
County Site ID Site Round 5 Round4 Round 5 Round4 Round 5 Round4

Cook 170310022 Chicago - Washington HS 14.1 14.8 14.0 14.6 13.9 14.4

Cook 170310052 Chicago - Mayfair 14.4 15.8 14.2 15.5 13.9 15.0

Cook 170310057 Chicago - Springfield 13.9 14.5 13.8 14.3 13.7 14.1

Cook 170310076 Chicago - Lawndale 13.8 14.5 13.7 14.3 13.6 14.1

Cook 170312001 Blue Island 13.7 14.5 13.6 14.3 13.4 14.1

Cook 170313301 Summit 14.2 14.8 14.0 14.6 13.9 14.4

Cook 170316005 Cicero 14.4 15.3 14.3 15.1 14.2 14.9

Madison 171191007 Granite City 15.1 16.0 14.9 15.8 14.3 15.5

St. Clair 171630010 E. St. Louis 14.1 14.9 13.9 14.7 13.4 14.5

Clark 180190005 Jeffersonville 13.8 15.5 13.7 15.0 13.4 14.4

Dubois 180372001 Jasper 12.4 13.8 12.2 13.5 11.8 13.0

Lake 180890031 Gary 13.0 12.8 12.4

Marion 180970078 Indy-Washington Park 12.8 14.5 12.6 14.2 12.0 13.7

Marion 180970083 Indy- Michigan Street 13.4 14.8 13.1 14.9 12.6 14.0

Wayne 261630001 Allen Park 13.0 14.5 12.8 14.1 12.4 13.3

Wayne 261630015 Southwest HS 14.2 15.8 13.9 15.3 13.5 14.4

Wayne 261630016 Linwood 13.1 14.1 12.8 13.7 12.5 13.0

Wayne 261630033 Dearborn 15.8 17.7 15.5 17.1 15.1 16.1

Wayne 261630036 Wyandotte 13.1 15.1 12.8 14.7 12.5 13.9

Butler 390170003 Middleton 13.5 14.2 13.2 13.7 12.8 13.1

Butler 390170016 Fairfield 13.1 13.5 12.9 12.9 12.5 12.2

Cuyahoga 390350027 Cleveland-28th Street 13.5 14.4 13.2 13.8 12.7 12.9

Cuyahoga 390350038 Cleveland-St. Tikhon 15.2 16.1 14.8 15.4 14.3 14.4

Cuyahoga 390350045 Cleveland-Broadway 14.4 14.6 14.0 14.0 13.5 13.1

Cuyahoga 390350060 Cleveland-GT Craig 15.0 15.3 14.6 14.7 14.1 13.7

Cuyahoga 390350065 Newburg Hts - Harvard Ave 14.0 14.1 13.6 13.5 13.1 12.6

Franklin 390490024 Columbus - Fairgrounds 12.9 14.6 12.6 14.0 12.0 13.0

Franklin 390490025 Columbus - Ann Street 12.7 14.1 12.4 13.5 11.9 12.5

Franklin 390490081 Columbus - Maple Canyon 11.7 14.0 11.4 13.4 10.9 12.5

Hamilton 390610014 Cincinnati - Seymour 14.5 15.5 14.3 14.8 13.8 14.0

Hamilton 390610040 Cincinnati - Taft Ave 12.8 13.6 12.6 13.0 12.2 12.3

Hamilton 390610042 Cincinnati - 8th Ave 14.0 14.6 13.8 14.0 13.4 13.2

Hamilton 390610043 Sharonville 12.9 13.6 12.7 13.0 12.3 12.2

Hamilton 390617001 Norwood 13.4 14.2 13.2 13.6 12.8 12.8

Hamilton 390618001 St. Bernard 14.7 15.2 14.4 14.6 14.0 13.8

Jefferson 390810016 Steubenville 12.8 16.3 12.5 15.9 12.7 16.2

Jefferson 390811001 Mingo Junction 13.5 15.5 13.2 15.0 13.4 15.3

Lawrence 390870010 Ironton 12.8 14.2 12.5 13.7 12.3 13.2

Montgomery 391130032 Dayton 13.2 13.7 12.9 13.2 12.4 12.3

Scioto 391450013 New Boston 12.1 15.4 11.9 14.8 11.6 14.2

Stark 391510017 Canton - Dueber 14.0 15.0 13.6 14.3 13.3 13.6

Stark 391510020 Canton - Market 12.6 13.6 12.3 13.0 11.9 12.2

Summit 391530017 Akron - Brittain 13.0 14.4 12.7 13.6 12.3 12.9

Summit 391530023 Akron - W. Exchange 12.3 13.6 12.0 13.0 11.5 12.2

2009 2012 2018

Table 10. Summary of PM2.5 Modeling Results
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 PM2.5 (annual average)–Base M
78

 2005 (observed) 2009 

2012 2018

Figure 62. Observed base year and projected future year design values for PM2.5 (24-hr average)-Base M
79

The number of monitors with design values above the standard are as follows:

Table 11. Number of sites above standard

 Ozone (8 hour: 85 ppb)

State 2002 2005 2009 2012 2018

BaseK Base M BaseK Base M BaseK Base M BaseK Base M

 IL 3 0 0 0 0 0 0 0

 IN 22 0 0 0 0 0 0 0

 MI 15 3 1 1 0 0 0 0

 OH 40 4 1 0 1 0 0 0

 WI 13 2 4 0 3 0 1 0

Total 93 9 6 1 4 0 1 0

PM2.5 (Annual: 15 ug/m3)

State 2002 2005 2009 2012 2018

BaseK Base M BaseK Base M BaseK Base M BaseK Base M

 IL 11 7 3 1 3 0 2 0

 IN 10 6 1 0 1 0 0 0

 MI 6 2 3 1 2 1 0 0

 OH 31 26 7 1 4 0 1 1

 WI 0 0 0 0 0 0 2 0

Total 58 41 14 3 10 1 5 1

The modeling results above reflect the “base” controls identified in Section 3.6, with EGU 

emissions based on IPM modeling (i.e., Round 4 – IPM2.1.9, and Round 5 – IPM3.0). In 

addition, two sets of alternative future year EGU emissions were examined in Round 5. First, 

alternative control assumptions were provided for several facilities by the states (i.e., “will do” 

and “may do” scenarios). In general, these scenarios produced a small change in future year 

ozone and PM2.5 concentrations (i.e., about 0.1 ug/m

3

 for PM2.5 and 0.1-0.2 ppb for ozone). 

Second, EPA suggested adjustments to the 2010 IPM emissions to reflect 2009 conditions. The 

revised (2009) SO2 emissions represent a 5-6% increase in domainwide SO2 emissions. The 

increased SO2 emissions result in slightly greater annual average PM2.5 concentrations (on the 

order of 0.1 – 0.2 ug/m3), but do not produce any new residual nonattainment areas.
80

The limited 4 km ozone modeling (based on Base K) performed by LADCO included a future 

year analysis for 2009. The figure below shows the 2009 values with 12 km and 4 km grid 

spacing for the LADCO modeling and similar modeling conducted by a stakeholder group

(Midwest Ozone Group).

70

75

80

85

90

95

Cook

Lake

LaPorte

Porter

Allegan

Muskegon

Kenosha

Milwaukee

Ozaukee

Racine

Sheboygan

MOG-12km MOG-4km LADCO-12km LADCO-4km

70

75

80

85

90

95

New Haven Warren Geauga Eastlake Portage Akron

MOG-12km MOG-4km LADCO-12km LADCO-4km

Figure 63. Future year (2009) values for Lake Michigan area (top) and Detroit-Cleveland region

(bottom)

These results show that the 12 km and 4 km values are similar, with the most notable changes 

in northwestern Indiana and northeastern Illinois (e.g., 4 km values are as much as 4 ppb lower 

than 12 km values). The differences in the southern part of the Lake Michigan area are 

plausible, given the tight emissions gradient there (i.e., finer grid resolution appears to provide 

more appropriate representation). 

In light of these findings, 12 km grid spacing can continue to be used for ozone modeling, but 

the Base K/Round 4 results for northwestern Indiana/northeastern Illinois should be viewed with 

caution (i.e., probably 1 – 4 ppb too high).
81

In summary, the ozone modeling provides the following information for the nonattainment areas 

in the region (see Table 12):

Table 12. Ozone Nonattainment Areas in the LADCO Region (as of December 31, 2007)

Area Name Category

Number of

Counties

Attainment 

Deadline

Detroit-Ann Arbor, MI Marginal 8 2007

Chicago-Gary-Lake County, IL-IN Moderate 10 2010

Cleveland-Akron-Lorain, OH Moderate 8 2010

Milwaukee-Racine, WI Moderate 6 2010

Sheboygan, WI Moderate 1 2010

St Louis, MO-IL Moderate 4 2010

Allegan Co, MI Subpart 1 1 2009

Cincinnati-Hamilton, OH-KY-IN Subpart 1 6 2009

Columbus, OH Subpart 1 6 2009

Door Co, WI Subpart 1 1 2009

Kewaunee Co, WI Subpart 1 1 2009

Manitowoc Co, WI Subpart 1 1 2009

53

Marginal Areas (2007 attainment date): No modeling was conducted for the 2006 SIP planning 

year. Rather, 2005 – 2007 air quality data are available to determine attainment.

Basic (Subpart 1) Areas (2009 attainment date): The modeling results for the 2008 SIP planning 

year show:

• Base K: all areas in attainment, except Cincinnati and Indianapolis

• Base M: all areas in attainment, except Holland (Allegan County) 

Moderate Areas (2010 attainment date): The modeling results for the 2009 SIP planning year 

show:

• Base K: all areas still in nonattainment

• Base M: all areas in attainment

The PM2.5 modeling results show:

• Base K: all areas in attainment, except for Chicago, Cincinnati, Cleveland, Detroit, 

Granite City (IL), Louisville, Portsmouth (OH), and Steubenville

• Base M: all areas in attainment, except for Cleveland, Detroit, and Granite City (IL)

With respect to the new lower 8-hour ozone standard, the modeling about 30 sites in 2012 and 

5 sites in 2018 with design values greater than 75 ppb. With respect to the new lower 24-hour 

PM2.5 standard, the modeling shows 13 sites in 2012 and 10 in 2018 with design values greater 

than 35 ug/m3.
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 supplemental 

analyses are recommended:

• additional modeling

• analyses of trends in ambient air quality and emissions, and

• observational models and diagnostic analyses

Furthermore, according to EPA’s guidelines, if the future year modeled values are “close” to the 

standard (i.e., 82 – 87 ppb for ozone and 14.5 – 15.5 ug/m3 for PM2.5), then the results of the 

primary modeling should be reviewed along with the supplemental information in a “weight of 

evidence” assessment of whether each area is likely to achieve timely attainment. 

A WOE determination for ozone and PM2.5 is provided in the following sections. Special 

attention is given to the following areas with future year modeled values that exceed or are 

“close” to the ambient standard (see Appendix I):

 Ozone PM2.5

Lake Michigan area Chicago, IL

Cleveland, OH Cleveland, OH

Cincinnati, OH Cincinnati, OH

Granite City, IL

Detroit, MI

4.3 Weight-of-Evidence Determination for Ozone

The WOE determination for ozone consists of the primary modeling and other supplemental 

analyses (some of which were discussed in Section 2). A summary of this information is 

provided below.

Primary (Guideline) Modeling: The guideline modeling is presented in Section 4.1. Key findings 

from this modeling include:

• Base M regional modeling shows attainment by 2008 and 2009 at all sites, except 

Holland (MI), and attainment at all sites by 2012.

• Base K modeling results reflect generally higher future year values, and show more 

sites in nonattainment compared to the Base M modeling. The difference in the two 

modeling analyses is due mostly to lower base year design values in Base M.

• Base K and Base M modeling analyses are considered “SIP quality”, so the 

attainment demonstration for ozone should reflect a weight-of-evidence approach, 

with consideration of monitoring based information.

• Base M modeling also shows that the proposed lower 8-hour standard will not be 

met at many sites, even by 2018, with existing controls.

Additional Modeling: Four additional modeling analyses were considered: (1) re-examination of 

the primary modeling to estimate attainment probabilities, (2) remodeling with different 

assumptions, (3) an unmonitored area analysis, and (4) EPA’s latest regional ozone modeling. 

Each of these analyses is described below.
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 estimates of future year ozone concentrations were calculated based 

on model-based RRFs and appropriate monitor-based concentrations for each year between 

2001 and 2007. RRF values for 2001, 2003, 2004, 2006, and 2007 were derived based on the 

2002 and 2005 modeling results. Monitor-based concentrations reflect 4th high values, design 

values, or average of three design values centered on the year in question. The probability of 

attainment was determined as the percentage of these seven estimates below the standard. 

The results indicate that sites in the Lake Michigan area (Chiwaukee, Sheboygan, Holland, 

Muskegon), Cleveland (Ashtabula), and St. Louis (W Alton) have a fairly low probability of 

attainment by 2009 (i.e., about 50% or less).

Second, the primary modeling analysis was redone with different types of assumptions for 

calculating base year design values (i.e., using the 3-year period centered on base year, and 

using the highest 3-year period that includes the base year), and for calculating RRFs (i.e.,

using all days with base year modeled value > 70 ppb, and using all days with base year 

modeled value > 85 ppb, with at least 10 days and “acceptable” model performance). The 

results for several high concentration sites are presented in Tables 13a and 13b for 2009. The 

different modeling assumptions produce eight estimates of future year ozone concentrations. 

The highest estimates are associated with base year design values representing the 3-year 

average for 2001-2003, and the lowest estimates are associated with base year design values 

representing the 3-year average 2004-2006. The different RRF approaches produce little 

change in future year ozone concentrations. This suggests that future year concentration 

estimates are most sensitive to the choice of the base year and the methodology used to derive 

the base year design values.

Third, EPA’s modeling guidelines recommend that an “unmonitored area analysis” be included 

as a supplemental analysis, particularly in nonattainment areas where the monitoring network 

just meets or minimally exceeds the size of the network required to report data to EPA’s Air 

Quality System. The purpose of this analysis is to identify areas where future year values are 

predicted to be greater than the NAAQS. 

Based on examination of the spatial plots in Figures 49a and 49b, the most notable areas of 

high modeled ozone concentrations are over the Great Lakes. Over-water monitoring, however, 

is not required by EPA12. A cursory analysis of unmonitored areas for ozone was performed by 

LADCO using an earlier version of the 2002 base year modeling (i.e, Base I) (Baker, 2005). 

Base year and future year “observed” values were derived for unmonitored grid cells using the 

absolute modeled concentrations (in all grid cells) and the observed values (in monitored grid 

cells). A spatial map of the estimated 2009 values is provided in Figure 64. As can be seen, 

there are very few (over land) grid cells where additional monitors may be desirable. This 

indicates that the current modeling analysis, which focuses on monitored locations, is 

addressing areas of high ozone throughout the region.

 

12 Air quality measurements over Lake Michigan were collected by LADCO previously to understand 

ozone transport in the area (see, for example, Figure 5). Due to cut-backs in USEPA funding, however, 

these measurements were discontinued in 2003.
84

Table 13a. Primary and Additional Ozone Modeling Results – Lake Michigan and Cleveland Areas (2009)

2009 Modeling Results Lake Michigan Area Cleveland Area

Chiwaukee Harr.Beach Sheboygan DoorCounty Holland Hammond MichiganCity Ashtabula Geauga Eastlake

550590019 550890009 551170006 550290004 260050003 180892008 180910005 390071001 390550004 390850003

Attainment Test

(based on EPA guidance-2002 baseyear)

Base Year Design Value

(average of three 3-year periods)

98.3 93.0 97.0 91.0 94.0 88.3 90.3 95.7 99.0 92.7

RRF (all days > 85 ppb, or at least 10 days) 0.935 0.918 0.916 0.899 0.888 0.980 0.958 0.865 0.897 0.894

Future Year Design Value 91.9 85.4 88.9 81.8 83.5 86.5 86.5 82.8 88.8 82.9

Attainment Test

(based on EPA guidance-2005 baseyear)

Base Year Design Value

(average of three 3-year periods)

84.7 83.3 88.0 88.7 90.0 77.7 77.0 89.0 79.3 86.3

RRF (all days > 85 ppb, or at least 10 days) 0.972 0.961 0.955 0.946 0.948 0.971 0.960 0.937 0.942 0.949

Future Year Design Value 82.3 80.1 84.0 83.9 85.3 75.4 73.9 83.4 74.7 81.9

Weight of Evidence

(alternative approaches-2002baseyear)

Alt 1 - Base Year Des. Value

(3-year period centered on 2002)

101.0 98.0 100.0 94.0 97.0 90.0 93.0 99.0 103.0 95.0

Alt 2 - Base Year Des. Value

(Highest 3-year period including 2002 )

101.0 98.0 100.0 94.0 97.0 92.0 93.0 99.0 103 95.0

RRF (all days > 85 ppb, or at least 10 days) 0.935 0.918 0.916 0.899 0.888 0.980 0.958 0.865 0.897 0.894

Alt 1 - Future Year Projected Value 94.4 90.0 91.6 84.5 86.1 88.2 89.1 85.6 92.4 84.9

Alt 2 - Future Year Projected Value 94.4 90.0 91.6 84.5 86.1 90.2 89.1 85.6 92.4 84.9

Alt 1 - RRF (all days > 70 ppb) 0.933 0.918 0.912 0.907 0.893 0.969 0.947 0.876 0.907 0.900

Alt 1 - Future Year Projected Value 94.2 90.0 91.2 85.3 86.6 87.2 88.1 86.7 93.4 85.5

Alt 2 - Future Year Projected Value 94.2 90.0 91.2 85.3 86.6 89.1 88.1 86.7 93.4 85.5

Alt 2 - RRF (all days > 85 ppb, or at least 10 

days; with acceptable model performance)

0.945 0.904 0.910 0.904 0.887 0.976 0.964 0.866 0.896 0.894

Alt 1 - Future Year Projected Value 95.4 88.6 91.0 85.0 86.0 87.8 89.7 85.7 92.3 84.9

Alt 2 - Future Year Projected Value 95.4 88.6 91.0 85.0 86.0 89.8 89.7 85.7 92.3 84.9

Weight of Evidence

(alternative approaches-2005baseyear)

Alt 1 - Base Year Des. Value

(3-year period centered on 2005)

83.0 79.0 86.0 86.0 88.0 76.0 76.0 86.0 77.0 86.0

Alt 2 - Base Year Des. Value

(Highest 3-year period including 2005)

86.0 88.0 89.0 90.0 93.0 79.0 78.0 91.0 86.0 89.0

Alt 1 - Future Year Projected Value 80.7 75.9 82.1 81.4 83.4 73.8 73.0 80.6 72.5 81.6

Alt 2 - Future Year Projected Value 83.6 84.6 85.0 85.1 88.2 76.7 74.9 85.3 81.0 84.5
85

Table 13b. Primary and Additional Ozone Modeling Results – Cincinnati, Columbus, St. Louis, Indianapolis, and Detroit (2009)

2009 Modeling Results Cincinnati Area Columbus St. Louis Area Indianapolis Area Detroit Area

Wilmington Lebanon Sycamore NewAlbany W. Alton OrchardFarm Noblesville Fortville New Haven

390271002 39165007 390610006 390490029 291831002 291831004 180571001 18059003 260990009

Attainment Test

(based on EPA guidance-2002 baseyear)

Base Year Design Value

(average of three 3-year periods)

94.3 90.7 90.7 94.0 90.0 90.0 93.7 91.3 92.3

RRF (all days > 85 ppb, or at least 10 days) 0.885 0.908 0.938 0.888 0.947 0.914 0.894 0.918 0.924

Future Year Design Value 83.5 82.4 85.1 83.5 85.2 82.3 83.8 83.8 85.3

Attainment Test

(based on EPA guidance-2005 baseyear)

Base Year Design Value

(average of three 3-year periods)

82.3 87.7 84.3 86.3 86.3 87.0 83.3 78.7 86.0

RRF (all days > 85 ppb, or at least 10 days) 0.941 0.947 0.967 0.947 0.938 0.942 0.945 0.947 0.947

Future Year Design Value 77.4 83.1 81.5 81.7 80.9 82.0 78.7 74.5 81.4

Weight of Evidence

(alternative approaches-2002baseyear)

Alt 1 - Base Year Des. Value

(3-year period centered on 2002)

96.0 92.0 93.0 95.0 91.0 92.0 96.0 94.0 97.0

Alt 2 - Base Year Des. Value

(Highest 3-year period including 2002 )

96.0 92.0 93.0 96.0 91.0 92.0 96.0 94.0 97.0

RRF (all days > 85 ppb, or at least 10 days) 0.885 0.908 0.938 0.888 0.947 0.914 0.894 0.918 0.924

Alt 1 - Future Year Projected Value 85.0 83.5 87.2 84.4 86.2 84.1 85.8 86.3 89.6

Alt 2 - Future Year Projected Value 85.0 83.5 87.2 85.2 86.2 84.1 85.8 86.3 89.6

Alt 1 - RRF (all days > 70 ppb) 0.885 0.914 0.940 0.901 0.945 0.911 0.912 0.907 0.918

Alt 1 - Future Year Projected Value 85.0 84.1 87.4 85.6 86.0 83.8 87.6 85.3 89.0

Alt 2 - Future Year Projected Value 85.0 84.1 87.4 86.5 86.0 83.8 87.6 85.3 89.0

Alt 2 - RRF (all days > 85 ppb, or at least 10 days; 

with acceptable model performance)

0.880 0.911 0.940 0.886 0.951 0.913 0.894 0.916 0.935

Alt 1 - Future Year Projected Value 84.5 83.8 87.4 84.2 86.5 84.0 85.8 86.1 90.7

Alt 2 - Future Year Projected Value 84.5 83.8 87.4 85.1 86.5 84.0 85.8 86.1 90.7

Weight of Evidence

(alternative approaches-2005baseyear)

Alt 1 - Base Year Des. Value

(3-year period centered on 2005)

80.0 86.0 81.0 84.0 85.0 86.0 80.0 76.0 82.0

Alt 2 - Base Year Des. Value

(Highest 3-year period including 2005)

85.0 89.0 86.0 88.0 89.0 89.0 87.0 81.0 90.0

Alt 1 - Future Year Projected Value 75.3 81.4 78.3 79.5 79.7 81.0 75.6 72.0 77.7

Alt 2 - Future Year Projected Value 80.0 84.3 83.2 83.3 83.5 83.8 82.2 76.7 85.2
86

Figure 64. Estimated Future Year Values (unmonitored grid cells)

Finally, EPA’s latest regional ozone modeling was considered as corroborative information. 

This modeling was performed as part of the June 2007 proposal to revise the ozone standard 

(EPA, 2007b). EPA applied the CMAQ model with 2001 meteorology to first estimate ozone 

levels in 2020 based on the current standard and national rules in effect or proposed (i.e., the 

baseline), and then to evaluate strategies for attaining a more stringent (70 ppb) primary 

standard. Baseline (2020) ozone levels were predicted to be below the current standard in 481 

of the 491 counties with ozone monitors. Of the 10 counties predicted to be above the 

standard, there is one county in the LADCO region (i.e., Kenosha County, WI at 86 ppb). This 

result is consistent with LADCO’s Base K modeling for 2018 (i.e., Kenosha County, WI at 86.7 

ppb), which is not surprising given that EPA’s modeling and LADCO’s Base K modeling have a

similar base year (2001 v. 2002).

Analysis of Trends: EPA’s modeling guidelines note that while air quality models are generally 

the most appropriate tools for assessing the expected impacts of a change in emissions, it may 

also be possible to extrapolate future trends based on measured historical trends of air quality 

and emissions. To do so, USEPA’s guidance suggests that ambient trends should first be 

normalized to account for year-to-year variations in meteorological conditions (EPA, 2002). 

Meterologically-adjusted 4th high 8-hour ozone concentrations were derived using the air quality 

– meteorological regression model developed by EPA (i.e., Cox method – see Section 2.1). 

The historical trend in these met-adjusted ozone concentrations were extrapolated to estimate 

future year ozone concentrations based on historical and projected trends in precursor 

emissions. Both VOC and NOx emissions affect ozone concentrations. Given that observationbased methods show that urban areas in the region are generally VOC-limited and rural areas 

in the region are NOx-limited (see Section 2.1), urban VOC emissions and regional NOx 

emissions are considered important. The trends in urban VOC and regional NOx emissions 

were calculated to produce appropriate weighting factors. 

The resulting 2009 and 2012 ozone values are provided in Figure 65, along with the primary 

and alternative modeling ozone values for key sites in the Lake Michigan, Cleveland, and 

Cincinnati areas. The results reflect a fairly wide scatter, but, on balance, the supplemental 

information is supportive of the primary modeling results (i.e., sites in the Lake Michigan area 

and Cleveland are expected to be close to the standard).
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 65. Estimates of Future Year Ozone Concentrations – Lake Michigan Area (Sheboygan and Holland), Cincinnati (Sycamore), and 

Cleveland (Ashtabula)

Note: Primary (guideline) modeling values (Base K and Base M results) are represented by large red diamonds, additional modeling 

values by small black circles, and trends-based values by small pink squares
88

Observational Models and Diagnostic Analyses: The observation-based modeling (i.e., 

MAPPER) is presented in Section 3. The key findings from this modeling are that most urban 

areas are VOC-limited and rural areas are NOx-limited.

The primary diagnostic analysis is source apportionment modeling with CAMx to provide more 

quantitative information on source region (and source sector) impacts (Baker, 2007a). 

Specifically, the model estimated the impact of 18 geographic source regions (which are 

identified in Figure 66) and 6 source sectors (EGU point, non-EGU point, on-road, off-road, 

area, and biogenic sources) at ozone monitoring sites in the region.

 

Figure 66. Source regions (left) and key monitoring sites (right) for ozone modeling analysis

Modeling results for 2009 (Base M) and 2012 (Base K) are provided in Appendix II for several 

key monitoring sites. For each monitoring site, there are two graphs: one showing sector-level 

contributions, and one showing source region and sector-level contributions in terms of 

percentages. (Note, in the sector-level graph, the contributions from NOx emissions are shown 

in blue, and from VOC emissions in green.)

The sector-level results (see, for example, Figure 67) show that on-road and nonroad NOx 

emissions generally have the largest contributions at the key monitor locations (> 15% each). 

EGU and non-EGU NOx emissions are also important contributors (> 10% each). The source 

group contributions vary by receptor location due to emissions inventory differences. 

Figure 67. Source-sector results for Holland (left) and Ashtabula (right) monitors – 2009 (Base M)

Kenosha County

Cook County

Indianapolis

Holland

St. Louis

Detroit

Cincinnati

Door County

Cleveland
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

nonattainment area contributes 25-35% to high sites in the Lake Michigan area, and Cleveland 

nonattainment counties contribute 20-25% to high sites in northeastern Ohio), there is an even 

larger regional impact (i.e., contribution from other states).

Figure 68. Source-region results for Holland (left) and Ashtabula (right) monitors – 2009 (Base M)

Summary: Air quality modeling and other supplemental analyses were performed to estimate 

future year ozone concentrations. Based on this information, the following general conclusions 

can be made:

• Existing (“on the books”) controls are expected to produce significant 

improvement in ozone air quality.

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

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

above, 2002 was more ozone conducive than 2005. The choice of which base 

year to use as the basis for the SIP is a policy decision (i.e., how much safeguard 

to incorporate).

• Most sites are expected to meet the current 8-hour standard by the applicable 

attainment date, except, for sites in western Michigan and, possibly, in eastern 

Wisconsin and northeastern Ohio.

• Current monitoring data show significant nonattainment in these areas (e.g., 

peak design values on the order of 90 – 93 ppb). It is not clear whether sufficient 

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

attainment.

• Attainment by the applicable attainment date is dependent on actual future year 

meteorology (e.g., if the weather conditions are consistent with [or less severe 

than] 2005, then attainment is likely) and actual future year emissions (e.g., if the 

emission reductions associated with the existing controls are achieved, then 

attainment is likely). On the other hand, if either of these conditions is not met, 

then attainment may be less likely.
90

4.3 Weight-of-Evidence Determination for PM2.5

The WOE determination for PM2.5 consists of the primary modeling and other supplemental 

analyses. A summary of this information is provided below.

Primary (Guideline) Modeling: The results of the guideline modeling are presented in Section 

4.1. Key findings from this modeling include:

• Base M regional modeling shows attainment by 2009 at all sites, except Detroit, 

Cleveland, and Granite City, and attainment at all sites by 2012, except for Detroit 

and Granite City.

The regional modeling for PM2.5 does not reflect any air quality benefit expected 

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

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

demonstrations for PM2.5

• Base K modeling results reflect generally higher future year values, and show more 

sites in nonattainment in 2009 and 2012 compared to the Base M modeling. The 

difference in the two modeling analyses is due mostly to lower base year design 

values in Base M.

• Base K and Base M modeling analyses are considered “SIP quality”, so the 

attainment demonstration for PM2.5 should reflect a weight-of-evidence approach, 

with consideration of monitoring based information.

• Base M modeling also shows that the new PM2.5 24-hour standard will not be met at 

many sites, even by 2018, with existing controls.

Additional Modeling: EPA’s latest regional PM2.5 modeling was considered as corroborative 

information. This modeling was performed as part of the September 2006 revision to the PM2.5

standard (USEPA, 2006). EPA applied the CMAQ model with 2001 meteorology to estimate 

PM2.5 levels in 2015 and 2020 first with national rules in effect or proposed, and then with 

additional controls to attain the current standard (15 ug/m3 annual/65 ug/m3 daily). Additional 

analyses were performed to evaluate strategies for attaining more stringent standards in 2020 

(15/35, and 14/35). Baseline (2015) PM2.5 levels were predicted to be above the current 

standard in four counties in the LADCO region: Madison County, IL at 15.2 ug/m3, Wayne 

County, MI at 17.4, Cuyahoga County, OH at 15.4, and Scioto County, OH at 15.6. These 

results are consistent with LADCO’s Base K modeling for 2012/2018, which is not surprising 

given that EPA’s modeling and LADCO’s Base K modeling have a similar base year (2001 v. 

2002).

Observational Models and Diagnostic Analyses: The observation-based modeling (i.e., 

application of thermodynamic equilibrium models) is presented in Section 3. The key findings 

from this modeling are that PM2.5 mass is sensitive to reductions in sulfate, nitric acid, and 

ammonia concentrations. Even though sulfate reductions cause more ammonia to be available 

to form ammonium nitrate (PM-nitrate increases slightly when sulfate is reduced), this increase 

is generally offset by the sulfate reductions, such that PM2.5 mass decreases. Under conditions 

with lower sulfate levels (i.e., proxy of future year conditions), PM2.5 is more sensitive to 

reductions in nitric acid compared to reductions in ammonia.
91

The primary diagnostic analysis is source apportionment modeling with CAMx to provide more 

quantitative information on source region (and source sector) impacts (Baker, 2007b). 

Specifically, the model estimated the impact of 18 geographic source regions (which are 

identified in Figure 69) and 6 source sectors (EGU point, non-EGU point, on-road, off-road, 

area, and biogenic sources) at PM2.5 monitoring sites in the region.

 

Figure 69. Source regions (left) and key monitoring sites (right) for PM2.5 modeling analysis

Modeling results for 2012 (Base K) and 2018 (Base M) are provided in Appendix III for several 

key monitoring sites. For each monitoring site, there are two graphs: one showing sector-level 

contributions, and one showing source region and sector-level contributions in terms of absolute 

modeled values.

The sector-level results (see, for example, Figure 70) show that EGU sulfate, non-EGU-sulfate, 

and area organic carbon emissions generally have the largest contributions at the key monitor 

locations (> 15% each). Ammonia emissions are also important contributors (> 10%). The 

source group contributions vary by receptor location due to emissions inventory differences. 

Figure 70. Source-sector results for Detroit (left) and Cleveland (right) monitors – 2018 (Base M)

Cook County

Steubenville

Granite City

Detroit

Cincinnati

Cleveland
92

The source region results (see, for example, Figure 71) show that while nearby areas generally 

have the highest impacts (e.g., Detroit nonattainment counties contribute 40% to high sites in 

southeastern Michigan, and Cleveland nonattainment counties contribute 35% to high sites in 

northeastern Ohio), there is an even larger regional impact (i.e., contribution from other states).

Figure 71. Source-region results for Detroit (left) and Cleveland (right) monitors – 2018 (Base M)

Summary: Air quality modeling and other supplemental analyses were performed to estimate 

future year PM2.5 concentrations. Based on this information, the following general conclusions 

can be made:

• Existing (“on the books”) controls are expected to produce significant 

improvement in PM2.5 air quality.

• The choice of the base year affects the future year model projections. It is not 

clear how much of this is attributable to differences in meteorology, because, as 

noted in Section 3, PM2.5 concentrations are not as strongly influenced by 

meteorology as ozone.

• Most sites are expected to meet the current PM2.5 standard by the applicable 

attainment date, except for sites in Detroit, Cleveland, and Granite City.

• Current monitoring data show significant nonattainment in these areas (e.g., 

peak design values on the order of 16 – 17 ug/m3). It is not clear whether 

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

attainment. States are conducting local-scale analyses for Detroit, Cleveland, 

and Granite City, in particular, to identify appropriate additional local controls.

• Attainment by the applicable attainment date is dependent (possibly) on actual 

future year meteorology and (more likely) on actual future year emissions (e.g., if 

the emission reductions associated with the “on the books” controls are 

achieved, then attainment is likely). On the other hand, if either of these 

conditions is not met (especially, with respect to emissions), then attainment may 

be less likely.
93

Section 5. Reasonable Progress Assessment for Regional Haze

Air quality modeling and other information were used to assess the improvement in visibility that 

would be provided by existing (“on the books”) controls and possible additional control 

programs. In determining reasonable progress for regional haze, Section 169A of the Clean Air 

Act and EPA’s visibility rule requires states to consider five factors:

• costs of compliance

• time necessary for compliance

• energy and non-air quality environmental impacts of compliance

• remaining useful life of any existing source subject to such requirements

• uniform rate of visibility improvement needed to attain natural visibility conditions 

by 2064

The uniform rate of visibility improvement requirement can be depicted graphically in the form of 

a “glide path” (see Figure 72).

Figure 72. Visibility “glide paths” for northern Class I areas (units: deciviews)

5.1 Class I Areas Impacted

EPA’s visibility rule requires a state to “address regional haze in each mandatory Class I 

Federal area located within the State and in each mandatory Class I Federal area located 

outside the State which may be affected by emissions from within the State.” (40 CFR Part 

51.308(d)) To meet this requirement, technical analyses conducted by the RPOs were 

consulted to obtain information on areas of influence and culpability for Class I areas in the 

eastern U.S. (MRPO, 2007). A summary of this information is provided in Table 1 (MRPO, 

2007). The table shows that every LADCO State impacts multiple Class I areas in the eastern 

U.S.

5

10

15

20

25

2004 2010 2020 2030 2040 2050 2060 2064

Voyageurs Bounday Waters

Isle Royale Seney

Voyageurs Boundry Waters

20% best visibility days

20% worst visibility days

Baseline

Conditions

Baseline

Conditions

Natural

Conditions
94

Table 14. Draft List of Class I Areas Impacted by LADCO States

AREA NAME IL IN MI OH WI

81.401 Alabama.

Sipsey Wilderness Area (1) (1)

81.404 Arkansas.

Caney Creek Wilderness Area (2), (4) (2), (4) (2), (4)

Upper Buffalo Wilderness Area (1),(2),(4),(5) (2), (4) (2), (4) (2)

81.408 Georgia.

Cohotta Wilderness Area

Okefenokee Wilderness Area

Wolf Island Wilderness Area

81.411 Kentucky.

Mammoth Cave NP (1), (2), (5) (1), (2), (5) (1), (2) (1), (2), (5)

81.412 Louisiana.

Breton Wilderness Area

81.413 Maine.

Acadia National Park (3) (3) (3) (3)

Moosehorn Wilderness Area. (3) (3) (3) (3)

81.414 Michigan.

Isle Royale NP. (1), (2) (1), (2) (1), (2) (1), (2)

Seney Wilderness Area (1), (2) (1), (2) (1), (2) (1), (2) (1), (2)

81.415 Minnesota.

Boundary Waters Canoe Area Wilderness (2) (2) (2) (1), (2)

Voyageurs NP (2) (2) (1), (2)

81.416 Missouri.

Hercules-Glades Wilderness Area (2), (4), (5) (2), (4), (5) (2), (4) (2)

Mingo Wilderness Area (2), (4), (5) (2), (4), (5) (2) (2), (4) (2)

81.419 New Hampshire.

Great Gulf Wilderness Area (3) (3) (3) (1), (3)

Pres. Range-Dry River Wilderness Area.

81.42 New Jersey.

Brigantine Wilderness Area (3) (3) (1), (3) (1), (3)
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) VISTAS 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, Voyageurs, Isle Royale, and 

Seney (see Section 2.3);

• use of the new IMPROVE light extinction equation; and

• use of EPA default values for natural conditions, based on the new IMPROVE 

light extinction equation.

The uniform rate of visibility improvement values for the 2018 planning year were derived (for 

the 20% worst visibility days) based on a straight line between baseline concentration value 

(plotted in the year 2004 -- end year of the 5-year baseline period) and natural condition value 

(plotted in the year 2064 -- date for achieving natural conditions). Plots of these “glide paths” 

with the Base M modeling results are presented in Figure 73 for Class I areas in the eastern 

U.S. A tabular summary of measured baseline and modeled future year deciview values for 

these Class I areas are provided in Table 15 (2002 base year) and Table 16 (2005 base year)13.

The haze results show that several Class I areas in the eastern U.S. are expected to be greater 

than (less improved than) the uniform rate of visibility improvement values (in 2018), including 

those in northern Michigan and several in the northeastern U.S. Many other Class I areas in the 

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

improvement values (in 2018). As noted above, states should consider these results, along with 

information on the other four factors, in setting reasonable progress goals. 

An assessment of the five factors was performed for LADCO and the State of Minnesota by a 

contractor (EC/R, 2007). Specifically, ECR examined reductions in SO2 and NOx emissions 

from EGUs and industrial, commercial and institutional (ICI) boilers; NOx emissions from mobile 

sources and reciprocating engines and turbines; and ammonia emissions from agricultural 

operations. The impacts of “on the books” controls were also examined to provide a frame of 

reference for assessing the impacts of the additional control measures.

The results of ECR’s analysis of the five factors are summarized below:

Factor 1 (Cost of Compliance): The average cost effectiveness values (in terms of $M 

per ton) are provided in Table 16. For comparison, cost-effectiveness estimates 

previously provided for “on the books” controls include:

CAIR SO2: $700 - $1,200, NOx: $1,400 – $2.600 ($/T)

BART SO2: $300 - $963, NOx: $248 - $1,770

MACT SO2: $1,500, NOx: $7,600

Most of the cost-effectiveness values for the additional controls are within the range of 

cost-effectiveness values for “on the books” controls.

 

13 Model results reflect the grid cell where the IMPROVE monitor is located.
97

Voyageurs Boundary 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 17. Estimated Cost Effectiveness for Potential Control Measures

Average Cost effectiveness ($/ton)

Emission category Control strategy Region SO2 NOX NH3

EGU EGU1 3-State 1,540 2,037

9-State 1,743 1,782

EGU2 3-State 1,775 3,016

9-State 1,952 2,984

ICI boilers ICI1 3-State 2,992 2,537

9-State 2,275 1,899

ICI Workgroup 3-State 2,731 3,814

9-State 2,743 2,311

3-State 538 Reciprocating engines 

emitting 100 tons/year or 

more 9-State 506

Reciprocating engines 

and turbines

3-State 754 Turbines emitting 100 

tons/year or more 9-State 754

3-State 1,286 Reciprocating engines 

emitting 10 tons/year or more 9-State 1,023

3-State 800 Turbines emitting 10 

tons/year or more 9-State 819

10% reduction 3-State 31 - 2,700 Agricultural sources

9-State 31 - 2,700

15% reduction 3-State 31 - 2,700

9-State 31 - 2,700

Mobile sources Low-NOX Reflash 3-State 241

9-State 241

MCDI 3-State 10,697

9-State 2,408

Anti-Idling 3-State (430) - 1,700

9-State (430) - 1,700

Cetane Additive Program 3-State 4,119

9-State 4,119

Cement Plants Process Modification Michigan -

Conversion to dry kiln Michigan 9,848

LoToxTM Michigan 1,399

Glass Manufacturing LNB Wisconsin 1,041

Oxy-firing Wisconsin 2,833

Electric boost Wisconsin 3,426

SCR Wisconsin 1,054

SNCR Wisconsin 1,094

Lime Manufacturing Mid-kiln firing Wisconsin 688

LNB Wisconsin 837

SNCR Wisconsin 1,210

SCR Wisconsin 5,037

FGD Wisconsin 128 - 4,828

Oil Refinery LNB Wisconsin 3,288

SNCR Wisconsin 4,260

SCR Wisconsin 17,997

LNB+FGR Wisconsin 4,768

ULNB Wisconsin 2,242

FGD Wisconsin 1,078
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 believed to be manageable. For example, the increased 

energy demand from add-on control equipment is less than 1% of the total electricity 

and steam production in the region, and solid waste disposal and wastewater treatment 

costs are less than 5% of the total operating costs of the pollution control equipment. It 

should also be noted that the SO2 and NOx controls would have beneficial 

environmental impacts (e.g., reduced acid deposition and nitrogen deposition).

Factor 4 (Remaining Useful Life): The additional control measures are intended to be 

market-based strategies applied over a broad geographic region. It is not expected that 

the control requirements will be applied to units that will be retired prior to the 

amortization period for the control equipment. Thus, this factor can be easily addressed.

Factor 5 (Visibility Impacts): The estimated incremental improvement in 2018 visibility 

levels for the additional measures is shown in Figure 74, along with the costeffectiveness expressed in $M per deciview improvement). These results show that 

although EGU and ICI boiler controls have higher cost-per-deciview values (compared 

to some of the other measures), their visibility impacts are larger.
103

Visibility Improvement (dv)

0.00

0.25

0.50

0.75

1.00

SO2NOx NH3 SO2NOx NH3 SO2 NOx NH3 SO2 NOx NH3 SO2NOx NH3

Strategy 1 Strategy 2

EGUs ICI Boilers Rec.Eng. Mobile Ag

Cost Effectiveness ($M/dv)

0

1000

2000

3000

4000

5000

6000

SO2 NOx NH3 SO2NOx NH3 SO2 NOx NH3 SO2 NOxNH3 SO2 NOx NH3

Strategy 1 Strategy 2

EGUs ICI Boilers Rec.Eng. Mobile Ag

Figure 74. Results of ECR analysis of reasonable progress factors – visibility improvement (Factor 

5) is on top, and cost effectiveness (Factor 1) is on bottom
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 areas are close 

to the glide path, whereas the northern Michigan Class I areas are above the glide 

path in 2018. Other sites in the eastern U.S. are close to (or below) the glide path, 

except for Mingo (MO), Brigantine (NJ), and Acadia (ME).

• Base K modeling results show that the northern Minnesota and northern Michigan 

Class I areas are above the glide path in 2018. Other sites in the eastern U.S. are 

close to (or below) the glide path. 

• The difference in the two modeling analyses is due mostly to differences in future 

year emission projections, especially for EGUs (e.g., use of IPM2.1.9 v. IPM3.0).

• Base K and Base M modeling analyses are considered “SIP quality”, so the 

attainment demonstration for haze should reflect a weight-of-evidence approach, 

with consideration of monitoring based information.

Additional Modeling: Two additional modeling analyses were considered: (1) the primary 

modeling redone with different baseline values, and (2) modeling by the State of Minnesota 

which looked at different receptor locations in the northern Class I areas (MPCA, 2008). Each 

of these analyses is described below.

First, the primary modeling analysis (Base M) was revised using an alternative baseline value. 

Specifically, the data for the period 2000-2005 were used to calculate the baseline, given that 

the Base M modeling reflects a 2005 base year. The results of this alternative analysis (see 

Table 18) are generally consistent with the primary modeling (see Table 16).

Second, Minnesota’s modeling reflects a 2002 base year and much of the data developed by 

LADCO for its modeling. (Note, Minnesota conducted modeling for LADCO’s domain at 36 km, 

and for a statewide domain at 12 km.) The purpose of the 12 km modeling was to address local 

scale impacts on the northern Class I areas at several locations, not just the location of the 

IMPROVE monitor. Results for the Boundary Waters on the 20% worst days range from 18.3 –

19.0 dv, with an average value of 18.7 dv, which is consistent with Minnesota’s 36 km modeling 

results at the IMPROVE monitor. This variability in visibility levels should be kept in mind when 

reviewing the values presented in Tables 15, 16, and 18, which reflect results at the IMPROVE 

monitor locations.
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

Observational Models and Diagnostic Analyses: The observation-based modeling (i.e., 

application of thermodynamic equilibrium models) is presented in Section 3. The key findings 

from this modeling are that PM2.5 mass is sensitive to reductions in sulfate, nitric acid, and 

ammonia concentrations. Even though sulfate reductions cause more ammonia to be available 

to form ammonium nitrate (PM-nitrate increases slightly when sulfate is reduced), this increase 

is generally offset by the sulfate reductions, such that PM2.5 mass decreases and visibility 

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

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

As discussed in Section 2, thermodynamic equilibrium modeling based on data collected at 

Seney indicates that PM2.5 there is most sensitive to reductions in sulfate, but also responsive to 

reductions in nitric acid (Blanchard, 2004). An analysis using data from the Midwest ammonia 

monitoring network for a site in Minnesota (i.e., Great River Bluffs, which is the closest ammonia 

monitoring site to the northern Class I areas) suggested that reductions in sulfate, nitric acid, 

and ammonia concentrations will lower PM2.5 concentrations and improve visibility levels in the 

northern Class I areas.

Trajectory analyses for the 20% worst visibility days for the four northern Class I areas are 

provided in Figure 75. (Note, this figure is similar to Figure 34, but the trajectory results for each 

Class I area are displayed separately here.) The orange areas are where the air is most likely 

to come from, and the green areas are where the air is least likely to come from. Darker 

shading represents higher frequency. As can be seen, bad air days are generally associated 

with transport from regions located to the south, and good air days with transport from Canada. 
107

Seney Isle Royale

Boundary Waters Voyageurs

Figure 75. Trajectory analysis results for northern Class I areas on 20% worst visibility days

The primary diagnostic analysis is source apportionment modeling with CAMx to provide more 

quantitative information on source region (and source sector) impacts (Baker, 2007b). 

Specifically, the CAMx model was applied to provide source contribution information. 

Specifically, the model estimated the impact of 18 geographic source regions (which are 

identified in Figure 76) and 6 source se ctors (EGU point, non-EGU point, on-road, off-road, 

area, and ammonia sources) at visibility/haze monitoring sites in the eastern U.S.
108

 

Figure 76. Source regions (left) and key monitoring sites (right) for haze modeling analysis

Modeling results for 2018 (Base K and Base M) are provided in Appendix IV for several key 

monitoring sites (Class I areas). For each monitoring site, there are two graphs: one showing 

sector-level contributions, and one showing source region and sector-level contributions in 

terms of absolute modeled values.

The sector-level results (see, for example, Figure 77) show that EGU sulfate, non-EGU-sulfate, 

and ammonia emissions generally have the largest contributions at the key monitor locations. 

The source group contributions vary by receptor location due to emissions inventory differences. 

Figure 77. Source-sector results for Seney (left) and Boundary Waters (right) – 2018 (Base M)

The source region results (see, for example, Figure 78) show that emissions from a number of 

nearby states contribute to regional haze levels.
109

Figure 78. Source-region results for Seney (left) and Boundary Waters (right) – 2018 (Base M)

Table 19 provides a summary of the estimated state-level culpabilities based on the LADCO 

back trajectory analyses and the PSAT analyses for 2018.

Summary: Air quality modeling and other supplemental analyses were performed to estimate 

future year visibility levels. Based on this information, the following general conclusions can be 

made:

• Existing (“on the books”) controls are expected to improve visibility levels in the 

northern Class I areas.

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

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

values in 2018, including those in northern Michigan and some in the 

northeastern U.S. 

• Visibility levels in many other Class I areas in the eastern U.S. are expected to 

be less than (more improved than) the uniform rate of visibility improvement 

values in 2018.
110

Table 19. State Culpabilities Based on PSAT Modeling and Trajectory Analyses

Boundary Waters Seney

LADCO -

Round 4 

PSAT

LADCO -

Round 5 

PSAT

MPCAPSATCENRAP -

PSAT

LADCO -

Traj. Analysis

LADCO -

Round 4 

PSAT

LADCO -

Round 5 

PSAT

CENRAP -

PSAT

LADCO -

Traj. Analysis

Michigan 3.4% 4.8% 3.0% 1.9% 0.7% 13.8% 18.1% 14.7%

Minnesota 30.5% 23.5% 28.0% 30.6% 37.6% 4.8% 1.6% 3.8%

Wisconsin 10.4% 10.9% 10.0% 6.4% 10.6% 12.6% 10.9% 8.4%

Illinois 5.2% 5.1% 6.0% 3.5% 2.7% 13.0% 14.3% 7.4%

Indiana 2.9% 3.9% 3.0% 1.8% 1.2% 9.6% 11.6% 2.2%

Iowa 7.6% 8.3% 8.0% 2.5% 7.4% 6.2% 3.8% 5.7%

Missouri 5.2% 3.4% 6.0% 2.1% 3.3% 6.5% 4.8% 3.2%

N. Dakota 5.7% 1.1% 6.0% 4.6% 5.9% 1.5% 0.1% 0.6%

Canada 1.9% 2.7% 3.0% 12.5% 15.1% 2.1% 1.2% 11.1%

CENRAPWRAP 10.9% 13.5% 4.2% 10.1% 13.1% 10.0% 7.0%

83.6% 77.2% 73.0% 70.2% 94.6% 83.3% 76.4% 64.1%

Voyageurs Isle Royale

LADCO -

Round 4 

PSAT

LADCO -

Round 5 

PSAT

MPCAPSATCENRAP -

PSAT

LADCO -

Traj. Analysis

LADCO -

Round 4 

PSAT

LADCO -

Round 5 

PSAT

CENRAP -

PSAT

LADCO -

Traj. Analysis

Michigan 2.0% 4.9% 2.0% 1.0% 1.6% 12.7% 13.4%

Minnesota 35.0% 20.2% 31.0% 31.5% 36.9% 14.1% 9.5%

Wisconsin 6.3% 7.9% 6.0% 3.7% 9.7% 16.3% 14.7%

Illinois 3.0% 7.1% 3.0% 1.8% 1.2% 7.0% 8.7%

Indiana 1.6% 4.6% 2.0% 0.8% 5.6% 5.2%

Iowa 7.4% 7.1% 7.0% 2.4% 10.2% 6.9% 8.3%

Missouri 4.3% 4.0% 4.0% 1.6% 0.3% 3.9% 4.6%

N. Dakota 10.3% 1.7% 13.0% 6.1% 7.1% 3.6% 0.3%

Canada 2.7% 3.3% 5.0% 17.2% 13.3% 2.2% 1.7%

CENRAPWRAP 10.2% 13.7% 6.1% 16.5% 12.5% 12.6%

82.7% 74.5% 73.0% 72.2% 96.8% 84.9% 79.0%
111

Section 6. Summary

To support the development of SIPs for ozone, PM2.5, and regional haze in the States of Illinois, 

Indiana, Michigan, Ohio, and Wisconsin, technical analyses were conducted by LADCO, its 

member states, and various contractors. The analyses include preparation of regional 

emissions inventories and meteorological modeling data for two base years, evaluation and 

application of regional chemical transport models, and review of ambient monitoring data. 

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

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

formation and distribution of pollutants in a given region. Key findings of the analyses include:

Ozone

• Current monitoring data show about 20 sites in violation of the 8-hour ozone 

standard of 85 ppb. Historical ozone data show a steady downward trend over the 

past 15 years, especially since 2001-2003, due likely to federal and state emission 

control programs.

• Ozone concentrations are strongly influenced by meteorological conditions, with 

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

temperatures.

• Inter- and intra-regional transport of ozone and ozone precursors affects many 

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

far from population or industrial centers

PM2.5

• Current monitoring data show 30 sites in violation of the annual PM2.5 standard of 15 

ug/m3. Nonattainment sites are characterized by an elevated regional background 

(about 12 – 14 ug/m3) and a significant local (urban) increment (about 2 – 3 ug/m3). 

Historical PM2.5 data show a slight downward trend since deployment of the PM2.5

monitoring network in 1999.

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

complex and less well understood compared to ozone.

• On an annual average basis, PM2.5 chemical composition consists of mostly sulfate, 

nitrate, and organic carbon in similar proportions.

Haze

• Current monitoring data show visibility levels in the Class I areas in northern 

Michigan are on the order of 22 – 24 deciviews. The goal of EPA’s visibility program 

is to achieve natural conditions, which is on the order of 12 deciviews for these 

Class I areas, by the year 2064.

• Visibility impairment is dominated by sulfate and nitrate.

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

used in the modeling analyses: 2002 and 2005. EPA’s modeling guidance recommends using 
112

2002 as the baseline inventory year, but also allows for use of an alternative baseline inventory 

year, especially a more recent year. Initially, LADCO conducted modeling with a 2002 base 

year (i.e., Base K modeling, which was completed in 2006). A decision was subsequently made 

to conduct modeling with a 2005 base year (i.e., Base M, which was completed in 2007). 

Statistical analyses showed that 2002 and 2005 both had above normal ozone-conducive 

conditions, although 2002 was more severe compared to 2005. Examination of multiple base 

years provides for a more complete technical assessment. Both sets of model runs are 

discussed in this document. 

Basecase modeling was conducted to evaluate model performance (i.e., assess the model's 

ability to reproduce the observed concentrations). This exercise was intended to assess 

whether, and to degree, confidence in the model is warranted (and to assess whether model 

improvements are necessary). Model performance for ozone and PM2.5 was generally

acceptable and can be characterized as follows:

Ozone

• Good agreement between modeled and monitored concentration for higher 

concentration levels (> 60 ppb) – i.e., bias within 30%

• Regional modeled concentrations appear to be underestimated in the 2002 base 

year, but show better agreement (with monitored data) in the 2005 base year due to 

model and inventory improvements.

• Day-to-day and hour-to-hour variation in and spatial patterns of modeled 

concentrations are consistent with monitored data

• Model accurately simulates the change in monitored ozone concentrations due to 

reductions in precursor emissions.

PM2.5

• Good agreement in the magnitude of fine particle mass, but some species are 

overestimated and some are underestimated

• Sulfates: good agreement in the 2002 base year, but underestimated in 

the summer in the 2005 base year due probably to meteorological factors

• Nitrates: slightly overestimated in the winter in the 2002 base year, but 

good agreement in the 2005 base year as a result of model and inventory 

improvements

• Organic Carbon: grossly underestimated in the 2002 and 2005 base 

years due likely to missing primary organic carbon emissions

• Temporal variation and spatial patterns of modeled concentrations are consistent 

with monitored data

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

controls would be sufficient to provide for attainment of the standards for ozone and PM2.5 and if 

not, then what additional emission reductions would be necessary for attainment. Traditionally, 

attainment demonstrations involved a “bright line” test in which a single modeled value (based 

on EPA guidance) was compared to the ambient standard. To provide a more robust 

assessment of expected future year air quality, other information was considered. Furthermore, 

according to EPA’s modeling guidance, if the future year modeled values are “close” to the 
113

standard (i.e., 82 – 87 ppb for ozone and 14.5 – 15.5 ug/m3 for PM2.5 ), then the results of the 

primary modeling should be reviewed along with the supplemental information in a “weight of 

evidence” (WOE) assessment of whether each area is likely to achieve timely attainment. Key 

findings of the WOE determination include:

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

PM2.5 concentrations and visibility levels.

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

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

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

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

• Most sites are expected to meet the current 8-hour standard by the applicable 

attainment date, except for sites in western Michigan and, possibly, in eastern 

Wisconsin and northeastern Ohio.

• Most sites are expected to meet the current PM2.5 standard by the applicable 

attainment date, except for sites in Detroit, Cleveland, and Granite City.

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

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

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

attainment demonstrations for PM2.5.

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

current (2005 – 2007) monitoring data which show significant nonattainment in 

the region (e.g., peak ozone design values on the order of 90 – 93 ppb, and peak 

PM2.5 design values on the order of 16 - 17 ug/m3). It is unlikely that sufficient 

emission reductions will occur in the next few of years to provide for attainment at 

all sites.

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

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

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

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

are achieved, then attainment is likely). If either of these conditions is not met, 

then attainment may be less likely.

• The new PM2.5 24-hour standard and the new lower ozone standard will not be 

met at several sites, even by 2018, with existing controls.

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

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

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

and some in the northeastern U.S. Visibility levels in many other Class I areas in 

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

rate of visibility improvement values in 2018.
114

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Research, Inc., August 2004.

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

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

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the Great Lakes Region”, Lake Michigan Air Directors Consortium, November 2007.

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Hour Ozone to Support Model Attainment Demonstrations”, Lake Michigan Air Directors 

Consortium, October 2007.

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Monitoring Sites in Illinois, Indiana, Michigan, Missouri, Ohio, and Wisconsin, 1998-2002”, 

Report to LADCO, February 2003..

Blanchard, C.L., and S. Tanenbaum, 2004, “The Effects of Changes in Sulfate, Ammonia, and 

Nitric Acid on Fine PM Composition at Monitoring Sites in Illinois, Indiana, Michigan, Missouri, 

Ohio, and Wisconsin, 2000-2002”, Report to LADCO, February 2004.

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Concentrations of Primary and Secondary Air Pollutants in Atlanta, Baltimore, Chicago, DallasFort Worth, Denver, Houston, New York, Phoenix, Washington, and Surrounding Areas”, 

Prepared for National Renewable Energy Laboratory, NREL Project ES04-1, July 30, 2005.

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Monitoring Project”, Draft Final Technical Memorandum, March 31, 2005.

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Camalier, L., Cox, W., Dolwick, P., 2007. The effects of meteorology on ozone in urban areas 

and their use in assessing ozone trends. Atmospheric Environment 41, 7127-7137

Cox, W.M, and S.-H. Chu, 1993, Meteorologically Adjusted Ozone Trends in Urban Areas: A 

Probabililistic Approach, Atmospheric Environment 27B(4):425-434 (1993).

DeBell, L.J., K. Gebhart, J. Hand, W. Malm, M. Pitchford, B. Schichtel, and W. White, 2006, 

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United States, Report IV”, Cooperative Institute for Research in the Atmosphere, November 

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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 Northern Midwest –

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

E.H. Pechan, 2004, “LADCO Nonroad Emissions Inventory Project – Development of Local 

Data for Construction and Agricultural Equipment”, Final Report, September 10, 2004

E.H. Pechan, 2005, “Development of Updated Growth and Control Factors for Lake Michigan 

Air Directors Consortium (LADCO)”, Draft Report, December 29, 2005.

E. H. Pechan, 2007, “Development of 2005 Base Year Growth and Control Factors for Lake 

Michigan Air Directors Consortium (LADCO)”, Final Report, September 2007.

Environ, 2004, “LADCO Nonroad Emissions Inventory Project for Locomotive, Commercial 

Marine, and Recreational Marine Emission Sources, Final Report, December 2004.

Environ, 2007a, “Modeling Weekend and Weekday Ozone in Southeast Michigan”, Draft Final 

Report, Prepared for National Renewable Energy Laboratory, July 30, 2007.

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

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

Environ, 2008, “LADCO On-Road Emission Inventory Development Using CONCEPT MV”, 

January 2008..

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

New York (1973).

Hopke, P. K., 2005, Analyses of Midwest PM-Related Measurements, Report to LADCO, March 

2005.

Kenski, D.M., 2004, Quantifying Transboundary Transport of PM2.5: A GIS Analysis, Proc. 

AWMA 97th Annual Conf., Indianapolis, June 2004.

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

2007.

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

Areas”, Donna Kenski, Midwest RPO, March 12, 2007 (revised 6/19/07).

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

Kenski, D.M. 2008b, “Updated CART Analysis for Ozone through 2007”, January 30, 2008.
116

LADCO, 2005, “Meteorological Modeling Performance Summary for Applications to 

PM2.5/Haze/Ozone Modeling Projects”, February 18, 2005.

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

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

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

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

LADCO, 2007c, “An Evaluation of an Annual 2005 MM5 Simulation to Support Photochemical 

and Emissions Modeling Applications”, January 10, 2007.

LADCO, 2007d, “Base M/Round 5 Modeling: Summary (DRAFT), October 10, 2007

LADCO, 2008a, “Base M Strategy Modeling: Emissions (Revised)”, February 27, 2008

LADCO, 2008b, “Critique: Modeled Attainment Probability (i.e., Bob Lopez’ analysis)”, January 

10, 2008

Lopez, 2007, “Assessing the Site-Specific and Region-wide Probability of Attainment Level 

Ozone Air Quality under varying seasonal conditions using the results of 2002 (Round 4) and 

2005 (Round 5) Regional Modeling”, Draft 2, Bob Lopez, WDNR, December 12, 2007

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

Improved Process-Based Ammonia Emissions Model for Agricultural Sources – Emission 

Estimates”

MPCA, 2008, “Technical Support Document for the Minnesota State Implementation Plan for 

Regional Haze”, Draft, March 4, 2008.

MRPO, 2001, “Principles for Regional Planning”, March 9, 2001.

MRPO, 2007, “Draft List of Class I Areas Located Within (or Impacted by) Midwest RPO 

States”, June 26, 2007.

MRPO, 2008, “Regional Haze in the Upper Midwest: Summary of Technical Information”, 

Version 2.2, February 22, 2008.

Millstein, D.E., R.A. Harley, and S.V. Hering, 2007, “Weekly cycles in fine particulate matter”, 

Atmospheric Environment (2007), doi:10.1016/j.atmosenv.2007.10.010.

Poirot, R. L.; Wishinski, P. R.; Hopke, P. K.; Polissar, A. V.; Comparative Application of Multiple 

Receptor Methods To Identify Aerosol Sources in Northern Vermont, Environ. Sci. Technol.,

36(4); 820-820 (2002).

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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-ES3, Sonoma Technology, Inc, August 5, 

2004.

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

STI-903520-2942-FR, Sonoma Technology, Inc. March 31, 2006.

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Final Report, STI-907018.03-3264-DRF, Sonoma Technology, Inc., February 2008.

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

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Rule”, EPA-454/B-03-005, September 2003.

EPA, 2002, “Recommended Approach for Performing Mid-Course Review of SIPs to Meet the 

1-Hour NAAQS for Ozone”, January 2002.

EPA, 2005, Final Rule to Implement the 8-Hour Ozone National Ambient Air Quality Standard, 

70 FR 71612, November 29, 2005

EPA, 2006, “Regulatory Impact Analysis of the Revisions to the National Ambient Air Quality 

Standards”, September 2006 (http://www.epa.gov/ttn/ecas/ria.html#ria2007)

EPA, 2007a, "Guidance for on the Use of Models and Other Analyses for Demonstrating 

Attainment of Air Quality Goals for Ozone, PM2.5, and Regional Haze”, EPA-454/B07-002, April 

2007. 

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Quality Standards”, EPA-452/R-07-008, July 2007

(http://www.epa.gov/ttn/ecas/ria.html#ria2007)

EPA, 2007c, Clean Air Fine Particle Implementation Rule, 72 FR 20586, April 25, 2007

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

Process-Based Ammonia Emissions Model for Agricultural Sources – Model Development”

Web Sites:

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

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

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

http://vista.cira.colostate.edu/views/
118

APPENDIX I

Ozone and PM2.5 Modeling Results
Key Sites 2005 BY 2002 BY

'03 '04 '05 '06 '07 '03-'05 '04-'06 '05-'07 Average Average RRF Round 5

Lake Michigan Area Lake Michigan Area

Chiwaukee 550590019 88 78 93 79 85 86 83 85 84.7 98.3 0.968 82.0 Chiwaukee

Racine 551010017 82 69 95 71 77 82 78 81 80.3 91.7 0.966 77.6 Racine

Milwaukee-Bayside 550190085 92 73 93 73 83 86 79 83 82.7 91.0 0.963 79.6 Milwaukee-Bayside

Harrington Beach 550890009 99 72 94 72 84 88 79 83 83.3 93.0 0.960 80.0 Harrington Beach

Manitowoc 550710007 92 74 95 78 85 87 82 86 85.0 87.0 0.957 81.3 Manitowoc

Sheboygan 551170006 93 78 97 83 88 89 86 89 88.0 97.0 0.959 84.4 Sheboygan

Kewaunee 550610002 97 73 88 76 85 86 79 83 82.7 89.3 0.954 78.9 Kewaunee

Door County 550290004 93 78 101 79 92 90 86 90 88.7 91.0 0.956 84.8 Door County

Hammond 180892008 81 67 87 75 77 78 76 79 77.7 88.3 0.971 75.4 Hammond

Whiting 180890030 64 88 81 88 76 77 85 79.3 0.971 77.0 Whiting

Michigan City 180910005 82 70 84 75 73 78 76 77 77.0 90.3 0.964 74.2 Michigan City

Ogden Dunes 181270020 77 69 90 70 84 78 76 81 78.3 86.3 0.967 75.7 Ogden Dunes

Holland 260050003 96 79 94 91 94 89 88 93 90.0 94.0 0.951 85.6 Holland

Jenison 261390005 91 69 86 83 88 82 79 85 82.0 86.0 0.950 77.9 Jenison

Muskegon 261210039 94 70 90 90 86 84 83 88 85.0 90.0 0.951 80.8 Muskegon

Indianapolis Area Indianapolis Area

Noblesville 189571001 101 75 87 77 84 87 79 82 82.7 93.7 0.944 78.0 Noblesville

Fortville 180590003 92 72 80 75 81 81 75 78 78.0 91.3 0.948 73.9 Fortville

Fort B. Harrison 180970050 91 73 80 76 83 81 76 79 78.7 90.0 0.951 74.8 Fort B. Harrison

Detroit Area Detroit Area

New Haven 260990009 102 81 88 78 93 90 82 86 86.0 92.3 0.962 82.7 New Haven

Warren 260991003 101 71 89 78 91 87 79 86 84.0 90.0 0.982 82.5 Warren

Port Huron 261470005 87 74 88 78 89 83 80 85 82.7 88.0 0.956 79.0 Port Huron

Cleveland Area Cleveland Area

Ashtabula 390071001 99 81 93 86 92 91 86 90 89.0 95.7 0.954 84.9 Ashtabula

Geauga 390550004 97 75 88 70 68 86 77 75 79.3 99.0 0.954 75.7 Geauga

Eastlake 390850003 92 79 97 83 74 89 86 84 86.3 92.7 0.959 82.8 Eastlake

Akron 391530020 89 77 89 77 91 85 81 85 83.7 93.3 0.948 79.3

Cincinnati Area Cincinnati Area

Wilmington 390271002 96 78 83 81 82 85 80 82 82.3 94.3 0.945 77.8 Wilmington

Sycamore 390610006 93 76 89 81 90 86 82 86 84.7 90.3 0.965 81.7 Sycamore

Lebanon 391650007 95 81 92 86 88 89 86 88 87.7 87.0 0.954 83.6 Lebanon

Columbus Area Columbus Area

London 390970007 90 75 81 76 83 82 77 80 79.7 88.7 0.946 75.4 London

New Albany 390490029 94 78 92 82 87 88 84 87 86.3 93.0 0.954 82.4 New Albany

Franklin 290490028 84 73 86 79 79 81 79 81 80.3 86.0 0.958 77.0 Franklin

St. Louis Area St. Louis Area

W. Alton (MO) 291831002 91 77 89 91 89 85 85 89 86.3 90.0 0.954 82.4 W. Alton (MO)

Orchard (MO) 291831004 90 76 92 92 83 86 86 89 87.0 90.0 0.958 83.3 Orchard (MO)

Sunset Hills (MO) 291890004 88 70 89 80 89 82 79 86 82.3 88.3 0.966 79.5 Sunset Hills (MO)

Arnold (MO) 290990012 82 70 92 79 87 81 80 86 82.3 84.7 0.956 78.7 Arnold (MO)

Margaretta (MO) 295100086 90 72 91 76 91 84 79 86 83.0 87.7 0.962 79.8 Margaretta (MO)

Maryland Heights (MO) 291890014 88 84 94 88 86 88 87.3 0.967 84.5 Maryland Heights (MO)

4th High 8-hour Value Des. Values (truncated) 2008 - OTB
Key Sites 2005 BY 2002 BY

'03 '04 '05 '06 '07 '03-'05 '04-'06 '05-'07 Average Average RRF Round 5 Round 4 RRF Round 5

Lake Michigan Area Lake Michigan Area

Chiwaukee 550590019 88 78 93 79 85 86 83 85 84.7 98.3 0.972 82.3 92.0 0.971 82.2 Chiwaukee

Racine 551010017 82 69 95 71 77 82 78 81 80.3 91.7 0.965 77.5 84.9 0.964 77.4 Racine

Milwaukee-Bayside 550190085 92 73 93 73 83 86 79 83 82.7 91.0 0.965 79.8 84.9 0.964 79.7 Milwaukee-Bayside

Harrington Beach 550890009 99 72 94 72 84 88 79 83 83.3 93.0 0.961 80.1 85.4 0.960 80.0 Harrington Beach

Manitowoc 550710007 92 74 95 78 85 87 82 86 85.0 87.0 0.951 80.8 78.9 0.949 80.7 Manitowoc

Sheboygan 551170006 93 78 97 83 88 89 86 89 88.0 97.0 0.955 84.0 88.9 0.953 83.9 Sheboygan

Kewaunee 550610002 97 73 88 76 85 86 79 83 82.7 89.3 0.945 78.1 81.0 0.943 78.0 Kewaunee

Door County 550290004 93 78 101 79 92 90 86 90 88.7 91.0 0.946 83.9 81.8 0.945 83.8 Door County

Hammond 180892008 81 67 87 75 77 78 76 79 77.7 88.3 0.971 75.4 86.6 0.970 75.3 Hammond

Whiting 180890030 64 88 81 88 76 77 85 79.3 0.971 77.0 0.970 77.0 Whiting

Michigan City 180910005 82 70 84 75 73 78 76 77 77.0 90.3 0.960 73.9 86.5 0.959 73.8 Michigan City

Ogden Dunes 181270020 77 69 90 70 84 78 76 81 78.3 86.3 0.965 75.6 82.8 0.964 75.5 Ogden Dunes

Holland 260050003 96 79 94 91 94 89 88 93 90.0 94.0 0.948 85.3 83.4 0.947 85.2 Holland

Jenison 261390005 91 69 86 83 88 82 79 85 82.0 86.0 0.940 77.1 77.6 0.939 77.0 Jenison

Muskegon 261210039 94 70 90 90 86 84 83 88 85.0 90.0 0.947 80.5 81.5 0.945 80.3 Muskegon

Indianapolis Area Indianapolis Area

Noblesville 189571001 101 75 87 77 84 87 79 82 82.7 93.7 0.945 78.1 83.7 0.946 78.2 Noblesville

Fortville 180590003 92 72 80 75 81 81 75 78 78.0 91.3 0.947 73.9 83.8 0.948 73.9 Fortville

Fort B. Harrison 180970050 91 73 80 76 83 81 76 79 78.7 90.0 0.955 75.1 83.7 0.956 75.2 Fort B. Harrison

Detroit Area Detroit Area

New Haven 260990009 102 81 88 78 93 90 82 86 86.0 92.3 0.947 81.4 85.3 0.947 81.4 New Haven

Warren 260991003 101 71 89 78 91 87 79 86 84.0 90.0 0.968 81.3 83.3 0.969 81.4 Warren

Port Huron 261470005 87 74 88 78 89 83 80 85 82.7 88.0 0.937 77.5 79.1 0.938 77.5 Port Huron

Cleveland Area Cleveland Area

Ashtabula 390071001 99 81 93 86 92 91 86 90 89.0 95.7 0.937 83.4 82.7 0.941 83.7 Ashtabula

Geauga 390550004 97 75 88 70 68 86 77 75 79.3 99.0 0.942 74.7 88.8 0.945 75.0 Geauga

Eastlake 390850003 92 79 97 83 74 89 86 84 86.3 92.7 0.949 81.9 82.8 0.954 82.4 Eastlake

Akron 391530020 89 77 89 77 91 85 81 85 83.7 93.3 0.934 78.1 81.4 0.935 78.2

Cincinnati Area Cincinnati Area

Wilmington 390271002 96 78 83 81 82 85 80 82 82.3 94.3 0.941 77.5 83.5 0.942 77.6 Wilmington

Sycamore 390610006 93 76 89 81 90 86 82 86 84.7 90.3 0.967 81.9 84.7 0.968 82.0 Sycamore

Lebanon 391650007 95 81 92 86 88 89 86 88 87.7 87.0 0.947 83.0 79.0 0.948 83.1 Lebanon

Columbus Area Columbus Area

London 390970007 90 75 81 76 83 82 77 80 79.7 88.7 0.941 75.0 78.4 0.942 75.0 London

New Albany 390490029 94 78 92 82 87 88 84 87 86.3 93.0 0.947 81.8 82.6 0.948 81.8 New Albany

Franklin 290490028 84 73 86 79 79 81 79 81 80.3 86.0 0.945 75.9 76.5 0.948 76.2 Franklin

St. Louis Area St. Louis Area

W. Alton (MO) 291831002 91 77 89 91 89 85 85 89 86.3 90.0 0.938 81.0 85.2 0.932 80.5 W. Alton (MO)

Orchard (MO) 291831004 90 76 92 92 83 86 86 89 87.0 90.0 0.942 82.0 82.2 0.939 81.7 Orchard (MO)

Sunset Hills (MO) 291890004 88 70 89 80 89 82 79 86 82.3 88.3 0.956 78.7 81.9 0.954 78.5 Sunset Hills (MO)

Arnold (MO) 290990012 82 70 92 79 87 81 80 86 82.3 84.7 0.938 77.2 77.4 0.937 77.1 Arnold (MO)

Margaretta (MO) 295100086 90 72 91 76 91 84 79 86 83.0 87.7 0.955 79.3 83.4 0.955 79.3 Margaretta (MO)

Maryland Heights (MO) 291890014 88 84 94 88 86 88 87.3 0.955 83.4 0.954 83.3 Maryland Heights (MO)

4th High 8-hour Value Des. Values (truncated) 2009 - OTB 2009 - Will Do
Key Sites 2005 BY 2002 BY

'03 '04 '05 '06 '07 '03-'05 '04-'06 '05-'07 Average Average RRF Round 5 Round 4 RRF Round 5

Lake Michigan Area Lake Michigan Area

Chiwaukee 550590019 88 78 93 79 85 86 83 85 84.7 98.3 0.956 80.9 90.3 0.900 76.2 Chiwaukee

Racine 551010017 82 69 95 71 77 82 78 81 80.3 91.7 0.947 76.1 82.9 0.886 71.2 Racine

Milwaukee-Bayside 550190085 92 73 93 73 83 86 79 83 82.7 91.0 0.944 78.0 82.3 0.880 72.7 Milwaukee-Bayside

Harrington Beach 550890009 99 72 94 72 84 88 79 83 83.3 93.0 0.939 78.3 82.9 0.870 72.5 Harrington Beach

Manitowoc 550710007 92 74 95 78 85 87 82 86 85.0 87.0 0.925 78.6 76.3 0.853 72.5 Manitowoc

Sheboygan 551170006 93 78 97 83 88 89 86 89 88.0 97.0 0.930 81.8 86.4 0.857 75.4 Sheboygan

Kewaunee 550610002 97 73 88 76 85 86 79 83 82.7 89.3 0.918 75.9 79.1 0.845 69.9 Kewaunee

Door County 550290004 93 78 101 79 92 90 86 90 88.7 91.0 0.919 81.5 79.3 0.843 74.7 Door County

Hammond 180892008 81 67 87 75 77 78 76 79 77.7 88.3 0.960 74.6 86.3 0.922 71.6 Hammond

Whiting 180890030 64 88 81 88 76 77 85 79.3 0.960 76.2 0.922 73.1 Whiting

Michigan City 180910005 82 70 84 75 73 78 76 77 77.0 90.3 0.942 72.5 85.4 0.884 68.1 Michigan City

Ogden Dunes 181270020 77 69 90 70 84 78 76 81 78.3 86.3 0.951 74.5 82.0 0.904 70.8 Ogden Dunes

Holland 260050003 96 79 94 91 94 89 88 93 90.0 94.0 0.920 82.8 81.0 0.846 76.1 Holland

Jenison 261390005 91 69 86 83 88 82 79 85 82.0 86.0 0.909 74.5 75.5 0.838 68.7 Jenison

Muskegon 261210039 94 70 90 90 86 84 83 88 85.0 90.0 0.918 78.0 79.4 0.846 71.9 Muskegon

Indianapolis Area Indianapolis Area

Noblesville 189571001 101 75 87 77 84 87 79 82 82.7 93.7 0.914 75.6 82.0 0.831 68.7 Noblesville

Fortville 180590003 92 72 80 75 81 81 75 78 78.0 91.3 0.916 71.4 82.1 0.835 65.1 Fortville

Fort B. Harrison 180970050 91 73 80 76 83 81 76 79 78.7 90.0 0.931 73.2 82.4 0.879 69.1 Fort B. Harrison

Detroit Area Detroit Area

New Haven 260990009 102 81 88 78 93 90 82 86 86.0 92.3 0.932 80.2 83.5 0.885 76.1 New Haven

Warren 260991003 101 71 89 78 91 87 79 86 84.0 90.0 0.961 80.7 81.9 0.924 77.6 Warren

Port Huron 261470005 87 74 88 78 89 83 80 85 82.7 88.0 0.913 75.5 77.0 0.858 70.9 Port Huron

Cleveland Area Cleveland Area

Ashtabula 390071001 99 81 93 86 92 91 86 90 89.0 95.7 0.910 81.0 80.2 0.844 75.1 Ashtabula

Geauga 390550004 97 75 88 70 68 86 77 75 79.3 99.0 0.916 72.7 86.2 0.848 67.3 Geauga

Eastlake 390850003 92 79 97 83 74 89 86 84 86.3 92.7 0.932 80.5 80.6 0.883 76.2 Eastlake

Akron 391530020 89 77 89 77 91 85 81 85 83.7 93.3 0.903 75.6 78.5 0.821 68.7 Akron

Cincinnati Area Cincinnati Area

Wilmington 390271002 96 78 83 81 82 85 80 82 82.3 94.3 0.910 74.9 81.1 0.830 68.3 Wilmington

Sycamore 390610006 93 76 89 81 90 86 82 86 84.7 90.3 0.948 80.3 82.9 0.881 74.6 Sycamore

Lebanon 391650007 95 81 92 86 88 89 86 88 87.7 87.0 0.921 80.7 77.0 0.846 74.2 Lebanon

Columbus Area Columbus Area

London 390970007 90 75 81 76 83 82 77 80 79.7 88.7 0.911 72.6 76.5 0.832 66.3 London

New Albany 390490029 94 78 92 82 87 88 84 87 86.3 93.0 0.922 79.6 80.2 0.845 73.0 New Albany

Franklin 290490028 84 73 86 79 79 81 79 81 80.3 86.0 0.923 74.1 74.7 0.859 69.0 Franklin

St. Louis Area St. Louis Area

W. Alton (MO) 291831002 91 77 89 91 89 85 85 89 86.3 90.0 0.911 78.6 84.0 0.868 74.9 W. Alton (MO)

Orchard (MO) 291831004 90 76 92 92 83 86 86 89 87.0 90.0 0.919 80.0 80.4 0.876 76.2 Orchard (MO)

Sunset Hills (MO) 291890004 88 70 89 80 89 82 79 86 82.3 88.3 0.937 77.1 80.6 0.897 73.9 Sunset Hills (MO)

Arnold (MO) 290990012 82 70 92 79 87 81 80 86 82.3 84.7 0.918 75.6 75.8 0.874 72.0 Arnold (MO)

Margaretta (MO) 295100086 90 72 91 76 91 84 79 86 83.0 87.7 0.939 77.9 82.5 0.896 74.4 Margaretta (MO)

Maryland Heights (MO) 291890014 88 84 94 88 86 88 87.3 0.936 81.7 0.894 78.1 Maryland Heights (MO)

4th High 8-hour Value Des. Values (truncated) 2012 - OTB 2018 - OTB
2005 BY 2002 BY

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

Average 

w/ 2007

Average

Round 5 Round4 Key Site

Chicago - Washington HS Cook 170310022 15.6 14.2 16.9 13.2 15.7 15.6 14.8 15.3 15.2 15.9 14.1 14.8 Chicago - Washington HS

Chicago - Mayfair Cook 170310052 15.9 15.3 17.0 14.5 15.5 16.1 15.6 15.7 15.8 17.1 14.4 15.8 Chicago - Mayfair

Chicago - Springfield Cook 170310057 15.6 13.8 16.7 13.5 15.1 15.4 14.7 15.1 15.0 15.6 13.9 14.5 Chicago - Springfield

Chicago - Lawndale Cook 170310076 14.8 14.2 16.6 13.5 14.3 15.2 14.8 14.8 14.9 15.6 13.8 14.5 Chicago - Lawndale

Blue Island Cook 170312001 14.9 14.1 16.4 13.2 14.3 15.1 14.6 14.6 14.8 15.6 13.7 14.5 Blue Island

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

Cicero Cook 170316005 16.8 15.2 16.3 14.3 14.8 16.1 15.3 15.1 15.5 16.4 14.4 15.3 Cicero

Granite City Madison 171191007 17.5 15.4 18.2 16.3 15.1 17.0 16.6 16.5 16.7 17.3 15.1 16.0 Granite City

E. St. Louis St. Clair 171630010 14.9 14.7 17.1 14.5 15.6 15.6 15.4 15.7 15.6 16.2 14.1 14.9 E. St. Louis

Jeffersonville Clark 180190005 15.8 15.1 18.5 15.0 16.5 16.5 16.2 16.7 16.4 17.2 13.8 15.5 Jeffersonville

Jasper Dubois 180372001 15.7 14.4 16.9 13.5 14.4 15.7 14.9 14.9 15.2 15.5 12.4 13.8 Jasper

Gary Lake 180890031 16.8 13.3 14.5 16.8 15.1 14.9 15.6 13.0 Gary

Indy-Washington Park Marion 180970078 15.5 14.3 16.4 14.1 15.8 15.4 14.9 15.4 15.3 16.2 12.8 14.5 Indy-Washington Park

Indy-W 18th Street Marion 180970081 16.2 15.0 17.9 14.2 16.1 16.4 15.7 16.1 16.0 13.4 Indy-W 18th Street

Indy- Michigan Street Marion 180970083 16.3 15.0 17.5 14.1 15.9 16.3 15.5 15.8 15.9 16.6 13.4 14.8 Indy- Michigan Street

Allen Park Wayne 261630001 15.2 14.2 15.9 13.2 12.8 15.1 14.4 14.0 14.5 15.8 13.0 14.5 Allen Park

Southwest HS Wayne 261630015 16.6 15.4 17.2 14.7 14.5 16.4 15.8 15.5 15.9 17.3 14.2 15.8 Southwest HS

Linwood Wayne 261630016 15.8 13.7 16.0 13.0 13.9 15.2 14.2 14.3 14.6 15.5 13.1 14.1 Linwood

Dearborn Wayne 261630033 19.2 16.8 18.6 16.1 16.9 18.2 17.2 17.2 17.5 19.3 15.8 17.7 Dearborn

Wyandotte Wayne 261630036 16.3 13.7 16.4 12.9 13.4 15.5 14.3 14.2 14.7 16.6 13.1 15.1 Wyandotte

Middleton Butler 390170003 17.2 14.1 19.0 14.1 15.4 16.8 15.7 16.2 16.2 16.5 13.5 14.2 Middleton

Fairfield Butler 390170016 15.8 14.7 17.9 14.0 14.9 16.1 15.5 15.6 15.8 15.9 13.1 13.5 Fairfield

Cleveland-28th Street Cuyahoga 390350027 15.4 15.6 17.3 13.0 14.5 16.1 15.3 14.9 15.4 16.5 13.5 14.4 Cleveland-28th Street

Cleveland-St. Tikhon Cuyahoga 390350038 17.6 17.5 19.2 14.9 16.2 18.1 17.2 16.8 17.4 18.4 15.2 16.1 Cleveland-St. Tikhon

Cleveland-Broadway Cuyahoga 390350045 16.4 15.3 19.3 14.1 15.3 17.0 16.2 16.2 16.5 16.7 14.4 14.6 Cleveland-Broadway

Cleveland-E14 & Orange Cuyahoga 390350060 17.2 16.4 19.4 15.0 15.9 17.7 16.9 16.8 17.1 17.6 15.0 15.3 Cleveland-E14 & Orange

Newburg Hts - Harvard Ave Cuyahoga 390350065 15.6 15.2 18.6 13.1 15.8 16.5 15.6 15.8 16.0 16.2 14.0 14.1 Newburg Hts - Harvard Ave

Columbus - Fairgrounds Franklin 390490024 16.4 15.0 16.4 13.6 14.6 15.9 15.0 14.9 15.3 16.5 12.9 14.6 Columbus - Fairgrounds

Columbus - Ann Street Franklin 390490025 15.3 14.6 16.5 13.8 14.7 15.5 15.0 15.0 15.1 16.0 12.7 14.1 Columbus - Ann Street

Columbus - Maple Canyon Franklin 390490081 14.9 13.6 14.6 12.9 13.1 14.4 13.7 13.5 13.9 16.0 11.7 14.0 Columbus - Maple Canyon

Cincinnati - Seymour Hamilton 390610014 17.0 15.9 19.8 15.5 16.5 17.6 17.1 17.3 17.3 17.7 14.5 15.5 Cincinnati - Seymour

Cincinnati - Taft Ave Hamilton 390610040 15.5 14.6 17.5 13.6 15.1 15.9 15.2 15.4 15.5 15.7 12.8 13.6 Cincinnati - Taft Ave

Cincinnati - 8th Ave Hamilton 390610042 16.7 16.0 19.1 14.9 15.9 17.3 16.7 16.6 16.9 17.3 14.0 14.6 Cincinnati - 8th Ave

Sharonville Hamilton 390610043 15.7 14.9 16.9 14.5 14.8 15.8 15.4 15.4 15.6 16.0 12.9 13.6 Sharonville

Norwood Hamilton 390617001 16.0 15.3 18.4 14.4 15.1 16.6 16.0 16.0 16.2 16.3 13.4 14.2 Norwood

St. Bernard Hamilton 390618001 17.3 16.4 20.0 15.9 16.1 17.9 17.4 17.3 17.6 17.3 14.7 15.2 St. Bernard

Steubenville Jefferson 390810016 17.7 15.9 16.4 13.8 16.2 16.7 15.4 15.5 15.8 17.7 12.8 16.3 Steubenville

Mingo Junction Jefferson 390811001 17.3 16.2 18.1 14.6 15.6 17.2 16.3 16.1 16.5 17.5 13.5 15.5 Mingo Junction

Ironton Lawrence 390870010 14.3 13.7 17.0 14.4 15.0 15.0 15.0 15.5 15.2 15.7 12.8 14.2 Ironton

Dayton Montgomery 391130032 15.9 14.5 17.4 13.6 15.6 15.9 15.2 15.5 15.5 15.9 13.2 13.7 Dayton

New Boston Scioto 391450013 14.7 13.0 16.2 14.3 14.0 14.6 14.5 14.8 14.7 17.1 12.1 15.4 New Boston

Canton - Dueber Stark 391510017 16.8 15.6 17.8 14.6 15.9 16.7 16.0 16.1 16.3 17.3 14.0 15.0 Canton - Dueber

Canton - Market Stark 391510020 15.0 14.1 16.6 11.9 14.4 15.2 14.2 14.3 14.6 15.7 12.6 13.6 Canton - Market

Akron - Brittain Summit 391530017 15.4 15.0 16.4 13.5 14.4 15.6 15.0 14.8 15.1 16.4 13.0 14.4 Akron - Brittain

Akron - W. Exchange Summit 391530023 14.2 13.9 15.7 12.8 13.7 14.6 14.1 14.1 14.3 15.6 12.3 13.6 Akron - W. Exchange

Annual Average Conc. Design Values 2009 Modeling Results
2005 BY 2002 BY

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

Average 

w/ 2007

Average

Round 5 Round4 Key Site

Chicago - Washington HS Cook 170310022 15.6 14.2 16.9 13.2 15.7 15.6 14.8 15.3 15.2 15.9 14.0 14.6 Chicago - Washington HS

Chicago - Mayfair Cook 170310052 15.9 15.3 17.0 14.5 15.5 16.1 15.6 15.7 15.8 17.1 14.2 15.5 Chicago - Mayfair

Chicago - Springfield Cook 170310057 15.6 13.8 16.7 13.5 15.1 15.4 14.7 15.1 15.0 15.6 13.8 14.3 Chicago - Springfield

Chicago - Lawndale Cook 170310076 14.8 14.2 16.6 13.5 14.3 15.2 14.8 14.8 14.9 15.6 13.7 14.3 Chicago - Lawndale

Blue Island Cook 170312001 14.9 14.1 16.4 13.2 14.3 15.1 14.6 14.6 14.8 15.6 13.6 14.3 Blue Island

Summit Cook 170313301 15.6 14.2 16.9 13.8 14.8 15.6 15.0 15.2 15.2 16.0 14.0 14.6 Summit

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

Granite City Madison 171191007 17.5 15.4 18.2 16.3 15.1 17.0 16.6 16.5 16.7 17.3 14.9 15.8 Granite City

E. St. Louis St. Clair 171630010 14.9 14.7 17.1 14.5 15.6 15.6 15.4 15.7 15.6 16.2 13.9 14.7 E. St. Louis

Jeffersonville Clark 180190005 15.8 15.1 18.5 15.0 16.5 16.5 16.2 16.7 16.4 17.2 13.7 15.0 Jeffersonville

Jasper Dubois 180372001 15.7 14.4 16.9 13.5 14.4 15.7 14.9 14.9 15.2 15.5 12.2 13.5 Jasper

Gary Lake 180890031 16.8 13.3 14.5 16.8 15.1 14.9 15.6 12.8 Gary

Indy-Washington Park Marion 180970078 15.5 14.3 16.4 14.1 15.8 15.4 14.9 15.4 15.3 16.2 12.6 14.2 Indy-Washington Park

Indy-W 18th Street Marion 180970081 16.2 15.0 17.9 14.2 16.1 16.4 15.7 16.1 16.0 13.2 Indy-W 18th Street

Indy- Michigan Street Marion 180970083 16.3 15.0 17.5 14.1 15.9 16.3 15.5 15.8 15.9 16.6 13.1 14.9 Indy- Michigan Street

Allen Park Wayne 261630001 15.2 14.2 15.9 13.2 12.8 15.1 14.4 14.0 14.5 15.8 12.8 14.1 Allen Park

Southwest HS Wayne 261630015 16.6 15.4 17.2 14.7 14.5 16.4 15.8 15.5 15.9 17.3 13.9 15.3 Southwest HS

Linwood Wayne 261630016 15.8 13.7 16.0 13.0 13.9 15.2 14.2 14.3 14.6 15.5 12.8 13.7 Linwood

Dearborn Wayne 261630033 19.2 16.8 18.6 16.1 16.9 18.2 17.2 17.2 17.5 19.3 15.5 17.1 Dearborn

Wyandotte Wayne 261630036 16.3 13.7 16.4 12.9 13.4 15.5 14.3 14.2 14.7 16.6 12.8 14.7 Wyandotte

Middleton Butler 390170003 17.2 14.1 19.0 14.1 15.4 16.8 15.7 16.2 16.2 16.5 13.2 13.7 Middleton

Fairfield Butler 390170016 15.8 14.7 17.9 14.0 14.9 16.1 15.5 15.6 15.8 15.9 12.9 12.9 Fairfield

Cleveland-28th Street Cuyahoga 390350027 15.4 15.6 17.3 13.0 14.5 16.1 15.3 14.9 15.4 16.5 13.2 13.8 Cleveland-28th Street

Cleveland-St. Tikhon Cuyahoga 390350038 17.6 17.5 19.2 14.9 16.2 18.1 17.2 16.8 17.4 18.4 14.8 15.4 Cleveland-St. Tikhon

Cleveland-Broadway Cuyahoga 390350045 16.4 15.3 19.3 14.1 15.3 17.0 16.2 16.2 16.5 16.7 14.0 14.0 Cleveland-Broadway

Cleveland-E14 & Orange Cuyahoga 390350060 17.2 16.4 19.4 15.0 15.9 17.7 16.9 16.8 17.1 17.6 14.6 14.7 Cleveland-E14 & Orange

Newburg Hts - Harvard Ave Cuyahoga 390350065 15.6 15.2 18.6 13.1 15.8 16.5 15.6 15.8 16.0 16.2 13.6 13.5 Newburg Hts - Harvard Ave

Columbus - Fairgrounds Franklin 390490024 16.4 15.0 16.4 13.6 14.6 15.9 15.0 14.9 15.3 16.5 12.6 14.0 Columbus - Fairgrounds

Columbus - Ann Street Franklin 390490025 15.3 14.6 16.5 13.8 14.7 15.5 15.0 15.0 15.1 16.0 12.4 13.5 Columbus - Ann Street

Columbus - Maple Canyon Franklin 390490081 14.9 13.6 14.6 12.9 13.1 14.4 13.7 13.5 13.9 16.0 11.4 13.4 Columbus - Maple Canyon

Cincinnati - Seymour Hamilton 390610014 17.0 15.9 19.8 15.5 16.5 17.6 17.1 17.3 17.3 17.7 14.3 14.8 Cincinnati - Seymour

Cincinnati - Taft Ave Hamilton 390610040 15.5 14.6 17.5 13.6 15.1 15.9 15.2 15.4 15.5 15.7 12.6 13.0 Cincinnati - Taft Ave

Cincinnati - 8th Ave Hamilton 390610042 16.7 16.0 19.1 14.9 15.9 17.3 16.7 16.6 16.9 17.3 13.8 14.0 Cincinnati - 8th Ave

Sharonville Hamilton 390610043 15.7 14.9 16.9 14.5 14.8 15.8 15.4 15.4 15.6 16.0 12.7 13.0 Sharonville

Norwood Hamilton 390617001 16.0 15.3 18.4 14.4 15.1 16.6 16.0 16.0 16.2 16.3 13.2 13.6 Norwood

St. Bernard Hamilton 390618001 17.3 16.4 20.0 15.9 16.1 17.9 17.4 17.3 17.6 17.3 14.4 14.6 St. Bernard

Steubenville Jefferson 390810016 17.7 15.9 16.4 13.8 16.2 16.7 15.4 15.5 15.8 17.7 12.5 15.9 Steubenville

Mingo Junction Jefferson 390811001 17.3 16.2 18.1 14.6 15.6 17.2 16.3 16.1 16.5 17.5 13.2 15.0 Mingo Junction

Ironton Lawrence 390870010 14.3 13.7 17.0 14.4 15.0 15.0 15.0 15.5 15.2 15.7 12.5 13.7 Ironton

Dayton Montgomery 391130032 15.9 14.5 17.4 13.6 15.6 15.9 15.2 15.5 15.5 15.9 12.9 13.2 Dayton

New Boston Scioto 391450013 14.7 13.0 16.2 14.3 14.0 14.6 14.5 14.8 14.7 17.1 11.9 14.8 New Boston

Canton - Dueber Stark 391510017 16.8 15.6 17.8 14.6 15.9 16.7 16.0 16.1 16.3 17.3 13.6 14.3 Canton - Dueber

Canton - Market Stark 391510020 15.0 14.1 16.6 11.9 14.4 15.2 14.2 14.3 14.6 15.7 12.3 13.0 Canton - Market

Akron - Brittain Summit 391530017 15.4 15.0 16.4 13.5 14.4 15.6 15.0 14.8 15.1 16.4 12.7 13.6 Akron - Brittain

Akron - W. Exchange Summit 391530023 14.2 13.9 15.7 12.8 13.7 14.6 14.1 14.1 14.3 15.6 12.0 13.0 Akron - W. Exchange

Annual Average Conc. Design Values 2012 Modeling Results
2005 BY 2002 BY

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

Average 

w/ 2007

Average Round 5

OTB

Round 5

Will Do Round4 Key Site

Chicago - Washington HS Cook 170310022 15.6 14.2 16.9 13.2 15.7 15.6 14.8 15.3 15.2 15.9 13.9 13.8 14.4 Chicago - Washington HS

Chicago - Mayfair Cook 170310052 15.9 15.3 17.0 14.5 15.5 16.1 15.6 15.7 15.8 17.1 13.9 13.8 15.0 Chicago - Mayfair

Chicago - Springfield Cook 170310057 15.6 13.8 16.7 13.5 15.1 15.4 14.7 15.1 15.0 15.6 13.7 13.5 14.1 Chicago - Springfield

Chicago - Lawndale Cook 170310076 14.8 14.2 16.6 13.5 14.3 15.2 14.8 14.8 14.9 15.6 13.6 13.4 14.1 Chicago - Lawndale

Blue Island Cook 170312001 14.9 14.1 16.4 13.2 14.3 15.1 14.6 14.6 14.8 15.6 13.4 13.3 14.1 Blue Island

Summit Cook 170313301 15.6 14.2 16.9 13.8 14.8 15.6 15.0 15.2 15.2 16.0 13.9 13.8 14.4 Summit

Cicero Cook 170316005 16.8 15.2 16.3 14.3 14.8 16.1 15.3 15.1 15.5 16.4 14.2 14.0 14.9 Cicero

Granite City Madison 171191007 17.5 15.4 18.2 16.3 15.1 17.0 16.6 16.5 16.7 17.3 14.3 14.2 15.5 Granite City

E. St. Louis St. Clair 171630010 14.9 14.7 17.1 14.5 15.6 15.6 15.4 15.7 15.6 16.2 13.4 13.3 14.5 E. St. Louis

Jeffersonville Clark 180190005 15.8 15.1 18.5 15.0 16.5 16.5 16.2 16.7 16.4 17.2 13.4 13.4 14.4 Jeffersonville

Jasper Dubois 180372001 15.7 14.4 16.9 13.5 14.4 15.7 14.9 14.9 15.2 15.5 11.8 11.9 13.0 Jasper

Gary Lake 180890031 16.8 13.3 14.5 16.8 15.1 14.9 15.6 12.4 12.4 Gary

Indy-Washington Park Marion 180970078 15.5 14.3 16.4 14.1 15.8 15.4 14.9 15.4 15.3 16.2 12.0 12.1 13.7 Indy-Washington Park

Indy-W 18th Street Marion 180970081 16.2 15.0 17.9 14.2 16.1 16.4 15.7 16.1 16.0 12.6 12.7 Indy-W 18th Street

Indy- Michigan Street Marion 180970083 16.3 15.0 17.5 14.1 15.9 16.3 15.5 15.8 15.9 16.6 12.6 12.6 14.0 Indy- Michigan Street

Allen Park Wayne 261630001 15.2 14.2 15.9 13.2 12.8 15.1 14.4 14.0 14.5 15.8 12.4 12.4 13.3 Allen Park

Southwest HS Wayne 261630015 16.6 15.4 17.2 14.7 14.5 16.4 15.8 15.5 15.9 17.3 13.5 13.5 14.4 Southwest HS

Linwood Wayne 261630016 15.8 13.7 16.0 13.0 13.9 15.2 14.2 14.3 14.6 15.5 12.5 12.5 13.0 Linwood

Dearborn Wayne 261630033 19.2 16.8 18.6 16.1 16.9 18.2 17.2 17.2 17.5 19.3 15.1 15.1 16.1 Dearborn

Wyandotte Wayne 261630036 16.3 13.7 16.4 12.9 13.4 15.5 14.3 14.2 14.7 16.6 12.5 12.5 13.9 Wyandotte

Middleton Butler 390170003 17.2 14.1 19.0 14.1 15.4 16.8 15.7 16.2 16.2 16.5 12.8 12.8 13.1 Middleton

Fairfield Butler 390170016 15.8 14.7 17.9 14.0 14.9 16.1 15.5 15.6 15.8 15.9 12.5 12.6 12.2 Fairfield

Cleveland-28th Street Cuyahoga 390350027 15.4 15.6 17.3 13.0 14.5 16.1 15.3 14.9 15.4 16.5 12.7 12.9 12.9 Cleveland-28th Street

Cleveland-St. Tikhon Cuyahoga 390350038 17.6 17.5 19.2 14.9 16.2 18.1 17.2 16.8 17.4 18.4 14.3 14.5 14.4 Cleveland-St. Tikhon

Cleveland-Broadway Cuyahoga 390350045 16.4 15.3 19.3 14.1 15.3 17.0 16.2 16.2 16.5 16.7 13.5 13.7 13.1 Cleveland-Broadway

Cleveland-E14 & Orange Cuyahoga 390350060 17.2 16.4 19.4 15.0 15.9 17.7 16.9 16.8 17.1 17.6 14.1 14.2 13.7 Cleveland-E14 & Orange

Newburg Hts - Harvard Ave Cuyahoga 390350065 15.6 15.2 18.6 13.1 15.8 16.5 15.6 15.8 16.0 16.2 13.1 13.3 12.6 Newburg Hts - Harvard Ave

Columbus - Fairgrounds Franklin 390490024 16.4 15.0 16.4 13.6 14.6 15.9 15.0 14.9 15.3 16.5 12.0 12.1 13.0 Columbus - Fairgrounds

Columbus - Ann Street Franklin 390490025 15.3 14.6 16.5 13.8 14.7 15.5 15.0 15.0 15.1 16.0 11.9 11.9 12.5 Columbus - Ann Street

Columbus - Maple Canyon Franklin 390490081 14.9 13.6 14.6 12.9 13.1 14.4 13.7 13.5 13.9 16.0 10.9 11.0 12.5 Columbus - Maple Canyon

Cincinnati - Seymour Hamilton 390610014 17.0 15.9 19.8 15.5 16.5 17.6 17.1 17.3 17.3 17.7 13.8 13.9 14.0 Cincinnati - Seymour

Cincinnati - Taft Ave Hamilton 390610040 15.5 14.6 17.5 13.6 15.1 15.9 15.2 15.4 15.5 15.7 12.2 12.3 12.3 Cincinnati - Taft Ave

Cincinnati - 8th Ave Hamilton 390610042 16.7 16.0 19.1 14.9 15.9 17.3 16.7 16.6 16.9 17.3 13.4 13.4 13.2 Cincinnati - 8th Ave

Sharonville Hamilton 390610043 15.7 14.9 16.9 14.5 14.8 15.8 15.4 15.4 15.6 16.0 12.3 12.4 12.2 Sharonville

Norwood Hamilton 390617001 16.0 15.3 18.4 14.4 15.1 16.6 16.0 16.0 16.2 16.3 12.8 12.8 12.8 Norwood

St. Bernard Hamilton 390618001 17.3 16.4 20.0 15.9 16.1 17.9 17.4 17.3 17.6 17.3 14.0 14.1 13.8 St. Bernard

Steubenville Jefferson 390810016 17.7 15.9 16.4 13.8 16.2 16.7 15.4 15.5 15.8 17.7 12.7 12.7 16.2 Steubenville

Mingo Junction Jefferson 390811001 17.3 16.2 18.1 14.6 15.6 17.2 16.3 16.1 16.5 17.5 13.4 13.4 15.3 Mingo Junction

Ironton Lawrence 390870010 14.3 13.7 17.0 14.4 15.0 15.0 15.0 15.5 15.2 15.7 12.3 12.3 13.2 Ironton

Dayton Montgomery 391130032 15.9 14.5 17.4 13.6 15.6 15.9 15.2 15.5 15.5 15.9 12.4 12.5 12.3 Dayton

New Boston Scioto 391450013 14.7 13.0 16.2 14.3 14.0 14.6 14.5 14.8 14.7 17.1 11.6 11.6 14.2 New Boston

Canton - Dueber Stark 391510017 16.8 15.6 17.8 14.6 15.9 16.7 16.0 16.1 16.3 17.3 13.3 13.3 13.6 Canton - Dueber

Canton - Market Stark 391510020 15.0 14.1 16.6 11.9 14.4 15.2 14.2 14.3 14.6 15.7 11.9 12.0 12.2 Canton - Market

Akron - Brittain Summit 391530017 15.4 15.0 16.4 13.5 14.4 15.6 15.0 14.8 15.1 16.4 12.3 12.3 12.9 Akron - Brittain

Akron - W. Exchange Summit 391530023 14.2 13.9 15.7 12.8 13.7 14.6 14.1 14.1 14.3 15.6 11.5 11.6 12.2 Akron - W. Exchange

Annual Average Conc. Design Values 2018 Modeling Results
24-Hour PM2.5 Base Year

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

Average

 w/ 2007 2009 2012 2018 Key Site

Chicago - Washington HS Cook 170310022 37.7 32.5 45.7 27.0 35.7 38.6 35.1 36.1 36.6 36 36 35 Chicago - Washington HS

Chicago - Mayfair Cook 170310052 37.3 38.8 48.3 31.6 39.4 41.5 39.6 39.8 40.3 36 36 36 Chicago - Mayfair

Chicago - Springfield Cook 170310057 36.4 33.1 46.5 27.7 38.9 38.7 35.8 37.7 37.4 32 32 31 Chicago - Springfield

Chicago - Lawndale Cook 170310076 32.6 39.7 45.1 29.0 37.2 39.1 37.9 37.1 38.1 35 35 34 Chicago - Lawndale

McCook Cook 170311016 43.0 39 39 38 McCook

Blue Island Cook 170312001 39.6 38.5 43.8 28.1 35.1 40.6 36.8 35.7 37.7 34 34 33 Blue Island

Schiller Park Cook 170313103 40.7 50.3 30.0 36.6 45.5 40.3 39.0 41.6 39 39 39 Schiller Park

Summit Cook 170313301 38.4 42.4 49.1 27.4 36.7 43.3 39.6 37.7 40.2 38 38 37 Summit

Maywood Cook 170316005 38.5 42.5 44.6 29.2 36.9 41.9 38.8 36.9 39.2 38 38 37 Maywood

Granite City Madison 171191007 40.8 35.4 44.1 36.3 36.0 40.1 38.6 38.8 39.2 33 33 32 Granite City

E. St. Louis St. Clair 171630010 32.6 30.2 39.6 29.2 33.1 34.1 33.0 34.0 33.7 28 28 28 E. St. Louis

Jeffersonville Clark 180190005 28.4 45.5 35.9 43.3 37.0 36.6 41.6 38.4 29 31 31 Jeffersonville

Jasper Dubois 180372001 39.5 30.0 41.2 31.6 39.5 36.9 34.3 37.4 36.2 28 29 28 Jasper

Gary - IITRI Lake 180890022 39.0 34 34 35 Gary - IITRI

Gary - Burr School Lake 180890026 39.0 33 34 32 Gary - Burr School

Gary Lake 180890031 38.7 27.1 36.2 38.7 32.9 34.0 35.2 24 24 27 Gary

Indy-West Street Marion 180970043 38.0 33 33 33 Indy-West Street

Indy-English Avenue Marion 180970066 38.0 32 32 32 Indy-English Avenue

Indy-Washington Park Marion 180970078 39.3 31.0 42.5 31.7 37.6 37.6 35.1 37.3 36.6 31 31 32 Indy-Washington Park

Indy-W 18th Street Marion 180970081 36.2 31.9 45.7 34.8 38.4 37.9 37.5 39.6 38.3 31 31 31 Indy-W 18th Street

Indy- Michigan Street Marion 180970083 36.7 31.3 40.3 33.5 37.2 36.1 35.0 37.0 36.0 28 28 29 Indy- Michigan Street

Luna Pier Monroe 261150005 34.7 35.0 49.3 32.6 32.2 39.7 39.0 38.0 38.9 32 32 31 Luna Pier

Oak Park Oakland 261250001 36.6 32.5 52.2 33.0 35.3 40.4 39.2 40.2 39.9 36 36 35 Oak Park

Port Huron St. Clair 261470005 37.2 32.2 47.6 37.9 36.3 39.0 39.2 40.6 39.6 34 34 33 Port Huron

Ypsilanti Washtenaw 261610008 38.8 31.5 52.1 31.3 34.5 40.8 38.3 39.3 39.5 35 35 34 Ypsilanti

Allen Park Wayne 261630001 40.5 36.9 43.0 34.1 35.9 40.1 38.0 37.7 38.6 35 34 33 Allen Park

Southwest HS Wayne 261630015 33.6 36.0 49.7 36.2 34.0 39.8 40.6 40.0 40.1 35 35 33 Southwest HS

Linwood Wayne 261630016 46.2 38.3 51.8 36.9 34.8 45.4 42.3 41.2 43.0 39 39 38 Linwood

E 7 Mile Wayne 261630019 37.1 35.0 52.3 36.2 33.0 41.5 41.2 40.5 41.0 38 38 37 E 7 Mile

Dearborn Wayne 261630033 42.8 39.4 50.2 43.1 36.6 44.1 44.2 43.3 43.9 40 40 39 Dearborn

Wyandotte Wayne 261630036 34.8 32.3 46.7 33.2 28.6 37.9 37.4 36.2 37.2 35 35 34 Wyandotte

Newberry Wayne 261630038 36.8 57.5 28.6 33.4 39.1 39.8 42.7 38 37 36 Newberry

FIA Wayne 261630039 43.9 32.4 34.8 37.0 39.7 33 33 31 FIA

Middleton Butler 390170003 38.6 37.2 47.6 30.2 37.1 41.1 38.3 38.3 39.3 28 28 27 Middleton

Fairfield Butler 390170016 34.8 32.2 43.4 35.2 34.5 36.8 36.9 37.7 37.1 27 28 27 Fairfield

Butler 390170017 34.6 34.3 44.9 37.9 39.6 40.8 29 29 28

Cleveland-28th Street Cuyahoga 390350027 41.3 40.9 35.7 31.5 39.0 39.3 36.0 35.4 36.9 32 32 31 Cleveland-28th Street

Cleveland-St. Tikhon Cuyahoga 390350038 47.3 42.5 51.2 36.1 39.7 44.9 47.0 42.3 44.2 36 35 34 Cleveland-St. Tikhon

Cleveland-Broadway Cuyahoga 390350045 42.2 36.1 46.2 29.5 37.0 41.5 37.3 37.6 38.8 31 30 29 Cleveland-Broadway

Cleveland-GT Craig Cuyahoga 390350060 45.5 42.2 49.5 31.0 38.7 45.7 40.9 39.7 42.1 37 37 35 Cleveland-GT Craig

Newburg Hts - Harvard Ave Cuyahoga 390350065 39.1 36.1 47.9 27.8 39.1 41.0 37.3 38.3 38.9 31 30 30 Newburg Hts - Harvard Ave

Columbus - Fairgrounds Franklin 390490024 39.2 35.1 45.0 34.0 34.2 39.8 38.0 37.7 38.5 33 32 31 Columbus - Fairgrounds

Columbus - Ann Street Franklin 390490025 37.0 35.5 44.9 34.0 35.5 39.1 38.1 38.1 38.5 31 31 30 Columbus - Ann Street

Cincinnait Hamilton 390610006 45.0 33.3 34.7 37.7 40.6 27 28 27 Cincinnait

Cincinnati - Seymour Hamilton 390610014 37.8 42.0 38.5 35.2 38.1 39.4 38.6 37.3 38.4 26 25 24 Cincinnati - Seymour

Cincinnati - Taft Ave Hamilton 390610040 31.9 30.5 45.8 32.8 34.7 36.1 36.4 37.8 36.7 24 24 23 Cincinnati - Taft Ave

Cincinnati - 8th Ave Hamilton 390610042 33.8 31.9 44.4 34.5 35.9 36.7 36.9 38.3 37.3 28 28 27 Cincinnati - 8th Ave

Sharonville Hamilton 390610043 37.3 31.4 39.9 34.9 34.0 36.2 35.4 36.3 36.0 28 28 27 Sharonville

Norwood Hamilton 390617001 37.1 34.6 47.1 34.0 33.7 39.6 38.6 38.3 38.8 30 30 29 Norwood

St. Bernard Hamilton 390618001 35.8 33.9 51.4 36.1 35.4 40.4 40.5 41.0 40.6 30 30 29 St. Bernard

Steubenville Jefferson 390810016 39.6 43.8 43.8 32.1 43.5 42.4 39.9 39.8 40.7 29 28 28 Steubenville

Mingo Junction Jefferson 390811001 40.9 51.5 44.2 32.9 35.4 45.5 42.9 37.5 42.0 30 30 30 Mingo Junction

Dayton Montgomery 391130032 42.7 32.5 45.0 30.3 36.9 40.1 35.9 37.4 37.8 30 30 30 Dayton

Canton - Dueber Stark 391510017 34.2 36.3 47.6 32.2 33.4 39.4 38.7 37.7 38.6 28 28 27 Canton - Dueber

Akron - Brittain Summit 391530017 36.9 36.9 45.2 31.5 33.3 39.7 37.9 36.7 38.1 30 30 29 Akron - Brittain

Green Bay - Est High Brown 550090005 33.5 32.3 41.5 36.9 37.1 35.8 36.9 38.5 37.1 35 34 32 Green Bay - Est High

Madison Dane 550250047 32.0 31.9 40.1 33.4 44.3 34.7 35.1 39.3 36.4 32 31 29 Madison

Milwaukee-Health Center Milwaukee 550790010 33.2 38.4 38.7 40.7 40.6 36.8 39.3 40.0 38.7 35 34 33 Milwaukee-Health Center

Milwaukee-SER Hdqs Milwaukee 550790026 29.6 28.7 41.5 42.6 39.8 33.3 37.6 41.3 37.4 34 34 33 Milwaukee-SER Hdqs

Milwaukee-Virginia FS Milwaukee 550790043 39.2 41.4 37.1 44.0 38 39.2 40.8 39.7 39.9 36 36 36 Milwaukee-Virginia FS

Milwaukee- Fire Dept Hdqs Milwaukee 550790099 33.7 38.9 37.1 38.3 40.7 36.6 38.1 38.7 37.8 33 32 32 Milwaukee- Fire Dept Hdqs

Waukesha Waukesha 551330027 29.1 38.4 41.1 28.2 33.8 36.2 35.9 34.4 35.5 31 31 29 Waukesha

98th Percentile (24-hour) Design Values Round 5 Modeling Results
Site ID State County Season Species

Species Comp. of Ave. 

FRM (fraction) Species RRF

1703100521 IL Cook winter so4 0.1772 0.9342

1703100521 IL Cook winter no3 0.3099 1.0128

1703100521 IL Cook winter ocm 0.2147 0.9942

1703100521 IL Cook winter ec 0.0372 0.888

1703100521 IL Cook winter soil 0.0242 1.1674

1703100521 IL Cook winter nh4 0.1421 0.97

1703100521 IL Cook winter pbw 0.0947 0.9678

1703100521 IL Cook spring so4 0.32 0.8018

1703100521 IL Cook spring no3 0.0609 0.9385

1703100521 IL Cook spring ocm 0.2742 1.0629

1703100521 IL Cook spring ec 0.0501 0.8712

1703100521 IL Cook spring soil 0.0505 1.1796

1703100521 IL Cook spring nh4 0.1203 0.8619

1703100521 IL Cook spring pbw 0.0984 0.8492

1703100521 IL Cook summer so4 0.3089 0.725

1703100521 IL Cook summer no3 0 1.0124

1703100521 IL Cook summer ocm 0.1599 1.069

1703100521 IL Cook summer ec 0.0351 0.8683

1703100521 IL Cook summer soil 0.0318 1.204

1703100521 IL Cook summer nh4 0.0932 0.7354

1703100521 IL Cook summer pbw 0.094 0.7217

1703100521 IL Cook fall so4 0.1872 0.9151

1703100521 IL Cook fall no3 0.1628 0.9408

1703100521 IL Cook fall ocm 0.2389 1.0091

1703100521 IL Cook fall ec 0.0403 0.8623

1703100521 IL Cook fall soil 0.0284 1.1443

1703100521 IL Cook fall nh4 0.1062 0.9247

1703100521 IL Cook fall pbw 0.0614 0.9233

1711910071 IL Madison winter so4 0.213 0.9195

1711910071 IL Madison winter no3 0.2705 1.0306

1711910071 IL Madison winter ocm 0.2093 0.9289

1711910071 IL Madison winter ec 0.0434 0.9083

1711910071 IL Madison winter soil 0.0306 1.1782

1711910071 IL Madison winter nh4 0.1528 0.9513

1711910071 IL Madison winter pbw 0.0804 0.9243

1711910071 IL Madison spring so4 0.3194 0.7717

1711910071 IL Madison spring no3 0.0189 0.8611

1711910071 IL Madison spring ocm 0.2455 1.1103

1711910071 IL Madison spring ec 0.0564 1.0046

1711910071 IL Madison spring soil 0.0459 1.2252

1711910071 IL Madison spring nh4 0.1121 0.7894

1711910071 IL Madison spring pbw 0.1085 0.7783

1711910071 IL Madison summer so4 0.313 0.705

1711910071 IL Madison summer no3 0 0.884

1711910071 IL Madison summer ocm 0.153 1.1546

1711910071 IL Madison summer ec 0.0345 1.0513

1711910071 IL Madison summer soil 0.0302 1.2532

1711910071 IL Madison summer nh4 0.102 0.7409

1711910071 IL Madison summer pbw 0.1096 0.7133

1711910071 IL Madison fall so4 0.2058 0.9037

1711910071 IL Madison fall no3 0.1308 0.9426

1711910071 IL Madison fall ocm 0.259 1.0233

1711910071 IL Madison fall ec 0.0563 0.9248

1711910071 IL Madison fall soil 0.0549 1.1412

1711910071 IL Madison fall nh4 0.1073 0.9185

1711910071 IL Madison fall pbw 0.0655 0.918

PM2.5 RRFs by Species and Season (2009)
Site ID State County Season Species

Species Comp. of Ave. 

FRM (fraction) Species RRF

1803720011 IN Dubois winter so4 0.2669 0.8833

1803720011 IN Dubois winter no3 0.2548 0.9526

1803720011 IN Dubois winter ocm 0.1747 0.9374

1803720011 IN Dubois winter ec 0.0313 0.9319

1803720011 IN Dubois winter soil 0.0192 1.1349

1803720011 IN Dubois winter nh4 0.1646 0.9069

1803720011 IN Dubois winter pbw 0.0885 0.9006

1803720011 IN Dubois spring so4 0.4141 0.6808

1803720011 IN Dubois spring no3 0.0022 0.8106

1803720011 IN Dubois spring ocm 0.178 0.9997

1803720011 IN Dubois spring ec 0.0324 0.9083

1803720011 IN Dubois spring soil 0.0218 1.1284

1803720011 IN Dubois spring nh4 0.1432 0.7075

1803720011 IN Dubois spring pbw 0.1556 0.6916

1803720011 IN Dubois summer so4 0.3687 0.644

1803720011 IN Dubois summer no3 0 0.8029

1803720011 IN Dubois summer ocm 0.1174 1.0136

1803720011 IN Dubois summer ec 0.0207 0.913

1803720011 IN Dubois summer soil 0.0213 1.1988

1803720011 IN Dubois summer nh4 0.1168 0.6789

1803720011 IN Dubois summer pbw 0.1246 0.6613

1803720011 IN Dubois fall so4 0.2964 0.8232

1803720011 IN Dubois fall no3 0.138 0.8797

1803720011 IN Dubois fall ocm 0.2116 0.9861

1803720011 IN Dubois fall ec 0.0437 0.9019

1803720011 IN Dubois fall soil 0.03 1.1387

1803720011 IN Dubois fall nh4 0.1449 0.8444

1803720011 IN Dubois fall pbw 0.0941 0.8558

1809700811 IN Marion winter so4 0.2358 0.9192

1809700811 IN Marion winter no3 0.2729 0.9769

1809700811 IN Marion winter ocm 0.1851 0.9546

1809700811 IN Marion winter ec 0.0385 0.8647

1809700811 IN Marion winter soil 0.0239 1.0835

1809700811 IN Marion winter nh4 0.1561 0.9446

1809700811 IN Marion winter pbw 0.0877 0.944

1809700811 IN Marion spring so4 0.3745 0.6868

1809700811 IN Marion spring no3 0.0167 0.8082

1809700811 IN Marion spring ocm 0.2034 0.9881

1809700811 IN Marion spring ec 0.0447 0.8547

1809700811 IN Marion spring soil 0.0376 1.0625

1809700811 IN Marion spring nh4 0.1313 0.7182

1809700811 IN Marion spring pbw 0.1309 0.7056

1809700811 IN Marion summer so4 0.3582 0.6529

1809700811 IN Marion summer no3 0 0.8099

1809700811 IN Marion summer ocm 0.1231 1.0043

1809700811 IN Marion summer ec 0.03 0.8444

1809700811 IN Marion summer soil 0.0253 1.0918

1809700811 IN Marion summer nh4 0.1114 0.6854

1809700811 IN Marion summer pbw 0.1163 0.6674

1809700811 IN Marion fall so4 0.2751 0.8538

1809700811 IN Marion fall no3 0.149 0.9452

1809700811 IN Marion fall ocm 0.223 0.9648

1809700811 IN Marion fall ec 0.0525 0.8412

1809700811 IN Marion fall soil 0.0358 1.089

1809700811 IN Marion fall nh4 0.1378 0.8905

1809700811 IN Marion fall pbw 0.0865 0.8888
Site ID State County Season Species

Species Comp. of Ave. 

FRM (fraction) Species RRF

2616300331 MI Wayne winter so4 0.1587 0.9206

2616300331 MI Wayne winter no3 0.2394 0.9813

2616300331 MI Wayne winter ocm 0.3193 1.0781

2616300331 MI Wayne winter ec 0.0383 0.9279

2616300331 MI Wayne winter soil 0.0541 1.0206

2616300331 MI Wayne winter nh4 0.1188 0.9518

2616300331 MI Wayne winter pbw 0.0714 0.9566

2616300331 MI Wayne spring so4 0.3383 0.7398

2616300331 MI Wayne spring no3 0.0259 0.8787

2616300331 MI Wayne spring ocm 0.3543 1.0234

2616300331 MI Wayne spring ec 0.0504 0.8671

2616300331 MI Wayne spring soil 0.0915 1.0153

2616300331 MI Wayne spring nh4 0.1191 0.7818

2616300331 MI Wayne spring pbw 0.1126 0.7619

2616300331 MI Wayne summer so4 0.3311 0.6681

2616300331 MI Wayne summer no3 0 0.8431

2616300331 MI Wayne summer ocm 0.2297 1.0029

2616300331 MI Wayne summer ec 0.0362 0.8332

2616300331 MI Wayne summer soil 0.061 1.0177

2616300331 MI Wayne summer nh4 0.1027 0.6974

2616300331 MI Wayne summer pbw 0.1073 0.6754

2616300331 MI Wayne fall so4 0.1898 0.854

2616300331 MI Wayne fall no3 0.1075 0.9367

2616300331 MI Wayne fall ocm 0.3689 1.0607

2616300331 MI Wayne fall ec 0.0546 0.8862

2616300331 MI Wayne fall soil 0.1676 1.0317

2616300331 MI Wayne fall nh4 0.0866 0.8919

2616300331 MI Wayne fall pbw 0.0553 0.8821

3903500381 OH Cuyahoga winter so4 0.2117 0.8993

3903500381 OH Cuyahoga winter no3 0.2665 0.9856

3903500381 OH Cuyahoga winter ocm 0.2048 0.9716

3903500381 OH Cuyahoga winter ec 0.0413 0.8903

3903500381 OH Cuyahoga winter soil 0.0465 1.0959

3903500381 OH Cuyahoga winter nh4 0.1459 0.9416

3903500381 OH Cuyahoga winter pbw 0.0832 0.9541

3903500381 OH Cuyahoga spring so4 0.3334 0.7145

3903500381 OH Cuyahoga spring no3 0.0374 0.8393

3903500381 OH Cuyahoga spring ocm 0.2068 1.0899

3903500381 OH Cuyahoga spring ec 0.052 0.9362

3903500381 OH Cuyahoga spring soil 0.0697 1.0601

3903500381 OH Cuyahoga spring nh4 0.1256 0.7666

3903500381 OH Cuyahoga spring pbw 0.115 0.7761

3903500381 OH Cuyahoga summer so4 0.3241 0.6303

3903500381 OH Cuyahoga summer no3 0 0.89

3903500381 OH Cuyahoga summer ocm 0.1306 1.0998

3903500381 OH Cuyahoga summer ec 0.0419 0.9354

3903500381 OH Cuyahoga summer soil 0.0583 1.0906

3903500381 OH Cuyahoga summer nh4 0.1074 0.7038

3903500381 OH Cuyahoga summer pbw 0.1183 0.6674

3903500381 OH Cuyahoga fall so4 0.2055 0.8193

3903500381 OH Cuyahoga fall no3 0.1275 0.9189

3903500381 OH Cuyahoga fall ocm 0.2234 1.0245

3903500381 OH Cuyahoga fall ec 0.0499 0.8913

3903500381 OH Cuyahoga fall soil 0.0675 1.0927

3903500381 OH Cuyahoga fall nh4 0.1034 0.8615

3903500381 OH Cuyahoga fall pbw 0.0637 0.8564
Site ID State County Season Species

Species Comp. of Ave. 

FRM (fraction) Species RRF

3904900241 OH Franklin winter so4 0.2555 0.8622

3904900241 OH Franklin winter no3 0.2373 1.0002

3904900241 OH Franklin winter ocm 0.2082 0.974

3904900241 OH Franklin winter ec 0.0375 0.8537

3904900241 OH Franklin winter soil 0.0259 1.0844

3904900241 OH Franklin winter nh4 0.1495 0.9261

3904900241 OH Franklin winter pbw 0.0861 0.9274

3904900241 OH Franklin spring so4 0.3754 0.6615

3904900241 OH Franklin spring no3 0.0176 0.8436

3904900241 OH Franklin spring ocm 0.2069 1.062

3904900241 OH Franklin spring ec 0.0405 0.8678

3904900241 OH Franklin spring soil 0.0371 1.0551

3904900241 OH Franklin spring nh4 0.1296 0.7212

3904900241 OH Franklin spring pbw 0.128 0.6992

3904900241 OH Franklin summer so4 0.3703 0.622

3904900241 OH Franklin summer no3 0 0.9056

3904900241 OH Franklin summer ocm 0.1343 1.0654

3904900241 OH Franklin summer ec 0.0311 0.8565

3904900241 OH Franklin summer soil 0.0267 1.0667

3904900241 OH Franklin summer nh4 0.1142 0.7021

3904900241 OH Franklin summer pbw 0.1186 0.6614

3904900241 OH Franklin fall so4 0.2692 0.8119

3904900241 OH Franklin fall no3 0.1186 0.9099

3904900241 OH Franklin fall ocm 0.2489 1.019

3904900241 OH Franklin fall ec 0.0533 0.8371

3904900241 OH Franklin fall soil 0.0423 1.0924

3904900241 OH Franklin fall nh4 0.1217 0.8539

3904900241 OH Franklin fall pbw 0.0821 0.8519

3906100141 OH Hamilton winter so4 0.2685 0.8104

3906100141 OH Hamilton winter no3 0.2378 1.0886

3906100141 OH Hamilton winter ocm 0.19 0.961

3906100141 OH Hamilton winter ec 0.035 0.8969

3906100141 OH Hamilton winter soil 0.0229 1.4146

3906100141 OH Hamilton winter nh4 0.1583 0.9077

3906100141 OH Hamilton winter pbw 0.0874 0.8687

3906100141 OH Hamilton spring so4 0.3583 0.6331

3906100141 OH Hamilton spring no3 0.0025 1.0155

3906100141 OH Hamilton spring ocm 0.1986 1.0798

3906100141 OH Hamilton spring ec 0.0466 0.9228

3906100141 OH Hamilton spring soil 0.0289 1.3785

3906100141 OH Hamilton spring nh4 0.1215 0.6968

3906100141 OH Hamilton spring pbw 0.128 0.6307

3906100141 OH Hamilton summer so4 0.3722 0.577

3906100141 OH Hamilton summer no3 0 1.0923

3906100141 OH Hamilton summer ocm 0.121 1.082

3906100141 OH Hamilton summer ec 0.0309 0.9099

3906100141 OH Hamilton summer soil 0.0199 1.537

3906100141 OH Hamilton summer nh4 0.1178 0.6441

3906100141 OH Hamilton summer pbw 0.1261 0.5734

3906100141 OH Hamilton fall so4 0.2608 0.7754

3906100141 OH Hamilton fall no3 0.1184 0.9857

3906100141 OH Hamilton fall ocm 0.213 1.0235

3906100141 OH Hamilton fall ec 0.0512 0.8876

3906100141 OH Hamilton fall soil 0.0328 1.4007

3906100141 OH Hamilton fall nh4 0.1254 0.846

3906100141 OH Hamilton fall pbw 0.0828 0.8172
Site ID State County Season Species

Species Comp. of Ave. 

FRM (fraction) Species RRF

3908110011 OH Jefferson winter so4 0.2367 0.8217

3908110011 OH Jefferson winter no3 0.1709 1.0522

3908110011 OH Jefferson winter ocm 0.3288 0.8819

3908110011 OH Jefferson winter ec 0.0435 0.9091

3908110011 OH Jefferson winter soil 0.0272 0.4368

3908110011 OH Jefferson winter nh4 0.1199 0.8904

3908110011 OH Jefferson winter pbw 0.073 0.8583

3908110011 OH Jefferson spring so4 0.3508 0.6666

3908110011 OH Jefferson spring no3 0.0154 0.9156

3908110011 OH Jefferson spring ocm 0.3078 0.9995

3908110011 OH Jefferson spring ec 0.0395 0.9853

3908110011 OH Jefferson spring soil 0.0407 0.4844

3908110011 OH Jefferson spring nh4 0.114 0.7054

3908110011 OH Jefferson spring pbw 0.1095 0.6713

3908110011 OH Jefferson summer so4 0.3779 0.6156

3908110011 OH Jefferson summer no3 0 1.0837

3908110011 OH Jefferson summer ocm 0.2098 1.0145

3908110011 OH Jefferson summer ec 0.0308 0.9689

3908110011 OH Jefferson summer soil 0.0323 0.3632

3908110011 OH Jefferson summer nh4 0.1065 0.6428

3908110011 OH Jefferson summer pbw 0.1007 0.625

3908110011 OH Jefferson fall so4 0.2315 0.7694

3908110011 OH Jefferson fall no3 0.0702 1.0302

3908110011 OH Jefferson fall ocm 0.372 0.9312

3908110011 OH Jefferson fall ec 0.051 0.9086

3908110011 OH Jefferson fall soil 0.0344 0.4555

3908110011 OH Jefferson fall nh4 0.0859 0.8284

3908110011 OH Jefferson fall pbw 0.0629 0.7951

3911300321 OH Montgomer winter so4 0.2613 0.8598

3911300321 OH Montgomer winter no3 0.2407 1.029

3911300321 OH Montgomer winter ocm 0.1954 0.9442

3911300321 OH Montgomer winter ec 0.036 0.8746

3911300321 OH Montgomer winter soil 0.0259 1.1295

3911300321 OH Montgomer winter nh4 0.1531 0.9304

3911300321 OH Montgomer winter pbw 0.0876 0.9205

3911300321 OH Montgomer spring so4 0.3659 0.6606

3911300321 OH Montgomer spring no3 0.0163 0.8639

3911300321 OH Montgomer spring ocm 0.1895 1.0976

3911300321 OH Montgomer spring ec 0.0442 0.9417

3911300321 OH Montgomer spring soil 0.0253 1.0873

3911300321 OH Montgomer spring nh4 0.1313 0.7149

3911300321 OH Montgomer spring pbw 0.1326 0.6839

3911300321 OH Montgomer summer so4 0.375 0.6234

3911300321 OH Montgomer summer no3 0 0.9474

3911300321 OH Montgomer summer ocm 0.128 1.1047

3911300321 OH Montgomer summer ec 0.029 0.9496

3911300321 OH Montgomer summer soil 0.0205 1.1299

3911300321 OH Montgomer summer nh4 0.1114 0.6931

3911300321 OH Montgomer summer pbw 0.1114 0.6482

3911300321 OH Montgomer fall so4 0.3062 0.8033

3911300321 OH Montgomer fall no3 0.1012 0.9634

3911300321 OH Montgomer fall ocm 0.2221 1.0158

3911300321 OH Montgomer fall ec 0.0514 0.877

3911300321 OH Montgomer fall soil 0.028 1.1391

3911300321 OH Montgomer fall nh4 0.1352 0.8625

3911300321 OH Montgomer fall pbw 0.0982 0.8475
Site ID State County Season Species

Species Comp. of Ave. 

FRM (fraction) Species RRF

3915100171 OH Stark winter so4 0.2362 0.8558

3915100171 OH Stark winter no3 0.2234 1.0222

3915100171 OH Stark winter ocm 0.2478 0.9255

3915100171 OH Stark winter ec 0.0414 0.8866

3915100171 OH Stark winter soil 0.0334 1.099

3915100171 OH Stark winter nh4 0.1376 0.925

3915100171 OH Stark winter pbw 0.0802 0.9155

3915100171 OH Stark spring so4 0.3581 0.6834

3915100171 OH Stark spring no3 0.0236 0.855

3915100171 OH Stark spring ocm 0.221 1.0892

3915100171 OH Stark spring ec 0.0501 1.0017

3915100171 OH Stark spring soil 0.058 1.0528

3915100171 OH Stark spring nh4 0.1288 0.7264

3915100171 OH Stark spring pbw 0.1256 0.7009

3915100171 OH Stark summer so4 0.3621 0.6277

3915100171 OH Stark summer no3 0 0.8203

3915100171 OH Stark summer ocm 0.1483 1.0984

3915100171 OH Stark summer ec 0.0403 1.016

3915100171 OH Stark summer soil 0.037 1.0781

3915100171 OH Stark summer nh4 0.1157 0.6739

3915100171 OH Stark summer pbw 0.124 0.651

3915100171 OH Stark fall so4 0.2293 0.8041

3915100171 OH Stark fall no3 0.1262 0.9363

3915100171 OH Stark fall ocm 0.2722 1.0226

3915100171 OH Stark fall ec 0.0545 0.9202

3915100171 OH Stark fall soil 0.0461 1.0959

3915100171 OH Stark fall nh4 0.1105 0.8549

3915100171 OH Stark fall pbw 0.0706 0.8428

3915300171 OH Summit winter so4 0.2511 0.8771

3915300171 OH Summit winter no3 0.2376 1.0052

3915300171 OH Summit winter ocm 0.2185 0.9429

3915300171 OH Summit winter ec 0.0334 0.8677

3915300171 OH Summit winter soil 0.0255 1.0835

3915300171 OH Summit winter nh4 0.1489 0.9374

3915300171 OH Summit winter pbw 0.0851 0.945

3915300171 OH Summit spring so4 0.387 0.7046

3915300171 OH Summit spring no3 0.0072 0.8466

3915300171 OH Summit spring ocm 0.1901 1.0967

3915300171 OH Summit spring ec 0.035 0.9482

3915300171 OH Summit spring soil 0.0304 1.0524

3915300171 OH Summit spring nh4 0.1294 0.7521

3915300171 OH Summit spring pbw 0.1342 0.7384

3915300171 OH Summit summer so4 0.3694 0.6378

3915300171 OH Summit summer no3 0 0.8587

3915300171 OH Summit summer ocm 0.1417 1.1077

3915300171 OH Summit summer ec 0.0332 0.9506

3915300171 OH Summit summer soil 0.0198 1.0744

3915300171 OH Summit summer nh4 0.1121 0.6961

3915300171 OH Summit summer pbw 0.1146 0.6691

3915300171 OH Summit fall so4 0.2443 0.8074

3915300171 OH Summit fall no3 0.1175 0.9392

3915300171 OH Summit fall ocm 0.2636 1.0252

3915300171 OH Summit fall ec 0.0623 0.8883

3915300171 OH Summit fall soil 0.0494 1.086

3915300171 OH Summit fall nh4 0.109 0.8622

3915300171 OH Summit fall pbw 0.0723 0.8506
119

APPENDIX II

Ozone Source Apportionment Modeling Results

















120

APPENDIX III

PM2.5 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, Michigan

2005 (Round 5)

2012 (Round 4)

2018 (Round 5)
Cincinnati, Ohio

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 IV

Haze Source Apportionment Modeling Results
Boundary Waters, Minnesota

2005 (Round 5)

2018 (Round 4)

2018 (Round 5)
Voyageurs, Minnesota

2005 (Round 5)

2018 (Round 4)

2018 (Round 5)
Seney, Michigan

2005 (Round 5)

2018 (Round 4)

2018 (Round 5)
Isle Royale, Michigan

2005 (Round 5)

2018 (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)