Technical Support Document for the 

Interstate Air Quality Rule

Air Quality Modeling Analyses

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Emissions Analysis and Monitoring Division

Research Triangle Park, NC 27711

January 2004
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I. Introduction

This document was prepared to describe the air quality modeling performed by EPA in

support of the proposed Interstate Air Quality Rule (IAQR). Included is information on (1) the

air quality models and the development of model inputs, (2) the performance of the models as

compared to measured data, (3) the procedures for projecting current air quality to future year

emissions scenarios, (4) the evaluation of interstate contribution to ozone and PM2.5 in

downwind nonattainment areas, (5) an analysis of the potential air quality improvements from

locally applied controls, (6) an assessment of the expected air quality improvements from the

regional SO2 and NOx emissions reductions, and (7) an analysis of the effects of SO2 emissions

reductions on nitrate concentrations. The following is an outline of the main sections of this

document:

I. Introduction

II. Emissions Inventories

III. Base Year Episodic Ozone Modeling

IV. Base Year PM2.5, Visibility, and Deposition Modeling

V. Procedures for Projecting Ozone and PM2.5 for Future Year Scenarios

VI. Modeling to Assess Interstate Ozone Contributions

VII. Modeling to Assess Interstate PM2.5 Contributions

VIII. Ozone Sensitivity Modeling of Local Emission Reductions

IX. PM2.5 Modeling of Locally Applied Emissions Reductions

X. Modeling of Regional SO2 and NOx Emissions Reductions

XI. Modeling to Examine Nitrate Replacement

XII. References

Appendix A. Emissions Summary

Appendix B. CAMx Model Performance Evaluation

Appendix C. REMSAD Model Performance Evaluation

Appendix D. 8-Hour Ozone Concentrations at Nonattainment Counties for the 2010 Base Case and

2015 Base Case

Appendix E. Procedures for Estimating Future PM2.5 Values by Application Speciated Model

Attainment Test (SMAT)

Appendix F. PM2.5 Concentrations Projected for the 2010 Base Cases and 2015 Base Case

Appendix G. Metrics for 8-Hour Ozone Contributions to Downwind Nonattainment Counties in 2010

Appendix H. PM2.5 Contributions to Downwind Nonattainment Counties in 2010

Appendix I. Background Information on the Development of Local Control Measures for PM2.5

Appendix J. 290 Counties Included in the Local Control Study

Appendix K Summary Emission Reductions from Local Control Measures for the 290 County Study

Appendix L. Summaries of Impacts on PM2.5 and PM2.5 Speices from Local Control Measures for

the 290 County Study

Appendix M. Projected Visibility Summaries for 20% Best and 20% Worst Days at IMPROVE

Monitoring Sites
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II. Emissions Inventories

A. Overview of Emissions Scenarios

In order to support the air quality modeling analyses for the proposed rule, emissions

inventories were developed for the 48 contiguous States and the District of Columbia. 

Inventories were developed for a 2001 base year, for 2010 and 2015 future baseline scenarios,

and for 2010 and 2015 future control scenarios. The 2001 base year and 2010 and 2015 future

base case inventories were in large part derived from a 1996 base year inventory and projections

of that 1996 inventory to 2007 and 2020 as developed for previous EPA rulemakings for Heavy

Duty Diesel Engines (HDDE)(EPA, 2000a; www.epa.gov/otaq/models/hd2007/r00020.pdf) and

Land-based Non-road Diesel Engines (LNDE)(EPA, 2003a;

www.epa.gov/nonroad/454r03009.pdf). 

The inventories were prepared at the county level for on-road vehicles, non-road engines,

and area sources. Emissions for electric generating units (EGUs) and large industrial and

commercial sources (non-EGUs) were prepared as individual point sources. The inventories

contain both annual and typical summer season day emissions for the following pollutants:

oxides of nitrogen (NOx); volatile organic compounds (VOC); carbon monoxide (CO); sulfur

dioxide (SO2); direct particulate matter with an aerodynamic diameter less than 10 micrometers

(PM10) and less than 2.5 micrometers (PM2.5); and ammonia (NH3). 

B. 2001 Base Year Emissions Inventory

Emissions inventory inputs representing the year 2001 were developed to provide a base

year for forecasting future air quality. Because the complete 2001 National Emissions Inventory

(NEI) and future year emissions projections consistent with that NEI were not available in a

form suitable for air quality modeling when needed for this analysis, the following approach was

used to develop a reasonably representative "proxy" inventory for 2001 in model-ready form that

retained the same consistency with the existing future year projected inventories as the 1996

model-ready inventory that was used as the basis for those projected inventories. 

The EPA had available model-ready emissions input files for a 1996 Base Year and a

projected 2010 Base Case from a previous analysis. In addition, robust NEI estimates were

available for 2001 for three of the five anthropogenic emissions sectors: EGUs; on-road vehicles;

and non-road engines. NEI estimates for the 2001 Base Year were not available on a basis

consistent with the 1996 and 2010 modeling files for the remaining two emission sectors:

non-EGU point sources and area sources. The 2001 Proxy modeling files were therefore

developed in a slightly different manner for each sector, as described below.

For the EGU sector, State-level emissions totals from the NEI 2001 were divided by

similar totals from the 1996 modeling inventory to create a set of 1996 to 2001 adjustment ratios. 

Ratios were developed for each State and pollutant. These ratios were applied to the
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model-ready 1996 EGU emissions file to produce the 2001 EGU emissions file. Adjustments

were thus made in the modeling file to account for emissions reductions that had occurred

between 1996 and 2001, but at an aggregated State-level, rather than for each individual source.

As previously stated, the NEI 2001 emissions estimates for the on-road vehicles and

non-road engines sectors were available from the MOBILE6 and NONROAD2002 models,

respectively. Because both of these models were updates of the versions used to produce the

existing 1996 model-ready emissions files and their associated projection year files, an approach

was developed to capture the relative 1996-to-2001 growth and control changes for these two

sectors, rather than producing absolute tonnage values in the 2001 Proxy modeling files that

would match the 2001 NEI.

The updated MOBILE6 and NONROAD2002 models were used to develop revised 1996

annual emissions estimates that were consistent with the 2001 NEI estimates. A set of

1996-to-2001 adjustment ratios were then created by dividing State-level total emissions for each

pollutant for 2001 by the corresponding consistent 1996 emissions. These adjustment ratios

were then multiplied by the older methodologies' existing gridded model-ready 1996 emissions

for these two sectors to produce model-ready files for 2001. These model-ready 2001 files,

therefore, maintain consistency with the future year projection files that were based on the older

emission model versions but also capture the effects of the 1996 to 2001 emission changes as

indicated by the latest versions of the two emissions models.

NEI estimates for the 2001 Base Year were not available on a basis consistent with the

1996 and 2010 modeling files for the non-EGU point source and area source sectors. Linear

interpolations were performed between the gridded 1996 emissions and the gridded 2010 Base

Case emissions to produce the 2001 gridded emissions files for these two sectors. These

interpolations were done separately for each of the two sectors, for each grid cell, for each

pollutant. As the 2010 Base Case inventory was itself a projection from the 1996 inventory, this

approach maintained consistency of methods and assumptions between the 2001 and 2010

emissions files, and also attempted to capture likely changes in the inventory from 1996 to 2010. 

(Note that the gridded area source files had been split into livestock versus non-livestock

categories. The grid cell by grid cell interpolations were therefore done separately for each of

these two sub-sectors of the area source inventory).

Appendix A, Tables 1 through 3 show the adjustment ratios that were developed for

EGUs, on-road vehicles and off-road engines. Tables 4 through 9 show the resulting State-level

emissions totals for the 2001 Proxy modeling inventory for these three sectors, as well as for the

non-egu and area source sectors which were developed from linear interpolation and a table for

all sectors combined. Because the gridded 1996 modeling files contained pollutant PM-coarse

(calculated from PM10 minus PM2.5), the three ratio tables include adjustment factors for

PM-coarse rather than PM10.
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C. 2010 and 2015 Base Case and IAQR Regional Control Case Emissions Inventories

The future Base Case scenarios represent predicted emissions in the absence of any

further controls beyond those State, local, and Federal measures already promulgated plus other

significant measures expected to be promulgated before the final IAQR is promulgated. Any

additional local control programs which may be necessary for areas to attain the annual PM2.5

NAAQS and the ozone NAAQS are not included in the future base case projections. The future

base case scenarios do reflect projected economic growth.

Specifically, the future base case scenarios include the effects of the LNDE as proposed,

the HDDE standards, the Tier 2 tailpipe standards, the NOx SIP Call as remanded (excludes

controls in Georgia and Missouri), and Reasonably Available Control Techniques (RACT) for

NOx in 1-hour ozone nonattainment areas. Adjustments were also made to the non-road sector

inventories to include the effects of the Large Spark Ignition and Recreational Vehicle rules; and

to the non-EGU sector inventories to include the SO2 and particulate matter co-benefit

effects of the proposed Maximum Achievable Control Technology (MACT) standard for

Industrial Boilers and Process Heaters. The future base case scenarios do not include the NOx

co-benefit effects of proposed MACT regulations for Gas Turbines or stationary Reciprocating

Internal Combustion Engines, which we estimate to be small compared to the overall inventory;

or the effects of NOx RACT in 8-hour ozone nonattainment areas, because these areas have not

yet been designated.

The 2010 and 2015 Base Case inventories used for this proposal were derived from

interpolations and adjustments to projection inventories developed for previous EPA

rulemakings. In particular, the 2007 inventory used to represent the control case for the Heavy

Duty Diesel Engines (HDDE) rule and the 2020 inventory used to represent the control case for

the Land-based Non-road Diesel Engines (LNDE) rule were used with appropriate adjustments

for this proposal. Full documentation of the procedures used to develop these earlier projection

inventories is available at www.epa.gov/otaq/models/hd2007/r00020.pdf and

www.epa.gov/nonroad/454r03009.pdf, respectively. A description of the adjustments that were

made beyond what is documented in those earlier documents is described in subsections 1.

through 4., below. 

The control case inventories for 2010 and 2015 were developed by replacing the EGU

emissions in the base case inventories with the projected EGU emissions under a proposed

emissions cap scenario. Appendix A, Tables 10 thru 21 contain the State-level emissions

summaries for each of the five sectors for the IAQR Base Case inventories for 2010 and 2015. 

Tables 22 thru 27 contain the Control Case summaries for the EGU sector, all sectors combined,

and the differences from the Base Cases for the two years.

1. Development of Emissions Inventories for Electric Generating Units

Base and Control Case EGU emissions for 2010 and 2015 used for the air quality strategy

modeling runs were obtained from version 2.1.6 of the Integrated Planning Model (IPM)

(www.epa.gov/airmarkets/epa-ipm/index.html). However, results from this version of the IPM
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Revisions to PM2.5 emissions from EGU sources were made to the 2010 Base-1 for sources in

Iowa, Louisiana, and North Dakota. These revisions were incorporated into an updated baseline referred

to as 2010 Base-1a. The 2010 Base-1a was used as the baseline for the zero-out REMSAD modeling of

Iowa, Louisiana, North Dakota combined with Vermont, Colorado, Montana, New Mexico, and

Wyoming. Information on the zero-out modeling to assess interstate contributions to PM2.5 can be found

in section VII.

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model were not available at the time that the air quality model runs used to determine interstate

contributions ("zero-out runs") were started. Therefore, EGU emissions from a previous IPM

version (v2.1.5) were used for the zero-out runs. Updates applied to the IPM model between

versions 2.1.5 and 2.1.6 include the update of coal and natural gas supply curves and the

incorporation of several State-mandated emission caps and New Source Review (NSR)

settlements. In this document we refer to the 2010 Base Case used in the zero out runs as 2010

Base-11, and the 2010 Base Case used for the control strategy runs as 2010 Base-2. The 2010

Base-2 and 2015 Base Case were developed from the same IPM. The 2010 Base-2 emissions are

also the same as the 2010 Base Case used for modeling in the 2003 Clear Skies analysis. 

Appendix A Tables 28 and 29 compare the State-level emissions totals of NOx and SO2 for

2010 Base-1 versus 2010 Base-2. 

2. Development of Emissions Inventories for On-road Vehicles

The 2010 and 2015 Base Case emissions files used for this proposal were developed as

straight-line interpolations between the 2007 on-road file used for the control case of the HDDE

rule and the 2020 on-road file used for both the base and control cases of the LNDE rule. Note

that the 2020 on-road vehicle emissions file developed for the LNDE rule includes the reductions

expected from implementation of the earlier HDDE rule. No adjustments were made for on-road

vehicles beyond the linear interpolations to produce the two intervening years.

As described in the referenced documents for the earlier rules, the 2007 and 2020 on-road

vehicle files were developed using a version of the MOBILE5b model which had been adjusted

to simulate the MOBILE6 model that was under development at that time. The 1996 on-road

vehicle emission file (and therefore the derived 2001 Proxy modeling file) had been developed

using the same adjusted version of MOBILE5b. 

3. Development of Emissions Inventories for Non-road Engines

The 2010 and 2020 non-road emissions files developed for EPA's analysis of the

preliminary controls of the LNDE rule (and as documented at

www.epa.gov/nonroad/454r03009.pdf) were modified to reflect that rule as finally proposed (68

FR 28327, May 23, 2003) and to incorporate the effects of the Large Spark Ignition and

Recreational Vehicle rules. These modifications were done using adjustment ratios developed

from national-level estimates of the benefits of these two rules. A 2015 emissions file for this

sector was then developed as a straight-line interpolation between the modified 2010 and 2020

files. Note that a 2010 emissions file for the non-road sector had been developed in a consistent

manner as the 2020 and 2030 files that were actually modeled for the LNDE proposal. However,
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2010 emissions files were not available from the LNDE analyses for the other emission sectors.

4. Development of Emissions Inventories for Non-EGU Point Sources and Area Sources

The 2010 and 2015 emissions files for these sectors that were used as part of the

interpolation to 2001 were themselves developed as straight-line interpolations between the 2007

and 2020 inventories described above for the on-road vehicle sector. The interpolated 2010 and

2015 emissions were adjusted to reflect the SO2, PM10, and PM2.5 co-control benefits of the

proposed Industrial Boiler and Process Heater MACT (68 FR 1660, January 13, 2003). The

2007 and 2020 projection inventories had been developed by applying State- and 2-digit

SIC-specific economic growth ratios to the 1996 NEI, followed by application of any emissions

control regulations.

5. Preparation of Emissions for Air Quality Modeling

The annual and summer day emissions inventory files were processed through the Sparse

Matrix Operator Kernel Emissions (SMOKE) Modeling System (Houyoux, 2000) to produce

36-km gridded input files for annual PM2.5 air quality modeling and 12-km input files for

episodic ozone air quality modeling. In addition to the U.S. anthropogenic emission sources

described above, hourly biogenic emissions were estimated for individual modeling days using

the BEIS model version 3.09 (ftp.epa.gov/amd/asmd/beis3v09/). Emissions inventories for

Canada and for U.S. offshore oil platforms were merged in using SMOKE to provide a more

complete modeling data set. The single set of biogenic, Canadian, and offshore U.S. emissions

was used in all scenarios modeled. That is, the emissions for these sources were not varied from

run to run.

III. Base Year Episodic Ozone Modeling

Air quality modeling analyses for ozone were conducted with the Comprehensive Air

Quality Model with Extensions (CAMx). CAMx is a non-proprietary computer modeling tool

that can be used to evaluate the impacts of proposed emissions reductions on future air quality

levels. For more information on the CAMx model, please see the model user’s guide (Environ,

2002). Version 3.10 of the CAMx model was employed for these analyses.

The modeling analyses were completed for an Eastern U.S. domain as shown in Figure

III-1. The domain has nested horizontal grids of 36 and 12 km. The model was applied and

evaluated over three episodes that occurred during the summer of 1995. Ozone model runs were

performed for emissions in 1996 in order to evaluate the ability of the model to replicate

measured concentrations. In addition, model runs were preformed for the 2001 Base Year and

the 2010 and 2015 Base and control case scenarios for all episodes. The model outputs from the

2001 base year and 2010 and 2015 base and control cases, combined with current air quality

data, were used to: 1) determine the degree and geographic extent of expected future

nonattainment, 2) determine the potential impacts of local controls on future nonattainment, 3)

assess the potential for transport of ozone and ozone precursors, and 4) determine the
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contribution from transport to future 8-hour ozone nonattainment. 

Figure III-1. Map of the Eastern U.S. modeling domain. The outer box denotes the entire

modeling domain (36 km) and the inner box shaded indicates the fine grid location (12 km).

A. Modeling Episodes

 There are several considerations involved in selecting episodes for an ozone modeling

analysis (EPA, 1999a). In general, the goal is to model several differing sets of meteorological

conditions leading to ambient ozone levels similar to an area’s design value. Warm

temperatures, light winds, cloud-free skies, and stable boundary layers are some of the typical

characteristics of ozone episodes. On a synoptic scale, these conditions usually result from a

combination of high pressure aloft (e.g., at the 500 millibar pressure level) and at the surface. 

On the local scale, the conditions that lead to ozone exceedances can vary from location to

location based on factors such as wind direction, sea/lake breezes, etc. The ozone episodes

modeled for the IAQR are listed in Table III-1. The meteorological and resultant ozone patterns

for these episodes are discussed in more detail in previous technical support documents for the

Tier-2/Low Sulfur rule (EPA, 1999b) and the Heavy-Duty Engine rule (EPA, 2000b). The first

three days of each period are considered ramp-up days and the results from these days were not

used in the analyses. In all, 30 episode days were modeled. 
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Table III-1. Dates of Ozone Episodes Modeled Including Ramp-Up Days.

Ozone Episodes

Episode 1 June 12-24, 1995

Episode 2 July 5-15, 1995

Episode 3 August 7-21, 1995

In order to determine whether the modeling days correspond to commonly occurring and

ozone-conducive meteorology, EPA has applied a multi variate statistical approach for

characterizing daily meteorological patterns and investigating their relationship to 8-hour ozone

concentrations in the Eastern U.S. (Battelle, 2004). The approach applies procedures presented

in Eder, et al. (1994). These analyses were conducted using meteorological data from the most

recent seven to ten years at 16 sites. In most locations, there were five to six distinct sets of

meteorological conditions, called regimes, that occurred during the ozone seasons studied. An

analysis of the 8-hour daily maximum ozone concentrations for each of the meteorological

regimes determined the distribution of ozone concentrations for each regime and the frequency

of regime occurrence. These two terms were combined to identify which regimes contribute the

most to ozone concentrations in the locations under investigation. Using the data base in which

each day in 1995 is assigned a meteorological regime, EPA determined that between 60 and 70

percent of the episode days modeled are associated with the most frequently occurring, high

ozone potential, meteorological regimes. In general, these results provide support that the

episodes modeled are representative of conditions present when elevated ozone is observed

throughout the modeling domain. 

B. Modeling Domain and Grid Configuration

As with episode selection, there are also several considerations involved in selecting the

domain and grid configuration to be used in the ozone modeling analysis. The modeling domain

should encompass the area of intended analysis with an additional buffer of grid cells to

minimize the effects of, sometimes uncertain, boundary condition inputs. When possible, grid

resolution should be equivalent to the resolution of the primary model inputs (emissions, winds,

etc.) and equivalent to the scale of the air quality issue being addressed. The CAMx modeling

was performed for the coarse and fine grid domains as defined below in Table III-2.
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 The inner nests were modeled with 34 layers while the outer 108 km domain was modeled with

28 layers.

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Table III-2. Configuration of Ozone Modeling Domain.

Eastern US Domain

Coarse Grid Fine Grid

Map Projection latitude/longitude latitude/longitude

Grid Resolution 1/2/ longitude, 1/3/ latitude 

(~ 36 km)

1/6/ longitude, 1/9/ latitude 

(~ 12 km)

East/West extent -99 W to -67 W -92 W to -69.5 W

North/South extent 26 N to 47 N 32 N to 44 N

Vertical extent 9 Layers: surface to 4 km 9 Layers: surface to 4 km

Dimensions 64 by 63 by 9 137 by 110 by 9

C. Meteorological and Other Model Inputs

In order to solve for the change in pollutant concentrations over time and space, the air

quality model requires certain meteorological inputs that, in part, govern the formation,

transport, and destruction of pollutant material. In particular, the CAMx model used in these

analyses requires seven meteorological input files: wind (u- and v-vector wind components),

temperature, water vapor mixing ratio, atmospheric air pressure, cloud cover, rainfall, and

vertical diffusion coefficient. Fine grid values of wind, pressure, and vertical diffusivity are also

used; the other fine grid meteorological inputs are interpolated from the coarse grid files.

The gridded meteorological data for the three historical 1995 episodes were developed

using the Regional Atmospheric Modeling System (RAMS), version 3b. RAMS (Pielke et. al.,

1992) is a numerical meteorological model that solves the full set of physical and

thermodynamic equations which govern atmospheric motions. The output data from RAMS,

which is run in a polar stereographic projection and a sigma-p coordinate system, are then

mapped to the CAMx grid. Two separate meteorological CAMx inputs, cloud fractions and

rainfall rates, were developed based on observed data.

RAMS was run in a nested-grid mode with three levels of resolution: 108 km, 36 km, and

12 km with 28-342 vertical layers. The top of the surface layer was 16.7 m in the 36 and 12km

grids. The two finer grids were at least as large as their CAMx counterparts. In order to keep

the model results in line with reality, the simulated fields were nudged to an European Center for

Medium-Range Weather Forecasting (ECMWF) analysis field every six hours. This assimilation
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data set was bolstered by every four-hourly special soundings regularly collected as part of the

North American Research Strategy on Tropospheric Ozone (NARSTO) field study in the

northeast U.S.

A limited model performance evaluation (Lagouvardos et al., 2000) was completed for a

portion of the 1995 meteorological modeling (July 12-15). Observed data not used in the

assimilation procedure were compared against modeled data at the surface and aloft. In general,

the model accurately reproduced the synoptic meteorological conditions of the episode days. 

Furthermore, the meteorological fields were compared before and after being processed into

CAMx inputs. It was concluded that this preprocessing did not distort the meteorological fields.

In addition to the meteorological data, the photochemical grid model requires several

other types of data. In general, most of these miscellaneous model files have been be taken from

existing regional modeling applications. Clean conditions were used to initialize the model and

were also used as lateral and top boundary conditions as in previous regional modeling

applications. The model also requires information regarding land use type and surface albedo

for all layer 1 grid cells in the domain. Existing regional data were used for these non-dayspecific files. Photolysis rates were developed using the JCALC preprocessor. Turbidity values

were set equal to a constant thought to be representative of regional conditions.

D. CAMx Model Performance Evaluation

The goal of the 1995 Base Year modeling was to reproduce the atmospheric processes

resulting in high ozone concentrations over the eastern United States during the three 1995

episodes selected for modeling. Note that the base year of the emissions was 1996 while the

eastern U.S. episodes are for 1995. The effects on model performance of using 1996 emissions

for the 1995 episodes are not known, but are not expected to be major. The ozone model

performance evaluation procedures and results are provided in Appendix B.

IV. Base Year PM2.5, Visibility, and Deposition Modeling

A. Introduction

This section describes the REgional Modeling System for Aerosols and Deposition

(REMSAD) model which was used as the tool for simulating base year and future concentrations

of PM, visibility, and deposition in support of the IAQR air quality assessments (ICF Kaiser,

2002) . Model runs were made for the 1996 and 2001 Base Years as well as for the 2010 and

2015 Base and control scenarios. As described below, each of these emissions scenarios was

simulated using 1996 meteorological data in order to provide the PM2.5 concentrations needed

for the projecting PM2.5, visibility and deposition for the future year baseline and control

scenarios.
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Two versions of REMSAD were used for the IAQR modeling. Version 7.03 was the

most current version available when EPA began the IAQR model runs. During the course of the

modeling process updates were made to REMSAD and incorporated into version 7.06. The

updates made to REMSAD between version 7.03 and 7.06 are noted below in the section

describing the scientific features of the model. Table IV-1 lists the IAQR emissions scenarios

and the version of REMSAD used for modeling each scenario.

Table IV-1. Emissions Scenarios Modeled and REMSAD Model Version.

Model

Version

2001

Proxy

2010

Base-1

2010

Base-2

2010

Zero-Out

Runs

2010 Control

Runsa

2015 Control

Runsa

7.03 X X - X X X

7.06 X - X Xb - -

a. The 2010 and 2015 Control runs include the IAQR regional strategy and a local control scenario for

each of these projection years.

b. The zero-out model run for New Jersey was rerun using the 2010 Base-2 emissions because the

emissions of SO2 in this State dropped by more than 10 percent compared to the emissions in the 2010

Base-1 scenario. Since the 2010 Base-2 scenario was modeled using version 7.06, the run of the New

Jersey zero-out was also modeled with version 7.06.

B. REMSAD Model Description

The basis for REMSAD is the atmospheric diffusion equation (also called the species

continuity or advection/diffusion equation). This equation represents a mass balance in which all

of the relevant emissions, transport, diffusion, chemical reactions, and removal processes are

expressed in mathematical terms. REMSAD employs finite-difference numerical techniques for

the solution of the advection/diffusion equation. 

REMSAD was run using a latitude/longitude horizontal grid structure in which the

horizontal grids are generally divided into areas of equal latitude and longitude. The vertical

layer structure of REMSAD is defined in terms of sigma-pressure coordinates. The top and

bottom of the domain are defined as 0 and 1 respectively. The vertical layers are defined as a

percent of the atmospheric pressure between the top and bottom of the domain. For example, a

vertical layer of 0.50 sigma is exactly halfway between the top and bottom of the domain as

defined by the local atmospheric pressure. The vertical layers were defined to match the vertical

layer structure of the meteorological model used to generate the REMSAD meteorological

inputs. 

1. Gas Phase Chemistry

REMSAD simulates gas phase chemistry using a reduced-form version of Carbon Bond

(CB4) chemical mechanism termed “micro-CB4” (mCB4) which treats fewer VOC species
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compared to the full CB4 mechanism. The inorganic and radical parts of the reduced mechanism

are identical to CB4. In this version of mCB4 the organic portion is based on three primary

species (VOC, ISOP, and TERP) and one primary and secondary carbonyl species (CARB). 

The VOC species was incorporated with kinetics representing an average anthropogenic

hydrocarbon species. The other two primary VOC species represent biogenic emissions of

isoprene and terpenes and are included with kinetic characteristics representing isoprene and

terpenes respectively. The intent of the mCB4 mechanism is to (a) provide a physically faithful

representation of the linkages between emissions of ozone precursor species and secondary PM

precursors species, (b) treat the oxidizing capacity of the troposphere, represented primarily by

the concentrations of radicals and hydrogen peroxide, and (c) simulate the rate of oxidation of

the nitrogen oxide (NOx) and sulfur dioxide (SO2) PM precursors. Box model testing of mCB4

has found that it performs very closely to the full CB4 that is contained in UAM-V (Whitten,

1999).

REMSAD version 7 (7.03 and 7.06) includes several updates to the mCB4 mechanism

relative to earlier versions of REMSAD. A new treatment for the NO3 and N2O5 species has

been implemented which results in improved agreement with rigorous solvers such as Gear and

eliminates nitrogen mass inconsistencies. Also, several additional reactions have been added to

the mCB4 mechanism which may be important for regional scale and annual applications where

wide ranges in temperature, pressure, and concentrations may be encountered. The reactions are

OH + H, OH + NO3, and HO2 + NO3. For the same reason three reactions involving peroxy

nitric acid (PNA), which were included in the original CB4 mechanism, were added to mCB4.

2. PM Chemistry

Primary PM emissions in REMSAD are treated as inert species. They are advected and

deposited without any chemical interaction with other species. Secondary PM species, such as

sulfate and nitrate are formed through chemical reactions within the model. SO2 is the gas phase

precursor for particulate sulfate, while nitric acid is the gas phase precursor for particulate

nitrate. Several other gas phase species are also involved in the secondary reactions.

There are two pathways for sulfate formation; gas phase and aqueous phase. Aqueous

phase reactions take place within clouds, rain, and/or fog. In-cloud processes can account for the

majority of atmospheric sulfate formation in many areas. In REMSAD, aqueous SO2 reacts with

hydrogen peroxide (H2O2), ozone (O3), and/or oxygen (O2) to form aerosol sulfate. REMSAD

version 7 reflects upgrades to include all three aqueous phase sulfate reactions. Previous

versions only contained the hydrogen peroxide reaction. The rate of the aqueous phase reactions

depends on the concentrations of the chemical reactants as well as cloud water content. SO2 also

reacts with OH radicals in the gas phase to form aerosol sulfate. The aqueous phase and gas

phase sulfate is typically added together to get the total sulfate concentration.

An equilibrium algorithm is used to calculate particulate nitrate concentrations. 

REMSAD version 7 uses the MARS-A equilibrium algorithm (Saxena et al., 1986) and (Kim et

al., 1993). In REMSAD, particulate nitrate is calculated in an equilibrium reaction between

nitric acid, sulfate, and ammonia. Nitric acid is a product of gas phase chemistry and is formed
3

An error was found in the SOA mechanism of REMSAD v7.01. This was corrected in version

7.03. The reference temperature from the literature to calculate the partitioning coefficient (K) was

assumed to be 298K when it should have been ~308K.

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through the mCB4 reactions. The acids are neutralized by ammonia with sulfate reacting more

quickly than nitric acid. An equilibrium is established among ammonium sulfate and

ammonium nitrate which strongly favors ammonium sulfate. If the available ammonia exceeds

twice the available sulfate then particulate nitrate is allowed to form as ammonium nitrate. 

Nitrate is then partitioned between particulate nitrate and gas phase nitric acid. The partitioning

of nitrate depends on the availability of ammonia as well meteorological factors such as

temperature and relative humidity. 

The updates to the REMSAD that were made between version 7.03 and 7.06 affect the

dry deposition velocity of all gas phase species and in particular ammonia. Several assumptions

contained in the REMSAD dry deposition code were removed. In previous versions of

REMSAD, the surface resistance (Rc) for ammonia gas was set equal to 30 s/m at all times for

the landuse categories of agriculture, range, and mixed agriculture and range. In addition, for the

landuse types of deciduous forest, coniferous forest, and mixed forest, the ammonia surface

resistance was set equal to the stomatal resistance only . Both of these assumptions were

removed from the code. As a result, version 7.06 more closely follows the original work by

Wesley (Wesley, 1989).

Organic aerosols can contribute a significant amount to the PM in the atmosphere. 

Primary organic aerosols (POA) are treated as a directly emitted species in REMSAD. In

REMSAD version 7, a calculation of the production of secondary organic aerosols (SOA) due to

atmospheric chemistry processes was added3

. A peer review of the REMSAD model (Seigneur

et al., 1999) recommended an SOA module based on the equilibrium approach of Pankow 

(Odum et al., 1997), (Griffin et al., 1999). The implementation of the SOA treatment in version 7

of REMSAD follows the recommendation of the peer review. This includes SOA formation

from anthropogenic and biogenic organic precursors. For both anthropogenic and biogenic

organics REMSAD includes gas phase secondary organic species and the corresponding aerosol

phase species. 

C. REMSAD Modeling Domain

The REMSAD domain used for the IAQR modeling is shown in Figure IV-1. The

geographic characteristics of the domain are as follows:

120 (E-W) X 84 (N-S) grid cells

Cell size (~36 km)

1⁄2 degree longitude (0.5)

1/3 degree latitude (0.3333)

E-W range: 66 degrees W - 126 degrees W
14

N-S range: 24 degrees N - 52 degrees N

Vertical extent: Ground to 16,200 meters (100mb) with 12 layers

Figure IV-1. REMSAD Modeling Domain. 

D. Meteorological and Other Model Inputs

REMSAD requires input of winds (u- and v-vector wind components), temperatures,

surface pressure, specific humidity, vertical diffusion coefficients, and rainfall rates. The

meteorological input files were developed from a 1996 annual MM5 model run that was

developed for previous projects. MM5 is the Fifth-Generation NCAR / Penn State Mesoscale

Model. MM5 (Grell et al., 1994) is a numerical meteorological model that solves the full set of

physical and thermodynamic equations which govern atmospheric motions. MM5 was run in a

nested-grid mode with 2 levels of resolution: 108 km, and 36km with 23 vertical layers sigma

layers extending from the surface to the 100 mb pressure level. The model was simulated in five

day segments with an eight hour ramp-up period. The MM5 runs were started at 00Z, which is

7:00 p.m. EST. The first eight hours of each five day period were removed before being input

into REMSAD. Table IV-2 provides the vertical grid structures for the MM5 and REMSAD

domains. Further detailed information concerning the development and evaluation of the 1996

MM5 datasets can be found in (Olerud, 2000).
15

Table IV-2. Vertical Grid Structure for 1996 MM5 and Clear Skies REMSAD Domains. 

Layer Heights Represent the Top of each Layer. The First Layer is from the Ground up to

38 meters.

REMSAD 

Layer MM5 Layer Sigma

Approximate

Height (m) Pressure (mb) 

0 0 1.000 0.0 1000.0

1 1 0.995 38.0 995.5

2 2 0.988 91.5 989.2

 3 0.980 152.9 982.0

3 4 0.970 230.3 973.0

 5 0.956 339.5 960.4

4 6 0.938 481.6 944.2

 7 0.916 658.1 924.4

5 8 0.893 845.8 903.7

 9 0.868 1053.9 881.2

6 10 0.839 1300.7 855.1

 11 0.808 1571.4 827.2

7 12 0.777 1849.6 799.3

 13 0.744 2154.5 769.6

8 14 0.702 2556.6 731.8

 15 0.648 3099.0 683.2

9 16 0.582 3805.8 623.8

 17 0.500 4763.7 550.0

10 18 0.400 6082.5 460.0

 19 0.300 7627.9 370.0

11 20 0.200 9510.5 280.0

 21 0.120 11465.1 208.0

 22 0.052 13750.2 146.0

12 23 0.000 16262.4 100.0

The MM5 model output cannot be directly input into REMSAD due to differences in the

grid coordinate systems and file formats. A postprocessor called MM5-REMSAD was

developed to convert the MM5 data into REMSAD format. This postprocessor was used to

develop hourly average meteorological input files from the MM5 output. Documentation of the

MM5REMSAD code and further details on the development of the input files is contained in

(Mansell, 2000). 
4

The ambient PM2.5 design values used for projecting future year concentrations were obtained

for monitoring sites which meet the completeness criteria in 40CFR Part 50, Appendix N and do not

reflect the application of any data substitution tests to fill in for incomplete data. However, the design

values reported in the Air Quality Data Analysis TSD do reflect the data substitution. As a consequence

of this difference, 2000-2002 design values reported in the Air Quality Data Analysis TSD may be higher

than those used in the modeling analysis for the following counties: New Haven, CT, Richmond, GA,

Lake, IN, Philadelphia, Hamilton, TN.

16

Application of the REMSAD modeling system requires data files specifying the initial

species concentration fields and lateral boundary species concentrations. Due to the extent of the

proposed modeling domains and the large-scale modeling domain, these inputs were developed

based on “clean” background concentration values. The IAQR modeling used temporally and

spatially (horizontal) invariant data for both initial and boundary conditions. Species

concentration values were allowed to decay vertically for most species. 

Land use characteristics were perpared for input to the REMSAD simulations. These

data provide the fraction in each grid cell of the 11 land specified in REMSAD. Land use

characteristics are used in the model for the calculation of deposition parameters. For this task,

land use data was obtained from the United States Geological Survey Global vegetation database

which contains the same data used in the 1996 MM5 models runs. This dataset provides 24

landuse categories, including urban. For the REMSAD application the 24 vegetation categories

were remapped to those required for application of REMSAD. 

E. REMSAD Model Performance Evaluation

The goal of the 1996 Base Year REMSAD modeling was to reproduce the atmospheric

processes resulting in formation and dispersion of fine particulate matter across the U.S. An

operational model performance evaluation for PM2.5 and its related speciated components (e.g.,

sulfate, nitrate, elemental carbon etc.) for 1996 was performed in order to estimate the ability of

the modeling system to replicate Base Year concentrations. A description of the evaluation

procedures and the results are provided in Appendix C.

V. Procedures for Projecting Ozone and PM2.5 for Future Year Scenarios

A. Introduction

In this section we describe the procedures used to project current air quality

concentrations to the future year baseline and control scenarios covered in this TSD. For this

analysis we started with current ambient 8-hour ozone and annual average PM2.5 design values

as calculated by EPA for individual monitoring sites. The development of these design values is

described in the report Air Quality Data Analysis Technical Support Document for the Proposed

Interstate Air Quality Rule (EPA, 2004)4. The procedures for projecting ozone concentrations is

presented first followed by the procedures for projecting PM2.5 concentrations. In general, the
17

procedures for projecting ozone and PM2.5 follow the same general approach. This approach

involves using the predictions from Base Year and future case air quality model runs in a

relative sense to adjust current design value concentrations up or down, depending on the

modeling results, to reflect expected future concentrations. 

B. Projection of Future 8-Hour Ozone Concentrations 

Ozone modeling for the 2001 Base Year was coupled with modeling for the future year

scenarios in 2010 and 2015 to project which counties are expected to be nonattainment for the

future year emissions scenarios. In general, the approach for projecting future 8-hour ozone

concentrations involves using the model in a relative sense to estimate the change in ozone

between 2001 and each future scenario. Concentrations of ozone in 2010 were estimated by

applying the relative change in model predicted ozone from 2001 to 2010 with present-day 8-

hour ozone design values (2000-2002). The procedures for calculating future case ozone design

values are consistent with EPA’s draft modeling guidance (EPA, 1999a) for 8-hour ozone

attainment demonstrations, “Draft Guidance on the Use of Models and Other Analyses in

Attainment Demonstrations for the 8-Hour Ozone NAAQS.” The draft guidance specifies the

use of the higher of the design values from (a) the period that straddles the emissions inventory

Base Year or (b) the design value period which was used to designate the area under the ozone

NAAQS. In this case, 2000-2002 is the design value period which straddles the 2001 Base Year

inventory and is also the latest period which is available for determining designation compliance

with the NAAQS. Therefore, 2000-2002 was the only period used as the basis for projections to

the future years of 2010 and 2015.

The procedures in the guidance for projecting future 8-hour ozone nonattainment are as

follows: 

Step 1: Hourly model predictions are processed to determine daily maximum 8-hour

concentrations for each episode day modeled. A relative reduction factor (RRF) is then

determined for each monitoring site. First, the multi-day mean (excluding ramp-up days) of the

8-hour daily maximum predictions in the nine grid cells that include or surround the site is

calculated using only those predictions greater than or equal to 70 ppb, as recommended in the

guidance. This calculation is performed for the base year 2001 scenario and the future-year

scenario. The RRF for a site is the ratio of the mean prediction in the future-year scenario (e.g.,

2010) to the mean prediction in the 2001 base year scenario. The RRFs were calculated on a

site-by-site basis. 

Step 2: The RRF for each site is then multiplied by the 2000-2002 ambient design value

for that site, yielding an estimate of the future design value at that particular monitoring location. 

In calculating the projected design values, any amount of the concentration less than 1 ppb (i.e.,

to the right of the decimal) were discarded (i.e., the concentrations were truncated to an integer

ppb value).
18

Step 3: For counties with only one monitoring site, the value at that site was selected as

the value for that county. For counties with more than one monitor, the highest value in the

county was selected as the value for that county. Counties with projected 8-hour ozone design

values of 85 ppb or more are projected to be nonattainment

.

As an example, consider Clay County, Alabama which has one ozone monitor. The

2000-2002 8-hour ambient ozone design value is 82 ppb. In the 2001 base year simulation, 24 of

the 30 episode modeling days have CAMx values of 70 ppb or more in one of the nine grid cells

that include or surround the monitor location. The average of these predicted ozone values is

88.62 ppb. In 2010, the average of the predicted values for these same grid cells was 70.32 ppb. 

Therefore, the RRF for this location is 0.79, and the projected 2010 design value is 82 multiplied

by 0.79 equals 65.07. All projected future case design values are truncated to the nearest ppb

(e.g., 65.07 becomes 65). Since there are no other monitoring locations in Clay County,

Alabama, the projected 2010 8-hour design value for this county is 65 ppb.

The RRF approach described above was applied for the 2010 and 2015 Base Case

scenarios. The 2010 Base and 2015 Base Case design values are provided in Appendix D. Of

the 287 counties that were nonattainment based on 2000-2002 design values, 47 are forecast to

be nonattainment in 2010 and 34 in 2015. None of the counties that were measuring attainment

in the period 2000-2002 are forecast to become nonattainment in the future. Those counties

projected to be nonattainment for the 2010 and 2015 Base Cases are listed in Table V-1. The

counties projected to be nonattainment for the 2010 Base Case are the nonattainment receptors

used for assessing the contribution of emissions in upwind States to downwind nonattainment

and for analyzing the impacts of emissions control scenarios.

Table V-1. Counties Projected to be Nonattainment for the 8-Hour Ozone NAAQS in the

2010 and 2015 Base Cases.

State 2010 Base-2 2015 Base Case

AR Crittenden Crittenden

CT Fairfield, Middlesex, New Haven Fairfield, Middlesex, New Haven

DC Washington, D.C. Washington D.C.

DE New Castle None

GA Fulton None

IL None Cook

IN Lake Lake

MD

Anne Arundel, Baltimore, Cecil, Harford,

Kent, Prince Georges Anne Arundel, Cecil, Harford

MI None Macomb
19

NJ

Bergen, Camden, Cumberland, Gloucester,

Hudson, Hunterdon, Mercer, Middlesex,

Monmouth, Morris, Ocean

Bergen, Camden, Gloucester, Hunterdon,

Mercer, Middlesex, Monmouth, Morris, Ocean

NY

Erie, Putnam, Richmond, Suffolk,

Westchester Erie, Richmond, Suffolk, Westchester

NC Mecklenburg None

OH Geauga, Summit Geauga

PA

Allegheny, Bucks, Delaware, Montgomery,

Philadelphia Bucks, Montgomery, Philadelphia

RI Kent Kent

TX Denton, Harris, Tarrant Harris

VA Arlington, Fairfax Arlington, Fairfax

WI Kenosha, Racine, Sheboygan Kenosha, Sheboygan

C. Projection of Future PM2.5 Concentrations

As with ozone, the approach for identifying areas expected to be nonattainment for

PM2.5 in the future involves using the model predictions in a relative way to forecast current

PM2.5 design values to 2010 and 2015. The modeling portion of this approach includes annual

simulations for 2001 emissions and for the 2010 and 2015 Base Case emissions scenarios. As

described below, the predictions from these runs were used to calculate RRFs which were then

applied to current PM2.5 design values. The approach we followed is consistent with the

procedures in the draft PM2.5 air quality modeling guidance (EPA, 2001) “Guidance for

Demonstrating Attainment of Air Quality Goals for PM2.5 and Regional Haze.” It should be

noted that the approach for PM2.5 differs from the approach recommended for projecting future

year 8-hour ozone design values in terms of the base period for design values. The approach for

ozone uses the higher of the ambient design values for two 3-year periods, as described above. 

In contrast, the PM2.5 guidance recommends selecting the highest design value from among the

three periods that straddle the base emissions year (i.e., 2001). The three periods that straddle

this year are 1999-2001, 2000-2002, and 2001-2003. The data from the first two design value

periods are readily available, but the data from the 2001-2003 period could not be used since the

2003 data were not yet available. Thus, we have relied on the data for the two periods 1999-

2001 and 2000-2002. The design values from the period 2000-2002, which is the most recent

period with available data, were used to identify which monitors are currently measuring

nonattainment (i.e., annual average PM2.5 of 15.05 μg/m3

 or more). To be consistent with

procedures in the modeling guideline, we selected the higher of the 1999-2001 or 2000-2002

design value from each nonattainment monitor for use in projecting future design values. The

recommendation in the guidance for selecting the highest values from among 3 periods is

applicable for nonattainment counties, but not necessarily for attainment counties. Thus, for

monitors that are measuring attainment (i.e., PM2.5 less than 15.05 μg/m3

) using the most recent
20

3 years of data, we used the 2000-2002 design values as the starting point for projecting future

year design values. Note that none of the counties that are attainment for the period 2000-2002

are forecast to become nonattainment in 2010 or 2015.

The modeling guidance recommends that model predictions be used in a relative sense to

estimate changes expected to occur in each major PM2.5 species. These species are sulfate,

nitrate, organic carbon, elemental carbon, crustal and un-attributed mass. Un-attributed mass is

defined as the difference between FRM PM2.5 and the sum of the other five components. The

procedure for calculating future year PM2.5 design values is called the Spectate Modeled

Attainment Test (SMAT). Details on the SMAT procedure are provided in Appendix E.

We are using the FRM data for projecting future design values since these data will be

used for nonattainment designations. In order to apply SMAT to the FRM data, information on

PM2.5 speciation is needed for the location of each FRM monitoring site. Only a small number

of the FRM sites have collocated species measurements. Therefore, spatial interpolation

techniques were applied to the spectate component averages from the IMPROVE and Speciation

Trends Network (STN) data to estimate concentrations of species mass at each FRM PM2.5

monitoring site.

The following is a brief summary of SMAT as applied to data for a given monitoring site:

Step 1: Calculate quarterly mean ambient concentrations (averaged over 3 years) for each

of the six major components of PM2.5 using the species concentrations estimated for the FRM

site. This is done by multiplying the monitored quarterly mean concentration of FRM-derived

PM2.5 by the estimated fractional composition of PM2.5 species for each quarter in 3

consecutive years (e.g., 20 percent sulfate multiplied by 15 μg/m3

 PM2.5 equals 3 μg/m3

sulfate). 

Step 2: For each quarter, calculate the ratio of future (e.g., 2010) to current (i.e., 2001)

model predictions for each component specie using the model output for the grid cell containing

the monitoring site. The result is a component-specific RRF (e.g., assume that 2001 predicted

sulfate for the grid cell containing the FRM site is 10 μg/m3

 and the 2010 Base concentration in

this same grid cell is 8 μg/m3

, then the RRF for sulfate at this site is 0.8).

Step 3: For each quarter and each component specie, multiply the current quarterly mean

component concentration (Step 1) by the component-specific RRF obtained in Step 2. This

produces an estimated future quarterly mean concentration for each component (e.g., 3 μg/m3

sulfate multiplied by 0.8 equals future sulfate of 2.4 μg/m3).

Step 4: Average the four quarterly mean future concentrations to get an estimated future

annual mean concentration for each component specie. Sum the annual mean concentrations of

the 6 components to obtain an estimated future annual average concentration for PM2.5. In

calculating the projected design values, any amount of the concentration less than 0.01 μg/m3

(i.e., more than two places to the right of the decimal) were discarded (i.e., truncated).
21

The preceding procedures for determining future year PM2.5 concentrations were applied

for each FRM site. For counties with only one FRM site, the forecast design value for that site

was used to determine whether or not the county will be nonattainment in the future. For

counties with multiple monitoring sites, the site with the highest future concentration was

selected for that county. Those counties with future year concentrations of 15.05 μg/m3

 or more

are predicted to be nonattainment. 

The SMAT technique was used for estimating future year PM2.5 concentrations for all

the scenarios modeled. For the 2010 Base-2 scenario there are 61 counties in the East that are

forecast to be nonattainment. Of these, 41 are forecast to remain nonattainment for the 2015

Base Case. The PM2.5 nonattainment counties for the 2010 Base-2 and 2015 Base Case are

listed in Table V-2. These nonattainment counties were used as receptors for quantifying the

impacts of the local control strategies and regional control strategies described in sections IX and

X, respectively.

Table V-2. Counties Projected to be Nonattainment for the PM2.5 NAAQS for the 2010

Base and 2015 Base Case.

State 2010 Base-2 2015 Base Case

AL

DeKalb, Jefferson, Montgomery, Russell,

Talladaga Jefferson, Montgomery, Russell, Talladaga

CT New Haven New Haven

DC Washington, D.C. None

DE New Castle None

GA

Clarke, Clayton, Cobb, DeKalb, Floyd,

Fulton, Hall, Muscogee, Paulding, Richmond,

Wilkinson

Clarke, Clayton, Cobb, DeKalb, Floyd, Fulton,

Hall, Muscogee, Richmond, Wilkinson

IL Cook, Madison, St. Clair, Will Cook, Madison, St. Clair

IN Clark, Marion Clark, Marion

KY Fayette, Jefferson Jefferson

MD Baltimore City Baltimore City

MI Wayne Wayne

MO St. Louis None

NY New York (Manhattan) New York (Manhattan)

NC Catawba, Davidson, Mecklenburg None

OH

Butler, Cuyahoga, Franklin, Hamilton,

Jefferson, Lawrence, Mahoning, Scioto, Stark,

Summit, Trumbull

Butler, Cuyahoga, Franklin, Hamilton,

Jefferson, Scioto, Stark, Summit

PA Allegheny, Bucks, Lancaster, York Allegheny, York
22

SC Greenville None

TN Davidson, Hamilton, Knox, Roane, Sullivan Hamilton, Knox

WV

Brooke, Cabell, Hancock, Kanawha, Marshal,

Wood

Brooke, Cabell, Hancock, Kanawha, Wood

As noted above in section II, the 2010 Base Case used for the zero-out PM2.5 modeling

included EGU emissions from an earlier simulation of the Integrated Planning Model. Of the 61

2010 Base-2 nonattainment counties listed in Table V-2, 4 counties (i.e., Catawba Co., NC,

Trumbull Co., OH, Greenville Co., SC, and Marshall Co., WV) were projected to be in

attainment in the 2010 Base-1 used for the zero-out modeling. Thus, 57 nonattainment counties

(i.e., the 61 counties in Table V-2 less these 4 counties) were used as downwind receptors for the

State-by-State zero-out modeling in the assessment of interstate PM2.5 contributions described

in section VII. The 2010 Base-1, 2010 Base-2, and 2015 Base Case PM2.5 concentrations

projected for each county that was nonattainment in the Base Year, are provided in Appendix F.

VI. Modeling to Assess Interstate Ozone Contributions

This section documents the procedures used by EPA to quantify the impact of ozone

precursor emissions in specific upwind States on air quality concentrations in projected

downwind 8-hour ozone nonattainment areas. These procedures are the first of the two-step

process for determining significant contribution, in which the second step involves a control cost

assessment to determine the amount of upwind emissions that should be reduced. In this section

we use the phase “significant contribution” to refer to the ozone air quality step of the

significance determination.

Included in this section are descriptions of: 1) the analytic approach for modeling the

contribution of upwind States to ozone in potential downwind nonattainment areas, 2) the

methodology for analyzing the modeling results, 3) the decision rules used to determine whether

individual States make a significant contribution (before considering cost), and d) the results of

the interstate ozone significant contribution analysis. As discussed in section III, the air quality

modeling analyses for ozone were conducted for an Eastern U.S. domain with CAMx, version

3.10. The air quality modeling for the interstate ozone contribution analysis focuses on the 47

counties predicted to be nonattainment for 8-hour ozone in the 2010 Base Case. These counties

are identified in section V. It should be noted that the approach used to identify the

nonattainment receptors for this analysis differed from that used in the NOx SIP Call where we

aggregated on a State-by-State basis all grid cells which were both (a) associated with counties

that violated the 8-hour NAAQS (based on 1994-1996 data) and (b) had future base case

predictions of 85 ppb or more. For the IAQR analysis of interstate ozone contributions, we have

treated each individual county projected to be nonattainment in the future as a downwind

nonattainment receptor.
23

A. Zero Out and Source Apportionment Techniques

The modeling approach used by EPA to quantify the impact of emissions in specific

upwind States on projected downwind nonattainment areas for 8-hour ozone includes two

different techniques, zero-out and source apportionment. The outputs of the two types of

modeling were used to calculate certain measures of contribution, called “metrics”. The metrics

were evaluated in terms of three key contribution factors to determine which States make a

significant contribution to downwind ozone nonattainment. The significant contribution analysis

completed for the IAQR analysis uses the same modeling techniques, the same metrics, and the

same three contribution factors as those used by EPA for the State-by-State determination in the

NOx SIP Call.

The zero-out and source apportionment modeling techniques provide different technical

approaches to quantifying the downwind impact of emissions in upwind States. The zero-out

modeling provides an estimate of downwind impacts by calculating the difference between the

model estimates from a base case run to the estimates from a simulation in which the base case

man-made emissions of NOx and VOC are removed from a specific State. Because of the

gridded nature of the modeling, State boundaries can only be approximated to the nearest grid. 

For grid cells that straddle State borders, assignments are made to the State in which the majority

of the grid cell resides. Thus, for low-level sources (i.e., onroad, nonroad, area, and point

sources with low plume rise) emissions were removed in the zero-out runs in grid cells which

closely approximate the State. However, because elevated point source emissions are located in

the model based on their actual latitude and longitude, only those sources within the State

boundaries had emissions removed in the zero-out runs.

EPA also used the source apportionment technique as part of the modeling analysis to

evaluate the downwind contributions of emissions in upwind States. The source apportionment

technique in CAMx was developed to provide modelers with a means of estimating the

contributions of many different source areas/categories to ozone formation in one single model

run. This is achieved by using multiple tracer species to track the fate of ozone precursor

emission (VOC and NOx) and the ozone formation caused by these emissions within a CAMx

simulation. The methodology is designed so that all ozone and precursor concentrations are

attributed to the selected source areas/categories at all times. Thus, for all receptor locations and

times, the ozone, VOC, and NOx concentrations predicted by the CAMx are attributed to the

source areas/categories selected for analysis. EPA used the Anthropogenic Precursor Culpability

Assessment (APCA) option in the IAQR source apportionment modeling. The key feature of

APCA is that it allocates the ozone production to the manmade precursor emissions, either

through reactions among various manmade sources and/or through reactions between manmade

emissions and biogenic emissions. Additional information on the source apportionment

technique can be found in the CAMx User’s Guide (Environ, 2002). In general, EPA found that

the source apportionment modeling tends to show greater magnitude and frequency of

contributions than the zero-out modeling for individual linkages. However, because there is no

technical evidence showing that one technique is clearly superior to the other for evaluating

contributions to ozone from various emission sources; both approaches were given equal

consideration in the significance analysis.
24

The EPA performed State-by-State zero-out modeling and source apportionment

modeling for 31 States in the Eastern U.S. These States are as follows: Alabama, Arkansas,

Connecticut, Delaware, Florida, Georgia, Illinois, Indiana, Iowa, Kentucky, Louisiana, Maine,

Maryland, Massachusetts, Michigan, Minnesota, Mississippi, Missouri, New Hampshire, New

Jersey, New York, North Carolina, Ohio, Pennsylvania, Rhode Island, South Carolina,

Tennessee, Vermont, Virginia, West Virginia, and Wisconsin. In both types of modeling,

emissions from the District of Columbia were combined with those from Maryland. 

B. Ozone Contribution Factors and Metrics

EPA selected several metrics to quantify the projected downwind contributions from

emissions in upwind States. The metrics were designed to provide information on three

fundamental factors for evaluating whether emissions in an upwind State make large and/or

frequent contributions to downwind nonattainment. These factors are: a) the magnitude of the

contribution, b) the frequency of the contribution, and c) the relative amount of the contribution. 

The magnitude of contribution factor refers to the actual amount of ozone contributed by

emissions in the upwind State to nonattainment in the downwind area. The frequency of the

contribution refers to how often contributions above certain thresholds occur. The relative

amount of the contribution is used to compare the total ozone contributed by the upwind State to

the total amount of nonattainment ozone in the downwind area. These factors are the basis for

eight separate metrics that can be used to assess a particular impact. These metrics are described

below for the zero-out modeling and for the source apportionment modeling. Table VI-1 lists

the four metrics for each factor.

Table VI-1. Ozone Contribution Factors and Metrics.

Factor: Zero-out Metrics Source Apportionment Metrics

Magnitude of

Contribution

1) Maximum contribution 5) Maximum contribution; and

6) Highest daily average contribution (ppb

and percent)

Frequency of

Contribution

2) Number and percent of exceedances

with contributions in various

concentration ranges

7) Number and percent of exceedances

with contributions in various concentration

ranges

Relative Amount of

Contribution

3) Total contribution relative to the total

exceedance ozone in the downwind area

4) Population-weighted total contribution

relative to the total population-weighted

exceedance ozone in the downwind area

8) Total average contribution to

exceedance ozone in the downwind area
25

The values for each metric were calculated using only those periods during which modelpredicted 8-hour average ozone concentration were of 85 ppb or more in at least one of the

model grid cells that are associated with the receptor county. That is, we only analyzed

interstate ozone contributions for the nonattainment receptor counties when the model predicted

an exceedance in the 2010 Base Case. Grid cells were linked to a specific nonattainment county

if any part of the grid cell covered any portion of the projected 2010 nonattainment county. In

cases where a grid cell covered two or more nonattainment counties, the grid was tied to the

nonattainment county that contained the largest portion of the area of the grid cell. The

exception to that rule involves cells that encompass a border of two adjacent States and more

than two counties. In that case, grids are assigned to the county in the State with the largest area

of the grid cell.

As in the NOx SIP Call, the ozone contribution metrics are calculated and evaluated for

each upwind State to each downwind nonattainment receptor. These source-receptor pairs are

referred to as “linkages”.

1. Zero-out Metrics

A central component of several of the metrics is the number of predicted exceedances in

the 2010 Base Case for each nonattainment receptor. The number of exceedances in a particular

nonattainment receptor is determined by the total number of daily predicted peak 8-hour

concentrations of 85 ppb or more across all the episode days in the model grid cells assigned to

the receptor. For example, the Fairfield County, CT receptor area consists of 11 grid cells. 

There are 30 days in the modeling simulations. Thus, the maximum possible number of

exceedances for this area is 330. The actual number of exceedances for this area was 27 griddays.

The Maximum Contribution Metric (metric 1) for a particular upwind State to an

individual downwind nonattainment receptor is determined by first calculating the concentration

differences between the 2010 Base Case and the zero-out simulation for that upwind State. This

calculation is performed for all 2010 Base Case exceedances predicted within the grid cells

associated with the nonattainment county. The largest difference (i.e., contribution) for the

linkage across all of the exceedances at the downwind receptor is identified as the maximum

contribution. 

The Frequency of Contribution Metric (metric 2) for a particular linkage is determined by

first sorting the contributions by concentration range (e.g., >= 2 ppb, >= 5 ppb, etc.). The

number of impacts in each range is used to assess the frequency of contribution. Frequency of

Contribution is also expressed in terms of the percent of the 2010 Base exceedances that receive

contributions in each range. For example, Ohio contributes 2 ppb or more to 9 of the 27

exceedances in Fairfield County, CT. Thus, Ohio contributes >= 2 ppb to 33% of the

exceedances predicted in this county.

Determining the Total Ozone Contribution Relative to the Base Case Exceedance Metric
26

(metric 3) for a particular linkage involves first calculating the total ozone of 85 ppb or more in

the 2010 Base Case and in the upwind State’s zero-out run. The calculation is performed by

summing the amount of ozone above the NAAQS for each predicted exceedance at the

downwind receptor area. Second, the amount of ozone above the NAAQS from the zero-out run

is subtracted from the amount of ozone above the NAAQS in the 2010 Base run. The difference

in contribution (between the base and zero-out run) is then divided by the total ozone above the

NAAQS in the base run to form this metric. For example in Fairfield County CT, the sum of the

ozone above 85 ppb for the 2010 Base run in the 27 exceedances equals 319.5 ppb. When the

emissions from Ohio are zeroed, the total ozone above the NAAQS equals 271.0 ppb. The

difference between the base and zero-out amounts is 48.5 ppb. Thus, the total relative

contribution from emissions in Ohio is 15 percent (48.5 divided by 319.5).

The Population-Weighted Relative Contribution Metric (metric 4) is similar to the total

ozone contribution metric described in the preceding paragraph, except that during the

calculation the amount of ozone above the NAAQS in both the base case and the zero-out

simulation is weighted by (i.e., multiplied by) the 2000 population in the receptor grid cell. Note

that this metric is used solely to provide an additional perspective. It is not considered as an

independent metric and it did not provide the basis for any decisions.

2. Source Apportionment Metrics

Despite the fundamental differences between the zero out and source apportionment

techniques, the definitions of the source apportionment metrics are generally similar to the zero

out metrics. One exception is that all 8-hour periods with averages above or equal to 85 ppb are

considered in the source apportionment metrics, as opposed to just the peak 8-hour average per

day. Similar analyses completed as part of the NOx SIP call concluded that the differences

resulting from considering only daily maximum 8-hour averages (zero out) versus considering

all 8-hour periods (source apportionment) was very small and did not influence the significance

determinations. Therefore, the number of “exceedance periods” are the total number of 8-hourly

predicted concentrations greater than or equal to 85 ppb within the downwind area on a cell-bycell basis. Again using the Fairfield County, CT receptor area as an example, the maximum

possible number of exceedances for this area is 5,610 (11 cells * 30 days * 17 eight-hour

averages per day). The actual number of exceedance periods for this area was 110.

For a given upwind State to downwind nonattainment receptor linkage, the Maximum

Contribution Metric (metric 5) is the highest contribution from among the contributions to all

exceedances at the downwind receptor. 

The Highest Daily Average Contribution Metric (metric 6) is determined for each day

with predicted exceedances at the downwind receptor. The metric is calculated by first summing

the contributions for that linkage over all exceedances on a particular day, then dividing by the

number of exceedances on that day to produce a daily average contribution to nonattainment. 

The daily average contribution values across all days with exceedances are examined to identify

the highest value which is then selected for use in the determination of significance . We also
27

express this metric as a percent by dividing the highest daily average contribution by the

corresponding ozone exceedance concentration on the same day. As an example of how this

metric is calculated, consider the following two modeling days in Fairfield County, CT.

7/13/95: There were 4 exceedance periods. The total contribution from Ohio was 11 ppb.

Therefore, the daily average contribution from Ohio to Fairfield County, CT was 2.8 ppb on that

day. The average exceedance ozone on that day was 87 ppb, so the percentage contribution from

Ohio on that day was 3.1 percent.

7/14/95: There were 68 exceedance periods. The total contribution from Ohio was 503

ppb for those cell-hours. Therefore the daily average contribution from Ohio to Fairfield

County, CT was 7.4 ppb on that day. The average exceedance ozone on that day was 103 ppb,

so the percentage contribution from Ohio on that day was 7.2 percent. This day had the highest

daily average contribution from Ohio to Fairfield County, CT (7.4 ppb) of any of the 30

modeling days, so the ppb and percent contributions on this day were used as the values for this

metric.

The Frequency of Contribution Metric (metric 7) for the source apportionment technique

is also determined in a similar way to which this metric is calculated for the zero-out modeling. 

Looking at the impact of Ohio man-made NOx and VOC emissions on Fairfield County, CT as

an example, 77 of the 110 exceedance hours (70 percent) were reduced by at least 2 ppb.

The Total Average Contribution Metric is determined for each of the three episodes

individually as well as for all 30 days (i.e., all three episodes) combined. There are three parts to

the calculation of this metric. In step 1, the ozone values for each of the exceedance periods in a

particular downwind area are summed over the episode(s). In step 2, the total ozone from the

previous step that is due to anthropogenic sources is calculated based on the source

apportionment results. In step 3, the contributions from a given source region to this downwind

area are summed over the exceedance periods. The total contribution calculated in step 3 is then

divided by the total ozone resulting from manmade sources in step 2 to determine the fraction of

ozone that is due to emissions from the upwind source area. This fraction can be multiplied by

100 to express the result as a percentage. For example, for the 110 exceedance periods in

Fairfield County, CT there is a total of 10,720 ppb of ozone. Of the total base ozone, the source

apportionment results indicate that 8,613 ppb is due to anthropogenic sources. The sources in

Ohio contribute a total of 535 ppb which is 6.2 percent of the base case total (i.e., 535 divided by

8,613).

C. Basis for Identifying which Linkages are Significant

EPA compiled the 8-hour metrics by downwind nonattainment receptor county (referred

to below as “downwind area”) in order to evaluate the contributions to downwind nonattainment

in 2010. The contribution metrics were reviewed to determine how large of a contribution a

particular upwind State makes to nonattainment in each downwind area in terms of the

magnitude of the contribution, the frequency of the contributions, and the relative amount of the
28

total contribution. Determining whether a particular linkage indicated a significant amount of

transport from an upwind source State to a downwind county is a four step process.

The first step in evaluating the contribution factors was to screen out linkages for which

the contributions were clearly small. This initial screening was based on: 1) a maximum

contribution of less than 2 ppb from either of the two modeling techniques and/or, 2) a percent of

total nonattainment of less than 1 percent. Any upwind State that contributed to a particular

downwind area in amounts that were less than the screening criteria was considered not to make

a significant contribution to that downwind area. As an example, Mississippi had a maximum

contribution of 3 ppb on Fulton County, GA in both the source apportionment and the zero out

modeling exercises, however, the percent of total nonattainment metric was less than 1 percent. 

Therefore, Mississippi was concluded not to have a significant impact on nonattainment in

Fulton County, GA as a result of the initial screening. Virginia also had a maximum

contribution of 3 ppb on Fulton County, GA in both the source apportionment and the zero out

modeling, but the percent of total nonattainment metric for Virginia/Fulton was 1 percent. This

linkage was carried on for further analysis.

Those linkages that had contributions which exceeded the screening criteria were

evaluated further in steps 2 through 4. In step 2 we evaluated the contributions in each linkage

based on the zero-out modeling and in step 3 we evaluated the contributions in each linkage

based on the source apportionment modeling. In step 4 we considered the results of both step 2

and step 3 to determine which of the linkages are significant. For both techniques, EPA

determined whether the linkage is significant by evaluating the magnitude, frequency, and

relative amount of the contributions. Each upwind State that made relatively large and/or

frequent contributions to nonattainment in the downwind area, based on these factors, is

considered as contributing significantly to nonattainment in the downwind area. The EPA

believes that each of the factors provides an independent legitimate measure of contribution. 

However, there had to be at least two different factors that indicate large and/or frequent

contributions in order for the linkage to be found significant. In this regard, the finding of a

significant contribution for an individual linkage was not based on any single factor.

As indicated above, in step 4 we considered the results of evaluating the contributions

zero-out contributions from step 1 and source apportionment contributions from step 2. For

many of the individual linkages the analyses of zero-out and source apportionment contributions

yield a consistent result (i.e., either large and/or frequent contributions or small and infrequent

contributions). Indeed, for each affected State, EPA’s proposed determination that the State

contributes significantly downwind is based on at least one linkage for which each of the factors

indicates large and/or frequent contributions. For some of the linkages, however, not all of the

factors are consistent. For upwind-downwind linkages in which some of the factors indicate

high and/or frequent contributions while other factors do not, EPA considered the overall

number and magnitude of those factors that indicate large and/or infrequent contributions

compared to those factors that do not. As part of the process of evaluating these types of

linkages, we required that two of the three factors had to indicate large and frequent

contributions for one of the modeling techniques and large and/or frequent contributions for at
29

least one factor in the other modeling technique in order to find that the linkage was significant. 

Thus, based on an assessment of all the factors in such cases, EPA determined that the upwind

State contributes significantly to nonattainment in the downwind area if, on balance, the factors

indicate large and frequent contributions from the upwind State to the downwind area. Table V2, below, provides examples of the four step process to illustrate how the metrics were evaluated

to determine whether individual linkages are significant. Contribution tables containing the

values of the metrics for each linkage are provided in Appendix G.

D. Results of Interstate Ozone Contribution Analysis 

Using the procedures described above, EPA determined which States contribute

significantly to nonattainment in the 47 specific downwind counties. Of the 31 States included

in the assessment of interstate ozone contributions, 25 States were found to have emissions

which make a significant contribution to downwind 8-hour ozone nonattainment. These States

are listed in Tables V-3 and V-4. The linkages which EPA found to be significant are listed in

Tables V-3 (by upwind State) and V-4 (by downwind nonattainment county) for the 8-hour

NAAQS. Each upwind State contributed to nonattainment problems in counties in at least two

downwind States (except for Louisiana and Arkansas which contributed to nonattainment only in

Texas counties). Of the 31 States included in the assessment of interstate ozone transport, the

following six States are found to not make a significant contribution to downwind

nonattainment: Florida, Maine, Minnesota, New Hampshire, Rhode Island, and Vermont.
30

Table VI-2a. Evaluation of the Contribution to Downwind Nonattainment in Middlesex Co., CT.

Receptor Steps Evaluation of ContributionsMiddlesex Co. Connecticut

Step 1: Evaluation ofContributionsAgainstScreeningCriteria

- 23 upwind States had contributions that did not exceed either or both of the screening criteria. These 

further. As an example, the contribution from WV did exceed the screening criteria for the Source Appexceed the criteria for the zero-out modeling, so this linkage was deemed not significant. Of the 23 linkcriteria (i.e., were not significant), 16 were not significant in both modeling techniques.

- 7 upwind States (MA, VA, MD, OH, NJ, PA, and NY) had contributions that exceeded both screening forward for evaluation in Steps 2 - 4.

Step 2: Evaluation ofContributionsfrom Zero-OutModeling

- Of the 7 States that exceeded the screening criteria, 6 (VA, MD, OH, NJ, PA, and NY) made contribut

considering the metrics for all three factors.- Contributions from VA, MD, OH, NJ, PA, and NY:

+ Magnitude: values ranged from 4.0 ppb (VA) up to 15.2 ppb (NY)+ Frequency: values ranged from VA which contributed 2 ppb or more to 19% of the exeedanccontributed 2 ppb or more to all of the exceedances

- Contributions from MA were large in terms of two of the three factors:

+ Magnitude: the maximum contribution was 7.0 ppb+ Frequency: MA contributed 2.0 ppb or more to 6% of the exceedances

Step 3: Evaluation ofContributionsfrom SourceApportionmentModeling

- The findings from the source apportionment modeling were similar to that of the zero-out modeling in 

exceeded the screening criteria (VA, MD, OH, NJ, PA, and NY) made contributions that were significa

all three factors:- Contributions from VA, MD, OH, NJ, PA, and NY:

+ Magnitude: values ranged from 7 ppb (VA) to 28 ppb (NJ and NY)+ Frequency: values ranged from VA which contributed 2 ppb or more to 44% of the exeedanccontributed 2 ppb or more to all of the exceedances

- Contributions from MA were large in terms of two of the three factors:

+ Magnitude: the maximum contribution was 7 ppb+ Frequency: MA contributed 2.0 ppb or more to 10% of the exceedances

Step 4: FinalDeterminationof Significance

- Since all 7 States had large and frequent contributions to Middlesex Co for at least two of the three co

modeling technique, we determined that each of these States makes a significant contribution to nonatta
31

Table VI-2b. Evaluation of the Contribution to Downwind Nonattainment in Bergen Co., NJ.

Receptor Steps Evaluation of Contributions

Bergen Co.New Jersey

Step 1: Evaluation ofContributionsAgainst ScreeningCriteria

- 25 upwind States had contributions that did not exceed either or both of the screening criteria. These lin

further. As an example, the contribution from DE did exceed the screening criteria for the Source Apport

exceed the criteria for the Zero-Out modeling, so this linkage was deemed not significant. Of the 25 linka

criteria (i.e., were not significant), 17 were not significant in both modeling techniques.

- 5 upwind States (PA, VA, MD, OH, and MI) had contributions that exceeded both screening criteria and 

evaluation in Steps 2 - 4.

Step 2: Evaluation ofContributionsfrom Zero-OutModeling

- Of the 5 States that exceeded the screening criteria, 3 (PA, VA, and OH) made contributions that were s

metrics for all three factors.- Contributions from PA, VA, and OH:

+ Magnitude: values ranged from 5.2 ppb (OH) up to 26.5 ppb (PA)+ Frequency: values ranged from OH which contributed 2 ppb or more to 60% of the exeedances 

ppb or more to all of the exceedances+ Relative Amount: values ranges from 21% (VA) up to 92% (PA)

- Contributions from MD were large in terms of two of the three factors: + Frequency: MD contributed 2.0 ppb or more to 30% of the exceedances

+ Relative Amount: the total contribution from MD is 12% of the total amount of nonattainment

- Contributions from MI were large in terms of one of the three factors:

+ Relative Amount: the total contribution from MI is 5% of the total amount of nonattainment

Step 3: Evaluation ofContributionsfrom SourceApportionmentModeling

- In the source apportionment modeling 4 of the 5 States that exceeded the screening criteria (MD, OH, PA 

that were significant considering the metrics from all three factors:- Contributions from (MD, OH, PA and VA):

+ Magnitude: maximum contributions ranged from 9 ppb (MD, OH and VA) to 37 ppb (PA)+ Frequency: values ranged from MD which contributed 2 ppb or more to 61% of the exeedance

ppb or more to all of the exceedances+ Relative Amount: values ranged from 4% (MD and VA) up to 31% (PA)

- Contributions from MI were large in terms of two of the three factors:

+ Magnitude: maximum contribution was 6 ppb+ Frequency: MI contributed 2.0 ppb or more to 21% of the exceedances

Step 4: FinalDetermination ofSignificance

- 4 of the States (MD, OH, PA and VA) had large and frequent contributions to Bergen Co. for at least tw

factors based on each modeling technique. Therefore, we determined that each of these States makes a sig

nonattainment in this county. In addition, the contributions from MI were large and frequent for two facto

Apportionment modeling and large based on one factor in the Zero-Out modeling. Therefore, we determi

contribution to Bergen Co.
32

Table VI-2c. Evaluation of the Contribution to Downwind Nonattainment in Suffolk Co., NY

 Receptor Steps Evaluation of ContributionsSuffolk Co.New York

Step 1: Evaluation ofContributionsAgainstScreeningCriteria

- 19 upwind States had contributions that did not exceed either or both of the screening criteria. These li

As an example, the contribution from IL did exceed the screening criteria for the Source Apportionment m

criteria for the Zero-Out modeling, so this linkage was deemed not significant. Of the 19 linkages that pa

were not significant), 17 were not significant in both modeling techniques.- 11 upwind States (NJ, PA, CT, VA, MD, DE, NC, OH, MA, WV, and MI) had contributions that exceedwere carried forward for evaluation in Steps 2 - 4.

Step 2: Evaluation ofContributionsfrom Zero-OutModeling

- Of the 11 States that exceeded the screening criteria, 8 States (NJ, PA, CT, VA, MD, NC, OH, and DE) significant considering the metrics for all three factors.- Contributions from NJ, PA, CT, VA, MD, NC, OH, and DE:

+ Magnitude: values ranged from 3.6 ppb (OH) up to 46.5 ppb (NJ)+ Frequency: values ranged from NC which contributed 2 ppb or more to 8% of the exeedances or more to all of the exceedances+ Relative Amount: values ranges from 3% (NC) up to 69% (NJ)

- Contributions from MI and WV were large in terms of two of the three factors: + Frequency: MI contributed 2.0 ppb or more to 3% of the exceedances; WV contributed 2.0 ppb 

exceedances+ Relative Amount: the total contribution from MI is 3% of the total amount of nonattainment; th3% of the total amount of nonattainment in Suffolk Co

.- Contributions from MA exceeded the screening criteria in step 1, but the Zero-Out metrics were determ

+ Magnitude: the maximum contribution (2.8 ppb) was just above the value of the screening crit

+ Frequency: MA contributed 2 ppb or more to only 1% of the exceedances

+ Relative Amount: the total contribution from MA was only 1% of the total amount of nonattain

Step 3: Evaluation ofContributionsfrom SourceApportionmentModeling

- In the source apportionment modeling 5 of the 11 States that exceeded the screening criteria (NJ, PA, V

contributions that were significant considering the metrics from all three factors:- Contributions from NJ, PA, VA, MD, and DE:

+ Magnitude: maximum contributions ranged from 8 ppb (DE) to 64 ppb (NJ)+ Frequency: values ranged from VA which contributed 2 ppb or more to 37% of the exeedance

ppb or more to all of the exceedances+ Relative Amount: values ranged from 3% (VA) up to 29% (NJ)

- Contributions from CT, NC, OH, MA, WV, and MI were large in terms of two of the three factors:

+ Magnitude: the maximum contributions ranged from 3 ppb (WV) to 23 ppb (CT)+ Frequency: values ranged from 6% (MA) to 25% (CT)

Step 4: FinalDetermination ofSignificance

- 10 States (NJ, PA, CT, VA, MD, DE, NC, OH, WV, and MI) had large and frequent contributions to Su

three contribution factors based on each modeling technique. Therefore, we determined that each of these 

contribution to nonattainment in this county. Although the contributions from MA based on the Source A

found to be large and frequent for two of the factors, the metrics based on the Zero-Out modeling did not 

contributions for any factor. Therefore, we determined that MA does not make a significant contribution 
33

Table VI-2d. Evaluation of the Contribution to Downwind Nonattainment in Fulton Co, GA.

 Receptor Steps Evaluation of ContributionsFulton Co.Georgia

Step 1: Evaluation ofContributionsAgainstScreeningCriteria

- 23 upwind States had contributions that did not exceed either or both of the screening criteria. These li

As an example, the contribution from FL did exceed the screening criteria for the Zero-Out modeling but 

Source Apportionment modeling, so this linkage was deemed not significant. Of the 23 linkages that pas

were not significant), 18 were not significant based on both modeling techniques.- 7 upwind States (AL, SC, TN, NC, KY, VA, and WV) had contributions that exceeded both screening c

for evaluation in Steps 2 - 4.

Step 2: Evaluation ofContributionsfrom Zero-OutModeling

- Of the 7 States that exceeded the screening criteria 5 States (AL, SC, TN, NC, and KY) made contributi

considering the metrics for all three factors.- Contributions from AL, SC, TN, NC, and KY:

+ Magnitude: values ranged from 3.6 ppb (KY) up to 22.2 ppb (AL)+ Frequency: values ranged from KY which contributed 2 ppb or more to 12% of the exeedance

ppb or more to 40% of the exceedances+ Relative Amount: values ranges from 4% (NC) up to 11% (TN)

- Contributions from WV were large in terms of one of the three factors: + Frequency: WV contributed 2.0 ppb or more to 5% of the exceedances- - Contributions from VA exceeded the screening criteria in step 1, but the Zero-Out metrics were determ

+ Magnitude: the maximum contribution (2.9 ppb) was just above the value of the screening crit

+ Frequency: VA contributed 2 ppb or more to only 2% of the exceedances+ Relative Amount: the total contribution from VA was only 2% of the total amount of nonattain

Step 3: Evaluation ofContributionsfrom SourceApportionmentModeling

- In the source apportionment modeling 3 of the 7 States that exceeded the screening criteria (AL, TN, an

were significant considering the metrics from all three factors:- Contributions from AL, TN, and KY:

+ Magnitude: maximum contributions ranged from 7 ppb (KY) to 25 ppb (AL)+ Frequency: values ranged from AL which contributed 2 ppb or more to 40% of the exeedances ppb or more to 78% of the exceedances+ Relative Amount: values ranged from 3% (KY) up to 5% (TN)

- Contributions from NC, SC, VA, and WV were large in terms of two of the three factors:

+ Magnitude: the maximum contributions ranged from 3 ppb (VA and WV) to 9 ppb (SC)+ Frequency: values ranged from 6% (VA) to 29% (SC)

Step 4: FinalDetermination ofSignificance

- 5 States (AL, TN, KY, SC, and NC) had large and frequent contributions to Fulton Co. for at least two 

based each modeling technique. Therefore, we determined that each of these States makes a significant c

this county. In addition, the contributions from WV based on the Source Apportionment modeling were l

three factors (magnitude and frequency) and the contributions based on the Zero-Out modeling were large (frequency). Therefore, we determined that WV makes a significant contribution to Fulton Co. Although 

on the Source Apportionment modeling were found to be large and frequent, this was not the case for any Out modeling. Therefore, we determined that the contribution from VA was not significant.
34

Table VI-3. Projected Downwind Counties to Which Sources in Upwind States Contribute

Significantly for the 8-Hour NAAQS.

Upwind State Downwind 2010 Nonattainment Counties

AL Crittenden AR, Fulton GA, Harris TX

AR Harris TX, Tarrant TX

CT Kent RI, Suffolk NY

DE

Bucks PA, Camden NJ, Cumberland NJ, Delaware PA, Gloucester NJ, Hunterdon NJ,

Mercer NJ, Middlesex NJ, Monmouth NJ, Montgomery PA, Morris NJ, Ocean NJ,

Philadelphia PA, Richmond NY, Suffolk NY

GA Crittenden AR, Mecklenburg NC

IA Kenosha WI, Lake IN, Racine WI

IL Allegheny PA, Crittenden AR, Erie NY, Geauga OH, Kenosha WI, Lake IN, Racine

WI, Sheboygan WI, Summit OH

IN Allegheny PA, Crittenden AR, Geauga OH, Kenosha WI, Racine WI, Sheboygan WI,

Summit OH

KY Allegheny PA, Crittenden AR, Fulton GA, Geauga OH

LA Harris TX, Tarrant TX

MA Kent RI, Middlesex CT

MD

Arlington VA, Bergen NJ, Bucks PA, Camden NJ, Cumberland NJ, Delaware PA, Erie

NY, Fairfax VA, Fairfield CT, Gloucester NJ, Hudson NJ, Hunterdon NJ, Mecklenburg

NC, Mercer NJ, Middlesex CT, Middlesex NJ, Monmouth NJ, Montgomery PA, 

Morris NJ, New Haven CT, Newcastle DE, Ocean NJ, Philadelphia PA, Putnam NY,

Richmond NY, Suffolk NY, Summit OH, Washington DC, Westchester NY

MI

Allegheny PA, Anne Arundel MD, Baltimore MD, Bergen NJ, Bucks PA, Camden NJ,

Cecil MD, Cumberland NJ, Delaware PA, Erie NY, Geauga OH, Gloucester NJ,

Harford MD, Hudson NJ, Hunterdon NJ, Kenosha WI, Kent MD, Lake IN, Mercer NJ,

Middlesex NJ, Monmouth NJ, Montgomery PA, Morris NJ, Newcastle DE, Ocean NJ,

Philadelphia PA, Prince Georges MD, Racine WI, Richmond NY, Suffolk NY, Summit

OH

MO Crittenden AR, Geauga OH, Kenosha WI, Lake IN, Racine WI, Sheboygan WI

MS Crittenden AR, Harris TX

NC

Anne Arundel MD, Baltimore MD, Camden NJ, Cecil MD, Cumberland NJ, Fulton

GA, Gloucester NJ, Harford MD, Kent MD, Newcastle DE, Ocean NJ, Philadelphia

PA, Suffolk NY

NJ

Bucks PA, Delaware PA, Erie NY, Fairfax VA, Fairfield CT, Kent RI, Middlesex CT,

Montgomery PA, New Haven CT, Philadelphia PA, Putnam NY, Richmond NY,

Suffolk NY, Westchester NY

NY Fairfield CT, Hudson NJ, Kent RI, Mercer NJ, Middlesex CT, Middlesex NJ,

Monmouth NJ, Morris NJ, New Haven CT

OH Allegheny PA, Anne Arundel MD, Arlington VA, Baltimore MD, Bergen NJ, Bucks

PA, Camden NJ, Cecil MD, Cumberland NJ, Delaware PA, Fairfax VA, Fairfield CT,
35

Gloucester NJ, Harford MD, Hudson NJ, Hunterdon NJ, Kenosha WI, Kent MD, Kent

RI, Lake IN, Mercer NJ, Middlesex CT, Middlesex NJ, Monmouth NJ, Montgomery

PA, Morris NJ, New Haven CT, Newcastle DE, Ocean NJ, Philadelphia PA, Prince

Georges MD, Racine WI, Richmond NY, Suffolk NY, Washington DC, Westchester

NY

PA

Anne Arundel MD, Arlington VA, Baltimore MD, Bergen NJ, Camden NJ, Cecil MD,

Cumberland NJ, Erie NY, Fairfax VA, Fairfield CT, Gloucester NJ, Harford MD,

Hudson NJ, Hunterdon NJ, Kenosha WI, Kent MD, Kent RI, Lake IN, Mecklenburg

NC, Mercer NJ, Middlesex CT, Middlesex NJ, Monmouth NJ, Morris NJ, New Haven

CT, Newcastle DE, Ocean NJ, Prince Georges MD, Putnam NY, Racine WI, Richmond

NY, Suffolk NY, Summit OH, Washington DC, Westchester NY

SC Fulton GA, Mecklenburg NC

TN Crittenden AR, Fulton GA, Lake IN, Mecklenburg NC, Tarrant TX

VA

Anne Arundel MD, Baltimore MD, Bergen NJ, Bucks PA, Camden NJ, Cecil MD,

Cumberland NJ, Delaware PA, Erie NY, Fairfield CT, Gloucester NJ, Harford MD,

Hudson NJ, Hunterdon NJ, Kent MD, Kent RI, Lake IN, Mecklenburg NC, Mercer NJ,

Middlesex CT, Middlesex NJ, Monmouth NJ, Montgomery PA, Morris NJ, New

Haven CT, Newcastle DE, Ocean NJ, Philadelphia PA, Prince Georges MD, Putnam

NY, Richmond NY, Suffolk NY, Summit OH, Washington DC, Westchester NY

WI Erie NY, Lake IN

WV

Allegheny PA, Anne Arundel MD, Baltimore MD, Bucks PA, Camden NJ, Cecil MD,

Cumberland NJ, Delaware PA, Fairfax VA, Fairfield CT, Fulton GA, Gloucester NJ,

Harford MD, Hunterdon NJ, Kent MD, Mercer NJ, Middlesex NJ, Monmouth NJ,

Montgomery PA, Morris NJ, New Haven CT, Newcastle DE, Ocean NJ, Philadelphia

PA, Prince Georges MD, Suffolk NY, Summit OH, Washington DC, Westchester NY

Table VI-4. Upwind States that Contain Emissions Sources that Contribute Significantly

to Projected 8-Hour Nonattainment in Downwind States.

Downwind

Nonattainment

Counties

Upwind States

Crittenden AR AL GA IL IN KY MO MS TN

Fairfield CT MD NJ NY OH PA VA WV

Middlesex CT MA MD NJ NY OH PA VA

New Haven CT MD NJ NY OH PA VA WV

Washington DC MD OH PA VA WV

Newcastle DE MD MI NC OH PA VA WV

Fulton GA AL KY NC SC TN WV

Lake IN IA IL MI MO OH PA TN VA WI

Anne Arundel MD MI NC OH PA VA WV

Baltimore MD MI NC OH PA VA WV

Cecil MD MI NC OH PA VA
36

Harford MD MI NC OH PA VA WV

Kent MD MI NC OH PA VA WV

Prince Georges MD MI OH PA VA WV

Mecklenburg NC GA MD SC TN VA

Bergen NJ MD MI OH PA VA

Camden NJ DE MD MI NC OH PA VA WV

Cumberland NJ DE MD MI NC OH PA VA WV

Gloucester NJ DE MD MI NC OH PA VA WV

Hudson NJ MD MI NY OH PA VA

Hunterdon NJ DE MD MI OH PA VA WV

Mercer NJ DE MD MI NY OH PA VA WV

Middlesex NJ DE MD MI NY OH PA VA WV

Monmouth NJ DE MD MI NY OH PA VA WV

Morris NJ DE MD MI NY OH PA VA WV

Ocean NJ DE MD MI NC OH PA VA WV

Erie NY IL MD MI NJ PA VA WI

Putnam NY MD NJ PA VA

Richmond NY DE MD MI NJ OH PA VA

Suffolk NY

CT DE MD MI NC NJ OH PA VA 

WV

Westchester NY MD NJ OH PA VA WV

Geauga OH IL IN KY MI MO

Summit OH IL IN MD MI PA VA WV

Allegheny PA IL IN KY MI OH WV

Bucks PA DE MD MI NJ OH VA WV

Delaware PA DE MD MI NJ OH VA WV

Montgomery PA DE MD MI NJ OH VA WV

Philadelphia PA DE MD MI NC NJ OH VA WV

Kent RI CT MA NJ NY OH PA VA

Denton TX None of the upwind States examined in this analysis were found to make a

significant contribution (before considering cost) to this nonattainment receptor

Harris TX AL AR LA MS

Tarrant TX AR LA TN

Arlington VA MD OH PA

Fairfax VA MD NJ OH PA WV

Kenosha WI IA IL IN MI MO OH PA

Racine WI IA IL IN MI MO OH PA

Sheboygan WI IL IN MO

As a refinement to the preceding procedures for evaluating the contributions for each

linkage, EPA prepared the following criteria for the three contribution factors to distinguish

between the values which comprise a significant contribution versus those that do not:
37

Magnitude Metrics: considered large enough to be significant if the contribution is >= 3 ppb.

Frequency Metrics: considered frequent enough to be significant if there is a 3 ppb or more

contribution to at least 3 percent of the exceedances and, for linkages in which the maximum

contribution was in the range of >= 2 to < 3 ppb, there has to be contributions in this range to at

least two exceedances in the downwind area.

Relative Amount Metrics: considered large enough to be significant if the total contribution

relative to the total amount of nonattainment is >= 3 percent.

Applying these criteria to the contribution metrics for each linkage in the evaluation steps 2

through 4 yields the same result in terms of which linkages are significant, as provided in Tables

V-3 and V-4.

VII. Modeling to Assess Interstate PM2.5 Contributions

This section documents the procedures used by EPA to quantify the impact of emissions

in specific States on projected downwind nonattainment for annual average PM2.5. The analytic

approach for modeling the contribution of upwind States to PM2.5 in downwind nonattainment

areas and the methodology for analyzing the modeling results are described in subsection A and

the findings as to whether individual States meet the air quality component of the significant

contribution test is provided in subsection B. These procedures are the first of the two-step

process for determining significant contribution, in which the second step involves a control cost

assessment to determine the amount of upwind emissions that should be reduced. In this section

we use the phase “significant contribution” to refer to the PM2.5 air quality step of the

significance determination.

A. Analytical Techniques for Modeling Interstate Contributions to Annual Average PM2.5

Nonattainment

1. State-by-State Zero-Out Modeling

The EPA performed State-by-State zero-out modeling to quantify the contribution from

emissions in each State to future PM2.5 nonattainment in other States. As part of the zero-out

modeling technique we removed the 2010 Base Case anthropogenic emissions of SO2 and NOx

for 41 States on a State-by-State basis in different model runs. The States EPA analyzed using

zero-out modeling are: Alabama, Arkansas, Colorado, Connecticut, Delaware, Florida, Georgia,

Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, Massachusetts,

Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, New Hampshire, New

Mexico, New Jersey, New York, North Carolina, North Dakota, Ohio, Oklahoma, Pennsylvania,

Rhode Island, South Carolina, South Dakota, Tennessee, Texas, Vermont, Virginia, West
5

For computational efficiency we performed zero-out modeling for six States as combination runs

in which emissions from two very distant States were removed (i.e., zero-out) in the same model run. The

States we combined in three separate runs are: Nebraska and Maine, South Dakota and New Hampshire,

and North Dakota and Vermont.

38

Virginia, Wisconsin, and Wyoming5. Emissions from the District of Columbia were combined

with those from Maryland. 

In processing emissions for zero-out modeling we removed the emissions of SO2 and

NOx from all anthropogenic source sectors in the given State. For elevated point sources, the

emissions were removed from individual sources located within the State. For low-level sources

(i.e., onroad, nonroad, area, and low-level point sources) we removed emissions using data in the

gridded emissions files. Thus, in order to zero-out emissions for these four source types we

identified the set of grid cells that covered the State then removed the emissions from just these

grid cells. In some cases a grid cell assigned to one State overlapped a portion of a neighboring

State in which a nonattainment receptor was located. In these situations the receptor was not

considered as a “downwind” receptor for that zero-out State.

The model predictions from the zero-out runs were used to calculate the contribution

from each State to PM2.5 at nonattainment receptors in other States through the following

procedures:

Step 1: The SMAT technique was applied for each zero-out run to calculate PM2.5

concentrations at each FRM site. That is, the outputs from each zero-out run was coupled with

the outputs from the 2001 proxy run to create specie-specific RRFs which were then applied to

ambient species concentrations estimated for the FRM sites in order to calculate PM2.5

concentrations at each site for each zero-out run.

Step 2: For the 57 receptor sites that were nonattainment in the 2010 Base-1, we

calculated the difference between the 2010 Base-1 PM2.5 concentration at the receptor and the

PM2.5 concentration for the zero-out run at that same receptor. This difference is the

contribution from the zero-out State to the downwind nonattainment receptor. The contribution

from each State to each downwind nonattainment receptor is provided in Appendix H.

2. Interstate PM2.5 Contribution Metrics

As described above in section VI, EPA used three fundamental factors for evaluating the

contribution of upwind States to downwind nonattainment, i.e., the magnitude, frequency, and

relative amount of contribution. One of these factors, the frequency of contribution, is not

relevant for an annual average NAAQS and thus, frequency was not considered in the evaluation

of interstate contributions to nonattainment of the PM2.5 NAAQS.

The EPA considered a number of metrics to quantify the magnitude and relative amount

of the PM2.5 contributions. These metrics are listed in Table VII-1. The EPA is proposing to
39

use the maximum downwind contribution metric as the means for evaluating the significance of

interstate PM2.5 transport. The maximum contribution from a given State is the highest

contribution made by that State when considering all downwind receptors.

Table VII-1. PM2.5 Contribution Metrics Considered by EPA.

Metric Description

Maximum Contribution Highest contribution from a given State to any downwind

nonattainment receptor

Sum of Contributions Sum of the contributions from a given State to all downwind

nonattainment receptors

Maximum Contribution per

MM Tons of SO2+NOx

Divide Maximum contribution from a given upwind State by the total

SO2+NOx emissions in that State

Sum of Contributions per MM

Tons of SO2+NOx

Divide the Sum of contributions from a given upwind State by the total

SO2+NOx emissions in that State

Sum Population-Weighted

Contribution

Multiply the contributions from a give State to each downwind receptor

by the population in the county in which the receptor is located; then

sum these population weighted values

Maximum Percent of

Downwind Nonattainment

For a given State, divide the contribution to each receptor by the

exceedance amount at that receptor (i.e., the difference between the

2010 Base concentration and 15.05 μg/m3

); express this value as a

percent; then select the highest value from among all downwind

receptors for that State

Maximum Percent of

Downwind PM2.5

For a given State, divide the contribution to each receptor by the 2010

Base Case concentration at that receptor; express this value as a

percent; then select the highest value from among all downwind

receptors for that State

The procedures for calculating the maximum contribution metric are as follows:

Step 1: Examine the contribution from each upwind State to PM2.5 at each downwind

nonattainment receptor; 

Step 2: Select the highest contribution from among those determined in Step 1. This is

the maximum downwind contribution.

B. Evaluation of Upwind State Contributions to Downwind PM2.5 Nonattainment

The EPA is proposing to use a criterion of 0.15 μg/m3 for determining whether emissions

in a State make a significant contribution to PM2.5 nonattainment in another State. The

rationale for choosing this criterion is described in the IAQR preamble. The maximum
40

downwind contribution from each upwind State to a downwind nonattainment county is

provided in Table VII-2. Of the States analyzed for this proposal, 28 States and the District of

Columbia contribute 0.15 μg/m3 or more to nonattainment in other States and therefore are found

to make a significant contribution to PM2.5. Although we are proposing to use 0.15 μg/m3 as

the air quality criterion, we have also analyzed the impacts of using 0.10 μg/m3

 Based on our

current modeling, two additional States, Oklahoma and North Dakota, would be included if we

were to adopt 0.10 μg/m3 as the air quality criterion. Table VII-3 provides a count of the number

of downwind counties that received contributions of 0.15 μg/m3 or more from each upwind State. 

This table also provides the number of downwind counties that received contributions of 0.10

μg/m3

 or more from each upwind State. 

Table VII-2. Maximum Downwind PM2.5 Contribution (μg/m3) for each of 41 Upwind

States.

Upwind State Maximum Downwind

Contribution

Downwind Nonattainment County

of Maximum Contribution

Alabama 1.17 Floyd, GA

Arkansas 0.29 St. Clair, IL

Connecticut 0.07 New York, NY

Colorado 0.04 Madison, IL

Delaware 0.17 Berks, PA

Florida 0.52 Russell, AL

Georgia 1.52 Russell, AL

Illinois 1.50 St. Louis, MO

Indiana 1.06 Hamilton, OH

Iowa 0.43 Madison, IL

Kansas 0.15 Madison, IL

Kentucky 1.10 Clark, IN

Louisiana 0.25 Jefferson, AL

Maryland/District of Columbia 0.85 York, PA

Maine 0.03 New Haven, CT

Massachusetts 0.21 New Haven, CT

Michigan 0.88 Cuyahoga, OH

Minnesota 0.39 Cook, IL

Mississippi 0.30 Jefferson, AL

Missouri 0.89 Madison, IL

Montana 0.03 Cook, IL

Nebraska 0.08 Madison, IL
41

New Hampshire 0.06 New Haven, CT

New Jersey 0.45 New York, NY

New Mexico 0.03 Knox, TN

New York 0.85 New Haven, CT

North Carolina 0.41 Sullivan, TN

North Dakota 0.12 Cook, IL

Ohio 1.90 Hancock, WV

Oklahoma 0.14 Madison, IL

Pennsylvania 1.17 New Castle, DE

Rhode Island 0.01 New Haven, CT

South Carolina 0.72 Richmond, GA

South Dakota 0.04 Madison, IL

Tennessee 0.57 Floyd, GA

Texas 0.37 St. Clair, IL

Vermont 0.06 New Haven, CT

Virginia 0.67 Washington, DC

West Virginia 0.89 Allegheny, PA

Wisconsin 1.00 Cook, IL

Wyoming 0.05 Madison, IL

Table VII-3. Number of Downwind PM2.5 Nonattainment Counties that Receive

Contributions 0.15 μg/m3 or More and 0.10 μg/m3 or More from each Upwind State.

Upwind State

Number of Downwind

Nonattainment Counties with

Contributions of 0.10 μg/m3

or More

Number of Downwind

Nonattainment Counties with

Contributions of 0.15 μg/m3

or More

Alabama 43 32

Arkansas 27 4

Delaware 4 1

Florida 23 19

Georgia 38 27

Illinois 53 53

Indiana 54 53

Iowa 30 13

Kansas 4 2

Kentucky 52 50
42

Louisiana 33 25

Maryland/District of Columbia 9 7

Massachusetts 2 1

Michigan 55 39

Minnesota 18 8

Mississippi 28 18

Missouri 47 31

New Jersey 8 7

New York 16 12

North Carolina 35 28

North Dakota 4 0

Ohio 47 47

Oklahoma 3 0

Pennsylvania 52 46

South Carolina 23 19

Tennessee 50 43

Texas 48 36

Virginia 35 17

West Virginia 46 32

Wisconsin 48 29

VIII. Ozone Sensitivity Modeling of Local Emission Reductions

As noted in the Preamble to the proposed rule, it is expected that reducing upwind

precursor emissions will assist downwind 8-hour ozone nonattainment areas in achieving the

National Ambient Air Quality Standards. Furthermore, it is expected that regional controls will

result in a more certain, equitable, and cost effective approach to attainment than by only local

emission reductions in the nonattainment areas. This section documents the procedures used in,

and presents the results of, a sensitivity modeling analysis designed to quantify the impact of

local ozone precursor emissions on projected residual nonattainment in 2010. 

As discussed in more detail in section III, the air quality modeling analyses completed to

assess the effect of local emission reductions on 8-hour ozone nonattainment were conducted for

an Eastern U.S. domain using CAMx, version 3.10. Two sets of modeling analyses were

completed focusing on nonattainment counties projected to be nonattainment in 2010. The first

analysis used the CAMx source apportionment probing tool, as discussed in section VI. The

total average contribution metric was used to determine the percentage of ozone that was formed

due to in-State vs. out-of-State emissions. The results are shown in Table VIII-1.
6

 Table VIII-1 was completed early in the analysis process and used 1999-2001 ambient data to

project the future design values. This results in a slightly different set of projected nonattainment

counties (37 of the 47 using 2000-2002 data are the same). The differing ambient data base is not

expected to impact the results. 

43

Table VIII-1. Projected 8-Hour Ozone Design Values and the Percent of Total Average

Contribution Resulting from Emissions in Upwind States6

2010 Nonattainment

Counties

Projected

2010 Design

Value

Percent of 8-Hour Ozone due to Out-of-State

Transport

New Haven CT 91 96

Middlesex CT 97 90

Ocean NJ 99 86

Cumberland NJ 85 86

Kent RI 87 85

Sheboygan WI 86 81

Fairfield CT 94 78

Ozaukee WI 86 77

Monmouth NJ 87 74

Middlesex NJ 93 71

Morris NJ 87 69

Gloucester NJ 92 68

Camden NJ 93 66

Door WI 85 65

Delaware PA 86 60

Hudson NJ 91 59

Montgomery PA 93 55

Richmond NY 92 54

Lehigh PA 86 54

Westchester NY 88 52

Kent MD 86 47

Anne Arundel MD 91 44

Bucks PA 98 43

Erie NY 85 43

Mercer NJ 99 41

Baltimore MD 85 40

New Castle DE 86 39

Kenosha WI 89 37

Prince Georges MD 87 37

Lake IN 85 36

Lancaster PA 85 36

Arlington VA 85 36
44

Fairfax VA 85 36

Galveston TX 92 35

Washington DC 87 35

Cecil MD 92 34

Harris TX 104 31

Northhampton PA 87 30

Harford MD 93 29

Tarrant TX 87 29

Shelby TN 85 29

Hunterdon NJ 93 28

Fulton GA 93 25

DeKalb GA 89 23

Rockdale GA 87 23

Denton TX 89 22

Collin TX 88 22

As seen from Table VIII-1, ozone transport constitutes a sizable portion of the projected

nonattainment problem in most eastern areas in 2010 (even after implementation of the NOx SIP

call). In many cases, over 50 percent of the ozone nonattainment problem is due to emissions in

other States. All of the future nonattainment areas show at least a 20 percent impact from

transported ozone or ozone precursors.

The second analysis considered the effects of 10 percent, 25 percent, and 50 percent

reductions in man-made NOx + VOC emissions in possible future nonattainment areas. Figure

VII-1 shows the counties in which the sensitivity controls were applied. In all, there were 271

counties over 29 possible future nonattainment areas. These projections were made using the

Clear Skies 2010 Base Case (EPA, 2003b) and 1999-2001 ambient data as a starting point. For

areas that might possibly be classified as marginal under the new 8-hour ozone implementation

rule, and therefore require a 2007 attainment date, the 2010 projections were interpolated to 2007

in order to assess future nonattainment. The sensitivity controls were applied to the 2010 Clear

Skies control case (i.e., after the application of a regional NOx reduction strategy). Only the

effects of the 25 percent controls were analyzed; this control level is indicative of substantial

local control. The results of the sensitivity modeling are shown in Table VIII-2.
45

Figure VIII-1. Counties in which Sensitivity Controls were Applied.

Table VIII-2. Results of CAMx 25 percent Local NOx + VOC Control in Projected Future

Nonattainment Areas.

CMSA-MSA

CSA 2007

Interpolated

CSA 2010

Base

CSA 2010

Control

CSA 2010

Control +

Local

Allentown-Bethlehem-Easton PA-NJ 87 86 81

Atlanta GA 93 92 86

Baton Rouge, LA 85 83 83 79

Boston-Lawrence-Worcester (E. MA-NH) 88 84 84 77

Buffalo-Niagara Falls, NY 85 84 81

Charlotte-Gastonia-Rock Hill NC-SC 89 84 85 80

Chicago-Gary-Lake County, IL-IN (WI) 89 88 86

Cincinnati-Hamilton, OH-KY-IN 85 81 80 77

Cleveland-Akron, OH 86 83 82 78

Dallas-Fort Worth, TX 89 89 84

Detroit-Ann Arbor-Flint, MI 85 84 83 84

Grand Rapids-Muskegon-Holland, MI 86 83 82 78

Greater Connecticut, CT 93 93 88

Green Bay-Appleton-Oshkosh-Neenah-Door, WI 85 82 79

Harrisburg-Lebanon-Carlisle, PA 86 83 82 78

Houston-Galveston-Brazoria, TX 104 104 103

Knoxville, TN 85 79 79 76

Lancaster, PA 85 85 80

Longview-Marshall, TX 85 80 82 80
46

Memphis, TN-AR-MS 85 85 83

Milwaukee-Racine, WI 89 88 86

New York-New Jersey-Long Island NY-NJ-CT- 99 99 93

Philadelphia-Wilmington-Atlantic City, PA-NJ- 98 98 93

Pittsburgh, PA (WV) 86 84 83 80

Providence (All RI), RI 86 85 80

Raleigh-Durham-Chapel Hill, NC 85 81 80 76

Reading, PA 87 84 84 78

Sheboygan, WI 86 85 81

Washington-Baltimore, DC-MD-VA-WV 93 93 87

Table VIII-2 shows that eight metropolitan areas (Atlanta, Greater Connecticut, Chicago,

Houston, Milwaukee, New York, Philadelphia, and Baltimore-Washington are projected to

remain above the standard in 2010, despite the application of significant amounts of local

control. 

IX. PM2.5 Modeling of Locally Applied Control Measures

The purpose of this section is to discuss modeling studies aimed at a preliminary

understanding of the effect of possible local control measures on PM2.5. We conducted two air

quality modeling analyses to assess the impact on PM2.5 concentrations of applying measures

only within the nonattainment areas. Both analyses were conducted :

C Identify a list of local control measures that could be applied in addition to those

measures already in place or required to be in place in the near future;

C Determine the emissions inventory categories that would be affected by those measures,

and the estimated percentage reduction; 

C Apply those percentage reductions to sources within a selected geographic area; and 

C Conduct regional air quality modeling using REMSAD to estimate the ambient impacts

from these control measures and the degree to which the measures would reduce the

expected number of nonattainment areas.

A. Control Measures and Percent Reductions

For the analysis of local controls, we developed a list of emission control measures as a

surrogate for measures that State, local and tribal air quality agencies might include in their PM

implementation plans. The list includes measures that such agencies might be able to carry out

to reach attainment in 2009 or as soon thereafter as possible. The measures addressed a broad
47

range of point, area, and mobile sources. In general, the measures represent what we consider to

be a highly ambitious but achievable level of control. We identified measures for direct PM2.5

and also for the following PM2.5 precursors: SO2, NOx, and VOC. We did not attempt to

address ammonia emissions, in part due to lower emissions of ammonia and the likelihood of

fewer controllable sources within the urban areas targeted for the analysis.

The percent reduction in emissions associated with each control measure was developed

in two ways. First, we developed percent reduction estimates for specific technologies to the

extent that information was available. These estimates were based on both the percent control

that might be achieved for sources applying that technology and the percent of the inventory the

measures might be applicable to (i.e., rule penetration). For example, assume that a given

technology is expected to reduce emissions of an individual source by 90 percent and it is

reasonable to install this technology on only 30 percent of the sources in this category. In this

case we applied a 27 percent reduction to all sources in this category (i.e., 90 percent control

efficiency multiplied by 30 percent of the source covered yields an overall reduction of 27

percent. 

Second, there were some groups of control measures where data and resources were not

available to develop technology-specific estimates in this manner. For these, we felt it preferable

to make broad judgments on the level of control that might be achieved rather than to leave these

control measures out of the analysis entirely. For example, the analysis reflects a reduction of 3

percent from onroad mobile source emissions relative to a 2010 and 2015 baseline. We judged

this 3 percent estimate to represent a reasonable upper bound on the degree to which

transportation control measures and other measures for reducing mobile source emissions could

reduce the overall inventory of mobile source emissions in a given area. 

Additionally, we believe that it may be possible to improve the performance of emissions

control devices such as baghouses and electrostatic precipitators for point sources, and in some

cases to upgrade to a more effective control device. In our current emissions inventories, we

have incomplete data on control equipment currently in use. As a result, data are not available to

calculate for each source the degree to which the control effectiveness could be improved. 

Nonetheless, we believed it important to include assumptions concerning point source controls

for direct PM. For this analysis, we assumed a 25 percent across-the-board that reduction in

PM2.5 emissions at all point sources. 

Table IX-1 shows the control measures selected for the analysis, the pollutants reduced

and the percentage reduction estimates. Documentation and references for the local control

measures are provided in Appendix I.
48

Table IX-1. Control Measures, Pollutants, and Percentage Reductions for the Local

Measures Analysis

Source Description Control Measure SO2 NOx PM2.5 Tol+Xyl (VOC)

Eff Eff App

%

Red Eff App

%

Red Eff App

%

Red

Utility boilers FGD scrubber for some or

all unscrubbed units

See

table 

IX-2

Coal-fired industrial

boilers > 250

MMBTU/hr

Coal switching 50

Petroleum fluid catalytic

cracking units

Wet gas scrubber 50

Refinery process heaters

- oil-fired

Switch to natural gas 50

Sulfuric acid plants Meet NSPS level 42-96

Coal-fired industrial

boilers

SNCR 50 20 10

Gas-fired industrial

boilers (large &

medium)

SNCR 45 20 9

Gas-fired industrial

boilers (small)

Low NOx burner 50 20 10

Gas-fired IC Engines

(reciprocating)

NSCR 94 10 9.4

Gas-fired turbine &

cogeneration

SCR 90 10 9

Asphalt Concrete, Lime

Manufacture

Low NOx burner 27 50 14

Cement Manufacturing Tire derived fuel & midkiln firing34 50 18

Petroleum Refinery Gasfired Process HeatersUltra-low NOx burner &

SNCR

93 50 46.5

All direct PM2.5 points

sources

Improve existing controls

(baghouses, ESPs)

25

Wood fireplaces and

woodstoves2 

Natural gas inserts for

fireplaces

80 30 24

Replace woostoves with

certified noncatalytic wood

stoves

71 30 21.4

HDDV including busesa Engine Modifications,

Diesel oxidation catalyst

40 5 2
Source Description Control Measure SO2 NOx PM2.5 Tol+Xyl (VOC)

Eff Eff App

%

Red Eff App

%

Red Eff App

%

Red

49

Particulate filter 90 30 27

Idling reduction4 1.7 1.7 1.7

Off-highway diesel

construction and mining

equipment

Engine modifcations, diesel

oxidation catalyst

40 73 29

particulate filter 25 73 18

Diesel Marine Vessels SCR 75 5 4

Particulate filter 90 30 27

Diesel locomotives SCR 72 5 4

Electrification of yard 2.5 2.5 6 0.2 2.5 6 0.2 2.5 6 0.2

Unpaved roads Gravel covering 60 30 18

Construction road Watering 50 30 15

Open burning Ban 100 75 75 10

0

75 75 100 75 75

Agricultural tilling Soil conservation measures,

unspecified

20 30 6

LDGV and LDGT1 Combination of unspecified

measures to reduce

highway vehicle miles and

emissions

333

_______________________________

a For the 1996 inventory woodstoves and fireplaces are combined into one SCC category. We assumed

for purpose of this analysis that woodstoves and fireplaces each comprise half of the total wood burned

for the category overall. Thus, the total percentage reduction is (24+21.4)/2 = 22.7 percent.
7

For the three-city study we chose the PMSA counties rather than the larger list of counties in the

consolidated metropolitan statistical area (CMSA). Both the PMSA and the CMSA classifications for

metropolitan areas are created by the Office of Management and Budget (OMB). For this study, we used

the classifications of counties in place as of spring 2003, rather than the revised classifications released by

OMB on June 6, 2003.

50

B. Development of Two Local Control Measure Studies

We conducted two studies for identifying the geographic area to which the control

measures were applied. These two studies were intended to address two separate issues related

to the effects of urban-based control measures.

The first study (3 City Study) was intended to illustrate the effect of the selected local

control measures within the geographic area to which controls were applied. For this, we

applied the control measures and associated emissions reductions to the inventories for three

cities –- Birmingham, Chicago, and Philadelphia. We selected these three urban areas because

each area was predicted to exceed the PM2.5 standard in 2010, albeit to varying degrees. 

Additionally, the three urban areas were selected because they are widely separated. 

Accordingly, we were able to conduct a single air quality analysis with less concerns for

overlapping impacts due to transport than if less separated cities were selected.

The 3 City Study control measures were applied to the projected 2010 Base Case

emission inventories for all counties within those Primary Metropolitan Statistical Areas

(PMSAs)7

. Thus, for Chicago, measures were applied to the 10 counties in Illinois, but were not

applied in northwest Indiana or Wisconsin. For Philadelphia, measures were applied to the New

Jersey and Pennsylvania counties within the Philadelphia urban area. For Birmingham,

measures were applied to 4 Alabama counties. 

The second Study (290 County Study) was intended to address the cumulative impact of

local control measures applied within nonattainment areas. In this study we applied the control

measures identified in IX-1 to all counties in Consolidated Metropolitan Statistical Areas

(CMSAs) which contained at least one county that was projected to be nonattainment in the

future baseline. A list of the counties included in this study is in Appendix J. The 290 County

Study included the application of the local control package in model runs for 2010 and 2015.

Judgments evolved over the process of conducting these two scenarios which resulted in

some differences in the measures that were applied. Table IX-2 outlines the differences in

control assumptions between the two studies. 
51

Table IX-2. Differences Between the Two Local Control Studies.

Pollutant Controls in 3 City Study, but

not in 290 County Study

Controls in 290 County Study, but

not in 3 City Study 

SO2 50% reduction from switch to

natural gas in oil-fired

commercial and industrial boilers 

For unscrubbed utility coal-fired

boilers, scrub to 0.15 lb/MMBTU

50% reduction from oil-fired refinery

process heaters

For unscrubbed utility coal-fired

boilers, apply 50% reduction

Sulfuric acid plants meet NSPS

NOx [no differences]

VOC 75% for solvent substitution for

cold cleaning

70% reduction for area source

coating use solvent substitution

75% reduction from metal pipe

coating solvent substitution

Direct PM 22.5% reduction from paved

roads for street sweeping

No reductions for PM2.5 for street

sweeping on advice from Tom Pace.

Measures that apply to

all pollutants

Mobile source across the board

assumption was 3% for Chicago

and Philadelphia, 5% for

Birmingham

Open burning 100% control, 30%

applicability

No assumptions for idling

We assumed 3% across the board

Open burning 100% control, 75%

applicability

Truck idling reductions

Diesel locomotive switching yard

reductions
52

C. Results of the Two Local Control Studies

Table IX-3 shows the results of applying the package of control measures in each of the

three urban areas addressed in the 3 City Study. The emission reductions were estimated to

achieve ambient PM2.5 reductions of about 0.5 to about 0.9 μg/m3

, less than needed to bring any

of the cities into attainment in 2010.

Table IX-3. Impact on PM2.5 in 2010 of the Emissions Reductions in the 3 City Study.

Metro Area 2010 Base

PM2.5

(μg/m3

)

PM2.5

Reduction

(μg/m3

)

Final PM2.5

(μg/m3

))

Attainment

Achieved?

Birmingham, AL 20.07 -0.84 19.23 No

Chicago, IL 18.01 -0.93 17.07 No

Philadelphia, PA 15.6 -.052 15.08 No

The results of the 290 County Study are summarized in Table IX-4. We were interested

in what part of the PM2.5 improvement that was attributable to SO2 reductions due to local

emissions reductions and due to emissions reductions in upwind States. Part B of Table IX-4 

shows a re-analysis of the modeling results in which the observed sulfate reductions were not

considered in calculating the PM2.5 effects of the control package. If, as we expect, the

observation from the earlier described modeling of Birmingham and two other cities that local

SO2 reductions have relatively small local effects on sulfate applies more generally, then the

difference between Parts A and B of Table IV-7 would generally represent the effect of upwind

reductions in SO2 from power plants and other sources in other urban areas. 

Table IX-4. Impact on PM2.5 of the Emissions Reductions in 2010 for the 290 County

Study.

2010 Base-2 With Local Controls

Part A - Full Modeling Results Considering All Pollutants and Species

Number of nonattainment counties 61 26

Average Reduction in PM2.5 Design

Value Not Applicable 1.26 μg/m3

Part B - Results Not Counting Reductions in Sulfate Component of PM2.5

Number of nonattainment counties 61 48

Average Reduction in PM2.5 Design

Value Not Applicable 0.37 μg/m3
53

The results of the two scenarios show that much of the difference between the baseline

case and the local control case is due to the sulfate component. 

D. Analysis of the 290 County Study

The application of control measures to emissions in the 290 counties generally resulted in

a somewhat modest percent reduction in emissions within an urban area in terms of the tons

reduced and percent reduction. This occurs because a substantial part of the local emissions are

attributable to mobile sources, small business, and household activities for which practical,

large-reduction, and quick-acting emission reductions measures could not be identified at this

time. Table IX-5 displays a ranking of measures by tons reduced for the various pollutants and

where available along with the costs associated with those measures, in $/ton.

We also note that the baseline emissions inventory used for this analysis has some known

gaps. For example, direct PM2.5 and VOC from commercial cooking (e.g., charbroiling) is not

included because no robust estimates were available for the 1996 base year used for this analysis. 

 Also, excess PM2.5 due to deterioration of engines in service, and emissions from open burning

of refuse, may not be well represented.

Table IX-5. Emission Reductions and Costs of Local Measures for the Second Scenario.

Pollutant Category/Measure Total tons

reduced in the

290 counties 

$/ton, if available

SO2 Utility boilers achieve 50 % reduction

overall

1,400,000 N/A

Industrial boilers >250 MMBTU/hr /

switch to lower sulfur coal to achieve

50% reduction 

73,000 N/A

Petroleum refinery catalytic cracking

units/ Wet gas scrubber 

36,000 N/A

Sulfuric acid plant/ Meet NSPS 8,300 N/A

Petroleum refinery oil-fired process

heaters / Switch to natural gas

6,000 N/A
Pollutant Category/Measure Total tons

reduced in the

290 counties 

$/ton, if available

54

NOx Off-highway diesel construction and

mining equipment/particulate filter

45,000 N/A

Heavy duty diesel vehicles including

buses / engine modifications

20,000 N/A

Petroleum refinery gas-fired process

heaters / ultra-low NOx burner +

SNCR

18,000 $800

Combination of unspecified measures

to reduce highway vehicle miles and

emissions

15,000 N/A

Coal-fired industrial boilers / SNCR 9,000 $1100

Open burning / ban open burning 8,300 N/A

Small Gas-fired industrial boilers /

Low NOx burner

7,000 $10,000

Diesel locomotives / SCR 5,900

Large and medium Gas-fired industrial

boilers / SNCR

4,800 $5000-5300

Diesel marine vessels / SCR 4,400

Cement manufacturing / mid-kiln firing 4,000 $150-680

Gas-fired reciprocating IC engines /

NSCR

2,800 $230

Asphalt plants, lime manufacturing /

Low NOx burner

2,400 $440 - $940

Gas-fired turbines and cogeneration /

SCR

<1000 $1500

Direct PM2.5 open burning / ban 42,000 N/A

All point source SCCs / 25% reduction

based upon improving existing controls

30,000 N/A

Construction roads / watering 10,000 $2000
Pollutant Category/Measure Total tons

reduced in the

290 counties 

$/ton, if available

55

Direct PM2.5 Unpaved roads / gravel covering 4,600 $2100-5900

Heavy duty diesel vehicles including

buses / particulate filter

4,300 < $4000

Fireplaces / natural gas inserts 3,600 $7500

Woodstoves / replace with certified

noncatalytic wood stove

3,200 $3800

Diesel marine vessels / particulate filter 2,600 < $4000

Off-highway diesel construction and

mining equipment / particulate filter

2,100 < $4000

Agricultural tilling / unspecified soil

conservation measures

< 1000 $19

Combination of unspecified measures

to reduce highway vehicle miles and

emissions

 300 N/A

Appendix K contains a detailed listing of the tons of each pollutant for each of the urban

areas included in the modeling. This Appendix contains nine individual tables, which show the

emissions reductions from the local control measures for the years 2010 and 2015 for the

following pollutants:

(1) SO2

(2) NOx

(3) VOC

(5) Total directly-emitted PM 2.5

(6 - 10), Individual primary PM2.5 species: elemental carbon (PEC), organic aerosol

(POA), primary nitrate (PNO3), primary sulfate(GSO4), and “other” (PMFINE, generally

crustal)

Each table compares emissions for a future year base case with a future year control case

reflecting the collection of control measures described in Tables IX-1 and IX-2. These tables

show varying degrees of control for the different pollutants. Because the patterns for 2010 and

2015 are very similar, this paragraph will focus on 2010 only. For 2010, total direct PM2.5

reductions ranged from 17 to 43 percent, and typically exceeded 25 percent. Reductions in

primary organic carbon ranged from 16 to 24 percent and primary elemental carbon reductions

ranged from 20-35 percent. NOx emissions in all areas were reduced by less than 10 percent. 

VOC emissions were typically less than 10 percent, except for a few areas which had reductions
56

as high as15 percent. For SO2, emissions reductions were more variable across the area and are

highly dependent on whether unscrubbed coal-fired utility boilers were located in the area. 

Some areas had SO2 emissions reductions approaching 50 percent, while other areas showed

very little reduction in SO2. Overall, the greatest cumulative reductions over the entire 290

county area was for SO2. Emissions of SO2 in the 2010 Base Case 4.2 million tons, which were

reduced to about 2.6 million tons in the control case. By comparison, total direct 2010 PM2.5

emissions for the 290 county area were reduced from about 380,000 tons to about 280,000 tons

per year.

Appendix L contains tables (the year 2010 and the year 2015) summarizing REMSAD

modeled air quality impacts from the control measures. In each table, the modeled impacts and

the difference from the base case are noted for each of the geographic areas in which controls

were applied:

- total PM2.5, and PM2.5 species:

+ crustal

+ elemental carbon

+ organic aerosol

+ ammonium sulfate

+ ammonium nitrate

Because most of the geographic areas consist of more than one county, each of these tables

indicates a “maximum” impact (i.e., in county with the greatest reduction in concentrations), a

“minimum” impact (i.e., in county with the smallest reduction in concentrations), and the

average impact across all counties in the metropolitan area. 

It is interesting to compare the year 2010 air quality impact to the emissions reductions

described above. The largest impact in terms of modeled reductions was for ammonium sulfate,

which is consistent with the sizeable emission reductions for SO2. As noted above, the overall

average total PM2.5 reduction across all the metropolitan areas was 1.26 μg/m3

. The detailed

tables in Appendix L show that in nearly all the areas, sulfate reductions were 2/3 or more of this

amount. For organic aerosol, Appendix L shows modeled reductions less than 10 percent and

typically 5-7 percent, which is significantly lower than the 16-24 percent reduction in primary

organic carbon emissions in the 290 counties. Because crustal and elemental carbon

concentrations are low, the overall reductions in emissions from these components do not have a

significant effect on PM2.5 reductions. The ammonium nitrate concentrations showed slight

increases for the control case, in all cases less than 0.1 μg/m3

. The increase in nitrate is likely to

reflect “nitrate replacement” which is a phenomena whereby SO2 emissions reductions lead to

an increase in nitrate concentrations. In the local control scenario, it is likely that the NOx

controls included in the local control packet were insufficient to overcome the formation of

additional nitrate as a result of the SO2 reductions. For more information on nitrate replacement

please see section XI.
8

In addition, summer season only EGU NOx controls are proposed for Connecticut which

significantly contributes to ozone, but not PM2.5 nonattainment in other States.

9

The modeled scenario reduces EGU emissions in the five New England States not covered by

the IAQR proposal by less than 3,000 tons per year. In the 15 States located to the west of the

region covered by the IAQR proposal, total EGU SO2 emissions decline by 17 percent.

57

X. Modeling of Regional SO2 and NOx Emissions Reductions 

A. Introduction

In this section, we describe the air quality modeling performed to determine the projected

impacts on PM2.5 and 8-hour ozone of the proposed regional SO2 and NOx emissions

reductions. The regional emissions reductions are associated with State emissions budgets in

2010 and 2015, as explained in the IAQR preamble. The impacts of the regional reductions in

2010 and 2015 are determined by comparing air quality modeling results for each of these

regional control scenarios to the modeling results for the corresponding 2010 and 2015 Base

Case scenarios. Descriptions of the 2010 Base-2 and 2015 Base Case are provided in section II. 

Note that neither the base cases nor the regional control strategy scenarios include any of the

local control measures discussed in section IX. Also note that the 2015 Base Case does not

include any 2010 emissions reductions from the regional strategy.

The 2010 and 2015 regional strategy budgets cover emissions from the power generation

sector in 28 eastern States plus the District of Columbia that contribute significantly to both

PM2.5 and ozone nonattainment in downwind States.8

 These annual SO2 and NOx budgets are

provided in the IAQR preamble. 

The EPA modeled a two-phase cap and trade strategy for SO2 and for NOx using the

IPM to assess the impacts of the budgets on air quality. For the purposes of air quality modeling,

we used a scenario that assumes a 48-State SO2 trading area and SO2 allowances. Most of the

SO2 emissions reductions in this scenario occur in the 28-State and DC control region; there are

only small changes in nearly all States.9

 We do not expect these latter changes to actually occur;

but, because they are only small changes, the results of using this IPM scenario are expected to

be very similar to the actual results of the IAQR proposal. For NOx, EPA modeled a NOx

trading scenario covering 31 States, DC, and the eastern half of Texas. The 31 States include

Arkansas, Iowa, Louisiana, Minnesota, Missouri, and all other States to the east of these five

States. Thus, the modeled strategy does not match the NOx reductions required in the IAQR

proposal for Kansas and western Texas. In addition, the modeled strategy includes NOx

reductions in Maine, New Hampshire, Rhode Island, and Vermont which do not have any

required reductions in the IAQR proposal.

Phase 1 of the regional strategy is forecast to reduce total EGU SO2 emissions in the 28-

States plus DC by 40 percent in 2010. Phase 2 is forecast to provide a 44 percent reduction in
58

EGU SO2 emissions compared to the base case in 2015. The net effect of the strategy on total

SO2 emissions in the 28-State plus DC States, considering all sectors of emissions, is a 27

percent reduction in 2010 and a 28 percent reduction in 2015. For NOx, Phase 1 of the strategy

is forecast to reduce EGU emissions by 44 percent and total emissions by 10 percent in the 28-

States plus DC in 2010. In Phase 2, EGU NOx emissions are projected to decline by 53 percent

in 2015. Total NOx emissions are projected to be reduced by 14 percent in 2015. The percent

change in emissions by State for SO2 and NOx in 2010 and 2015 for the regional strategy are

provided in the Appendix A.

B. PM2.5 Modeling of the Proposed Regional SO2 and NOx Strategy

The PM modeling platform described in section IV was used by EPA to model the

impacts of the proposed SO2 and NOx emissions reductions on annual average PM2.5

concentrations and visibility. In brief, we ran the REMSAD model for the meteorological

conditions in the year of 1996 using our nationwide modeling domain. Modeling was performed

for both 2010 and 2015 to assess the expected effects of the proposed regional strategy in each of

these years on projected PM2.5 concentrations and nonattainment. The procedures used to

project future PM2.5 design values and nonattainment are described in section V. The counties

that are projected to be nonattainment for the PM2.5 NAAQS are listed in Table X-1 for the

2010 Base-2 and the 2010 regional strategy scenario and in Table X-2 for the 2015 Base Case

and 2015 regional strategy scenario. The projected 2010 Base-2 and control scenario PM2.5

design values are provided in Table X-3. The projected 2015 Base Case and control PM2.5

design values are provided in Table X-4. 

Table X-1. Projected PM2.5 Nonattainment Counties for 2010 Base Case and SO2+NOx

Regional Strategy.

State 2010 Base-2 2010 Regional Strategy 

AL

DeKalb, Jefferson, Montgomery, Russell,

Talladaga Jefferson, Russell, Talladaga

CT New Haven None

DC Washington D.C. None

DE New Castle None

GA

Clarke, Clayton, Cobb, DeKalb, Floyd,

Fulton, Hall, Muscogee, Paulding, Richmond,

Wilkinson

Clarke, Clayton, Cobb, DeKalb, Floyd, Fulton,

Muscogee, Wilkinson

IL Cook, Madison, St. Clair, Will Cook, Madison, St. Clair

IN Clark, Marion None

KY Fayette, Jefferson None
59

MD Baltimore City None

MI Wayne Wayne

MO St. Louis None

NY New York (Manhattan) New York (Manhattan)

NC Catawba, Davidson, Mecklenburg None

OH

Butler, Cuyahoga, Franklin, Hamilton,

Jefferson, Lawrence, Mahoning, Scioto, Stark,

Summit, Trumbull

Cuyahoga, Hamilton, Jefferson, Scioto, Stark

PA Allegheny, Berks, Lancaster, York Allegheny

SC Greenville None

TN Davidson, Hamilton, Knox, Roane, Sullivan Knox

WV

Brooke, Cabell, Hancock, Kanawha, Marshal,

Wood

None

Table X-2. Projected PM2.5 Nonattainment Counties for 2015 Base Case and SO2+NOx

Regional Strategy.

State 2015 Base Case 2015 Regional Strategy

AL Jefferson, Montgomery, Russell, Talladaga Jefferson, Russell

CT New Haven None

GA

Clarke, Clayton, Cobb, DeKalb, Floyd,

Fulton, Hall, Muscogee, Richmond,

Wilkinson

Clayton, DeKalb, Fulton

IL Cook, Madison, St. Clair Cook

IN Clark, Marion None

KY Jefferson None

MD Baltimore City None

MI Wayne Wayne

NY New York County (Manhattan) None

OH

Butler, Cuyahoga, Franklin, Hamilton,

Jefferson, Scioto, Stark, Summit

Cuyahoga, Hamilton, Jefferson, Scioto

PA Allegheny, York Allegheny

TN Hamilton, Knox Knox

WV Brooke, Cabell, Hancock, Kanawha, Wood None
60

Table X-3. Projected PM2.5 Design Values for the 2010 Base Case and SO2 + NOx

Regional Strategy.

State County 2010 Base-2 2010 Regional Strategy

Alabama DeKalb 15.22 13.92

Alabama Jefferson 20.03 18.85

Alabama Montgomery 15.69 14.60

Alabama Russell 17.07 15.77

Alabama Talladega 16.44 15.26

Connecticut New Haven 15.43 14.50

Delaware New Castle 15.43 14.12

District of Columbia District of Columbia 15.48 13.70

Georgia Clarke 17.04 15.56

Georgia Clayton 17.73 16.43

Georgia Cobb 16.80 15.56

Georgia DeKalb 18.26 16.92

Georgia Floyd 16.99 15.65

Georgia Fulton 19.79 18.37

Georgia Hall 15.62 14.24

Georgia Muscogee 16.68 15.41

Georgia Paulding 15.40 14.17

Georgia Richmond 15.99 14.65

Georgia Wilkinson 16.68 15.51

Illinois Cook 17.90 16.90

Illinois Madison 16.41 15.33

Illinois St. Clair 16.31 15.11

Illinois Will 15.21 14.25

Indiana Clark 15.86 14.34

Indiana Marion 15.89 14.39

Kentucky Fayette 15.21 13.55

Kentucky Jefferson 15.79 14.23

Maryland Baltimore City 16.58 14.82

Michigan Wayne 18.78 17.65

Missouri St. Louis City 15.25 14.14

New York New York 16.30 15.25

North Carolina Catawba 15.26 13.87

North Carolina Davidson 15.52 14.22

North Carolina Mecklenburg County 15.18 13.92

Ohio Butler 16.01 14.53

Ohio Cuyahoga 19.13 17.68

Ohio Franklin 16.69 15.04

Ohio Hamilton 17.75 15.96
61

Ohio Jefferson 18.04 16.06

Ohio Lawrence 15.48 13.67

Ohio Mahoning 15.39 13.76

Ohio Scioto 18.40 16.33

Ohio Stark 17.09 15.19

Ohio Summit 16.35 14.71

Ohio Trumbull 15.13 13.56

Pennsylvania Allegheny 19.52 16.92

Pennsylvania Berks 15.39 13.84

Pennsylvania Lancaster 15.46 13.71

Pennsylvania York 15.68 13.93

South Carolina Greenville 15.06 13.75

Tennessee Davidson 15.36 13.92

Tennessee Hamilton 16.14 14.74

Tennessee Knox 18.36 16.60

Tennessee Roane 15.18 13.69

Tennessee Sullivan 15.24 13.77

West Virginia Brooke 16.60 14.77

West Virginia Cabell 16.39 14.41

West Virginia Hancock 16.69 14.85

West Virginia Kanawha 17.11 14.81

West Virginia Marshall 15.53 13.25

West Virginia Wood 16.30 14.15

Table X-4. Projected PM2.5 Design Values for the 2015 Base Case and SO2+NOx Regional

Strategy.

State County 2015 Base Case 2015 Regional Strategy

Alabama Jefferson 19.57 18.11

Alabama Montgomery 15.35 14.05

Alabama Russell 16.68 15.05

Alabama Talladega 15.97 14.57

Connecticut New Haven 15.13 14.13

Georgia Clarke 16.46 14.58

Georgia Clayton 17.26 15.49

Georgia Cobb 16.28 14.37

Georgia DeKalb 17.93 16.22

Georgia Floyd 16.51 14.71

Georgia Fulton 19.44 17.62

Georgia Hall 15.05 13.16

Georgia Muscogee 16.31 14.71
62

Georgia Richmond 15.51 13.82

Georgia Wilkinson 16.40 14.88

Illinois Cook 17.52 16.40

Illinois Madison 16.03 14.88

Illinois St. Clair 15.91 14.67

Indiana Clark 15.40 13.69

Indiana Marion 15.31 13.79

Kentucky Jefferson 15.32 13.57

Maryland Baltimore City 16.11 14.20

Michigan Wayne 18.28 17.06

New York

New York

(Manhattan) 15.82 14.69

Ohio Butler 15.39 13.77

Ohio Cuyahoga 18.58 17.05

Ohio Franklin 16.18 14.46

Ohio Hamilton 17.07 15.15

Ohio Jefferson 17.49 15.51

Ohio Scioto 17.62 15.49

Ohio Stark 16.42 14.52

Ohio Summit 15.78 14.14

Pennsylvania Allegheny 18.64 16.09

Pennsylvania York 15.13 13.26

Tennessee Hamilton 15.63 13.91

Tennessee Knox 17.73 15.59

West Virginia Brooke 16.10 14.26

West Virginia Cabell 15.70 13.71

West Virginia Hancock 16.18 14.33

West Virginia Kanawha 16.45 14.10

West Virginia Wood 15.58 13.49

As described in section V, the air quality modeling results indicate that 61 counties in the

East are expected to be nonattainment for PM2.5 in the 2010 Base-2. Of these 61 counties, 38

are projected to come into attainment in 2010 following the SO2 and NOx emissions reductions

resulting from the regional control strategy. The 23 counties projected to remain nonattainment

after the application of the regional strategy are expected to experience a sizeable reduction in

PM2.5 from this strategy, which will bring them closer to attainment. Specifically, the average

reduction in these 23 residual 2010 nonattainment counties is 1.50 μg/m3

 with a range of 0.93 to

2.60 μg/m3.
63

In 2015, the SO2 and NOx reductions are expected to reduce the number of PM2.5

nonattainment counties in the East from 41 to 13. The regional strategy is predicted to provide

large reductions in PM2.5 in those 13 residual nonattainment counties. Specifically, the average

reduction in these 13 residual 2015 nonattainment counties is 1.70 μg/m3 with a range of 1.00 to

2.54 μg/m3

.

Thus, the SO2 and NOx emissions reductions will greatly reduce the extent of PM2.5

nonattainment by 2010 and beyond. These emissions reductions are expected to substantially

reduce the number of PM2.5 nonattainment counties in the East and make attainment easier for

those counties that remain nonattainment by substantially lowering PM2.5 concentrations in

these residual nonattainment counties.

C. Ozone Modeling of the Proposed Regional NOx Strategy

The EPA used the ozone modeling platform described in section III to model the impacts

of the proposed EGU NOx controls on 8-hour ozone concentrations. In brief, we ran the CAMx

model for the meteorological conditions in each of the three 1995 ozone episodes using the

Eastern U.S. modeling domain. Ozone modeling was performed for both 2010 and 2015 to

assess the projected effects of the regional strategy in each of these years on projected 8-hour

ozone nonattainment.

The results of the regional strategy ozone modeling are expressed in terms of the

expected reduction in projected 8-hour design value concentrations and the implications for

future nonattainment. The procedures used to project future 8-hour ozone design values and

nonattainment are described in section V. The counties that are projected to be nonattainment

for the 8-hour ozone NAAQS are listed in Table X-5 for the 2010 Base-2 and the 2010 regional

strategy scenario and in Table X-6 for the 2015 Base Case and 2015 regional strategy scenario. 

The projected 2010 Base Case and control scenario 8-hour ozone design values are provided in

Table X-7. The projected 2015 Base and control 8-hour ozone design values are provided in

Table X-8. Predicted exceedance counts for the 2010 Base-2 and control scenarios are provided

in Tables X-9 for those counties that are projected to be nonattainment in the 2010 Base Case. 

The same information is provided in Table X-10 for the 2015 Base and control scenarios.

Table X-5. Projected 8-Hour Ozone Nonattainment Counties for 2010 Base and NOx

Regional Strategy.

State 2010 Base-2 2010 Regional Strategy

AR Crittenden Crittenden

CT Fairfield, Middlesex, New Haven Fairfield, Middlesex, New Haven

DC Washington D.C. Washington D.C.

DE New Castle New Castle
64

GA Fulton Fulton

IL None None

IN Lake Lake

MD

Anne Arundel, Baltimore, Cecil, Harford,

Kent, Prince Georges

Anne Arundel, Baltimore, Cecil, Harford,

Kent, Prince Georges

MI None None

NJ

Bergen, Camden, Cumberland, Gloucester,

Hudson, Hunterdon, Mercer, Middlesex,

Monmouth, Morris, Ocean

Bergen, Camden, Cumberland, Gloucester,

Hunterdon, Mercer, Middlesex, Monmouth,

Morris, Ocean

NY

Erie, Putnam, Richmond, Suffolk,

Westchester Erie, Putnam, Richmond, Suffolk, Westchester

NC Mecklenburg Mecklenburg

OH Geauga, Summit Geauga

PA

Allegheny, Bucks, Delaware, Montgomery,

Philadelphia Bucks, Delaware, Montgomery, Philadelphia

RI Kent Kent

TX Denton, Harris, Tarrant Denton, Harris, Tarrant

VA Arlington, Fairfax Arlington, Fairfax

WI Kenosha, Racine, Sheboygan Kenosha, Racine, Sheboygan

Table X-6. Projected 8-Hour Ozone Nonattainment Counties for 2015 Base Case and NOx

Regional Strategy.

State 2015 Base Case 2015 Regional Strategy

AR Crittenden None

CT Fairfield, Middlesex, New Haven Fairfield, Middlesex, New Haven

DC Washington D.C. Washington D.C.

DE None None

GA None None

IL Cook None

IN Lake Lake

MD Anne Arundel, Cecil, Harford Anne Arundel, Cecil, Harford

MI Macomb None

NJ

Bergen, Camden, Gloucester, Hunterdon,

Mercer, Middlesex, Monmouth, Morris,

Ocean

Bergen, Camden, Gloucester, Hunterdon,

Mercer, Middlesex, Monmouth, Ocean

NY Erie, Richmond, Suffolk, Westchester Erie, Richmond, Suffolk, Westchester
65

NC None None

OH Geauga None

PA Bucks, Montgomery, Philadelphia Bucks, Montgomery, Philadelphia

RI Kent None

TX Harris Harris

VA Arlington, Fairfax Arlington

WI Kenosha, Sheboygan Kenosha

Table X-7. Projected 8-Hour Ozone Design Values for the 2010 Base Case and NOx

Regional Strategy. 

State County 2010 Base-2

2010 Regional

Strategy

Arkansas Crittenden 86 86

Connecticut Fairfield 94 94

Connecticut Middlesex 91 91

Connecticut New Haven 92 92

District of Columbia District of Columbia 88 88

Delaware New Castle 87 86

Georgia Fulton 86 85

Indiana Lake 87 86

Maryland Anne Arundel 91 91

Maryland Baltimore 85 85

Maryland Cecil 90 90

Maryland Harford 93 93

Maryland Kent 89 88

Maryland Prince Georges 86 85

New Jersey Bergen 88 87

New Jersey Camden 93 92

New Jersey Cumberland 86 85

New Jersey Gloucester 95 95

New Jersey Hudson 85 84

New Jersey Hunterdon 89 89

New Jersey Mercer 98 98

New Jersey Middlesex 95 95

New Jersey Monmouth 89 89

New Jersey Morris 88 87

New Jersey Ocean 105 104

New York Erie 90 89

New York Putnam 85 85
66

New York Richmond 90 89

New York Suffolk 90 90

New York Westchester 86 85

North Carolina Mecklenburg 85 86

Ohio Geauga 88 88

Ohio Summit 85 84

Pennsylvania Allegheny 85 84

Pennsylvania Bucks 97 97

Pennsylvania Delaware 87 86

Pennsylvania Montgomery 90 89

Pennsylvania Philadelphia 92 92

Rhode Island Kent 89 88

Texas Denton 87 87

Texas Harris 100 100

Texas Tarrant 88 87

Virginia Arlington 88 88

Virginia Fairfax 87 87

Wisconsin Kenosha 94 93

Wisconsin Racine 86 85

Wisconsin Sheboygan 90 89

Table X-8. Projected 8-Hour Ozone Design Values for the 2015 Base Case and NOx

Regional Strategy.

State County 2015 Base Case 2015 Regional Strategy

Arkansas Crittenden 85 83

Connecticut Fairfield 94 93

Connecticut Middlesex 89 88

Connecticut New Haven 90 89

District of

Columbia District of Columbia 86 85

Illinois Cook 85 84

Indiana Lake 87 86

Maryland Anne Arundel 87 86

Maryland Cecil 86 85

Maryland Harford 89 88

Michigan Macomb 86 84

New Jersey Bergen 87 86

New Jersey Camden 91 90

New Jersey Gloucester 93 92

New Jersey Hunterdon 87 86
67

New Jersey Mercer 96 95

New Jersey Middlesex 92 92

New Jersey Monmouth 87 86

New Jersey Morris 85 83

New Jersey Ocean 102 101

New York Erie 88 86

New York Richmond 87 87

New York Suffolk 89 89

New York Westchester 86 85

Ohio Geauga 85 83

Pennsylvania Bucks 95 94

Pennsylvania Montgomery 89 88

Pennsylvania Philadelphia 91 90

Rhode Island Kent 85 84

Texas Harris 99 98

Virginia Arlington 87 86

Virginia Fairfax 85 84

Wisconsin Kenosha 93 91

Wisconsin Sheboygan 86 84

Table X-9. Count of Predicted 8-Hour Ozone Exceedances for the 2010 Base and NOx

Regional Strategy.

State

FIPs

County

FIPs State County 2010 Base-2

2010 Regional

Strategy

5 35 Arkansas Crittenden 36 36

9 1 Connecticut Fairfield 27 26

9 7 Connecticut Middlesex 31 30

9 9 Connecticut New Haven 35 34

10 3 Delaware New Castle 25 22

11 1 D.C. Washington 2 2

13 121 Georgia Fulton 204 205

18 89 Indiana Lake 36 32

24 3 Maryland Anne Arundel 56 53

24 5 Maryland Baltimore 71 68

24 15 Maryland Cecil 31 31

24 25 Maryland Harford 37 36

24 29 Maryland Kent 30 29

24 33 Maryland Prince Georges 56 52

34 3 New Jersey Bergen 10 10

34 7 New Jersey Camden 37 36
68

34 11 New Jersey Cumberland 16 13

34 15 New Jersey Gloucester 26 25

34 17 New Jersey Hudson 5 5

34 19 New Jersey Hunterdon 36 30

34 21 New Jersey Mercer 17 17

34 23 New Jersey Middlesex 37 34

34 25 New Jersey Monmouth 63 60

34 27 New Jersey Morris 45 41

34 29 New Jersey Ocean 75 71

36 29 New York Erie 13 13

36 79 New York Putnam 14 14

36 85 New York Richmond 8 8

36 103 New York Suffolk 176 171

36 119 New York Westchester 17 16

37 119 North Carolina Mecklenburg 24 27

39 55 Ohio Geauga 21 21

39 153 Ohio Summit 45 42

42 3 Pennsylvania Allegheny 118 112

42 17 Pennsylvania Bucks 40 36

42 45 Pennsylvania Delaware 11 11

42 91 Pennsylvania Montgomery 22 16

42 101 Pennsylvania Philadelphia 13 12

44 3 Rhode Island Kent 22 22

48 121 Texas Denton 6 6

48 201 Texas Harris 324 320

48 439 Texas Tarrant 3 3

51 13 Virginia Arlington 3 2

51 59 Virginia Fairfax 25 24

55 59 Wisconsin Kenosha 17 17

55 101 Wisconsin Racine 33 32

55 117 Wisconsin Sheboygan 12 11
69

Table X-10. Count of Predicted 8-Hour Ozone Exceedances for the 2015 Base and NOx

Regional Strategy.

State

FIPs

County

FIPs State County 2015 Base

2015 Regional

Strategy

5 35 Arkansas Crittenden 31 23

9 1 Connecticut Fairfield 30 25

9 7 Connecticut Middlesex 26 22

9 9 Connecticut New Haven 30 28

11 1 D.C. Washington 3 3

18 89 Indiana Lake 29 24

24 3 Maryland Anne Arundel 42 37

24 15 Maryland Cecil 21 18

24 25 Maryland Harford 30 27

34 3 New Jersey Bergen 11 10

34 7 New Jersey Camden 28 27

34 15 New Jersey Gloucester 20 19

34 19 New Jersey Hunterdon 22 16

34 21 New Jersey Mercer 17 17

34 23 New Jersey Middlesex 32 31

34 25 New Jersey Monmouth 57 57

34 27 New Jersey Morris 38 30

34 29 New Jersey Ocean 59 56

36 29 New York Erie 12 9

36 85 New York Richmond 9 9

36 103 New York Suffolk 169 161

36 119 New York Westchester 20 20

39 55 Ohio Geauga 9 8

42 17 Pennsylvania Bucks 28 25

42 91 Pennsylvania Montgomery 12 10

42 101 Pennsylvania Philadelphia 12 12

44 3 Rhode Island Kent 17 16

48 201 Texas Harris 292 279

51 13 Virginia Arlington 2 2

51 59 Virginia Fairfax 18 16

55 59 Wisconsin Kenosha 15 15

55 117 Wisconsin Sheboygan 8 8
10In 2010, the modeling predicts an increase in the number of exceedances. This increase in

ozone is caused by local predicted NOx increases in the IPM model from certain power plants. These

power plants were predicted to be controlled under the NOxSIP call trading program (which is assumed

in the 2010 IAQR Base Case). Under the IAQR regional control case, the plants trade under a new

trading program which is year-round and expanded to additional states. The predicted emissions patterns

from IPM are slightly different under the two trading programs. Therefore, some power plants that were

predicted to put on controls under the NOxSIP call may not be predicted to do so under the IAQR (and

vice versa). It is important to note that the overall summer utility NOx emissions in the States with

NOxSIP call area are predicted to be lower under IAQR than under the NOxSIP call. So overall, the

IAQR will provide regional ozone benefits in the NOxSIP call area.

70

In the 2010 Base-2, 47 counties in the East are forecast to be nonattainment for ozone. 

With the implementation of the proposed regional NOx strategy, three of the 47 2010 Base Case

nonattainment counties are forecast to come into attainment. Of the 44 counties that are

projected to remain nonattainment in 2010 after the regional controls, 12 are projected to have

design values within 2 ppb of attainment (i.e., counties that have design values of 85 or 86 ppb). 

In addition, the model predicted exceedances in nonattainment areas of the East show an overall

decline of 4 percent between the 2010 Base Case and the control case10. Among the areas

predicted to have the largest percent reduction in exceedances in 2010 are Montgomery Co., PA

(27 percent reduction), Cumberland Co., NJ (19 percent reduction), and Hunterdon Co., NJ (17

percent reduction).

In 2015, the number of nonattainment counties is expected to decline from 34 counties in

the Base Case to 26 counties after the NOx emissions reductions in the IAQR proposal. The

proposed regional NOx strategy is projected to reduce nonattainment ozone design values in the

East by 1 to 2 ppb in all but three of the 34 2015 Base Case nonattainment counties. Of the 26

counties that are forecast to remain nonattainment in the control case, ten are projected to be

within 2 ppb of attainment. In addition, the overall number of model predicted exceedances in

nonattainment areas of the East are projected to decline by 8 percent in 2015 with the regional

strategy NOx reductions. Among the areas predicted to have the largest percent reduction in

exceedances in 2015 are Morris Co., NJ (21 percent reduction), Fairfield Co., CT (17 percent

reduction), and Anne Arundel Co., MD (12 percent reduction). Thus, our modeling indicates

that by 2010 and 2015 the regional NOx controls will reduce ozone concentrations throughout

the East and help bring areas into attainment with the 8-hour ozone NAAQS.

D. Visibility Modeling of the Proposed Regional SO2 and NOx Strategy

The impacts of the regional SO2 and NOx emissions reductions were examined in terms

of the projected improvements in visibility on the 20 percent best and worst days from 1996 at

each IMPROVE site with complete data. The future year base and control visibility was

calculated using a methodology which applies modeling results in a relative sense similar to

SMAT. The draft modeling guidance recommends the calculation of future year changes in
11 The 1996 data was used because it is coincident with the REMSAD meteorology. The changes

in visibility are representative of emissions changes from 2001 into the future (not 1996). Due to the lack

of complete IMPROVE baseline ambient data and due to the fact that 1996 meteorology was used, it was

not possible to replicate the Regional Haze guidance (the modeling guidance and the procedures for

calculating the baseline 20 percent best and worst days.) The resultant values are believed to be

representative of the expected improvement in visibility.

71

visibility in a similar manner to the calculation of changes in PM2.5. The extinction coefficient

and deciview values are made up of individual component species (sulfate, nitrate, organics, etc). 

The predicted change in visibility (on the 20 percent best and worst days) is calculated as the

percent change in the extinction coefficient for each of the PM species (on a daily basis). The

individual daily species extinction coefficients are summed to get a daily total extinction value. 

The daily extinction coefficients are then converted to deciviews and averaged across all 20

percent best and worst days (best and worst days separately). In this way, we can calculate an

average change in deciviews from the base case to a future case at each IMPROVE site. 

Additionally, subtracting the future IAQR control case deciview values from the future base case

deciview values gives an estimate of the visibility benefits in Class I areas from the SO2 + NOx

regional strategy. 

Appendix M contains an example calculation of the predicted improvement in visibility

on the 20 percent worst days at an IMPROVE site. The predicted improvements in visibility at

Class I areas on the 20 percent best visibility days and the 20 percent worst visibility days for the

2010 and 2015 base and regional control scenarios are also provided in Appendix M. There is a

separate table in this appendix for the 20 percent best days and 20 percent worst days. The

calculated reductions in deciviews is based on the model predicted changes in PM species

between the 2001 proxy Base Year and the 2010 and/or 2015 model runs. The 1996 ambient

data were used as a starting point to calculate the deciview reductions and thus, the visibility

improvements are from a 1996 ambient baseline.11 The visibility benefits solely from the

regional strategy are also provided in Appendix M.

As an example, the expected improvement in visibility at the Great Smoky Mountain

National Park (GRSM) from 2001 to the 2010 Base Case on the 20 percent worst visibility days

is 1.38 deciviews. The expected improvement from 2001 to 2010 with the regional SO2+NOx

controls (in addition to all other expected controls) is 3.55 deciviews. The improvement in

visibility due only to the regional strategy in 2010 is 2.17 deciviews. The expected improvement

in visibility in 2015 is even larger. The visibility improvement from 2001 to the 2015 Base is

1.94 deciviews. The improvement from 2001 from 2015 with the regional strategy emissions

reductions is 4.52 deciviews. The improvement in 2015 between the base case and the regional

strategy is 2.58 deciviews. The modeling predicts smaller improvements in visibility on the 20

percent best days forecast for both 2010 and 2015. Note that there are no cases in which

visibility deteriorated due to the regional strategy. 
72

XI. Modeling to Examine Nitrate Replacement

The chemical interactions involved in the formation of sulfates and nitrates have

consequences for the effectiveness of SO2 emissions reductions in lowering regional and urban

PM2.5 concentrations. The formation of ammonium nitrate is favored by availability of

ammonia and nitric acid vapor, low temperatures, high relative humidity, and the absence of acid

sulfate particles. At higher summer temperatures when photochemical processes and

meteorological conditions in the East produce high sulfate levels, ammonia and nitric acid vapor

tend to remain in the gas phase rather than forming ammonium nitrate particles. In winter

months, with cooler temperatures and lower sulfur-related acidity, the presence of sufficient

nitric acid and ammonia favors formation of nitrate particles. The air quality modeling , as

described in section X, indicates that regional SO2 reductions are effective at reducing sulfates

and PM2.5. When SO2 reductions reach a certain point in relation to other relevant reactants

and conditions, however, the ammonia formerly associated with sulfate can react with excess

nitric acid vapor to form nitrate particles, effectively replacing at least part of the PM2.5

reduction due to sulfate. This phenomenon is termed “nitrate replacement”. The EPA

performed several air quality modeling sensitivity simulations to provide information on the

potential magnitude of nitrate replacement. The model simulations include zero-out runs with

REMSAD for nine States in which emissions of SO2 from all source sectors were removed from

an individual State. These nine States we modeled are: Alabama, Indiana, Michigan, Missouri,

North Carolina, Pennsylvania, Tennessee, Texas, and West Virginia. These States were chosen

to obtain information on nitrate replacement in various parts of the East that experience different

meteorological conditions. 

The results of the sensitivity runs were examined to determine the increase in nitrate

concentrations in counties (i.e., receptors) projected to be nonattainment in the 2010 Base Case. 

Receptor specific impacts were calculated using the SMAT technique described in section V. 

Table XI-1 provides the mean and maximum annual average increase in nitrate particles

calculated from these model runs. Mean and maximum values are given for both in-State

impacts and impacts across all downwind nonattainment receptors and are expressed in terms of

the concentration increase in μg/m3

 and as a percent of the 2010 Base nitrate concentration at the

receptor location. The results indicate that the amount of nitrate replacement can be substantial

for both in-State impacts and downwind impacts. The in-State maximum increase in nitrate

ranges from 0.08 μg/m3

 (North Carolina and Tennessee) up to 0.59 μg/m3 (West Virginia). 

Mean in-State increases range from 0.05 μg/m3

 (Missouri and North Carolina) up to 0.13 μg/m3

(Alabama and Indiana). In terms the percent of base nitrate concentration, the amount of inState nitrate replacement ranges from 2 percent (both mean and maximum) up to 10 percent, as a

statewide mean value and 14 percent, as a statewide maximum value. Considering the amount of

nitrate replacement in downwind States, the maximum amount ranges from less than 0.05 μg/m3

(locations downwind of Alabama, Missouri, North Carolina, Tennessee, and Texas) up to more

than 0.10 μg/m3 (locations downwind of Indiana, Michigan, Pennsylvania, and West Virginia). 

The maximum downwind increases in nitrate represent amounts that are between 2 percent and 7

percent of the 2010 Base nitrate concentration at downwind locations.
73

Table XI-1. Results of Nitrate Replacement Sensitivity Modeling.

State

In-State Increase in Nitrates Downwind Increase in Nitrates

Maximum Mean Maximum Mean

μg/m3 percent μg/m3 percent μg/m3 percent μg/m3 percent

AL 0.19 14% 0.13 10% 0.04 7% 0.02 1%

IN 0.17 5% 0.13 4% 0.10 4% 0.04 2%

MI 0.32 6% 0.32 6% 0.18 4% 0.03 1%

MO 0.05 2% 0.05 2% 0.04 2% 0.01 1%

NC 0.08 6% 0.05 4% 0.02 2% <0.01 < 1%

PA 0.18 5% 0.11 4% 0.20 7% 0.03 1%

TN 0.08 7% 0.06 5% 0.04 3% 0.02 1%

TX NA NA NA NA 0.03 2% 0.01 1%

WV 0.59 25% 0.27 12% 0.11 4% 0.02 1%

The preceding information is useful for indicating the possible extent of nitrate replacement

associated with all SO2 emissions in States in the East. Although not examined in this analysis,

one would expect that the amount of nitrate replacement would be a function of the amount of

SO2 emissions removed together with meteorological conditions and the amount of ammonia

present in the State and in downwind areas. Also, these results are based single State model

runs. One would expect that the amount of nitrate replacement would be larger for SO2

emissions reductions made across a multi-State region compared to the amount that would result

from a single State.
74

XII. References

Battelle, 2004: Technical Report: Application of Automated Classification Scheme to Identify

Meteorological Regimes and Their Relationship to Ozone Concentrations in the Eastern United

States, Contract No. 68-D-02-061, Work Assignment 1-12, September 2003.

Battelle, 2004: Draft Paper: Identification of Meteorological Regimes and Their Relationship to

Ozone Concentrations, in conjunction with US EPA and Sonoma Technology Inc., January

2004. 

Eder, B.K., J.M. Davis, P. Bloomfield, (1994), “A Characterization of the Spatiotemporal

Variability of Non-urban Ozone Concentrations over the Eastern United States”, Atmospheric

Environment, 30 (14), 2615-2625.

Environ, 2002: User’s Guide: Comprehensive Air Quality Model with Extensions (CAMx),

Novato, CA.

EPA, 1999a: Draft Guidance on the Use of Models and Other Analyses in Attainment

Demonstrations for the 8-Hour Ozone NAAQS, Office of Air Quality Planning and Standards,

Research Triangle Park, NC.

EPA, 1999b: Technical Support Document for the Tier 2/Gasoline Sulfur Ozone Modeling

Analyses, EPA420-R-99-031, Research Triangle Park, NC.

EPA, 2000a: Procedures for Developing Base Year and Future Year Mass and Modeling

Inventories for the Heavy Duty Engine and Vehicle Standards and Highway Diesel Fuel (HDD)

Rulemaking: Air Quality Modeling Analyses, EPA420-R-00-0208, Research Triangle Park, NC.

EPA, 2000b: Technical Support Document for the Heavy Duty Engine and Vehicle Standards

and Highway Diesel Fuel Sulfur Control Requirements: Air Quality Modeling Analyses,

EPA420-R-00-0208, Research Triangle Park, NC.

EPA, 2001: Guidance for Demonstrating Attainment of Air Quality Goals for PM2.5 and

Regional Haze; Draft 1.1, Office of Air Quality Planning and Standards, Research Triangle Park,

NC.

EPA, 2003a: Procedures for Developing Base Year and Future Year Mass Emissions Inventories

for the Nonroad Diesel Engine Rulemaking, Office of Air Quality Planning and Standards,

Research Triangle Park, NC.

EPA, 2003b: Technical Support Document for the Clear Skies Act 2003: Air Quality Modeling

Analyses, Office of Air Quality Planning and Standards, Research Triangle Park, NC. Available

at http://www.epa.gov/clearskies/aq_modeling_tsd_csa2003.pdf
75

EPA, 2004: Air Quality Data Analysis Technical Support Document for the Proposed Interstate

Air Quality Rule, Office of Air Quality Planning and Standards, Research Triangle Park, NC.

Grell, G., J. Dudhia, and D. Stauffer, 1994: A Description of the Fifth-Generation Penn

State/NCAR Mesoscale Model (MM5), NCAR/TN-398+STR., 138 pp, National Center for

Atmospheric Research, Boulder CO.

Griffin, R.J., D.R. Cocker III, R.C. Flagan, and J.H. Seinfeld, 1999: “Organic aerosol formation

from the oxidation of biogenic hydrocarbons” J. Geophysical Research, Vol. 104, pp. 3555-

3567.

Houyoux, M.; Vukovich, J.; Brandmeyer, J. Sparse Matrix Operator Kernal Emissions

Modeling System (SMOKE) User Manual, Version 1.1.2 Draft, MCNC-North Carolina

Supercomputing Center Environmental Programs, 2000. (Updates at

http://www.cmascenter.org/modelclear.html#smoke)

IMPROVE, 2000. Spatial and Seasonal Patterns and Temporal Variability of Haze and its

Constituents in the United States: Report III. Cooperative Institute for Research in the

Atmosphere, ISSN: 0737-5352-47.

ICF Kaiser, 2002: User’s Guide to the Regional Modeling System for Aerosols and Deposition

(REMSAD) Version 7, San Rafael, CA.

Kim, Y.P., J.H. Seinfeld, and P. Saxena, 1993. “Atmospheric Gas-Aerosol Equilibrium I.

Thermodynamic Model.” Aerosol Science and Technology, Vol. 19, pp. 157-181.

Lagouvardos, K., G. Kallos, V. Kotroni, and S.T. Rao, 2000: “An Analysis of the Meteorological

and Air Quality Conditions during an Extreme Ozone Episode over the Northeastern USA.” Int.

J. Environment and Pollution, Vol. 14, Nos. 1-6, pp. 581-587.

Mansell, G., 2000: User’s Instructions for the Phase 2 REMSAD Preprocessors, Environ

International, Novato, CA.

NCAR Fields Manual. Home page of the Geophysical Statistic Project. URL address located:

http://www.cgd.ucar.edu/stats/Software/Fields/MAN.shtml.

Odum, J.R., T.P.W. Jungkamp, R.J. Griffin, R.C. Flagan, and J.H. Seinfeld, 1997: “The

Atmospheric Aerosol-Forming Potential of Whole Gasoline Vapor.” Science, Vol. 276, pp. 96-

99.

Olerud, D., K. Alapaty, and N. Wheeler, 2000: Meteorological Modeling of 1996 for the United

States with MM5. MCNC-Environmental Programs, Research Triangle Park, NC. 

Pielke, R.A., W.R. Cotton, R.L. Walko, C.J. Tremback, W.A. Lyons, L.D. Grasso, M.E.
76

Nicholls, M.D. Moran, D.A. Wesley, T.J. Lee, and J.H. Copeland, 1992: A Comprehensive

Meteorological Modeling System - RAMS, Meteor. Atmos. Phys., Vol. 49, pp. 69-91.

RTI, Analysis of Speciation Network Carbon Blank Data. Draft Report submitted to EPA,

August 30, 2002 under Work Assignment 4-05 for contract 68-D-032, Research Triangle

Institute.

SAIC, 2001: “Interpolating Relative Humidity Weighting Factors to Calculate Visibility

Impairment and the Effects of Improve Monitor Outliers” EPA Contract No. 68-D-98-113. 

http://vista.cira.colostate.edu/improve/Publications/GuidanceDocs/DraftReportSept20.pdf

Saxena, P., A.B. Hudischewskyj, C. Seigneur, and J.H. Seinfeld, 1986: “A Comparative Study of

Equilibrium Approaches to the Chemical Characterization of Secondary Aerosols.” Atmospheric

Environment, Vol. 20, pp. 1471-1483.

Seigneur, C., G. Hidy, I. Tombach, J. Vimont, P. Amar, 1999: “Scientific Peer-Review of the

Regulatory Modeling System for Aerosols and Deposition (REMSAD).” The KEVRIC

Company, Inc., Durham, NC.

Wesley, M.L., 1989: “Parameterization of Surface Resistances to Gaseous Dry Deposition in

Regional-Scale Numerical Models” Atmospheric Environment, Vol. 23, No 6, pp. 1293-1304.

Whitten, Gary Z., 1999: Computer Efficient Photochemistry for Simultaneous Modeling of

Smog and Secondary Particulate Precursors, Systems Application International, San Rafael, CA.
Technical Support Document for the 

Interstate Air Quality Rule

Air Quality Modeling Analyses

Appendix A

Emissions Summary
A-1

Table 1. 1996 to 2001 Adjustment Ratios for EGU Sector

FIPS State VOC NOX CO SO2 PM-coarse PM2_5 NH3

01 Alabama 2.0945 0.7681 1.1934 0.7969 0.8595 0.9529 1.0436

04 Arizona 1.3613 1.3129 1.3961 0.6095 0.5264 0.6819 4.0316

05 Arkansas 0.7825 0.9404 0.5202 0.8248 0.8655 0.7512 0.8460

06 California 1.6865 1.3923 2.4643 1.0738 2.8898 1.7167 7.3337

08 Colorado 1.4226 0.9013 1.2024 1.0024 0.7279 0.6510 1.4924

09 Connecticut 1.1894 0.9765 1.6837 0.9366 4.0814 2.3472 0.9930

10 Delaware 0.9918 0.6944 1.0597 0.8404 0.8406 0.7182 0.9574

11 DC 1.1666 1.8784 1.2121 1.0117 1.2383 1.2122 1.2692

12 Florida 1.3012 1.0128 1.3290 0.8771 1.0521 0.9264 1.3229

13 Georgia 1.0956 0.9934 1.1147 1.0328 1.1189 1.1291 1.4102

16 Idaho 1.0000 4.4955 1.0000 4.3333 1.0000 1.0000 1.0000

17 Illinois 1.3303 0.6952 1.3984 0.5023 0.9420 0.9096 1.0347

18 Indiana 1.2160 0.8405 1.1252 0.8521 1.0061 1.0316 11.7019

19 Iowa 1.4793 1.0019 1.2398 0.8813 0.8670 0.9725 1.3102

20 Kansas 0.7806 0.9094 1.1991 1.0276 0.9523 1.0961 1.4990

21 Kentucky 1.1045 0.6294 1.0966 0.8337 0.6247 0.6187 1.0824

22 Louisiana 1.2568 1.0345 1.2482 1.1145 0.5666 1.5345 1.3203

23 Maine 14.2874 1.7712 32.0844 1.2063 1.3772 1.1463 1.7137

24 Maryland 1.1110 0.6790 0.9630 1.0031 0.8429 0.7690 1.7242

25 Massachusetts 0.9275 0.9876 0.7850 0.9846 1.0107 1.0057 1.1115

26 Michigan 1.0462 0.7862 1.0577 0.9271 0.9150 0.9218 2.2040

27 Minnesota 1.5712 0.9240 1.2561 1.0802 0.7451 0.9564 0.9518

28 Mississippi 3.4328 1.2552 2.3265 1.2550 1.2216 3.6208 1.8421

29 Missouri 1.2171 0.7866 1.2667 0.6590 0.9751 1.1990 1.1486

30 Montana 1.3587 1.5482 1.3393 1.4233 1.4576 1.4746 1.9565

31 Nebraska 1.3671 1.0020 1.5316 1.0726 1.4117 1.2847 1.6633

32 Nevada 1.3512 0.7309 1.2092 1.0276 0.4602 0.5897 1.4032

33 New Hampshire 1.2334 0.4080 3.2519 0.9537 1.2403 1.4975 0.5677

34 New Jersey 0.8673 0.9647 0.9575 1.1145 1.4005 1.1526 1.6309

35 New Mexico 1.0206 1.0315 0.9533 0.7943 0.9587 1.0431 1.1756

36 New York 1.2013 1.0838 1.1562 1.0434 1.0883 1.2421 2.3763

37 North Carolina 1.2398 0.5313 1.4623 0.9668 0.6209 0.6342 1.4202

38 North Dakota 1.0464 0.7430 0.7585 0.8744 1.1621 0.9900 0.8977

39 Ohio 0.9924 0.5990 1.0266 0.7610 1.0138 1.0206 2.4965

40 Oklahoma 1.0905 0.9463 1.1384 0.9517 1.0056 0.9837 0.9736

41 Oregon 2.7528 2.5515 2.4914 3.2359 3.2808 1.7384 3.4427

42 Pennsylvania 1.1263 0.7977 1.3806 0.9321 0.3768 0.4114 1.2189

44 Rhode Island 5.7597 0.7888 37.1047 0.5263 1.0000 1.0000 1.0000

45 South Carolina 1.2868 0.7422 2.3359 1.0050 0.7064 0.7648 1.8615

46 South Dakota 3.5138 1.0131 1.9764 0.9704 3.8807 2.1509 1.1248

47 Tennessee 1.0430 0.6030 1.0546 0.6968 0.1049 0.4203 1.0864

48 Texas 0.9678 0.7840 0.8039 0.8134 1.1211 0.8143 0.9183

49 Utah 1.2401 0.9868 1.2064 0.8844 0.5453 0.6310 3.2197

50 Vermont 0.8178 1.1201 5.6593 2.0400 0.6224 0.8198 12.7125

51 Virginia 1.6943 0.7934 2.1968 1.1288 0.7839 0.9162 5.1739

53 Washington 1.4558 0.8584 2.5344 0.8549 0.9737 1.0384 1.4307

54 West Virginia 0.9693 0.6890 0.9683 0.7566 0.9728 0.7952 1.0238

55 Wisconsin 1.1978 0.9573 1.2889 0.9176 0.8599 1.1143 1.3196

56 Wyoming 0.9763 0.8176 1.0194 0.8735 0.5601 0.7204 1.0506
A-2

Table 2. 1996 to 2001 Adjustment Ratios for On-road Sector

FIPS State VOC NOX CO SO2 PM-coarse PM2_5 NH3

01 Alabama 0.7921 0.9455 0.9426 0.9799 0.9421 0.7064 1.1782

04 Arizona 0.8660 1.0318 1.0214 1.0388 1.0229 0.7686 1.2647

05 Arkansas 0.7667 0.8962 0.8963 0.8812 0.8830 0.6488 1.1260

06 California 0.7416 0.9204 1.0026 0.5488 0.9669 0.6912 1.1723

08 Colorado 0.8154 1.0213 0.9620 1.0486 0.9924 0.7418 1.2387

09 Connecticut 0.7150 0.8973 0.9489 0.6109 0.9550 0.6885 1.1697

10 Delaware 0.7211 0.8890 0.8945 0.5618 0.9307 0.6682 1.1505

11 DC 0.7098 0.8526 0.8479 0.5638 0.9389 0.6826 1.1255

12 Florida 0.8953 1.0240 1.0343 1.0738 1.0183 0.7710 1.2524

13 Georgia 0.8298 1.0239 1.0084 1.0723 1.0227 0.7773 1.2605

16 Idaho 0.7623 0.8834 0.9239 0.8737 0.8771 0.6426 1.1229

17 Illinois 0.7498 0.8955 0.9216 0.7417 0.9269 0.6825 1.1378

18 Indiana 0.7669 0.9042 0.9112 0.8881 0.9133 0.6756 1.1476

19 Iowa 0.7649 0.9227 0.8885 0.9143 0.9289 0.6875 1.1753

20 Kansas 0.7932 0.9219 0.9140 0.9807 0.9231 0.6875 1.1637

21 Kentucky 0.7819 0.9379 0.9388 0.8669 0.9399 0.6952 1.1782

22 Louisiana 0.7660 0.9250 0.9246 0.9267 0.9146 0.6800 1.1500

23 Maine 0.8104 0.9358 1.0091 0.9751 0.9328 0.6934 1.1890

24 Maryland 0.7257 0.9071 0.8797 0.6208 0.9487 0.6885 1.1618

25 Massachusetts 0.6854 0.8681 0.8712 0.5968 0.9283 0.6713 1.1301

26 Michigan 0.7759 0.9279 0.9063 0.9851 0.9360 0.7046 1.1607

27 Minnesota 0.8794 1.0129 1.0523 1.0043 1.0157 0.7561 1.2678

28 Mississippi 0.8498 0.9955 1.0044 0.9816 0.9853 0.7316 1.2477

29 Missouri 0.7758 0.9360 0.9442 0.8585 0.9460 0.7012 1.1750

30 Montana 0.7165 0.8800 0.8455 0.8827 0.8833 0.6557 1.1235

31 Nebraska 0.7840 0.9446 0.9189 0.9706 0.9480 0.7076 1.1942

32 Nevada 0.9011 1.0912 1.0166 1.0844 1.0667 0.7880 1.3366

33 New Hampshire 0.7663 0.9189 0.9491 0.8093 0.9324 0.6841 1.1733

34 New Jersey 0.7978 0.9189 1.0095 0.5730 0.9489 0.6836 1.1562

35 New Mexico 0.7579 0.9313 0.9130 0.9029 0.9015 0.6722 1.1351

36 New York 0.7352 0.9139 0.9565 0.7961 0.9502 0.7047 1.1627

37 North Carolina 0.8378 0.9672 0.9896 1.0177 0.9697 0.7247 1.2159

38 North Dakota 0.7138 0.8911 0.8432 0.8847 0.9014 0.6650 1.1503

39 Ohio 0.7173 0.8779 0.8560 0.8964 0.8893 0.6676 1.1000

40 Oklahoma 0.8252 0.9677 0.9687 0.9885 0.9588 0.7203 1.1936

41 Oregon 0.8475 0.9925 1.0541 0.9960 0.9941 0.7465 1.2366

42 Pennsylvania 0.7216 0.8973 0.8938 0.8358 0.9132 0.6778 1.1338

44 Rhode Island 0.8221 0.9877 1.0529 0.6615 1.0345 0.7502 1.2544

45 South Carolina 0.8351 0.9873 0.9816 1.0239 0.9750 0.7296 1.2287

46 South Dakota 0.7248 0.9033 0.8532 0.8917 0.9091 0.6698 1.1626

47 Tennessee 0.8238 0.9739 0.9773 1.0069 0.9688 0.7284 1.2051

48 Texas 0.8054 1.0203 0.9450 0.9160 1.0353 0.7720 1.2693

49 Utah 0.8311 1.0171 0.9663 1.0568 1.0006 0.7546 1.2393

50 Vermont 0.7473 0.8897 0.9354 0.8857 0.9016 0.6657 1.1473

51 Virginia 0.7885 0.8964 0.9465 0.8587 0.9006 0.6657 1.1249

53 Washington 0.7781 0.9445 1.0129 0.9335 0.9361 0.7003 1.1558

54 West Virginia 0.7935 0.9268 0.9375 0.9205 0.9185 0.6800 1.1682

55 Wisconsin 0.7455 0.9118 0.8969 0.8263 0.9281 0.6838 1.1628

56 Wyoming 0.7750 0.9345 0.9276 0.9550 0.9268 0.6874 1.1817
A-3

Table 3. 1996 to 2001 Adjustment Ratios for Non-road Sector

FIPS State VOC NOX CO SO2 PM-coarse PM2_5 NH3

01 Alabama 0.9292 0.9691 1.0168 1.0317 0.9431 0.9293 1.1155

04 Arizona 0.8653 1.0032 1.0109 1.0871 0.9564 0.9495 1.1309

05 Arkansas 0.9601 0.9729 1.0126 1.0524 0.9001 0.9022 1.0235

06 California 0.8583 1.0381 1.0142 1.0299 0.9448 0.9443 1.1219

08 Colorado 0.8731 1.0020 1.0134 1.1052 0.9320 0.9200 1.0590

09 Connecticut 0.8701 1.0457 1.0121 1.1156 0.9540 0.9558 1.1423

10 Delaware 0.8935 0.9633 1.0054 1.0225 0.9382 0.9314 1.1113

11 DC 0.8376 1.0263 0.9958 1.1578 0.9232 0.9268 1.1369

12 Florida 0.8799 1.0358 1.0143 1.1087 0.9622 0.9620 1.1448

13 Georgia 0.8817 1.0058 1.0160 1.0885 0.9403 0.9363 1.1283

16 Idaho 0.9581 0.9747 1.0168 1.0729 0.9019 0.8993 0.9785

17 Illinois 0.8677 0.9941 1.0131 1.0628 0.9177 0.9117 1.0618

18 Indiana 0.8754 0.9709 1.0085 1.0427 0.9032 0.9003 1.0661

19 Iowa 0.8925 0.9857 1.0073 1.1038 0.8822 0.8793 0.9174

20 Kansas 0.8617 0.9784 1.0059 1.0911 0.8829 0.8783 0.9295

21 Kentucky 0.9247 0.9718 1.0125 1.0332 0.9334 0.9295 1.0800

22 Louisiana 0.9315 0.9369 1.0094 0.9908 0.9228 0.9213 1.0380

23 Maine 0.9669 0.9599 1.0148 1.0434 0.9323 0.9295 1.1165

24 Maryland 0.8708 1.0024 1.0110 1.0427 0.9400 0.9407 1.1242

25 Massachusetts 0.8655 1.0124 1.0073 1.1220 0.9429 0.9348 1.1470

26 Michigan 0.9333 0.9825 1.0124 1.0376 0.9396 0.9351 1.1092

27 Minnesota 0.9474 0.9792 1.0114 1.0629 0.9115 0.9103 1.0072

28 Mississippi 0.9559 0.9569 1.0132 1.0218 0.9171 0.9139 1.0578

29 Missouri 0.9001 0.9831 1.0137 1.0553 0.9199 0.9168 1.0464

30 Montana 0.9467 0.9588 1.0115 1.0258 0.8956 0.8899 0.8104

31 Nebraska 0.8837 0.9668 1.0062 1.0533 0.8863 0.8821 0.8616

32 Nevada 0.8676 0.9947 1.0056 1.1029 0.9385 0.9333 1.1374

33 New Hampshire 0.9382 1.0759 1.0154 1.1534 0.9582 0.9636 1.1422

34 New Jersey 0.8685 1.0525 1.0173 1.1165 0.9597 0.9604 1.1498

35 New Mexico 0.8963 0.9638 1.0119 1.0166 0.9371 0.9308 1.0637

36 New York 0.8909 1.0073 1.0214 1.0724 0.9427 0.9423 1.1413

37 North Carolina 0.8850 1.0076 1.0116 1.0917 0.9317 0.9264 1.1207

38 North Dakota 0.9015 0.9754 0.9979 1.1070 0.8647 0.8631 0.6901

39 Ohio 0.8785 0.9935 1.0110 1.0655 0.9361 0.9300 1.0997

40 Oklahoma 0.8934 0.9850 1.0118 1.0836 0.9034 0.8982 1.0116

41 Oregon 0.9125 1.0457 1.0159 1.1528 0.9537 0.9540 1.1035

42 Pennsylvania 0.8877 1.0295 1.0141 1.1004 0.9568 0.9541 1.1251

44 Rhode Island 0.8622 1.0748 1.0166 1.1242 0.9748 0.9707 1.1415

45 South Carolina 0.8945 1.0658 1.0114 1.1901 0.9770 0.9678 1.1215

46 South Dakota 0.9097 0.9904 1.0034 1.1430 0.8640 0.8615 0.7545

47 Tennessee 0.9116 0.9894 1.0136 1.0558 0.9378 0.9327 1.1084

48 Texas 0.8641 1.0222 1.0136 1.1053 0.9555 0.9504 1.0514

49 Utah 0.9300 0.9833 1.0063 1.1129 0.9241 0.9203 1.0980

50 Vermont 0.9513 1.0456 1.0151 1.1495 0.9398 0.9390 1.1075

51 Virginia 0.8715 0.9221 1.0086 0.9495 0.9179 0.9023 1.1177

53 Washington 0.8970 0.9501 1.0137 1.0128 0.9170 0.9105 1.1027

54 West Virginia 1.0198 1.2908 1.0381 1.4083 1.1704 1.1798 1.1019

55 Wisconsin 0.9376 1.0042 1.0122 1.0909 0.9275 0.9224 1.0743

56 Wyoming 0.9638 0.9543 1.0133 1.0050 0.9204 0.9137 0.9932
A-4

Table 4. 2001 Proxy Inventory for EGU Sector (Annual Tons)

FIPS State VOC NOx CO SO2 PM-10 PM-2.5 NH3

01 Alabama 2,084 168,221 9,629 466,155 8,385 4,161 16

04 Arizona 885 97,966 7,482 73,361 5,418 2,842 98

05 Arkansas 441 47,557 2,314 78,708 1,289 847 51

06 California 3,757 25,318 34,892 2,152 1,395 1,292 3,608

08 Colorado 888 75,288 9,431 92,963 1,530 844 24

09 Connecticut 404 11,196 2,176 34,147 1,270 756 152

10 Delaware 136 10,915 1,032 35,431 414 213 40

11 DC 9 348 31 934 45 43 5

12 Florida 3,672 299,320 27,833 569,980 9,119 5,739 1,197

13 Georgia 973 162,672 8,204 490,399 8,582 4,042 23

16 Idaho 0 0 0 0 0 00

17 Illinois 2,433 203,139 16,467 371,106 5,719 3,170 78

18 Indiana 2,144 310,458 15,759 802,556 11,765 6,431 325

19 Iowa 847 81,129 6,428 139,735 3,064 1,845 15

20 Kansas 907 87,177 7,034 120,358 2,349 1,550 60

21 Kentucky 1,420 231,062 13,427 536,744 11,207 5,053 16

22 Louisiana 1,709 80,365 14,625 112,806 3,550 2,882 794

23 Maine 491 2,105 3,821 6,818 84 73 32

24 Maryland 551 71,741 3,216 253,060 2,205 1,041 92

25 Massachusetts 721 34,945 3,785 103,451 1,534 963 267

26 Michigan 1,339 144,125 10,568 351,578 5,586 3,211 87

27 Minnesota 1,014 83,896 6,256 94,327 3,799 2,086 13

28 Mississippi 1,401 57,881 8,228 138,563 2,689 2,171 222

29 Missouri 1,577 147,125 10,896 240,199 3,526 2,460 19

30 Montana 349 39,553 2,743 24,402 6,030 3,023 6

31 Nebraska 468 48,868 4,110 70,541 1,424 958 9

32 Nevada 612 44,186 3,579 54,701 2,464 1,259 66

33 New Hampshire 152 6,831 2,049 48,137 331 244 15

34 New Jersey 3,597 66,513 18,767 68,210 7,493 7,213 32

35 New Mexico 578 83,864 4,332 62,355 8,874 4,186 59

36 New York 2,121 83,487 9,272 255,982 3,849 2,731 1,259

37 North Carolina 985 130,946 10,323 415,113 7,161 3,299 16

38 North Dakota 860 79,188 7,268 155,308 3,813 2,067 8

39 Ohio 1,657 336,761 14,008 1,145,322 15,143 6,950 61

40 Oklahoma 1,183 83,476 11,532 101,444 2,309 1,602 191

41 Oregon 137 24,683 1,641 28,316 395 283 1

42 Pennsylvania 1,556 203,131 14,968 945,019 10,911 5,330 119

44 Rhode Island 68 118 1,381 0 0 00

45 South Carolina 478 82,157 7,321 202,573 6,269 2,779 10

46 South Dakota 271 17,849 723 14,363 139 81 1

47 Tennessee 1,081 157,993 7,323 375,899 8,856 7,270 10

48 Texas 7,110 333,280 67,832 542,067 19,966 13,071 1,511

49 Utah 545 71,518 4,257 28,335 2,213 1,052 25

50 Vermont 44 1,125 987 109 45 43 1

51 Virginia 864 81,841 7,666 217,847 3,617 1,937 126

53 Washington 247 18,863 3,689 67,027 1,921 1,346 3

54 West Virginia 1,008 204,344 8,050 497,988 8,336 3,739 16

55 Wisconsin 928 102,564 8,070 190,060 3,316 2,212 15

56 Wyoming 784 87,879 6,506 87,906 4,643 2,979 9

Total 57,485 4,824,967 451,932 10,714,558 224,044 129,369 10,803
A-5

Table 5. 2001 Proxy Inventory for non-EGU Sector (Annual Tons)

FIPS State VOC NOx CO SO2 PM-10 PM-2.5 NH3

01 Alabama 80,573 92,760 201,009 120,101 37,338 22,005 3,778

04 Arizona 18,674 92,129 21,293 104,999 29,821 16,264 5

05 Arkansas 13,492 22,446 104,432 16,953 30,709 18,147 15,767

06 California 73,353 131,142 84,548 42,289 30,602 17,268 15,218

08 Colorado 37,403 47,126 29,186 14,689 20,649 12,432 278

09 Connecticut 6,947 10,887 2,831 7,586 1,120 844 59

10 Delaware 7,058 10,317 15,271 40,499 1,178 839 668

11 DC 243 809 259 2,235 102 72 10

12 Florida 22,895 55,868 54,511 86,525 15,870 10,533 7,153

13 Georgia 40,220 65,153 171,012 85,644 29,481 20,604 15,114

16 Idaho 427 6,418 4,659 24,957 12,138 7,731 2

17 Illinois 149,255 133,197 119,543 261,252 87,262 45,692 12,158

18 Indiana 41,041 53,875 233,903 145,385 15,628 12,335 8,094

19 Iowa 8,897 25,759 6,957 84,608 8,095 4,483 8,481

20 Kansas 22,298 102,792 79,014 14,761 12,861 8,802 13,083

21 Kentucky 61,734 34,908 66,523 40,369 16,560 10,650 1,265

22 Louisiana 112,092 280,691 736,737 172,782 34,625 25,928 66,094

23 Maine 5,108 15,124 10,220 22,379 5,220 3,437 132

24 Maryland 7,493 21,665 44,471 22,879 4,000 2,349 311

25 Massachusetts 7,622 18,656 7,547 15,795 3,543 2,569 89

26 Michigan 82,758 159,031 125,560 130,191 22,522 12,818 502

27 Minnesota 35,312 78,404 86,078 40,256 82,519 36,493 1,114

28 Mississippi 55,415 68,081 109,202 69,182 14,095 10,101 26,768

29 Missouri 57,813 28,190 102,988 121,869 49,152 18,367 23,645

30 Montana 6,639 18,323 49,602 30,902 11,681 6,285 459

31 Nebraska 10,865 13,368 11,907 6,810 8,561 3,327 14

32 Nevada 1,438 5,064 11,898 2,924 13,020 4,556 8

33 New Hampshire 5,144 4,049 6,059 8,066 1,340 839 23

34 New Jersey 85,237 48,657 20,754 71,990 11,017 7,651 497

35 New Mexico 11,224 70,418 21,847 106,197 7,836 5,932 35

36 New York 54,443 41,428 30,814 188,988 49,686 34,209 225

37 North Carolina 78,423 66,906 77,927 90,282 21,250 14,930 113

38 North Dakota 261 7,499 3,614 62,497 1,418 1,263 13

39 Ohio 70,331 83,068 685,198 354,237 39,673 26,678 2,756

40 Oklahoma 48,837 120,822 221,112 36,790 10,161 6,246 17,616

41 Oregon 13,433 16,493 77,785 6,496 10,483 7,684 15

42 Pennsylvania 64,364 190,652 379,584 143,330 39,804 26,123 6,224

44 Rhode Island 4,909 871 1,716 2,578 1,240 912 8

45 South Carolina 40,208 45,743 65,518 58,738 8,134 5,937 55

46 South Dakota 1,516 4,518 0 1,308 857 448 1

47 Tennessee 108,302 88,697 102,384 135,863 18,367 10,738 80

48 Texas 243,265 505,461 404,608 303,841 37,478 25,726 1,318

49 Utah 18,849 28,769 117,815 27,835 21,530 17,629 1,218

50 Vermont 1,555 765 1,452 2,050 993 615 3

51 Virginia 55,898 70,598 65,376 111,065 16,083 10,187 746

53 Washington 18,682 41,059 177,380 46,629 10,818 7,308 5,049

54 West Virginia 23,097 52,440 110,439 64,213 11,661 7,339 409

55 Wisconsin 52,047 51,745 60,878 87,850 15,243 10,855 897

56 Wyoming 18,537 47,994 73,459 56,385 29,758 17,471 458

Total 1,985,624 3,180,835 5,196,881 3,696,048 963,181 581,654 258,025
A-6

Table 6. 2001 Proxy Inventory for On-road Sector (Annual Tons)

FIPS State VOC NOx CO SO2 PM-10 PM-2.5 NH3

01 Alabama 90,967 191,356 1,255,115 6,311 4,936 3,631 5,568

04 Arizona 65,235 159,946 743,136 5,335 4,133 3,013 4,890

05 Arkansas 43,394 107,044 587,436 3,146 2,641 1,957 2,856

06 California 504,308 741,570 4,917,346 18,346 23,184 16,413 30,442

08 Colorado 57,530 142,904 782,750 4,702 3,545 2,595 4,134

09 Connecticut 40,483 98,070 503,158 2,081 2,382 1,697 3,065

10 Delaware 10,957 27,642 123,413 529 711 522 816

11 DC 4,485 8,390 54,361 214 244 170 352

12 Florida 267,721 481,541 3,330,519 16,935 12,349 8,951 15,153

13 Georgia 162,666 354,467 2,234,620 11,829 9,058 6,648 10,372

16 Idaho 20,979 49,766 304,092 1,451 1,219 901 1,325

17 Illinois 136,417 313,673 1,830,288 8,624 7,703 5,495 10,225

18 Indiana 111,679 244,554 1,617,597 7,375 6,121 4,491 6,978

19 Iowa 45,437 110,184 673,726 3,145 2,684 1,990 2,883

20 Kansas 41,203 100,675 619,475 3,210 2,485 1,834 2,768

21 Kentucky 67,842 168,246 903,618 4,665 4,138 3,048 4,598

22 Louisiana 68,422 149,575 905,798 4,445 3,694 2,742 4,017

23 Maine 20,295 53,403 311,775 1,615 1,321 984 1,383

24 Maryland 48,128 137,842 600,932 3,429 3,634 2,565 4,984

25 Massachusetts 56,206 135,741 722,639 3,465 3,489 2,427 5,279

26 Michigan 137,830 298,757 2,046,372 10,830 7,811 5,636 9,683

27 Minnesota 84,039 191,340 1,063,057 5,582 4,549 3,339 5,187

28 Mississippi 54,907 127,897 737,093 3,847 3,261 2,421 3,472

29 Missouri 76,380 210,112 1,131,261 6,430 5,330 3,866 6,622

30 Montana 14,409 39,793 213,828 1,086 947 708 962

31 Nebraska 27,481 66,437 416,446 2,006 1,643 1,218 1,772

32 Nevada 29,513 59,225 316,200 1,896 1,459 1,061 1,751

33 New Hampshire 17,581 43,974 246,304 1,129 1,065 785 1,180

34 New Jersey 81,796 181,198 938,311 4,203 4,581 3,183 6,730

35 New Mexico 37,571 84,822 523,730 2,480 2,083 1,545 2,230

36 New York 155,161 356,584 2,155,370 11,236 9,163 6,490 12,832

37 North Carolina 104,114 258,350 1,421,369 9,849 6,736 4,837 8,821

38 North Dakota 11,398 28,534 167,894 775 681 508 703

39 Ohio 157,237 351,592 2,226,531 11,390 8,908 6,502 10,486

40 Oklahoma 67,759 151,575 967,480 4,869 3,831 2,817 4,330

41 Oregon 47,206 126,382 615,341 3,799 3,114 2,299 3,445

42 Pennsylvania 148,739 345,513 1,982,662 9,888 8,730 6,433 10,106

44 Rhode Island 13,196 25,718 166,391 560 621 439 837

45 South Carolina 72,321 166,086 971,279 5,192 4,157 3,086 4,460

46 South Dakota 12,399 34,152 184,761 910 805 602 823

47 Tennessee 101,361 229,694 1,411,463 7,331 5,695 4,183 6,488

48 Texas 292,834 704,713 3,405,481 20,436 19,312 14,295 21,839

49 Utah 39,742 79,109 562,108 2,551 1,920 1,406 2,240

50 Vermont 10,829 26,182 164,165 728 628 467 665

51 Virginia 99,235 246,828 1,347,176 7,572 6,448 4,741 7,395

53 Washington 79,260 184,161 1,070,697 5,649 4,395 3,195 5,297

54 West Virginia 28,180 73,972 411,936 2,106 1,798 1,340 1,878

55 Wisconsin 69,708 191,803 1,015,476 5,429 5,085 3,753 5,630

56 Wyoming 12,049 32,946 187,636 913 767 573 790

Total 3,948,588 8,694,038 51,089,610 261,526 225,193 163,798 270,740
A-7

Table 7. 2001 Proxy Inventory for Non-road Sector (Annual Tons)

FIPS State VOC NOx CO SO2 PM-10 PM-2.5 NH3

01 Alabama 49,843 64,704 384,401 6,271 6,415 5,426 641

04 Arizona 49,263 51,488 516,299 4,207 8,060 5,038 497

05 Arkansas 30,576 41,132 235,198 3,940 4,174 3,776 426

06 California 253,515 438,815 2,747,237 13,734 26,413 23,456 4,532

08 Colorado 41,018 68,051 418,847 5,344 5,722 4,817 720

09 Connecticut 29,589 21,401 302,235 2,125 2,292 2,096 468

10 Delaware 10,349 17,079 82,028 1,372 1,345 1,210 96

11 DC 2,173 5,990 19,165 396 277 246 69

12 Florida 208,411 166,054 1,795,514 26,118 21,351 18,432 1,682

13 Georgia 70,317 79,983 728,751 9,392 8,031 7,091 1,196

16 Idaho 21,578 20,394 149,004 1,869 2,795 2,111 187

17 Illinois 102,314 176,679 1,053,290 13,858 13,599 12,380 1,970

18 Indiana 50,657 108,974 569,186 10,011 7,986 7,280 1,103

19 Iowa 35,447 69,050 329,620 7,523 7,788 7,129 591

20 Kansas 26,047 96,062 285,058 9,694 7,768 7,002 612

21 Kentucky 37,055 80,563 305,463 18,042 5,993 5,333 570

22 Louisiana 59,991 202,756 403,665 31,907 11,551 10,467 1,541

23 Maine 26,934 10,722 146,963 1,192 1,479 1,329 138

24 Maryland 49,250 43,271 481,748 12,020 4,666 4,147 494

25 Massachusetts 53,835 83,928 544,236 8,099 6,810 6,194 929

26 Michigan 141,309 75,516 1,016,464 6,835 9,160 8,337 1,535

27 Minnesota 99,352 75,615 578,010 7,845 9,473 8,678 891

28 Mississippi 33,202 49,193 226,986 5,452 4,810 4,192 395

29 Missouri 55,302 73,101 504,459 6,683 7,261 6,540 885

30 Montana 13,426 39,733 99,677 2,869 3,174 2,813 158

31 Nebraska 19,307 67,930 195,153 6,660 5,812 5,293 341

32 Nevada 19,029 29,938 178,300 2,592 3,213 2,421 172

33 New Hampshire 18,797 7,943 130,522 785 1,156 1,009 169

34 New Jersey 72,143 99,953 735,916 69,747 9,796 8,933 1,186

35 New Mexico 13,768 11,905 128,337 1,192 2,176 1,654 198

36 New York 135,077 107,083 1,287,956 11,061 11,727 10,556 2,084

37 North Carolina 74,100 74,746 744,771 7,285 9,049 7,784 1,334

38 North Dakota 14,378 49,288 111,394 5,433 5,586 5,059 196

39 Ohio 107,455 139,873 1,102,599 16,300 17,243 14,367 1,887

40 Oklahoma 33,152 47,907 305,507 4,872 5,170 4,554 823

41 Oregon 42,483 67,131 359,989 5,146 4,246 3,881 504

42 Pennsylvania 96,413 99,336 985,292 12,070 8,727 7,813 1,634

44 Rhode Island 6,994 6,864 80,475 3,779 698 638 120

45 South Carolina 41,617 39,206 379,582 4,630 4,064 3,630 546

46 South Dakota 12,046 29,095 96,483 3,424 3,843 3,484 170

47 Tennessee 55,168 149,755 477,648 17,540 8,229 7,510 894

48 Texas 163,338 491,346 1,831,200 56,608 30,521 26,142 5,006

49 Utah 25,881 38,389 201,705 3,912 3,593 3,124 322

50 Vermont 9,312 4,766 62,366 474 598 535 77

51 Virginia 61,874 82,788 611,529 9,959 9,383 7,942 739

53 Washington 61,445 85,804 543,499 14,625 7,787 6,782 836

54 West Virginia 17,107 77,029 129,518 49,287 5,575 5,096 231

55 Wisconsin 80,529 63,844 569,468 5,469 6,673 6,036 919

56 Wyoming 9,537 27,106 61,747 1,553 1,478 1,278 160

Total 2,741,704 4,059,278 25,234,462 531,203 354,734 311,039 42,879
A-8

Table 8. 2001 Proxy Inventory for Area Sector (Annual Tons)

FIPS State VOC NOx CO SO2 PM-10 PM-2.5 NH3

01 Alabama 156,928 66,927 547,935 52,248 194,840 80,885 80,092

04 Arizona 141,063 72,837 219,632 4,079 122,689 70,962 31,535

05 Arkansas 128,066 42,433 233,465 20,366 141,399 46,361 141,454

06 California 451,587 125,210 721,418 10,724 365,655 123,700 166,925

08 Colorado 123,489 55,752 79,398 4,552 144,770 35,401 97,929

09 Connecticut 85,850 10,601 129,781 531 37,799 7,030 5,357

10 Delaware 23,057 7,115 19,566 10,450 12,476 5,907 11,008

11 DC 9,477 2,033 870 6,029 1,573 611 983

12 Florida 351,830 52,481 501,295 43,645 235,571 89,998 69,625

13 Georgia 262,571 72,717 1,139,960 6,571 349,662 155,621 82,562

16 Idaho 71,583 27,822 455,387 8,324 185,906 66,120 62,242

17 Illinois 292,488 125,113 84,547 37,260 248,897 57,565 139,749

18 Indiana 224,455 36,584 81,933 2,079 159,995 32,724 94,342

19 Iowa 133,300 30,088 54,195 13,980 155,592 35,239 296,305

20 Kansas 120,974 70,489 81,998 3,384 217,109 47,697 213,171

21 Kentucky 140,706 73,687 164,588 56,501 102,479 38,138 88,425

22 Louisiana 123,698 101,234 151,815 93,687 139,903 58,872 66,540

23 Maine 49,242 5,556 58,526 12,387 26,470 12,555 6,064

24 Maryland 67,466 16,652 92,986 815 61,053 14,317 23,992

25 Massachusetts 139,347 26,696 48,451 65,893 71,517 20,102 8,128

26 Michigan 292,289 121,040 174,268 34,249 148,878 48,123 61,016

27 Minnesota 187,817 24,178 100,651 5,932 233,041 53,342 186,179

28 Mississippi 144,824 54,080 389,556 78,950 149,265 60,216 65,945

29 Missouri 156,821 14,252 160,545 31,955 332,368 72,527 180,671

30 Montana 56,138 18,172 239,130 1,415 150,392 45,230 88,533

31 Nebraska 77,465 14,811 23,181 9,962 160,061 30,602 227,271

32 Nevada 37,930 7,539 14,057 3,637 38,129 9,103 14,815

33 New Hampshire 36,337 13,649 38,526 89,896 19,396 8,567 2,170

34 New Jersey 158,069 84,626 52,949 46,291 76,027 24,380 8,677

35 New Mexico 57,522 28,830 99,253 8,462 265,621 50,899 45,681

36 New York 357,680 108,956 156,257 136,978 191,008 61,562 54,759

37 North Carolina 367,929 36,056 786,318 33,098 168,657 79,942 158,947

38 North Dakota 57,186 19,519 14,250 59,452 100,334 19,926 87,818

39 Ohio 302,230 83,225 136,451 62,840 176,768 54,446 79,446

40 Oklahoma 102,999 31,221 69,593 5,201 252,911 50,579 186,990

41 Oregon 148,786 39,443 897,808 19,715 278,811 132,568 59,039

42 Pennsylvania 273,343 121,781 228,637 92,518 153,451 55,278 79,834

44 Rhode Island 20,324 3,185 6,246 4,801 7,582 2,623 1,091

45 South Carolina 155,638 24,801 303,014 14,765 119,894 47,430 27,600

46 South Dakota 39,619 7,183 26,981 20,903 109,024 23,323 128,208

47 Tennessee 252,289 50,799 268,451 45,530 123,923 53,156 78,194

48 Texas 563,395 40,784 322,518 9,014 857,360 177,511 454,360

49 Utah 63,761 20,626 37,317 11,828 63,460 15,507 30,345

50 Vermont 24,406 13,383 31,272 13,462 22,136 7,788 8,845

51 Virginia 213,685 45,048 373,847 9,388 146,335 30,034 66,400

53 Washington 166,279 21,779 248,796 3,532 121,003 50,183 46,611

54 West Virginia 61,474 22,148 83,522 11,458 46,182 18,010 15,735

55 Wisconsin 193,368 60,381 151,495 44,669 115,160 38,924 119,948

56 Wyoming 19,796 67,208 44,209 16,403 177,504 30,896 47,944

Total 7,686,575 2,220,728 10,346,847 1,379,810 7,780,035 2,352,479 4,299,503
A-9

Table 9. 2001 Proxy Inventory for All Sectors (Annual Tons)

FIPS State VOC NOx CO SO2 PM-10 PM-2.5 NH3

01 Alabama 380,395 583,967 2,398,089 651,086 251,915 116,107 90,095

04 Arizona 275,120 474,366 1,507,841 191,981 170,120 98,120 37,023

05 Arkansas 215,970 260,611 1,162,845 123,114 180,213 71,089 160,555

06 California 1,286,520 1,462,055 8,505,442 87,245 447,250 182,129 220,725

08 Colorado 260,328 389,121 1,319,611 122,250 176,217 56,089 103,085

09 Connecticut 163,272 152,156 940,181 46,470 44,861 12,422 9,100

10 Delaware 51,556 73,068 241,310 88,282 16,124 8,691 12,628

11 DC 16,386 17,570 74,687 9,809 2,240 1,142 1,420

12 Florida 854,529 1,055,264 5,709,673 743,202 294,260 133,652 94,810

13 Georgia 536,746 734,992 4,282,547 603,836 404,814 194,007 109,267

16 Idaho 114,567 104,400 913,142 36,602 202,057 76,863 63,756

17 Illinois 682,907 951,800 3,104,135 692,100 363,180 124,302 164,179

18 Indiana 429,975 754,446 2,518,378 967,405 201,494 63,261 110,842

19 Iowa 223,929 316,211 1,070,926 248,992 177,223 50,687 308,274

20 Kansas 211,429 457,195 1,072,580 151,408 242,572 66,885 229,696

21 Kentucky 308,758 588,465 1,453,618 656,321 140,377 62,221 94,873

22 Louisiana 365,911 814,622 2,212,640 415,627 193,322 100,891 138,986

23 Maine 102,069 86,908 531,305 44,392 34,573 18,377 7,750

24 Maryland 172,888 291,171 1,223,353 292,204 75,558 24,419 29,873

25 Massachusetts 257,730 299,965 1,326,658 196,704 86,893 32,255 14,692

26 Michigan 655,525 798,470 3,373,233 533,683 193,956 78,124 72,823

27 Minnesota 407,534 453,434 1,834,051 153,942 333,380 103,938 193,385

28 Mississippi 289,749 357,132 1,471,065 295,994 174,121 79,102 96,802

29 Missouri 347,893 472,780 1,910,149 407,134 397,636 103,760 211,842

30 Montana 90,961 155,573 604,980 60,674 172,224 58,059 90,118

31 Nebraska 135,587 211,414 650,798 95,979 177,501 41,398 229,407

32 Nevada 88,522 145,952 524,033 65,750 58,286 18,400 16,811

33 New Hampshire 78,010 76,445 423,460 148,013 23,289 11,444 3,556

34 New Jersey 400,842 480,947 1,766,697 260,441 108,914 51,360 17,122

35 New Mexico 120,664 279,839 777,500 180,685 286,590 64,216 48,202

36 New York 704,481 697,539 3,639,669 604,245 265,433 115,547 71,159

37 North Carolina 625,552 567,003 3,040,707 555,627 212,852 110,791 169,232

38 North Dakota 84,083 184,028 304,421 283,465 111,832 28,824 88,738

39 Ohio 638,909 994,519 4,164,787 1,590,091 257,735 108,942 94,635

40 Oklahoma 253,931 435,001 1,575,225 153,176 274,383 65,798 209,951

41 Oregon 252,045 274,132 1,952,563 63,471 297,050 146,714 63,006

42 Pennsylvania 584,415 960,412 3,591,144 1,202,826 221,623 100,978 97,918

44 Rhode Island 45,490 36,756 256,208 11,718 10,141 4,612 2,056

45 South Carolina 310,262 357,994 1,726,715 285,898 142,518 62,861 32,671

46 South Dakota 65,850 92,796 308,948 40,908 114,669 27,939 129,203

47 Tennessee 518,201 676,938 2,267,269 582,163 165,069 82,857 85,667

48 Texas 1,269,942 2,075,584 6,031,639 931,966 964,637 256,744 484,034

49 Utah 148,777 238,410 923,202 74,462 92,716 38,718 34,151

50 Vermont 46,145 46,221 260,242 16,822 24,401 9,448 9,591

51 Virginia 431,556 527,102 2,405,594 355,830 181,866 54,841 75,407

53 Washington 325,914 351,666 2,044,061 137,462 145,924 68,814 57,795

54 West Virginia 130,866 429,934 743,465 625,052 73,552 35,524 18,269

55 Wisconsin 396,581 470,337 1,805,388 333,476 145,477 61,781 127,409

56 Wyoming 60,703 263,132 373,558 163,161 214,150 53,197 49,362

Total 16,419,976 22,979,846 92,319,732 16,583,144 9,547,186 3,538,339 4,881,950
A-10

Table 10. IAQR 2010 Base EGU Annual Emissions (Tons)

FIPS State VOC NOx CO SO2 PM-10 PM-2.5 NH3

01 Alabama 1,073 134,134 21,015 473,043 8,322 4,323 9

04 Arizona 642 84,567 25,830 47,779 3,707 2,016 4

05 Arkansas 393 52,511 8,429 122,667 1,724 1,213 3

06 California 1,606 17,671 56,798 17,317 2,432 1,138 707

08 Colorado 446 82,714 7,924 73,089 1,594 888 4

09 Connecticut 99 5,168 5,984 6,284 829 282 0

10 Delaware 79 10,271 1,377 46,355 688 319 1

11 DC 0 42 41 0 1 1 0

12 Florida 1,224 161,846 44,976 233,241 9,414 4,225 62

13 Georgia 1,163 150,582 18,179 609,154 10,114 4,877 10

16 Idaho 21 1,197 2,273 0 38 38 0

17 Illinois 1,386 171,443 10,401 600,836 6,503 3,709 10

18 Indiana 1,704 239,713 14,292 670,365 12,150 6,408 37

19 Iowa 463 86,090 4,314 169,861 3,228 1,983 4

20 Kansas 538 100,942 4,234 63,532 2,800 1,734 4

21 Kentucky 1,352 195,883 13,849 363,145 8,874 4,092 11

22 Louisiana 459 49,767 11,683 112,534 3,378 1,384 3

23 Maine 48 2,103 4,592 3,210 349 148 0

24 Maryland 424 60,629 4,263 232,229 3,374 1,445 4

25 Massachusetts 239 10,392 10,920 15,650 1,237 646 23

26 Michigan 973 125,394 11,972 387,627 6,121 3,517 19

27 Minnesota 525 104,535 6,899 91,561 3,205 1,676 4

28 Mississippi 275 43,163 8,384 73,467 2,072 855 2

29 Missouri 1,150 137,009 9,132 293,093 3,950 2,685 9

30 Montana 220 38,465 2,021 17,923 2,874 1,378 2

31 Nebraska 299 57,826 2,452 97,630 1,285 881 3

32 Nevada 271 37,403 7,037 16,408 2,467 1,322 11

33 New Hampshire 113 3,647 4,437 7,289 363 252 0

34 New Jersey 196 29,322 5,790 41,255 2,037 796 1

35 New Mexico 359 76,400 3,233 48,577 1,939 919 3

36 New York 863 68,413 18,085 214,077 3,818 1,790 252

37 North Carolina 943 62,069 9,471 219,369 9,074 3,806 9

38 North Dakota 670 77,927 7,726 160,938 3,219 1,574 5

39 Ohio 1,664 266,798 15,149 1,258,684 15,245 6,907 16

40 Oklahoma 603 82,115 15,056 133,009 2,759 1,714 39

41 Oregon 132 13,346 9,654 15,187 381 310 0

42 Pennsylvania 1,483 209,760 16,238 853,431 15,288 6,453 14

44 Rhode Island 22 1,440 2,383 0 40 40 0

45 South Carolina 507 64,737 5,691 199,745 10,059 4,553 5

46 South Dakota 64 11,748 546 36,304 327 74 1

47 Tennessee 746 102,819 5,839 306,082 3,953 2,079 7

48 Texas 3,711 200,909 102,753 487,740 20,215 12,771 445

49 Utah 369 69,368 3,080 31,541 2,329 1,041 3

50 Vermont 0 1 2 0 0 0 0

51 Virginia 471 55,530 5,863 187,772 5,761 2,133 4

53 Washington 266 28,432 16,403 5,959 811 635 9

54 West Virginia 1,204 155,157 9,764 550,629 11,006 4,817 11

55 Wisconsin 697 111,540 8,011 214,063 3,124 2,046 6

56 Wyoming 506 90,500 4,241 47,276 3,145 2,090 5

Total 32,660 3,943,438 588,685 9,856,926 217,623 109,983 1,783
A-11

Table 11. IAQR 2010 Base non-EGU Annual Emissions (Tons)

FIPS State VOC NOx CO SO2 PM-10 PM-2.5 NH3

01 Alabama 59,099 83,403 217,668 121,267 38,080 22,112 4,054

04 Arizona 14,898 118,162 26,092 120,829 36,813 19,730 5

05 Arkansas 12,038 23,484 117,195 17,464 31,597 18,058 17,198

06 California 61,577 137,347 88,706 43,980 32,863 18,143 15,950

08 Colorado 37,311 44,879 28,358 15,909 24,074 14,406 351

09 Connecticut 5,811 11,252 3,160 7,567 1,151 869 59

10 Delaware 5,447 8,492 16,025 38,381 1,174 855 692

11 DC 239 812 273 2,123 97 71 8

12 Florida 21,531 59,032 57,372 90,435 15,931 10,385 6,816

13 Georgia 34,833 71,428 202,252 92,752 29,824 20,856 17,460

16 Idaho 327 6,645 4,906 26,758 10,629 7,184 2

17 Illinois 131,654 134,916 125,786 277,244 94,437 48,689 13,677

18 Indiana 38,674 45,385 250,845 152,198 16,468 12,993 8,996

19 Iowa 5,985 26,522 7,708 84,015 8,213 4,603 9,147

20 Kansas 16,738 108,813 84,638 16,013 13,728 9,370 14,038

21 Kentucky 61,947 34,826 76,182 42,912 18,374 11,864 1,469

22 Louisiana 87,220 297,110 822,849 193,555 35,295 26,138 73,111

23 Maine 4,940 15,551 10,688 22,206 4,877 3,173 132

24 Maryland 7,167 19,129 44,248 22,514 3,756 2,230 285

25 Massachusetts 7,064 18,221 8,024 15,337 3,491 2,561 89

26 Michigan 72,709 160,968 134,751 134,973 23,650 13,316 586

27 Minnesota 29,586 83,849 101,257 41,178 91,600 40,064 1,331

28 Mississippi 52,402 74,439 123,188 77,530 12,636 9,042 29,901

29 Missouri 55,860 29,745 111,022 128,569 54,393 20,189 26,844

30 Montana 5,225 20,759 55,176 34,720 14,252 7,546 562

31 Nebraska 10,840 14,459 13,092 7,302 9,751 3,699 14

32 Nevada 1,750 5,988 12,717 3,461 15,792 5,525 9

33 New Hampshire 4,866 4,231 6,268 7,948 1,341 816 23

34 New Jersey 76,222 51,016 21,876 70,783 10,870 7,590 472

35 New Mexico 9,044 68,718 21,539 115,204 7,556 5,504 37

36 New York 50,073 36,692 31,657 168,553 44,612 31,918 206

37 North Carolina 75,673 63,283 88,170 95,437 23,043 16,194 120

38 North Dakota 179 7,225 3,568 56,097 1,446 1,304 13

39 Ohio 56,202 77,462 724,782 337,560 37,671 25,030 2,962

40 Oklahoma 36,190 120,968 239,431 41,168 11,259 6,938 19,773

41 Oregon 8,578 16,785 82,183 6,558 9,217 6,617 16

42 Pennsylvania 48,210 172,998 350,381 141,002 39,014 25,787 6,842

44 Rhode Island 3,250 876 1,793 2,423 1,278 942 7

45 South Carolina 30,894 45,978 76,025 63,865 8,143 6,017 60

46 South Dakota 1,887 4,722 0 1,366 864 449 1

47 Tennessee 96,883 78,009 113,496 134,335 18,933 11,290 83

48 Texas 203,123 523,815 437,225 318,637 40,730 27,852 1,420

49 Utah 15,180 31,647 127,753 30,303 23,711 19,349 1,398

50 Vermont 1,413 780 1,540 2,024 975 575 3

51 Virginia 52,649 66,479 74,527 112,675 16,836 10,587 849

53 Washington 13,992 47,008 190,821 51,577 11,164 7,504 5,821

54 West Virginia 20,753 50,132 113,347 62,211 11,426 7,309 412

55 Wisconsin 45,276 54,295 66,172 88,506 16,203 11,496 986

56 Wyoming 13,652 49,464 82,803 59,741 35,812 20,952 533

Total 1,707,060 3,228,201 5,599,537 3,799,163 1,015,051 605,691 284,824
A-12

Table 12. IAQR 2010 Base On-road Annual Emissions (Tons)

FIPS State VOC NOx CO SO2 PM-10 PM-2.5 NH3

01 Alabama 80,971 110,191 1,200,284 604 3,783 2,448 6,480

04 Arizona 48,868 91,317 600,172 555 3,302 2,102 5,990

05 Arkansas 40,377 64,910 578,870 335 2,216 1,459 3,561

06 California 255,609 401,906 2,528,853 3,446 20,857 13,333 38,090

08 Colorado 42,946 80,596 666,048 466 2,840 1,824 5,015

09 Connecticut 19,334 48,516 259,998 326 2,008 1,291 3,596

10 Delaware 8,754 17,423 137,544 92 606 399 1,008

11 DC 2,386 4,821 40,218 41 219 133 457

12 Florida 225,359 293,897 3,610,060 1,728 9,938 6,254 18,742

13 Georgia 102,256 189,194 1,570,358 1,137 6,898 4,419 12,257

16 Idaho 18,591 32,658 319,856 164 1,080 710 1,740

17 Illinois 91,971 177,741 1,489,301 1,121 6,471 4,054 12,296

18 Indiana 85,881 142,865 1,526,256 769 4,872 3,163 8,261

19 Iowa 35,789 61,607 641,840 308 2,032 1,338 3,282

20 Kansas 33,953 59,091 591,143 309 1,973 1,286 3,308

21 Kentucky 53,653 95,692 866,316 494 3,235 2,121 5,316

22 Louisiana 61,112 89,284 896,379 445 2,741 1,763 4,768

23 Maine 14,779 30,608 305,955 153 1,029 681 1,618

24 Maryland 29,581 73,126 451,132 552 3,143 1,955 6,073

25 Massachusetts 33,901 74,353 547,767 578 3,194 1,969 6,417

26 Michigan 104,326 171,375 2,049,577 1,014 5,852 3,676 10,959

27 Minnesota 56,188 103,429 927,184 535 3,334 2,156 5,747

28 Mississippi 46,140 68,761 644,765 357 2,368 1,559 3,800

29 Missouri 57,371 117,844 992,687 724 3,978 2,442 7,797

30 Montana 12,436 24,821 230,038 117 801 534 1,235

31 Nebraska 21,938 37,730 400,851 192 1,251 820 2,048

32 Nevada 20,414 36,277 305,382 202 1,207 770 2,178

33 New Hampshire 13,105 25,744 228,352 130 881 583 1,405

34 New Jersey 41,543 93,102 578,048 717 3,960 2,433 7,938

35 New Mexico 34,521 54,524 552,017 278 1,813 1,188 2,965

36 New York 87,991 181,546 1,617,694 1,296 7,191 4,440 14,218

37 North Carolina 87,854 150,027 1,482,472 988 5,496 3,384 10,631

38 North Dakota 9,126 16,449 164,300 77 528 351 818

39 Ohio 111,178 201,346 1,925,363 1,174 7,094 4,542 12,673

40 Oklahoma 56,863 86,790 931,020 468 2,918 1,886 5,034

41 Oregon 29,270 67,386 486,003 381 2,402 1,559 4,088

42 Pennsylvania 104,305 200,618 1,799,671 1,104 6,541 4,146 11,937

44 Rhode Island 6,979 12,265 91,584 81 480 304 902

45 South Carolina 64,158 94,175 921,836 491 3,202 2,098 5,230

46 South Dakota 10,037 20,183 184,474 96 659 439 1,013

47 Tennessee 85,533 132,898 1,422,081 715 4,416 2,848 7,683

48 Texas 220,557 399,631 3,025,769 2,288 12,974 8,085 24,981

49 Utah 30,313 48,995 542,229 263 1,584 1,013 2,839

50 Vermont 7,457 15,976 139,292 78 523 345 830

51 Virginia 74,439 147,032 1,162,361 859 5,221 3,337 9,326

53 Washington 65,982 114,579 1,249,051 638 3,770 2,396 6,907

54 West Virginia 22,222 40,379 359,078 200 1,357 900 2,125

55 Wisconsin 47,319 109,650 911,995 619 3,826 2,456 6,666

56 Wyoming 9,073 18,620 170,136 87 596 396 923

Total 2,824,708 4,931,947 44,323,659 29,790 178,660 113,788 323,171
A-13

Table 13. IAQR 2010 Base Non-road Annual Emissions (Tons)

FIPS State VOC NOx CO SO2 PM-10 PM-2.5 NH3

01 Alabama 38,010 55,830 427,088 1,567 5,672 4,647 771

04 Arizona 35,574 43,609 587,042 716 9,058 4,605 608

05 Arkansas 24,641 35,395 263,509 475 3,100 2,774 483

06 California 162,983 276,098 3,104,650 12,967 21,719 19,082 5,369

08 Colorado 32,342 56,971 476,272 759 4,907 3,832 831

09 Connecticut 19,862 17,273 333,208 368 1,854 1,688 580

10 Delaware 6,850 16,801 89,348 310 1,218 1,092 115

11 DC 2,276 5,424 22,243 116 225 195 84

12 Florida 143,736 147,942 2,038,938 15,133 18,699 15,927 2,075

13 Georgia 50,978 66,365 814,963 2,636 6,582 5,727 1,465

16 Idaho 17,783 17,306 168,419 250 2,736 1,666 210

17 Illinois 76,333 150,172 1,159,501 1,724 10,260 9,281 2,318

18 Indiana 37,404 90,417 623,186 1,091 5,945 5,359 1,288

19 Iowa 25,895 57,564 353,199 624 5,245 4,773 605

20 Kansas 18,930 79,483 310,188 805 5,512 4,853 617

21 Kentucky 28,834 73,055 328,529 1,849 5,231 4,539 670

22 Louisiana 48,151 205,029 453,763 21,143 10,973 9,907 1,625

23 Maine 22,186 8,797 158,919 228 1,269 1,110 168

24 Maryland 35,791 38,923 549,345 8,132 4,084 3,576 576

25 Massachusetts 37,464 69,973 594,543 1,218 5,408 4,897 1,147

26 Michigan 107,260 63,196 1,083,795 1,316 7,399 6,682 1,861

27 Minnesota 78,587 64,800 611,775 1,082 6,963 6,359 1,006

28 Mississippi 26,628 44,790 255,001 2,007 4,158 3,542 456

29 Missouri 40,742 64,161 559,607 867 5,430 4,819 1,036

30 Montana 11,336 33,985 111,963 274 2,276 1,919 149

31 Nebraska 14,539 57,396 213,024 578 3,941 3,549 333

32 Nevada 14,473 25,367 203,541 357 3,155 2,039 208

33 New Hampshire 14,362 6,212 144,603 154 1,063 869 210

34 New Jersey 48,940 86,387 828,276 53,543 9,153 8,321 1,426

35 New Mexico 11,107 10,714 149,663 218 2,161 1,445 228

36 New York 97,406 90,922 1,450,391 2,226 9,414 8,372 2,588

37 North Carolina 52,331 60,101 814,384 1,237 7,734 6,502 1,629

38 North Dakota 11,115 41,798 115,381 405 3,737 3,320 136

39 Ohio 78,264 116,893 1,208,863 5,716 15,265 12,353 2,251

40 Oklahoma 24,505 40,022 340,543 608 3,974 3,411 870

41 Oregon 32,264 52,552 398,748 815 3,159 2,878 597

42 Pennsylvania 73,514 80,601 1,092,556 3,338 6,971 6,105 1,996

44 Rhode Island 4,714 5,633 90,238 2,883 617 563 148

45 South Carolina 28,816 29,879 417,852 1,193 3,197 2,821 664

46 South Dakota 9,170 24,422 100,351 248 2,570 2,284 134

47 Tennessee 42,874 138,923 518,158 2,771 7,251 6,600 1,075

48 Texas 114,781 432,118 2,046,992 33,434 26,184 21,664 5,489

49 Utah 20,821 31,535 223,250 395 2,899 2,369 382

50 Vermont 7,528 3,855 68,423 83 499 434 93

51 Virginia 44,498 76,591 683,777 4,592 8,656 7,211 888

53 Washington 45,488 78,757 609,995 9,459 6,901 5,814 996

54 West Virginia 14,605 57,047 148,138 33,597 4,491 4,100 265

55 Wisconsin 61,885 50,959 594,423 770 5,163 4,603 1,083

56 Wyoming 8,200 22,918 69,681 165 1,173 945 164

Total 2,006,777 3,404,962 28,010,246 236,446 295,253 251,423 49,964
A-14

Table 14. IAQR 2010 Base Area Annual Emissions (Tons)

FIPS State VOC NOx CO SO2 PM-10 PM-2.5 NH3

01 Alabama 144,297 69,410 521,658 51,945 191,882 78,833 85,120

04 Arizona 139,902 78,053 213,271 4,333 122,253 70,998 31,614

05 Arkansas 118,428 44,794 198,105 21,156 135,966 42,502 149,698

06 California 472,160 129,306 638,780 10,684 347,785 117,527 165,486

08 Colorado 120,713 59,928 70,880 4,653 151,900 36,511 95,332

09 Connecticut 65,405 9,341 86,813 470 34,955 7,016 5,741

10 Delaware 22,724 6,872 19,292 10,223 13,424 6,238 12,672

11 DC 8,458 1,935 861 5,751 1,815 680 1,085

12 Florida 342,177 53,238 473,626 44,726 248,625 91,811 70,327

13 Georgia 257,398 74,729 1,117,469 6,661 350,201 155,686 89,400

16 Idaho 70,040 29,393 441,270 8,782 162,309 61,564 60,653

17 Illinois 263,817 115,848 69,185 36,367 244,904 56,227 144,138

18 Indiana 214,228 37,879 73,152 2,240 163,801 33,756 98,380

19 Iowa 128,567 31,091 44,865 14,630 150,839 33,704 306,177

20 Kansas 117,523 74,256 75,870 3,451 209,431 45,971 209,259

21 Kentucky 126,045 76,895 128,309 58,005 103,229 35,523 87,718

22 Louisiana 109,098 103,534 139,790 94,015 142,402 58,926 70,145

23 Maine 39,134 4,928 39,259 10,827 25,446 10,840 6,303

24 Maryland 63,097 15,936 91,552 898 64,349 13,901 26,160

25 Massachusetts 122,857 24,894 37,787 61,255 72,583 19,804 8,727

26 Michigan 254,186 115,555 127,662 32,688 150,240 44,957 62,070

27 Minnesota 180,126 24,850 77,015 5,692 229,854 51,182 190,979

28 Mississippi 133,143 56,688 366,353 82,727 144,645 57,758 69,378

29 Missouri 139,362 14,808 114,101 31,930 322,307 67,290 181,815

30 Montana 52,893 18,383 226,282 1,401 152,449 44,634 86,259

31 Nebraska 76,757 15,374 19,892 10,122 157,152 29,914 224,619

32 Nevada 38,369 8,455 12,709 3,913 38,023 9,386 14,545

33 New Hampshire 31,981 13,910 28,450 90,762 19,414 7,804 2,201

34 New Jersey 140,373 79,814 47,276 42,601 83,105 25,993 9,183

35 New Mexico 56,240 32,427 91,749 9,447 254,567 48,963 43,927

36 New York 319,026 88,071 106,257 122,071 188,701 57,721 54,593

37 North Carolina 353,839 36,969 727,973 33,810 175,321 77,486 170,679

38 North Dakota 57,618 21,197 13,578 64,078 98,021 19,417 87,088

39 Ohio 277,948 82,187 104,105 63,253 182,980 53,484 81,898

40 Oklahoma 92,653 33,165 52,658 5,528 243,098 47,946 186,307

41 Oregon 139,000 39,925 850,783 20,897 257,281 125,782 58,126

42 Pennsylvania 240,416 114,330 193,078 80,948 155,235 52,980 81,910

44 Rhode Island 18,047 2,766 4,454 4,108 7,957 2,578 1,205

45 South Carolina 151,220 26,093 279,698 15,619 118,616 45,755 29,031

46 South Dakota 40,456 7,880 24,025 23,819 110,488 23,431 125,939

47 Tennessee 235,564 52,303 214,772 47,789 125,280 49,494 78,243

48 Texas 558,052 43,065 304,178 9,570 857,424 178,493 444,795

49 Utah 64,449 23,536 34,962 13,107 67,559 16,411 29,630

50 Vermont 22,148 11,533 21,636 12,963 21,374 6,990 8,618

51 Virginia 202,313 45,680 302,317 9,471 147,719 33,371 67,726

53 Washington 152,211 22,999 203,537 3,732 117,415 46,448 46,814

54 West Virginia 53,461 21,321 59,801 11,332 45,549 15,976 15,950

55 Wisconsin 175,512 58,670 122,456 45,889 117,111 37,010 118,345

56 Wyoming 18,444 71,685 41,227 17,309 166,817 29,141 45,892

Total 7,221,877 2,225,898 9,254,775 1,367,643 7,693,802 2,285,814 4,341,905
A-15

Table 15. IAQR 2010 Base All Sectors Annual Emissions (Tons)

FIPS State VOC NOx CO SO2 PM-10 PM-2.5 NH3

01 Alabama 323,451 452,969 2,387,712 648,426 247,738 112,362 96,434

04 Arizona 239,883 415,709 1,452,407 174,212 175,132 99,452 38,221

05 Arkansas 195,877 221,094 1,166,108 162,096 174,602 66,006 170,944

06 California 953,934 962,328 6,417,787 88,393 425,656 169,224 225,601

08 Colorado 233,758 325,088 1,249,482 94,875 185,315 57,461 101,533

09 Connecticut 110,510 91,552 689,163 15,014 40,796 11,146 9,978

10 Delaware 43,855 59,859 263,586 95,361 17,110 8,903 14,488

11 DC 13,359 13,033 63,635 8,031 2,357 1,080 1,635

12 Florida 734,026 715,956 6,224,972 385,263 302,607 128,602 98,023

13 Georgia 446,628 552,298 3,723,221 712,339 403,619 191,565 120,591

16 Idaho 106,763 87,199 936,723 35,953 176,792 71,161 62,605

17 Illinois 565,161 750,119 2,854,174 917,292 362,575 121,960 172,440

18 Indiana 377,892 556,258 2,487,732 826,664 203,237 61,680 116,962

19 Iowa 196,700 262,874 1,051,926 269,439 169,557 46,401 319,214

20 Kansas 187,682 422,586 1,066,073 84,111 233,444 63,213 227,226

21 Kentucky 271,831 476,351 1,413,185 466,405 138,942 58,140 95,184

22 Louisiana 306,041 744,724 2,324,464 421,691 194,790 98,118 149,653

23 Maine 81,087 61,986 519,413 36,624 32,969 15,951 8,220

24 Maryland 136,060 207,743 1,140,541 264,325 78,706 23,107 33,098

25 Massachusetts 201,524 197,833 1,199,042 94,038 85,913 29,878 16,403

26 Michigan 539,454 636,488 3,407,757 557,619 193,262 72,148 75,494

27 Minnesota 345,012 381,462 1,724,130 140,049 334,956 101,437 199,067

28 Mississippi 258,589 287,842 1,397,691 236,088 165,879 72,756 103,538

29 Missouri 294,485 363,568 1,786,549 455,182 390,058 97,425 217,502

30 Montana 82,111 136,413 625,481 54,434 172,654 56,011 88,208

31 Nebraska 124,374 182,786 649,310 115,823 173,379 38,863 227,016

32 Nevada 75,276 113,490 541,387 24,340 60,645 19,041 16,951

33 New Hampshire 64,426 53,744 412,110 106,284 23,063 10,324 3,838

34 New Jersey 307,274 339,640 1,481,267 208,899 109,126 45,133 19,021

35 New Mexico 111,272 242,782 818,201 173,724 268,036 58,018 47,160

36 New York 555,359 465,644 3,224,084 508,223 253,736 104,242 71,857

37 North Carolina 570,641 372,450 3,122,470 350,841 220,669 107,372 183,067

38 North Dakota 78,709 164,596 304,552 281,595 106,951 25,967 88,060

39 Ohio 525,256 744,686 3,978,263 1,666,387 258,255 102,317 99,799

40 Oklahoma 210,814 363,060 1,578,707 180,781 264,007 61,894 212,024

41 Oregon 209,243 189,993 1,827,370 43,838 272,440 137,146 62,828

42 Pennsylvania 467,928 778,307 3,451,925 1,079,823 223,050 95,471 102,699

44 Rhode Island 33,011 22,980 190,452 9,495 10,373 4,428 2,262

45 South Carolina 275,595 260,862 1,701,102 280,913 143,216 61,244 34,990

46 South Dakota 61,614 68,955 309,395 61,834 114,908 26,677 127,088

47 Tennessee 461,600 504,953 2,274,345 491,691 159,834 72,311 87,091

48 Texas 1,100,224 1,599,537 5,916,917 851,669 957,527 248,866 477,130

49 Utah 131,133 205,081 931,274 75,609 98,082 40,183 34,253

50 Vermont 38,545 32,147 230,893 15,148 23,371 8,344 9,544

51 Virginia 374,371 391,312 2,228,845 315,369 184,192 56,638 78,793

53 Washington 277,939 291,775 2,269,807 71,365 140,061 62,797 60,547

54 West Virginia 112,245 324,035 690,128 657,970 73,830 33,102 18,762

55 Wisconsin 330,688 385,113 1,703,056 349,847 145,427 57,610 127,086

56 Wyoming 49,875 253,187 368,088 124,577 207,543 53,524 47,518

Total 13,793,082 17,734,447 87,776,902 15,289,969 9,400,388 3,366,700 5,001,647
A-16

Table 16. IAQR 2015 Base EGU Annual Emissions (Tons)

FIPS State VOC NOx CO SO2 PM-10 PM-2.5 NH3

01 Alabama 1,157 128,592 29,397 415,985 8,524 4,491 9

04 Arizona 673 85,975 29,226 47,779 3,764 2,073 4

05 Arkansas 408 52,786 10,022 122,667 1,751 1,240 3

06 California 1,440 19,597 71,879 17,317 2,760 1,466 503

08 Colorado 464 83,632 9,893 73,089 1,620 914 4

09 Connecticut 100 5,260 6,126 6,284 831 285 0

10 Delaware 84 10,843 1,658 48,275 717 335 1

11 DC 1 86 106 0 2 2 0

12 Florida 1,784 170,803 52,590 230,295 9,473 4,283 383

13 Georgia 1,228 153,295 25,136 600,315 10,236 4,996 10

16 Idaho 22 1,229 2,428 0 41 41 0

17 Illinois 1,509 179,581 12,281 539,206 6,854 3,910 11

18 Indiana 1,757 245,844 16,389 531,563 12,481 6,585 37

19 Iowa 491 90,805 4,729 178,041 3,376 2,084 4

20 Kansas 546 102,025 4,492 65,316 2,855 1,769 5

21 Kentucky 1,380 200,732 14,511 363,166 9,097 4,198 12

22 Louisiana 478 50,164 13,762 112,534 3,413 1,419 3

23 Maine 49 2,138 4,640 3,210 350 148 0

24 Maryland 436 62,037 5,165 229,578 3,444 1,476 4

25 Massachusetts 277 11,923 14,228 16,259 1,337 720 25

26 Michigan 1,028 131,114 15,432 390,753 6,324 3,656 19

27 Minnesota 546 108,222 7,567 92,830 3,368 1,767 5

28 Mississippi 358 44,939 17,374 73,467 2,223 1,007 2

29 Missouri 1,254 145,066 11,285 317,556 4,292 2,928 9

30 Montana 224 38,547 2,474 17,718 2,882 1,386 2

31 Nebraska 300 57,820 2,595 97,391 1,285 881 3

32 Nevada 298 41,284 8,606 17,314 2,616 1,403 11

33 New Hampshire 114 3,813 4,449 7,289 376 258 0

34 New Jersey 215 30,713 7,780 39,237 2,121 842 1

35 New Mexico 362 76,538 3,308 48,577 1,940 920 4

36 New York 801 70,461 24,469 214,077 3,932 1,904 176

37 North Carolina 1,025 63,472 12,048 144,369 9,673 4,087 9

38 North Dakota 689 80,541 7,857 171,995 3,330 1,622 6

39 Ohio 1,761 261,431 18,861 1,047,580 15,822 7,205 16

40 Oklahoma 692 86,711 17,434 133,009 2,782 1,737 84

41 Oregon 140 13,504 10,515 15,187 395 325 0

42 Pennsylvania 1,561 215,027 21,059 812,610 15,849 6,703 14

44 Rhode Island 29 1,989 3,211 0 54 54 0

45 South Carolina 529 66,243 7,797 195,541 10,122 4,601 5

46 South Dakota 74 13,552 639 42,118 379 85 1

47 Tennessee 756 102,714 6,114 309,626 3,994 2,102 7

48 Texas 3,737 201,284 110,660 487,068 20,355 12,911 416

49 Utah 369 69,402 3,110 31,541 2,330 1,042 3

50 Vermont 0 4 5 0 0 00

51 Virginia 496 57,948 8,307 186,498 5,851 2,191 4

53 Washington 255 26,336 15,235 5,959 791 615 9

54 West Virginia 1,211 148,246 9,974 485,118 11,097 4,852 11

55 Wisconsin 715 103,469 9,349 189,552 3,011 1,975 6

56 Wyoming 506 90,502 4,245 47,240 3,145 2,090 5

Total 34,332 4,008,241 700,418 9,222,097 223,265 113,584 1,850
A-17

Table 17. IAQR 2015 Base non-EGU Annual Emissions (Tons)

FIPS State VOC NOx CO SO2 PM-10 PM-2.5 NH3

01 Alabama 62,259 86,324 227,779 123,496 39,909 23,175 4,233

04 Arizona 16,315 126,302 27,969 126,366 40,157 21,484 6

05 Arkansas 12,726 24,269 124,409 18,050 33,583 19,098 18,064

06 California 64,866 140,646 91,712 45,231 34,639 18,943 16,596

08 Colorado 39,457 44,303 28,096 16,572 25,629 15,303 373

09 Connecticut 6,057 11,769 3,319 7,765 1,187 896 61

10 Delaware 5,691 8,568 16,691 38,341 1,207 879 716

11 DC 243 840 282 2,164 100 73 8

12 Florida 22,873 61,083 59,391 93,694 16,716 10,876 6,829

13 Georgia 37,244 75,076 218,771 97,537 31,537 22,045 18,727

16 Idaho 334 6,833 5,068 27,825 10,996 7,463 2

17 Illinois 138,171 138,214 128,953 286,281 98,941 50,807 14,496

18 Indiana 40,812 46,176 258,837 157,305 17,034 13,404 9,468

19 Iowa 6,281 27,203 8,041 85,366 8,579 4,809 9,584

20 Kansas 17,474 111,768 88,084 16,668 14,372 9,758 14,659

21 Kentucky 65,902 35,997 81,025 44,661 19,486 12,595 1,558

22 Louisiana 91,246 305,063 869,350 204,181 36,848 27,270 76,805

23 Maine 5,154 16,078 11,084 22,731 5,068 3,292 136

24 Maryland 7,463 19,476 44,646 22,680 3,818 2,255 278

25 Massachusetts 7,304 18,925 8,296 15,561 3,562 2,611 91

26 Michigan 77,038 166,567 139,295 138,305 24,630 13,803 615

27 Minnesota 31,688 79,225 109,210 42,319 97,130 42,572 1,447

28 Mississippi 55,822 77,917 130,604 81,892 13,203 9,446 31,584

29 Missouri 59,087 30,766 115,122 133,606 57,424 21,287 28,608

30 Montana 5,472 21,792 58,055 36,510 15,623 8,254 598

31 Nebraska 11,424 15,062 13,626 7,637 10,342 3,899 14

32 Nevada 1,926 6,459 13,172 3,723 17,089 5,980 9

33 New Hampshire 5,087 4,362 6,442 8,092 1,390 842 23

34 New Jersey 79,765 52,739 22,676 71,653 11,025 7,694 465

35 New Mexico 9,095 68,196 21,555 119,341 7,821 5,668 38

36 New York 51,448 37,130 32,035 165,704 45,138 32,292 204

37 North Carolina 80,836 65,662 93,521 99,577 24,547 17,275 125

38 North Dakota 176 7,158 3,584 54,300 1,476 1,335 13

39 Ohio 59,521 79,281 745,465 334,133 38,902 25,813 3,090

40 Oklahoma 37,750 121,811 250,339 42,928 11,863 7,295 20,893

41 Oregon 8,946 17,175 85,362 6,714 9,389 6,714 16

42 Pennsylvania 50,102 173,185 340,856 141,871 39,699 26,160 7,183

44 Rhode Island 3,421 894 1,815 2,453 1,299 958 7

45 South Carolina 33,416 47,562 81,190 67,001 8,632 6,397 62

46 South Dakota 2,090 4,891 0 1,416 909 471 1

47 Tennessee 102,907 79,932 119,656 136,735 19,957 11,938 85

48 Texas 214,252 535,436 455,799 327,249 42,772 29,147 1,454

49 Utah 15,860 32,801 132,818 31,263 24,763 20,150 1,477

50 Vermont 1,486 803 1,595 2,064 1,007 590 3

51 Virginia 55,622 68,327 79,345 115,850 17,700 11,117 880

53 Washington 14,961 50,092 197,803 53,700 11,740 7,888 6,254

54 West Virginia 21,456 51,005 116,334 62,824 11,576 7,413 416

55 Wisconsin 48,251 56,094 69,181 91,386 16,971 12,034 1,041

56 Wyoming 14,202 50,184 84,785 61,094 38,814 22,666 569

Total 1,800,977 3,307,415 5,823,044 3,893,813 1,066,198 634,132 299,862
A-18

Table 18. IAQR 2015 Base On-road Annual Emissions (Tons)

FIPS State VOC NOx CO SO2 PM-10 PM-2.5 NH3

01 Alabama 73,623 78,804 1,274,667 657 3,313 1,932 7,005

04 Arizona 42,794 64,120 617,336 618 2,983 1,712 6,619

05 Arkansas 36,663 46,228 614,446 365 1,920 1,137 3,862

06 California 210,076 275,210 2,385,266 3,786 19,093 11,182 41,530

08 Colorado 35,646 56,073 691,043 515 2,535 1,467 5,506

09 Connecticut 16,070 33,251 249,639 355 1,816 1,069 3,887

10 Delaware 7,062 11,830 147,363 101 538 322 1,095

11 DC 1,942 3,299 42,572 45 210 118 500

12 Florida 201,790 211,115 3,956,434 1,932 9,096 5,168 20,798

13 Georgia 90,406 133,254 1,606,409 1,256 6,162 3,558 13,438

16 Idaho 15,875 23,108 342,514 180 940 555 1,897

17 Illinois 76,888 123,878 1,491,270 1,216 5,925 3,400 13,234

18 Indiana 74,868 101,430 1,603,040 834 4,252 2,491 8,894

19 Iowa 31,147 43,585 674,725 332 1,743 1,033 3,517

20 Kansas 29,737 42,095 625,521 335 1,715 1,007 3,568

21 Kentucky 45,693 67,101 915,803 536 2,817 1,670 5,725

22 Louisiana 54,241 62,772 944,778 481 2,413 1,402 5,125

23 Maine 12,523 21,805 324,327 166 887 527 1,752

24 Maryland 25,508 50,672 465,492 603 2,950 1,693 6,584

25 Massachusetts 27,298 50,409 575,702 629 3,018 1,724 6,928

26 Michigan 90,077 122,230 2,161,156 1,091 5,227 2,980 11,703

27 Minnesota 46,835 72,259 983,535 581 2,919 1,703 6,206

28 Mississippi 41,812 48,738 680,140 387 2,037 1,207 4,089

29 Missouri 50,197 82,868 1,030,139 787 3,685 2,067 8,417

30 Montana 10,562 17,594 243,933 127 687 411 1,339

31 Nebraska 19,082 26,809 423,544 208 1,080 637 2,204

32 Nevada 17,171 25,719 335,181 228 1,102 633 2,441

33 New Hampshire 10,924 18,395 241,797 142 772 464 1,521

34 New Jersey 35,699 64,681 566,351 777 3,737 2,130 8,539

35 New Mexico 29,560 38,716 594,947 308 1,593 938 3,259

36 New York 72,821 127,098 1,638,456 1,382 6,579 3,736 15,054

37 North Carolina 77,471 106,425 1,597,593 1,090 5,132 2,880 11,629

38 North Dakota 7,687 11,569 172,264 83 448 268 875

39 Ohio 94,185 140,294 1,943,742 1,265 6,215 3,596 13,566

40 Oklahoma 50,700 62,063 987,603 509 2,557 1,490 5,437

41 Oregon 25,133 47,165 500,520 420 2,119 1,238 4,469

42 Pennsylvania 86,641 137,144 1,907,047 1,195 5,874 3,386 12,822

44 Rhode Island 5,401 8,360 83,640 88 439 255 974

45 South Carolina 58,895 67,410 984,012 538 2,793 1,646 5,697

46 South Dakota 8,711 14,353 196,221 105 566 338 1,100

47 Tennessee 74,531 93,790 1,517,880 781 3,891 2,262 8,336

48 Texas 197,443 277,366 3,157,126 2,506 12,029 6,862 27,155

49 Utah 25,041 34,523 589,164 294 1,429 824 3,142

50 Vermont 6,015 10,908 148,439 86 452 268 902

51 Virginia 64,957 103,491 1,230,113 941 4,743 2,762 10,143

53 Washington 55,723 80,559 1,349,970 707 3,398 1,950 7,592

54 West Virginia 19,629 28,484 376,185 216 1,158 691 2,277

55 Wisconsin 39,874 76,085 960,707 673 3,418 1,992 7,194

56 Wyoming 7,648 13,145 179,073 94 509 304 994

Total 2,440,276 3,458,279 46,328,823 32,551 160,910 93,083 350,542
A-19

Table 19. IAQR 2015 Base Non-road Annual Emissions (Tons)

FIPS State VOC NOx CO SO2 PM-10 PM-2.5 NH3

01 Alabama 32,798 47,802 438,141 1,494 5,271 4,249 850

04 Arizona 32,630 36,972 612,706 619 9,124 4,275 673

05 Arkansas 20,093 28,970 267,038 365 2,576 2,289 532

06 California 149,152 235,899 3,232,631 13,249 19,931 17,437 5,888

08 Colorado 29,365 48,537 492,746 635 4,393 3,288 902

09 Connecticut 18,216 13,758 344,742 315 1,605 1,459 643

10 Delaware 6,039 14,822 91,633 295 1,136 1,017 127

11 DC 2,321 4,903 23,286 113 186 158 92

12 Florida 131,965 128,917 2,135,831 15,560 17,513 14,812 2,303

13 Georgia 46,179 55,313 847,928 2,514 5,798 5,000 1,622

16 Idaho 14,621 14,425 170,800 197 2,593 1,423 232

17 Illinois 69,021 127,051 1,190,122 1,380 8,585 7,728 2,563

18 Indiana 32,945 75,451 632,355 877 4,907 4,394 1,423

19 Iowa 21,912 46,265 354,808 367 4,062 3,680 671

20 Kansas 16,758 67,088 314,750 589 4,496 3,900 668

21 Kentucky 25,540 64,356 344,264 1,813 4,819 4,133 738

22 Louisiana 42,261 185,369 467,393 21,260 10,555 9,516 1,691

23 Maine 18,074 7,200 158,723 206 1,105 953 184

24 Maryland 33,924 33,441 574,791 8,033 3,769 3,274 633

25 Massachusetts 34,177 56,508 610,497 1,008 4,505 4,062 1,270

26 Michigan 88,876 51,442 1,099,629 1,140 6,303 5,663 2,059

27 Minnesota 63,307 53,064 602,546 847 5,651 5,151 1,114

28 Mississippi 22,382 38,257 258,017 1,943 3,756 3,168 502

29 Missouri 35,616 53,844 576,935 693 4,587 4,031 1,148

30 Montana 9,362 29,264 112,732 193 1,886 1,542 161

31 Nebraska 12,666 48,569 215,901 416 3,148 2,814 366

32 Nevada 13,251 21,225 211,624 290 2,999 1,794 230

33 New Hampshire 11,858 4,967 145,837 134 952 756 233

34 New Jersey 44,962 75,085 864,066 52,970 8,697 7,898 1,573

35 New Mexico 9,969 8,730 155,365 180 2,070 1,306 246

36 New York 85,796 73,472 1,505,260 1,895 7,983 7,045 2,879

37 North Carolina 46,044 47,811 834,169 1,049 6,883 5,707 1,804

38 North Dakota 9,195 34,383 111,990 226 2,908 2,549 149

39 Ohio 70,182 98,358 1,241,363 5,524 13,983 11,154 2,481

40 Oklahoma 21,373 33,257 350,407 487 3,406 2,881 919

41 Oregon 28,613 44,646 412,499 714 2,678 2,435 658

42 Pennsylvania 65,649 67,095 1,129,376 3,168 6,048 5,230 2,209

44 Rhode Island 4,383 4,713 93,192 2,901 570 520 163

45 South Carolina 25,445 24,160 428,653 1,130 2,807 2,460 734

46 South Dakota 7,495 19,450 98,197 116 1,973 1,729 149

47 Tennessee 38,116 123,363 536,210 2,720 6,674 6,066 1,186

48 Texas 104,746 380,897 2,124,134 33,591 24,085 19,607 5,818

49 Utah 17,269 24,644 225,855 268 2,413 1,888 418

50 Vermont 6,084 3,147 68,785 71 426 365 103

51 Virginia 40,859 65,601 710,236 4,552 8,029 6,630 980

53 Washington 40,593 67,516 631,226 9,535 6,282 5,202 1,100

54 West Virginia 12,171 51,505 154,504 34,261 4,417 4,031 286

55 Wisconsin 50,629 41,306 597,605 605 4,281 3,778 1,197

56 Wyoming 6,676 20,230 70,111 139 1,034 802 170

Total 1,771,559 2,903,048 28,871,613 232,644 263,857 221,249 54,742
A-20

Table 20. IAQR 2015 Base Area Annual Emissions (Tons)

FIPS State VOC NOx CO SO2 PM-10 PM-2.5 NH3

01 Alabama 147,717 71,360 516,799 54,764 192,257 78,819 88,825

04 Arizona 147,153 80,293 212,314 4,443 124,052 71,502 32,022

05 Arkansas 121,987 46,195 190,819 21,833 135,645 41,884 154,846

06 California 491,456 132,987 620,067 10,897 350,983 117,982 167,727

08 Colorado 126,190 62,177 69,352 4,803 156,189 37,404 95,837

09 Connecticut 63,114 9,139 77,436 459 34,881 7,159 5,986

10 Delaware 23,804 6,946 19,656 10,428 13,956 6,461 13,608

11 DC 8,586 1,980 869 5,921 1,953 721 1,141

12 Florida 359,609 54,736 467,578 46,656 256,975 93,670 71,704

13 Georgia 268,166 76,381 1,114,791 6,898 352,511 156,723 93,851

16 Idaho 71,986 30,150 438,105 9,087 157,148 60,605 60,889

17 Illinois 273,244 116,003 66,517 37,232 246,317 56,584 145,952

18 Indiana 223,977 38,714 71,471 2,319 166,247 34,442 100,158

19 Iowa 132,804 31,811 43,276 14,996 150,631 33,646 310,204

20 Kansas 121,553 76,256 74,930 3,542 208,336 45,823 210,514

21 Kentucky 128,788 78,586 121,362 58,831 104,934 35,421 88,617

22 Louisiana 111,966 105,559 137,606 95,933 144,292 59,277 72,447

23 Maine 39,714 4,864 35,079 10,762 25,591 10,562 6,499

24 Maryland 65,591 15,960 93,687 933 66,161 13,988 27,484

25 Massachusetts 126,635 24,961 35,602 61,044 73,418 19,994 9,087

26 Michigan 260,658 116,735 117,357 32,875 151,877 44,719 62,904

27 Minnesota 186,991 25,540 72,193 5,695 229,766 50,999 193,373

28 Mississippi 137,505 58,172 361,720 85,352 143,913 57,404 71,865

29 Missouri 141,001 15,216 104,212 32,278 319,757 66,248 184,169

30 Montana 53,937 18,603 223,364 1,411 152,991 44,580 86,519

31 Nebraska 79,635 15,776 19,373 10,298 156,165 29,804 226,312

32 Nevada 41,269 8,967 12,548 4,107 39,156 9,732 14,720

33 New Hampshire 32,873 14,137 26,249 91,996 19,701 7,713 2,247

34 New Jersey 144,404 80,210 46,277 42,517 86,477 26,893 9,465

35 New Mexico 58,749 34,044 90,335 9,927 252,411 48,715 44,037

36 New York 325,497 82,286 95,010 118,216 189,190 57,251 55,389

37 North Carolina 367,073 37,921 717,184 34,478 180,595 78,175 175,663

38 North Dakota 59,430 21,882 13,516 65,873 97,732 19,396 87,433

39 Ohio 287,903 83,817 97,786 64,303 186,617 54,044 83,569

40 Oklahoma 94,792 34,141 49,281 5,690 242,364 47,764 188,583

41 Oregon 141,650 40,205 839,787 21,452 252,063 124,175 58,493

42 Pennsylvania 246,274 110,183 185,871 80,565 157,655 53,177 83,820

44 Rhode Island 18,741 2,701 4,072 3,996 8,222 2,620 1,260

45 South Carolina 157,237 26,841 275,218 16,186 118,876 45,672 30,006

46 South Dakota 42,288 8,246 23,544 25,135 112,100 23,760 126,624

47 Tennessee 243,272 53,973 204,066 49,292 127,498 49,253 79,402

48 Texas 579,751 44,600 300,113 9,885 863,796 180,445 448,202

49 Utah 68,759 24,951 34,634 13,897 69,389 16,898 29,783

50 Vermont 22,630 11,110 19,428 13,165 21,146 6,827 8,688

51 Virginia 210,021 46,708 290,029 9,742 150,838 35,101 69,379

53 Washington 157,056 23,927 193,592 3,845 118,429 46,061 47,621

54 West Virginia 53,973 21,489 55,088 11,484 45,472 15,624 16,348

55 Wisconsin 181,851 59,470 117,604 47,355 119,630 37,172 119,348

56 Wyoming 18,855 73,828 40,585 17,756 165,055 28,893 45,852

Total 7,468,115 2,260,738 9,037,352 1,390,552 7,741,355 2,291,781 4,408,472
A-21

Table 21. IAQR 2015 Base All Sectors Annual Emissions (Tons)

FIPS State VOC NOx CO SO2 PM-10 PM-2.5 NH3

01 Alabama 317,555 412,881 2,486,784 596,395 249,274 112,667 100,922

04 Arizona 239,566 393,661 1,499,552 179,824 180,080 101,046 39,324

05 Arkansas 191,878 198,448 1,206,734 163,280 175,475 65,648 177,308

06 California 916,990 804,339 6,401,555 90,479 427,406 167,010 232,245

08 Colorado 231,122 294,722 1,291,130 95,613 190,366 58,377 102,622

09 Connecticut 103,557 73,176 681,262 15,178 40,319 10,867 10,578

10 Delaware 42,680 53,010 277,002 97,441 17,554 9,013 15,546

11 DC 13,093 11,107 67,115 8,243 2,450 1,072 1,741

12 Florida 718,021 626,654 6,671,824 388,137 309,773 128,808 102,018

13 Georgia 443,224 493,319 3,813,036 708,520 406,244 192,322 127,649

16 Idaho 102,839 75,745 958,915 37,289 171,718 70,088 63,021

17 Illinois 558,833 684,727 2,889,143 865,314 366,621 122,429 176,256

18 Indiana 374,359 507,615 2,582,093 692,897 204,921 61,316 119,981

19 Iowa 192,636 239,669 1,085,579 279,102 168,389 45,252 323,979

20 Kansas 186,068 399,231 1,107,777 86,450 231,775 62,258 229,413

21 Kentucky 267,303 446,772 1,476,965 469,007 141,152 58,017 96,650

22 Louisiana 300,192 708,927 2,432,889 434,391 197,521 98,884 156,071

23 Maine 75,514 52,084 533,854 37,076 33,000 15,482 8,571

24 Maryland 132,921 181,585 1,183,782 261,826 80,142 22,687 34,983

25 Massachusetts 195,691 162,726 1,244,325 94,500 85,840 29,111 17,401

26 Michigan 517,677 588,089 3,532,868 564,164 194,361 70,821 77,299

27 Minnesota 329,367 338,309 1,775,051 142,274 338,834 102,192 202,145

28 Mississippi 257,879 268,023 1,447,855 243,040 165,132 72,232 108,044

29 Missouri 287,156 327,759 1,837,693 484,920 389,745 96,560 222,351

30 Montana 79,557 125,799 640,558 55,959 174,069 56,174 88,620

31 Nebraska 123,107 164,036 675,039 115,949 172,019 38,036 228,898

32 Nevada 73,915 103,653 581,130 25,663 62,962 19,542 17,412

33 New Hampshire 60,856 45,674 424,775 107,652 23,191 10,034 4,024

34 New Jersey 305,045 303,427 1,507,150 207,154 112,056 45,457 20,044

35 New Mexico 107,734 226,223 865,510 178,332 265,836 57,546 47,584

36 New York 536,363 390,447 3,295,230 501,274 252,821 102,228 73,703

37 North Carolina 572,450 321,291 3,254,516 280,562 226,829 108,123 189,231

38 North Dakota 77,177 155,534 309,211 292,476 105,895 25,170 88,475

39 Ohio 513,552 663,181 4,047,217 1,452,804 261,539 101,813 102,721

40 Oklahoma 205,308 337,983 1,655,064 182,624 262,972 61,166 215,916

41 Oregon 204,482 162,695 1,848,682 44,487 266,643 134,886 63,637

42 Pennsylvania 450,228 702,633 3,584,209 1,039,410 225,125 94,656 106,048

44 Rhode Island 31,975 18,657 185,929 9,438 10,584 4,407 2,404

45 South Carolina 275,522 232,216 1,776,870 280,395 143,230 60,776 36,504

46 South Dakota 60,657 60,492 318,600 68,890 115,927 26,383 127,874

47 Tennessee 459,582 453,773 2,383,926 499,152 162,013 71,621 89,015

48 Texas 1,099,929 1,439,583 6,147,833 860,301 963,037 248,972 483,046

49 Utah 127,299 186,322 985,581 77,262 100,324 40,801 34,824

50 Vermont 36,215 25,972 238,251 15,386 23,032 8,051 9,695

51 Virginia 371,956 342,075 2,318,030 317,584 187,162 57,800 81,386

53 Washington 268,588 248,430 2,387,825 73,746 140,640 61,716 62,576

54 West Virginia 108,440 300,729 712,084 593,903 73,720 32,609 19,339

55 Wisconsin 321,319 336,423 1,754,446 329,571 147,311 56,951 128,787

56 Wyoming 47,887 247,890 378,800 126,323 208,557 54,755 47,589

Total 13,515,259 15,937,721 90,761,250 14,771,657 9,455,584 3,353,829 5,115,469
A-22

Table 22. IAQR 2010 Control EGU Annual Emissions (Tons)

FIPS State VOC NOx CO SO2 PM-10 PM-2.5 NH3

01 Alabama 1,081 73,687 22,180 354,454 8,526 4,426 9

04 Arizona 641 84,483 25,711 47,779 3,705 2,014 4

05 Arkansas 378 38,169 6,782 77,934 2,039 1,421 3

06 California 1,605 17,855 56,973 17,317 2,438 1,144 706

08 Colorado 449 82,804 8,207 73,089 1,597 892 4

09 Connecticut 99 5,172 5,994 5,318 829 283 0

10 Delaware 83 8,545 1,842 34,020 685 322 1

11 DC 0 30 30 0 1 1 0

12 Florida 1,197 63,135 45,338 192,948 9,427 4,237 42

13 Georgia 1,153 65,104 19,234 407,671 10,752 5,272 10

16 Idaho 21 1,194 2,255 0 38 38 0

17 Illinois 1,478 113,864 11,313 246,121 6,593 3,805 11

18 Indiana 1,699 137,248 14,276 381,404 12,233 6,450 37

19 Iowa 424 38,307 3,917 156,234 2,979 1,835 4

20 Kansas 537 100,659 4,228 62,842 2,817 1,741 4

21 Kentucky 1,331 76,290 13,833 311,149 8,551 3,943 11

22 Louisiana 395 37,057 12,394 79,840 3,525 1,489 3

23 Maine 48 2,077 4,557 3,210 348 147 0

24 Maryland 412 22,904 4,140 67,079 3,384 1,441 4

25 Massachusetts 229 9,998 11,172 14,661 1,164 614 20

26 Michigan 995 94,310 13,069 376,726 6,086 3,504 32

27 Minnesota 482 42,698 4,191 77,332 3,001 1,555 4

28 Mississippi 301 19,633 11,215 73,467 2,119 903 2

29 Missouri 1,050 67,141 8,769 244,403 3,475 2,363 8

30 Montana 220 38,461 2,002 17,718 2,874 1,378 2

31 Nebraska 299 57,730 2,447 97,391 1,282 879 3

32 Nevada 273 37,789 7,089 16,535 2,486 1,331 11

33 New Hampshire 109 3,129 4,402 5,626 324 235 0

34 New Jersey 172 10,997 5,305 25,497 1,930 754 1

35 New Mexico 359 76,378 3,204 48,577 1,939 918 3

36 New York 814 60,728 19,082 113,726 3,838 1,810 216

37 North Carolina 995 62,004 10,042 219,369 9,585 4,021 9

38 North Dakota 717 84,889 8,259 68,024 3,464 1,656 6

39 Ohio 1,667 118,712 15,204 368,186 15,366 6,976 16

40 Oklahoma 624 83,133 15,930 133,009 2,770 1,725 47

41 Oregon 131 13,328 9,552 15,187 379 309 0

42 Pennsylvania 1,449 81,494 18,037 179,711 15,111 6,367 13

44 Rhode Island 23 1,504 2,489 0 42 42 0

45 South Carolina 496 33,570 5,586 162,980 9,778 4,435 5

46 South Dakota 97 17,608 835 2,865 500 111 1

47 Tennessee 747 50,199 5,919 258,130 3,957 2,082 7

48 Texas 3,782 198,229 102,008 401,700 20,228 12,760 455

49 Utah 369 69,368 3,080 31,541 2,329 1,041 3

50 Vermont 0 1 2 0 0 0 0

51 Virginia 456 33,536 5,615 160,665 5,618 2,070 4

53 Washington 263 28,321 16,109 5,959 806 630 9

54 West Virginia 1,179 41,356 9,654 221,065 10,836 4,740 11

55 Wisconsin 656 74,189 7,840 200,978 2,977 1,954 6

56 Wyoming 506 90,500 4,241 47,276 3,145 2,090 5

Total 32,488 2,569,519 595,553 6,106,708 217,876 110,155 1,753
A-23

Table 23. IAQR 2010 Control All Sectors Annual Emissions (Tons)

FIPS State VOC NOx CO SO2 PM-10 PM-2.5 NH3

01 Alabama 323,460 392,522 2,388,878 529,837 247,942 112,466 96,434

04 Arizona 239,882 415,625 1,452,287 174,212 175,130 99,450 38,221

05 Arkansas 195,862 206,752 1,164,461 117,363 174,917 66,214 170,944

06 California 953,933 962,512 6,417,962 88,393 425,662 169,230 225,600

08 Colorado 233,760 325,177 1,249,765 94,875 185,319 57,465 101,533

09 Connecticut 110,510 91,556 689,172 14,048 40,796 11,146 9,978

10 Delaware 43,858 58,133 264,052 83,025 17,107 8,906 14,488

11 DC 13,359 13,022 63,624 8,031 2,356 1,080 1,635

12 Florida 734,000 617,245 6,225,334 344,970 302,619 128,614 98,003

13 Georgia 446,618 466,821 3,724,276 510,856 404,257 191,960 120,591

16 Idaho 106,763 87,196 936,705 35,953 176,791 71,161 62,605

17 Illinois 565,253 692,540 2,855,086 562,577 362,665 122,056 172,441

18 Indiana 377,887 453,794 2,487,715 537,702 203,319 61,722 116,962

19 Iowa 196,661 215,091 1,051,530 255,811 169,309 46,253 319,214

20 Kansas 187,681 422,303 1,066,068 83,420 233,460 63,221 227,226

21 Kentucky 271,809 356,759 1,413,169 414,409 138,620 57,991 95,184

22 Louisiana 305,977 732,014 2,325,175 388,997 194,937 98,223 149,652

23 Maine 81,087 61,960 519,377 36,624 32,969 15,950 8,220

24 Maryland 136,049 170,018 1,140,417 99,175 78,716 23,102 33,098

25 Massachusetts 201,514 197,438 1,199,294 93,049 85,840 29,847 16,400

26 Michigan 539,476 605,404 3,408,855 546,718 193,228 72,136 75,508

27 Minnesota 344,969 319,626 1,721,422 125,819 334,751 101,317 199,067

28 Mississippi 258,614 264,311 1,400,522 236,088 165,927 72,804 103,538

29 Missouri 294,385 293,700 1,786,185 406,492 389,583 97,102 217,501

30 Montana 82,110 136,409 625,461 54,229 172,653 56,010 88,208

31 Nebraska 124,373 182,690 649,306 115,585 173,376 38,861 227,016

32 Nevada 75,279 113,876 541,439 24,467 60,664 19,050 16,951

33 New Hampshire 64,422 53,226 412,076 104,621 23,024 10,306 3,838

34 New Jersey 307,250 321,315 1,480,781 193,141 109,018 45,092 19,020

35 New Mexico 111,272 242,760 818,172 173,724 268,036 58,018 47,160

36 New York 555,310 457,958 3,225,081 407,872 253,756 104,262 71,821

37 North Carolina 570,692 372,385 3,123,041 350,841 221,179 107,587 183,068

38 North Dakota 78,756 171,558 305,085 188,680 107,196 26,049 88,061

39 Ohio 525,259 596,601 3,978,318 775,888 258,376 102,386 99,799

40 Oklahoma 210,834 364,079 1,579,581 180,781 264,019 61,906 212,032

41 Oregon 209,242 189,974 1,827,268 43,838 272,438 137,145 62,828

42 Pennsylvania 467,893 650,041 3,453,723 406,103 222,872 95,385 102,698

44 Rhode Island 33,012 23,044 190,558 9,495 10,375 4,430 2,262

45 South Carolina 275,583 229,695 1,700,997 244,148 142,936 61,126 34,989

46 South Dakota 61,647 74,815 309,685 28,394 115,082 26,714 127,088

47 Tennessee 461,601 452,332 2,274,425 443,739 159,838 72,314 87,091

48 Texas 1,100,295 1,596,858 5,916,172 765,629 957,540 248,855 477,141

49 Utah 131,133 205,081 931,274 75,609 98,082 40,183 34,253

50 Vermont 38,545 32,147 230,893 15,148 23,371 8,344 9,544

51 Virginia 374,356 369,319 2,228,598 288,262 184,049 56,576 78,793

53 Washington 277,936 291,665 2,269,514 71,365 140,056 62,792 60,547

54 West Virginia 112,220 210,234 690,017 328,406 73,659 33,025 18,762

55 Wisconsin 330,647 347,762 1,702,885 336,762 145,280 57,518 127,085

56 Wyoming 49,875 253,187 368,088 124,577 207,543 53,524 47,518

Total 13,792,910 16,360,528 87,783,770 11,539,750 9,400,641 3,366,871 5,001,617
A-24

Table 24. IAQR 2010 Control Minus Base All Sectors Annual Emissions

FIPS State NOx Tons Delta SO2 Tons Delta NOx % Delta SO2 % Delta

01 Alabama -60,446 -118,589 -13.3 -18.3

04 Arizona -84 0 -0.0 0.0

05 Arkansas -14,342 -44,733 -6.5 -27.6

06 California 184 0 0.0 0.0

08 Colorado 89 0 0.0 0.0

09 Connecticut 4 -966 0.0 -6.4

10 Delaware -1,726 -12,336 -2.9 -12.9

11 DC -12 0 -0.1 0.0

12 Florida -98,711 -40,293 -13.8 -10.5

13 Georgia -85,478 -201,483 -15.5 -28.3

16 Idaho -3 0 -0.0 0.0

17 Illinois -57,579 -354,715 -7.7 -38.7

18 Indiana -102,464 -288,961 -18.4 -35.0

19 Iowa -47,783 -13,628 -18.2 -5.1

20 Kansas -283 -691 -0.1 -0.8

21 Kentucky -119,593 -51,996 -25.1 -11.1

22 Louisiana -12,710 -32,694 -1.7 -7.8

23 Maine -26 0 -0.0 0.0

24 Maryland -37,725 -165,150 -18.2 -62.5

25 Massachusetts -395 -989 -0.2 -1.1

26 Michigan -31,084 -10,901 -4.9 -2.0

27 Minnesota -61,836 -14,229 -16.2 -10.2

28 Mississippi -23,531 0 -8.2 0.0

29 Missouri -69,868 -48,690 -19.2 -10.7

30 Montana -4 -205 -0.0 -0.4

31 Nebraska -95 -239 -0.1 -0.2

32 Nevada 386 127 0.3 0.5

33 New Hampshire -519 -1,663 -1.0 -1.6

34 New Jersey -18,325 -15,758 -5.4 -7.5

35 New Mexico -21 0 -0.0 0.0

36 New York -7,686 -100,351 -1.7 -19.7

37 North Carolina -65 0 -0.0 0.0

38 North Dakota 6,962 -92,914 4.2 -33.0

39 Ohio -148,085 -890,498 -19.9 -53.4

40 Oklahoma 1,018 0 0.3 0.0

41 Oregon -19 0 -0.0 0.0

42 Pennsylvania -128,267 -673,720 -16.5 -62.4

44 Rhode Island 64 0 0.3 0.0

45 South Carolina -31,167 -36,765 -11.9 -13.1

46 South Dakota 5,860 -33,440 8.5 -54.1

47 Tennessee -52,621 -47,952 -10.4 -9.8

48 Texas -2,679 -86,040 -0.2 -10.1

49 Utah 0 0 0.0 0.0

50 Vermont 0 0 0.0 0.0

51 Virginia -21,994 -27,107 -5.6 -8.6

53 Washington -110 0 -0.0 0.0

54 West Virginia -113,801 -329,564 -35.1 -50.1

55 Wisconsin -37,351 -13,084 -9.7 -3.7

56 Wyoming 0 -0 0.0 -0.0

Total -1,373,919 -3,750,219 -7.7 -24.5
A-25

Table 25. IAQR 2015 Control EGU Annual Emissions (Tons)

FIPS State VOC NOx CO SO2 PM-10 PM-2.5 NH3

01 Alabama 1,165 59,250 31,101 334,173 8,699 4,587 9

04 Arizona 677 85,861 29,738 47,779 3,773 2,082 4

05 Arkansas 405 8,587 9,736 77,935 2,089 1,471 3

06 California 1,389 19,586 72,692 17,317 2,789 1,495 465

08 Colorado 464 83,648 9,944 73,089 1,621 915 4

09 Connecticut 100 5,269 6,119 5,318 831 285 0

10 Delaware 92 9,088 2,707 34,574 710 342 1

11 DC 1 78 127 0 2 2 0

12 Florida 1,703 54,558 54,143 173,799 9,500 4,311 325

13 Georgia 1,203 51,838 25,151 196,833 10,647 5,274 10

16 Idaho 22 1,229 2,428 0 41 41 0

17 Illinois 1,569 96,049 13,039 262,563 6,918 3,987 11

18 Indiana 1,750 77,976 16,421 335,700 13,001 7,060 37

19 Iowa 454 39,694 4,268 164,217 3,132 1,937 4

20 Kansas 543 101,556 4,459 59,532 2,841 1,759 5

21 Kentucky 1,371 54,871 14,578 288,002 8,960 4,135 11

22 Louisiana 407 14,549 13,669 79,841 3,546 1,510 3

23 Maine 49 2,138 4,640 3,210 350 148 0

24 Maryland 432 24,707 5,402 39,592 3,490 1,492 4

25 Massachusetts 261 11,289 14,033 10,117 1,244 676 22

26 Michigan 1,045 99,367 16,329 385,221 6,267 3,624 33

27 Minnesota 500 44,813 4,530 79,335 3,118 1,622 4

28 Mississippi 380 14,583 19,887 43,279 2,265 1,049 2

29 Missouri 1,188 73,163 11,130 289,353 4,042 2,759 9

30 Montana 224 38,547 2,474 17,031 2,882 1,386 2

31 Nebraska 303 58,001 2,882 97,391 1,289 886 3

32 Nevada 300 41,649 8,520 17,506 2,641 1,414 11

33 New Hampshire 109 3,124 4,403 5,602 324 235 0

34 New Jersey 223 14,368 8,184 21,186 2,247 881 2

35 New Mexico 366 76,710 3,322 48,577 1,941 920 7

36 New York 663 57,818 26,997 117,490 3,986 1,959 75

37 North Carolina 1,052 54,849 12,909 144,369 9,910 4,192 10

38 North Dakota 717 84,890 8,259 68,024 3,464 1,656 6

39 Ohio 1,752 102,175 18,924 312,501 15,784 7,204 16

40 Oklahoma 703 87,241 18,259 131,902 2,795 1,750 86

41 Oregon 140 13,504 10,515 15,187 395 325 0

42 Pennsylvania 1,496 79,078 22,162 176,683 15,218 6,460 13

44 Rhode Island 29 1,985 3,204 0 54 54 0

45 South Carolina 521 30,780 7,809 145,254 9,852 4,489 5

46 South Dakota 97 17,613 841 2,865 501 111 1

47 Tennessee 757 31,580 6,267 192,419 3,996 2,104 7

48 Texas 3,716 160,282 111,554 365,440 19,983 12,630 415

49 Utah 369 69,400 3,108 31,541 2,330 1,042 3

50 Vermont 0 4 5 0 0 0 0

51 Virginia 487 33,794 8,384 117,255 5,756 2,149 4

53 Washington 254 26,234 15,112 5,959 789 613 9

54 West Virginia 1,199 35,873 9,942 138,781 10,964 4,796 11

55 Wisconsin 689 60,426 9,036 182,174 2,923 1,922 6

56 Wyoming 506 90,502 4,245 45,792 3,145 2,090 5

Total 33,846 2,304,175 713,590 5,401,704 223,046 113,828 1,663
A-26

Table 26. IAQR 2015 Control All Sectors Annual Emissions (Tons)

FIPS State VOC NOx CO SO2 PM-10 PM-2.5 NH3

01 Alabama 317,563 343,540 2,488,487 514,583 249,448 112,762 100,922

04 Arizona 239,570 393,546 1,500,063 179,824 180,088 101,054 39,324

05 Arkansas 191,875 154,249 1,206,448 118,547 175,813 65,878 177,308

06 California 916,939 804,329 6,402,367 90,479 427,435 167,039 232,206

08 Colorado 231,122 294,738 1,291,182 95,613 190,367 58,377 102,622

09 Connecticut 103,557 73,185 681,255 14,211 40,319 10,867 10,578

10 Delaware 42,687 51,255 278,051 83,740 17,547 9,020 15,546

11 DC 13,093 11,099 67,137 8,243 2,451 1,073 1,741

12 Florida 717,941 510,410 6,673,377 331,641 309,801 128,836 101,959

13 Georgia 443,198 391,862 3,813,050 305,038 406,655 192,600 127,649

16 Idaho 102,839 75,745 958,915 37,289 171,718 70,088 63,021

17 Illinois 558,893 601,195 2,889,901 588,671 366,686 122,506 176,256

18 Indiana 374,353 339,747 2,582,125 497,034 205,441 61,791 119,981

19 Iowa 192,598 188,558 1,085,118 265,277 168,146 45,105 323,979

20 Kansas 186,065 398,762 1,107,743 80,667 231,760 62,247 229,413

21 Kentucky 267,294 300,911 1,477,033 393,843 141,016 57,954 96,650

22 Louisiana 300,121 673,312 2,432,795 401,697 197,654 98,975 156,071

23 Maine 75,514 52,084 533,854 37,076 33,000 15,482 8,571

24 Maryland 132,917 144,255 1,184,018 71,839 80,188 22,702 34,983

25 Massachusetts 195,675 162,092 1,244,130 88,359 85,747 29,067 17,397

26 Michigan 517,694 556,341 3,533,766 558,632 194,303 70,790 77,313

27 Minnesota 329,322 274,900 1,772,014 128,779 338,584 102,046 202,144

28 Mississippi 257,901 237,667 1,450,368 212,853 165,173 72,274 108,044

29 Missouri 287,089 255,856 1,837,538 456,717 389,494 96,391 222,350

30 Montana 79,557 125,799 640,558 55,273 174,069 56,174 88,620

31 Nebraska 123,110 164,217 675,326 115,949 172,024 38,041 228,898

32 Nevada 73,917 104,018 581,044 25,854 62,987 19,553 17,412

33 New Hampshire 60,850 44,986 424,728 105,965 23,139 10,010 4,024

34 New Jersey 305,053 287,082 1,507,554 189,103 112,182 45,495 20,045

35 New Mexico 107,739 226,396 865,525 178,332 265,836 57,546 47,587

36 New York 536,225 377,804 3,297,758 404,687 252,876 102,282 73,602

37 North Carolina 572,476 312,668 3,255,376 280,562 227,066 108,228 189,231

38 North Dakota 77,205 159,882 309,614 188,505 106,028 25,205 88,475

39 Ohio 513,543 503,925 4,047,280 717,725 261,500 101,811 102,721

40 Oklahoma 205,318 338,513 1,655,890 181,516 262,985 61,179 215,918

41 Oregon 204,482 162,695 1,848,682 44,487 266,643 134,886 63,637

42 Pennsylvania 450,163 566,685 3,585,311 403,482 224,494 94,413 106,047

44 Rhode Island 31,975 18,653 185,923 9,438 10,583 4,407 2,404

45 South Carolina 275,514 196,753 1,776,882 230,108 142,960 60,665 36,504

46 South Dakota 60,681 64,553 318,803 29,637 116,048 26,408 127,875

47 Tennessee 459,583 382,638 2,384,080 381,946 162,015 71,624 89,015

48 Texas 1,099,908 1,398,581 6,148,727 738,673 962,665 248,691 483,045

49 Utah 127,299 186,320 985,579 77,262 100,324 40,801 34,824

50 Vermont 36,215 25,972 238,251 15,386 23,032 8,051 9,695

51 Virginia 371,947 317,920 2,318,107 248,341 187,067 57,758 81,386

53 Washington 268,587 248,329 2,387,703 73,746 140,638 61,714 62,576

54 West Virginia 108,428 188,356 712,052 247,566 73,587 32,554 19,339

55 Wisconsin 321,293 293,381 1,754,133 322,193 147,223 56,898 128,786

56 Wyoming 47,887 247,890 378,800 124,875 208,557 54,755 47,589

Total 13,514,774 14,233,656 90,774,422 10,951,264 9,455,366 3,354,073 5,115,282
A-27

Table 27. IAQR 2015 Control Minus Base All Sectors Annual Emissions

FIPS State NOx Tons Delta SO2 Tons Delta NOx % Delta SO2 % Delta

01 Alabama -69,341 -81,812 -16.8 -13.7

04 Arizona -115 0 -0.0 0.0

05 Arkansas -44,199 -44,732 -22.3 -27.4

06 California -10 0 -0.0 0.0

08 Colorado 16 0 0.0 0.0

09 Connecticut 9 -966 0.0 -6.4

10 Delaware -1,756 -13,701 -3.3 -14.1

11 DC -8 0 -0.1 0.0

12 Florida -116,245 -56,496 -18.6 -14.6

13 Georgia -101,457 -403,482 -20.6 -56.9

16 Idaho 0 0 0.0 0.0

17 Illinois -83,532 -276,643 -12.2 -32.0

18 Indiana -167,868 -195,863 -33.1 -28.3

19 Iowa -51,111 -13,824 -21.3 -5.0

20 Kansas -469 -5,784 -0.1 -6.7

21 Kentucky -145,860 -75,164 -32.6 -16.0

22 Louisiana -35,615 -32,694 -5.0 -7.5

23 Maine 0 0 0.0 0.0

24 Maryland -37,331 -189,987 -20.6 -72.6

25 Massachusetts -634 -6,142 -0.4 -6.5

26 Michigan -31,747 -5,531 -5.4 -1.0

27 Minnesota -63,409 -13,495 -18.7 -9.5

28 Mississippi -30,356 -30,187 -11.3 -12.4

29 Missouri -71,903 -28,203 -21.9 -5.8

30 Montana 0 -686 0.0 -1.2

31 Nebraska 181 0 0.1 0.0

32 Nevada 365 191 0.4 0.7

33 New Hampshire -689 -1,687 -1.5 -1.6

34 New Jersey -16,345 -18,051 -5.4 -8.7

35 New Mexico 173 0 0.1 0.0

36 New York -12,643 -96,587 -3.2 -19.3

37 North Carolina -8,623 0 -2.7 0.0

38 North Dakota 4,348 -103,971 2.8 -35.5

39 Ohio -159,256 -735,079 -24.0 -50.6

40 Oklahoma 530 -1,107 0.2 -0.6

41 Oregon 0 0 0.0 0.0

42 Pennsylvania -135,949 -635,927 -19.3 -61.2

44 Rhode Island -4 0 -0.0 0.0

45 South Carolina -35,463 -50,287 -15.3 -17.9

46 South Dakota 4,060 -39,254 6.7 -57.0

47 Tennessee -71,134 -117,207 -15.7 -23.5

48 Texas -41,003 -121,628 -2.8 -14.1

49 Utah -2 0 -0.0 0.0

50 Vermont 0 0 0.0 0.0

51 Virginia -24,155 -69,243 -7.1 -21.8

53 Washington -102 0 -0.0 0.0

54 West Virginia -112,373 -346,336 -37.4 -58.3

55 Wisconsin -43,043 -7,378 -12.8 -2.2

56 Wyoming 0 -1,448 0.0 -1.1

Total -1,704,065 -3,820,393 -10.7 -25.9
A-28

Table 28. NOx Comparison of 2010 Base-1 Versus 2010 Base-2

FIPS State

2010 Base-2 2010 Base-1 Base-2 Minus 2010 Base-1 All Base-2 Minus

EGUs (tpy) EGUs (tpy) Base-1 (tpy) Sectors (tpy) Base-1 (% of All)

01 Alabama 134,134 129,543 4,590 448,379 1.0

04 Arizona 84,567 88,190 -3,623 419,331 -0.9

05 Arkansas 52,511 52,570 -58 221,152 -0.0

06 California 17,671 18,221 -550 962,878 -0.1

08 Colorado 82,714 87,047 -4,333 329,420 -1.3

09 Connecticut 5,168 6,682 -1,514 93,065 -1.6

10 Delaware 10,271 11,503 -1,232 61,091 -2.0

11 DC 42 70 -28 13,062 -0.2

12 Florida 161,846 162,927 -1,082 717,038 -0.2

13 Georgia 150,582 152,535 -1,953 554,251 -0.4

16 Idaho 1,197 1,398 -201 87,400 -0.2

17 Illinois 171,443 194,241 -22,798 772,917 -2.9

18 Indiana 239,713 223,339 16,373 539,885 3.0

19 Iowa 86,090 95,351 -9,261 272,135 -3.4

20 Kansas 100,942 101,358 -416 423,001 -0.1

21 Kentucky 195,883 186,325 9,558 466,794 2.0

22 Louisiana 49,767 64,710 -14,943 759,667 -2.0

23 Maine 2,103 6,047 -3,944 65,930 -6.0

24 Maryland 60,629 60,515 114 207,629 0.1

25 Massachusetts 10,392 27,805 -17,412 215,245 -8.1

26 Michigan 125,394 126,212 -818 637,307 -0.1

27 Minnesota 104,535 109,707 -5,173 386,635 -1.3

28 Mississippi 43,163 49,726 -6,563 294,404 -2.2

29 Missouri 137,009 144,698 -7,689 371,257 -2.1

30 Montana 38,465 38,528 -64 136,477 -0.0

31 Nebraska 57,826 58,111 -285 183,071 -0.2

32 Nevada 37,403 44,778 -7,375 120,865 -6.1

33 New Hampshire 3,647 3,031 616 53,128 1.2

34 New Jersey 29,322 39,956 -10,634 350,274 -3.0

35 New Mexico 76,400 77,261 -861 243,643 -0.4

36 New York 68,413 58,665 9,749 455,895 2.1

37 North Carolina 62,069 64,705 -2,636 375,086 -0.7

38 North Dakota 77,927 81,093 -3,166 167,762 -1.9

39 Ohio 266,798 249,054 17,743 726,942 2.4

40 Oklahoma 82,115 97,721 -15,607 378,667 -4.1

41 Oregon 13,346 18,048 -4,701 194,694 -2.4

42 Pennsylvania 209,760 212,124 -2,364 780,671 -0.3

44 Rhode Island 1,440 1,343 97 22,884 0.4

45 South Carolina 64,737 67,477 -2,740 263,602 -1.0

46 South Dakota 11,748 13,846 -2,099 71,054 -3.0

47 Tennessee 102,819 106,702 -3,883 508,836 -0.8

48 Texas 200,909 246,216 -45,308 1,644,845 -2.8

49 Utah 69,368 68,411 957 204,124 0.5

50 Vermont 1 18 -17 32,163 -0.1

51 Virginia 55,530 55,794 -264 391,576 -0.1

53 Washington 28,432 26,567 1,865 289,910 0.6

54 West Virginia 155,157 142,549 12,608 311,427 4.0

55 Wisconsin 111,540 116,180 -4,640 389,753 -1.2

56 Wyoming 90,500 90,261 239 252,948 0.1

Total 3,943,438 4,079,159 -135,721 17,870,168 -0.8
A-29

Table 29. SO2 Comparison of 2010 Base-1 Versus 2010 Base-2

FIPS State

2010 Base-2 2010 Base-1 Base-2 Minus 2010 Base-1 All Base-2 Minus

EGUs (tpy) EGUs (tpy) Base-1 (tpy) Sectors (tpy) Base-1 (% of All)

01 Alabama 473,043 494,704 -21,661 670,087 -3.2

04 Arizona 47,779 47,779 0 174,212 0.0

05 Arkansas 122,667 119,310 3,357 158,739 2.1

06 California 17,317 17,317 0 88,393 0.0

08 Colorado 73,089 90,389 -17,300 112,175 -15.4

09 Connecticut 6,284 6,579 -295 15,310 -1.9

10 Delaware 46,355 36,760 9,595 85,766 11.2

11 DC 0 0 0 8,031 0.0

12 Florida 233,241 230,295 2,946 382,317 0.8

13 Georgia 609,154 609,978 -825 713,164 -0.1

16 Idaho 0 0 0 35,953 0.0

17 Illinois 600,836 591,479 9,357 907,935 1.0

18 Indiana 670,365 599,035 71,330 755,333 9.4

19 Iowa 169,861 186,213 -16,351 285,790 -5.7

20 Kansas 63,532 71,466 -7,934 92,045 -8.6

21 Kentucky 363,145 393,296 -30,151 496,556 -6.1

22 Louisiana 112,534 96,341 16,194 405,498 4.0

23 Maine 3,210 4,707 -1,496 38,120 -3.9

24 Maryland 232,229 261,406 -29,177 293,502 -9.9

25 Massachusetts 15,650 17,723 -2,073 96,111 -2.2

26 Michigan 387,627 375,812 11,815 545,803 2.2

27 Minnesota 91,561 94,176 -2,615 142,663 -1.8

28 Mississippi 73,467 84,629 -11,163 247,251 -4.5

29 Missouri 293,093 261,017 32,076 423,106 7.6

30 Montana 17,923 17,718 205 54,229 0.4

31 Nebraska 97,630 97,151 478 115,345 0.4

32 Nevada 16,408 56,670 -40,262 64,602 -62.3

33 New Hampshire 7,289 7,289 -0 106,284 -0.0

34 New Jersey 41,255 85,348 -44,092 252,992 -17.4

35 New Mexico 48,577 48,274 302 173,421 0.2

36 New York 214,077 211,427 2,651 505,572 0.5

37 North Carolina 219,369 221,529 -2,161 353,001 -0.6

38 North Dakota 160,938 172,194 -11,256 292,851 -3.8

39 Ohio 1,258,684 979,332 279,352 1,387,034 20.1

40 Oklahoma 133,009 133,009 -0 180,781 -0.0

41 Oregon 15,187 15,187 0 43,838 0.0

42 Pennsylvania 853,431 670,161 183,270 896,553 20.4

44 Rhode Island 0 0 0 9,495 0.0

45 South Carolina 199,745 191,473 8,273 272,641 3.0

46 South Dakota 36,304 42,118 -5,814 67,647 -8.6

47 Tennessee 306,082 317,250 -11,168 502,859 -2.2

48 Texas 487,740 539,915 -52,175 903,844 -5.8

49 Utah 31,541 31,240 301 75,308 0.4

50 Vermont 0 0 0 15,148 0.0

51 Virginia 187,772 180,633 7,139 308,230 2.3

53 Washington 5,959 5,960 -0 71,365 -0.0

54 West Virginia 550,629 456,778 93,852 564,118 16.6

55 Wisconsin 214,063 217,221 -3,159 353,005 -0.9

56 Wyoming 47,276 47,120 156 124,422 0.1

Total 9,856,926 9,435,405 421,521 14,868,447 2.8
Technical Support Document for the 

Interstate Air Quality Rule

Air Quality Modeling Analyses

Appendix B

CAMx Model Performance Evaluation
B-1

Introduction

An operational model performance evaluation for surface ozone for the five episodes was

performed in order to estimate the ability of the modeling system to replicate base year ozone

concentrations. This evaluation is comprised principally of statistical assessments of model

versus observed pairs. The robustness of an operational evaluation is directly proportional to the

amount and quality of the ambient data available for comparison.

a. Statistical Definitions

Below are the definitions of those statistics used for the evaluation. The format of all the

statistics is such that negative values indicate model ozone predictions that were less than their

observed counterparts. Positively-valued statistics indicate model overestimation of surface

ozone. Statistics were not generated for the first three days of an episode to avoid the

initialization period. The statistics were calculated for (a) the entire HDE domain, (b) four

quadrants (Midwest, Northeast, Southeast, Southwest), and (c) 51 local areas. The statistics that

were calculated for each of these sets of areas are described below.

Domainwide unpaired peak prediction accuracy: This metric simply compares the peak

concentration modeled anywhere in the selected area against the peak ambient concentration

anywhere in the same area. The difference of the peaks (model - observed) is then normalized

by the peak observed concentration.

Peak prediction accuracy: This metric averages the paired peak prediction accuracy calculated

for each monitor in the subregion. It characterizes the capacity of the model to replicate peak

(afternoon) ozone over a subregion. The daily peak model versus daily peak observed residuals

are paired in space but not in time.

Mean normalized bias: This performance statistic averages the normalized (by observation)

difference (model - observed) over all pairs in which the observed values were greater than 60

ppb. A value of zero would indicate that the model over predictions and model under

predictions exactly cancel each other out.

Mean normalized gross error: The last metric used to assess the performance of the HDE base

cases is similar to the above statistic, except in this case it is the absolute value of the residual

which is normalized by the observation, and then averaged over all sites. A zero gross error

value would indicate that all model concentrations (in which their observed counterpart was

greater than 60 ppb) exactly matched the ambient values.

b. Domainwide Model Performance

As with previous regional photochemical modeling studies, the degree that model

predictions replicate observed concentrations varies by day and location over the large eastern

U.S. modeling domain. From a qualitative standpoint, there appears to be considerable
B-2

similarity on most days between the observed and simulated ozone patterns. Additionally, where

possible to discern, the model appears to follow the day-to-day variations in synoptic-scale

ozone fairly closely. More quantitative comparisons of the model predictions and ambient data

are provided below.

When all hourly observed ozone values (greater than 60 ppb) are compared to their

model counterparts for the 30 episode modeling days in the eastern U.S. simulations, the mean

normalized bias is -1.1 percent and the mean normalized gross error is 20.5 percent As shown in

Table III-3, the model generally underestimates observed ozone values for the June and July

episodes, but predicts higher than observed amounts for the August episode.

Table III-3. Performance statistics for hourly ozone in the Eastern U.S. CAMx simulations.

Average Accuracy of the Peak Mean Normalized Bias Mean Normalized Gross Error

June 1995 -7.3 -8.8 19.6

July 1995 -3.3 -5.0 19.1

August 1995 9.6 8.6 23.3

Depending on the episode and region, the normalized biases can range from an

underestimation of 18 percent to an overestimation of 16 percent. Gross errors tend to average

between 17 and 25 percent. As shown in Table III-4, when the model domain is subdivided into

four quadrants, it is found that most of the underestimations in the June and July episodes are

driven by the Northeast and Midwest quadrants (i.e., the two northern ones). Conversely, most

of the overestimated ozone in the August episode is due to the Midwest, Southeast and

Southwest quadrants. Hourly ozone is consistently underestimated in the Northeast quadrant. 

The model does slightly better in replicating the peak values for each monitoring site than it does

at replicating the mean values, especially in the Northeast where the underpredictions are not as

large for the highest ozone observations.

Table III-4. Regional/Episodic performance statistics for IAQR hourly ozone predictions.

Average Accuracy of the

Peak

Mean Normalized Bias Mean Normalized Gross

Error

June July August June July August June July August

Whole Grid -7.3 -3.3 9.6 -8.8 -5.0 8.6 19.6 19.1 23.3

Northeast -14.7 -5.0 -4.3 -18.4 -7.2 -6.0 24.7 19.1 22.6

Midwest -7.3 -6.2 15.5 -8.7 -7.2 15.5 18.0 19.4 23.7

Southeast -2.9 1.9 15.1 -3.0 1.3 14.7 17.4 19.1 24.1

Southwest -0.9 1.3 7.0 0.7 3.1 10.3 19.0 20.0 22.6

At present, there are no accepted criteria by which one can determine if a regional ozone
B-3

modeling exercise is exhibiting adequate model performance. As a result, EPA compares the

evaluation results of regional models against applicable previous analyses. For instance, the

Heavy Duty Engine (HDE) base case simulations were determined to be appropriate for use

based on comparisons to previously accepted modeling analyses (e.g., OTAG and Tier-2). 

Model performance in the base year IAQR simulations is generally similar or better than its

predecessor regional ozone modeling efforts. In particular, the gross error metric is almost

universally improved in the more recent IAQR modeling. In general, the IAQR CAMx

modeling results are approximately 3-6 ppb higher on average than what was generated in the

HDE/UAM-V modeling. In some previous regional modeling applications, there had been a

tendency for the model to underestimate ozone in the early parts of an episode and then

overestimate ozone at the end of an episode. The trend toward positive bias would increase

throughout the episode, which may be a sign of an imbalance in the model chemistry which in

turn could affect control strategy signal. In general, there does not appear to be an issue with

bias creep in the base case IAQR modeling. Finally, as noted above, the IAQR base case CAMx

modeling has been used before to support proposed emission control regulations (i.e., Clear

Skies and the Non-Road rulemaking). 

Table III-5 presents the results from the eight-hourly ozone evaluation. In general, the

gross error is noticeably less for the eight-hour ambient versus observed ozone comparisons. 

However, the eight-hour ozone model predictions are large overestimates of the actual observed

values for the August episode, especially outside of the Northeast quadrant.

Table III-5. Regional/Episodic performance statistics for IAQR 8-hourly ozone predictions. 

Average Accuracy of the

Peak

Mean Normalized Bias Mean Normalized Gross

Error

June July August June July August June July August

Whole Grid -3.9 0.9 13.9 -5.7 -2.1 11.0 17.5 16.4 22.6

Northeast -13.5 -2.4 -1.6 -15.4 -4.9 -3.8 21.3 14.6 20.8

Midwest -4.0 -0.9 20.6 -5.8 -4.4 17.6 16.0 16.7 23.7

Southeast 1.3 5.3 20.5 0.9 4.0 18.4 16.4 17.5 24.1

Southwest 5.0 8.2 16.2 3.9 3.6 12.4 17.8 18.1 21.1

c. Local-scale Model Performance

The CAMx modeling results were also evaluated at a “local” level. For this analysis, the

modeling domain was broken up into 51 local subregions as shown in Figure III-2. The primary

statistics for each of the 51 subregions is shown in Table III-6.

As noted above, there is no set of established statistical benchmarks to determine the

adequacy of a regional modeling operation evaluation. If one were to evaluate the performance
B-4

of the 1995 eastern base cases against existing EPA requirements for acceptable levels of

accuracy, bias, and error in local attainment demonstration modeling, 69% of the regions would

pass for the June episode, 80% of the regions would pass for the July episodes, and 61% of the

regions would pass for the August episode. This is an improvement from the HDE base case

analyses where the numbers were: 57%, 45%, and 55%, respectively. The local eight-hour

metrics (not shown) generally do not greatly differ from their hourly counterparts. There is a

slight tendency toward greater overprediction of the eight-hourly values.

Table III-6. Local performance statistics for IAQR hourly ozone predictions.

Average Accuracy of the

Peak

Mean Normalized Bias Mean Normalized Gross

Error

June July August June July August June July August

Dallas -9.6 -12.3 2.2 -10.6 -11.5 3.2 16.6 18.7 15.7

Houston/Galveston -3.0 -5.1 0.3 -3.5 -3.9 2.2 20.8 19.0 25.7

Beaumont/Port 14.0 16.7 8.8 16.0 19.3 12.9 20.4 24.5 24.6

Baton Rouge 15.6 24.7 31.4 22.6 26.6 37.4 26.1 31.0 40.5

New Orleans 15.6 29.1 42.1 15.9 28.9 48.9 21.9 32.0 50.2

St. Louis -0.5 -4.0 8.4 -0.6 0.6 10.5 17.0 18.4 18.2

Memphis -7.7 -4.9 13.7 -5.9 -0.3 13.6 15.5 19.3 22.0

Alabama 5.2 -1.7 16.0 6.5 6.7 23.1 14.4 16.6 25.2

Atlanta -3.1 5.4 19.0 -3.4 6.8 26.1 16.7 20.1 31.0

Nashville -2.9 7.8 31.5 -2.4 9.1 36.1 18.1 24.7 37.4

Eastern TN -14.2 -16.0 -2.7 -21.0 -17.1 -5.9 22.7 20.7 18.3

Charlotte 8.3 -2.1 6.0 5.8 4.1 14.5 13.0 16.3 18.2

Greensboro -1.7 -1.1 17.2 -4.2 1.2 18.2 14.1 15.3 21.7

Raleigh-Durham -11.8 1.3 -2.3 -10.7 4.2 -1.9 14.6 13.9 16.9

Evansville/Owensbor 1.2 -0.9 28.3 4.5 5.4 32.8 15.1 21.2 33.9

Indianapolis -8.3 -13.5 15.9 -3.6 -14.4 18.0 13.1 19.3 19.7

Louisville 2.8 4.2 36.6 4.8 6.1 42.1 14.7 17.9 42.5

Cincinnati/Dayton -4.7 -8.5 29.0 0.1 -5.6 32.7 12.8 19.1 33.5

Columbus -8.5 -14.5 9.2 -6.2 -11.0 14.2 14.6 17.3 18.7

West Virginia -8.8 -5.7 12.7 -7.5 -3.2 13.7 15.7 16.6 24.5

Chicago -9.9 -4.3 10.4 -17.1 -11.1 3.5 24.5 23.5 22.3

Milwaukee -14.8 -12.9 21.5 -16.5 -16.9 12.3 19.1 23.3 18.2

Muskegon/Grand

Rapids

-10.8 -12.3 3.1 -11.6 -12.9 1.7 17.7 20.4 16.4
B-5

Gary/South Bend -13.0 -10.0 11.8 -15.0 -14.5 9.3 19.2 24.4 20.7

Detroit -17.2 -5.8 3.9 -20.1 -13.2 -3.2 25.1 22.5 23.4

Pittsburgh -10.0 -3.2 9.2 -9.2 -2.1 7.9 23.1 16.1 20.4

Central PA -6.0 -7.6 1.0 -8.5 -6.0 1.1 21.9 15.5 18.6

Norfolk -9.0 0.0 8.3 -13.4 -5.6 5.7 19.1 18.6 24.7

Richmond -1.2 4.8 2.6 -1.3 10.7 4.5 8.4 18.3 20.3

Baltimore/Washingto -4.7 -3.1 1.7 -6.8 -5.2 0.7 18.6 15.6 23.4

Delaware -6.1 -5.2 2.3 -6.3 -0.2 7.5 12.9 11.6 16.2

Philadelphia -14.1 -1.8 -8.7 -22.0 -10.5 -13.9 26.4 19.5 28.9

New York City -16.2 -3.9 -12.2 -24.6 -14.1 -17.9 31.3 22.5 29.8

Hartford -16.9 -5.0 -9.9 -18.5 -4.0 -7.7 23.6 18.2 20.1

Boston -13.7 -4.7 -15.6 -19.6 -9.2 -19.6 25.9 20.9 26.5

Maine -20.4 -4.7 -6.9 -25.0 -9.4 -6.9 25.3 19.0 15.5

Longview/Shreveport -2.1 11.3 7.7 0.8 11.1 11.4 16.2 16.5 17.9

Kansas City -8.5 -7.8 -4.3 -7.9 -1.5 -8.3 15.7 13.0 12.4

Western NY -23.1 -20.6 -9.0 -25.6 -20.5 -12.1 28.1 23.8 19.0

Northeast OH -4.0 -6.5 6.9 -6.6 -6.8 7.7 20.4 15.5 16.5

South Carolina -2.5 1.3 11.4 -3.4 1.5 15.7 12.5 17.7 19.4

Gulf Coast 0.5 23.1 29.3 4.5 30.0 33.7 15.4 31.6 34.9

FL West Coast -6.4 22.8 41.2 -7.3 11.9 42.8 11.3 22.7 43.7

FL East Coast -15.9 16.2 23.3 -16.8 16.6 26.3 18.0 18.4 29.4

Jackson 0.6 10.9 21.0 1.8 10.0 24.0 16.0 16.0 24.9

Central MI -6.9 -10.4 12.0 -9.6 -14.8 6.6 18.1 18.7 17.5

Macon/Columbus -9.5 -11.1 21.6 -8.8 -5.7 26.4 10.9 13.0 26.9

Austin/San Antonio -14.1 -19.6 -1.9 -11.0 -15.5 4.1 14.1 17.2 12.4

Oklahoma -12.3 -5.6 -5.2 -12.9 -3.2 -2.8 17.2 14.6 12.6

Ft. Wayne/Lima -9.1 -13.1 3.9 -8.3 -14.1 5.1 16.0 18.2 10.6

Bangor/Hancock Co. -17.8 -6.9 -17.7 -24.4 -8.5 -19.9 25.2 15.3 21.0
B-6

Figure III-2. Map of the 51 local-scale evaluation zones.
Technical Support Document for the 

Interstate Air Quality Rule

Air Quality Modeling Analyses

Appendix C

REMSAD Model Performance Evaluation
1

The dividing line between the West and East was defined as the 100th meridian.

2

The same completeness criteria was used for all of the monitoring networks.

C-1

Introduction

This evaluation of REMSAD is comprised principally of statistical assessments of model

versus observed pairs. The robustness of any evaluation is directly proportional to the amount

and quality of the ambient data available for comparison. Unfortunately, there are few PM2.5

monitoring networks with available data for this evaluation. Critical limitations of the 1996

databases are a lack of urban monitoring sites with speciated measurements and poor geographic

representation of ambient concentration in the East. PM2.5 monitoring networks were expanded

in 1999 to include more than 1000 Federal Reference Method (FRM) monitoring sites. The

purpose of this network is to monitor PM2.5 mass levels in urban areas. These monitors only

measure total PM2.5 mass and do not measure PM species. In 2001 a new network of ~300

urban oriented speciation monitor sites began operation across the country. These monitors

collect a full range of PM2.5 species that are necessary to evaluate models and to develop PM2.5

control strategies. Future modeling efforts will be able to take advantage of these newer

speciated PM2.5 measurements.

The evaluation used data from the IMPROVE, CASTNet dry deposition, and NADP

monitoring networks (IMPROVE, 2000), (EPA, 2002), (NADP, 2003). The IMPROVE and

NADP networks were in full operation during 1996. The CASTNet dry deposition network was

partially shutdown during the first half of the year. There were 65 CASTNet sites with at least

one season of complete data. There were 16 sites which had complete annual data. The

CASTNet visibility network was also partially operating in 1996. Data from the 7 visibility sites

is only complete from September-December. This only provides a single season (fall) of

complete data. Therefore, the limited data from these sites was not used in the evaluation. The

mercury deposition network (MDN) was in its first year of operation in 1996. There was not

adequate data to fully evaluate the wet deposition of total mercury.

The largest available ambient database for 1996 comes from the Interagency Monitoring

of PROtected Visual Environments (IMPROVE) network. IMPROVE is a cooperative visibility

monitoring effort between EPA, federal land management agencies, and state air agencies. Data

is collected at Class I areas across the United States mostly at National Parks, National

Wilderness Areas, and other protected pristine areas. There were approximately 60 IMPROVE

sites that had complete annual PM2.5 mass and/or PM2.5 species data for 1996. Forty two sites

were in the West1

 and 18 sites were in the East. Figure C-1 shows the locations of the

IMPROVE monitoring sites used in this evaluation. IMPROVE data is collected twice weekly

(Wednesday and Saturday). Thus, there is a total of 104 possible samples per year or 26 samples

per season. For this analysis, a 50% completeness criteria was used2. That is, in order to be

counted in the statistics a site had to have > 50% complete data in all 4 seasons. If any season

was missing, an annual average was not calculated for the site. See Appendix D for a list of the
3

For the performance evaluation and the calculation of PM2.5mass, POA is multiplied by 1.167. 

The IMPROVE organic carbon mass is multiplied by a 1.4 factor to account for additional mass attached

to the carbon (this follows standard IMPROVE procedures). In REMSAD, the “additional” mass is

already accounted for in the SOA predictions (by using a molecular weight of 160 g/mole). The POA

emissions have been multiplied by1.2 prior to processing by the emissions model (the 1.2 factor is applied

to the organic carbon in the PM2.5 speciation profiles). The post-processed POA concentrations are then

multiplied by 1.167 to simulate an equivalent 1.4 factor (1.2 * 1.167 = 1.4).

C-2

IMPROVE sites used in the evaluation. The observed IMPROVE data used for the performance

evaluation was PM2.5 mass, sulfate ion, nitrate ion, elemental carbon, organic aerosols, and

crustal material (soils). The REMSAD model output species were postprocessed in order to

achieve compatibility with the observation species. The following is the translation of

REMSAD output species into PM2.5 and related species:

Sulfate Ion: TSO4 = ASO4 + GSO4

Nitrate Ion: PNO3

Organic aerosols: TOA = 1.167*POA + SOA1 + SOA2 + SOA3 + SOA4

Elemental Carbon: PEC

Crustal Material (soils): PMFINE

PM2.5: PM2.5= PMFINE + ASO4 + GSO4 + NH4S +

PNO3 + NH4N + 1.167*POA + PEC + 

SOA1 + SOA2 + SOA3 + SOA4

where, TSO4 is total sulfate ion, ASO4 is aqueous path sulfate, GSO4 is gaseous path sulfate,

NH4S is ammonium associated with sulfate, PNO3 is nitrate ion, NH4N is ammonium

associated with nitrate, TOA is total organic aerosols, POA is primary organic aerosol3, SOA1

and SOA2 are anthropogenic secondary organic aerosol, SOA3 and SOA4 are biogenic

secondary organic aerosol, PEC is primary elemental carbon, and PMFINE is primary fine

particles (other unspeciated primary PM2.5). PM2.5 is defined as the sum of the individual

species. 
C-3

Figure C-1. Map of 1996 IMPROVE monitoring sites used in the REMSAD model performance

evaluation.

Model performance was also calculated using data from the CASTNet dry deposition

monitoring network. The sulfate and total nitrate data was used in the evaluation. CASTNet

data is collected and reported as weekly average data. The data is collected in filter packs that

sample the ambient air continuously during the week. The sulfate data is of high quality since

sulfate is a very stable compound. But the particulate nitrate concentration data collected by

CASTNet is subject to volatility due to the length of the sampling period. Therefore, we chose

not to use the CASTNet particulate nitrate data in this evaluation. CASTNet also reports a total

nitrate measurement. This is the combined total of particulate nitrate and nitric acid. Since the

total nitrate measurement is not affected by the partitioning back and forth between particulate

nitrate and nitric acid, it should be a fairly accurate measurement. 

Wet deposition data from the National Acid Deposition Program (NADP) was also used

in the model evaluation. There were a total of 160 NADP sites with complete annual data in

1996. Model results were compared to observed values of ammonium, sulfate, and nitrate wet

deposition.
C-4

1. Statistical Definitions

Below are the definitions of statistics used for the evaluation. The format of all the

statistics is such that negative values indicate model predictions that were less than their

observed counterparts. Positive statistics indicate model overestimation of observed PM.. The

statistics were calculated for the entire REMSAD domain and separated for the east and the west. 

The dividing line between East and West is the 100th meridian.

Mean Observation: The mean observed value (in :g/m3

) averaged over all monitored days in

the year and then averaged over all sites in the region.

Mean REMSAD Prediction: The mean predicted value (in :g/m3

) paired in time and space

with the observations and then averaged over all sites in the region.

Ratio of the Means: Ratio of the predicted over the observed values. A ratio of greater than 1

indicates on overprediction and a ratio of less than 1 indicates an underprediction.

Mean Bias (:g/m3

): This performance statistic averages the difference (model - observed) over

all pairs in which the observed values were greater than zero. A mean bias of zero indicates that

the model over predictions and model under predictions exactly cancel each other out. Note that

the model bias is defined such that it is a positive quantity when model prediction exceeds the

observation, and vice versa. This model performance estimate is used to make statements about

the absolute or unnormalized bias in the model simulation

Mean Fractional Bias (percent): Normalized bias can become very large when a minimum

threshold is not used. Therefore fractional bias is used as a substitute. The fractional bias for

cases with factors of 2 under- and over-prediction are -67 and + 67 percent, respectively (as
C-5

opposed to -50 and +100 percent, when using normalized bias, which is not presented here). 

Fractional bias is a useful model performance indicator because it has the advantage of equally

weighting positive and negative bias estimates. The single largest disadvantage in this estimate

of model performance is that the estimated concentration (i.e., prediction, Pred) is found in both

the numerator and denominator. 

 Mean Error ( :g/m3): This performance statistic averages the absolute value of the difference

(model - observed) over all pairs in which the observed values were greater than zero. It is

similar to mean bias except that the absolute value of the difference is used so that the error is

always positive. 

Mean Fractional Error (percent): Normalized error can become very large when a minimum

threshold is not used. Therefore fractional error is used as a substitute. It is similar to the

fractional bias except the absolute value of the difference is used so that the error is always

positive. 

Correlation Coefficient: This performance statistic measures the degree to which two variables

are linearly related. A correlation coefficient of 1 indicates a perfect linear relationship, whereas

a correlation coefficient of 0 means that there is no linear relationship between the variables.
C-6

2. Results of REMSAD Performance Evaluation

The statistics described above are presented for the entire domain, the Eastern sites, and

the Western sites. The statistics were calculated in two different ways. The bias, error, and R2

statistics in the tables below were calculated for all days and all sites. Observations and model

predictions were paired in time and space on a daily basis. These statistics represent the ability

of the model to replicate each day of year with measurements. 

Following the statistical tables are scatterplots of seasonal and annual average predictions

at each ambient data site. These scatterplots represent the ability of the model to represent a

seasonal average or annual average measurement. The correlation coefficients for the

scatterplots represent the correlation of the site average (seasonal and/or annual) predictions to

the site average measurements. 

a. IMPROVE Performance

a.1. PM2.5 Performance

Table C-1 lists the performance statistics for PM2.5 at the IMPROVE sites. For the full

domain, PM2.5 is underpredicted by 18%. Overall, the performance of REMSAD (v7.06) has

improved from underpredicting PM2.5 by 34% in version 7.01. The ratio of the means is 0.82

with a bias of -1.10 :g/m3

. It can be seen that most of this underprediction is due to the Western

sites. The West is underpredicted by 33% while the East is underpredicted by 2%. The

fractional bias is ~9% in the East, while the fractional error is 46%. The fractional bias and error

in the West is ~30% and 63% respectively. The observed PM2.5 concentrations in the East are

relatively high compared to the West. REMSAD displays an ability to differentiate between

generally high and low PM2.5 areas. 

Table C-1. Annual mean PM2.5 performance at IMPROVE sites.

No. of

Sites

Mean

REMSAD

Predictions

(:g/m3

)

Mean

Observations

(:g/m3

)

Ratio

of

Means

(pred/o

bs)

 Bias

(:g/m3

)

Fractional

Bias (%)

 Error

(:g/m3)

Fractional

Error (%)

Correla

tion

Coeffic

ient

National 54 5.11 6.21 0.82 -1.10 -24.1 3.01 58.2 0.46

East 15 10.93 11.15 0.98 -0.22 -8.9 4.99 46.1 0.39

West 39 2.87 4.31 0.67 -1.44 -29.9 2.44 62.8 0.09
C-7

0

2

4

6

8

10

12

14

16

18

20

0 2 4 6 8 10 12 14 16 18 20

IM PROVE Observations (ug/m3

)

REMSAD Predictions (ug/m3

)

0

5

10

15

20

25

0 5 10 15 20 25

IMPROVE Observations (ug/m3

)

REMSAD Predictions (ug/m3

)

summer

fall

spring

winter

Figures C-2 and C-3 show the annual and seasonal average PM2.5 1996 IMPROVE observations

versus REMSAD predictions respectively. The annual and seasonal scatterplots showed some

scatter, but good agreement, with strong correlations (annual: R2 = 0.79; summer: R2 = 0.69; fall:

R2 = 0.62; spring: R2 = 0.60; and winter: R2 = 0.78). 

Figure C-2. Annual average PM2.5 1996 IMPROVE observations versus REMSAD predictions.

Figure C-3. Seasonal average PM2.5 1996 IMPROVE observations versus REMSAD

predictions.
C-8

0

1

2

3

4

5

6

7

8

012345678

IMPROVE Observations (ug/m3

)

REMSAD Predictions (ug/m3

)

a.2. Sulfate Performance

Table C-2 lists the performance statistics for particulate sulfate at the IMPROVE sites. 

Domainwide, sulfate is underpredicted by 21%. The annual average sulfate underprediction in

the east is 12% and 41% in the West. The sulfate performance (especially in the East) is better

than most of the other PM2.5 species. The fractional error in the East is ~60% and the R2

 is

0.51. 

Table C-2. Annual mean sulfate ion performance at IMPROVE sites.

No. of

Sites

Mean

REMSAD

Predictions

(:g/m3

)

Mean

Observations

(:g/m3

)

Ratio of

Means

(pred/obs)

 Bias

(:g/m3

)

Fractional

Bias (%)

 Error

(:g/m3)

Fractional

Error (%)

Correlation

Coefficient

National 58 1.25 1.59 0.79 -0.34 -40.7 0.80 69.3 0.66

East 16 3.47 3.93 0.88 -0.46 -29.8 1.80 60.2 0.51

West 42 0.41 0.69 0.59 -0.29 -44.8 0.41 72.8 0.13

 

Figures C-4 and C-5 show the annual and seasonal average sulfate 1996 IMPROVE

observations versus REMSAD predictions respectively. The scatterplots and linear regressions

displayed strong correlations (annual: R2 = 0.96; summer: R2 = 0.92; fall: R2 = 0.91; spring: R2 =

0.90; and winter: R2 = 0.86). 

Figure C-4. Annual average sulfate 1996 IMPROVE observations versus REMSAD predictions.
C-9

0

2

4

6

8

10

12

14

0 2 4 6 8 10 12 14

IMPR OVE Observations (ug/m 3

)

REMSAD Predictions (ug/m3

)

Summer

Fall

Spring

W inter

Figure C-5. Seasonal average sulfate 1996 IMPROVE observations versus REMSAD

predictions.

Overall, the model shows an ability to replicate the annual and seasonal sulfate

concentrations. This is particularly important for this application of REMSAD. The IAQR

emissions controls mainly reduce SO2 and lead to large predicted sulfate reductions. It is

important to have good model performance for the species that is being reduced the most. 

a.3. Elemental Carbon Performance

Table C-3 lists the performance statistics for primary elemental carbon at the IMPROVE

sites. Elemental carbon concentrations at IMPROVE sites are relatively low, but performance is

generally good. There is a domainwide underprediction of 14% and a western underprediction of

29%. 

Table C-3. Annual mean elemental carbon performance at IMPROVE sites.

No. of

Sites

Mean

REMSAD

Predictions

(:g/m3

)

Mean

Observations

(:g/m3

)

Ratio of

Means

(pred/obs)

 Bias

(:g/m3

)

Fractional

Bias (%)

 Error

(:g/m3)

Fractional

Error (%)

Correlation

Coefficient

National 47 0.27 0.32 0.86 -0.05 -13.6 0.17 58.7 0.33

East 15 0.49 0.48 1.01 0.01 1.78 0.20 41.7 0.47

West 32 0.17 0.24 0.71 -0.07 -20.9 0.16 66.7 0.07

Figures C-6 and C-7 show scatterplots of annual and seasonal average elemental carbon

1996 IMPROVE observations versus REMSAD predictions respectively. The annual scatterplot
C-10

0

0.5

1

1.5

2

2.5

3

0 0.5 1 1.5 2 2.5 3

IMPROVE Observations (ug/m3

)

REMSAD Predictions (ug/m3

)

0

0.5

1

1.5

2

2.5

3

0 0.5 1 1.5 2 2.5 3

IMPROVE Observations (ug/m3

)

REMSAD Predictions (ug/m3

)

Summer

Fall

Spring

Winter

and linear regression displayed some scatter, however good agreement with a R2 of 0.53. 

Overall, summer and fall linear regressions had relatively good agreement (summer: R2

 = 0.63;

fall: R2 = 0.62), whereas spring and winter had the weakest correlations (spring: R2 = 0.49; and

winter: R2 = 0.39).

Figure C-6. Annual average elemental carbon 1996 IMPROVE observations versus REMSAD

predictions.

Figure C-7. Seasonal average elemental carbon 1996 IMPROVE observations versus REMSAD

predictions.
C-11

a.4. Organic Aerosol Performance

Table C-4 lists the performance statistics for organic aerosols at the IMPROVE sites. 

Organic aerosols performance is generally good. The nationwide bias and errors are low. But

the correlation coefficient is also low. There is much uncertainty in the predictions of organic

carbon. There are several different forms of organic carbon predicted in the model. There is

primary organic carbon, secondary biogenic organic carbon, and secondary anthropogenic

organic carbon. Both the model and the ambient data contains a mix of these different types of

organics which all originate from different sources. Unfortunately, given limitations in

measurement techniques, it is currently not possible to quantify the different types of organic

carbon in the ambient air. 

This latest version of REMSAD (7.06) contains science updates and code fixes that result

in predicted concentrations of secondary organic carbon that are much higher than in previous

versions of REMSAD. The model predictions for organics are tempered by the fact that

wildfires (a significant source of organic carbon) are not included in the current modeling

inventory. The performance for organics should be viewed relative to the uncertainties in the

measurements and the emissions inventories.

Table C-4. Annual mean organic aerosol performance at IMPROVE sites.

No. of

Sites

Mean

REMSAD

Predictions

(:g/m3

)

Mean

Observations

(:g/m3

)

Ratio of

Means

(pred/obs)

 Bias

(:g/m3

)

Fractional

Bias (%)

 Error

(:g/m3)

Fractional

Error (%)

Correlation

Coefficient

National 47 1.76 1.76 1.00 0.004 -5.58 1.13 62.0 0.18

East 15 2.58 2.49 1.04 0.09 -11.83 1.42 54.7 0.21

West 32 1.38 1.42 0.97 -0.04 -2.64 1.00 65.4 0.10

Annual and seasonal scatterplots (Figures C-8 and C-9) of average organic aerosol for 1996

IMPROVE observations versus REMSAD predictions displayed some scatter, with an annual R2

= 0.40 and seasonal correlations of: summer: R2 = 0.43; fall: R2 = 0.23; spring: R2 = 0.45; and

winter: R2

 = 0.45.
C-12

0

1

2

3

4

5

012345

IMPROVE Observations (ug/m3)

REMSAD Predictions (ug/m 3) 0

1

2

3

4

5

6

7

01234567

IMPROVE Observations (ug/m3)

REMSAD Predictions (ug/m 3)

Summer

Fall

Spring

Winter

Figure C-8. Annual average organic aerosol 1996 IMPROVE observations versus REMSAD

predictions.

Figure C-9. Seasonal average organic aerosol 1996 IMPROVE observations versus REMSAD

predictions.
C-13

0

1

2

3

4

5

012345

IMPROVE Observations (ug/m3

)

REMSAD Predictions (ug/m3

)

a.5. Nitrate Performance

Table C-5 lists the performance statistics for nitrate ion at the IMPROVE sites. Nitrate is

generally overpredicted in the East and underpredicted in the West. Nitrate is overpredicted by

166% in the east and underpredicted by 31% in the west. Domainwide there is an overprediction

of 55%. 

Table C-5. Annual mean nitrate ion performance at IMPROVE sites.

No. of

Sites

Mean

REMSAD

Predictions

(:g/m3

)

Mean

Observations

(:g/m3

)

Ratio of

Means

(pred/obs)

 Bias

(:g/m3

)

Fractional

Bias (%)

 Error

(:g/m3)

Fractional

Error (%)

Correlation

Coefficient

National 48 0.61 0.39 1.55 0.21 -59.4 0.57 129.8 0.19

East 15 1.47 0.55 2.66 0.91 13.0 1.11 109.3 0.29

West 33 0.22 0.32 0.69 -0.10 -91.9 0.32 139.0 0.15

Likewise, this overprediction is depicted in Figures C-10 and C-11, which show the

scatterplots of the annual (R2= 0.37) and seasonal (summer: R2= 0.24; fall: R2= 0.17; spring: 

R2= 0.36; winter: R2= 0.52) average nitrate ion for 1996 IMPROVE observations verus

REMSAD predictions.

Figure C-10. Annual average nitrate ion 1996 IMPROVE observations versus REMSAD

predictions.
C-14

0

1

2

3

4

5

6

7

01234567

IMPROVE Observations (ug/m3)

REMSAD Predictions (ug/m 3)

Summer

Fall

Spring

Winter

Figure C-11. Seasonal average nitrate ion 1996 IMPROVE observations versus REMSAD

predictions.

It is important to consider these results in the context that the observed nitrate

concentrations at the IMPROVE sites are very low. The mean nationwide observations are only

0.40 :g/m3. It is often difficult for models to replicate very low concentrations of secondarily

formed pollutants. Nitrate is generally a small percentage of the measured PM2.5 at almost all

of the IMPROVE sites. Nonetheless, it has been recognized that the current generation of PM

air quality models generally overpredict particulate nitrate. There are numerous ongoing efforts

to improve particulate nitrate model performance through emissions inventory improvements

(ammonia emissions and dry deposition of gaseous precursors) and improvements in the

scientific formulations of the models. 

More recent ambient data has shown that nitrate can be an important contributor to

PM2.5 in some urban areas (particularly in California and the upper Midwest) but performance

for those areas could not be assessed due to the lack of urban area speciated nitrate data for 1996.

a.6. PMFINE-Other (crustal) Performance 

Table C-6 lists the performance statistics for PMFINE-other or primary crustal emissions. 

The observations show crustal PM2.5 to be generally higher in the West than in the East. 

However, REMSAD is predicting higher crustal concentrations in the East. Performance

statistics show an underprediction of 19% in the west, with an overprediction nationally of

~33%. The largest categories of PMFINE-other are fugitive dust sources such as paved roads,

unpaved roads, construction, and animal feed lots. 
C-15

0

0.5

1

1.5

2

2.5

3

0 0.5 1 1.5 2 2.5 3

IMPROVE Observations (ug/m3

)

REMSAD Predictions (ug/m3

)

There is a large uncertainty as to how emissions for such sources should be treated in

grid-based air quality models since a large fraction of the emissions either deposit or are

removed by vegetation within a few meters of the source. Work is underway to develop

improved methods for estimating emissions from these sources for the purpose of air quality

modeling.

Table C-6. Annual mean PMFINE (crustal) performance at IMPROVE sites.

No. of

Sites

Mean

REMSAD

Predictions

(:g/m3

)

Mean

Observations

(:g/m3

)

Ratio of

Means

(pred/obs)

 Bias

(:g/m3

)

Fractional

Bias (%)

 Error

(:g/m3)

Fractional

Error (%)

Correlation

Coefficient

National 57 0.86 0.64 1.33 0.22 38.8 0.80 93.9 0.003

East 16 1.64 0.53 3.08 1.10 103.8 1.36 116.1 0.002

West 41 0.56 0.69 0.81 -0.13 13.5 0.58 85.3 0.00

Figures C-12 and C-13 show the annual and seasonal average concentration scatterplots

for PMFINE-other.

Figure C-12. Annual average PMFINE (crustal) 1996 IMPROVE observations versus REMSAD

predictions
C-16

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

IMPROVE Observations (ug/m3

)

REMSAD Predictions (ug/m3

)

Summer

Fall

Spring

Winter

0

0.5

1

1.5

2

2.5

3

3.5

0123456

NADP Observations (kg/ha)

REMSAD Predictions (kg/ha)

.

Figure C-13. Seasonal average PMFINE (crustal) 1996 IMPROVE observations versus

REMSAD predictions

 b. NADP Wet Deposition Performance

Figures C-14, C-15, and C-16 show the annual 1996 NADP observations versus

REMSAD predictions for ammonium, nitrate, and sulfate wet deposition respectively. The

scatterplots and linear regressions show some scatter (e.g. underprediction bias for nitrate and

especially sulfate wet deposition), but good agreement, with strong correlations (NH4: R2 = 0.65;

NO3: R2

 = 0.78; SO4: R2 = 0.78).

 

Figure C-14. Annual total ammonium (NH4) wet deposition 1996 NADP observations versus

REMSAD predictions.
C-17

0

5

10

15

20

25

30

0 5 10 15 20 25 30

NADP Observations (Kg/Ha)

REMSAD Predictions (ug/m 3)

0

5

10

15

20

25

30

35

40

0 10 20 30 40

NADP Observations (Kg/Ha)

REMSAD Predictions (ug/m3)

 

Figure C-15. Annual total nitrate (NO3) wet deposition 1996 NADP observations versus

REMSAD predictions

Figure C-16. Annual total sulfate (SO4) wet deposition 1996 NADP observations versus

REMSAD predictions.
C-18

0

3

6

9

12

15

0 3 6 9 12 15

CASTNet Observations (ug/m3)

REMSAD Predictions (ug/m 3)

Summer

Fall

Spring

Winter

c. CASTNet Performance

Figures C-17 and C-18 show the seasonal 1996 CASTNet observations versus REMSAD

predictions for total sulfate and total nitrate, respectively. The scatterplot and linear regression

of sulfate showed good agreement, with strong correlations among all seasons (summer: R2 =

0.80; fall: R2

 = 0.92; spring: R2 = 0.81; winter: R2 = 0.78). The performance of sulfate at the

CASTNet sites looks better than at the IMPROVE sites. The CASTNet sites measure data on a

weekly average basis as opposed to the IMPROVE twice weekly sampling schedule. There are

also more CASTNet sites in the high sulfate region of the East (e.g. the Ohio Valley). The

CASTNet long term averaging of data seems particularly well suited for comparisons to seasonal

average modeled concentrations. 

The scatterplot and linear regression of total nitrate showed modest agreement, with

weaker correlations within each season (summer: R2

 = 0.48; fall: R2 = 0.67; spring: R2 = 0.74;

winter: R2

 = 0.51). There is an indication of an overprediction bias. This is not surprising given

the overprediction bias of modeled particulate nitrate. The overprediction of total nitrate

indicates that nitric acid concentrations may be overpredicted. This may be one of the reasons

for the general overprediction of particulate nitrate. Model developers are continuing to examine

the nitric acid production and destruction pathways. There are continuing improvements being

made to the daytime and nighttime nitric acid formation reactions. Dry deposition of nitric acid

is also being studied as a possible cause of overprediction.

Figure C-17. Seasonal average sulfate (SO4) 1996 CASTNet observations versus REMSAD

predictions.
C-19

0

1

2

3

4

5

6

7

8

9

10

11

12

0 1 2 3 4 5 6 7 8 9 10 11 12

CASTNet Observations (ug/m3)

REMSAD Predictions (ug/m 3)

Summer

Fall

Spring

Winter

Figure C-18. Seasonal average total nitrate (NO3 + HNO3) 1996 CASTNet observations versus

REMSAD predictions.

e. Visibility performance

For the purpose of model performance evaluation, visibility was calculated in a manner

similar to recommendations for the Regional Haze rule. For the Regional Haze rule, states must

look at the change in visibility on the 20% best days and the 20% worst days (in units of

deciviews) at each Class I area. A certain improvement in visibility on the 20% worst days is

needed in the future at each Class I area. Visibility on the 20% best days cannot degrade in the

future. 

 EPA has released a draft version of guidance that details the calculation of base period

visibility (EPA, 2001a). The 20% best and worst days for the “base period” are to be calculated

from the 2000-2004 IMPROVE data at each Class I area. The daily average extinction

coefficient (bext) values are calculated using the following formula:

bext = 10.0 + [3.0 * f(RH) * (1.375 * sulfate) + 3.0 * f(RH) * (1.29 * nitrate)+

 4.0 * (organic aerosols) + 10.0 * (elemental carbon) + 1.0 * (crustal) + 0.6 * (coarse PM)]

Bext is in units of inverse megameters (Mm-1). The 10.0 initial value accounts for atmospheric

background (i.e., Rayleigh) scattering. F(RH) refers to the relative humidity correction function

as defined by IMPROVE (2000). The relative humidity correction factor was derived from

historical climatological meteorological data. There is a published f(rh) value for each month of

the year for each Class I area (SAIC, 2001). The climatological f(rh) values will be used to

calculate bext for the Regional Haze rule. 
C-20

0

50

100

150

200

250

300

350

400

0 50 100 150 200 250 300 350 400

IMPROVE Extinction Coefficient (M m -1)

REMSAD Extinction Coefficient (M m-1)

East- 20% Best days

East- 20% Worst days

The formula to calculate bext from REMSAD output species is as follows:

bext = 10.0 + [3.0 * f(RH) * (1.375 * (GSO4 + ASO4)) + 3.0 * f(RH) * (1.29 * PNO3)+

 4.0 * (TOA) + 10.0 * PEC + 1.0 * (PMFINE) + 0.6 * (PMCOARS)]

The daily average bext values are converted to deciview values using the following formula:

The 20% best and worst days are identified based on the daily average observed deciview values

at each Class I areas. For the purpose of this model performance evaluation, we have calculated

the 20% best and worst days from 1996 (the meteorological year we are using) at each

IMPROVE site with complete data. The following scatter plots show the observed vs. predicted

bext values at the IMPROVE sites on the 20% best and worst days. 

Figure C-19. IMPROVE observed versus REMSAD predicted light extinction coefficient values

on the 20% best and worst days in the East. 
C-21

0

25

50

75

100

125

150

175

200

225

250

0 25 50 75 100 125 150 175 200 225 250

IMPROVE Extinction Coefficient (M m-1)

REMSAD Extinction Coefficient (M m-1)

West- 20% Best days

West- 20% Worst days

Figure C-20. IMPROVE observed versus REMSAD predicted light extinction coefficient values

on the 20% best and worst days in the West. 

REMSAD was generally able to predict the highest bext values on the observed worst days

in the East. The 20% worst days in the East show little bias, but a large amount of scatter. The

20% best days in the East are generally overpredicted. The 20% worst days in the West are

underpredicted. REMSAD rarely predicted high bext values in the West. The model predictions

on the 20% best and worst days are similar. 

3. Summary of Model Performance

The purpose of this model performance evaluation was to evaluate the capabilities of the

REMSAD modeling system in reproducing annual average concentrations and deposition at all

IMPROVE, CASTNet, and NADP sites in the contiguous U.S. for fine particulate mass, its

associated speciated components, visibility, and wet deposition. When considering annual

average statistics (e.g., predicted versus observed), which are computed and aggregated over all

sites and all days, REMSAD underpredicted fine particulate mass (PM2.5), by 18%. PM2.5 in

the Eastern U.S. was underpredicted by 2%, while PM2.5 in the West was underpredicted by

33%. All PM2.5 component species were underpredicted in the west. In the East, nitrate and

crustal material are overestimated. Elemental carbon shows neither over or underprediction in

the east with a bias near 0%. Eastern sulfate is slightly underpredicted with a bias of 12%. 

Organic aerosols show little or no bias in the East and West.

The comparisons to the CASTNet data show generally good model performance for
C-22

particulate sulfate. Comparison of total nitrate indicate an overestimate, possible due to

overpredictions of nitric acid in the model.

Performance at the NADP sites for wet deposition of ammonium, sulfate, and nitrate

were reasonably good. There is a an underprediction bias of nitrate, and especially sulfate wet

deposition. The model predictions of total mercury wet deposition at the MDN sites were also

underpredicted.

Given the state of the science relative to PM modeling, it is inappropriate to judge PM

model performance using criteria derived for other pollutants, like ozone. The overall model

performance results may be limited by our current knowledge of PM science and chemistry, by

the emissions inventories for direct PM and secondary PM precursor pollutants, by the relatively

sparse ambient data available for comparisons to model output, and by uncertainties in

monitoring techniques. The model performance for sulfate in the East is quite reasonable, which

is key since sulfate compounds comprise a large portion of PM2.5 in the East.

Negative effects of relatively poor model performance for some of the smaller (i.e., lower

concentration) components of PM2.5, such as crustal mass, are mitigated to some extent by the

way we use the modeling results in projecting future year nonattainment and downwind

contributions. As described in more detail below, each measured component of PM2.5 is

adjusted upward or downward based on the percent change in that component, as determined by

the ratio of future year to base year model predictions. Thus, we are using the model predictions

in a relative way, rather than relying on the absolute model predictions for the future year

scenarios. By using the modeling in this way, we are reducing the risk that large overprediction

or underprediction will unduly affect our projection of future year concentrations. For example,

REMSAD may overpredict the crustal component at a particular location by a factor of 2, but

since measured crustal concentrations are generally a small fraction of ambient PM2.5, the future

crustal concentration will remain as a small fraction of PM2.5.

A number of factors need to be considered when interpreting the results of this

performance analysis. First, simulating the formation and fate of particles, especially secondary

organic aerosols and nitrates is part of an evolving science. In this regard, the science in air

quality models is continually being reviewed and updated as new research results become

available. Also, there are a number of issues associated with the emissions and meteorological

inputs, as well as ambient air quality measurements and how these should be paired to model

predictions that are currently under investigation by EPA and others. The process of building

consensus within the scientific community on ways for doing PM model performance

evaluations has not yet progressed to the point of having a defined set of common approaches or

criteria for judging model performance. Unlike ozone, there is a limited data base of past

performance statistics against which to measure the performance of regional/national PM

modeling. Thus, the approach used for this analysis may be modified or expanded in future

evaluation analyses.
Technical Support Document for the 

Interstate Air Quality Rule

Air Quality Modeling Analyses

Appendix D

8-Hour Ozone Concentrations at Nonattainment

Counties for the 2010 Base Case and 2015 Base Case
D-1

The tables below provide 8-hour ozone concentration design values projected for the 2010 Base

Case and 2015 Base Case. Concentrations for the two projection years are provided for each

county in the East with ambient 2000-2002 8-hour design values >= 85 ppb (i.e., nonattainment). 

For counties with multiple monitoring sites, the data in the table below represent the highest

concentration from among the monitors in the county. Note that in all but four counties the same

site has the highest concentration in both the 2010 and 2015 Base Cases. The counties in which

the highest concentration for the 2015 Base Case is at a different site than the 2010 Base Case

are as follows:

County 2015 Base High Site 

DeKalb Co, GA: 130893001

Mecklenburg Co, NC: 371190041

Alleghany Co, PA: 420030010

Knox Co, TN: 470930021

State

FIPs

Cnty

FIPs State County AIRS Site ID

2000-2002

Ambient

8-Hr Ozone

DV

2010

Base

2015

Base

1 73 Alabama Jefferson 010732006 88 73 68

1 103 Alabama Morgan 011030011 85 73 69

1 117 Alabama Shelby 011170004 92 76 70

5 35 Arkansas Crittenden 050350005 94 86 85

5 119 Arkansas Pulaski 051191002 86 76 72

9 1 Connecticut Fairfield 090011123 98 94 94

9 3 Connecticut Hartford 090031003 90 82 78

9 7 Connecticut Middlesex 090070007 97 91 89

9 9 Connecticut New Haven 090093002 98 92 90

9 11 Connecticut New London 090110008 89 82 79

9 13 Connecticut Tolland 090131001 94 84 80

10 1 Delaware Kent 100010002 92 79 75

10 3 Delaware New Castle 100031010 96 87 84

10 5 Delaware Sussex 100051002 94 81 77

11 1 D.C. Washington 110010043 95 88 86

13 21 Georgia Bibb 130210012 92 65 61

13 67 Georgia Cobb 130670003 98 81 75

13 77 Georgia Coweta 130770002 93 76 72

13 89 Georgia De Kalb 130890002 95 82 79

13 97 Georgia Douglas 130970004 95 79 74

13 113 Georgia Fayette 131130001 90 75 70

13 121 Georgia Fulton 131210055 99 86 81

13 135 Georgia Gwinnett 131350002 89 74 68
D-2

13 151 Georgia Henry 131510002 98 77 72

13 213 Georgia Murray 132130003 87 68 63

13 223 Georgia Paulding 132230003 90 70 65

13 245 Georgia Richmond 132450091 87 74 70

13 247 Georgia Rockdale 132470001 96 80 74

17 31 Illinois Cook 170310032 88 84 85

17 83 Illinois Jersey 170831001 89 78 74

17 163 Illinois St Clair 171630010 85 77 75

18 3 Indiana Allen 180030002 88 78 74

18 11 Indiana Boone 180110001 88 79 76

18 19 Indiana Clark 180190003 90 79 76

18 55 Indiana Greene 180550001 89 77 74

18 57 Indiana Hamilton 180571001 93 83 80

18 59 Indiana Hancock 180590003 92 82 79

18 63 Indiana Hendricks 180630004 88 79 76

18 69 Indiana Huntington 180690002 86 76 72

18 71 Indiana Jackson 180710001 85 72 69

18 81 Indiana Johnson 180810002 87 75 72

18 89 Indiana Lake 180892008 92 87 87

18 91 Indiana La Porte 180910005 92 84 82

18 95 Indiana Madison 180950010 91 80 76

18 97 Indiana Marion 180970050 90 81 78

18 109 Indiana Morgan 181090005 88 78 75

18 127 Indiana Porter 181270024 90 84 83

18 129 Indiana Posey 181290003 87 75 73

18 141 Indiana St Joseph 181411007 90 78 75

18 145 Indiana Shelby 181450001 93 83 79

21 13 Kentucky Bell 210130002 86 69 65

21 15 Kentucky Boone 210150003 86 71 68

21 19 Kentucky Boyd 210190017 88 76 73

21 29 Kentucky Bullitt 210290006 85 75 73

21 37 Kentucky Campbell 210370003 94 83 80

21 47 Kentucky Christian 210470006 85 65 62

21 111 Kentucky Jefferson 211110027 85 76 74

21 117 Kentucky Kenton 211170007 88 77 75

21 185 Kentucky Oldham 211850004 87 73 71

21 227 Kentucky Warren 212270008 86 70 67

22 33 Louisiana East Baton Rou 220330003 86 79 77

22 47 Louisiana Iberville 220470012 86 80 78

22 51 Louisiana Jefferson 220511001 85 79 77

22 121 Louisiana West Baton Rou 221210001 85 78 76
D-3

23 5 Maine Cumberland 230052003 86 78 75

23 9 Maine Hancock 230090102 93 81 76

23 31 Maine York 230312002 90 82 80

24 3 Maryland Anne Arundel 240030019 102 91 87

24 5 Maryland Baltimore 240053001 93 85 83

24 13 Maryland Carroll 240130001 92 82 78

24 15 Maryland Cecil 240150003 104 90 86

24 17 Maryland Charles 240170010 94 79 75

24 21 Maryland Frederick 240210037 91 81 77

24 25 Maryland Harford 240251001 104 93 89

24 29 Maryland Kent 240290002 102 89 84

24 31 Maryland Montgomery 240313001 89 82 79

24 33 Maryland Prince Georges 240330002 95 86 82

24 43 Maryland Washington 240430009 87 75 71

25 1 Massachusetts Barnstable 250010002 93 81 77

25 5 Massachusetts Bristol 250051002 90 80 76

25 9 Massachusetts Essex 250092006 90 82 80

25 13 Massachusetts Hampden 250130008 92 83 80

25 15 Massachusetts Hampshire 250154002 88 80 78

25 17 Massachusetts Middlesex 250171102 89 79 76

25 25 Massachusetts Suffolk 250250041 89 79 75

25 27 Massachusetts Worcester 250270015 85 76 73

26 5 Michigan Allegan 260050003 92 82 79

26 19 Michigan Benzie 260190003 86 78 75

26 21 Michigan Berrien 260210014 87 77 74

26 27 Michigan Cass 260270003 90 78 74

26 91 Michigan Lenawee 260910007 85 76 74

26 99 Michigan Macomb 260991003 88 84 86

26 105 Michigan Mason 261050007 87 78 74

26 121 Michigan Muskegon 261210039 89 80 77

26 125 Michigan Oakland 261250001 86 81 82

26 139 Michigan Ottawa 261390005 85 76 74

26 147 Michigan St Clair 261470005 88 82 80

26 161 Michigan Washtenaw 261610008 87 79 77

26 163 Michigan Wayne 261630016 85 80 83

28 33 Mississippi De Soto 280330002 86 75 72

29 47 Missouri Clay 290470005 85 78 75

29 99 Missouri Jefferson 290990012 86 75 72

29 183 Missouri St Charles 291831002 90 81 78

29 189 Missouri St Louis 291890004 89 81 78

29 510 Missouri St Louis City 295100086 88 80 77
D-4

33 11 New Hampshire Hillsborough 330111010 85 76 73

34 1 New Jersey Atlantic 340010005 91 80 76

34 3 New Jersey Bergen 340030005 91 88 87

34 7 New Jersey Camden 340070003 103 93 91

34 11 New Jersey Cumberland 340110007 98 86 81

34 15 New Jersey Gloucester 340150002 104 95 93

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34 19 New Jersey Hunterdon 340190001 96 89 87

34 21 New Jersey Mercer 340210005 104 98 96

34 23 New Jersey Middlesex 340230011 101 95 92

34 25 New Jersey Monmouth 340250005 97 89 87

34 27 New Jersey Morris 340273001 98 88 85

34 29 New Jersey Ocean 340290006 115 105 102

34 31 New Jersey Passaic 340315001 88 82 80

36 13 New York Chautauqua 360130006 92 83 81

36 27 New York Dutchess 360270007 93 83 80

36 29 New York Erie 360290002 97 90 88

36 31 New York Essex 360310002 86 80 78

36 45 New York Jefferson 360450002 91 82 80

36 55 New York Monroe 360551004 85 77 75

36 63 New York Niagara 360631006 91 83 81

36 79 New York Putnam 360790005 92 85 83

36 85 New York Richmond 360850067 96 90 87

36 103 New York Suffolk 361030009 97 90 89

36 119 New York Westchester 361192004 90 86 86

37 3 North Carolina Alexander 370030003 91 73 68

37 21 North Carolina Buncombe 370210030 85 68 63

37 27 North Carolina Caldwell 370270003 86 69 65

37 33 North Carolina Caswell 370330001 91 75 71

37 51 North Carolina Cumberland 370510008 87 73 68

37 59 North Carolina Davie 370590002 95 78 73

37 63 North Carolina Durham 370630013 91 77 72

37 65 North Carolina Edgecombe 370650099 88 75 71

37 67 North Carolina Forsyth 370670022 94 76 71

37 69 North Carolina Franklin 370690001 91 77 72

37 77 North Carolina Granville 370770001 94 79 75

37 81 North Carolina Guilford 370810011 93 76 71

37 87 North Carolina Haywood 370870036 87 69 65

37 99 North Carolina Jackson 370990005 86 69 64

37 101 North Carolina Johnston 371010002 85 72 67

37 109 North Carolina Lincoln 371090004 94 77 72
D-5

37 119 North Carolina Mecklenburg 371191009 102 85 79

37 145 North Carolina Person 371450003 90 74 71

37 157 North Carolina Rockingham 371570099 90 72 67

37 159 North Carolina Rowan 371590022 101 82 77

37 179 North Carolina Union 371790003 88 73 67

37 183 North Carolina Wake 371830015 94 81 75

37 199 North Carolina Yancey 371990003 87 70 66

39 3 Ohio Allen 390030002 88 78 75

39 7 Ohio Ashtabula 390071001 94 84 82

39 17 Ohio Butler 390170004 89 77 74

39 23 Ohio Clark 390230001 90 78 74

39 25 Ohio Clermont 390250022 90 78 75

39 27 Ohio Clinton 390271002 96 82 77

39 35 Ohio Cuyahoga 390355002 86 78 75

39 41 Ohio Delaware 390410002 89 79 75

39 55 Ohio Geauga 390550004 99 88 85

39 57 Ohio Greene 390570006 86 74 70

39 61 Ohio Hamilton 390610006 89 79 76

39 81 Ohio Jefferson 390810016 86 77 75

39 83 Ohio Knox 390830002 90 80 77

39 85 Ohio Lake 390850003 92 83 80

39 87 Ohio Lawrence 390870006 86 74 71

39 89 Ohio Licking 390890005 90 80 76

39 93 Ohio Lorain 390930017 85 78 76

39 95 Ohio Lucas 390950081 89 81 79

39 97 Ohio Madison 390970007 89 78 75

39 99 Ohio Mahoning 390990013 87 76 72

39 103 Ohio Medina 391030003 87 77 73

39 109 Ohio Miami 391090005 87 76 72

39 113 Ohio Montgomery 391130019 86 75 71

39 133 Ohio Portage 391331001 91 80 77

39 151 Ohio Stark 391510021 89 79 75

39 153 Ohio Summit 391530020 95 85 81

39 155 Ohio Trumbull 391550011 90 79 75

39 165 Ohio Warren 391650006 89 77 74

39 167 Ohio Washington 391670004 87 74 67

39 173 Ohio Wood 391730003 86 77 74

40 143 Oklahoma Tulsa 401430137 85 76 74

42 3 Pennsylvania Allegheny 420031005 95 85 82

42 5 Pennsylvania Armstrong 420050001 91 79 76

42 7 Pennsylvania Beaver 420070005 90 82 79
D-6

42 11 Pennsylvania Berks 420110009 92 81 77

42 17 Pennsylvania Bucks 420170012 104 97 95

42 21 Pennsylvania Cambria 420210011 88 76 73

42 27 Pennsylvania Centre 420270100 85 74 70

42 29 Pennsylvania Chester 420290100 95 84 80

42 33 Pennsylvania Clearfield 420334000 87 75 72

42 43 Pennsylvania Dauphin 420431100 91 80 76

42 45 Pennsylvania Delaware 420450002 95 87 84

42 49 Pennsylvania Erie 420490003 88 79 77

42 55 Pennsylvania Franklin 420550001 94 80 76

42 59 Pennsylvania Greene 420590002 90 78 73

42 69 Pennsylvania Lackawanna 420690101 85 74 69

42 71 Pennsylvania Lancaster 420710007 94 83 80

42 77 Pennsylvania Lehigh 420770004 93 83 80

42 85 Pennsylvania Mercer 420850100 92 80 76

42 91 Pennsylvania Montgomery 420910013 97 90 89

42 95 Pennsylvania Northampton 420950025 92 82 79

42 101 Pennsylvania Philadelphia 421010024 98 92 91

42 125 Pennsylvania Washington 421255001 88 80 78

42 129 Pennsylvania Westmoreland 421290008 86 76 73

42 133 Pennsylvania York 421330008 92 81 78

44 3 Rhode Island Kent 440030002 97 89 85

44 7 Rhode Island Providence 440071010 91 82 78

44 9 Rhode Island Washington 440090007 93 84 80

45 1 South Carolina Abbeville 450010001 85 69 64

45 3 South Carolina Aiken 450030003 88 75 71

45 7 South Carolina Anderson 450070003 88 74 69

45 21 South Carolina Cherokee 450210002 87 71 67

45 31 South Carolina Darlington 450310003 86 73 69

45 77 South Carolina Pickens 450770002 85 69 64

45 79 South Carolina Richland 450791001 93 77 72

45 83 South Carolina Spartanburg 450830009 90 74 69

47 1 Tennessee Anderson 470010101 92 72 67

47 9 Tennessee Blount 470090101 94 77 72

47 65 Tennessee Hamilton 470650028 93 75 70

47 75 Tennessee Haywood 470750003 86 74 71

47 89 Tennessee Jefferson 470890002 95 78 73

47 93 Tennessee Knox 470931020 96 77 72

47 121 Tennessee Meigs 471210104 93 73 68

47 141 Tennessee Putnam 471410004 86 72 68

47 155 Tennessee Sevier 471550101 98 79 74
D-7

47 157 Tennessee Shelby 471570021 90 80 78

47 163 Tennessee Sullivan 471632003 92 74 70

47 165 Tennessee Sumner 471650007 88 76 73

47 187 Tennessee Williamson 471870106 87 72 69

47 189 Tennessee Wilson 471890103 85 74 70

48 29 Texas Bexar 480290059 86 72 69

48 39 Texas Brazoria 480391003 86 80 78

48 85 Texas Collin 480850005 93 83 79

48 113 Texas Dallas 481130069 91 82 79

48 121 Texas Denton 481210034 99 87 83

48 139 Texas Ellis 481390015 86 75 71

48 167 Texas Galveston 481670014 89 83 82

48 183 Texas Gregg 481830001 88 74 71

48 201 Texas Harris 482010024 107 100 99

48 251 Texas Johnson 482510003 89 78 74

48 339 Texas Montgomery 483390078 91 82 79

48 367 Texas Parker 483670081 86 75 71

48 439 Texas Tarrant 484392003 98 88 84

48 453 Texas Travis 484530014 85 75 72

51 13 Virginia Arlington 510130020 96 88 87

51 36 Virginia Charles City 510360002 90 77 74

51 41 Virginia Chesterfield 510410004 86 74 71

51 59 Virginia Fairfax 510590018 97 87 85

51 69 Virginia Frederick 510690010 85 73 70

51 87 Virginia Henrico 510870014 90 77 74

51 107 Virginia Loudoun 511071005 90 81 78

51 113 Virginia Madison 511130003 85 71 67

51 153 Virginia Prince William 511530009 85 75 72

51 161 Virginia Roanoke 511611004 87 73 69

51 179 Virginia Stafford 511790001 86 74 70

51 510 Virginia Alexandria City 515100009 90 83 81

51 650 Virginia Hampton City 516500004 89 80 77

51 800 Virginia Suffolk City 518000004 88 79 77

54 11 West Virginia Cabell 540110006 88 75 72

54 29 West Virginia Hancock 540291004 85 76 74

54 39 West Virginia Kanawha 540390010 85 69 66

54 69 West Virginia Ohio 540690007 85 74 70

54 107 West Virginia Wood 541071002 88 72 66

55 29 Wisconsin Door 550290004 91 83 79

55 59 Wisconsin Kenosha 550590019 100 94 93

55 61 Wisconsin Kewaunee 550610002 88 80 77
D-8

55 71 Wisconsin Manitowoc 550710007 88 80 77

55 79 Wisconsin Milwaukee 550790085 91 83 81

55 89 Wisconsin Ozaukee 550890009 93 84 81

55 101 Wisconsin Racine 551010017 93 86 84

55 117 Wisconsin Sheboygan 551170006 99 90 86
Technical Support Document for the 

Interstate Air Quality Rule

Air Quality Modeling Analyses

Appendix E

Procedures for Estimating Future PM2.5 Values 

by Application of the Speciated Modeled 

Attainment Test (SMAT)
E-1

Introduction

EPA has issued draft guidance (EPA, 2001a) that describes a procedure for combining

monitoring data with outputs from simulation models to estimate future concentrations of PM2.5

mass. The guidance recommends that model predictions be used in a relative sense to estimate

changes expected to occur in each major PM2.5 species. PM2.5 species are sulfates, nitrates,

organic carbon, elemental carbon, crustal and un-attributed mass which is defined as the

difference between measured PM2.5and the sum of the five component species. EPA is using

the “SMAT” procedure to estimate the ambient impact of national rules and legislation,

including the Clear Skies Act and the Interstate Air Quality Rule (IAQR). 

The draft guidance includes a sequence of key steps that are recommended for processing

the data. The following is a brief summary of those steps:

(1) Derive current quarterly mean concentrations (averaged over three years) for each of the

six major components of PM2.5. This is done by multiplying the monitored quarterly

mean concentration of Federal Reference Method (FRM) derived PM2.5 by the

monitored fractional composition of PM2.5 species (at speciation monitor sites) for each

quarter in three consecutive years. (e.g., 20% sulfate x 15 :g/m3 PM2.5 = 3 :g/m3

sulfate).

(2) For each quarter, apply an air quality model to estimate current and future concentrations

for each of the six components of PM2.5. Take the ratio of future to current predictions

for each component. The result is a component-specific relative reduction factor (RRF).

(e.g., given model predicted sulfate for base is 10 :g/m3

 and future is 8 :g/m3 then RRF

for sulfate is 0.8).

(3) For each quarter, multiply the current quarterly mean component concentration (step 1)

times the component-specific RRF obtained in step 2. This leads to an estimated future

quarterly mean concentration for each component. (e.g., 3 :g/m3 sulfate x 0.8 = future

sulfate of 2.4 :g/m3).

(4) Average the four quarterly mean future concentrations to get an estimated future annual

mean concentration for each component. Sum the annual mean concentrations of the six

components to obtain an estimated future annual concentration for PM2.5.

EPA will use the Federal Reference Monitor (FRM) data for nonattainment designations. 

Therefore it is important that FRM data is used in the speciated modeled attainment test

described above. As can be seen from the list of steps, the modeled attainment test is dependent

on the availability of species component mass at FRM sites. Since roughly 80% of the FRM

sites will not have collocated speciation monitors, a spatial interpolation methodology was

developed to estimate component species mass at the FRM locations. This method was further

utilized to estimate PM2.5 and component species mass at every grid cell in the study domain.

Additional ambient data handling procedures were also developed. Below we describe an
1

The network is referred to as the “STN”, but all urban speciation sites were used, not just the

trends sites.

2

There are certain differences in sampling and analysis techniques which may affect the results of

this application. The data from both networks were treated similarly whenever possible. Further

comparison studies and analyses are needed to develop data sampling and handling procedures that may

make the data from the two networks more similar.

3

 The 2nd quarter of 2002 was the most recent quarter of data available from both the IMPROVE

and STN networks at the time of the analysis. The ambient speciation data will be updated as newer data

and more sites become available.

E-2

example application of the procedures, for a study domain that extends over a large portion of

eastern US. The study domain is defined for grids of dimension 1/2 degree longitude by 1/3

degree latitude (~36 km X 36 km) covering the area enclosed within -100 to -67 longitude and

25 to 49 latitude. Base year and future year model predictions are available for each grid cell (72

rows by 66 columns) that make up the study domain.

Ambient Data preparation

PM2.5 quarterly averages at FRM sites for 1999-2001 were calculated using data from

the Air Quality System (AQS). The resulting data set contained 325 sites that meet the

completeness criteria needed to determine the PM2.5 NAAQS attainment status. Each of the

PM2.5 sites was uniquely associated with one of the grid cells in the study domain. 

Speciated PM2.5 data from both the Interagency Monitoring of Protected Visual

Environments (IMPROVE) and EPA’s speciation trends network1

 (STN) were used to derive

mean concentrations of each of the six PM2.5 components. No attempt was made to resolve

differences in measurement and analysis methodology between the two networks2

. Since three

years of urban speciation data were not available, the latest full year of data was used. Quarterly

average concentrations between July 2001 through June 20023 were retained for sites that had at

least 15 monitored values (50% completeness for 1 in 3 day sampling). The quarters were

defined as follows: Q3 = July 2001 - September 2001; Q4 = October - December 2001; Q1 = 

January - March 2002; and Q2 = April - June 2001. Figure 1 shows the spatial distribution of

IMPROVE and STN stations that met this completeness criteria for first quarter of 2002. 
4

As recommended in the modeling guidance, organic carbon was multiplied by 1.4 and

particulate nitrate was multiplied by 1.29.

E-3

Figure 1. Speciated stations with at least 15 quarterly samples

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 Note: The number of stations meeting completeness criteria for the four quarters:

 Quarter 3, 2001 – 103 sites

 Quarter 4, 2001 – 106 sites

 Quarter 1, 2002 – 105 sites

 Quarter 2, 2002 – 117 sites

As noted in the modeling guidance, the mass associated with each component must be

estimated based on assumptions about chemical composition. Table 3.4 in the modeling

guidance provides recommended default assumptions which were applied for each of the species

except sulfate and carbon compounds4

. Because ammonium is reported in the STN, it was

possible to analyze the degree to which sulfate measured on the filter was actually neutralized. 

The analysis concluded that, on average, sulfate was not completely neutralized resulting in use

of the factor 1.25 rather than the value of 1.375 recommended in the guidance. The 1.25 factor

was derived through a mass balance of measured ammonium, sulfate, and nitrate at the STN

sites. It was assumed that all particulate nitrate was in the form of ammonium nitrate. The

measurements of nitrate ion and particulate ammonium are known to be uncertain. The
E-4

calculation of the ammoniation of sulfate is subject to these uncertainties. Therfore, a single

domainwide annual average value of 1.25 was used for all sites due to the uncertainties in the

measurements of ammonium and nitrate. This value assumes that sulfate is, on average, partway

between ammonium bisulfate and ammonium sulfate.

The elemental and organic carbon mass from the STN was adjusted downward based on 

measurements from field blanks which indicate a positive bias. The blank corrections were

based on a draft report which examined the blank carbon data in the STN network (RTI, 2002). 

The carbon corrections are shown below in Table 1. The values were taken from Table 4.1 from

the RTI report. The monitor dependent blank corrections were made to the quarterly average

concentrations at each STN site. The IMPROVE carbon measurements are blank corrected by

the IMPROVE program. 

Sampler Type Elemental Carbon 

(:gC/m3

)

Organic Carbon

(:gC/m3)

URG MASS 0.03 0.29

R and P 2300 0.22 0.90

Anderson RAAS 0.09 1.19

R&P 2025 0.07 0.77

MetOne SASS 0.11 1.42

Table 1. Carbon blank corrections

Finally, un-attributed mass was calculated for each of the STN monitors with a colocated FRM monitor. Un-attributed mass was not calculated for the IMPROVE sites since there

were no collocated FRM PM2.5 data available. The results produced generally small positive

estimates of un-attributed mass although for some sites, the estimate was negative. The unattributed mass did not follow any clear spatial or temporal patterns. Due to the relatively

random pattern of the un-attributed mass, a single quarterly value of un-attributed mass was used

at each site. Table 2 summarizes the quarterly average un-attributed mass data. A quarterly

average un-attributed mass value was calculated at each STN site by applying the un-attributed

percentage to the quarterly average site specific FRM mass.
E-5

 Quarter 1

 (Jan-Mar 02)

 Quarter 2 

(Apr-June 01)

 Quarter 3 

(July-Sept 01)

 Quarter 4

(Oct - Dec 01)

Num of Monitoring sites 47 31 43 46

Avg FRM PM2.5 mass 

(:g/m3

)

 12.17 13.51 14.43 11.97

Avg species mass sum

(:g/m3

)

 12.12 13.41 13.70 11.74

Un-attributed (:g/m3) 0.05 0.10 0.73 0.23

Percent Un-attributed 0.4 % 0.7 % 5.0 % 1.9 %

Table 2. Average Un-attributed Mass of PM2.5

Species Component Estimation

 Only a small fraction of PM2.5 sites have measured species information. For this

reason, an objective procedure was developed for using the speciated component averages from

the IMPROVE and STN networks to estimate concentrations of species mass at all FRM PM2.5

monitoring sites. Kriging was adopted as the method for estimating PM2.5 component mass at

PM2.5 sites since software is readily available and can produce estimates of prediction error. 

Kriging was performed using an S-PLUS software package known as FIELDS (NCAR, 2002)

developed by scientists at NCAR to perform generalized kriging and efficient spatial analysis of

large data sets.

The Krig function in FIELDS estimates the parameters of the spatial field using the

Generalized Cross Validation (GCV) error as the criterion for parameter estimation. A simple

exponential covariance function was used to describe the variogram. Outputs from Krig include

the parameter estimates (range, nugget and sill) along with predicted values at each of the PM2.5

monitor locations. Once the kriging equations were established for each species, quarterly

average species concentrations were estimated for each of the FRM sites and for each grid cell in

the modeling domain. The latter predictions were made so that estimated PM2.5 concentrations

could be obtained for the entire modeling domain, allowing for a more complete spatial

assessment of future PM2.5 levels. Figures 2 and 3 illustrate the spatially interpolated

concentration fields for nitrates (quarter 1) and sulfates (quarter 3).
E-6

Figure 2 Spatially Interpolated Nitrate Quarterly Average 

Concentrations (quarter 1)

Figure 3 Spatially Interpolated Sulfate Quarterly Average

Concentrations (quarter 3)

Kriging was not used for spatial interpolation of un-attributed mass since it only available for
E-7

some of the STN sites and because there was no discernable spatial trend. Instead the quarterly

average of the un-attributed mass from the STN sites was first expressed as a fraction of the

average PM2.5 mass. The estimated fractions for each quarter were previously shown in Table

2.

For each quarter, predicted concentrations for each of the six species are combined with

quarterly PM2.5 FRM averages to derive composition concentrations in the following manner. 

First, the un-attributed mass at each PM2.5 site was estimated by multiplying the average

fraction of un-attributed mass by the quarterly average PM2.5 concentration for that site. For

example, if a site in quarter 3 had an average PM2.5 mass of 20 :g/m3, then the un-attributed

mass would be 20 :g/m3 x 0.05 = 1 :g/m3. The total PM2.5 mass that is identifiable was

calculated by subtracting the estimated un-attributed mass from each quarterly average PM2.5

value. Next, the component mass of each of the five identifiable species was estimated by

multiplying the fraction of each species by the identifiable portion of the quarterly PM2.5mass. 

This procedure is repeated for each PM2.5 site and quarter to complete the calculation of current

or baseline ambient concentrations used as the basis for future estimates of PM2.5 mass and its

components. Table 3a shows an example of the un-attributed mass calculation and the species

fractions for an FRM site in quarter 2. The species fractions in table 3a are derived from the

quarterly interpolated (Kriged) spatial fields for each of the five species. Multiplying the unattributed mass fraction of 0.7% (from table 2) times 17.0 (FRM mass from table 3a) yields the

identifiable mass of 16.88. The identifiable mass can then be split into individual species

component mass estimates by using the fractions in table 3a.

FRM

Mass

(:g/m3

)

% 

Un-atributed

mass 

Identifiable

Mass

(:g/m3)

%

Sulfate

%

Nitrate

%

Organic

aerosol

%

Elemental

Carbon

%

Crustal

17.0 0 .7 16.88 32.1 11.4 38.9 9.9 7.7

Table 3a. Un-attributed mass and species fractions for an FRM site in quarter 2

Table 3b shows the resultant mass for each of the component species at the same FRM site. The

species mass is calculated by multiplying the fraction of each component by the identifiable

mass. The sum of the components is the observed FRM PM2.5 mass concentration (17.0

:g/m3)

FRM

Mass

(:g/m3

)

Un-atributed

Mass 

(:g/m3)

Sulfate

Mass

(:g/m3)

Nitrate Mass

(:g/m3

)

Organic

aerosol Mass 

(:g/m3

)

Elemental

Carbon Mass

(:g/m3)

Crustal

Mass

(:g/m3)

17.0 0.12 5.42 1.92 6.57 1.67 1.30

Table 3b. Resultant species mass at an FRM site in quarter 2
5

The species fractions at co-located FRM and speciation sites can be calculated without the use of

spatial fields. However, for this application, the species fractions for all FRM sites were derived from the

spatial fields. This allowed for consistent calculations at all sites. 

E-8

Estimating Future Year PM2.5

Future concentrations of PM2.5 component species are estimated by assuming that the

quarterly average component concentration will change in exactly the same proportion as the

model predicted change. Model predicted changes in species concentrations (from a current year

to a future year) are used to calculate “relative reduction factors”. Relative reduction factors are

calculated for each grid cell and species as the ratio of the quarterly average future predictions to

the current base predictions. The relative reduction factor for each species is then multiplied by

the estimated current year ambient species mass for the site to estimate future species

concentrations. These future species concentrations at each FRM site are then summed over the

five species to estimate the identifiable portion of future quarterly average PM2.5 concentration. 

The current year quarterly average estimate of un-attributed PM2.5 mass is added to the future

quarterly average identifiable PM2.5 mass estimate. The four quarterly values are then averaged

to obtain the estimated future annual average PM2.5 for each FRM site.

FRM sites close to or co-located with an STN monitor will have the least “error” in the

estimation of species fractions5

. There is more uncertainty associated with FRM monitoring

sites that are not located near a speciation site. It should be noted that the sole use of the

interpolated speciation data is to calculate the mass fractions of each of the PM2.5 components.

All of the future year design value calculations at FRM sites are “anchored” by the FRM data

itself. 

The results of the analysis at each of the FRM monitoring sites (with complete data) were 

used in analyses such as Clear Skies and the IAQR. Application of SMAT with Kriged spatial

fields allows us to take advantage of the design value information at each FRM site. In this way,

a more complete attainment/nonattainment picture can be derived by not limiting the predictions

of future year design values to only speciation monitoring sites. 

Additional Spatial Information

PM2.5 concentrations can also be estimated over the entire field of grid locations that

define the study domain (i.e., 72 x 66 grid cells). This requires that the quarterly average PM2.5

also be kriged to estimate PM2.5 average concentrations for each grid cell. Because the majority

of PM2.5 measurement sites are urban oriented, the PM2.5 mass reported for the IMPROVE

sites are also included in the spatial interpolation process to help minimize potential urban bias

in more rural locations. Figure 4 shows the spatially interpolated base year (1999-2001) PM2.5

annual concentration field and figure 5 shows the projected future base case (2010) PM2.5

concentration field. 
E-9

Figure 4 Example spatial fields of future year (2010) annual

average PM2.5 design values (calculated from relative

reduction factors from the REMSAD model) 

Figure 5 Example spatial fields of base year (1999-2001)

annual average PM2.5 design values
E-10

Summary of Outputs

Future year design values can be calculated at monitoring sites which have co-located

FRM and speciation monitors. Kriging of speciation data allows the calculation of future year

design values at all FRM monitoring sites. Additional Kriging of all PM2.5 data (FRM and

IMPROVE) allows the calculation of future year design values at all model grid cells. Table 4

shows the available outputs of the modeled attainment test with spatial fields. 

Ambient PM2.5

Data From:

Ambient Speciation

Data From:

Case1- FRM monitoring sites FRM monitor Interpolated (Kriged)

speciation data

Case 2-All grid cells Interpolated

(Kriged) PM2.5

data

Interpolated (Kriged)

speciation data

Table 4. Sources of data for speciated modeled attainment test with spatial fields

 There are uncertainties associated with many aspects of the analysis. There is

uncertainty associated with collection and analysis of the ambient data (e.g. positive organic

carbon artifacts and negative particulate nitrate artifacts associated with the ambient data

collection and analysis), post-processing of the ambient data (e.g, assumptions regarding the 1.25

factor for sulfate or the 1.4 factor applied to organic carbon), interpolation of the data to the

FRM sites and grids (e.g. Kriging error and replication of species gradients), use of the model

predicted changes in species (e.g. errors and uncertainty in the model science and inventories),

etc. 

We have the most confidence in future estimates of PM2.5 at FRM monitoring locations

(case 1). Therefore, the results of this analysis at each of the FRM monitoring sites (with

complete data) will be used for regulatory purposes. 

Caveats on use of SMAT with Spatial Fields

The details of this application of SMAT are specific to the short term use of the FRM and

STN data in estimating future year PM2.5 concentrations. The use of a single year of speciation

data interpolated to a modeling grid is necessary at this time, due to the relatively sparse ambient

data sets. The amount of available ambient data will increase significantly in the future. As a

resul, for many areas, the coverage of speciation data may be adequate so that interpolation of

the data through spatial fields is not necessary. This application should serve as an example that

can be replicated in the short term, but the techniques and assumptions will likely evolve over

the long term. 
E-11

Example Future Year Design Value Calculations

The following example shows the SMAT steps for the 2010 Base Case for several FRM

PM2.5 sites in Alabama. The example follows the calculations for the future design values for

each model run. There are four tables. One each for the 2010 Base Case design value

calculations, the 2010 control case, the 2015 Base Case, and the 2015 control case. Each table

contains three sections. The “Quarterly All Sites” section shows the quarterly average

calculations for all FRM sites (only sites with complete date). The “Annual All Sites” section

averages the quarterly data for each monitoring site and reports the annual average design

values. The “Annual High Sites” section filters the data to show only the highest monitoring site

in each county. 

We start with the 1999-2001 and 2000-2002 design values at each FRM site (with

complete data). For those sites that are measuring nonattainment in 2000-2002, the higher of the

two design values is used in the analysis. The 2000-2002 design value are used for those sites

that are attainment during this period. The design value is then broken down into quarterly

averages. The following excerpt from the 2010 Base Case table (Quarterly All Sites section)

shows the ambient design values for each quarter (column I) for several sites in Alabama.

A B C D E FG H I

State

Fip

County

Fip

State Name County Name AIRS Site

Code

Row Column Quarter

1999-2001/

2000-2002 

Ambient

FRM DV

1 49 Alabama DeKalb County 010491003 31 81 1 13.7857115

1 49 Alabama DeKalb County 010491003 31 81 2 14.10473748

1 49 Alabama DeKalb County 010491003 31 81 3 21.92974988

1 49 Alabama DeKalb County 010491003 31 81 4 17.22333333

1 53 Alabama Escambia County 010530002 22 78 1 12.98124247

1 53 Alabama Escambia County 010530002 22 78 2 12.94027641

1 53 Alabama Escambia County 010530002 22 78 3 15.45416667

1 53 Alabama Escambia County 010530002 22 78 4 13.47417442

1 73 Alabama Jefferson County 010730023 29 79 1 17.88685887

1 73 Alabama Jefferson County 010730023 29 79 2 19.89304794

1 73 Alabama Jefferson County 010730023 29 79 3 26.24857762

 

1 73 Alabama Jefferson County 010730023 29 79 4 22.28314749
E-12

The next step is to remove the unattributed mass from the design value. The unatributed

mass is treated as a fixed fraction of FRM mass that varies by quarter. Column J shows the

unatributed fraction and column K shows the quarterly averages with the unatributed mass

removed.

J K

Unatributed

Fraction

FRM Mass 

without 

Unatributed

0.004108463 13.72907341

0.007401925 14.00033528

0.050589051 20.82034465

0.019214703 16.89239209

0.004108463 12.92790951

0.007401925 12.84449346

0.050589051 14.67235505

0.019214703 13.21527216

0.004108463 17.81337136

0.007401925 19.7458011

0.050589051 24.920687

0.019214703 21.85498341
E-13

Next the FRM PM2.5 mass is divided into the species components. This is done by calculating

the species fractions from the Kriged surfaces. In the following example, The sulfate fraction for

quarter 1 at the site in DeKalb county Alabama is 43.4% (column O). This is the fraction of total

PM2.5 from column K.

LMNOP

Crustal

Fraction

EC Fraction OC Fraction Sulfate

Fraction

Nitrate Fraction

0.040554422 0.053856416 0.32917698 0.434901805 0.141510376

0.062691722 0.038491135 0.364702132 0.473525669 0.060589341

0.050784161 0.046509019 0.295064061 0.573991559 0.0336512

0.046598479 0.058149979 0.412990795 0.365473292 0.116787456

0.04323078 0.057282481 0.32142633 0.410296926 0.167763484

0.067217832 0.045197205 0.359264032 0.465347001 0.06297393

0.072306094 0.046501861 0.296886604 0.540717388 0.043588054

0.054660758 0.058832206 0.376710307 0.399193305 0.110603425

0.049615803 0.072993526 0.420536616 0.331436705 0.125417349

0.10227892 0.077315923 0.376565645 0.375782385 0.068057127

0.063257188 0.061957184 0.365119889 0.463406204 0.046259536

0.078780385 0.087789986 0.50144869 0.236116133 0.095864806

We can then get the quarterly species mass values at each site by multiplying the species

fractions by the total PM2.5 (e.g. column O multiplied by Column K = column U)

QRSTUV

1999-

2001/2000-2002

Ambient FRM

Unatributed

PM2.5 Mass

1999-

2001/2000-2002

Ambient Crustal

Mass

1999-

2001/2000-2002

Ambient

Elemental

Carbon Mass

1999-

2001/2000-2002

Ambient

Organic Aerosol

Mass

1999-

2001/2000-2002

Ambient

Ammonium

Sulfate Mass

1999-

2001/2000-2002

Ambient

Ammonium

Nitrate Mass

0.056638092 0.556774643 0.739398691 4.519294924 5.970798805 1.942806345

0.104402202 0.87770513 0.538888802 5.105952133 6.629518135 0.848271083

1.109405226 1.05734374 0.968333805 6.143335444 11.95070209 0.700629572

0.330941242 0.787159772 0.98229225 6.976402432 6.173718139 1.972819499

0.05333296 0.558883608 0.740542727 4.155370505 5.304281528 2.168831145
E-14

0.095782949 0.863379009 0.580535205 4.61456451 5.97714651 0.808868227

0.781811619 1.060900676 0.68229182 4.356025659 7.933597492 0.6395394

0.258902265 0.722356789 0.777483614 4.97832923 5.275448164 1.461654358

0.073487506 0.883824732 1.300260794 7.491174911 5.90400511 2.234105814

0.147246839 2.019579202 1.526664845 7.435590321 7.420124237 1.3438425

1.327890621 1.576412586 1.544015587 9.099038465 11.54840096 1.152819409

0.42816407 1.721744015 1.918648684 10.9591528 5.16031417 2.095123751

The relative reduction factors (RRF) are calculated from the REMSAD model results. 

The RRFs represent the percentage change for each specie for each site for each quarter. For

example, the RRF for elemental carbon for quarter 1 at the DeKalb county site is 0.73 (column

X), which represents a 26.6% reduction in elemental carbon mass between 2001 and 2010. 

W X Y Z AA

RRF - IAQR

2010b Crustal

Mass

RRF - IAQR

2010b

Elemental

Carbon Mass

RRF - IAQR

2010b Organic

Aerosol Mass

RRF - IAQR

2010b

Ammonium

Sulfate Mass

RRF - IAQR

2010b

Ammonium

Nitrate Mass

0.984387805 0.734007875 0.894215346 1.004490725 0.936200299

1.010292927 0.75741159 0.901596815 0.899538483 0.722048795

1.023778779 0.699817783 0.876176087 0.881970652 0.522850118

1.003476273 0.717172235 0.913843769 0.977859617 0.979772342

0.976329385 0.878162793 0.950120354 0.966785809 0.933484255

1.000018254 0.902456596 0.951021008 0.90147797 0.896162069

1.000782489 0.836301085 0.925816993 0.916565498 0.829103207

0.983348583 0.84418583 0.950149933 0.938169407 0.961194783

1.031068875 0.724041957 0.94061741 0.991943586 0.939327718

1.048690141 0.741519805 0.933041132 0.916765785 0.790046338

1.054997488 0.693010181 0.910243813 0.927926735 0.756365551

1.039458529 0.707563728 0.951575967 0.974710839 0.986069043
E-15

The RRFs are applied to each of the species to get the future year 2010 base species mass

values (columns AB-AF). The species mass values are then added together (along with the

previuosly calculated unatributed mass from column Q) to get the total 2010 basecase mass by

quarter (column AG).

AB AC AD AE AF AG

2010b IAQR

Crustal Mass

2010b IAQR

Elemental

Carbon Mass

2010b IAQR

Organic Aerosol

Mass

2010b IAQR

Ammonium

Sulfate Mass

2010b IAQR

Ammonium

Nitrate Mass

2010 base

IAQR DV

0.548082169 0.542724462 4.041222874 5.997612021 1.818855881 13.0051355

0.886739285 0.408160624 4.603510181 5.963506689 0.612493114 12.57881209

1.082486083 0.677657217 5.38264361 10.54016852 0.366324255 19.15868491

0.789896154 0.704472728 6.375341896 6.037029653 1.93291398 16.17059565

0.545654489 0.65031707 3.948102093 5.12810411 2.024569726 12.35008045

0.863394769 0.523907825 4.38854779 5.388265905 0.724877024 11.98477626

1.06173082 0.570601389 4.032882577 7.271661737 0.530244168 14.24893231

0.710328525 0.65634065 4.730159187 4.949264077 1.404934544 12.70992925

0.911284172 0.94144337 7.046329543 5.856440004 2.098557516 16.92754211

2.117912797 1.132052218 6.937711612 6.802516022 1.061697846 18.19913733

1.663111318 1.070018521 8.282343471 10.71606999 0.871952887 23.93138681

1.789681501 1.357566216 10.42846642 5.029814154 2.065936673 21.09962904

The quarterly average mass from column AG is then averaged for all four quarters to get

the annual average future year design value for each monitoring site. The result of this

calculation is in the “Annual All Sites” worksheet (column H). The Annual All Sites worksheet

also contains annual average summary information of species mass and RRFs for each

monitoring site. The species mass and RRFs in this worksheet are for informational purposes

only and are not used as part of the future year design value calculations. All calculations are

done on a quarterly average basis and then summed at the end.

The “Annual High Sites” worksheet contains the final county level design values. Only

the highest design value site in each county is retained for counties with multiple FRM sites. 

The values in this worksheet were used to determine future year attainment status for each

county. Note that each projected PM2.5 design value is truncated at two places to the right of

the decimal in order to determine whether the concentration is $15.05 :g/m3

 (i.e, nonattainment).
Technical Support Document for the 

Interstate Air Quality Rule

Air Quality Modeling Analyses

Appendix F

PM2.5 Concentrations Projected for the

2010 Base Cases and 2015 Base Case
F-1

The table below contains PM2.5 annual average design values (:g/m3) by county for those

counties with PM2.5 concentrations above the National Ambient Air Quality Standard (i.e.,

design value >=15.05 :g/m3) for the period 2000-2002. The ambient data listed for each site are

the higher of the design values over the two periods: 1999-2001 and 2000-2002, as measured at

Federal Reference Method (FRM) sites in the East. Thus, the ambient data are the highest

values in each county during the periods 1999-2001 and 2000-2002 at those sites that measured

concentrations >=15.05 in 2000-2002. Counties with measured concentrations below the

NAAQS are not shown. In addition to the ambient data, the table provides the highest projected

design value in each of these counties for the 2010 Base-1, 2010 Base-2, and 2015 Base Case

scenarios. In all three future base cases, the highest concentration in each county occurs at the

same site that has the highest concentration in 1999-2001/2000-2002. Note that these data have

been truncated at two places to the right of the decimal. Row and Col denote the row and

column coordinates of the REMSAD grid cell in which the monitoring site is located.

State

FIPs

Cnty

FIPs State County AIRS Site ID Row Col

1999-2001/

2000-2002

Ambient

FRM

2010

Base-1

2010

Base-2

2015

Base

1 49 Alabama DeKalb County 010491003 31 81 16.76 15.24 15.22 14.75

1 73 Alabama Jefferson County 010730023 29 79 21.57 20.12 20.03 19.57

1 101 Alabama Montgomery County 011010007 26 80 16.79 15.72 15.69 15.35

1 113 Alabama Russell County 011130001 26 83 18.39 17.31 17.07 16.68

1 121 Alabama Talladega County 011210002 28 80 17.75 16.46 16.44 15.97

9 9 Connecticut New Haven County 090090018 52 107 16.80 15.45 15.43 15.13

10 3 Delaware New Castle County 100032004 48 101 16.61 15.49 15.43 15.01

11 1 DC District of Columbia 110010041 45 99 16.55 15.35 15.48 14.98

13 59 Georgia Clarke County 130590001 30 86 18.61 17.05 17.04 16.46

13 63 Georgia Clayton County 130630091 29 84 19.16 17.82 17.73 17.26

13 67 Georgia Cobb County 130670003 31 83 18.56 17.24 16.80 16.28

13 89 Georgia DeKalb County 130892001 30 84 19.56 18.26 18.26 17.93

13 115 Georgia Floyd County 131150005 31 82 18.45 17.14 16.99 16.51

13 121 Georgia Fulton County 131210039 30 84 21.20 19.79 19.79 19.44

13 139 Georgia Hall County 131390003 31 85 17.24 15.61 15.62 15.05

13 215 Georgia Muscogee County 132150011 26 83 17.97 16.92 16.68 16.31

13 223 Georgia Paulding County 132230003 30 82 16.76 15.52 15.40 14.93

13 245 Georgia Richmond County 132450091 29 88 17.36 16.03 15.99 15.51

13 319 Georgia Wilkinson County 133190001 27 86 17.75 16.89 16.68 16.40

17 31 Illinois Cook County 170310052 54 77 18.79 18.07 17.90 17.52

17 43 Illinois DuPage County 170434002 54 76 15.44 14.91 14.74 14.34

17 119 Illinois Madison County 171191007 45 72 17.45 16.48 16.41 16.03

17 163 Illinois St. Clair County 171630010 44 72 17.42 16.32 16.31 15.91

17 197 Illinois Will County 171971002 53 76 15.87 15.54 15.21 14.86

18 19 Indiana Clark County 180190005 43 81 17.34 15.79 15.86 15.40

18 35 Indiana Delaware County 180350006 49 82 15.07 13.88 13.93 13.41

18 39 Indiana Elkhart County 180390003 54 81 15.45 14.32 14.34 13.83

18 43 Indiana Floyd County 180431004 43 81 15.60 14.20 14.26 13.84

18 67 Indiana Howard County 180670003 50 80 15.10 13.98 14.05 13.48

18 89 Indiana Lake County 180890006 53 78 15.62 14.89 14.83 14.44

18 97 Indiana Marion County 180970083 48 80 17.00 15.76 15.89 15.31

18 163 Indiana

Vanderburgh

County 181630016 42 77 15.70 14.24 14.25 13.78
F-2

State

FIPs

Cnty

FIPs State County AIRS Site ID Row Col

1999-2001/

2000-2002

Ambient

FRM

2010

Base-1

2010

Base-2

2015

Base

18 167 Indiana Vigo County 181670018 47 78 15.15 13.82 14.00 13.38

21 19 Kentucky Boyd County 210190017 44 87 15.67 14.27 14.56 13.99

21 29 Kentucky Bullitt County 210290006 42 81 16.03 14.18 14.31 13.79

21 37 Kentucky Campbell County 210370003 46 84 15.45 14.05 14.21 13.65

21 67 Kentucky Fayette County 210670014 43 83 16.81 15.05 15.21 14.66

21 93 Kentucky Hardin County 210930006 42 81 15.10 13.35 13.48 12.99

21 111 Kentucky Jefferson County 211110044 43 81 17.28 15.71 15.79 15.32

21 117 Kentucky Kenton County 211170007 46 83 15.86 14.37 14.52 14.01

24 3 Maryland

Anne Arundel

County 240031003 46 99 15.81 14.66 14.72 14.30

24 5 Maryland Baltimore County 240053001 46 100 15.10 13.77 13.81 13.38

24 510 Maryland Baltimore city 245100040 46 99 17.82 16.53 16.58 16.11

26 115 Michigan Monroe County 261150005 54 86 15.57 14.63 14.68 14.26

26 163 Michigan Wayne County 261630033 55 86 19.85 18.76 18.78 18.28

29 510 Missouri St. Louis city 295100085 44 72 16.28 15.26 15.25 14.89

34 17 New Jersey Hudson County 340171003 51 104 15.88 13.46 13.49 13.20

34 39 New Jersey Union County 340390004 50 104 16.26 14.13 14.11 13.93

36 5 New York Bronx County 360050080 51 105 16.13 14.55 14.56 14.12

36 61 New York New York County 360610056 51 105 18.04 16.29 16.30 15.82

37 25 North Carolina Cabarrus County 370250004 35 91 15.67 13.53 13.68 13.13

37 35 North Carolina Catawba County 370350004 36 90 17.10 15.04 15.26 14.62

37 57 North Carolina Davidson County 370570002 36 92 17.27 15.32 15.52 14.92

37 67 North Carolina Forsyth County 370670022 37 92 16.23 14.27 14.44 13.82

37 111 North Carolina McDowell County 371110004 36 89 16.16 14.34 14.54 14.00

37 119 North Carolina

Mecklenburg

County 371190010 34 91 16.77 15.07 15.18 14.61

39 17 Ohio Butler County 390170003 47 84 17.40 15.87 16.01 15.39

39 35 Ohio Cuyahoga County 390350038 53 89 20.25 18.99 19.13 18.58

39 49 Ohio Franklin County 390490024 48 87 18.13 16.45 16.69 16.18

39 61 Ohio Hamilton County 390610014 46 84 19.29 17.57 17.75 17.07

39 81 Ohio Jefferson County 390810016 50 91 18.90 17.69 18.04 17.49

39 87 Ohio Lawrence County 390870010 44 87 16.65 15.19 15.48 14.88

39 99 Ohio Mahoning County 390990005 52 91 16.42 15.13 15.39 14.82

39 113 Ohio Montgomery County 391130031 48 84 15.89 14.62 14.71 14.15

39 133 Ohio Portage County 391330002 52 90 15.29 14.25 14.41 13.90

39 145 Ohio Scioto County 391450013 45 87 20.03 18.02 18.40 17.62

39 151 Ohio Stark County 391510017 51 90 18.28 16.80 17.09 16.42

39 153 Ohio Summit County 391530017 52 90 17.34 16.17 16.35 15.78

39 155 Ohio Trumbull County 391550007 52 91 16.15 14.89 15.13 14.58

42 3 Pennsylvania Allegheny County 420030064 49 93 21.42 18.86 19.52 18.64

42 7 Pennsylvania Beaver County 420070014 51 92 15.99 14.53 14.89 14.37

42 11 Pennsylvania Berks County 420110009 49 101 16.67 15.28 15.39 14.95

42 21 Pennsylvania Cambria County 420210011 49 95 15.76 14.10 14.52 13.89

42 43 Pennsylvania Dauphin County 420430401 49 99 15.64 14.05 14.36 13.90

42 45 Pennsylvania Delaware County 420450002 48 102 15.74 14.88 14.85 14.57

42 71 Pennsylvania Lancaster County 420710007 49 100 17.08 15.27 15.46 14.87

42 101 Pennsylvania Philadelphia County 421010136 48 102 15.29 14.46 14.43 14.15

42 125 Pennsylvania Washington County 421250005 49 93 15.69 13.80 14.32 13.65

42 129 Pennsylvania

Westmoreland

County 421290008 49 93 15.61 13.70 14.19 13.53

42 133 Pennsylvania York County 421330008 48 99 17.05 15.50 15.68 15.13
F-3

State

FIPs

Cnty

FIPs State County AIRS Site ID Row Col

1999-2001/

2000-2002

Ambient

FRM

2010

Base-1

2010

Base-2

2015

Base

45 45

South

Carolina Greenville County 450450009 33 88 16.50 14.93 15.06 14.53

47 37 Tennessee Davidson County 470370023 37 79 17.04 15.31 15.36 14.90

47 65 Tennessee Hamilton County 470654002 34 82 17.62 16.11 16.14 15.63

47 93 Tennessee Knox County 470931017 36 85 20.41 18.16 18.36 17.73

47 107 Tennessee McMinn County 471071002 35 83 16.07 14.36 14.45 13.95

47 145 Tennessee Roane County 471450004 36 83 17.02 15.13 15.18 14.63

47 163 Tennessee Sullivan County 471631007 38 87 16.97 15.06 15.24 14.69

51 520 Virginia Bristol city 515200006 38 88 16.01 13.99 14.20 13.64

51 770 Virginia Roanoke city 517700014 40 93 15.23 13.69 13.93 13.41

51 775 Virginia Salem city 517750010 40 92 15.31 13.72 13.96 13.38

54 3 West Virginia Berkeley County 540030003 47 97 16.24 14.59 14.96 14.38

54 9 West Virginia Brooke County 540090005 50 91 17.40 16.28 16.60 16.10

54 11 West Virginia Cabell County 540110006 44 88 17.84 15.98 16.39 15.70

54 29 West Virginia Hancock County 540291004 50 91 17.49 16.37 16.69 16.18

54 39 West Virginia Kanawha County 540391005 44 89 18.39 16.67 17.11 16.45

54 49 West Virginia Marion County 540490006 47 92 15.74 13.99 14.50 13.82

54 51 West Virginia Marshall County 540511002 48 91 16.52 14.90 15.53 14.78

54 69 West Virginia Ohio County 540690008 49 91 15.65 14.15 14.64 13.96

54 107 West Virginia Wood County 541071002 46 89 17.61 15.85 16.30 15.58
Technical Support Document for the 

Interstate Air Quality Rule

Air Quality Modeling Analyses

Appendix G

Metrics for 8-Hour Ozone Contributions to 

Downwind Nonattainment Counties in 2010
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

Downwind NonattainmentReceptor

CAMx Source Apportionment Modeling CAMx State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 133 Base Case: Total Number of Exce

Crittenden AR Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

TN 58% 45 52% 133 100% 51 100% 100% GA 5% 10 11% 51 38% 11 23% 28% AL 6% 12 13% 51 38% 14 34% 48% MS 4% 8 9% 73 55% 8 29% 53% IL 6% 10 11% 79 59% 11 11% 10% KY 4% 5 6% 80 60% 6 24% 29% MO 3% 6 6% 80 60% 7 11% 9% IN 1% 4 5% 21 16% 4 7% 14% 

Contributions do notexceed screening criteria

OH 1% 2 3% 19 14% 3 2% 2% FL 0% 2 2% 0 0% 2 4% 12% LA 1% 2 2% 0 0% 2 2% 2% NC 1% 1 1% 0 0% 1 2% 3% SC 0% 1 1% 0 0% 1 3% 7% VA 1% 1 2% 0 0% 1 3% 2% WV 0% 0 1% 0 0% 1 1% 2% IA 0% 1 1% 0 0% 2 1% 2% WI 0% 1 2% 0 0% 2 1% 2% MI 0% 1 1% 0 0% 1 0% 0% PA 0% 1 1% 0 0% 1 1% 0% MD 0% 0 1% 0 0% 0 0% 0% MN 0% 0 0% 0 0% 0 0% 2% NY 0% 0 0% 0 0% 0 0% 0% CT 0% 0 0% 0 0% 0 0% 0% DE 0% 0 0% 0 0% 0 0% 0% MA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% NJ 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

Downwind NonattainmentReceptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 110 Base Case: Total Number of Exce

Fairfield CT Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

NY 21% 36 41% 110 100% 37 -10% -14% PA 23% 25 29% 108 98% 30 59% 57% NJ 27% 21 24% 110 100% 28 47% 41% VA 3% 7 8% 68 62% 7 10% 9% OH 6% 7 7% 77 70% 10 15% 13% MD 3% 7 8% 68 62% 7 9% 8% WV 2% 3 3% 68 62% 3 7% 6% 

Contributions do notexceed screening criteria

MI 1% 4 5% 13 12% 4 3% 3% NC 2% 2 2% 43 39% 3 3% 3% DE 2% 2 2% 33 30% 3 3% 3% IN 2% 2 2% 28 25% 3 3% 3% IL 2% 2 2% 17 15% 2 3% 2% KY 1% 2 2% 20 18% 3 2% 2% WI 1% 2 2% 0 0% 2 1% 1% MA 0% 1 1% 0 0% 1 0% 0% MO 1% 1 1% 0 0% 1 1% 1% IA 1% 2 2% 0 0% 2 1% 1% MN 0% 1 1% 0 0% 1 0% 0% TN 0% 0 0% 0 0% 0 0% 0% FL 0% 0 0% 0 0% 0 0% 0% GA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% SC 0% 0 0% 0 0% 0 0% 0% AL 0% 0 0% 0 0% 0 0% 0% AR 0% 0 0% 0 0% 0 0% 0% LA 0% 0 0% 0 0% 0 0% 0% MS 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 227 Base Case: Total Number of Exceedanc

Middlesex CT Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

NY 20% 18 21% 227 100% 25 35% 37% PA 22% 23 25% 224 99% 28 46% 49% NJ 23% 19 20% 227 100% 28 44% 46% MA 1% 7 7% 23 10% 9 2% 2% OH 5% 7 6% 110 48% 10 10% 14% MD 4% 9 11% 182 80% 9 7% 7% VA 3% 5 6% 101 44% 7 5% 6% 

Contributions do notexceed screening criteria

WV 2% 2 3% 92 41% 3 4% 5% MI 1% 2 2% 32 14% 3 3% 2% NC 1% 3 3% 76 33% 4 2% 2% DE 2% 4 4% 82 36% 5 3% 3% IN 2% 2 2% 41 18% 3 3% 3% NH 0% 1 1% 0 0% 2 0% 0% IL 1% 2 2% 23 10% 2 2% 3% KY 1% 2 2% 17 7% 2 1% 2% RI 0% 1 1% 0 0% 2 0% 0% WI 0% 1 2% 0 0% 1 1% 1% VT 0% 0 0% 0 0% 1 0% 0% MO 0% 1 1% 0 0% 1 0% 1% IA 0% 1 1% 0 0% 1 0% 1% TN 0% 0 0% 0 0% 0 0% 0% AR 0% 0 0% 0 0% 0 0% 0% FL 0% 0 0% 0 0% 0 0% 0% GA 0% 0 1% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 1 0% 0% MN 0% 0 0% 0 0% 0 0% 0% SC 0% 0 0% 0 0% 0 0% 0% AL 0% 0 0% 0 0% 0 0% 0% LA 0% 0 0% 0 0% 0 0% 0% MS 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 178 Base Case: Total Number of Exceedanc

New Haven CT Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

PA 24% 24 27% 176 99% 28 51% 50% NJ 24% 18 21% 178 100% 28 46% 46% NY 20% 35 40% 178 100% 36 23% 24% OH 6% 8 7% 117 66% 10 12% 14% VA 4% 6 7% 103 58% 7 12% 11% MD 4% 8 9% 139 78% 9 13% 12% WV 2% 2 3% 85 48% 3 6% 6% 

Contributions do notexceed screening criteria

MI 1% 2 3% 24 13% 3 4% 4% NC 2% 2 3% 57 32% 3 3% 3% IN 2% 2 2% 38 21% 3 3% 4% DE 2% 3 4% 46 26% 4 4% 3% IL 2% 2 2% 24 13% 3 3% 3% KY 1% 2 2% 22 12% 3 2% 2% WI 1% 1 1% 0 0% 1 1% 1% MA 0% 7 8% 6 3% 7 0% 0% IA 0% 1 1% 0 0% 1 1% 1% MO 0% 1 1% 0 0% 1 1% 1% MN 0% 0 0% 0 0% 0 0% 1% TN 0% 0 0% 0 0% 0 0% 0% AL 0% 0 0% 0 0% 0 0% 0% FL 0% 0 0% 0 0% 0 0% 0% GA 0% 0 0% 0 0% 0 0% 0% LA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% NH 0% 1 1% 0 0% 1 0% 0% RI 0% 1 1% 0 0% 1 0% 0% SC 0% 0 0% 0 0% 0 0% 0% AR 0% 0 0% 0 0% 0 0% 0% MS 0% 0 0% 0 0% 0 0% 0% VT 0% 1 1% 0 0% 1 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 149 Base Case: Total Number of Exceedanc

Newcastle DE Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

MD 48% 39 45% 149 100% 48 100% 100% VA 7% 8 8% 105 70% 11 47% 33% PA 8% 9 10% 107 72% 18 39% 51% OH 8% 9 10% 128 86% 9 25% 20% WV 4% 4 5% 81 54% 6 23% 17% NC 3% 5 6% 63 42% 9 10% 5% MI 2% 3 3% 54 36% 5 9% 10% 

Contributions do notexceed screening criteria

NJ 0% 1 2% 0 0% 2 2% 2% IN 2% 3 3% 39 26% 3 6% 5% IL 3% 4 4% 65 44% 4 6% 5% IA 1% 2 2% 7 5% 2 4% 3% KY 2% 2 3% 40 27% 4 4% 3% NY 0% 1 1% 0 0% 1 1% 1% MO 2% 2 2% 51 34% 2 2% 1% WI 1% 1 1% 0 0% 1 2% 2% CT 0% 0 1% 0 0% 0 0% 0% MA 0% 1 1% 0 0% 1 0% 0% AR 0% 1 1% 0 0% 1 0% 0% LA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% MN 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% SC 0% 0 1% 0 0% 1 0% 0% TN 1% 1 1% 0 0% 1 0% 0% AL 0% 0 0% 0 0% 0 0% 0% FL 0% 0 0% 0 0% 1 0% 0% GA 0% 0 0% 0 0% 1 0% 0% MS 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 8 Base Case: Total Number of Exceedanc

Washington DC Upwind State Average 4- episode %

contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

VA 11% 9 9% 8 100% 10 79% 79% OH 5% 6 6% 7 88% 6 30% 30% PA 7% 6 6% 8 100% 6 11% 11% WV 2% 3 3% 4 50% 3 13% 13% 

Contributions do notexceed screening criteria

IL 3% 3 3% 4 50% 3 8% 8% IN 2% 2 2% 1 13% 2 7% 7% MI 2% 2 2% 0 0% 2 4% 4% KY 0% 0 0% 0 0% 0 2% 2% MO 2% 2 2% 0 0% 2 4% 4% IA 0% 0 0% 0 0% 0 2% 2% LA 2% 2 2% 0 0% 2 1% 1% AR 1% 1 1% 0 0% 1 1% 1% TN 0% 0 0% 0 0% 0 1% 1% WI 0% 0 0% 0 0% 0 1% 1% MS 1% 0 1% 0 0% 1 0% 0% AL 0% 0 0% 0 0% 0 0% 0% CT 0% 0 0% 0 0% 0 0% 0% DE 1% 1 2% 0 0% 2 0% 0% FL 0% 0 0% 0 0% 0 0% 0% GA 0% 0 0% 0 0% 0 0% 0% MA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% MN 0% 0 0% 0 0% 0 0% 0% NC 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% NJ 1% 2 2% 1 13% 2 0% 0% NY 1% 1 1% 0 0% 1 0% 0% RI 0% 0 0% 0 0% 0 0% 0% SC 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 1366 Base Case: Total Number of Exceedanc

Fulton GA Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

AL 4% 18 20% 550 40% 25 9% 6% SC 2% 7 7% 390 29% 9 5% 4% TN 5% 8 8% 1062 78% 10 11% 9% NC 2% 5 5% 334 24% 6 4% 4% KY 3% 6 6% 733 54% 7 5% 4% VA 1% 2 2% 80 6% 3 2% 2% WV 1% 3 3% 135 10% 3 2% 2% 

Contributions do notexceed screening criteria

FL 0% 5 5% 95 7% 6 1% 1% MS 0% 2 2% 39 3% 3 1% 0% AR 1% 2 2% 46 3% 3 1% 1% OH 1% 3 3% 142 10% 4 1% 1% PA 0% 2 3% 83 6% 3 0% 0% IL 1% 2 2% 117 9% 2 1% 1% IN 1% 2 2% 0 0% 2 1% 1% MO 1% 2 2% 0 0% 2 1% 1% LA 0% 0 1% 0 0% 1 0% 0% MD 0% 1 1% 0 0% 1 0% 0% NY 0% 1 1% 0 0% 1 0% 0% CT 0% 0 0% 0 0% 0 0% 0% IA 0% 0 0% 0 0% 0 0% 0% MA 0% 0 0% 0 0% 0 0% 0% NJ 0% 1 1% 0 0% 1 0% 0% WI 0% 0 0% 0 0% 0 0% 0% DE 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% MI 0% 0 0% 0 0% 0 0% 0% MN 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 75 Base Case: Total Number of Exceedanc

Lake IN Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

IL 42% 40 45% 75 100% 47 92% 98% MO 4% 4 4% 58 77% 4 30% 38% OH 5% 15 18% 26 35% 15 2% 2% WI 3% 3 4% 29 39% 6 8% 8% IA 5% 8 9% 40 53% 8 15% 12% PA 2% 11 12% 14 19% 11 1% 1% MI 3% 6 7% 29 39% 7 2% 3% TN 2% 2 2% 10 13% 2 7% 7% VA 1% 3 3% 13 17% 3 1% 1% 

Contributions do notexceed screening criteria

AR 1% 2 2% 0 0% 2 9% 14% AL 1% 2 2% 0 0% 2 6% 8% GA 1% 2 2% 0 0% 2 6% 7% WV 1% 2 2% 7 9% 2 1% 1% LA 1% 2 2% 6 8% 2 2% 2% MD 1% 3 3% 13 17% 3 1% 1% KY 0% 0 1% 0 0% 1 2% 2% MS 1% 1 1% 0 0% 1 3% 4% NC 1% 2 2% 6 8% 2 1% 1% MN 0% 1 1% 0 0% 1 0% 0% FL 0% 1 1% 0 0% 1 0% 0% NY 0% 3 4% 1 1% 3 0% 0% SC 0% 1 1% 0 0% 1 1% 1% CT 0% 0 0% 0 0% 0 0% 0% DE 0% 0 0% 0 0% 0 0% 0% MA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% NJ 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 237 Base Case: Total Number of Exceedanc

Anne Arundel MD Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

VA 9% 16 18% 227 96% 18 61% 59% OH 8% 10 10% 206 87% 12 26% 23% PA 5% 11 13% 163 69% 13 18% 21% WV 3% 4 5% 126 53% 5 13% 12% NC 1% 5 6% 38 16% 10 3% 2% MI 2% 3 4% 77 32% 3 5% 5% 

Contributions do notexceed screening criteria

WI 1% 3 4% 37 16% 4 3% 4% IL 3% 4 4% 106 45% 5 6% 5% IN 2% 3 3% 95 40% 3 5% 4% KY 2% 3 3% 95 40% 4 3% 2% NJ 0% 1 2% 0 0% 1 1% 1% IA 1% 1 2% 0 0% 2 2% 2% MO 2% 2 2% 69 29% 3 2% 1% DE 0% 1 1% 0 0% 1 0% 1% MN 0% 1 1% 0 0% 1 1% 1% NY 0% 1 1% 0 0% 1 1% 1% LA 0% 1 1% 0 0% 1 0% 0% MA 0% 1 1% 0 0% 1 0% 0% CT 0% 0 1% 0 0% 0 0% 0% FL 0% 0 0% 0 0% 1 0% 0% TN 1% 1 1% 0 0% 1 0% 0% AR 0% 1 1% 0 0% 1 0% 0% GA 0% 0 0% 0 0% 1 0% 0% ME 0% 0 0% 0 0% 0 0% 0% MS 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% SC 0% 0 1% 0 0% 1 0% 0% AL 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 296 Base Case: Total Number of Exceedanc

Baltimore MD Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

VA 10% 15 17% 269 91% 17 50% 44% PA 7% 7 8% 234 79% 10 30% 24% NC 3% 13 14% 61 21% 15 8% 5% OH 4% 9 10% 157 53% 11 17% 16% WV 2% 5 6% 73 25% 6 12% 9% MI 1% 3 3% 60 20% 3 6% 6% 

Contributions do notexceed screening criteria

WI 1% 3 4% 10 3% 4 3% 3% DE 1% 2 2% 20 7% 3 2% 1% IL 2% 4 4% 77 26% 4 5% 5% KY 0% 3 4% 20 7% 4 1% 1% NJ 1% 2 2% 21 7% 3 2% 1% IN 1% 3 3% 37 13% 3 4% 3% SC 0% 2 2% 20 7% 4 1% 0% IA 0% 1 2% 0 0% 2 1% 1% MO 1% 2 3% 26 9% 3 2% 2% MN 0% 1 1% 0 0% 1 1% 1% NY 1% 1 2% 0 0% 2 1% 1% LA 1% 1 1% 0 0% 2 1% 1% MA 0% 1 1% 0 0% 1 0% 0% AR 0% 1 1% 0 0% 1 0% 0% CT 0% 0 1% 0 0% 0 0% 0% FL 0% 1 1% 0 0% 1 0% 0% MS 0% 0 0% 0 0% 1 0% 0% AL 0% 0 0% 0 0% 0 0% 0% GA 0% 1 1% 0 0% 1 0% 0% ME 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% TN 0% 1 1% 0 0% 1 0% 0% RI 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 210 Base Case: Total Number of Exceedanc

Cecil MD Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

PA 9% 18 21% 167 80% 21 43% 46% VA 6% 14 16% 129 61% 15 40% 37% OH 7% 9 10% 159 76% 10 20% 20% WV 3% 5 5% 77 37% 6 16% 17% MI 2% 3 3% 73 35% 4 7% 7% NC 2% 4 5% 55 26% 8 4% 4% 

Contributions do notexceed screening criteria

IL 3% 4 4% 75 36% 4 5% 5% IN 2% 3 3% 96 46% 3 5% 4% KY 2% 3 3% 48 23% 4 3% 3% IA 1% 2 2% 0 0% 2 3% 2% DE 0% 1 1% 3 1% 3 1% 1% NJ 0% 1 2% 0 0% 2 1% 1% NY 0% 1 1% 0 0% 1 1% 1% MO 1% 2 2% 64 30% 3 2% 1% LA 0% 1 1% 0 0% 1 0% 0% MA 0% 1 1% 0 0% 1 0% 0% WI 1% 3 4% 3 1% 3 1% 1% CT 0% 0 1% 0 0% 0 0% 0% FL 0% 1 1% 0 0% 1 0% 0% AR 0% 1 1% 0 0% 1 0% 0% GA 0% 1 1% 0 0% 1 0% 0% MN 0% 1 1% 0 0% 1 0% 0% MS 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% SC 0% 0 1% 0 0% 1 0% 0% TN 0% 1 1% 0 0% 1 0% 0% AL 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 187 Base Case: Total Number of Exceedanc

Harford MD Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

PA 9% 12 14% 156 83% 16 38% 36% VA 7% 14 15% 104 56% 17 41% 36% WV 2% 6 6% 29 16% 6 11% 10% OH 6% 9 11% 139 74% 11 17% 18% NC 1% 4 5% 33 18% 5 4% 5% MI 2% 5 5% 83 44% 5 6% 7% 

Contributions do notexceed screening criteria

KY 1% 3 4% 24 13% 4 2% 2% WI 1% 3 4% 17 9% 4 2% 3% IL 2% 4 4% 68 36% 4 4% 5% IN 1% 2 3% 41 22% 3 4% 4% IA 1% 2 2% 0 0% 2 2% 2% NJ 0% 1 2% 0 0% 2 1% 1% MN 0% 1 1% 0 0% 1 1% 1% MO 1% 2 3% 31 17% 3 1% 1% NY 0% 1 1% 0 0% 1 1% 1% LA 0% 1 1% 0 0% 2 1% 0% FL 0% 1 1% 0 0% 1 0% 0% MA 0% 1 1% 0 0% 1 0% 0% CT 0% 0 1% 0 0% 0 0% 0% DE 0% 1 1% 0 0% 1 0% 0% GA 0% 1 1% 0 0% 1 0% 0% SC 0% 0 1% 0 0% 1 0% 0% AL 0% 0 0% 0 0% 0 0% 0% AR 0% 1 1% 0 0% 1 0% 0% ME 0% 0 0% 0 0% 0 0% 0% MS 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% TN 0% 1 1% 0 0% 1 0% 0% RI 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 210 Base Case: Total Number of Exceedanc

Kent MD Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

VA 8% 14 16% 159 76% 16 48% 50% PA 5% 9 10% 140 67% 13 15% 15% OH 10% 9 10% 188 90% 12 24% 25% NC 3% 7 7% 58 28% 10 6% 6% WV 4% 4 5% 124 59% 5 15% 16% MI 2% 4 5% 49 23% 5 4% 4% 

Contributions do notexceed screening criteria

IN 3% 3 3% 123 59% 3 6% 6% IL 3% 3 4% 91 43% 5 5% 6% KY 2% 3 3% 100 48% 4 3% 2% IA 1% 1 2% 0 0% 2 2% 2% NJ 0% 2 2% 0 0% 2 1% 1% MO 1% 2 2% 62 30% 3 1% 2% NY 0% 1 1% 0 0% 1 1% 0% DE 0% 1 1% 0 0% 1 0% 0% MA 0% 1 1% 0 0% 1 0% 0% WI 1% 2 2% 1 0% 3 1% 1% CT 0% 0 1% 0 0% 0 0% 0% MN 0% 0 1% 0 0% 1 0% 0% AR 0% 1 1% 0 0% 1 0% 0% ME 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% SC 0% 1 1% 0 0% 1 0% 0% TN 1% 1 1% 0 0% 1 0% 0% AL 0% 0 0% 0 0% 0 0% 0% FL 0% 0 1% 0 0% 1 0% 0% GA 0% 0 0% 0 0% 1 0% 0% LA 0% 1 1% 0 0% 1 0% 0% MS 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 176 Base Case: Total Number of Exceedanc

=

Prince Georges MD Upwind State Average 4- episode %

contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

VA 9% 16 19% 150 85% 16 77% 71% PA 7% 7 7% 144 82% 10 20% 19% OH 6% 10 11% 112 64% 11 23% 21% WV 2% 4 5% 51 29% 4 11% 11% MI 3% 3 3% 99 56% 4 6% 5% 

Contributions do notexceed screening criteria

WI 1% 3 4% 5 3% 3 4% 3% NC 0% 6 6% 3 2% 7 2% 3% IL 3% 4 4% 130 74% 4 7% 7% KY 1% 3 3% 32 18% 4 2% 2% DE 0% 1 2% 0 0% 2 1% 1% NJ 0% 2 2% 2 1% 2 1% 1% IN 2% 3 3% 32 18% 3 4% 4% IA 1% 1 2% 0 0% 2 2% 2% MO 2% 2 3% 29 16% 3 2% 2% MN 0% 1 1% 0 0% 1 1% 1% NY 0% 2 2% 0 0% 2 1% 1% LA 1% 1 1% 0 0% 2 1% 1% AR 0% 1 1% 0 0% 1 0% 0% CT 0% 0 0% 0 0% 0 0% 0% MA 0% 0 0% 0 0% 0 0% 0% MS 0% 0 0% 0 0% 1 0% 0% TN 0% 1 1% 0 0% 1 0% 0% AL 0% 0 0% 0 0% 0 0% 0% FL 0% 0 0% 0 0% 0 0% 0% GA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% SC 0% 1 1% 0 0% 1 0% 0% RI 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 82 Base Case: Total Number of Exceedanc

Bergen NJ Upwind State Average 4- episode %

contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

PA 31% 33 34% 82 100% 37 92% 97% VA 4% 9 9% 48 59% 9 21% 21% OH 5% 7 7% 52 63% 9 23% 25% MD 4% 7 7% 50 61% 9 12% 15% MI 2% 6 6% 17 21% 6 5% 10% 

Contributions do notexceed screening criteria

DE 3% 4 5% 37 45% 5 6% 8% WV 2% 3 3% 35 43% 3 13% 16% NC 2% 2 2% 22 27% 3 8% 12% IN 1% 2 2% 16 20% 3 5% 6% KY 1% 2 2% 18 22% 3 3% 3% WI 1% 1 1% 0 0% 2 2% 4% IA 1% 2 2% 7 9% 2 2% 3% IL 2% 2 2% 12 15% 2 4% 5% MN 0% 0 0% 0 0% 0 1% 1% MO 1% 1 1% 0 0% 1 1% 1% FL 0% 0 1% 0 0% 0 0% 0% NY 3% 9 10% 20 24% 11 -5% -11% SC 0% 0 0% 0 0% 0 0% 0% AL 0% 0 0% 0 0% 0 0% 0% AR 0% 0 0% 0 0% 0 0% 0% CT 0% 0 0% 0 0% 0 0% 0% GA 0% 0 0% 0 0% 0 0% 0% LA 0% 0 0% 0 0% 0 0% 0% MA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% MS 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% TN 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 106 Base Case: Total Number of Exceedanc

Camden NJ Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

PA 26% 30 35% 104 98% 39 71% 67% MD 21% 27 29% 99 93% 32 45% 50% VA 4% 6 7% 48 45% 8 16% 14% DE 15% 13 15% 106 100% 16 42% 42% OH 6% 8 9% 81 76% 8 16% 13% MI 3% 4 5% 45 42% 6 11% 11% WV 2% 4 4% 36 34% 5 9% 7% NC 2% 5 5% 30 28% 6 4% 3% 

Contributions do notexceed screening criteria

WI 1% 2 3% 3 3% 2 3% 3% IL 3% 3 4% 47 44% 4 4% 4% IA 1% 2 2% 27 25% 2 3% 2% NY 0% 2 2% 1 1% 2 2% 2% IN 2% 2 3% 8 8% 2 4% 3% KY 1% 2 2% 14 13% 3 1% 1% MN 0% 0 1% 0 0% 0 1% 1% CT 0% 0 0% 0 0% 0 0% 0% MA 0% 0 0% 0 0% 0 0% 0% MO 1% 2 2% 0 0% 2 1% 1% SC 0% 0 1% 0 0% 0 0% 0% GA 0% 0 0% 0 0% 0 0% 0% LA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% TN 0% 0 0% 0 0% 1 0% 0% AL 0% 0 0% 0 0% 0 0% 0% AR 0% 1 1% 0 0% 1 0% 0% FL 0% 0 0% 0 0% 1 0% 0% MS 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 55 Base Case: Total Number of Exceedanc

Cumberland NJ Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

PA 9% 34 40% 35 64% 37 85% 92% MD 34% 30 35% 51 93% 35 83% 80% VA 6% 8 10% 25 45% 10 37% 20% DE 8% 8 9% 48 87% 11 86% 90% NC 4% 6 7% 20 36% 8 17% 12% WV 3% 4 4% 21 38% 4 27% 18% OH 9% 8 9% 51 93% 9 74% 62% MI 2% 2 2% 12 22% 3 30% 38% 

Contributions do notexceed screening criteria

NY 0% 2 3% 2 4% 3 4% 6% IL 4% 4 4% 31 56% 4 30% 28% IA 2% 2 2% 0 0% 2 18% 20% KY 2% 2 2% 10 18% 3 9% 6% IN 2% 2 2% 1 2% 2 18% 16% WI 1% 1 1% 0 0% 1 13% 14% MO 2% 2 2% 0 0% 2 8% 8% FL 0% 0 1% 0 0% 1 0% 0% MN 0% 0 0% 0 0% 0 3% 2% SC 0% 0 1% 0 0% 1 1% 0% TN 1% 1 1% 0 0% 1 1% 0% AL 0% 0 0% 0 0% 0 0% 0% AR 1% 1 1% 0 0% 1 1% 0% CT 0% 0 0% 0 0% 0 0% 0% GA 0% 0 0% 0 0% 0 0% 0% LA 0% 0 0% 0 0% 0 0% 0% MA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% MS 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 85 Base Case: Total Number of Exceedanc

Gloucester NJ Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

PA 14% 40 47% 65 76% 42 66% 62% MD 32% 33 36% 81 95% 39 69% 72% DE 13% 14 16% 80 94% 19 65% 62% VA 5% 6 7% 44 52% 8 23% 22% OH 7% 6 7% 75 88% 7 17% 14% WV 3% 4 5% 38 45% 5 12% 11% MI 3% 4 4% 37 44% 5 12% 10% NC 3% 5 5% 38 45% 6 5% 4% 

Contributions do notexceed screening criteria

IL 3% 3 4% 37 44% 4 6% 5% IA 2% 2 2% 10 12% 2 4% 3% IN 2% 2 2% 9 11% 2 5% 4% NY 0% 2 2% 1 1% 2 1% 1% KY 2% 2 2% 23 27% 3 2% 2% WI 1% 1 1% 0 0% 1 3% 2% MO 1% 2 2% 0 0% 2 1% 1% SC 0% 0 0% 0 0% 0 0% 0% GA 0% 0 0% 0 0% 0 0% 0% LA 0% 0 0% 0 0% 0 0% 0% MN 0% 0 0% 0 0% 0 1% 1% TN 0% 1 1% 0 0% 1 0% 0% AL 0% 0 0% 0 0% 0 0% 0% AR 0% 1 1% 0 0% 1 0% 0% CT 0% 0 0% 0 0% 0 0% 0% FL 0% 0 0% 0 0% 1 0% 0% MA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% MS 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 47 Base Case: Total Number of Exceedanc

Hudson NJ Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

PA 29% 32 32% 47 100% 37 100% 100% MD 4% 7 7% 20 43% 8 16% 18% NY 3% 6 6% 15 32% 8 -21% -39% VA 2% 7 8% 18 38% 8 12% 13% MI 3% 6 6% 24 51% 7 14% 14% OH 3% 5 5% 18 38% 6 12% 13% 

Contributions do notexceed screening criteria

WV 1% 3 3% 14 30% 3 8% 9% DE 2% 5 5% 18 38% 6 7% 7% NC 1% 3 3% 13 28% 3 7% 8% WI 1% 1 1% 0 0% 1 4% 4% IA 1% 2 2% 9 19% 2 3% 3% IL 2% 3 3% 13 28% 3 4% 5% IN 1% 2 2% 2 4% 2 4% 4% KY 1% 2 2% 3 6% 2 1% 1% MN 0% 0 0% 0 0% 0 1% 1% MO 1% 1 1% 0 0% 1 1% 1% AL 0% 0 0% 0 0% 0 0% 0% AR 0% 0 0% 0 0% 0 0% 0% CT 0% 0 0% 0 0% 0 0% 0% FL 0% 0 1% 0 0% 0 0% 0% GA 0% 0 1% 0 0% 0 0% 0% LA 0% 0 0% 0 0% 0 0% 0% MA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% MS 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% SC 0% 0 0% 0 0% 0 0% 0% TN 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 149 Base Case: Total Number of Exceedanc

Hunterdon NJ Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

PA 55% 42 48% 149 100% 52 97% 95% VA 2% 10 12% 22 15% 11 12% 15% MD 4% 10 12% 68 46% 13 14% 15% OH 3% 7 8% 49 33% 8 13% 13% DE 5% 7 8% 115 77% 8 14% 14% MI 3% 4 4% 72 48% 4 10% 9% WV 1% 4 4% 13 9% 4 6% 7% 

Contributions do notexceed screening criteria

NY 2% 2 2% 35 23% 5 8% 9% IN 1% 2 3% 11 7% 3 3% 4% KY 1% 3 3% 11 7% 3 2% 3% NC 0% 2 3% 9 6% 3 3% 4% WI 1% 2 2% 11 7% 2 4% 4% IA 1% 1 1% 0 0% 1 1% 1% IL 1% 2 2% 1 1% 2 4% 4% MN 0% 0 1% 0 0% 0 1% 1% MO 0% 1 1% 0 0% 1 1% 1% FL 0% 1 1% 0 0% 1 0% 0% GA 0% 0 1% 0 0% 0 0% 0% LA 0% 0 0% 0 0% 1 0% 0% SC 0% 0 0% 0 0% 0 0% 0% TN 0% 0 0% 0 0% 0 0% 0% AL 0% 0 0% 0 0% 0 0% 0% AR 0% 0 0% 0 0% 0 0% 0% CT 0% 0 0% 0 0% 0 0% 0% MA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% MS 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 89 Base Case: Total Number of Exceedanc

Mercer NJ Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

PA 46% 44 45% 89 100% 51 87% 90% MD 10% 13 15% 60 67% 20 16% 18% DE 7% 8 9% 81 91% 10 10% 11% MI 3% 6 6% 39 44% 7 8% 9% VA 2% 5 5% 32 36% 7 6% 6% OH 4% 5 6% 66 74% 7 6% 7% WV 2% 4 4% 24 27% 4 4% 4% NY 1% 3 3% 14 16% 3 4% 4% 

Contributions do notexceed screening criteria

NC 1% 4 4% 24 27% 5 2% 3% IA 1% 2 2% 16 18% 2 2% 2% IL 2% 3 3% 19 21% 3 3% 3% IN 1% 2 2% 3 3% 2 2% 2% KY 1% 2 2% 10 11% 3 1% 1% WI 1% 2 2% 0 0% 2 2% 2% MO 1% 2 2% 0 0% 2 1% 1% CT 0% 0 0% 0 0% 0 0% 0% MN 0% 0 0% 0 0% 0 0% 0% TN 0% 0 0% 0 0% 0 0% 0% AL 0% 0 0% 0 0% 0 0% 0% AR 0% 0 1% 0 0% 1 0% 0% FL 0% 0 0% 0 0% 1 0% 0% GA 0% 0 0% 0 0% 0 0% 0% LA 0% 0 0% 0 0% 0 0% 0% MA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% MS 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% SC 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 175 Base Case: Total Number of Exceedanc

Middlesex NJ Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

PA 39% 34 40% 175 100% 46 72% 72% VA 2% 4 4% 44 25% 6 6% 6% MD 5% 11 12% 92 53% 14 9% 8% MI 3% 6 6% 85 49% 7 6% 6% DE 4% 5 6% 94 54% 8 5% 5% NY 2% 4 4% 45 26% 6 5% 5% OH 3% 5 5% 99 57% 7 5% 5% WV 1% 3 3% 41 23% 4 3% 3% 

Contributions do notexceed screening criteria

NC 1% 3 3% 39 22% 4 2% 2% IA 1% 2 2% 31 18% 2 1% 1% IL 2% 3 3% 39 22% 3 2% 2% IN 1% 2 2% 5 3% 2 2% 1% WI 1% 1 1% 0 0% 1 1% 1% KY 1% 2 2% 16 9% 3 1% 1% MN 0% 0 0% 0 0% 0 0% 0% MO 1% 1 1% 0 0% 1 0% 0% CT 0% 0 0% 0 0% 0 0% 0% FL 0% 0 0% 0 0% 0 0% 0% GA 0% 0 0% 0 0% 0 0% 0% SC 0% 0 0% 0 0% 0 0% 0% TN 0% 0 0% 0 0% 0 0% 0% AL 0% 0 0% 0 0% 0 0% 0% AR 0% 0 0% 0 0% 0 0% 0% LA 0% 0 0% 0 0% 0 0% 0% MA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% MS 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 341 Base Case: Total Number of Exceedanc

Monmouth NJ Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

PA 37% 33 38% 339 99% 50 66% 65% MD 9% 14 14% 244 72% 23 13% 13% DE 6% 7 7% 278 82% 10 10% 10% VA 3% 6 6% 131 38% 8 7% 7% NY 1% 7 8% 37 11% 8 4% 4% OH 4% 6 7% 210 62% 7 5% 5% MI 2% 5 5% 103 30% 6 5% 4% WV 1% 3 3% 80 23% 4 4% 4% 

Contributions do notexceed screening criteria

NC 1% 4 4% 84 25% 5 2% 2% IN 1% 2 2% 7 2% 2 2% 2% IA 1% 2 2% 44 13% 2 1% 1% IL 2% 3 3% 91 27% 3 2% 2% WI 1% 1 2% 0 0% 1 1% 1% MA 0% 1 1% 0 0% 1 0% 0% CT 0% 1 1% 0 0% 1 0% 0% KY 1% 1 1% 23 7% 3 1% 1% SC 0% 1 1% 0 0% 1 0% 0% MN 0% 0 0% 0 0% 0 0% 0% MO 1% 1 1% 0 0% 2 0% 0% FL 0% 0 0% 0 0% 1 0% 0% GA 0% 1 1% 0 0% 1 0% 0% ME 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% TN 0% 0 0% 0 0% 0 0% 0% AL 0% 0 0% 0 0% 0 0% 0% AR 0% 0 0% 0 0% 1 0% 0% LA 0% 0 0% 0 0% 0 0% 0% MS 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 223 Base Case: Total Number of Exceedanc

Morris NJ Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

PA 42% 34 39% 223 100% 52 80% 79% VA 4% 9 10% 83 37% 10 21% 22% MD 4% 8 9% 75 34% 11 11% 10% OH 4% 8 9% 96 43% 10 11% 11% DE 3% 5 6% 124 56% 7 9% 8% MI 2% 6 7% 45 20% 7 5% 5% NY 2% 4 5% 105 47% 7 5% 6% WV 1% 3 4% 42 19% 3 6% 6% 

Contributions do notexceed screening criteria

NC 1% 2 2% 13 6% 3 3% 3% IN 1% 2 3% 35 16% 3 2% 2% KY 1% 3 3% 36 16% 4 2% 2% WI 1% 2 2% 0 0% 2 2% 2% IL 1% 2 3% 24 11% 3 2% 2% IA 1% 2 3% 4 2% 2 1% 1% MN 0% 0 0% 0 0% 0 1% 1% MO 0% 1 1% 0 0% 1 1% 1% FL 0% 0 1% 0 0% 0 0% 0% GA 0% 0 0% 0 0% 0 0% 0% LA 0% 0 0% 0 0% 0 0% 0% SC 0% 0 0% 0 0% 0 0% 0% AL 0% 0 0% 0 0% 0 0% 0% AR 0% 0 1% 0 0% 0 0% 0% CT 0% 0 0% 0 0% 0 0% 0% MA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% MS 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% TN 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 406 Base Case: Total Number of Exceedanc

Ocean NJ Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

PA 35% 32 38% 406 100% 49 81% 82% MD 14% 19 20% 353 87% 26 25% 29% VA 3% 7 7% 198 49% 9 12% 14% NC 1% 5 5% 78 19% 6 3% 3% DE 10% 9 10% 396 98% 12 23% 23% OH 5% 7 7% 304 75% 8 10% 9% WV 2% 4 4% 101 25% 4 6% 6% MI 3% 4 4% 167 41% 6 6% 6% 

Contributions do notexceed screening criteria

NY 0% 3 4% 2 0% 3 2% 2% IL 3% 3 3% 194 48% 4 3% 3% IA 1% 2 2% 77 19% 2 2% 2% IN 2% 2 2% 19 5% 2 3% 2% WI 1% 2 2% 0 0% 2 2% 1% MA 0% 1 1% 0 0% 1 0% 1% CT 0% 1 1% 0 0% 1 0% 0% KY 1% 2 2% 17 4% 3 1% 1% SC 0% 1 1% 0 0% 1 0% 0% FL 0% 0 1% 0 0% 1 0% 0% GA 0% 1 1% 0 0% 1 0% 0% MN 0% 0 0% 0 0% 0 0% 0% MO 1% 1 2% 0 0% 2 1% 1% TN 0% 0 0% 0 0% 1 0% 0% AL 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% AR 0% 0 1% 0 0% 1 0% 0% LA 0% 0 0% 0 0% 0 0% 0% MS 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 73 Base Case: Total Number of Exceedanc

Erie NY Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

PA 5% 13 15% 16 22% 15 1% 0% WI 7% 6 7% 57 78% 7 54% 67% NJ 2% 6 7% 16 22% 6 1% 0% MD 3% 8 9% 16 22% 10 1% 0% VA 2% 6 7% 16 22% 7 1% 0% IL 4% 4 4% 57 78% 7 19% 20% MI 4% 3 4% 56 77% 6 31% 39% 

Contributions do notexceed screening criteria

DE 1% 2 3% 16 22% 3 1% 0% NC 1% 3 4% 16 22% 5 1% 0% MO 3% 2 3% 40 55% 3 14% 17% IA 3% 2 3% 53 73% 3 12% 14% AR 1% 1 1% 0 0% 1 5% 6% CT 0% 1 1% 0 0% 1 0% 0% MA 0% 1 1% 0 0% 1 0% 0% MN 0% 0 0% 0 0% 1 2% 2% OH 0% 0 0% 0 0% 0 1% 1% SC 0% 1 1% 0 0% 1 0% 0% TN 0% 0 0% 0 0% 0 1% 1% AL 0% 0 0% 0 0% 0 0% 0% FL 0% 0 0% 0 0% 0 0% 0% GA 0% 0 0% 0 0% 0 0% 0% IN 0% 0 0% 0 0% 0 1% 1% KY 0% 0 0% 0 0% 0 0% 0% LA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% MS 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% WV 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 81 Base Case: Total Number of Exceedanc

Putnam NY Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

NJ 40% 38 40% 81 100% 53 99% 99% PA 20% 20 21% 81 100% 27 54% 64% VA 6% 7 8% 47 58% 8 29% 29% MD 5% 7 7% 47 58% 9 13% 13% 

Contributions do notexceed screening criteria

DE 2% 3 3% 41 51% 4 6% 7% OH 2% 3 3% 47 58% 3 5% 5% WV 1% 1 1% 0 0% 1 4% 4% MI 1% 1 2% 0 0% 2 5% 5% NC 1% 1 1% 0 0% 1 2% 1% WI 1% 1 1% 0 0% 1 3% 3% CT 0% 0 0% 0 0% 1 0% 0% IA 0% 0 0% 0 0% 0 1% 1% IL 1% 1 1% 0 0% 1 2% 2% IN 0% 0 1% 0 0% 1 1% 1% KY 0% 0 0% 0 0% 0 0% 0% LA 0% 0 0% 0 0% 0 0% 0% MN 0% 0 0% 0 0% 0 1% 1% MO 0% 0 0% 0 0% 0 0% 0% SC 0% 0 0% 0 0% 0 0% 0% AL 0% 0 0% 0 0% 0 0% 0% AR 0% 0 0% 0 0% 0 0% 0% FL 0% 0 0% 0 0% 0 0% 0% GA 0% 0 0% 0 0% 0 0% 0% MA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% MS 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% TN 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 66 Base Case: Total Number of Exceedanc

Richmond NY Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

NJ 42% 56 59% 66 100% 61 72% 74% PA 28% 32 37% 66 100% 37 62% 56% VA 2% 7 8% 22 33% 7 7% 7% MD 5% 10 12% 38 58% 11 8% 8% MI 2% 5 5% 27 41% 6 5% 5% DE 3% 6 5% 28 42% 7 4% 4% OH 3% 5 5% 35 53% 6 5% 4% 

Contributions do notexceed screening criteria

WV 1% 3 3% 15 23% 3 3% 3% NC 1% 3 3% 15 23% 4 3% 2% IL 2% 3 2% 14 21% 3 2% 1% IA 1% 2 2% 9 14% 2 1% 1% WI 1% 1 1% 0 0% 1 1% 1% IN 1% 2 1% 0 0% 2 1% 1% KY 1% 2 2% 4 6% 2 0% 0% MN 0% 0 0% 0 0% 0 0% 0% FL 0% 0 1% 0 0% 1 0% 0% GA 0% 0 1% 0 0% 0 0% 0% MO 1% 1 1% 0 0% 1 0% 0% SC 0% 0 0% 0 0% 0 0% 0% TN 0% 0 0% 0 0% 0 0% 0% AL 0% 0 0% 0 0% 0 0% 0% AR 0% 0 0% 0 0% 0 0% 0% CT 0% 0 0% 0 0% 0 0% 0% LA 0% 0 0% 0 0% 0 0% 0% MA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% MS 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 1337 Base Case: Total Number of Exceedanc

Suffolk NY Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

NJ 29% 23 26% 1337 100% 64 69% 65% PA 21% 28 29% 1324 99% 38 41% 40% CT 2% 5 5% 339 25% 23 9% 6% VA 3% 9 8% 492 37% 12 8% 6% MD 7% 12 14% 1004 75% 15 12% 10% DE 4% 5 6% 873 65% 8 8% 6% NC 1% 5 5% 310 23% 7 3% 2% OH 2% 4 5% 405 30% 6 4% 5% MA 1% 2 2% 82 6% 6 1% 0% WV 1% 2 2% 237 18% 3 3% 3% MI 2% 4 4% 302 23% 5 3% 4% 

Contributions do notexceed screening criteria

WI 1% 1 1% 0 0% 2 1% 2% IN 1% 1 1% 0 0% 2 1% 2% IA 1% 2 2% 41 3% 2 1% 1% IL 1% 2 2% 215 16% 3 1% 2% MN 0% 0 0% 0 0% 1 1% 1% KY 1% 1 1% 0 0% 2 0% 1% RI 0% 1 1% 0 0% 2 0% 0% MO 0% 1 1% 0 0% 1 0% 0% NH 0% 0 0% 0 0% 1 0% 0% AL 0% 0 0% 0 0% 0 0% 0% AR 0% 0 0% 0 0% 0 0% 0% GA 0% 0 0% 0 0% 1 0% 0% ME 0% 0 0% 0 0% 1 0% 0% SC 0% 0 0% 0 0% 1 0% 0% TN 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% FL 0% 1 1% 0 0% 1 0% 0% LA 0% 0 0% 0 0% 0 0% 0% MS 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 62 Base Case: Total Number of Exceedanc

Westchester NY Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

NJ 33% 47 50% 62 100% 54 58% 8% PA 28% 26 27% 62 100% 31 70% 79% VA 5% 8 8% 45 73% 9 14% 22% OH 7% 7 8% 50 81% 10 15% 19% MD 4% 7 7% 45 73% 8 8% 13% MI 1% 4 5% 4 6% 5 3% 2% WV 2% 3 3% 39 63% 3 7% 12% 

Contributions do notexceed screening criteria

NC 2% 2 2% 22 35% 3 4% 10% DE 2% 3 4% 22 35% 4 4% 6% WI 1% 1 1% 0 0% 1 2% 2% IN 2% 2 2% 21 34% 3 3% 4% IL 2% 2 2% 11 18% 2 2% 3% KY 2% 2 2% 17 27% 3 2% 2% IA 1% 2 2% 0 0% 2 1% 1% MN 0% 0 1% 0 0% 0 1% 1% MO 1% 1 1% 0 0% 1 1% 1% AR 0% 0 0% 0 0% 0 0% 0% CT 0% 0 0% 0 0% 0 0% 0% FL 0% 0 0% 0 0% 0 0% 0% LA 0% 0 0% 0 0% 0 0% 0% SC 0% 0 0% 0 0% 0 0% 0% AL 0% 0 0% 0 0% 0 0% 0% GA 0% 0 0% 0 0% 0 0% 0% MA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% MS 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% TN 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 108 Base Case: Total Number of Exceedanc

Mecklenburg NC Upwind State Average 4- episode %

contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

SC 14% 22 25% 89 82% 22 76% 69% GA 5% 12 15% 71 66% 14 33% 27% VA 3% 8 9% 31 29% 9 8% 7% MD 1% 7 8% 8 7% 7 1% 1% TN 2% 3 4% 23 21% 4 15% 15% PA 1% 4 5% 10 9% 5 1% 1% 

Contributions do notexceed screening criteria

OH 1% 3 4% 29 27% 3 4% 3% WV 1% 2 2% 2 2% 2 4% 3% FL 1% 2 3% 4 4% 3 5% 4% AL 1% 2 2% 0 0% 2 5% 4% KY 2% 2 3% 4 4% 2 7% 6% IN 1% 3 3% 4 4% 3 3% 3% DE 0% 1 1% 0 0% 1 0% 0% IL 2% 2 2% 0 0% 2 3% 3% MO 1% 3 3% 4 4% 3 3% 3% NJ 0% 1 1% 0 0% 1 0% 0% AR 0% 1 1% 0 0% 1 1% 1% LA 0% 1 1% 0 0% 1 0% 0% MI 0% 0 1% 0 0% 1 0% 0% NY 0% 3 3% 2 2% 3 0% 0% CT 0% 0 1% 0 0% 0 0% 0% IA 0% 0 0% 0 0% 0 1% 0% MS 0% 0 0% 0 0% 0 0% 0% MA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% MN 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% WI 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 89 Base Case: Total Number of Exceedanc

Geauga OH Upwind State Average 4- episode %

contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

IL 12% 16 18% 85 96% 17 46% 42% MI 11% 15 17% 78 88% 18 42% 46% IN 3% 8 9% 65 73% 8 24% 16% KY 1% 8 10% 2 2% 9 6% 2% MO 4% 6 7% 72 81% 6 19% 13% PA 1% 19 23% 4 4% 19 2% 2% 

Contributions do notexceed screening criteria

WV 0% 5 6% 4 4% 5 2% 2% VA 0% 6 7% 4 4% 6 1% 2% MD 0% 4 4% 3 3% 4 1% 1% AR 1% 2 2% 0 0% 2 4% 3% IA 2% 2 2% 0 0% 2 7% 5% LA 2% 2 2% 35 39% 3 5% 5% WI 1% 4 4% 9 10% 4 4% 3% NY 0% 2 2% 0 0% 2 0% 0% NJ 0% 1 1% 0 0% 1 0% 0% TN 1% 1 2% 0 0% 2 1% 0% CT 0% 0 0% 0 0% 0 0% 0% DE 0% 0 0% 0 0% 0 0% 0% MA 0% 1 1% 0 0% 1 0% 0% MN 0% 0 0% 0 0% 0 1% 0% MS 1% 1 1% 0 0% 1 1% 1% NH 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% AL 0% 1 1% 0 0% 1 0% 0% FL 0% 0 0% 0 0% 0 0% 0% GA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% NC 0% 4 4% 1 1% 4 0% 0% RI 0% 0 0% 0 0% 0 0% 0% SC 0% 1 1% 0 0% 1 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 195 Base Case: Total Number of Exceedanc

Summit OH Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

PA 14% 22 25% 101 52% 27 32% 30% MI 9% 14 15% 100 51% 20 48% 49% IL 4% 7 8% 100 51% 8 17% 16% WV 3% 4 4% 87 45% 7 12% 12% VA 2% 5 6% 48 25% 6 7% 8% IN 1% 2 3% 19 10% 3 5% 5% MD 1% 2 3% 38 19% 5 5% 5% 

Contributions do notexceed screening criteria

WI 2% 3 4% 79 41% 4 8% 7% MO 1% 1 2% 0 0% 2 7% 6% NC 1% 4 4% 38 19% 4 4% 4% NY 1% 2 2% 19 10% 5 2% 2% SC 0% 1 2% 1 1% 2 2% 2% LA 1% 2 2% 0 0% 2 2% 1% AR 1% 2 2% 4 2% 2 3% 2% IA 1% 1 1% 0 0% 2 3% 3% NJ 0% 0 0% 0 0% 1 0% 0% AL 0% 1 1% 0 0% 1 1% 1% FL 0% 0 0% 0 0% 1 0% 0% GA 0% 0 0% 0 0% 1 1% 1% TN 1% 1 1% 0 0% 1 1% 1% CT 0% 0 0% 0 0% 0 0% 0% DE 0% 0 0% 0 0% 0 0% 0% KY 0% 0 0% 0 0% 1 0% 1% MA 0% 0 0% 0 0% 0 0% 0% MN 0% 0 0% 0 0% 0 0% 0% MS 0% 1 1% 0 0% 1 1% 1% NH 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 392 Base Case: Total Number of Exceedanc

Allegheny PA Upwind State Average 4- episode %

contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

OH 28% 30 35% 387 99% 40 78% 78% WV 5% 7 8% 248 63% 12 31% 31% MI 5% 8 9% 267 68% 10 31% 34% IN 5% 6 7% 250 64% 7 31% 32% IL 5% 9 10% 356 91% 10 25% 27% KY 2% 7 8% 43 11% 7 7% 8% 

Contributions do notexceed screening criteria

VA 0% 5 6% 13 3% 6 2% 1% MD 0% 5 5% 13 3% 6 2% 1% MO 2% 4 5% 100 26% 5 11% 11% AR 2% 3 4% 108 28% 4 5% 5% TN 1% 2 2% 23 6% 2 3% 3% IA 1% 1 1% 0 0% 1 3% 3% LA 1% 2 2% 0 0% 2 2% 2% NY 0% 1 1% 0 0% 1 1% 0% WI 1% 1 1% 0 0% 1 4% 3% MN 0% 0 0% 0 0% 0 0% 0% MS 0% 1 1% 0 0% 1 1% 1% NC 0% 0 0% 0 0% 1 0% 0% AL 0% 0 1% 0 0% 1 1% 0% GA 0% 0 0% 0 0% 0 0% 0% NJ 0% 0 0% 0 0% 0 0% 0% CT 0% 0 0% 0 0% 0 0% 0% DE 0% 0 0% 0 0% 0 0% 0% FL 0% 0 0% 0 0% 0 0% 0% MA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% SC 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 129 Base Case: Total Number of Exceedanc

Bucks PA Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

NJ 5% 10 11% 52 40% 18 35% 24% MD 9% 12 13% 109 84% 19 23% 29% VA 2% 4 4% 32 25% 6 6% 7% MI 3% 7 8% 45 35% 7 14% 13% DE 10% 9 10% 125 97% 14 21% 21% OH 4% 6 7% 79 61% 9 13% 13% WV 2% 4 4% 30 23% 4 6% 7% 

Contributions do notexceed screening criteria

NC 1% 3 3% 23 18% 5 2% 3% WI 1% 2 3% 10 8% 3 5% 4% NY 1% 1 1% 0 0% 2 5% 5% IA 1% 2 3% 8 6% 2 2% 3% KY 1% 2 3% 22 17% 4 2% 2% IL 2% 3 4% 11 9% 3 5% 5% IN 1% 2 2% 15 12% 3 3% 4% MN 0% 0 1% 0 0% 1 1% 1% LA 0% 1 1% 0 0% 1 1% 1% MO 1% 2 2% 0 0% 2 1% 1% AR 0% 1 1% 0 0% 1 0% 0% FL 0% 0 0% 0 0% 0 0% 0% GA 0% 0 0% 0 0% 0 0% 0% MS 0% 0 0% 0 0% 0 0% 0% SC 0% 0 0% 0 0% 0 0% 0% TN 0% 0 0% 0 0% 0 0% 0% AL 0% 0 0% 0 0% 0 0% 0% CT 0% 0 0% 0 0% 0 0% 0% MA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 50 Base Case: Total Number of Exceedanc

Delaware PA Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

MD 30% 26 30% 50 100% 33 73% 71% DE 16% 25 29% 50 100% 27 56% 57% NJ 1% 3 4% 8 16% 4 12% 11% MI 2% 5 6% 11 22% 5 9% 10% VA 4% 4 5% 31 62% 7 13% 12% WV 3% 5 5% 18 36% 5 11% 9% OH 6% 6 7% 31 62% 7 14% 12% 

Contributions do notexceed screening criteria

WI 1% 1 1% 0 0% 1 3% 3% NC 2% 3 4% 17 34% 5 3% 3% IA 1% 2 2% 7 14% 2 2% 2% IL 2% 3 4% 11 22% 4 4% 4% KY 2% 3 3% 14 28% 4 2% 2% IN 2% 2 2% 10 20% 3 3% 3% MN 0% 0 0% 0 0% 0 1% 1% NY 0% 1 1% 0 0% 1 2% 2% LA 0% 1 1% 0 0% 1 0% 0% MO 1% 2 2% 5 10% 2 1% 1% TN 0% 0 1% 0 0% 1 0% 0% AL 0% 0 0% 0 0% 0 0% 0% AR 0% 1 1% 0 0% 1 0% 0% CT 0% 0 0% 0 0% 0 0% 0% FL 0% 0 0% 0 0% 0 0% 0% GA 0% 0 0% 0 0% 0 0% 0% MA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% MS 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% SC 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 78 Base Case: Total Number of Exceedanc

Montgomery PA Upwind State Average 4- episode %

contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

DE 11% 16 17% 69 88% 18 46% 41% MD 15% 27 31% 78 100% 28 59% 50% NJ 6% 17 20% 32 41% 19 25% 22% OH 5% 7 8% 44 56% 9 24% 21% WV 2% 4 5% 23 29% 4 12% 11% MI 2% 4 4% 7 9% 4 17% 13% VA 4% 10 12% 35 45% 11 12% 11% 

Contributions do notexceed screening criteria

NY 1% 3 3% 20 26% 4 8% 5% WI 1% 3 3% 2 3% 3 6% 5% KY 1% 3 3% 19 24% 4 5% 4% NC 1% 3 4% 20 26% 4 4% 4% IN 2% 2 3% 17 22% 3 9% 6% CT 0% 1 1% 0 0% 1 2% 1% IL 2% 2 2% 3 4% 2 9% 6% MN 0% 1 1% 0 0% 1 2% 1% LA 0% 1 1% 0 0% 1 2% 1% MO 1% 1 1% 0 0% 1 3% 2% MA 0% 1 1% 0 0% 1 1% 0% IA 1% 1 1% 0 0% 1 2% 2% AL 0% 0 0% 0 0% 0 0% 0% AR 0% 0 0% 0 0% 0 1% 1% MS 0% 0 0% 0 0% 0 1% 0% RI 0% 0 0% 0 0% 0 0% 0% TN 0% 0 0% 0 0% 0 1% 0% FL 0% 0 1% 0 0% 1 0% 0% GA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% SC 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 59 Base Case: Total Number of Exceedanc

Philadelphia PA Upwind State Average 4- episode %

contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

MD 22% 25 26% 58 98% 32 62% 65% NJ 3% 7 8% 12 20% 9 15% 14% DE 17% 18 20% 59 100% 21 49% 52% MI 3% 6 6% 14 24% 6 14% 15% VA 4% 5 6% 33 56% 8 15% 16% WV 2% 4 4% 19 32% 5 9% 10% OH 5% 6 6% 36 61% 7 14% 14% NC 2% 4 4% 19 32% 6 5% 5% 

Contributions do notexceed screening criteria

WI 1% 1 1% 0 0% 1 4% 3% IA 1% 2 2% 12 20% 2 3% 4% IL 2% 3 4% 14 24% 4 5% 5% NY 0% 1 1% 0 0% 1 3% 3% IN 2% 2 2% 7 12% 2 4% 4% KY 1% 2 2% 11 19% 3 1% 1% MN 0% 0 0% 0 0% 0 1% 1% MO 1% 2 2% 0 0% 2 1% 1% LA 0% 0 1% 0 0% 1 0% 0% SC 0% 0 0% 0 0% 0 0% 0% TN 0% 0 0% 0 0% 0 0% 0% AL 0% 0 0% 0 0% 0 0% 0% AR 0% 1 1% 0 0% 1 0% 0% CT 0% 0 0% 0 0% 0 0% 0% FL 0% 0 0% 0 0% 1 0% 0% GA 0% 0 0% 0 0% 0 0% 0% MA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% MS 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 183 Base Case: Total Number of Exceedanc

Kent RI Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

MA 2% 19 22% 12 7% 28 4% 3% NY 20% 17 19% 183 100% 24 58% 57% NJ 21% 17 19% 180 98% 24 57% 52% PA 20% 25 27% 171 93% 26 39% 37% CT 8% 10 11% 161 88% 17 24% 31% OH 4% 6 6% 83 45% 8 8% 10% VA 3% 5 5% 83 45% 7 5% 4% 

Contributions do notexceed screening criteria

NH 0% 3 3% 11 6% 5 2% 2% MD 4% 5 6% 150 82% 6 6% 5% MI 1% 2 2% 33 18% 3 3% 4% WV 1% 2 3% 67 37% 3 3% 3% DE 2% 4 4% 75 41% 4 3% 3% NC 2% 3 3% 72 39% 4 1% 1% IN 1% 2 2% 11 6% 2 3% 3% ME 0% 1 1% 0 0% 1 1% 1% IL 1% 2 2% 2 1% 2 2% 3% KY 1% 2 2% 0 0% 2 1% 1% VT 0% 0 0% 0 0% 0 0% 0% TN 0% 0 0% 0 0% 0 0% 0% WI 0% 1 1% 0 0% 1 1% 1% AR 0% 0 0% 0 0% 0 0% 0% IA 0% 0 1% 0 0% 1 0% 0% MO 0% 0 1% 0 0% 1 0% 1% AL 0% 0 0% 0 0% 0 0% 0% FL 0% 0 0% 0 0% 0 0% 0% GA 0% 0 0% 0 0% 0 0% 0% LA 0% 0 0% 0 0% 0 0% 0% MN 0% 0 0% 0 0% 0 0% 0% MS 0% 0 0% 0 0% 0 0% 0% SC 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 13 Base Case: Total Number of Exceedanc

Denton TX Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions do notexceed screening criteria

LA 5% 3 4% 13 100% 4 84% 87% AR 2% 1 2% 0 0% 2 57% 55% OH 2% 1 1% 0 0% 1 33% 30% KY 1% 1 1% 0 0% 1 26% 25% TN 2% 1 1% 0 0% 1 28% 26% IL 1% 1 1% 0 0% 1 23% 21% IN 1% 1 1% 0 0% 1 25% 23% MO 1% 1 1% 0 0% 1 22% 19% MS 1% 1 1% 0 0% 1 13% 11% AL 1% 1 1% 0 0% 1 8% 8% GA 1% 0 0% 0 0% 0 6% 6% MI 1% 0 0% 0 0% 0 8% 8% NC 1% 1 1% 0 0% 1 8% 8% PA 0% 0 0% 0 0% 0 8% 8% VA 1% 0 0% 0 0% 0 7% 6% WV 1% 0 0% 0 0% 0 9% 8% CT 0% 0 0% 0 0% 0 0% 0% DE 0% 0 0% 0 0% 0 0% 0% FL 0% 0 0% 0 0% 0 0% 0% IA 0% 0 0% 0 0% 0 1% 2% MA 0% 0 0% 0 0% 0 0% 0% MD 0% 0 0% 0 0% 0 1% 2% ME 0% 0 0% 0 0% 0 0% 0% MN 0% 0 0% 0 0% 0 1% 2% NH 0% 0 0% 0 0% 0 0% 0% NJ 0% 0 0% 0 0% 0 0% 0% NY 0% 0 0% 0 0% 0 0% 2% RI 0% 0 0% 0 0% 0 0% 0% SC 0% 0 0% 0 0% 0 3% 4% VT 0% 0 0% 0 0% 0 0% 0% WI 0% 0 0% 0 0% 0 3% 4% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 1547 Base Case: Total Number of Exceedanc

Harris TX Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

LA 13% 13 15% 1547 100% 17 52% 49% AL 3% 4 5% 650 42% 5 8% 8% MS 3% 4 5% 744 48% 5 11% 10% AR 2% 3 3% 423 27% 4 8% 7% 

Contributions do notexceed screening criteria

GA 1% 2 3% 376 24% 3 4% 4% TN 2% 3 3% 572 37% 3 6% 5% NC 1% 2 2% 5 0% 2 2% 2% IL 1% 2 2% 0 0% 2 2% 2% MO 1% 1 1% 0 0% 1 2% 2% IN 0% 1 1% 0 0% 1 2% 1% KY 1% 2 2% 0 0% 2 3% 2% OH 1% 1 2% 0 0% 2 2% 2% VA 1% 1 1% 0 0% 1 2% 2% SC 0% 1 1% 0 0% 1 1% 1% WV 0% 1 1% 0 0% 1 1% 1% FL 0% 1 1% 0 0% 1 1% 1% MI 0% 1 1% 0 0% 1 1% 1% PA 0% 1 1% 0 0% 1 1% 1% IA 0% 0 0% 0 0% 0 0% 0% MD 0% 0 0% 0 0% 0 0% 0% WI 0% 0 0% 0 0% 0 0% 0% CT 0% 0 0% 0 0% 0 0% 0% DE 0% 0 0% 0 0% 0 0% 0% MA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% MN 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% NJ 0% 0 0% 0 0% 0 0% 0% NY 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 12 Base Case: Total Number of Exceedanc

Tarrant TX Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

LA 10% 8 10% 12 100% 9 100% 100% AR 4% 7 8% 7 58% 7 90% 88% TN 3% 4 5% 2 17% 4 59% 52% 

Contributions do notexceed screening criteria

MS 3% 2 3% 9 75% 3 73% 78% KY 1% 1 2% 0 0% 2 27% 20% AL 2% 1 1% 0 0% 1 41% 37% OH 1% 1 1% 0 0% 1 20% 15% GA 1% 1 1% 0 0% 1 33% 29% IL 1% 1 1% 0 0% 1 16% 12% IN 0% 1 1% 0 0% 1 18% 14% MO 1% 1 1% 0 0% 1 16% 12% FL 0% 0 0% 0 0% 0 8% 8% MI 0% 0 0% 0 0% 0 4% 3% NC 1% 1 1% 0 0% 1 16% 14% PA 0% 0 0% 0 0% 0 4% 3% SC 0% 0 0% 0 0% 0 12% 11% VA 0% 0 0% 0 0% 0 8% 6% WV 0% 0 0% 0 0% 0 8% 5% CT 0% 0 0% 0 0% 0 0% 0% DE 0% 0 0% 0 0% 0 0% 0% IA 0% 0 0% 0 0% 0 0% 0% MA 0% 0 0% 0 0% 0 0% 0% MD 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% MN 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% NJ 0% 0 0% 0 0% 0 0% 0% NY 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% WI 0% 0 0% 0 0% 0 2% 2% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 11 Base Case: Total Number of Exceedanc

Arlington VA Upwind State Average 4- episode %

contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

MD 51% 37 41% 11 100% 42 20% 20% OH 5% 5 5% 7 64% 5 23% 23% PA 7% 6 7% 11 100% 6 9% 9% 

Contributions do notexceed screening criteria

MI 2% 2 2% 1 9% 2 4% 4% WV 2% 2 2% 5 45% 2 8% 8% IL 3% 2 3% 7 64% 3 7% 7% IN 2% 2 2% 0 0% 2 5% 5% IA 1% 0 1% 0 0% 0 2% 2% KY 0% 0 0% 0 0% 0 2% 2% MO 2% 2 2% 0 0% 2 3% 3% WI 1% 0 1% 0 0% 0 1% 1% LA 2% 1 1% 0 0% 2 1% 1% NY 1% 1 2% 0 0% 2 0% 1% MN 0% 0 0% 0 0% 0 0% 1% TN 0% 0 0% 0 0% 0 0% 0% AR 1% 1 1% 0 0% 1 0% 1% MS 0% 0 0% 0 0% 0 0% 0% AL 0% 0 0% 0 0% 0 0% 0% CT 0% 0 0% 0 0% 1 0% 0% DE 1% 2 2% 1 9% 2 0% 0% FL 0% 0 0% 0 0% 0 0% 0% GA 0% 0 0% 0 0% 0 0% 0% MA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% NC 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% NJ 1% 2 2% 3 27% 2 0% 0% RI 0% 0 0% 0 0% 0 0% 0% SC 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 85 Base Case: Total Number of Exceedanc

Fairfax VA Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

MD 47% 37 43% 85 100% 54 55% 58% PA 12% 19 22% 85 100% 23 26% 29% NJ 4% 8 9% 23 27% 9 7% 7% OH 3% 3 3% 59 69% 5 8% 10% WV 2% 2 2% 16 19% 3 8% 8% 

Contributions do notexceed screening criteria

MI 1% 1 1% 0 0% 1 3% 3% NY 2% 2 3% 16 19% 3 5% 5% DE 2% 3 4% 23 27% 4 5% 6% IN 2% 2 2% 19 22% 3 4% 4% IL 2% 2 2% 0 0% 2 4% 4% MO 1% 1 1% 0 0% 2 2% 3% IA 0% 0 0% 0 0% 0 1% 1% LA 1% 1 1% 0 0% 1 1% 2% WI 0% 0 0% 0 0% 0 1% 1% AR 1% 1 1% 0 0% 1 1% 1% CT 0% 0 0% 0 0% 1 1% 1% MA 0% 0 0% 0 0% 0 0% 0% MN 0% 0 0% 0 0% 0 0% 0% MS 0% 0 0% 0 0% 0 0% 1% AL 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% FL 0% 0 0% 0 0% 0 0% 0% GA 0% 0 0% 0 0% 0 0% 0% KY 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% NC 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% SC 0% 0 0% 0 0% 0 0% 0% TN 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 76 Base Case: Total Number of Exceedanc

Kenosha WI Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

IL 54% 54 56% 76 100% 61 100% 100% IN 8% 11 11% 63 83% 16 22% 28% MI 3% 13 14% 16 21% 14 3% 2% MO 7% 10 11% 63 83% 12 26% 30% PA 2% 9 10% 11 14% 9 2% 0% OH 2% 9 10% 15 20% 9 2% 1% IA 1% 4 5% 8 11% 5 4% 5% 

Contributions do notexceed screening criteria

AR 2% 3 3% 24 32% 3 4% 6% AL 1% 3 3% 5 7% 3 1% 2% VA 1% 3 3% 11 14% 3 1% 0% GA 0% 2 2% 0 0% 2 1% 2% LA 2% 4 4% 23 30% 4 2% 3% MS 1% 2 2% 5 7% 2 1% 2% WV 0% 1 2% 0 0% 1 0% 0% MD 0% 2 3% 11 14% 2 0% 0% NC 0% 2 2% 0 0% 2 0% 0% TN 1% 2 2% 0 0% 2 1% 1% NY 0% 1 2% 0 0% 1 0% 0% SC 0% 1 1% 0 0% 1 0% 0% FL 0% 0 0% 0 0% 0 0% 0% KY 0% 1 1% 0 0% 1 0% 0% MN 0% 1 1% 0 0% 1 0% 0% NJ 0% 0 0% 0 0% 0 0% 0% CT 0% 0 0% 0 0% 0 0% 0% DE 0% 0 0% 0 0% 0 0% 0% MA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 126 Base Case: Total Number of Exceedanc

Racine WI Upwind

State

Average 4-episode %contribution

Highest dailyaverage (ppb)

Highestdailyaverage(%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

IL 52% 54 54% 126 100% 61 100% 100% IN 9% 11 11% 108 86% 15 23% 23% MI 3% 14 17% 27 21% 16 2% 2% MO 7% 10 10% 112 89% 12 28% 29% PA 1% 9 10% 14 11% 9 0% 0% OH 1% 8 9% 16 13% 9 0% 0% IA 1% 5 5% 7 6% 5 6% 7% 

Contributions do notexceed screening criteria

AR 2% 3 3% 48 38% 3 4% 5% AL 1% 3 3% 11 9% 3 2% 2% LA 2% 4 4% 46 37% 4 3% 3% VA 0% 3 3% 14 11% 3 0% 0% GA 0% 2 2% 0 0% 2 1% 1% MS 1% 2 2% 10 8% 2 1% 2% MD 0% 2 3% 14 11% 2 0% 0% NC 0% 2 2% 0 0% 2 0% 0% WV 0% 1 1% 0 0% 1 0% 0% TN 1% 2 2% 0 0% 2 1% 1% NY 0% 1 2% 0 0% 2 0% 0% SC 0% 1 1% 0 0% 1 0% 0% FL 0% 0 0% 0 0% 0 0% 0% KY 0% 1 1% 0 0% 1 0% 0% MN 0% 0 0% 0 0% 0 0% 0% NJ 0% 0 0% 0 0% 0 0% 0% CT 0% 0 0% 0 0% 0 0% 0% DE 0% 0 0% 0 0% 0 0% 0% MA 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% 
_________________________

Note that due to rounding, some of the maximum contribution values that appear in the table as "2 ppb" for source apportion

zero-out are actually less than the 2 ppb screening criteria. These occurances are denoted by a value "0" in the column lab

ppb". The linkages for these cases are listed in the tables as having contributions that do not exceed the screening criteria.

DownwindNonattainment Receptor

CAMX Source Apportionment Modeling CAMX State Zero-O

Base Case: Total Number of Exceedances (grid-hours) = 41 Base Case: Total Number of Exceedanc

Sheboygan WI Upwind State Average 4- episode %

contribution

Highest dailyaverage (ppb)

Highestdailyaverage (%)

# reduced>= 2 ppb

% reduced>= 2 ppb

max 8-hr ppbcontribution

% total ppbreduced

% pop-wgt totalppb reduced

# reduc>= 2 pp

Contributions exceedscreening criteria

IL 52% 36 39% 41 100% 41 100% 100% IN 10% 7 8% 41 100% 10 34% 27% MO 13% 9 10% 41 100% 10 43% 36% 

Contributions do notexceed screening criteria

IA 2% 1 1% 0 0% 1 12% 19% MI 1% 1 1% 0 0% 1 1% 1% AR 0% 0 0% 0 0% 0 -1% -1% MN 0% 0 0% 0 0% 0 1% 1% AL 0% 0 0% 0 0% 0 0% 0% CT 0% 0 0% 0 0% 0 0% 0% DE 0% 0 0% 0 0% 0 0% 0% FL 0% 0 0% 0 0% 0 0% 0% GA 0% 0 0% 0 0% 0 0% 0% KY 0% 0 0% 0 0% 0 0% 0% LA 0% 0 0% 0 0% 0 0% 0% MA 0% 0 0% 0 0% 0 0% 0% MD 0% 0 0% 0 0% 0 0% 0% ME 0% 0 0% 0 0% 0 0% 0% MS 0% 0 0% 0 0% 0 0% 0% NC 0% 0 0% 0 0% 0 0% 0% NH 0% 0 0% 0 0% 0 0% 0% NJ 0% 0 0% 0 0% 0 0% 0% NY 0% 0 0% 0 0% 0 0% 0% OH 0% 0 0% 0 0% 0 0% 0% PA 0% 0 0% 0 0% 0 0% 0% RI 0% 0 0% 0 0% 0 0% 0% SC 0% 0 0% 0 0% 0 0% 0% TN 0% 0 0% 0 0% 0 0% 0% VA 0% 0 0% 0 0% 0 0% 0% VT 0% 0 0% 0 0% 0 0% 0% WV 0% 0 0% 0 0% 0 0% 0% 
Technical Support Document for the 

Interstate Air Quality Rule

Air Quality Modeling Analyses

Appendix H

PM2.5 Contributions to 

Downwind Nonattainment Counties in 2010
H-1

The tables below show the contribution from the State-by-State zero-out modeling to annual

average PM2.5 concentrations at nonattainment receptors in other States. In these tables “NA”

indicates that the given nonattainment county is not downwind of that particular upwind source

State. That is, the county is either located within the source State or within that portion of an

adjacent State that shares a model grid cell with the source State. States denoted as “combined”

indicate those States that were paired in zero-out runs. The combined State runs were performed

for North Dakota with Vermont; Nebraska with Maine; and South Dakota with New Hampshire. 

The maximum downwind contribution from each of the three Plains States included in combined

runs(i.e., Nebraska, North Dakota, and South Dakota) was determined by indentifying the

highest contribution to nonattainment counties in the Midwest. The maximum contribution from

each of the three New England States included in combined runs (i.e., Maine, New Hampshire,

and Vermont) was determined by identifying the highest contribution to nonattainment counties

in the Northeast.
H-2

Downwind 2010 Nonattainment

Counties

Downwind PM2.5 Contributions (ug/m3) from Upwind Source States based on

Zero-Out Modeling of 2010 SO2+NOx Emissions. 

State Name County Name 2010 Base-1

Case PM2.5

(:g/m3)

AL AR CO CT DE FL GA IA IL IN KS 

Alabama DeKalb County 15.24 NA 0.11 0.03 0.00 0.01 0.22 1.32 0.09 0.34 0.29 0.04 Alabama Jefferson County 20.12 NA 0.12 0.03 0.00 0.01 0.26 0.82 0.10 0.33 0.25 0.05 Alabama Montgomery County 15.72 NA 0.10 0.03 0.00 0.01 0.44 0.74 0.08 0.25 0.20 0.05 Alabama Russell County 17.31 NA 0.10 0.03 0.00 0.01 0.52 1.52 0.09 0.28 0.23 0.05 Alabama Talladega County 16.46 NA 0.10 0.03 0.00 0.01 0.33 0.88 0.09 0.30 0.24 0.05 Connecticut New Haven County 15.45 0.05 0.01 0.01 NA 0.06 0.03 0.08 0.04 0.15 0.13 0.01 Delaware New Castle County 15.49 0.08 0.02 0.01 0.02 NA 0.03 0.11 0.05 0.19 0.18 0.02 District ofColumbia

District of Columbia 15.35 0.12 0.03 0.01 0.01 0.10 0.04 0.15 0.06 0.24 0.23 0.02 

Georgia Clarke County 17.05 0.75 0.10 0.03 0.00 0.02 0.27 NA 0.07 0.27 0.26 0.04 Georgia Clayton County 17.82 0.90 0.10 0.03 0.00 0.01 0.30 NA 0.07 0.26 0.23 0.04 Georgia Cobb County 17.24 0.97 0.10 0.03 0.00 0.01 0.23 NA 0.08 0.28 0.26 0.04 Georgia DeKalb County 18.26 0.93 0.11 0.03 0.00 0.01 0.27 NA 0.08 0.27 0.25 0.04 Georgia Floyd County 17.14 1.17 0.11 0.03 0.00 0.01 0.24 NA 0.09 0.33 0.30 0.05 Georgia Fulton County 19.79 0.99 0.11 0.03 0.00 0.01 0.28 NA 0.08 0.29 0.27 0.05 Georgia Hall County 15.61 0.76 0.10 0.03 0.00 0.01 0.22 NA 0.07 0.26 0.25 0.04 Georgia Muscogee County 16.92 NA 0.10 0.03 0.00 0.01 0.51 NA 0.08 0.27 0.22 0.05 Georgia Paulding County 15.52 1.14 0.10 0.03 0.00 0.01 0.26 NA 0.08 0.29 0.26 0.04 Georgia Richmond County 16.03 0.55 0.06 0.02 0.00 0.02 0.28 NA 0.06 0.22 0.21 0.03 Georgia Wilkinson County 16.89 0.65 0.07 0.02 0.00 0.02 0.37 NA 0.07 0.22 0.20 0.03 Illinois Cook County 18.07 0.08 0.11 0.03 0.00 0.00 0.01 0.04 0.33 NA 0.79 0.11 Illinois Madison County 16.48 0.11 0.27 0.04 0.00 0.00 0.02 0.07 0.43 NA 0.45 0.15 Illinois St. Clair County 16.32 0.12 0.29 0.04 0.00 0.00 0.02 0.07 0.40 NA 0.50 0.15 Illinois Will County 15.54 0.08 0.11 0.03 0.00 0.00 0.01 0.04 0.35 NA 0.76 0.09 Indiana Clark County 15.79 0.43 0.12 0.03 0.00 0.00 0.06 0.34 0.19 0.84 NA 0.06 Indiana Marion County 15.76 0.19 0.10 0.03 0.00 0.00 0.03 0.12 0.25 1.11 NA 0.07 Kentucky Fayette County 15.05 0.42 0.10 0.03 0.00 0.00 0.07 0.38 0.17 0.71 0.80 0.05 Kentucky Jefferson County 15.71 0.42 0.12 0.03 0.00 0.00 0.06 0.35 0.19 0.85 NA 0.06 Maryland Baltimore city 16.53 0.10 0.03 0.02 0.01 0.10 0.04 0.14 0.06 0.24 0.23 0.02 Michigan Wayne County 18.76 0.10 0.06 0.02 0.00 0.00 0.03 0.08 0.16 0.70 0.57 0.05 Missouri St. Louis city 15.26 0.12 0.27 0.04 0.00 0.00 0.02 0.07 0.38 1.50 0.45 0.14 New York New York County 16.29 0.05 0.02 0.01 0.07 0.09 0.02 0.08 0.04 0.16 0.15 0.01 North Carolina Davidson County 15.32 0.27 0.06 0.02 0.00 0.02 0.11 0.54 0.06 0.28 0.29 0.02 North Carolina Mecklenburg County 15.07 0.33 0.06 0.02 0.00 0.02 0.14 0.74 0.06 0.25 0.26 0.02 Ohio Butler County 15.87 0.24 0.08 0.02 0.00 0.00 0.04 0.19 0.16 0.75 0.91 0.05 
H-3

Downwind 2010 Nonattainment

Counties

Downwind PM2.5 Contributions (ug/m3) from Upwind Source States based on

Zero-Out Modeling of 2010 SO2+NOx Emissions. 

State Name County Name 2010 Base-1

Case PM2.5

(:g/m3)

AL AR CO CT DE FL GA IA IL IN KS 

Ohio Franklin County 16.45 0.20 0.06 0.02 0.00 0.00 0.04 0.17 0.14 0.59 0.67 0.04 Ohio Hamilton County 17.57 0.32 0.09 0.03 0.00 0.00 0.05 0.26 0.18 0.83 1.06 0.06 Ohio Jefferson County 17.69 0.15 0.04 0.01 0.00 0.01 0.04 0.15 0.10 0.39 0.39 0.03 Ohio Lawrence County 15.19 0.26 0.06 0.02 0.00 0.00 0.05 0.28 0.12 0.49 0.54 0.03 Ohio Mahoning County 15.13 0.13 0.04 0.02 0.00 0.00 0.03 0.11 0.10 0.38 0.36 0.03 Ohio Scioto County 18.02 0.30 0.08 0.02 0.00 0.01 0.06 0.31 0.14 0.59 0.63 0.04 Ohio Stark County 16.80 0.17 0.05 0.02 0.00 0.00 0.04 0.15 0.13 0.49 0.49 0.04 Ohio Summit County 16.17 0.14 0.04 0.02 0.00 0.00 0.04 0.12 0.12 0.46 0.44 0.04 Pennsylvania Allegheny County 18.86 0.17 0.05 0.02 0.00 0.02 0.04 0.19 0.11 0.43 0.43 0.03 Pennsylvania Berks County 15.28 0.08 0.02 0.01 0.02 0.17 0.03 0.11 0.05 0.20 0.19 0.02 Pennsylvania Lancaster County 15.27 0.09 0.03 0.02 0.02 0.09 0.03 0.13 0.06 0.23 0.22 0.02 Pennsylvania York County 15.50 0.09 0.03 0.01 0.01 0.11 0.03 0.13 0.06 0.23 0.22 0.02 Tennessee Davidson County 15.31 0.85 0.16 0.03 0.00 0.00 0.11 0.49 0.17 0.68 0.54 0.06 Tennessee Hamilton County 16.11 0.94 0.12 0.03 0.00 0.01 0.17 1.08 0.11 0.41 0.39 0.04 Tennessee Knox County 18.16 0.77 0.13 0.03 0.00 0.01 0.18 0.98 0.13 0.51 0.51 0.05 Tennessee Roane County 15.13 0.80 0.13 0.03 0.00 0.01 0.15 0.77 0.12 0.48 0.46 0.05 Tennessee Sullivan County 15.06 0.43 0.09 0.02 0.00 0.01 0.11 0.57 0.10 0.41 0.43 0.04 West Virginia Brooke County 16.28 0.13 0.04 0.01 0.00 0.01 0.03 0.14 0.09 0.36 0.36 0.03 West Virginia Cabell County 15.98 0.28 0.07 0.02 0.00 0.01 0.06 0.31 0.13 0.51 0.54 0.03 West Virginia Hancock County 16.37 0.13 0.04 0.01 0.00 0.01 0.03 0.13 0.09 0.36 0.36 0.03 West Virginia Kanawha County 16.67 0.27 0.06 0.02 0.00 0.01 0.06 0.31 0.12 0.47 0.49 0.03 West Virginia Wood County 15.85 0.23 0.06 0.02 0.00 0.01 0.05 0.25 0.13 0.49 0.53 0.03 
H-4

Downwind 2010 

Counties

Nonattainment Downwind PM2.5 Contributions (ug/m3) from Upwind Source States based

on Zero-Out Modeling of 2010 SO2+NOx Emissions. 

State Name County Name 2010 Base-1

Case PM2.5

(:g/m3)

MI MN MO MS MT NC ND & VT

(Combined)

NE & ME

(Combined)

NJ NM 

Alabama DeKalb County 15.24 0.12 0.05 0.18 0.19 0.01 0.20 0.04 0.03 0.02 0Alabama Jefferson County 20.12 0.10 0.06 0.19 0.30 0.01 0.15 0.04 0.03 0.02 0Alabama Montgomery County 15.72 0.08 0.05 0.15 0.26 0.01 0.15 0.04 0.03 0.02 0Alabama Russell County 17.31 0.10 0.05 0.15 0.22 0.01 0.21 0.04 0.03 0.03 0Alabama Talladega County 16.46 0.10 0.06 0.17 0.25 0.01 0.15 0.04 0.03 0.02 0Connecticut New Haven County 15.45 0.20 0.05 0.04 0.02 0.01 0.12 0.06 0.03 0.32 0Delaware New Castle County 15.49 0.24 0.05 0.06 0.02 0.01 0.15 0.04 0.02 0.21 0District ofColumbia

District of Columbia 15.35 0.24 0.06 0.08 0.04 0.01 0.26 0.04 0.02 0.14 0

Georgia Clarke County 17.05 0.14 0.04 0.14 0.15 0.01 0.34 0.04 0.03 0.03 0Georgia Clayton County 17.82 0.11 0.04 0.14 0.17 0.01 0.23 0.04 0.03 0.02 0Georgia Cobb County 17.24 0.12 0.05 0.15 0.16 0.01 0.24 0.04 0.03 0.01 0Georgia DeKalb County 18.26 0.12 0.04 0.15 0.17 0.01 0.27 0.04 0.03 0.02 0Georgia Floyd County 17.14 0.13 0.05 0.17 0.19 0.01 0.23 0.05 0.03 0.02 0Georgia Fulton County 19.79 0.13 0.05 0.16 0.18 0.01 0.29 0.04 0.03 0.02 0Georgia Hall County 15.61 0.13 0.04 0.14 0.14 0.01 0.33 0.04 0.03 0.02 0Georgia Muscogee County 16.92 0.10 0.05 0.15 0.22 0.01 0.21 0.04 0.03 0.03 0Georgia Paulding County 15.52 0.12 0.05 0.16 0.18 0.01 0.21 0.04 0.03 0.02 0Georgia Richmond County 16.03 0.12 0.03 0.10 0.12 0.01 0.38 0.04 0.02 0.03 0Georgia Wilkinson County 16.89 0.11 0.04 0.11 0.15 0.01 0.26 0.04 0.02 0.03 0Illinois Cook County 18.07 0.73 0.39 0.30 0.05 0.03 0.01 0.12 0.06 0.00 0Illinois Madison County 16.48 0.24 0.27 0.89 0.10 0.03 0.02 0.10 0.08 0.00 0Illinois St. Clair County 16.32 0.24 0.25 NA 0.11 0.03 0.02 0.10 0.08 0.00 0Illinois Will County 15.54 0.58 0.33 0.30 0.05 0.03 0.01 0.11 0.05 0.00 0Indiana Clark County 15.79 0.29 0.13 0.27 0.13 0.02 0.06 0.07 0.04 0.01 0Indiana Marion County 15.76 0.51 0.19 0.25 0.08 0.02 0.02 0.08 0.04 0.00 0Kentucky Fayette County 15.05 0.30 0.12 0.24 0.12 0.02 0.08 0.06 0.04 0.01 0Kentucky Jefferson County 15.71 0.30 0.13 0.28 0.13 0.02 0.07 0.07 0.04 0.01 0Maryland Baltimore city 16.53 0.25 0.06 0.07 0.03 0.01 0.23 0.04 0.02 0.16 0Michigan Wayne County 18.76 NA 0.19 0.14 0.04 0.02 0.03 0.07 0.04 0.00 0Missouri St. Louis city 15.26 0.22 0.23 NA 0.11 0.02 0.02 0.09 0.08 0.00 0New York New York County 16.29 0.21 0.05 0.05 0.02 0.01 0.13 0.05 0.02 0.45 0North Carolina Davidson County 15.32 0.16 0.04 0.11 0.06 0.01 NA 0.03 0.02 0.05 0North Carolina Mecklenburg County 15.07 0.14 0.03 0.11 0.08 0.01 NA 0.03 0.02 0.04 0Ohio Butler County 15.87 0.52 0.14 0.20 0.08 0.02 0.05 0.07 0.04 0.00 0
H-5

Downwind 2010 

Counties

Nonattainment Downwind PM2.5 Contributions (ug/m3) from Upwind Source States based

on Zero-Out Modeling of 2010 SO2+NOx Emissions. 

State Name County Name 2010 Base-1

Case PM2.5

(:g/m3)

MI MN MO MS MT NC ND & VT

(Combined)

NE & ME

(Combined)

NJ NM 

Ohio Franklin County 16.45 0.61 0.13 0.16 0.06 0.02 0.06 0.06 0.03 0.01 0Ohio Hamilton County 17.57 0.53 0.15 0.24 0.10 0.02 0.06 0.08 0.04 0.01 0Ohio Jefferson County 17.69 0.48 0.09 0.11 0.04 0.01 0.09 0.05 0.02 0.01 0Ohio Lawrence County 15.19 0.33 0.08 0.16 0.07 0.01 0.13 0.05 0.03 0.01 0Ohio Mahoning County 15.13 0.55 0.09 0.10 0.04 0.01 0.06 0.05 0.03 0.01 0Ohio Scioto County 18.02 0.43 0.11 0.19 0.09 0.02 0.13 0.07 0.03 0.01 0Ohio Stark County 16.80 0.70 0.11 0.14 0.05 0.02 0.07 0.06 0.03 0.01 0Ohio Summit County 16.17 0.71 0.11 0.12 0.05 0.01 0.06 0.06 0.03 0.01 0Pennsylvania Allegheny County 18.86 0.50 0.09 0.13 0.06 0.02 0.15 0.06 0.03 0.04 0Pennsylvania Berks County 15.28 0.25 0.06 0.06 0.02 0.01 0.14 0.04 0.02 0.21 0Pennsylvania Lancaster County 15.27 0.28 0.06 0.07 0.03 0.01 0.16 0.04 0.02 0.23 0Pennsylvania York County 15.50 0.26 0.06 0.07 0.03 0.01 0.17 0.04 0.02 0.17 0Tennessee Davidson County 15.31 0.19 0.10 0.32 0.22 0.02 0.12 0.06 0.05 0.01 0Tennessee Hamilton County 16.11 0.16 0.06 0.20 0.17 0.01 0.21 0.05 0.03 0.01 0Tennessee Knox County 18.16 0.22 0.08 0.23 0.16 0.02 0.35 0.06 0.04 0.02 0Tennessee Roane County 15.13 0.18 0.07 0.23 0.16 0.01 0.19 0.05 0.04 0.01 0Tennessee Sullivan County 15.06 0.20 0.06 0.17 0.10 0.01 0.41 0.04 0.03 0.02 0West Virginia Brooke County 16.28 0.44 0.08 0.10 0.04 0.01 0.08 0.04 0.02 0.01 0West Virginia Cabell County 15.98 0.35 0.09 0.16 0.08 0.01 0.16 0.06 0.03 0.01 0West Virginia Hancock County 16.37 0.45 0.08 0.10 0.04 0.01 0.08 0.04 0.02 0.01 0West Virginia Kanawha County 16.67 0.33 0.09 0.15 0.07 0.01 0.19 0.05 0.03 0.01 0West Virginia Wood County 15.85 0.41 0.09 0.15 0.07 0.01 0.14 0.06 0.03 0.01 0
H-6

Downwind 2010 Nonattainment

Counties

Downwind PM2.5 Contributions (ug/m3) from Upwind Source States

based on Zero-Out Modeling of 2010 SO2+NOx Emissions. 

 

State Name County Name 2010 Base-1

Case PM2.5

(:g/m3)

PA RI SC SD & NH

(Combined)

TN TX VA WI WV WY

Alabama DeKalb County 15.24 0.15 0.00 0.16 0.02 0.55 0.21 0.10 0.11 0.13 0.03Alabama Jefferson County 20.12 0.14 0.00 0.13 0.02 0.45 0.22 0.09 0.11 0.11 0.03Alabama Montgomery County 15.72 0.15 0.00 0.15 0.01 0.30 0.20 0.09 0.09 0.10 0.03Alabama Russell County 17.31 0.17 0.00 0.26 0.02 0.36 0.21 0.11 0.10 0.11 0.03Alabama Talladega County 16.46 0.14 0.00 0.14 0.01 0.38 0.20 0.09 0.10 0.10 0.03Connecticut New Haven County 15.45 0.57 0.01 0.04 0.06 0.07 0.05 0.16 0.09 0.14 0.01Delaware New Castle County 15.49 1.17 0.00 0.05 0.02 0.10 0.06 0.35 0.10 0.26 0.01District ofColumbia

District of Columbia 15.35 0.86 0.00 0.09 0.02 0.15 0.08 0.67 0.13 0.37 0.02

Georgia Clarke County 17.05 0.22 0.00 0.47 0.01 0.46 0.20 0.15 0.09 0.16 0.03Georgia Clayton County 17.82 0.17 0.00 0.28 0.01 0.40 0.19 0.12 0.09 0.13 0.03Georgia Cobb County 17.24 0.15 0.00 0.24 0.01 0.52 0.21 0.11 0.10 0.13 0.03Georgia DeKalb County 18.26 0.18 0.00 0.30 0.01 0.46 0.21 0.13 0.10 0.14 0.03Georgia Floyd County 17.14 0.16 0.00 0.21 0.02 0.57 0.22 0.11 0.12 0.14 0.03Georgia Fulton County 19.79 0.19 0.00 0.32 0.02 0.49 0.23 0.14 0.11 0.15 0.03Georgia Hall County 15.61 0.18 0.00 0.37 0.01 0.47 0.20 0.14 0.09 0.15 0.03Georgia Muscogee County 16.92 0.17 0.00 0.26 0.02 0.35 0.21 0.11 0.09 0.11 0.03Georgia Paulding County 15.52 0.16 0.00 0.19 0.01 0.47 0.20 0.11 0.11 0.13 0.03Georgia Richmond County 16.03 0.22 0.00 0.72 0.01 0.35 0.17 0.16 0.08 0.16 0.02Georgia Wilkinson County 16.89 0.19 0.00 0.39 0.01 0.35 0.17 0.12 0.08 0.13 0.03Illinois Cook County 18.07 0.11 0.00 0.00 0.04 0.14 0.21 0.01 1.00 0.04 0.04Illinois Madison County 16.48 0.11 0.00 0.01 0.04 0.22 0.37 0.02 0.35 0.07 0.05Illinois St. Clair County 16.32 0.10 0.00 0.01 0.04 0.24 0.37 0.02 0.32 0.08 0.05Illinois Will County 15.54 0.10 0.00 0.00 0.04 0.12 0.21 0.01 0.84 0.05 0.04Indiana Clark County 15.79 0.16 0.00 0.04 0.02 0.53 0.20 0.08 0.26 0.19 0.03Indiana Marion County 15.76 0.18 0.00 0.01 0.03 0.24 0.18 0.03 0.37 0.12 0.03Kentucky Fayette County 15.05 0.17 0.00 0.05 0.02 0.53 0.20 0.09 0.24 0.24 0.03Kentucky Jefferson County 15.71 0.18 0.00 0.04 0.02 0.52 0.20 0.08 0.26 0.20 0.03Maryland Baltimore city 16.53 1.01 0.00 0.08 0.02 0.15 0.08 0.58 0.13 0.38 0.02Michigan Wayne County 18.76 0.20 0.00 0.01 0.02 0.14 0.12 0.03 0.41 0.15 0.03Missouri St. Louis city 15.26 0.09 0.00 0.01 0.04 0.22 0.35 0.02 0.30 0.07 0.04New York New York County 16.29 0.95 0.00 0.05 0.04 0.08 0.05 0.21 0.10 0.17 0.01North Carolina Davidson County 15.32 0.29 0.00 0.38 0.01 0.38 0.13 0.32 0.10 0.25 0.02North Carolina Mecklenburg County 15.07 0.26 0.00 0.66 0.01 0.38 0.14 0.24 0.08 0.21 0.02Ohio Butler County 15.87 0.20 0.00 0.02 0.02 0.30 0.16 0.05 0.28 0.22 0.03
H-7

Downwind 2010 Nonattainment

Counties

Downwind PM2.5 Contributions (ug/m3) from Upwind Source States

based on Zero-Out Modeling of 2010 SO2+NOx Emissions. 

 

State Name County Name 2010 Base-1

Case PM2.5

(:g/m3)

PA RI SC SD & NH

(Combined)

TN TX VA WI WV WY

Ohio Franklin County 16.45 0.30 0.00 0.03 0.02 0.27 0.14 0.08 0.27 0.39 0.03Ohio Hamilton County 17.57 0.22 0.00 0.03 0.03 0.39 0.19 0.07 0.31 0.29 0.03Ohio Jefferson County 17.69 0.73 0.00 0.04 0.02 0.19 0.10 0.12 0.21 NA 0.02Ohio Lawrence County 15.19 0.21 0.00 0.07 0.02 0.35 0.14 0.13 0.18 0.60 0.02Ohio Mahoning County 15.13 0.70 0.00 0.03 0.02 0.16 0.10 0.09 0.20 0.53 0.02Ohio Scioto County 18.02 0.23 0.00 0.07 0.02 0.42 0.18 0.13 0.22 0.61 0.03Ohio Stark County 16.80 0.71 0.00 0.03 0.02 0.21 0.13 0.09 0.27 0.42 0.03Ohio Summit County 16.17 0.59 0.00 0.03 0.02 0.17 0.11 0.07 0.24 0.32 0.03Pennsylvania Allegheny County 18.86 NA 0.00 0.05 0.02 0.25 0.14 0.26 0.23 0.89 0.03Pennsylvania Berks County 15.28 NA 0.00 0.05 0.02 0.11 0.07 0.32 0.11 0.28 0.01Pennsylvania Lancaster County 15.27 NA 0.00 0.06 0.02 0.13 0.08 0.40 0.12 0.35 0.02Pennsylvania York County 15.50 NA 0.00 0.06 0.02 0.13 0.08 0.44 0.12 0.39 0.02Tennessee Davidson County 15.31 0.15 0.00 0.08 0.02 NA 0.27 0.07 0.19 0.16 0.03Tennessee Hamilton County 16.11 0.16 0.00 0.15 0.02 NA 0.23 0.09 0.14 0.16 0.03Tennessee Knox County 18.16 0.21 0.00 0.23 0.02 NA 0.28 0.15 0.17 0.24 0.04Tennessee Roane County 15.13 0.18 0.00 0.14 0.02 NA 0.24 0.10 0.15 0.19 0.03Tennessee Sullivan County 15.06 0.19 0.00 0.18 0.01 NA 0.20 0.16 0.13 0.26 0.03West Virginia Brooke County 16.28 0.67 0.00 0.03 0.01 0.17 0.10 0.11 0.20 NA 0.02West Virginia Cabell County 15.98 0.25 0.00 0.09 0.02 0.36 0.16 0.16 0.18 NA 0.02West Virginia Hancock County 16.37 0.68 0.00 0.03 0.01 0.17 0.09 0.11 0.20 NA 0.02West Virginia Kanawha County 16.67 0.28 0.00 0.10 0.02 0.36 0.16 0.19 0.17 NA 0.02West Virginia Wood County 15.85 0.31 0.00 0.07 0.02 0.30 0.15 0.14 0.20 NA 0.02
Technical Support Document for the 

Interstate Air Quality Rule

Air Quality Modeling Analyses

Appendix I

Background Information on the Development 

of Local Control Measures for PM2.5
I-1

Appendix I. Memo from ECR summarizing references for cost estimates

To: Scott Mathias

From: Becky Battye, EC/R

Subject: Revised Costs of Local Control Measures

Date: June 20, 2003

This memorandum is an update from the June 2, 2003 memorandum - documenting the

selection and costs for the recently modeled control measures. Note: this memo describes the

costs for the measures we wanted to model, not the levels that were actually modeled, and many

of the measures have been modified based on comments received and information obtained

during the development of the costs. Major changes to this memo include the addition of the

source category codes for the measures, separately referencing the source of the cost and control

efficiency information, and providing more information on the NOx and VOC controls. This

memo also incorporates the information you have forwarded from OTAQ (email from you

(5/6/03) and from Katayama (5/20/03)).
I-2

Costs for the PM local control measures

Control Measures Efficiency Cost/ton Notes Reference (efficiency) 

Replace fireplaceswith natural gasinserts2104008001

80 7508 Cost-effectiveness is calculated for

PM10 precursors and assumes a$300/retrofit incentive

Not known - shouldn’t thisbe 100%?

Air Qfor tCentComCA, 

Replace with noncatalytic certifiedwoodstoves2104008001

71 3872 “ Residential Wood

Combustion - PM2.5. 

Prepared for Westar byOMNI. July 1998.

Air Qfor tCentComCA, 

84-91 Final Report to the

Govenor’s Air QualityStrategies Task Force fromthe PM-10 Subcommittee(1/98)

Combination ofmeasures to reducegasoline highwayvehicle emissions2201001***2201020***

3 - 5 Costs

applied to

VOC

Costs were developed based on VOCreduction. Efficiency for LDGV &LDGT1 - 5% in Birmingham and 3%in Chicago and Philadelphia. Assume no reduction in VMT forLDGT2 (2201040*** - commercialapplications).

From OTAQ email Nati

2002 MitiProg

Diesel ParticulateFilter2230070***

90 4000 Cost is probably high - (based on

lack of LSD availability - whichshouldn’t be an issue in 2010). Filters cost about $7,500/vehicle

http://www.adeq.state.az.us/environ/air/browncloud/download/onroad/1002haze2.pdf

http:nvirooad/
Control Measures Efficiency Cost/ton Notes Reference (efficiency) 

I-3

Work day restrictions(commercial lawnand garden)226004016; 021;026; 031; 071

11 Cost

applied to

NOx

Cost-effectiveness for lawn servicerestrictions is calculated and reportedfor NOx. Effectiveness based on 1ton reduction from 9.6 tons of NOxin Houston.

Emission reduction iscalculated from SIPinventory - these numbershave not been verified

TNRdrafHouPublwebs

Buy back program(residential lawn andgarden)226004015; 020;025; 030

Need relative emissions from 2-stroke and 4-stroke (we have a costfor marine buy back program forVOC emissions)

Diesel Oxidationcatalyst for Non-roaddiesel2270002***

25 1,000 Cost based on the Big Dig in Boston

(which isn’t clear if this is for PM orNOx). Cost is applied to both PMand NOx.

Retrofitting EmissionControls on Diesel-PoweredVehicles, MECA 3/2002,pg.8

CleaTranEngi1/20

Marine -diesel Have something for NOx -

since we don’t know themeasures we don’t know ifthere is a PM co-benefit

Marine 2-strokebuy backprogram2282005010; 015

Have a cost for VOC reduction- need a ratio of PM and NOxemissions to VOC emissionsfor 2- vs. 4- stroke engines

OutBacWissurvwerimp

Vacuum sweeping ofpaved roads2294000000

75 1,070 Best Management Practices

document and FHWA(Sutherland & Jelen, 1996)

PropRACsourprec4/20
Control Measures Efficiency Cost/ton Notes Reference (efficiency) 

I-4

Gravel covering ofunpaved roads2296000000

90 2160 -

5920

Not sure where efficiencycame from - believe its anold FACA number

PropRACsourprec4/20

Wateringconstruction road2311000100

50 1960 Cost is actually for disturbed soils

after demolition completion or at endof each day of cleanup (constructionactivities). Much lower cost forlimiting speeds on unpaved parkinglots and water suppression($1960/ton)

Air Quality Modeling ofElevated ParticulatesConcentrations in Tucson in1999 Arizona DEQ. 6/2001

PropRACsourprec4/20

Ban Open Burning261001000026100200002610030000

100 0 SJV

AmeBurn(PreHaza11/0

Soil conservationmeasures for tillingoperations2801000003

20 19 Additional Control Measure

Evaluation for the IntegratedImplementation of the Ozoneand Particulate MatterNational Ambient Air QualityStandards, and RegionalHaze Program. July 17,1997.

BACDemPM1the S
I-5

Reductions for the LDGV, LDGT 1&2, and LDGT 3&4, are currently listed as reduce VMT and turn ove

email suggested we change the name to “combination of measures to reduce highway vehicle emissions.” We ha

ridesharing, vanpool programs, employer trip reduction programs, and bike/pedestrian improvements” - but need distributing the costs between pollutants. We show the reductions for all 3 pollutants & put the cost on VOC. A

would work toward improving the basis for three subcategories “accelerated fleet turnover, technology-based pro

based programs” (item 4 of email). Is there any additional information from them? Meanwhile they want us to lexpectations on overall control efficiency (which we have done). The costs we have are only applied to LDGV aassume that LDGT2 are more commercial in nature and not amenable to the three programs for which we have cadditional information (non-CMAQ measures) in the CMAQ document that we will extract to get the remainialso to address LDGT2.

For the diesel particulate filter, the first email from OTAQ said an overall HDDV reduction of 37% is app

referred to the Katayama information when applying the diesel particulate filter. We had assumed a 30% penetra

restricted the use of the particulate filter to model years 1996 to 2006 and only for class 5-8 vehicles. Using refe

we estimate that 57% of the HDDV fleet is the applicable class and 54% of the fleet is the correct model year. T

31% could retrofit with the filter. When he said 30% market penetration I assume he means only 30% could use stick with the 90% efficiency and 30% applicability. We use a cost of $4,000 ton (middle of the range but proba

The costs for the lawn service restriction are reported for NOx. The cost is presented for NOx but the me

pollutants. We are currently just reporting the restrictions for commercial lawn and garden use with the Texas prNo efficiency is provided in the Texas document. We calculated an 11% reduction for the NOx and applied the said they would look into buy-back programs for lawn and garden.

First email (item 6) refers to the diesel oxidation catalyst as achieving 25% control (of PM10?). Neither 

a good feel for pre-2007 non-road engines. To achieve the desired 18.3% overall control efficiency - the overall have to increase to 73% (because the efficiency decreased from 61 to 25%).

Banning open burning is listed as a free measure in the SJV analysis. We have some costs for collecting 

CA but would need to work the numbers to get an efficiency and a cost effectiveness.Costs for the NOx local control measures
I-6

Control Measures Efficiency Cost/ton Notes Reference (efficiency) 

Point Sources

Low NOx burners forlime calcining kiln andasphalt concrete rotarydryer3050160430500201

27 440 -

940

Technology transfer fromcement kiln operations. Cost isdependent upon whether theburner is direct fired or indirectfired

NOx Control Technologies forthe Cement Industry, EPAreport, 9/2000

same

Cement kiln - mid-kilnfiring30500606

33 55 for dry process kiln NOx Control Technologies for

the Cement Industry, EPAreport, 9/2000

same

Cement kiln - tirederived fuel30500623

35 (1900) for preheater/precalciner kiln NOx Control Technologies for

the Cement Industry, EPAreport, 9/2000

same

Point and Area Source Combustion Categories

SNCR for coal-firedpulverized boilers102002022102002000

50 1055 Middle of the range for both

efficiency and cost effectiveness. Efficiency is much higher at aslightly higher cost for SCR

EPA, TTN NAAQS OTAGTechnical Supporting Document Chapter 5 Appendix C Chartspage 4

same

SNCR for coal-firedstoker boilers1020010410200204

50 1160 Middle of the range for both

efficiency and cost effectiveness. Efficiency is much higher at aslightly higher cost for SCR

EPA, TTN NAAQS OTAGTechnical Supporting Document Chapter 5 Appendix C Chartspage 4

same

SNCR for mediumindustrial externalcombustion natural gasfired boilers10200602

45 5315 Middle of the range for both

efficiency and cost effectiveness. Range is for 50 mmBTU/hrnatural gas fired boilers

EPA, TTN NAAQS OTAGTechnical Supporting Document Chapter 5 Appendix C Chartspage 2

same
Control Measures Efficiency Cost/ton Notes Reference (efficiency) 

I-7

SNCR for largeindustrial externalcombustion natural gasfired boilers10200601

45 4950 Middle of the range for both

efficiency and cost effectiveness. Range is for 150 mmBTU/hrnatural gas fired boilers

EPA, TTN NAAQS OTAGTechnical Supporting Document Chapter 5 Appendix C Chartspage 2

same

Low NOx burner forsmall industrial naturalgas fired boilers10200603

50 10,200 Middle of the range for cost

effectiveness. Range is for 10mmBTU/hr natural gas firedboilers

EPA, TTN NAAQS OTAGTechnical Supporting Document Chapter 5 Appendix C Chartspage 2

same

SCR for continuousgas-fired turbine2020020120200203

90 1530 costs are dependent on size (3

sizes listed, 5MW, 25MW, and100 MW) Average used.

EPA, TTN NAAQS OTAGTechnical Supporting Document Chapter 5 Appendix C Chartspage 12

same

NSCR for industrialreciprocating gas firedengine20200202

94 230 Assume spark ignition, gas rich

engine. Middle of the range forboth efficiency and costeffectiveness. 

EPA, TTN NAAQS OTAGTechnical Supporting Document Chapter 5 Appendix C Chartspage 9

same

ULNB & SNCR forPetroleum RefiningProcess Heaters3060010430600106

93 806 Assume medium size process

heater (75 MMBtu/hr) and verygood reduction

Petroleum Refinery Tier 2 BACTAnalysis Report. ERG. 3/2000

PetroleuAnalysis 

Area Sources

Combination ofmeasures to reducegasoline highwayvehicle emissions22010001***2201020***

3-5 Costs

applied

to VOC

An average cost effectiveness forregional ridesharing, vanpoolprograms, and employer tripreduction programs (did notinclude bike/pedestrianimprovements - too expensive)

Efficiency recommendation fromOTAQ - first email

NRC, 20Program
Control Measures Efficiency Cost/ton Notes Reference (efficiency) 

I-8

Work day restrictionsfor commercial lawnand garden2260004016; 021; 031;036; 071

11 16,600 Cost-effectiveness for lawn

service restrictions is calculatedand reported for NOx. Effectiveness based on 1 tonreduction from 9.6 tons of NOxin Houston.

TNRCC Ozone August 2000draft SIP in Clearing Houston'sAir,from Texas Public PolicyFoundation website:http://www.tppf.org

TNRCC draft SIP Air,from Foundathttp://ww

Diesel oxidationcatalyst are applied toHDDV & nonroadengines to control PMand NOx2230070***2270002***

40 1,000 the 40% reduction for diesel

oxidation catalyst was notreflected in the initialspreadsheets (Costs are in bothPM and NOX since report saysper ton of pollutant withoutspecifying pollutant)

Retrofitting Emission Controlson Diesel-Powered Vehicles,MECA 3/2002

Clean ADiesel EDOT/FHBig Dig 

SCR for diesellocomotives2285002000

72 1700 Controlling Locomotive

Emissions in California, Engine,Fuel, and EmissionsEngineering, Inc. 3/95

same

1160 The Car

Annual S3/2002

DOC for locomotives2285002000

1200 Clean A

Diesel EDOT/FHCarl Mo

Diesel boat retrofits,repowers, diesel tugretrofits2280002000

900,

1200,

1300

From the presentation for theConference on Marine Vesselsand Air Quality, San Francisco,CA, 2/2001

EconomVessels, 

3044 The Car

Annual S3/2002
Control Measures Efficiency Cost/ton Notes Reference (efficiency) 

I-9

Buy back program(residential lawn andgarden)

Need relative emissions from 2-stroke and 4-stroke (we have acost for marine buy backprogram)

Marine 2-stroke buyback program

Have a cost for VOC reduction -need a ratio of PM and NOxemissions to VOC emissions for2- vs. 4- stroke engines

Outboard Engine Buy-BackProgram, EPA Wisconsinconducted a survey but the costswere too high - not implemented

Ban Open Burning261001000026100200002610030000

100 0 SJVUAPD Draft Staff Report

Amendments to 4103 (OpenBurning) and New Rule 4106(Prescribed Burning and HazardReduction Burning) 11/00

SJVUAPAmendmBurning(PrescriReductio

The use of low NOx burners for the lime calcining, asphalt rotary dryers, and industrial natural gas boiler

seems reasonable. The costs are transferred from the cement document which is probably appropriate for the limrotary dryer but may be too low a cost for an industrial boiler (lower fuel consumption).

The use of SNCR technology on area sources of coal boilers and natural gas boilers and IC engines may n

(the boilers may be too small) but the point source efficiencies and costs are applied.Costs for the VOC local control measures
I-10

Control Measures Efficiency Cost/ton Notes 

Point

Solvent Substitution 2226 This is only based on solvent cleaning

operations.

Technical AssRegulation 8, Cleaning OpeAQMD 5/199

Area

Combination of measures toreduce gasoline highwayvehicle emissions

3-5 13,500

(average cost

for the 3

measures)

Costs were developed based on VOCreduction. Efficiency for LDGV &LDGT1 - 5% in Birmingham and 3% inChicago and Philadelphia. Assume noreduction in VMT for LDGT2 (commercialapplications).

NRC, 2002 (T

Work day restrictions(commercial lawn andgarden)

11 Cost-effectiveness for lawn service

restrictions is calculated and reported forNOx. Effectiveness based on 1 tonreduction from 9.6 tons of NOx inHouston.

TNRCC Ozonin Clearing HPublic Policy http://www.tpp

Buy back program(residential lawn and garden)

Need relative emissions from 2-stroke and4-stroke (we have a cost for marine buyback program)

Marine 2-stroke buy backprogram

4,000 - 10,000 Have a cost for VOC reduction (100 hp

engine vs. 10 hp engine) - need a ratio ofPM and NOx emissions to VOC emissionsfor 2- vs. 4- stroke engines 

Outboard EngEPA Wisconsbut the costs wimplemented

Ban Open Burning 100 0 SJVUAPD Dr

Amendments and New Rule Burning and HBurning) 11/0
Technical Support Document for the 

Interstate Air Quality Rule

Air Quality Modeling Analyses

Appendix J

290 Counties Included 

in the Local Control Study
J-1

STATE

FIPS

COUNTY

FIPS

STATE 

NAME

COUNTY 

NAME MSA/CMSA/NECMA NAME

17 031 Illinois Cook Chicago--Gary--Kenosha, IL--IN--WI CMSA

17 037 Illinois DeKalb Chicago--Gary--Kenosha, IL--IN--WI CMSA

17 043 Illinois DuPage Chicago--Gary--Kenosha, IL--IN--WI CMSA

17 063 Illinois Grundy Chicago--Gary--Kenosha, IL--IN--WI CMSA

17 089 Illinois Kane Chicago--Gary--Kenosha, IL--IN--WI CMSA

17 091 Illinois Kankakee Chicago--Gary--Kenosha, IL--IN--WI CMSA

17 093 Illinois Kendall Chicago--Gary--Kenosha, IL--IN--WI CMSA

17 097 Illinois Lake Chicago--Gary--Kenosha, IL--IN--WI CMSA

17 111 Illinois McHenry Chicago--Gary--Kenosha, IL--IN--WI CMSA

17 197 Illinois Will Chicago--Gary--Kenosha, IL--IN--WI CMSA

18 089 Indiana Lake Chicago--Gary--Kenosha, IL--IN--WI CMSA

18 127 Indiana Porter Chicago--Gary--Kenosha, IL--IN--WI CMSA

55 059 Wisconsin Kenosha Chicago--Gary--Kenosha, IL--IN--WI CMSA

18 029 Indiana Dearborn Cincinnati--Hamilton, OH--KY--IN CMSA

18 115 Indiana Ohio Cincinnati--Hamilton, OH--KY--IN CMSA

21 015 Kentucky Boone Cincinnati--Hamilton, OH--KY--IN CMSA

21 037 Kentucky Campbell Cincinnati--Hamilton, OH--KY--IN CMSA

21 077 Kentucky Gallatin Cincinnati--Hamilton, OH--KY--IN CMSA

21 081 Kentucky Grant Cincinnati--Hamilton, OH--KY--IN CMSA

21 117 Kentucky Kenton Cincinnati--Hamilton, OH--KY--IN CMSA

21 191 Kentucky Pendleton Cincinnati--Hamilton, OH--KY--IN CMSA

39 015 Ohio Brown Cincinnati--Hamilton, OH--KY--IN CMSA

39 017 Ohio Butler Cincinnati--Hamilton, OH--KY--IN CMSA

39 025 Ohio Clermont Cincinnati--Hamilton, OH--KY--IN CMSA

39 061 Ohio Hamilton Cincinnati--Hamilton, OH--KY--IN CMSA

39 165 Ohio Warren Cincinnati--Hamilton, OH--KY--IN CMSA

39 007 Ohio Ashtabula Cleveland--Akron, OH CMSA

39 035 Ohio Cuyahoga Cleveland--Akron, OH CMSA

39 055 Ohio Geauga Cleveland--Akron, OH CMSA

39 085 Ohio Lake Cleveland--Akron, OH CMSA

39 093 Ohio Lorain Cleveland--Akron, OH CMSA

39 103 Ohio Medina Cleveland--Akron, OH CMSA

39 133 Ohio Portage Cleveland--Akron, OH CMSA

39 153 Ohio Summit Cleveland--Akron, OH CMSA

26 049 Michigan Genesee Detroit--Ann Arbor--Flint, MI CMSA

26 087 Michigan Lapeer Detroit--Ann Arbor--Flint, MI CMSA

26 091 Michigan Lenawee Detroit--Ann Arbor--Flint, MI CMSA

26 093 Michigan Livingston Detroit--Ann Arbor--Flint, MI CMSA

26 099 Michigan Macomb Detroit--Ann Arbor--Flint, MI CMSA
J-2

26 115 Michigan Monroe Detroit--Ann Arbor--Flint, MI CMSA

26 125 Michigan Oakland Detroit--Ann Arbor--Flint, MI CMSA

26 147 Michigan St. Clair Detroit--Ann Arbor--Flint, MI CMSA

26 161 Michigan Washtenaw Detroit--Ann Arbor--Flint, MI CMSA

26 163 Michigan Wayne Detroit--Ann Arbor--Flint, MI CMSA

09 005 Connecticut Litchfield

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

09 007 Connecticut Middlesex

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

34 003 New Jersey Bergen

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

34 013 New Jersey Essex

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

34 017 New Jersey Hudson

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

34 019 New Jersey Hunterdon

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

34 021 New Jersey Mercer

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

34 023 New Jersey Middlesex

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

34 025 New Jersey Monmouth

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

34 027 New Jersey Morris

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

34 029 New Jersey Ocean

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

34 031 New Jersey Passaic

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

34 035 New Jersey Somerset

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

34 037 New Jersey Sussex

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

34 039 New Jersey Union

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

34 041 New Jersey Warren

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

36 005 New York Bronx

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

36 027 New York Dutchess

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

36 047 New York Kings

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

36 059 New York Nassau

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

36 061 New York New York

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA
J-3

36 071 New York Orange

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

36 079 New York Putnam

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

36 081 New York Queens

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

36 085 New York Richmond

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

36 087 New York Rockland

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

36 103 New York Suffolk

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

36 119 New York Westchester

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

42 103 Pennsylvania Pike

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA

09 001 Connecticut Fairfield

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA 

09 009 Connecticut New Haven

New York--Northern New Jersey--Long Island, NY--NJ--CT--PA

CMSA 

10 003 Delaware New Castle Philadelphia--Wilmington--Atlantic City, PA--NJ--DE--MD CMSA

24 015 Maryland Cecil Philadelphia--Wilmington--Atlantic City, PA--NJ--DE--MD CMSA

34 001 New Jersey Atlantic Philadelphia--Wilmington--Atlantic City, PA--NJ--DE--MD CMSA

34 005 New Jersey Burlington Philadelphia--Wilmington--Atlantic City, PA--NJ--DE--MD CMSA

34 007 New Jersey Camden Philadelphia--Wilmington--Atlantic City, PA--NJ--DE--MD CMSA

34 009 New Jersey Cape May Philadelphia--Wilmington--Atlantic City, PA--NJ--DE--MD CMSA

34 011 New Jersey Cumberland Philadelphia--Wilmington--Atlantic City, PA--NJ--DE--MD CMSA

34 015 New Jersey Gloucester Philadelphia--Wilmington--Atlantic City, PA--NJ--DE--MD CMSA

34 033 New Jersey Salem Philadelphia--Wilmington--Atlantic City, PA--NJ--DE--MD CMSA

42 017 Pennsylvania Bucks Philadelphia--Wilmington--Atlantic City, PA--NJ--DE--MD CMSA

42 029 Pennsylvania Chester Philadelphia--Wilmington--Atlantic City, PA--NJ--DE--MD CMSA

42 045 Pennsylvania Delaware Philadelphia--Wilmington--Atlantic City, PA--NJ--DE--MD CMSA

42 091 Pennsylvania Montgomery Philadelphia--Wilmington--Atlantic City, PA--NJ--DE--MD CMSA

42 101 Pennsylvania Philadelphia Philadelphia--Wilmington--Atlantic City, PA--NJ--DE--MD CMSA

11 001

District of

Columbia

District of

Columbia Washington--Baltimore, DC--MD--VA--WV CMSA

24 003 Maryland Anne Arundel Washington--Baltimore, DC--MD--VA--WV CMSA

24 005 Maryland Baltimore Washington--Baltimore, DC--MD--VA--WV CMSA

24 009 Maryland Calvert Washington--Baltimore, DC--MD--VA--WV CMSA

24 013 Maryland Carroll Washington--Baltimore, DC--MD--VA--WV CMSA

24 017 Maryland Charles Washington--Baltimore, DC--MD--VA--WV CMSA

24 021 Maryland Frederick Washington--Baltimore, DC--MD--VA--WV CMSA

24 025 Maryland Harford Washington--Baltimore, DC--MD--VA--WV CMSA

24 027 Maryland Howard Washington--Baltimore, DC--MD--VA--WV CMSA
J-4

24 031 Maryland Montgomery Washington--Baltimore, DC--MD--VA--WV CMSA

24 033 Maryland Prince George's Washington--Baltimore, DC--MD--VA--WV CMSA

24 035 Maryland Queen Anne's Washington--Baltimore, DC--MD--VA--WV CMSA

24 043 Maryland Washington Washington--Baltimore, DC--MD--VA--WV CMSA

24 510 Maryland Baltimore City Washington--Baltimore, DC--MD--VA--WV CMSA

51 013 Virginia Arlington Washington--Baltimore, DC--MD--VA--WV CMSA

51 043 Virginia Clarke Washington--Baltimore, DC--MD--VA--WV CMSA

51 047 Virginia Culpeper Washington--Baltimore, DC--MD--VA--WV CMSA

51 059 Virginia Fairfax Washington--Baltimore, DC--MD--VA--WV CMSA

51 061 Virginia Fauquier Washington--Baltimore, DC--MD--VA--WV CMSA

51 099 Virginia King George Washington--Baltimore, DC--MD--VA--WV CMSA

51 107 Virginia Loudoun Washington--Baltimore, DC--MD--VA--WV CMSA

51 153 Virginia Prince William Washington--Baltimore, DC--MD--VA--WV CMSA

51 177 Virginia Spotsylvania Washington--Baltimore, DC--MD--VA--WV CMSA

51 179 Virginia Stafford Washington--Baltimore, DC--MD--VA--WV CMSA

51 187 Virginia Warren Washington--Baltimore, DC--MD--VA--WV CMSA

51 510 Virginia Alexandria Washington--Baltimore, DC--MD--VA--WV CMSA

51 600 Virginia Fairfax City Washington--Baltimore, DC--MD--VA--WV CMSA

51 610 Virginia Falls Church Washington--Baltimore, DC--MD--VA--WV CMSA

51 630 Virginia Fredericksburg Washington--Baltimore, DC--MD--VA--WV CMSA

51 683 Virginia Manassas Washington--Baltimore, DC--MD--VA--WV CMSA

51 685 Virginia Manassas Park Washington--Baltimore, DC--MD--VA--WV CMSA

54 003

West

Virginia Berkeley Washington--Baltimore, DC--MD--VA--WV CMSA

54 037

West

Virginia Jefferson Washington--Baltimore, DC--MD--VA--WV CMSA

13 059 Georgia Clarke Athens, GA MSA

13 195 Georgia Madison Athens, GA MSA

13 219 Georgia Oconee Athens, GA MSA

13 013 Georgia Barrow Atlanta, GA MSA

13 015 Georgia Bartow Atlanta, GA MSA

13 045 Georgia Carroll Atlanta, GA MSA

13 057 Georgia Cherokee Atlanta, GA MSA

13 063 Georgia Clayton Atlanta, GA MSA

13 067 Georgia Cobb Atlanta, GA MSA

13 077 Georgia Coweta Atlanta, GA MSA

13 089 Georgia DeKalb Atlanta, GA MSA

13 097 Georgia Douglas Atlanta, GA MSA

13 113 Georgia Fayette Atlanta, GA MSA

13 117 Georgia Forsyth Atlanta, GA MSA

13 121 Georgia Fulton Atlanta, GA MSA

13 135 Georgia Gwinnett Atlanta, GA MSA

13 151 Georgia Henry Atlanta, GA MSA
J-5

13 217 Georgia Newton Atlanta, GA MSA

13 223 Georgia Paulding Atlanta, GA MSA

13 227 Georgia Pickens Atlanta, GA MSA

13 247 Georgia Rockdale Atlanta, GA MSA

13 255 Georgia Spalding Atlanta, GA MSA

13 297 Georgia Walton Atlanta, GA MSA

13 073 Georgia Columbia Augusta--Aiken, GA--SC MSA

13 189 Georgia McDuffie Augusta--Aiken, GA--SC MSA

13 245 Georgia Richmond Augusta--Aiken, GA--SC MSA

45 003

South

Carolina Aiken Augusta--Aiken, GA--SC MSA

45 037

South

Carolina Edgefield Augusta--Aiken, GA--SC MSA

01 009 Alabama Blount Birmingham, AL MSA

01 073 Alabama Jefferson Birmingham, AL MSA

01 115 Alabama St. Clair Birmingham, AL MSA

01 117 Alabama Shelby Birmingham, AL MSA

39 019 Ohio Carroll Canton--Massillon, OH MSA

39 151 Ohio Stark Canton--Massillon, OH MSA

54 039

West

Virginia Kanawha Charleston, WV MSA

54 079

West

Virginia Putnam Charleston, WV MSA

37 025

North

Carolina Cabarrus Charlotte--Gastonia--Rock Hill, NC--SC MSA

37 071

North

Carolina Gaston Charlotte--Gastonia--Rock Hill, NC--SC MSA

37 109

North

Carolina Lincoln Charlotte--Gastonia--Rock Hill, NC--SC MSA

37 119

North

Carolina Mecklenburg Charlotte--Gastonia--Rock Hill, NC--SC MSA

37 159

North

Carolina Rowan Charlotte--Gastonia--Rock Hill, NC--SC MSA

37 179

North

Carolina Union Charlotte--Gastonia--Rock Hill, NC--SC MSA

45 091

South

Carolina York Charlotte--Gastonia--Rock Hill, NC--SC MSA

13 047 Georgia Catoosa Chattanooga, TN--GA MSA

13 083 Georgia Dade Chattanooga, TN--GA MSA

13 295 Georgia Walker Chattanooga, TN--GA MSA

47 065 Tennessee Hamilton Chattanooga, TN--GA MSA
J-6

47 115 Tennessee Marion Chattanooga, TN--GA MSA

01 113 Alabama Russell Columbus, GA--AL MSA

13 053 Georgia Chattahoochee Columbus, GA--AL MSA

13 145 Georgia Harris Columbus, GA--AL MSA

13 215 Georgia Muscogee Columbus, GA--AL MSA

39 041 Ohio Delaware Columbus, OH MSA

39 045 Ohio Fairfield Columbus, OH MSA

39 049 Ohio Franklin Columbus, OH MSA

39 089 Ohio Licking Columbus, OH MSA

39 097 Ohio Madison Columbus, OH MSA

39 129 Ohio Pickaway Columbus, OH MSA

37 001

North

Carolina Alamance Greensboro--Winston-Salem--High Point, NC MSA

37 057

North

Carolina Davidson Greensboro--Winston-Salem--High Point, NC MSA

37 059

North

Carolina Davie Greensboro--Winston-Salem--High Point, NC MSA

37 067

North

Carolina Forsyth Greensboro--Winston-Salem--High Point, NC MSA

37 081

North

Carolina Guilford Greensboro--Winston-Salem--High Point, NC MSA

37 151

North

Carolina Randolph Greensboro--Winston-Salem--High Point, NC MSA

37 169

North

Carolina Stokes Greensboro--Winston-Salem--High Point, NC MSA

37 197

North

Carolina Yadkin Greensboro--Winston-Salem--High Point, NC MSA

45 007

South

Carolina Anderson Greenville--Spartanburg--Anderson, SC MSA

45 021

South

Carolina Cherokee Greenville--Spartanburg--Anderson, SC MSA

45 045

South

Carolina Greenville Greenville--Spartanburg--Anderson, SC MSA

45 077

South

Carolina Pickens Greenville--Spartanburg--Anderson, SC MSA

45 083

South

Carolina Spartanburg Greenville--Spartanburg--Anderson, SC MSA

37 003

North

Carolina Alexander Hickory--Morganton--Lenoir, NC MSA

37 023

North

Carolina Burke Hickory--Morganton--Lenoir, NC MSA
J-7

37 027

North

Carolina Caldwell Hickory--Morganton--Lenoir, NC MSA

37 035

North

Carolina Catawba Hickory--Morganton--Lenoir, NC MSA

21 019 Kentucky Boyd Huntington--Ashland, WV--KY--OH MSA

21 043 Kentucky Carter Huntington--Ashland, WV--KY--OH MSA

21 089 Kentucky Greenup Huntington--Ashland, WV--KY--OH MSA

39 087 Ohio Lawrence Huntington--Ashland, WV--KY--OH MSA

54 011

West

Virginia Cabell Huntington--Ashland, WV--KY--OH MSA

54 099

West

Virginia Wayne Huntington--Ashland, WV--KY--OH MSA

18 011 Indiana Boone Indianapolis, IN MSA

18 057 Indiana Hamilton Indianapolis, IN MSA

18 059 Indiana Hancock Indianapolis, IN MSA

18 063 Indiana Hendricks Indianapolis, IN MSA

18 081 Indiana Johnson Indianapolis, IN MSA

18 095 Indiana Madison Indianapolis, IN MSA

18 097 Indiana Marion Indianapolis, IN MSA

18 109 Indiana Morgan Indianapolis, IN MSA

18 145 Indiana Shelby Indianapolis, IN MSA

47 019 Tennessee Carter Johnson City--Kingsport--Bristol, TN--VA MSA

47 073 Tennessee Hawkins Johnson City--Kingsport--Bristol, TN--VA MSA

47 163 Tennessee Sullivan Johnson City--Kingsport--Bristol, TN--VA MSA

47 171 Tennessee Unicoi Johnson City--Kingsport--Bristol, TN--VA MSA

47 179 Tennessee Washington Johnson City--Kingsport--Bristol, TN--VA MSA

51 169 Virginia Scott Johnson City--Kingsport--Bristol, TN--VA MSA

51 191 Virginia Washington Johnson City--Kingsport--Bristol, TN--VA MSA

51 520 Virginia Bristol Johnson City--Kingsport--Bristol, TN--VA MSA

47 001 Tennessee Anderson Knoxville, TN MSA

47 009 Tennessee Blount Knoxville, TN MSA

47 093 Tennessee Knox Knoxville, TN MSA

47 105 Tennessee Loudon Knoxville, TN MSA

47 155 Tennessee Sevier Knoxville, TN MSA

47 173 Tennessee Union Knoxville, TN MSA

42 071 Pennsylvania Lancaster Lancaster, PA MSA

21 017 Kentucky Bourbon Lexington, KY MSA

21 049 Kentucky Clark Lexington, KY MSA
J-8

21 067 Kentucky Fayette Lexington, KY MSA

21 113 Kentucky Jessamine Lexington, KY MSA

21 151 Kentucky Madison Lexington, KY MSA

21 209 Kentucky Scott Lexington, KY MSA

21 239 Kentucky Woodford Lexington, KY MSA

18 019 Indiana Clark Louisville, KY--IN MSA

18 043 Indiana Floyd Louisville, KY--IN MSA

18 061 Indiana Harrison Louisville, KY--IN MSA

18 143 Indiana Scott Louisville, KY--IN MSA

21 029 Kentucky Bullitt Louisville, KY--IN MSA

21 111 Kentucky Jefferson Louisville, KY--IN MSA

21 185 Kentucky Oldham Louisville, KY--IN MSA

01 001 Alabama Autauga Montgomery, AL MSA

01 051 Alabama Elmore Montgomery, AL MSA

01 101 Alabama Montgomery Montgomery, AL MSA

47 021 Tennessee Cheatham Nashville, TN MSA

47 037 Tennessee Davidson Nashville, TN MSA

47 043 Tennessee Dickson Nashville, TN MSA

47 147 Tennessee Robertson Nashville, TN MSA

47 149 Tennessee Rutherford Nashville, TN MSA

47 165 Tennessee Sumner Nashville, TN MSA

47 187 Tennessee Williamson Nashville, TN MSA

47 189 Tennessee Wilson Nashville, TN MSA

39 167 Ohio Washington Parkersburg--Marietta, WV--OH MSA

54 107

West

Virginia Wood Parkersburg--Marietta, WV--OH MSA

42 003 Pennsylvania Allegheny Pittsburgh, PA MSA

42 007 Pennsylvania Beaver Pittsburgh, PA MSA

42 019 Pennsylvania Butler Pittsburgh, PA MSA

42 051 Pennsylvania Fayette Pittsburgh, PA MSA

42 125 Pennsylvania Washington Pittsburgh, PA MSA

42 129 Pennsylvania Westmoreland Pittsburgh, PA MSA

42 011 Pennsylvania Berks Reading, PA MSA

17 027 Illinois Clinton St. Louis, MO--IL MSA

17 083 Illinois Jersey St. Louis, MO--IL MSA

17 119 Illinois Madison St. Louis, MO--IL MSA

17 133 Illinois Monroe St. Louis, MO--IL MSA
J-9

17 163 Illinois St. Clair St. Louis, MO--IL MSA

29 071 Missouri Franklin St. Louis, MO--IL MSA

29 099 Missouri Jefferson St. Louis, MO--IL MSA

29 113 Missouri Lincoln St. Louis, MO--IL MSA

29 183 Missouri St. Charles St. Louis, MO--IL MSA

29 189 Missouri St. Louis St. Louis, MO--IL MSA

29 219 Missouri Warren St. Louis, MO--IL MSA

29 510 Missouri St. Louis St. Louis, MO--IL MSA

39 081 Ohio Jefferson Steubenville--Weirton, OH--WV MSA

54 009

West

Virginia Brooke Steubenville--Weirton, OH--WV MSA

54 029

West

Virginia Hancock Steubenville--Weirton, OH--WV MSA

39 013 Ohio Belmont Wheeling, WV--OH MSA

54 051

West

Virginia Marshall Wheeling, WV--OH MSA

54 069

West

Virginia Ohio Wheeling, WV--OH MSA

42 133 Pennsylvania York York, PA MSA

39 029 Ohio Columbiana Youngstown--Warren, OH MSA

39 099 Ohio Mahoning Youngstown--Warren, OH MSA

39 155 Ohio Trumbull Youngstown--Warren, OH MSA

01 49 Alabama DeKalb County Rural County

01 121 Alabama

Talladega

County Rural County

13 115 Georgia Floyd County Rural County

13 139 Georgia Hall County Rural County

13 319 Georgia

Wilkinson

County Rural County

39 145 Ohio Scioto County Rural County

47 145 Tennessee Roane County Rural County
Technical Support Document for the 

Interstate Air Quality Rule

Air Quality Modeling Analyses

Appendix K

Summary Emission Reductions from Local Control

Measures for the 290 County Study
K-1

VOC Summary

VOC Emissions Scenerio Totals Difference Percent Difference

CMSA/MSA/FIP

VOC 

 2010 Base

VOC 

 2010 Control

VOC

 2015 Base

VOC

 2015 Control

VOC 

 (2010C -

2010B)

VOC 

 (2015B -

2010B)

VOC 

(2015C

-2015B)

VOC

 ( 2010C -

2010B)

 / 2010B

 VOC 

 (2015B -

2010B)

 / 2010B

VOC

 ( 2015C -

2015B) 

 / 2015B

Hall County, Georgia 2583.5 1844.0 2473.7 1699.0 -739.5 -109.8 -774.7 -28.6 -4.3 -31.3

Floyd County, Georgia 3237.2 2589.1 3129.7 2453.6 -648.1 -107.5 -676.1 -20.0 -3.3 -21.6

Atlanta, GA 52209.2 44246.3 47814.6 39546.1 -7962.9 -4394.6 -8268.4 -15.3 -8.4 -17.3

Nashville, TN 25406.9 21670.3 23027.8 19196.3 -3736.6 -2379.1 -3831.5 -14.7 -9.4 -16.6

Wilkinson County, Georgia 262.5 224.6 242.2 203.0 -37.9 -20.4 -39.2 -14.5 -7.8 -16.2

Washington-Baltimore,

DC-MD-VA-WV 47772.6 41324.4 42926.7 36160.2 -6448.2 -4845.9 -6766.5 -13.5 -10.1 -15.8

Roane County, Tennessee 1041.7 905.2 955.8 814.7 -136.5 -85.9 -141.1 -13.1 -8.2 -14.8

DeKalb County, Alabama 1165.0 1028.3 1064.7 925.7 -136.7 -100.3 -139.0 -11.7 -8.6 -13.1

Charlotte-Gastonia-Rock Hill,

NC-SC 25769.2 23151.3 23763.8 21088.5 -2617.9 -2005.4 -2675.3 -10.2 -7.8 -11.3

Cincinnati-Hamilton, OH-KY-IN 23192.1 20837.6 20480.8 18139.6 -2354.5 -2711.3 -2341.3 -10.2 -11.7 -11.4

Greensboro--Winston-Salem--Hig

h Point, NC 32699.6 29469.2 31302.3 27965.2 -3230.4 -1397.3 -3337.1 -9.9 -4.3 -10.7

Athens, GA 2998.0 2710.7 2727.6 2436.2 -287.3 -270.4 -291.4 -9.6 -9.0 -10.7

Scioto County, Ohio 1022.7 928.2 892.1 797.5 -94.5 -130.6 -94.5 -9.2 -12.8 -10.6

Louisville, KY-IN 25825.8 23728.0 23988.9 21894.5 -2097.8 -1836.9 -2094.4 -8.1 -7.1 -8.7

Indianapolis, IN 24328.0 22387.2 21835.4 19913.3 -1940.9 -2492.6 -1922.1 -8.0 -10.2 -8.8

Chattanooga, TN-GA 11589.0 10686.4 10793.8 9883.7 -902.7 -795.3 -910.1 -7.8 -6.9 -8.4

Augusta-Aiken, GA-SC 9279.7 8567.8 8658.5 7936.4 -711.8 -621.2 -722.0 -7.7 -6.7 -8.3

Huntington-Ashland, WV-KY-OH 6888.3 6385.0 6324.8 5821.3 -503.3 -563.5 -503.5 -7.3 -8.2 -8.0

Talladega County, Alabama 2583.9 2396.5 2594.9 2401.7 -187.4 11.0 -193.2 -7.3 0.4 -7.4

Greenville-Spartanburg-Anderson,

SC 17159.3 15927.3 15757.4 14514.7 -1232.0 -1402.0 -1242.7 -7.2 -8.2 -7.9

Philadelphia-Wilmington-Atlantic

City, PA-NJ-DE-MD 68335.2 63475.5 63376.0 58449.7 -4859.7 -4959.2 -4926.4 -7.1 -7.3 -7.8

Hickory-Morganton-Lenoir, NC 17559.9 16331.5 17684.9 16406.1 -1228.3 125.0 -1278.7 -7.0 0.7 -7.2

Knoxville, TN 13673.0 12738.0 12517.4 11577.1 -934.9 -1155.6 -940.3 -6.8 -8.5 -7.5

Birmingham, AL 22982.4 21522.9 21650.3 20187.6 -1459.5 -1332.1 -1462.7 -6.4 -5.8 -6.8

Columbus, GA-AL 7276.4 6841.1 7008.9 6570.4 -435.3 -267.5 -438.5 -6.0 -3.7 -6.3

Wheeling, WV-OH 4332.8 4085.8 4097.5 3854.1 -247.0 -235.3 -243.4 -5.7 -5.4 -5.9

Steubenville-Weirton, OH-WV 4264.7 4028.7 4081.9 3845.6 -236.0 -182.8 -236.3 -5.5 -4.3 -5.8

Montgomery, AL 8435.4 7988.8 8175.7 7732.7 -446.5 -259.6 -443.0 -5.3 -3.1 -5.4

Lexington, KY 12386.6 11821.9 12336.6 11774.5 -564.6 -50.0 -562.1 -4.6 -0.4 -4.6

Youngstown-Warren, OH 6379.2 6102.7 5472.6 5215.7 -276.4 -906.5 -256.9 -4.3 -14.2 -4.7

Johnson City-Kingsport-Bristol,

TN-VA 38385.1 37090.2 38951.9 37574.1 -1295.0 566.8 -1377.8 -3.4 1.5 -3.5

Cleveland-Akron, OH 21191.4 20608.0 17589.1 17100.0 -583.4 -3602.3 -489.0 -2.8 -17.0 -2.8

Columbus, OH 24089.8 23508.4 21836.3 21303.2 -581.4 -2253.5 -533.1 -2.4 -9.4 -2.4

St. Louis, MO-IL 38584.5 37726.4 35300.4 34510.9 -858.2 -3284.1 -789.5 -2.2 -8.5 -2.2

York, PA 3773.5 3694.3 3131.7 3068.3 -79.2 -641.8 -63.4 -2.1 -17.0 -2.0

Pittsburgh, PA 21398.8 20954.1 18329.9 17965.6 -444.7 -3068.9 -364.3 -2.1 -14.3 -2.0

Canton-Massillon, OH 6662.6 6526.5 6242.4 6117.8 -136.2 -420.3 -124.6 -2.0 -6.3 -2.0

Reading, PA 3771.3 3696.6 3244.3 3184.8 -74.6 -526.9 -59.5 -2.0 -14.0 -1.8

New York-Northern New

Jersey-Long Island, NY-NJ-CT-PA 78789.2 77290.4 70804.3 69529.7 -1498.8 -7984.8 -1274.6 -1.9 -10.1 -1.8

Detroit-Ann Arbor-Flint, MI 71046.8 69888.2 66511.9 65516.8 -1158.7 -4534.9 -995.1 -1.6 -6.4 -1.5

Chicago-Gary-Kenosha, IL-IN-WI 107228.9 105530.1 101143.4 99659.2 -1698.8 -6085.5 -1484.2 -1.6 -5.7 -1.5

Parkersburg-Marietta, WV-OH 6604.6 6501.0 6584.0 6485.6 -103.6 -20.6 -98.4 -1.6 -0.3 -1.5

New

Haven-Bridgeport-Stamford-Water

bury-Danbury, CT 12783.4 12587.7 10753.5 10590.7 -195.7 -2029.8 -162.8 -1.5 -15.9 -1.5

Lancaster, PA 6153.8 6064.1 5655.5 5582.1 -89.7 -498.3 -73.4 -1.5 -8.1 -1.3

Hartford, CT 1292.4 1275.1 1078.3 1063.9 -17.4 -214.1 -14.4 -1.3 -16.6 -1.3

Charleston, WV 8071.8 7972.9 7718.5 7633.3 -98.9 -353.2 -85.2 -1.2 -4.4 -1.1

Litchfield County, Connecticut 1407.9 1392.7 1154.7 1142.2 -15.1 -253.2 -12.5 -1.1 -18.0 -1.1
K-2

SO2 Summary

SO2 Emissions Scenerio Totals Difference Percent Difference

CMSA/MSA/FIP

SO2

2010 Base

SO2 

2010 Control 

SO2

2015 Base

SO2

2015 Control

SO2

 (2010C -

2010B)

SO2 

 (2015B -

2010B)

SO2 

( 2015C -

2015B)

SO2 

 ( 2010C -

2010B) 

/ 2010B

SO2

( 2015B - 

2010B) 

/ 2010B

SO2 

( 2015C - 

2015B)

 / 2015B

Roane County, Tennessee 78936.7 39671.4 78943.9 39651.4 -39265.2 7.2 -39292.5 -49.7 0.0 -49.8

Wheeling, WV-OH 221842.5 112435.7 166254.4 84615.6 -109406.8 -55588.0 -81638.8 -49.3 -25.1 -49.1

Charleston, WV 128870.4 65594.6 128780.0 65559.5 -63275.8 -90.4 -63220.5 -49.1 -0.1 -49.1

Atlanta, GA 251829.0 130483.4 251269.0 130326.0 -121345.6 -560.0 -120943.0 -48.2 -0.2 -48.1

Birmingham, AL 206784.4 110920.5 191125.5 103141.5 -95863.9 -15659.0 -87984.0 -46.4 -7.6 -46.0

Indianapolis, IN 70720.1 38822.2 42807.8 24937.6 -31897.9 -27912.3 -17870.2 -45.1 -39.5 -41.7

Charlotte-Gastonia-Rock Hill, NC-SC 60938.8 33942.5 62617.2 35577.2 -26996.3 1678.5 -27040.1 -44.3 2.8 -43.2

Talladega County, Alabama 12651.8 7099.3 13019.0 7339.2 -5552.5 367.1 -5679.8 -43.9 2.9 -43.6

Washington-Baltimore, DC-MD-VA-WV 282046.5 159048.2 279273.8 161885.7 -122998.3 -2772.7 -117388.0 -43.6 -1.0 -42.0

Floyd County, Georgia 52769.7 29824.6 45337.4 26325.4 -22945.1 -7432.3 -19012.0 -43.5 -14.1 -41.9

Nashville, TN 60041.6 34045.4 60082.9 34072.0 -25996.2 41.3 -26010.9 -43.3 0.1 -43.3

Steubenville-Weirton, OH-WV 252834.7 145786.1 242428.0 139581.1 -107048.6 -10406.6 -102846.9 -42.3 -4.1 -42.4

Detroit-Ann Arbor-Flint, MI 308294.8 185486.5 316431.8 189984.2 -122808.3 8137.0 -126447.6 -39.8 2.6 -40.0

Cincinnati-Hamilton, OH-KY-IN 280390.3 177154.5 247621.8 159967.1 -103235.9 -32768.5 -87654.8 -36.8 -11.7 -35.4

Greenville-Spartanburg-Anderson, SC 21137.5 13444.4 21749.4 13803.8 -7693.1 611.9 -7945.6 -36.4 2.9 -36.5

Cleveland-Akron, OH 338088.2 215882.8 283841.6 187605.2 -122205.4 -54246.6 -96236.4 -36.1 -16.0 -33.9

St. Louis, MO-IL 316680.5 207279.8 349077.2 226184.8 -109400.7 32396.7 -122892.4 -34.5 10.2 -35.2

Chicago-Gary-Kenosha, IL-IN-WI 300742.0 197154.8 299980.5 197992.4 -103587.2 -761.5 -101988.1 -34.4 -0.3 -34.0

Youngstown-Warren, OH 8722.2 5863.7 7969.5 7398.4 -2858.5 -752.7 -571.1 -32.8 -8.6 -7.2

Knoxville, TN 69578.6 46957.8 69984.4 47343.1 -22620.8 405.8 -22641.4 -32.5 0.6 -32.4

Johnson City-Kingsport-Bristol, TN-VA 93930.3 66225.3 94221.3 66470.3 -27705.0 291.0 -27751.0 -29.5 0.3 -29.5

Augusta-Aiken, GA-SC 28253.8 20300.6 27040.6 19880.6 -7953.2 -1213.2 -7160.0 -28.1 -4.3 -26.5

Parkersburg-Marietta, WV-OH 25891.6 19036.9 23397.7 13882.5 -6854.7 -2493.9 -9515.2 -26.5 -9.6 -40.7

Lancaster, PA 1062.6 787.0 1073.6 790.1 -275.6 11.0 -283.4 -25.9 1.0 -26.4

Philadelphia-Wilmington-Atlantic City,

PA-NJ-DE-MD 107863.1 81521.6 109500.6 82394.9 -26341.6 1637.5 -27105.7 -24.4 1.5 -24.8

Lexington, KY 11317.1 8555.9 13819.7 10380.7 -2761.2 2502.6 -3439.0 -24.4 22.1 -24.9

Huntington-Ashland, WV-KY-OH 20915.7 16085.1 21575.7 16449.9 -4830.7 660.0 -5125.8 -23.1 3.2 -23.8

Pittsburgh, PA 108480.2 86765.8 87787.6 67779.8 -21714.4 -20692.5 -20007.9 -20.0 -19.1 -22.8

Columbus, OH 18569.7 15254.9 18706.0 18705.8 -3314.8 136.3 -0.2 -17.9 0.7 -0.0

Louisville, KY-IN 71330.7 59393.6 71567.0 59650.8 -11937.1 236.3 -11916.3 -16.7 0.3 -16.7

Canton-Massillon, OH 2739.2 2374.0 2817.0 2429.3 -365.2 77.7 -387.7 -13.3 2.8 -13.8

Greensboro--Winston-Salem--High

Point, NC 19820.7 17511.6 19581.6 17427.0 -2309.2 -239.1 -2154.6 -11.7 -1.2 -11.0

New York-Northern New Jersey-Long

Island, NY-NJ-CT-PA 166583.9 150517.6 163870.5 147794.1 -16066.3 -2713.4 -16076.4 -9.6 -1.6 -9.8

 Chattanooga, TN-GA 10115.5 9810.2 10346.3 10047.5 -305.3 230.8 -298.7 -3.0 2.3 -2.9

York, PA 107820.5 105123.8 111486.0 108786.5 -2696.7 3665.5 -2699.5 -2.5 3.4 -2.4

Athens, GA 144.3 144.1 142.2 142.2 -0.2 -2.1 -0.0 -0.1 -1.4 -0.0

Columbus, GA-AL 2216.1 2213.7 2301.5 2301.5 -2.3 85.5 -0.0 -0.1 3.9 -0.0

Hall County, Georgia 71.6 71.6 70.9 70.9 -0.0 -0.7 -0.0 -0.0 -1.0 -0.1

Reading, PA 17790.0 17782.9 19587.9 19587.8 -7.1 1797.9 -0.1 -0.0 10.1 -0.0

Hickory-Morganton-Lenoir, NC 1249.2 1248.7 1299.9 1299.8 -0.5 50.7 -0.1 -0.0 4.1 -0.0

DeKalb County, Alabama 143.7 143.7 145.7 145.7 -0.0 2.0 -0.0 -0.0 1.4 -0.0

Hartford, CT 773.2 773.1 790.0 789.9 -0.1 16.8 -0.0 -0.0 2.2 -0.0

Scioto County, Ohio 3040.9 3040.7 3069.0 3069.0 -0.2 28.1 -0.0 -0.0 0.9 -0.0

Litchfield County, Connecticut 146.5 146.5 146.8 146.8 0.0 0.3 -0.0 0.0 0.2 -0.0

Wilkinson County, Georgia 107.2 107.3 109.5 109.4 0.0 2.2 -0.0 0.0 2.1 -0.0

New

Haven-Bridgeport-Stamford-WaterburyDanbury, CT 7279.7 7282.1 7326.1 7325.9 2.4 46.5 -0.2 0.0 0.6 -0.0

Montgomery, AL 5156.0 5161.8 5404.7 5379.3 5.8 248.7 -25.4 0.1 4.8 -0.5

4156713.5 2654278.4 3975780.7 2566529.3
K-3

NOX Summary

NOX Emissions Scenerio Totals Difference Percent Difference

CMSA/MSA/FIP

NOX

2010 Base

NOX 

2010 Control

NOX 

2015 Base

NOX 

2015 Control

NOX

 (2010C -

2010B)

NOX

(2015B -

2010B)

NOX

(2015C -

2015B)

NOX

 ( 2010C -

2010B)

 / 2010B

NOX

(2015B -

2010B)

 / 2010B

NOX

 (2015C -

2015B)

 / 2015B

Philadelphia-Wilmington-Atlantic City,

PA-NJ-DE-MD 174144.8 157996.8 148766.9 134273.5 -16147.9 -25377.9 -14493.4 -9.3 -14.6 -9.7

Hall County, Georgia 3667.8 3332.7 2725.9 2437.2 -335.1 -941.9 -288.7 -9.1 -25.7 -10.6

Hickory-Morganton-Lenoir, NC 7926.2 7277.2 6069.7 5528.9 -648.9 -1856.5 -540.8 -8.2 -23.4 -8.9

Huntington-Ashland, WV-KY-OH 35264.7 32597.1 32726.1 30100.9 -2667.6 -2538.6 -2625.1 -7.6 -7.2 -8.0

Scioto County, Ohio 3069.0 2844.1 2589.5 2391.8 -225.0 -479.5 -197.8 -7.3 -15.6 -7.6

Athens, GA 5048.3 4692.9 3881.2 3595.6 -355.4 -1167.2 -285.6 -7.0 -23.1 -7.4

DeKalb County, Alabama 2280.9 2124.7 1821.8 1687.6 -156.2 -459.1 -134.2 -6.8 -20.1 -7.4

Greensboro--Winston-Salem--High

Point, NC 39157.0 36616.5 31440.0 29389.6 -2540.6 -7717.0 -2050.4 -6.5 -19.7 -6.5

St. Louis, MO-IL 150203.4 140689.1 143718.4 134668.5 -9514.2 -6485.0 -9049.8 -6.3 -4.3 -6.3

New

Haven-Bridgeport-Stamford-WaterburyDanbury, CT 30932.6 28997.7 23620.2 22301.3 -1934.9 -7312.4 -1319.0 -6.3 -23.6 -5.6

Columbus, OH 37995.8 35630.3 29646.1 27918.2 -2365.5 -8349.7 -1727.9 -6.2 -22.0 -5.8

Nashville, TN 53314.0 50040.3 44263.8 41567.2 -3273.8 -9050.2 -2696.7 -6.1 -17.0 -6.1

New York-Northern New Jersey-Long

Island, NY-NJ-CT-PA 301357.1 283489.7 243392.9 230519.1 -17867.4 -57964.2 -12873.8 -5.9 -19.2 -5.3

Greenville-Spartanburg-Anderson, SC 35709.1 33610.0 29907.5 28154.0 -2099.1 -5801.6 -1753.6 -5.9 -16.2 -5.9

Lancaster, PA 10405.4 9795.2 7722.7 7254.9 -610.2 -2682.7 -467.9 -5.9 -25.8 -6.1

Canton-Massillon, OH 10945.3 10307.4 8769.8 8268.0 -637.9 -2175.5 -501.8 -5.8 -19.9 -5.7

Columbus, GA-AL 12120.5 11434.5 10716.3 10150.9 -685.9 -1404.2 -565.5 -5.7 -11.6 -5.3

Youngstown-Warren, OH 16580.5 15646.7 13258.3 12567.6 -933.8 -3322.2 -690.7 -5.6 -20.0 -5.2

Charlotte-Gastonia-Rock Hill, NC-SC 54441.8 51392.6 47425.6 45003.0 -3049.2 -7016.2 -2422.6 -5.6 -12.9 -5.1

Reading, PA 11778.8 11119.8 10045.5 9479.1 -659.0 -1733.3 -566.4 -5.6 -14.7 -5.6

Knoxville, TN 34947.3 33010.3 30078.2 28412.6 -1937.0 -4869.1 -1665.6 -5.5 -13.9 -5.5

Indianapolis, IN 62322.1 58934.8 52765.1 50129.6 -3387.4 -9557.1 -2635.5 -5.4 -15.3 -5.0

Litchfield County, Connecticut 2482.1 2347.7 1729.1 1637.8 -134.4 -752.9 -91.4 -5.4 -30.3 -5.3

Chicago-Gary-Kenosha, IL-IN-WI 285964.2 270636.6 245603.1 232857.8 -15327.6 -40361.1 -12745.3 -5.4 -14.1 -5.2

Louisville, KY-IN 91194.1 86377.1 83293.7 78925.8 -4816.9 -7900.4 -4367.9 -5.3 -8.7 -5.2

Lexington, KY 23219.4 22010.0 21871.4 20817.7 -1209.4 -1348.0 -1053.7 -5.2 -5.8 -4.8

Chattanooga, TN-GA 17205.2 16336.5 13772.8 13082.1 -868.7 -3432.4 -690.7 -5.0 -19.9 -5.0

Atlanta, GA 171312.1 162850.1 143810.9 136976.6 -8462.0 -27501.2 -6834.3 -4.9 -16.1 -4.8

Montgomery, AL 15877.5 15101.6 14264.6 13511.0 -775.9 -1612.8 -753.6 -4.9 -10.2 -5.3

Cleveland-Akron, OH 86488.2 82304.4 72649.0 69573.3 -4183.8 -13839.2 -3075.7 -4.8 -16.0 -4.2

Washington-Baltimore, DC-MD-VA-WV 213187.6 202890.3 180789.9 172799.8 -10297.3 -32397.7 -7990.1 -4.8 -15.2 -4.4

Johnson City-Kingsport-Bristol, TN-VA 42335.2 40316.5 38769.2 36933.7 -2018.7 -3566.0 -1835.5 -4.8 -8.4 -4.7

Hartford, CT 3609.5 3440.4 2848.4 2724.3 -169.1 -761.1 -124.1 -4.7 -21.1 -4.4

Augusta-Aiken, GA-SC 25435.1 24339.5 22922.6 22008.4 -1095.6 -2512.4 -914.2 -4.3 -9.9 -4.0

York, PA 35769.1 34258.0 34226.1 32859.9 -1511.0 -1543.0 -1366.1 -4.2 -4.3 -4.0

Wilkinson County, Georgia 926.7 888.8 809.9 780.0 -37.9 -116.9 -29.9 -4.1 -12.6 -3.7

Parkersburg-Marietta, WV-OH 10897.6 10466.2 9754.2 9373.6 -431.4 -1143.4 -380.6 -4.0 -10.5 -3.9

Birmingham, AL 84872.2 81680.3 75596.9 72699.1 -3191.9 -9275.3 -2897.9 -3.8 -10.9 -3.8

Pittsburgh, PA 122868.2 118493.1 108262.2 104788.7 -4375.1 -14606.0 -3473.5 -3.6 -11.9 -3.2

Detroit-Ann Arbor-Flint, MI 211671.1 204318.2 191429.0 185626.6 -7352.9 -20242.2 -5802.4 -3.5 -9.6 -3.0

Cincinnati-Hamilton, OH-KY-IN 123066.7 118924.7 112209.5 108768.1 -4142.0 -10857.2 -3441.4 -3.4 -8.8 -3.1

Talladega County, Alabama 5992.1 5795.0 5589.8 5409.6 -197.2 -402.4 -180.1 -3.3 -6.7 -3.2

Wheeling, WV-OH 43899.2 42885.8 42724.3 41808.6 -1013.4 -1174.9 -915.7 -2.3 -2.7 -2.1

Charleston, WV 59044.9 57686.0 56644.7 55438.2 -1358.8 -2400.1 -1206.6 -2.3 -4.1 -2.1

Floyd County, Georgia 17372.4 17049.2 17040.7 16752.5 -323.2 -331.6 -288.3 -1.9 -1.9 -1.7

Roane County, Tennessee 12775.6 12654.8 12444.4 12363.6 -120.7 -331.1 -80.8 -0.9 -2.6 -0.6

Steubenville-Weirton, OH-WV 64511.5 63994.9 62928.7 62440.3 -516.6 -1582.8 -488.4 -0.8 -2.5 -0.8
K-4

 Total Direct PM2_5 Summary

PM2.5 Emissions Scenerio Totals Difference Percent Difference

CMSA/MSA/FIP

PM2_5

2010 Base

PM2_5

2010 Control

PM2_5 

2015 Base

PM2_5 

2015 Control

PM2_5

 (2010C -

2010B)

PM2_5

 ( 2015B -

2010B)

PM2_5 

(2015C -

2015B)

PM2_5

 ( 2010C - 

2010B) 

/ 2010B

PM2_5

 ( 2015B -

2010B) 

/ 2010B

PM2_5

(2015C -

2015B) 

/ 2015B

Hall County, Georgia 1485.4 841.8 1456.6 803.6 -643.7 -28.8 -653.0 -43.3 -1.9 -44.8

Nashville, TN 8202.4 5019.1 8076.9 4868.7 -3183.3 -125.5 -3208.2 -38.8 -1.5 -39.7

Hickory-Morganton-Lenoir, NC 3720.3 2337.7 3674.0 2275.3 -1382.6 -46.3 -1398.7 -37.2 -1.2 -38.1

Floyd County, Georgia 1896.4 1250.1 1948.0 1277.3 -646.3 51.6 -670.7 -34.1 2.7 -34.4

Greensboro--Winston-Salem--High

Point, NC 9839.1 6502.2 9718.2 6349.2 -3336.9 -120.9 -3369.0 -33.9 -1.2 -34.7

Johnson City-Kingsport-Bristol, TN-VA 4706.0 3123.5 4682.6 3094.6 -1582.5 -23.4 -1588.0 -33.6 -0.5 -33.9

Atlanta, GA 22425.9 14900.1 22219.4 14589.0 -7525.9 -206.5 -7630.4 -33.6 -0.9 -34.3

Youngstown-Warren, OH 2678.5 1814.5 2598.0 1747.3 -864.1 -80.5 -850.7 -32.3 -3.0 -32.7

Detroit-Ann Arbor-Flint, MI 20544.8 13987.6 20266.4 13744.5 -6557.2 -278.4 -6521.9 -31.9 -1.4 -32.2

Charlotte-Gastonia-Rock Hill, NC-SC 9222.8 6288.4 9235.2 6278.4 -2934.4 12.5 -2956.8 -31.8 0.1 -32.0

Athens, GA 912.9 626.8 868.0 588.2 -286.1 -44.8 -279.9 -31.3 -4.9 -32.2

Canton-Massillon, OH 1814.6 1254.2 1777.9 1224.0 -560.4 -36.7 -553.9 -30.9 -2.0 -31.2

Cleveland-Akron, OH 14901.8 10391.7 14803.9 10312.0 -4510.1 -97.8 -4492.0 -30.3 -0.7 -30.3

Lancaster, PA 2299.5 1603.9 2248.0 1562.4 -695.7 -51.5 -685.5 -30.3 -2.2 -30.5

Huntington-Ashland, WV-KY-OH 4628.0 3253.4 4588.2 3217.7 -1374.6 -39.8 -1370.6 -29.7 -0.9 -29.9

DeKalb County, Alabama 588.3 414.7 553.5 387.5 -173.7 -34.9 -166.0 -29.5 -5.9 -30.0

Chattanooga, TN-GA 3970.1 2799.5 3917.8 2753.4 -1170.5 -52.3 -1164.4 -29.5 -1.3 -29.7

Parkersburg-Marietta, WV-OH 2064.4 1457.9 2029.0 1429.3 -606.5 -35.5 -599.6 -29.4 -1.7 -29.6

Philadelphia-Wilmington-Atlantic City,

PA-NJ-DE-MD 29574.9 20889.8 29661.1 20919.9 -8685.1 86.2 -8741.2 -29.4 0.3 -29.5

Louisville, KY-IN 6785.7 4794.8 6828.8 4821.8 -1990.9 43.1 -2007.0 -29.3 0.6 -29.4

Columbus, GA-AL 2051.3 1453.5 2083.2 1473.0 -597.8 31.9 -610.2 -29.1 1.6 -29.3

Reading, PA 2605.9 1852.2 2610.6 1856.8 -753.7 4.6 -753.8 -28.9 0.2 -28.9

New York-Northern New Jersey-Long

Island, NY-NJ-CT-PA 45165.2 32213.1 44214.3 31460.3 -12952.1 -951.0 -12754.0 -28.7 -2.1 -28.8

Talladega County, Alabama 1876.2 1352.6 1931.5 1393.2 -523.6 55.3 -538.2 -27.9 2.9 -27.9

Roane County, Tennessee 831.1 601.6 838.1 604.6 -229.6 7.0 -233.5 -27.6 0.8 -27.9

Scioto County, Ohio 889.7 645.2 863.6 625.6 -244.5 -26.0 -238.1 -27.5 -2.9 -27.6

Lexington, KY 2261.0 1642.4 2265.9 1655.1 -618.6 4.9 -610.8 -27.4 0.2 -27.0

Columbus, OH 6031.0 4401.8 6134.9 4536.2 -1629.2 103.9 -1598.6 -27.0 1.7 -26.1

Montgomery, AL 2697.9 1970.1 2736.4 1999.9 -727.8 38.5 -736.5 -27.0 1.4 -26.9

Greenville-Spartanburg-Anderson, SC 5913.3 4347.1 5657.7 4144.6 -1566.2 -255.6 -1513.1 -26.5 -4.3 -26.7

Knoxville, TN 4867.9 3580.4 4764.1 3500.2 -1287.4 -103.8 -1263.9 -26.4 -2.1 -26.5

Pittsburgh, PA 13052.8 9645.1 12906.2 9546.4 -3407.7 -146.7 -3359.8 -26.1 -1.1 -26.0

York, PA 2428.1 1797.7 2378.1 1757.6 -630.4 -50.1 -620.5 -26.0 -2.1 -26.1

Indianapolis, IN 6813.4 5061.9 6870.1 5134.1 -1751.5 56.7 -1735.9 -25.7 0.8 -25.3

Cincinnati-Hamilton, OH-KY-IN 12200.1 9066.9 12148.0 9030.8 -3133.3 -52.1 -3117.2 -25.7 -0.4 -25.7

Wilkinson County, Georgia 2109.9 1574.7 2177.6 1624.6 -535.2 67.7 -553.0 -25.4 3.2 -25.4

Augusta-Aiken, GA-SC 5143.4 3867.1 5197.1 3898.6 -1276.2 53.7 -1298.5 -24.8 1.0 -25.0

Steubenville-Weirton, OH-WV 9136.4 7098.9 9248.2 7193.7 -2037.5 111.8 -2054.5 -22.3 1.2 -22.2

Chicago-Gary-Kenosha, IL-IN-WI 41544.2 32697.0 42001.8 33095.6 -8847.2 457.6 -8906.2 -21.3 1.1 -21.2

Birmingham, AL 10290.4 8124.1 10374.0 8197.9 -2166.2 83.7 -2176.2 -21.1 0.8 -21.0

Hartford, CT 404.0 320.6 379.6 302.6 -83.4 -24.4 -77.0 -20.7 -6.0 -20.3

St. Louis, MO-IL 24942.5 19989.0 25303.6 20336.9 -4953.4 361.2 -4966.7 -19.9 1.4 -19.6

Litchfield County, Connecticut 526.0 422.4 494.9 399.6 -103.6 -31.1 -95.3 -19.7 -5.9 -19.3

Charleston, WV 3116.0 2508.0 3038.6 2450.7 -607.9 -77.3 -587.9 -19.5 -2.5 -19.3

Washington-Baltimore, DC-MD-VA-WV 18514.4 15044.5 17917.6 14646.7 -3469.9 -596.8 -3270.9 -18.7 -3.2 -18.3

Wheeling, WV-OH 1661.3 1350.9 1625.6 1325.0 -310.3 -35.7 -300.5 -18.7 -2.1 -18.5

New

Haven-Bridgeport-Stamford-WaterburyDanbury, CT 2630.1 2169.7 2438.4 2029.6 -460.4 -191.7 -408.7 -17.5 -7.3 -16.8
K-5

Primary Organic Aerosol (POA) 

Summary

Primary Organic Aerosol Emissions Scenerio Totals Difference Percent Difference

CMSA/MSA/FIP

POA

2010 Base

POA

2010 Control

POA

2015 Base

POA 

2015 Control

POA

 (2010C -

2010B)

POA

 ( 2015B -

2010B)

POA

(2015C -

2015B)

POA

 ( 2010C - 

2010B) 

/ 2010B

POA

 ( 2015B -

2010B) 

/ 2010B

POA

(2015C -

2015B) 

/ 2015B

Wilkinson County, Georgia 499.8 375.3 507.4 381.4 -124.4 7.7 -126.0 -24.9 1.5 -24.8

Steubenville-Weirton, OH-WV 1764.7 1329.7 1771.1 1335.6 -435.0 6.3 -435.5 -24.6 0.4 -24.6

Roane County, Tennessee 284.5 215.7 283.9 216.0 -68.8 -0.6 -67.9 -24.2 -0.2 -23.9

Parkersburg-Marietta, WV-OH 376.4 285.8 359.0 272.9 -90.6 -17.4 -86.1 -24.1 -4.6 -24.0

Huntington-Ashland, WV-KY-OH 959.7 729.8 911.4 693.7 -229.9 -48.3 -217.7 -24.0 -5.0 -23.9

Talladega County, Alabama 253.4 193.4 250.5 191.6 -60.0 -2.9 -58.9 -23.7 -1.1 -23.5

Charleston, WV 476.4 365.4 447.4 343.9 -111.0 -29.0 -103.5 -23.3 -6.1 -23.1

Augusta-Aiken, GA-SC 1020.3 783.6 1010.8 777.2 -236.6 -9.4 -233.6 -23.2 -0.9 -23.1

Hickory-Morganton-Lenoir, NC 1018.7 783.7 952.2 732.0 -235.0 -66.5 -220.2 -23.1 -6.5 -23.1

Scioto County, Ohio 174.9 134.9 158.5 122.5 -40.0 -16.5 -36.0 -22.9 -9.4 -22.7

Johnson City-Kingsport-Bristol, TN-VA 963.4 744.1 901.3 698.7 -219.2 -62.1 -202.5 -22.8 -6.4 -22.5

Greensboro--Winston-Salem--High

Point, NC 2156.0 1672.4 2007.6 1561.8 -483.6 -148.4 -445.8 -22.4 -6.9 -22.2

Chattanooga, TN-GA 778.1 605.3 726.2 568.6 -172.7 -51.9 -157.5 -22.2 -6.7 -21.7

Hall County, Georgia 170.3 132.5 154.4 120.2 -37.8 -15.9 -34.2 -22.2 -9.3 -22.1

Knoxville, TN 1060.9 826.0 976.1 765.9 -234.9 -84.8 -210.2 -22.1 -8.0 -21.5

Columbus, GA-AL 305.8 238.1 298.6 233.5 -67.7 -7.2 -65.1 -22.1 -2.4 -21.8

Floyd County, Georgia 162.7 126.8 158.8 124.8 -35.9 -3.9 -33.9 -22.1 -2.4 -21.4

DeKalb County, Alabama 134.4 104.8 120.8 94.8 -29.6 -13.6 -26.0 -22.0 -10.1 -21.6

Chicago-Gary-Kenosha, IL-IN-WI 7107.1 5544.5 7152.1 5598.2 -1562.6 45.0 -1553.9 -22.0 0.6 -21.7

Greenville-Spartanburg-Anderson, SC 1446.1 1130.1 1336.0 1048.8 -316.1 -110.1 -287.2 -21.9 -7.6 -21.5

Reading, PA 273.0 213.4 259.1 204.4 -59.6 -13.9 -54.7 -21.8 -5.1 -21.1

Cleveland-Akron, OH 1949.6 1524.7 1848.0 1453.2 -424.9 -101.6 -394.8 -21.8 -5.2 -21.4

Canton-Massillon, OH 308.9 241.6 288.4 226.8 -67.3 -20.6 -61.6 -21.8 -6.7 -21.4

Wheeling, WV-OH 233.5 182.7 214.5 169.2 -50.9 -19.1 -45.3 -21.8 -8.2 -21.1

Youngstown-Warren, OH 427.2 334.6 391.6 308.1 -92.6 -35.6 -83.5 -21.7 -8.3 -21.3

York, PA 373.4 292.7 339.9 268.4 -80.8 -33.6 -71.5 -21.6 -9.0 -21.0

Nashville, TN 1312.8 1029.0 1187.4 937.0 -283.8 -125.4 -250.4 -21.6 -9.6 -21.1

Philadelphia-Wilmington-Atlantic City,

PA-NJ-DE-MD 3744.4 2936.3 3580.4 2825.8 -808.1 -164.0 -754.6 -21.6 -4.4 -21.1

Litchfield County, Connecticut 152.5 119.6 139.1 109.6 -32.9 -13.5 -29.5 -21.6 -8.8 -21.2

Lancaster, PA 443.5 347.9 417.9 329.5 -95.6 -25.6 -88.5 -21.6 -5.8 -21.2

Charlotte-Gastonia-Rock Hill, NC-SC 1641.4 1288.0 1525.0 1202.2 -353.4 -116.4 -322.8 -21.5 -7.1 -21.2

Hartford, CT 118.4 93.1 109.1 86.2 -25.3 -9.3 -22.8 -21.4 -7.9 -20.9

Montgomery, AL 359.0 282.8 354.2 281.5 -76.2 -4.7 -72.8 -21.2 -1.3 -20.5

Athens, GA 170.4 134.3 155.2 123.1 -36.1 -15.2 -32.1 -21.2 -8.9 -20.7

Cincinnati-Hamilton, OH-KY-IN 1495.7 1182.8 1421.6 1132.7 -312.9 -74.2 -288.9 -20.9 -5.0 -20.3

Pittsburgh, PA 1281.9 1017.0 1197.9 962.3 -264.9 -84.0 -235.6 -20.7 -6.6 -19.7

St. Louis, MO-IL 2542.5 2019.3 2458.6 1963.3 -523.2 -84.0 -495.2 -20.6 -3.3 -20.1

Lexington, KY 415.3 329.9 387.9 311.5 -85.4 -27.3 -76.4 -20.6 -6.6 -19.7

Birmingham, AL 968.5 769.5 951.9 762.4 -199.1 -16.6 -189.5 -20.6 -1.7 -19.9

Detroit-Ann Arbor-Flint, MI 2558.7 2034.3 2400.7 1922.8 -524.4 -158.0 -478.0 -20.5 -6.2 -19.9

Atlanta, GA 2498.2 2009.9 2324.1 1891.8 -488.3 -174.2 -432.3 -19.5 -7.0 -18.6

Columbus, OH 900.2 724.5 891.6 734.1 -175.7 -8.6 -157.4 -19.5 -1.0 -17.7

New York-Northern New Jersey-Long

Island, NY-NJ-CT-PA 5965.2 4802.6 5637.5 4569.7 -1162.6 -327.6 -1067.9 -19.5 -5.5 -18.9

Louisville, KY-IN 704.1 570.1 680.5 557.6 -134.0 -23.6 -122.9 -19.0 -3.3 -18.1

Washington-Baltimore, DC-MD-VA-WV 3278.1 2672.9 3058.8 2525.6 -605.3 -219.3 -533.2 -18.5 -6.7 -17.4

New

Haven-Bridgeport-Stamford-WaterburyDanbury, CT 575.2 475.6 519.5 435.6 -99.6 -55.7 -83.9 -17.3 -9.7 -16.1

Indianapolis, IN 631.9 527.4 625.3 534.4 -104.5 -6.6 -90.9 -16.5 -1.1 -14.5
K-6

PEC (primary elemental carbon) Summary

Primary Elemental Carbon Emissions Scenerio Totals Difference Percent Difference

CMSA/MSA/FIP

PEC

2010 Base

PEC

2010 Control

PEC 

2015 Base

PEC

2015 Control

PEC

 (2010C -

2010B)

PEC

 ( 2015B -

2010B)

PEC

(2015C -

2015B)

PEC

 ( 2010C - 

2010B) 

/ 2010B

PEC

 ( 2015B -

2010B) 

/ 2010B

PEC

(2015C -

2015B) 

/ 2015B

Hall County, Georgia 103.0 66.9 85.2 53.8 -36.1 -17.7 -31.4 -35.1 -17.2 -36.9

Floyd County, Georgia 90.9 60.6 82.2 54.4 -30.4 -8.8 -27.8 -33.4 -9.7 -33.8

Hickory-Morganton-Lenoir, NC 333.7 236.9 292.4 205.1 -96.9 -41.4 -87.2 -29.0 -12.4 -29.8

Nashville, TN 831.9 590.7 685.5 485.4 -241.1 -146.4 -200.0 -29.0 -17.6 -29.2

Johnson City-Kingsport-Bristol, TN-VA 390.1 279.6 345.3 252.3 -110.5 -44.7 -93.0 -28.3 -11.5 -26.9

Atlanta, GA 2108.5 1514.0 1669.3 1200.2 -594.5 -439.2 -469.1 -28.2 -20.8 -28.1

Greensboro--Winston-Salem--High

Point, NC 886.3 637.5 751.3 535.5 -248.8 -135.0 -215.8 -28.1 -15.2 -28.7

Lancaster, PA 201.9 146.1 173.9 128.9 -55.8 -28.0 -45.0 -27.6 -13.9 -25.9

Detroit-Ann Arbor-Flint, MI 1839.9 1332.9 1441.1 1044.4 -506.9 -398.7 -396.7 -27.6 -21.7 -27.5

Reading, PA 154.6 112.1 140.7 105.3 -42.5 -14.0 -35.4 -27.5 -9.0 -25.1

DeKalb County, Alabama 53.4 38.8 45.7 34.0 -14.6 -7.7 -11.7 -27.3 -14.4 -25.7

Parkersburg-Marietta, WV-OH 417.4 303.6 397.3 289.8 -113.8 -20.1 -107.5 -27.3 -4.8 -27.1

Canton-Massillon, OH 159.4 116.1 122.6 89.8 -43.3 -36.8 -32.7 -27.2 -23.1 -26.7

Huntington-Ashland, WV-KY-OH 1080.3 787.3 1034.8 756.7 -293.0 -45.5 -278.1 -27.1 -4.2 -26.9

Athens, GA 98.5 72.2 78.1 57.8 -26.4 -20.4 -20.3 -26.8 -20.7 -26.0

Youngstown-Warren, OH 267.7 196.3 199.4 145.6 -71.4 -68.4 -53.7 -26.7 -25.5 -26.9

Chattanooga, TN-GA 328.1 240.7 283.3 212.2 -87.5 -44.8 -71.1 -26.7 -13.6 -25.1

Louisville, KY-IN 604.1 443.4 527.7 398.3 -160.6 -76.4 -129.5 -26.6 -12.6 -24.5

Cleveland-Akron, OH 2038.1 1496.2 1774.4 1307.8 -541.9 -263.7 -466.6 -26.6 -12.9 -26.3

Charlotte-Gastonia-Rock Hill, NC-SC 854.5 627.4 699.9 512.1 -227.1 -154.5 -187.9 -26.6 -18.1 -26.8

Philadelphia-Wilmington-Atlantic City,

PA-NJ-DE-MD 2986.9 2193.2 2621.3 1933.9 -793.7 -365.6 -687.4 -26.6 -12.2 -26.2

Talladega County, Alabama 151.8 111.9 151.2 112.5 -39.9 -0.6 -38.7 -26.3 -0.4 -25.6

Charleston, WV 852.7 629.2 811.9 600.4 -223.4 -40.8 -211.5 -26.2 -4.8 -26.0

York, PA 190.8 140.9 157.6 117.6 -49.9 -33.2 -40.1 -26.2 -17.4 -25.4

Roane County, Tennessee 80.3 59.4 77.4 57.8 -20.9 -2.9 -19.6 -26.1 -3.6 -25.3

Scioto County, Ohio 64.6 47.9 53.7 40.2 -16.7 -10.9 -13.5 -25.8 -16.9 -25.1

Greenville-Spartanburg-Anderson, SC 596.4 443.3 478.8 360.3 -153.1 -117.6 -118.5 -25.7 -19.7 -24.7

Montgomery, AL 276.4 205.6 261.4 198.4 -70.9 -15.0 -63.0 -25.6 -5.4 -24.1

Columbus, GA-AL 161.6 120.2 138.2 104.9 -41.3 -23.3 -33.4 -25.6 -14.4 -24.1

Wilkinson County, Georgia 61.6 45.8 61.0 45.5 -15.7 -0.6 -15.5 -25.5 -0.9 -25.4

Lexington, KY 218.2 163.1 189.8 147.7 -55.1 -28.4 -42.0 -25.2 -13.0 -22.1

Cincinnati-Hamilton, OH-KY-IN 987.3 738.3 826.4 631.7 -249.0 -160.8 -194.7 -25.2 -16.3 -23.6

New York-Northern New Jersey-Long

Island, NY-NJ-CT-PA 7287.2 5449.8 6205.8 4640.0 -1837.3 -1081.4 -1565.8 -25.2 -14.8 -25.2

Augusta-Aiken, GA-SC 338.1 252.9 292.7 222.0 -85.2 -45.3 -70.7 -25.2 -13.4 -24.2

Knoxville, TN 470.4 352.2 399.0 306.3 -118.1 -71.4 -92.7 -25.1 -15.2 -23.2

Pittsburgh, PA 984.4 738.7 858.9 666.9 -245.7 -125.5 -192.0 -25.0 -12.8 -22.4

Wheeling, WV-OH 213.4 160.3 192.8 145.9 -53.2 -20.6 -46.9 -24.9 -9.7 -24.3

Hartford, CT 101.5 76.6 87.5 66.4 -24.9 -14.1 -21.0 -24.5 -13.8 -24.1

Indianapolis, IN 735.9 556.9 662.3 532.5 -179.0 -73.6 -129.8 -24.3 -10.0 -19.6

Steubenville-Weirton, OH-WV 206.6 156.5 193.3 148.0 -50.1 -13.3 -45.3 -24.3 -6.5 -23.4

Litchfield County, Connecticut 67.6 51.4 50.7 39.0 -16.2 -16.9 -11.7 -24.0 -24.9 -23.0

Birmingham, AL 502.7 382.6 445.0 353.0 -120.1 -57.7 -92.0 -23.9 -11.5 -20.7

Columbus, OH 703.6 541.2 737.6 617.2 -162.4 34.0 -120.4 -23.1 4.8 -16.3

St. Louis, MO-IL 1248.6 964.4 1104.5 874.9 -284.2 -144.1 -229.5 -22.8 -11.5 -20.8

Washington-Baltimore, DC-MD-VA-WV 2708.2 2096.7 2201.3 1747.0 -611.5 -506.9 -454.3 -22.6 -18.7 -20.6

New

Haven-Bridgeport-Stamford-WaterburyDanbury, CT 568.4 444.1 402.4 318.6 -124.3 -166.0 -83.8 -21.9 -29.2 -20.8

Chicago-Gary-Kenosha, IL-IN-WI 2632.2 2061.6 2185.5 1759.2 -570.6 -446.7 -426.3 -21.7 -17.0 -19.5
K-7

Primary nitrate (PNO3) Summary

Primary nitrate Emissions Scenerio Totals Difference Percent Difference

CMSA/MSA/FIP

PNO3

2010 Base

PNO3

2010 Control

PNO3 

2015 Base

PNO3

2015 Control

PNO3

 (2010C -

2010B)

PNO3

 ( 2015B -

2010B)

PNO3

(2015C -

2015B)

PNO3

 ( 2010C - 

2010B) 

/ 2010B

PNO3

 ( 2015B -

2010B) 

/ 2010B

PNO3

(2015C -

2015B) 

/ 2015B

Athens, GA 1.2 0.9 1.1 0.9 -0.2 -0.1 -0.2 -20.7 -7.6 -20.3

Atlanta, GA 20.8 16.8 19.8 16.1 -4.0 -1.0 -3.7 -19.4 -4.9 -18.7

Augusta-Aiken, GA-SC 210.6 158.1 225.4 169.2 -52.5 14.8 -56.2 -24.9 7.0 -24.9

Birmingham, AL 19.0 14.6 19.2 14.8 -4.3 0.2 -4.4 -22.9 1.3 -22.8

Canton-Massillon, OH 3.1 2.4 3.0 2.3 -0.7 -0.1 -0.7 -22.2 -3.1 -22.0

Charleston, WV 3.0 2.3 2.8 2.2 -0.7 -0.2 -0.7 -23.9 -5.2 -23.8

Charlotte-Gastonia-Rock Hill,

NC-SC 11.6 9.1 11.3 8.9 -2.5 -0.3 -2.4 -21.2 -2.7 -21.0

Chattanooga, TN-GA 7.2 5.6 7.1 5.5 -1.6 -0.1 -1.6 -22.8 -1.4 -22.6

Chicago-Gary-Kenosha, IL-IN-WI 181.0 137.4 186.9 141.9 -43.6 5.9 -44.9 -24.1 3.2 -24.0

Cincinnati-Hamilton, OH-KY-IN 19.0 14.7 19.1 14.8 -4.3 0.1 -4.3 -22.6 0.5 -22.4

Cleveland-Akron, OH 28.4 21.8 28.0 21.5 -6.6 -0.4 -6.5 -23.2 -1.3 -23.1

Columbus, GA-AL 4.4 3.4 4.6 3.5 -1.0 0.1 -1.1 -23.4 2.9 -23.3

Columbus, OH 8.3 6.7 8.3 6.8 -1.6 0.1 -1.5 -19.5 0.9 -18.1

DeKalb County, Alabama 0.8 0.6 0.7 0.6 -0.2 -0.1 -0.1 -20.4 -8.9 -19.9

Detroit-Ann Arbor-Flint, MI 25.7 20.2 24.8 19.6 -5.5 -0.9 -5.2 -21.4 -3.4 -21.1

Floyd County, Georgia 2.6 2.0 2.7 2.1 -0.6 0.1 -0.6 -23.4 5.4 -23.1

Greensboro--Winston-Salem--High

Point, NC 10.7 8.4 10.1 7.9 -2.3 -0.6 -2.1 -21.5 -5.9 -21.3

Greenville-Spartanburg-Anderson,

SC 8.9 7.0 8.4 6.6 -1.9 -0.5 -1.7 -21.2 -5.8 -20.8

Hall County, Georgia 1.2 1.0 1.1 0.9 -0.2 -0.1 -0.2 -20.0 -8.6 -19.5

Hartford, CT 0.7 0.6 0.7 0.5 -0.2 -0.0 -0.1 -22.0 -6.0 -21.6

Hickory-Morganton-Lenoir, NC 6.2 4.8 6.1 4.7 -1.4 -0.1 -1.4 -23.2 -1.9 -23.3

Huntington-Ashland, WV-KY-OH 8.4 6.3 8.3 6.3 -2.0 -0.1 -2.0 -24.3 -1.2 -24.2

Indianapolis, IN 8.8 7.1 8.8 7.2 -1.6 0.0 -1.6 -18.6 0.1 -17.8

Johnson City-Kingsport-Bristol,

TN-VA 7.2 5.5 7.1 5.5 -1.7 -0.1 -1.6 -23.3 -1.3 -23.1

Knoxville, TN 7.8 6.1 7.6 5.9 -1.8 -0.3 -1.7 -22.5 -3.5 -22.3

Lancaster, PA 2.4 2.0 2.2 1.8 -0.4 -0.2 -0.4 -18.6 -6.3 -17.8

Lexington, KY 2.8 2.2 2.7 2.2 -0.6 -0.1 -0.5 -20.0 -3.3 -19.2

Litchfield County, Connecticut 0.9 0.7 0.8 0.7 -0.2 -0.1 -0.2 -21.3 -6.8 -20.8

Louisville, KY-IN 6.2 5.0 6.2 5.0 -1.2 -0.1 -1.2 -19.9 -0.9 -19.3

Montgomery, AL 17.3 13.1 17.8 13.4 -4.2 0.5 -4.3 -24.3 2.7 -24.2

Nashville, TN 7.2 5.8 6.6 5.3 -1.4 -0.6 -1.3 -19.8 -8.2 -19.1

New

Haven-Bridgeport-Stamford-Water

bury-Danbury, CT 3.9 3.2 3.6 3.0 -0.7 -0.4 -0.6 -18.1 -9.1 -17.2

New York-Northern New

Jersey-Long Island, NY-NJ-CT-PA 47.6 38.4 45.4 36.8 -9.2 -2.2 -8.6 -19.4 -4.6 -18.9

Parkersburg-Marietta, WV-OH 22.8 17.1 23.1 17.4 -5.7 0.3 -5.8 -24.9 1.5 -24.9

Philadelphia-Wilmington-Atlantic

City, PA-NJ-DE-MD 41.9 32.5 41.1 31.9 -9.5 -0.8 -9.2 -22.6 -1.9 -22.4

Pittsburgh, PA 97.2 73.3 102.0 76.9 -23.9 4.8 -25.1 -24.6 4.9 -24.6

Reading, PA 4.9 3.7 4.9 3.7 -1.1 -0.0 -1.1 -23.3 -0.3 -23.2

Roane County, Tennessee 0.8 0.6 0.8 0.6 -0.2 -0.0 -0.2 -23.4 -5.4 -23.2

Scioto County, Ohio 1.2 0.9 1.1 0.9 -0.3 -0.1 -0.3 -23.3 -5.3 -23.2

St. Louis, MO-IL 188.0 142.1 198.1 149.7 -45.9 10.1 -48.4 -24.4 5.4 -24.4

Steubenville-Weirton, OH-WV 32.2 24.2 32.5 24.4 -8.0 0.3 -8.1 -25.0 1.1 -24.9

Talladega County, Alabama 3.8 2.9 4.0 3.0 -0.9 0.2 -1.0 -24.2 4.0 -24.2

Washington-Baltimore,

DC-MD-VA-WV 26.6 21.3 25.2 20.4 -5.3 -1.3 -4.8 -19.8 -5.0 -19.1

Wheeling, WV-OH 1.6 1.2 1.5 1.2 -0.4 -0.1 -0.3 -22.8 -4.7 -22.3

Wilkinson County, Georgia 9.3 7.0 9.7 7.2 -2.3 0.3 -2.4 -24.9 3.5 -24.9

York, PA 3.0 2.4 2.9 2.3 -0.7 -0.1 -0.6 -21.7 -4.2 -21.4

Youngstown-Warren, OH 5.2 4.0 5.1 3.9 -1.2 -0.1 -1.2 -22.9 -2.8 -22.7
K-8

GSO4 Summary

GSO4 Emissions Scenerio Totals Difference Percent Difference

CMSA/MSA/FIP

GSO4 

2010 Base

GSO4 

2010 Control

GSO4 

2015 Base

GSO4 

2015 Control

GSO4 

 (2010C -

2010B)

GSO4 

 ( 2015B

- 2010B)

GSO4 

(2015C -

2015B)

GSO4 

 ( 2010C - 

2010B) 

/ 2010B

GSO4 

 ( 2015B -

2010B) 

/ 2010B

GSO4 

(2015C -

2015B) 

/ 2015B

Athens, GA 18.5 7.6 19.1 7.6 -11.0 0.5 -11.4 -59.2 2.8 -59.9

Atlanta, GA 972.6 563.7 1008.9 578.6 -408.9 36.3 -430.3 -42.0 3.7 -42.7

Augusta-Aiken, GA-SC 446.0 336.7 467.1 352.0 -109.3 21.1 -115.1 -24.5 4.7 -24.6

Birmingham, AL 999.8 779.7 1024.6 797.8 -220.1 24.8 -226.8 -22.0 2.5 -22.1

Canton-Massillon, OH 112.6 72.9 116.1 75.1 -39.7 3.4 -40.9 -35.3 3.1 -35.3

Charleston, WV 189.0 165.6 188.7 164.8 -23.4 -0.3 -23.9 -12.4 -0.2 -12.7

Charlotte-Gastonia-Rock Hill, NC-SC 545.7 349.9 584.3 377.5 -195.8 38.6 -206.8 -35.9 7.1 -35.4

Chattanooga, TN-GA 339.8 235.2 351.9 243.5 -104.6 12.1 -108.4 -30.8 3.5 -30.8

Chicago-Gary-Kenosha, IL-IN-WI 3745.9 2847.5 3831.9 2912.2 -898.4 86.0 -919.7 -24.0 2.3 -24.0

Cincinnati-Hamilton, OH-KY-IN 1056.0 786.9 1077.0 801.7 -269.1 21.0 -275.3 -25.5 2.0 -25.6

Cleveland-Akron, OH 1230.4 857.7 1251.9 873.2 -372.7 21.6 -378.7 -30.3 1.8 -30.3

Columbus, GA-AL 393.4 286.2 422.5 307.5 -107.3 29.1 -115.0 -27.3 7.4 -27.2

Columbus, OH 221.3 134.7 234.4 145.7 -86.6 13.2 -88.8 -39.1 6.0 -37.9

DeKalb County, Alabama 9.7 3.9 10.0 4.0 -5.8 0.3 -6.0 -60.0 2.9 -60.0

Detroit-Ann Arbor-Flint, MI 1729.8 1183.5 1761.1 1203.4 -546.3 31.2 -557.6 -31.6 1.8 -31.7

Floyd County, Georgia 374.1 271.4 403.5 292.9 -102.6 29.5 -110.6 -27.4 7.9 -27.4

Greensboro--Winston-Salem--High

Point, NC 456.1 276.1 470.7 282.4 -179.9 14.7 -188.4 -39.4 3.2 -40.0

Greenville-Spartanburg-Anderson, SC 135.5 82.9 138.8 84.7 -52.5 3.4 -54.1 -38.8 2.5 -39.0

Hall County, Georgia 46.1 13.0 48.2 13.4 -33.2 2.1 -34.8 -71.9 4.5 -72.2

Hartford, CT 21.7 16.4 22.0 16.7 -5.2 0.3 -5.3 -24.2 1.6 -24.1

Hickory-Morganton-Lenoir, NC 214.9 126.4 228.7 134.9 -88.5 13.9 -93.8 -41.2 6.4 -41.0

Huntington-Ashland, WV-KY-OH 421.2 298.5 436.5 309.7 -122.8 15.3 -126.9 -29.1 3.6 -29.1

Indianapolis, IN 300.5 196.5 316.0 209.1 -104.0 15.5 -106.9 -34.6 5.2 -33.8

Johnson City-Kingsport-Bristol, TN-VA 391.6 262.1 412.8 276.6 -129.5 21.2 -136.1 -33.1 5.4 -33.0

Knoxville, TN 162.8 112.4 167.1 115.0 -50.3 4.3 -52.1 -30.9 2.6 -31.2

Lancaster, PA 54.6 24.5 55.6 24.9 -30.1 1.0 -30.7 -55.1 1.9 -55.2

Lexington, KY 120.6 82.3 130.1 90.0 -38.3 9.6 -40.1 -31.7 7.9 -30.8

Litchfield County, Connecticut 11.9 9.1 11.9 9.1 -2.8 0.0 -2.8 -23.4 0.4 -23.4

Louisville, KY-IN 390.3 274.8 402.2 282.4 -115.4 11.9 -119.8 -29.6 3.0 -29.8

Montgomery, AL 300.4 217.7 323.5 235.0 -82.7 23.0 -88.5 -27.5 7.7 -27.3

Nashville, TN 293.6 131.0 301.3 132.4 -162.5 7.7 -168.9 -55.4 2.6 -56.1

New

Haven-Bridgeport-Stamford-Waterbury

-Danbury, CT 68.3 55.9 65.8 53.9 -12.5 -2.6 -11.9 -18.2 -3.7 -18.1

New York-Northern New Jersey-Long

Island, NY-NJ-CT-PA 4341.2 3024.9 4379.2 3050.5 -1316.3 38.0 -1328.6 -30.3 0.9 -30.3

Parkersburg-Marietta, WV-OH 107.2 73.4 107.8 73.7 -33.8 0.5 -34.1 -31.5 0.5 -31.6

Philadelphia-Wilmington-Atlantic City,

PA-NJ-DE-MD 2668.5 1873.3 2709.5 1899.8 -795.1 41.1 -809.7 -29.8 1.5 -29.9

Pittsburgh, PA 1383.0 1037.3 1387.5 1041.4 -345.7 4.5 -346.1 -25.0 0.3 -24.9

Reading, PA 318.1 228.4 325.0 233.6 -89.7 6.8 -91.4 -28.2 2.2 -28.1

Roane County, Tennessee 33.5 24.4 34.5 25.1 -9.1 1.0 -9.5 -27.1 3.0 -27.4

Scioto County, Ohio 67.7 48.5 68.9 49.4 -19.2 1.2 -19.6 -28.3 1.8 -28.4

St. Louis, MO-IL 1961.3 1527.5 2058.1 1607.0 -433.8 96.8 -451.1 -22.1 4.9 -21.9

Steubenville-Weirton, OH-WV 2741.1 2088.6 2780.4 2119.1 -652.5 39.3 -661.3 -23.8 1.4 -23.8

Talladega County, Alabama 340.8 250.6 366.4 269.7 -90.2 25.6 -96.7 -26.5 7.5 -26.4

Washington-Baltimore, DC-MD-VA-WV 760.6 606.9 760.3 607.3 -153.6 -0.3 -153.0 -20.2 -0.0 -20.1

Wheeling, WV-OH 125.3 108.8 127.3 110.5 -16.5 1.9 -16.8 -13.2 1.5 -13.2

Wilkinson County, Georgia 339.3 253.6 350.5 261.8 -85.7 11.1 -88.6 -25.3 3.3 -25.3

York, PA 236.5 175.9 244.8 182.0 -60.7 8.3 -62.8 -25.6 3.5 -25.7

Youngstown-Warren, OH 235.2 158.0 239.0 160.2 -77.2 3.8 -78.8 -32.8 1.6 -33.0
K-9

Summary for other unspecified PMFINE

emissions 

 PMFINE Emissions Scenerio Totals Difference Percent Difference

CMSA/MSA/FIP

 PMFINE

2010 Base

 PMFINE

2010 Control

 PMFINE

2015 Base

 PMFINE

2015 Control

 PMFINE 

 (2010C -

2010B)

 PMFINE 

 ( 2015B -

2010B)

 PMFINE 

(2015C -

2015B)

 PMFINE

 ( 2010C - 

2010B) 

/ 2010B

 PMFINE 

 ( 2015B -

2010B) 

/ 2010B

 PMFINE

(2015C -

2015B) 

/ 2015B

Athens, GA 624.3 411.7 614.5 398.7 -212.6 -9.8 -215.8 -34.1 -1.6 -35.1

Atlanta, GA 16819.7 10783.8 17197.4 10902.4 -6035.9 377.7 -6295.0 -35.9 2.2 -36.6

Augusta-Aiken, GA-SC 3128.7 2336.0 3201.1 2378.3 -792.8 72.3 -822.8 -25.3 2.3 -25.7

Birmingham, AL 7804.0 6174.1 7933.3 6269.8 -1629.9 129.3 -1663.4 -20.9 1.7 -21.0

Canton-Massillon, OH 1229.8 821.1 1247.9 829.9 -408.7 18.1 -418.0 -33.2 1.5 -33.5

Charleston, WV 1593.9 1346.7 1587.8 1339.4 -247.2 -6.2 -248.4 -15.5 -0.4 -15.6

Charlotte-Gastonia-Rock Hill, NC-SC 6162.8 4016.6 6414.6 4177.7 -2146.2 251.9 -2237.0 -34.8 4.1 -34.9

Chattanooga, TN-GA 2518.4 1712.0 2549.3 1723.5 -806.4 30.8 -825.8 -32.0 1.2 -32.4

Chicago-Gary-Kenosha, IL-IN-WI 27874.2 22090.6 28645.4 22684.0 -5783.7 771.2 -5961.4 -20.7 2.8 -20.8

Cincinnati-Hamilton, OH-KY-IN 8640.8 6346.7 8804.0 6449.9 -2294.1 163.2 -2354.0 -26.5 1.9 -26.7

Cleveland-Akron, OH 9654.4 6491.4 9901.6 6656.2 -3163.0 247.2 -3245.4 -32.8 2.6 -32.8

Columbus, GA-AL 1186.4 804.8 1219.3 823.7 -381.6 32.9 -395.7 -32.2 2.8 -32.5

Columbus, OH 4197.1 2995.7 4262.9 3032.4 -1201.4 65.8 -1230.5 -28.6 1.6 -28.9

DeKalb County, Alabama 389.8 266.7 376.3 254.1 -123.1 -13.5 -122.1 -31.6 -3.5 -32.5

Detroit-Ann Arbor-Flint, MI 14383.7 9411.5 14638.7 9554.3 -4972.2 254.9 -5084.4 -34.6 1.8 -34.7

Floyd County, Georgia 1265.5 790.3 1300.8 803.1 -475.2 35.4 -497.7 -37.5 2.8 -38.3

Greensboro--Winston-Salem--High

Point, NC 6331.0 3907.8 6478.5 3961.6 -2423.2 147.5 -2516.9 -38.3 2.3 -38.8

Greenville-Spartanburg-Anderson, SC 3724.4 2682.0 3695.7 2644.1 -1042.4 -28.8 -1051.5 -28.0 -0.8 -28.5

Hall County, Georgia 1164.3 628.5 1167.7 615.3 -535.8 3.4 -552.4 -46.0 0.3 -47.3

Hartford, CT 162.1 133.9 160.4 132.7 -28.3 -1.7 -27.7 -17.4 -1.1 -17.3

Hickory-Morganton-Lenoir, NC 2146.5 1186.5 2194.6 1198.6 -960.1 48.1 -996.1 -44.7 2.2 -45.4

Huntington-Ashland, WV-KY-OH 2159.9 1431.6 2197.2 1451.2 -728.3 37.3 -745.9 -33.7 1.7 -33.9

Indianapolis, IN 5143.2 3774.7 5257.7 3850.9 -1368.5 114.5 -1406.8 -26.6 2.2 -26.8

Johnson City-Kingsport-Bristol, TN-VA 2953.3 1833.9 3016.1 1861.4 -1119.4 62.8 -1154.7 -37.9 2.1 -38.3

Knoxville, TN 3167.2 2281.8 3214.4 2307.1 -885.4 47.2 -907.3 -28.0 1.5 -28.2

Lancaster, PA 1597.4 1084.0 1598.3 1077.3 -513.5 0.8 -520.9 -32.1 0.1 -32.6

Lexington, KY 1503.7 1065.8 1555.4 1103.6 -437.8 51.7 -451.8 -29.1 3.4 -29.0

Litchfield County, Connecticut 292.6 241.3 292.3 241.2 -51.3 -0.3 -51.1 -17.5 -0.1 -17.5

Louisville, KY-IN 5081.1 3501.3 5212.2 3578.5 -1579.8 131.1 -1633.7 -31.1 2.6 -31.3

Montgomery, AL 1744.2 1250.1 1779.5 1271.5 -494.1 35.3 -507.9 -28.3 2.0 -28.5

Nashville, TN 5761.4 3267.1 5896.2 3308.6 -2494.3 134.8 -2587.6 -43.3 2.3 -43.9

New

Haven-Bridgeport-Stamford-Waterbury

-Danbury, CT 1414.9 1190.1 1447.1 1218.5 -224.9 32.2 -228.6 -15.9 2.3 -15.8

New York-Northern New Jersey-Long

Island, NY-NJ-CT-PA 27504.2 18880.4 27946.4 19163.3 -8623.8 442.2 -8783.1 -31.4 1.6 -31.4

Parkersburg-Marietta, WV-OH 1140.3 777.9 1141.8 775.5 -362.4 1.4 -366.2 -31.8 0.1 -32.1

Philadelphia-Wilmington-Atlantic City,

PA-NJ-DE-MD 20130.6 13856.8 20708.7 14228.4 -6273.8 578.1 -6480.3 -31.2 2.9 -31.3

Pittsburgh, PA 9306.0 6776.4 9360.0 6798.9 -2529.6 53.9 -2561.0 -27.2 0.6 -27.4

Reading, PA 1855.5 1294.6 1880.9 1309.7 -560.9 25.4 -571.2 -30.2 1.4 -30.4

Roane County, Tennessee 432.8 300.8 441.5 305.1 -132.0 8.8 -136.4 -30.5 2.0 -30.9

Scioto County, Ohio 581.0 413.3 581.4 412.6 -167.7 0.4 -168.8 -28.9 0.1 -29.0

St. Louis, MO-IL 18996.3 15335.7 19484.3 15742.0 -3660.6 488.0 -3742.4 -19.3 2.6 -19.2

Steubenville-Weirton, OH-WV 4391.8 3495.4 4470.9 3566.7 -896.4 79.1 -904.2 -20.4 1.8 -20.2

Talladega County, Alabama 1125.9 795.3 1159.4 816.4 -330.6 33.5 -343.0 -29.4 3.0 -29.6

Washington-Baltimore, DC-MD-VA-WV 11745.4 9644.6 11872.0 9746.5 -2100.7 126.6 -2125.5 -17.9 1.1 -17.9

Wheeling, WV-OH 1087.3 898.5 1089.5 898.3 -188.8 2.2 -191.2 -17.4 0.2 -17.6

Wilkinson County, Georgia 1200.0 892.7 1249.0 928.6 -307.3 49.0 -320.4 -25.6 4.1 -25.7

York, PA 1624.9 1185.9 1632.8 1187.3 -439.1 7.9 -445.4 -27.0 0.5 -27.3

Youngstown-Warren, OH 1744.0 1121.4 1762.9 1129.4 -622.6 18.9 -633.6 -35.7 1.1 -35.9
Technical Support Document for the 

Interstate Air Quality Rule

Air Quality Modeling Analyses

Appendix L

Summaries of Impacts on PM2.5 and 

PM2.5 Species from Local Control Measures 

for the 290 County Study
L- 1

Table L-1 Results for 2010 Base Case vs. Local Control Case

PM2.5 Crustal Elemental Carbon Organic Aerosol Ammonium Sulfate Ammonium Nitrate

MSA/CMSA Reduction

(Local -

Base)

%

Change

 Local vs

Base

(Local -

 Base)

%

Change 

Local vs

Base

(Local 

- Base)

%

Change 

Local vs

Base

(Local -

 Base)

%

Change

 Local vs

Base

(Local - 

Base)

%

Change

 Local vs

Base

(Local - 

Base)

%

Change 

Local vs

Base

Athens, GA MSA Minimum -1.11 -6.5% -0.11 -12.4% -0.06 -8.6% -0.12 -2.0% -0.83 -10.8% 0.02 1.7%

Athens, GA MSA Maximum -1.11 -6.5% -0.11 -12.4% -0.06 -8.6% -0.12 -2.0% -0.83 -10.8% 0.02 1.7%

Athens, GA MSA Average -1.11 -6.5% -0.11 -12.4% -0.06 -8.6% -0.12 -2.0% -0.83 -10.8% 0.02 1.7%

Atlanta, GA MSA Minimum -0.94 -6.1% -0.08 -10.1% -0.05 -7.9% -0.09 -1.6% -0.75 -10.8% 0.03 2.6%

Atlanta, GA MSA Maximum -1.53 -8.8% -0.18 -22.0% -0.13 -14.6% -0.26 -3.3% -1.03 -14.1% 0.02 1.5%

Atlanta, GA MSA Average -1.34 -7.6% -0.14 -16.7% -0.10 -12.4% -0.19 -2.8% -0.92 -12.4% 0.03 2.0%

Augusta-Aiken, GA-SC MSA Minimum -1.05 -6.6% -0.10 -11.8% -0.06 -9.7% -0.21 -3.5% -0.69 -10.0% 0.01 0.8%

Augusta-Aiken, GA-SC MSA Maximum -1.05 -6.6% -0.10 -11.8% -0.06 -9.7% -0.21 -3.5% -0.69 -10.0% 0.01 0.8%

Augusta-Aiken, GA-SC MSA Average -1.05 -6.6% -0.10 -11.8% -0.06 -9.7% -0.21 -3.5% -0.69 -10.0% 0.01 0.8%

Birmingham, AL MSA Minimum -1.33 -6.6% -0.25 -15.4% -0.12 -10.7% -0.25 -3.1% -0.76 -10.7% 0.03 2.0%

Birmingham, AL MSA Maximum -1.33 -6.6% -0.25 -15.4% -0.12 -10.7% -0.25 -3.1% -0.76 -10.7% 0.03 2.0%

Birmingham, AL MSA Average -1.33 -6.6% -0.25 -15.4% -0.12 -10.7% -0.25 -3.1% -0.76 -10.7% 0.03 2.0%

Canton-Massillon, OH MSA Minimum -1.29 -7.5% -0.12 -13.8% -0.07 -11.3% -0.20 -4.9% -0.95 -11.8% 0.07 2.3%

Canton-Massillon, OH MSA Maximum -1.29 -7.5% -0.12 -13.8% -0.07 -11.3% -0.20 -4.9% -0.95 -11.8% 0.07 2.3%

Canton-Massillon, OH MSA Average -1.29 -7.5% -0.12 -13.8% -0.07 -11.3% -0.20 -4.9% -0.95 -11.8% 0.07 2.3%

Charleston, WV MSA Minimum -1.66 -9.7% -0.09 -12.2% -0.14 -20.6% -0.33 -7.2% -1.16 -12.7% 0.06 3.8%

Charleston, WV MSA Maximum -1.66 -9.7% -0.09 -12.2% -0.14 -20.6% -0.33 -7.2% -1.16 -12.7% 0.06 3.8%

Charleston, WV MSA Average -1.66 -9.7% -0.09 -12.2% -0.14 -20.6% -0.33 -7.2% -1.16 -12.7% 0.06 3.8%

Charlotte-Gastonia-Rock Hill, NC-SC

MSA Minimum -1.13 -7.4% -0.11 -14.9% -0.07 -12.1% -0.24 -4.1% -0.72 -11.2% 0.01 0.8%

Charlotte-Gastonia-Rock Hill, NC-SC

MSA Maximum -1.13 -7.4% -0.11 -14.9% -0.07 -12.1% -0.24 -4.1% -0.72 -11.2% 0.01 0.8%

Charlotte-Gastonia-Rock Hill, NC-SC

MSA Average -1.13 -7.4% -0.11 -14.9% -0.07 -12.1% -0.24 -4.1% -0.72 -11.2% 0.01 0.8%

Chattanooga, TN-GA MSA Minimum -1.34 -8.3% -0.13 -13.5% -0.07 -12.1% -0.23 -4.6% -0.92 -11.6% 0.02 1.6%

Chattanooga, TN-GA MSA Maximum -1.34 -8.3% -0.13 -13.5% -0.07 -12.1% -0.23 -4.6% -0.92 -11.6% 0.02 1.6%

Chattanooga, TN-GA MSA Average -1.34 -8.3% -0.13 -13.5% -0.07 -12.1% -0.23 -4.6% -0.92 -11.6% 0.02 1.6%

Chicago-Gary-Kenosha, IL-IN-WI

CMSA Minimum -0.99 -6.5% -0.08 -11.4% -0.04 -7.4% -0.35 -8.5% -0.56 -10.2% 0.06 1.5%

Chicago-Gary-Kenosha, IL-IN-WI

CMSA Maximum -1.28 -7.2% -0.13 -15.7% -0.08 -11.0% -0.57 -10.5% -0.57 -10.2% 0.06 1.2%

Chicago-Gary-Kenosha, IL-IN-WI

CMSA Average -1.14 -6.8% -0.10 -13.5% -0.06 -9.2% -0.46 -9.5% -0.57 -10.2% 0.06 1.4%

Cincinnati-Hamilton, OH-KY-IN CMSA Minimum -1.09 -6.8% -0.10 -12.7% -0.05 -9.4% -0.20 -4.6% -0.79 -11.0% 0.06 1.9%

Cincinnati-Hamilton, OH-KY-IN CMSA Maximum -1.32 -7.4% -0.13 -16.5% -0.08 -13.3% -0.22 -5.4% -0.97 -11.6% 0.05 1.6%

Cincinnati-Hamilton, OH-KY-IN CMSA Average -1.20 -7.1% -0.12 -14.6% -0.06 -11.4% -0.21 -5.0% -0.88 -11.3% 0.06 1.7%

Cleveland-Akron, OH CMSA Minimum -1.16 -7.1% -0.12 -14.3% -0.08 -11.5% -0.19 -4.6% -0.87 -12.0% 0.13 3.1%

Cleveland-Akron, OH CMSA Maximum -1.44 -7.5% -0.29 -22.3% -0.11 -12.5% -0.23 -4.8% -0.93 -12.4% 0.09 2.8%

Cleveland-Akron, OH CMSA Average -1.30 -7.3% -0.20 -18.3% -0.10 -12.0% -0.21 -4.7% -0.90 -12.2% 0.11 3.0%

Columbus, GA-AL MSA Minimum -0.77 -4.6% -0.08 -9.0% -0.03 -4.0% -0.07 -1.2% -0.64 -8.7% 0.06 4.0%

Columbus, GA-AL MSA Maximum -0.80 -4.7% -0.09 -9.8% -0.04 -5.4% -0.08 -1.4% -0.65 -8.8% 0.05 3.4%

Columbus, GA-AL MSA Average -0.78 -4.7% -0.08 -9.4% -0.04 -4.7% -0.07 -1.3% -0.64 -8.8% 0.06 3.7%

Columbus, OH MSA Minimum -1.12 -6.7% -0.11 -12.4% -0.05 -9.3% -0.17 -4.2% -0.84 -11.2% 0.07 2.1%

Columbus, OH MSA Maximum -1.12 -6.7% -0.11 -12.4% -0.05 -9.3% -0.17 -4.2% -0.84 -11.2% 0.07 2.1%

Columbus, OH MSA Average -1.12 -6.7% -0.11 -12.4% -0.05 -9.3% -0.17 -4.2% -0.84 -11.2% 0.07 2.1%

Detroit-Ann Arbor-Flint, MI CMSA Minimum -1.25 -6.7% -0.19 -20.2% -0.09 -10.8% -0.27 -5.4% -0.81 -12.5% 0.10 1.9%

Detroit-Ann Arbor-Flint, MI CMSA Maximum -1.25 -6.7% -0.19 -20.2% -0.09 -10.8% -0.27 -5.4% -0.81 -12.5% 0.10 1.9%

Detroit-Ann Arbor-Flint, MI CMSA Average -1.25 -6.7% -0.19 -20.2% -0.09 -10.8% -0.27 -5.4% -0.81 -12.5% 0.10 1.9%
L- 2

Greensboro--Winston-Salem--High

Point, NC MSA Minimum -1.37 -8.8% -0.16 -20.8% -0.09 -16.1% -0.33 -6.0% -0.83 -12.0% 0.04 2.9%

Greensboro--Winston-Salem--High

Point, NC MSA Maximum -1.37 -8.8% -0.16 -20.8% -0.09 -16.1% -0.33 -6.0% -0.83 -12.0% 0.04 2.9%

Greensboro--Winston-Salem--High

Point, NC MSA Average -1.37 -8.8% -0.16 -20.8% -0.09 -16.1% -0.33 -6.0% -0.83 -12.0% 0.04 2.9%

Greenville-Spartanburg-Anderson, SC

MSA Minimum -1.15 -7.6% -0.08 -11.8% -0.06 -11.1% -0.19 -3.5% -0.84 -11.9% 0.03 3.0%

Greenville-Spartanburg-Anderson, SC

MSA Maximum -1.15 -7.6% -0.08 -11.8% -0.06 -11.1% -0.19 -3.5% -0.84 -11.9% 0.03 3.0%

Greenville-Spartanburg-Anderson, SC

MSA Average -1.15 -7.6% -0.08 -11.8% -0.06 -11.1% -0.19 -3.5% -0.84 -11.9% 0.03 3.0%

Hickory-Morganton-Lenoir, NC MSA Minimum -1.35 -8.8% -0.14 -20.0% -0.08 -13.3% -0.33 -6.3% -0.82 -11.5% 0.02 1.7%

Hickory-Morganton-Lenoir, NC MSA Maximum -1.35 -8.8% -0.14 -20.0% -0.08 -13.3% -0.33 -6.3% -0.82 -11.5% 0.02 1.7%

Hickory-Morganton-Lenoir, NC MSA Average -1.35 -8.8% -0.14 -20.0% -0.08 -13.3% -0.33 -6.3% -0.82 -11.5% 0.02 1.7%

Huntington-Ashland, WV-KY-OH MSA Minimum -1.35 -8.2% -0.08 -11.8% -0.12 -17.6% -0.28 -6.0% -0.92 -11.0% 0.06 3.9%

Huntington-Ashland, WV-KY-OH MSA Maximum -1.41 -9.1% -0.10 -15.2% -0.12 -18.2% -0.31 -7.1% -0.93 -11.8% 0.05 3.1%

Huntington-Ashland, WV-KY-OH MSA Average -1.38 -8.7% -0.09 -13.5% -0.12 -17.9% -0.30 -6.6% -0.92 -11.4% 0.05 3.5%

Indianapolis, IN MSA Minimum -0.95 -6.0% -0.11 -16.9% -0.06 -11.5% -0.15 -3.4% -0.69 -10.7% 0.04 1.1%

Indianapolis, IN MSA Maximum -0.95 -6.0% -0.11 -16.9% -0.06 -11.5% -0.15 -3.4% -0.69 -10.7% 0.04 1.1%

Indianapolis, IN MSA Average -0.95 -6.0% -0.11 -16.9% -0.06 -11.5% -0.15 -3.4% -0.69 -10.7% 0.04 1.1%

Johnson City-Kingsport-Bristol, TN-VA

MSA Minimum -1.32 -8.7% -0.11 -16.4% -0.07 -11.5% -0.24 -5.2% -0.93 -12.0% 0.04 3.3%

Johnson City-Kingsport-Bristol, TN-VA

MSA Maximum -1.32 -8.7% -0.11 -16.4% -0.07 -11.5% -0.24 -5.2% -0.93 -12.0% 0.04 3.3%

Johnson City-Kingsport-Bristol, TN-VA

MSA Average -1.32 -8.7% -0.11 -16.4% -0.07 -11.5% -0.24 -5.2% -0.93 -12.0% 0.04 3.3%

Knoxville, TN MSA Minimum -1.55 -8.4% -0.14 -15.4% -0.08 -12.1% -0.31 -5.7% -1.06 -11.2% 0.04 2.8%

Knoxville, TN MSA Maximum -1.55 -8.4% -0.14 -15.4% -0.08 -12.1% -0.31 -5.7% -1.06 -11.2% 0.04 2.8%

Knoxville, TN MSA Average -1.55 -8.4% -0.14 -15.4% -0.08 -12.1% -0.31 -5.7% -1.06 -11.2% 0.04 2.8%

Lancaster, PA MSA Minimum -1.11 -7.2% -0.10 -16.4% -0.06 -11.3% -0.25 -6.2% -0.70 -10.5% 0.01 0.3%

Lancaster, PA MSA Maximum -1.11 -7.2% -0.10 -16.4% -0.06 -11.3% -0.25 -6.2% -0.70 -10.5% 0.01 0.3%

Lancaster, PA MSA Average -1.11 -7.2% -0.10 -16.4% -0.06 -11.3% -0.25 -6.2% -0.70 -10.5% 0.01 0.3%

Lexington, KY MSA Minimum -1.01 -6.6% -0.07 -10.1% -0.04 -7.8% -0.11 -3.0% -0.81 -10.3% 0.02 0.9%

Lexington, KY MSA Maximum -1.01 -6.6% -0.07 -10.1% -0.04 -7.8% -0.11 -3.0% -0.81 -10.3% 0.02 0.9%

Lexington, KY MSA Average -1.01 -6.6% -0.07 -10.1% -0.04 -7.8% -0.11 -3.0% -0.81 -10.3% 0.02 0.9%

Louisville, KY-IN MSA Minimum -1.24 -7.8% -0.15 -19.5% -0.07 -13.2% -0.18 -4.7% -0.85 -11.0% 0.04 1.6%

Louisville, KY-IN MSA Maximum -1.26 -8.0% -0.16 -20.3% -0.07 -13.2% -0.19 -4.8% -0.87 -11.1% 0.03 1.3%

Louisville, KY-IN MSA Average -1.25 -7.9% -0.16 -19.9% -0.07 -13.2% -0.18 -4.7% -0.86 -11.0% 0.03 1.5%

Montgomery, AL MSA Minimum -0.82 -5.2% -0.11 -11.5% -0.05 -7.5% -0.08 -1.5% -0.62 -9.1% 0.03 2.2%

Montgomery, AL MSA Maximum -0.82 -5.2% -0.11 -11.5% -0.05 -7.5% -0.08 -1.5% -0.62 -9.1% 0.03 2.2%

Montgomery, AL MSA Average -0.82 -5.2% -0.11 -11.5% -0.05 -7.5% -0.08 -1.5% -0.62 -9.1% 0.03 2.2%

Nashville, TN MSA Minimum -1.09 -7.1% -0.15 -14.6% -0.06 -12.2% -0.17 -4.3% -0.72 -9.4% 0.01 0.5%

Nashville, TN MSA Maximum -1.09 -7.1% -0.15 -14.6% -0.06 -12.2% -0.17 -4.3% -0.72 -9.4% 0.01 0.5%

Nashville, TN MSA Average -1.09 -7.1% -0.15 -14.6% -0.06 -12.2% -0.17 -4.3% -0.72 -9.4% 0.01 0.5%

New

Haven-Bridgeport-Stamford-Waterbury

-Danbury, CT NECMA Minimum -0.71 -4.6% -0.08 -10.5% -0.07 -10.1% -0.14 -3.2% -0.55 -8.6% 0.12 4.1%

New

Haven-Bridgeport-Stamford-Waterbury

-Danbury, CT NECMA Maximum -0.71 -4.6% -0.08 -10.5% -0.07 -10.1% -0.14 -3.2% -0.55 -8.6% 0.12 4.1%

New

Haven-Bridgeport-Stamford-Waterbury

-Danbury, CT NECMA Average -0.71 -4.6% -0.08 -10.5% -0.07 -10.1% -0.14 -3.2% -0.55 -8.6% 0.12 4.1%

New York-Northern New Jersey-Long

Island, NY-NJ-CT-PA CMSA Minimum -1.27 -7.8% -0.18 -21.7% -0.16 -15.4% -0.23 -5.1% -0.73 -11.7% 0.05 1.5%

New York-Northern New Jersey-Long

Island, NY-NJ-CT-PA CMSA Maximum -1.27 -7.8% -0.18 -21.7% -0.16 -15.4% -0.23 -5.1% -0.73 -11.7% 0.05 1.5%
L- 3

New York-Northern New Jersey-Long

Island, NY-NJ-CT-PA CMSA Average -1.27 -7.8% -0.18 -21.7% -0.16 -15.4% -0.23 -5.1% -0.73 -11.7% 0.05 1.5%

Parkersburg-Marietta, WV-OH MSA Minimum -1.38 -8.5% -0.09 -12.3% -0.11 -16.2% -0.23 -5.9% -1.03 -11.5% 0.06 3.6%

Parkersburg-Marietta, WV-OH MSA Maximum -1.38 -8.5% -0.09 -12.3% -0.11 -16.2% -0.23 -5.9% -1.03 -11.5% 0.06 3.6%

Parkersburg-Marietta, WV-OH MSA Average -1.38 -8.5% -0.09 -12.3% -0.11 -16.2% -0.23 -5.9% -1.03 -11.5% 0.06 3.6%

Philadelphia-Wilmington-Atlantic City,

PA-NJ-DE-MD CMSA Minimum -1.20 -7.8% -0.17 -27.4% -0.11 -17.5% -0.23 -5.3% -0.78 -12.6% 0.09 2.7%

Philadelphia-Wilmington-Atlantic City,

PA-NJ-DE-MD CMSA Maximum -1.20 -7.8% -0.17 -27.4% -0.11 -17.5% -0.23 -5.3% -0.78 -12.6% 0.09 2.7%

Philadelphia-Wilmington-Atlantic City,

PA-NJ-DE-MD CMSA Average -1.20 -7.8% -0.17 -27.4% -0.11 -17.5% -0.23 -5.3% -0.78 -12.6% 0.09 2.7%

Pittsburgh, PA MSA Minimum -1.70 -8.7% -0.20 -19.6% -0.10 -13.5% -0.24 -4.7% -1.21 -12.7% 0.03 1.2%

Pittsburgh, PA MSA Maximum -1.70 -8.7% -0.20 -19.6% -0.10 -13.5% -0.24 -4.7% -1.21 -12.7% 0.03 1.2%

Pittsburgh, PA MSA Average -1.70 -8.7% -0.20 -19.6% -0.10 -13.5% -0.24 -4.7% -1.21 -12.7% 0.03 1.2%

Reading, PA MSA Minimum -1.04 -6.8% -0.12 -19.7% -0.07 -13.2% -0.21 -5.4% -0.71 -10.8% 0.07 2.0%

Reading, PA MSA Maximum -1.04 -6.8% -0.12 -19.7% -0.07 -13.2% -0.21 -5.4% -0.71 -10.8% 0.07 2.0%

Reading, PA MSA Average -1.04 -6.8% -0.12 -19.7% -0.07 -13.2% -0.21 -5.4% -0.71 -10.8% 0.07 2.0%

St. Louis, MO-IL MSA Minimum -0.96 -6.3% -0.15 -12.8% -0.07 -10.3% -0.22 -4.9% -0.55 -9.5% 0.03 1.0%

St. Louis, MO-IL MSA Maximum -1.14 -6.9% -0.15 -14.0% -0.07 -10.8% -0.31 -6.5% -0.64 -10.2% 0.02 0.6%

St. Louis, MO-IL MSA Average -1.04 -6.5% -0.15 -13.3% -0.07 -10.6% -0.26 -5.4% -0.59 -9.8% 0.02 0.8%

Steubenville-Weirton, OH-WV MSA Minimum -1.77 -10.6% -0.13 -16.2% -0.08 -13.3% -0.39 -10.0% -1.33 -15.2% 0.18 7.4%

Steubenville-Weirton, OH-WV MSA Maximum -1.92 -10.7% -0.15 -17.3% -0.08 -14.5% -0.43 -10.2% -1.44 -15.3% 0.16 7.1%

Steubenville-Weirton, OH-WV MSA Average -1.82 -10.6% -0.14 -16.9% -0.08 -14.1% -0.41 -10.1% -1.37 -15.2% 0.17 7.3%

Washington-Baltimore, DC-MD-VA-WV

CMSA Minimum -1.05 -6.8% -0.07 -9.5% -0.08 -12.5% -0.18 -4.1% -0.80 -11.3% 0.08 3.6%

Washington-Baltimore, DC-MD-VA-WV

CMSA Maximum -1.26 -7.6% -0.08 -9.6% -0.10 -13.5% -0.30 -5.8% -0.84 -12.2% 0.06 2.3%

Washington-Baltimore, DC-MD-VA-WV

CMSA Average -1.16 -7.2% -0.08 -9.5% -0.09 -13.0% -0.24 -4.9% -0.82 -11.8% 0.07 3.0%

Wheeling, WV-OH MSA Minimum -1.39 -9.0% -0.08 -10.8% -0.07 -13.0% -0.17 -4.9% -1.17 -13.6% 0.08 4.4%

Wheeling, WV-OH MSA Maximum -1.39 -9.0% -0.08 -10.8% -0.07 -13.0% -0.17 -4.9% -1.17 -13.6% 0.08 4.4%

Wheeling, WV-OH MSA Average -1.39 -9.0% -0.08 -10.8% -0.07 -13.0% -0.17 -4.9% -1.17 -13.6% 0.08 4.4%

York, PA MSA Minimum -1.16 -7.4% -0.09 -14.1% -0.08 -13.3% -0.23 -5.7% -0.83 -11.9% 0.07 2.3%

York, PA MSA Maximum -1.16 -7.4% -0.09 -14.1% -0.08 -13.3% -0.23 -5.7% -0.83 -11.9% 0.07 2.3%

York, PA MSA Average -1.16 -7.4% -0.09 -14.1% -0.08 -13.3% -0.23 -5.7% -0.83 -11.9% 0.07 2.3%

Youngstown-Warren, OH MSA Minimum -1.22 -8.1% -0.14 -16.5% -0.07 -11.7% -0.22 -5.8% -0.89 -12.9% 0.10 3.8%

Youngstown-Warren, OH MSA Maximum -1.26 -8.2% -0.15 -17.2% -0.08 -13.3% -0.22 -5.9% -0.92 -12.9% 0.10 3.7%

Youngstown-Warren, OH MSA Average -1.24 -8.1% -0.14 -16.9% -0.08 -12.5% -0.22 -5.8% -0.90 -12.9% 0.10 3.8%

DeKalb County, Alabama Minimum -0.93 -6.1% -0.07 -8.5% -0.04 -6.9% -0.08 -1.6% -0.76 -10.7% 0.01 0.8%

DeKalb County, Alabama Maximum -0.93 -6.1% -0.07 -8.5% -0.04 -6.9% -0.08 -1.6% -0.76 -10.7% 0.01 0.8%

DeKalb County, Alabama Average -0.93 -6.1% -0.07 -8.5% -0.04 -6.9% -0.08 -1.6% -0.76 -10.7% 0.01 0.8%

Talladega County, Alabama Minimum -0.99 -6.0% -0.10 -10.5% -0.05 -7.2% -0.09 -1.5% -0.80 -11.3% 0.05 3.6%

Talladega County, Alabama Maximum -0.99 -6.0% -0.10 -10.5% -0.05 -7.2% -0.09 -1.5% -0.80 -11.3% 0.05 3.6%

Talladega County, Alabama Average -0.99 -6.0% -0.10 -10.5% -0.05 -7.2% -0.09 -1.5% -0.80 -11.3% 0.05 3.6%

Floyd County, Georgia Minimum -1.34 -7.9% -0.18 -18.6% -0.07 -10.9% -0.15 -2.6% -0.96 -12.2% 0.03 2.4%

Floyd County, Georgia Maximum -1.34 -7.9% -0.18 -18.6% -0.07 -10.9% -0.15 -2.6% -0.96 -12.2% 0.03 2.4%

Floyd County, Georgia Average -1.34 -7.9% -0.18 -18.6% -0.07 -10.9% -0.15 -2.6% -0.96 -12.2% 0.03 2.4%

Hall County, Georgia Minimum -1.31 -8.4% -0.15 -19.5% -0.08 -12.5% -0.17 -3.1% -0.93 -12.8% 0.01 1.0%

Hall County, Georgia Maximum -1.31 -8.4% -0.15 -19.5% -0.08 -12.5% -0.17 -3.1% -0.93 -12.8% 0.01 1.0%

Hall County, Georgia Average -1.31 -8.4% -0.15 -19.5% -0.08 -12.5% -0.17 -3.1% -0.93 -12.8% 0.01 1.0%

Wilkinson County, Georgia Minimum -1.05 -6.3% -0.09 -10.8% -0.05 -6.8% -0.22 -3.6% -0.76 -10.5% 0.07 5.3%

Wilkinson County, Georgia Maximum -1.05 -6.3% -0.09 -10.8% -0.05 -6.8% -0.22 -3.6% -0.76 -10.5% 0.07 5.3%

Wilkinson County, Georgia Average -1.05 -6.3% -0.09 -10.8% -0.05 -6.8% -0.22 -3.6% -0.76 -10.5% 0.07 5.3%
L- 4

Scioto County, Ohio Minimum -1.37 -7.4% -0.09 -11.2% -0.09 -12.5% -0.27 -5.4% -0.99 -10.9% 0.06 2.5%

Scioto County, Ohio Maximum -1.37 -7.4% -0.09 -11.2% -0.09 -12.5% -0.27 -5.4% -0.99 -10.9% 0.06 2.5%

Scioto County, Ohio Average -1.37 -7.4% -0.09 -11.2% -0.09 -12.5% -0.27 -5.4% -0.99 -10.9% 0.06 2.5%

Roane County, Tennessee Minimum -1.08 -7.1% -0.07 -9.2% -0.04 -7.5% -0.11 -2.5% -0.89 -11.3% 0.04 3.2%

Roane County, Tennessee Maximum -1.08 -7.1% -0.07 -9.2% -0.04 -7.5% -0.11 -2.5% -0.89 -11.3% 0.04 3.2%

Roane County, Tennessee Average -1.08 -7.1% -0.07 -9.2% -0.04 -7.5% -0.11 -2.5% -0.89 -11.3% 0.04 3.2%

Overall Minimum -0.71 -4.6% -0.07 -8.5% -0.03 -4.0% -0.07 -1.2% -0.55 -8.6% 0.18 7.4%

Overall Maximum -1.92 -10.7% -0.29 -27.4% -0.16 -20.6% -0.57 -10.5% -1.44 -15.3% 0.01 0.3%

Overall Average -1.23 -7.5% -0.13 -14.9% -0.08 -11.8% -0.22 -4.8% -0.86 -11.5% 0.05 2.5%
L- 5

Table L-2 Results for 2015 Base Case vs. Local Control Case

PM2.5 Crustal Elemental Carbon Organic Aerosol Ammonium Sulfate Ammonium Nitrate

MSA/CMSA Reduction

(Local -

 Base)

%

Change

 Local vs

Base

(Local -

 Base)

%

Change

 Local vs

Base

(Local - 

Base)

%

Change

 Local vs

Base

(Local -

 Base)

%

Change 

Local vs

Base

(Local -

 Base)

%

Change 

Local vs

Base

(Local -

 Base)

%

Change

 Local vs

Base

Athens, GA Minimum -1.05 -6.4% -0.11 -12.1% -0.05 -8.1% -0.12 -2.0% -0.78 -10.6% 0.00 0.0%

Athens, GA Maximum -1.05 -6.4% -0.11 -12.1% -0.05 -8.1% -0.12 -2.0% -0.78 -10.6% 0.00 0.0%

Athens, GA Average -1.05 -6.4% -0.11 -12.1% -0.05 -8.1% -0.12 -2.0% -0.78 -10.6% 0.00 0.0%

Atlanta, GA Minimum -1.28 -7.4% -0.14 -15.6% -0.08 -11.0% -0.18 -2.6% -0.83 -11.9% 0.02 1.7%

Atlanta, GA Maximum -1.44 -8.7% -0.18 -21.4% -0.11 -13.9% -0.23 -3.0% -0.98 -13.8% 0.01 0.7%

Atlanta, GA Average -1.37 -7.7% -0.16 -18.5% -0.09 -11.9% -0.20 -2.8% -0.93 -12.6% 0.01 1.0%

Augusta-Aiken, GA-SC Minimum -1.00 -6.4% -0.10 -11.5% -0.04 -7.3% -0.20 -3.4% -0.65 -9.7% 0.01 0.9%

Augusta-Aiken, GA-SC Maximum -1.00 -6.4% -0.10 -11.5% -0.04 -7.3% -0.20 -3.4% -0.65 -9.7% 0.01 0.9%

Augusta-Aiken, GA-SC Average -1.00 -6.4% -0.10 -11.5% -0.04 -7.3% -0.20 -3.4% -0.65 -9.7% 0.01 0.9%

Birmingham, AL Minimum -1.29 -6.6% -0.25 -15.0% -0.09 -8.8% -0.24 -3.0% -0.72 -10.5% 0.01 0.7%

Birmingham, AL Maximum -1.29 -6.6% -0.25 -15.0% -0.09 -8.8% -0.24 -3.0% -0.72 -10.5% 0.01 0.7%

Birmingham, AL Average -1.29 -6.6% -0.25 -15.0% -0.09 -8.8% -0.24 -3.0% -0.72 -10.5% 0.01 0.7%

Canton-Massillon, OH Minimum -1.20 -7.3% -0.13 -14.6% -0.06 -11.3% -0.19 -4.8% -0.89 -11.6% 0.05 1.7%

Canton-Massillon, OH Maximum -1.20 -7.3% -0.13 -14.6% -0.06 -11.3% -0.19 -4.8% -0.89 -11.6% 0.05 1.7%

Canton-Massillon, OH Average -1.20 -7.3% -0.13 -14.6% -0.06 -11.3% -0.19 -4.8% -0.89 -11.6% 0.05 1.7%

Charleston, WV Minimum -1.56 -9.5% -0.10 -13.2% -0.12 -19.0% -0.30 -6.8% -1.10 -12.6% 0.06 3.9%

Charleston, WV Maximum -1.56 -9.5% -0.10 -13.2% -0.12 -19.0% -0.30 -6.8% -1.10 -12.6% 0.06 3.9%

Charleston, WV Average -1.56 -9.5% -0.10 -13.2% -0.12 -19.0% -0.30 -6.8% -1.10 -12.6% 0.06 3.9%

Chattanooga, TN-GA Minimum -1.27 -8.1% -0.14 -14.1% -0.05 -9.8% -0.22 -4.5% -0.88 -11.4% 0.02 1.7%

Chattanooga, TN-GA Maximum -1.27 -8.1% -0.14 -14.1% -0.05 -9.8% -0.22 -4.5% -0.88 -11.4% 0.02 1.7%

Chattanooga, TN-GA Average -1.27 -8.1% -0.14 -14.1% -0.05 -9.8% -0.22 -4.5% -0.88 -11.4% 0.02 1.7%

Chicago-Gary-Kenosha,

IL-IN-WI Minimum -1.28 -7.3% -0.13 -15.1% -0.06 -9.7% -0.58 -10.6% -0.55 -10.2% 0.04 0.8%

Chicago-Gary-Kenosha,

IL-IN-WI Maximum -1.28 -7.3% -0.13 -15.1% -0.06 -9.7% -0.58 -10.6% -0.55 -10.2% 0.04 0.8%

Chicago-Gary-Kenosha,

IL-IN-WI Average -1.28 -7.3% -0.13 -15.1% -0.06 -9.7% -0.58 -10.6% -0.55 -10.2% 0.04 0.8%

Cincinnati-Hamilton,

OH-KY-IN Minimum -1.03 -6.7% -0.10 -12.3% -0.04 -8.9% -0.18 -4.3% -0.73 -10.6% 0.04 1.4%

Cincinnati-Hamilton,

OH-KY-IN Maximum -1.23 -7.2% -0.13 -16.0% -0.05 -9.8% -0.20 -5.1% -0.91 -11.3% 0.04 1.3%

Cincinnati-Hamilton,

OH-KY-IN Average -1.13 -6.9% -0.12 -14.2% -0.04 -9.3% -0.19 -4.7% -0.82 -11.0% 0.04 1.3%

Cleveland-Akron, OH Minimum -1.09 -6.9% -0.12 -14.0% -0.07 -10.8% -0.17 -4.3% -0.80 -11.6% 0.11 2.7%

Cleveland-Akron, OH Maximum -1.39 -7.5% -0.29 -21.8% -0.09 -12.7% -0.22 -4.7% -0.90 -12.4% 0.08 2.6%

Cleveland-Akron, OH Average -1.24 -7.2% -0.20 -17.9% -0.08 -11.8% -0.20 -4.5% -0.85 -12.0% 0.09 2.7%

Columbus, GA-AL Minimum -0.75 -4.6% -0.08 -8.8% -0.03 -4.3% -0.07 -1.2% -0.61 -8.5% 0.05 3.4%

Columbus, GA-AL Maximum -0.76 -4.6% -0.09 -9.6% -0.03 -4.3% -0.08 -1.3% -0.61 -8.6% 0.04 2.8%

Columbus, GA-AL Average -0.75 -4.6% -0.08 -9.2% -0.03 -4.3% -0.08 -1.3% -0.61 -8.5% 0.04 3.1%

Columbus, OH Minimum -1.04 -6.4% -0.12 -13.0% -0.04 -7.7% -0.15 -3.8% -0.79 -11.0% 0.05 1.6%

Columbus, OH Maximum -1.04 -6.4% -0.12 -13.0% -0.04 -7.7% -0.15 -3.8% -0.79 -11.0% 0.05 1.6%

Columbus, OH Average -1.04 -6.4% -0.12 -13.0% -0.04 -7.7% -0.15 -3.8% -0.79 -11.0% 0.05 1.6%

Detroit-Ann Arbor-Flint, MI Minimum -1.22 -6.7% -0.21 -21.6% -0.07 -9.6% -0.25 -5.1% -0.78 -12.3% 0.08 1.6%

Detroit-Ann Arbor-Flint, MI Maximum -1.22 -6.7% -0.21 -21.6% -0.07 -9.6% -0.25 -5.1% -0.78 -12.3% 0.08 1.6%

Detroit-Ann Arbor-Flint, MI Average -1.22 -6.7% -0.21 -21.6% -0.07 -9.6% -0.25 -5.1% -0.78 -12.3% 0.08 1.6%

Huntington-Ashland,

WV-KY-OH Minimum -1.26 -8.0% -0.08 -11.6% -0.11 -18.0% -0.26 -5.8% -0.86 -10.8% 0.05 3.2%
L- 6

Huntington-Ashland,

WV-KY-OH Maximum -1.26 -8.0% -0.08 -11.6% -0.11 -18.0% -0.26 -5.8% -0.86 -10.8% 0.05 3.2%

Huntington-Ashland,

WV-KY-OH Average -1.26 -8.0% -0.08 -11.6% -0.11 -18.0% -0.26 -5.8% -0.86 -10.8% 0.05 3.2%

Indianapolis, IN Minimum -0.87 -5.7% -0.11 -16.4% -0.04 -8.5% -0.13 -3.0% -0.62 -10.1% 0.03 0.9%

Indianapolis, IN Maximum -0.87 -5.7% -0.11 -16.4% -0.04 -8.5% -0.13 -3.0% -0.62 -10.1% 0.03 0.9%

Indianapolis, IN Average -0.87 -5.7% -0.11 -16.4% -0.04 -8.5% -0.13 -3.0% -0.62 -10.1% 0.03 0.9%

Knoxville, TN Minimum -1.47 -8.3% -0.14 -14.9% -0.07 -11.9% -0.29 -5.6% -1.01 -11.0% 0.03 2.2%

Knoxville, TN Maximum -1.47 -8.3% -0.14 -14.9% -0.07 -11.9% -0.29 -5.6% -1.01 -11.0% 0.03 2.2%

Knoxville, TN Average -1.47 -8.3% -0.14 -14.9% -0.07 -11.9% -0.29 -5.6% -1.01 -11.0% 0.03 2.2%

Louisville, KY-IN Minimum -1.19 -7.7% -0.16 -20.0% -0.07 -14.6% -0.17 -4.5% -0.82 -10.9% 0.03 1.3%

Louisville, KY-IN Maximum -1.21 -7.9% -0.17 -20.7% -0.07 -14.6% -0.18 -4.7% -0.84 -11.1% 0.02 0.9%

Louisville, KY-IN Average -1.20 -7.8% -0.16 -20.4% -0.07 -14.6% -0.18 -4.6% -0.83 -11.0% 0.02 1.1%

Montgomery, AL Minimum -0.79 -5.1% -0.11 -11.2% -0.04 -6.3% -0.08 -1.5% -0.60 -9.1% 0.03 2.3%

Montgomery, AL Maximum -0.79 -5.1% -0.11 -11.2% -0.04 -6.3% -0.08 -1.5% -0.60 -9.1% 0.03 2.3%

Montgomery, AL Average -0.79 -5.1% -0.11 -11.2% -0.04 -6.3% -0.08 -1.5% -0.60 -9.1% 0.03 2.3%

New

Haven-Bridgeport-StamfordWaterbury-Danbury, CT Minimum -0.68 -4.5% -0.09 -11.5% -0.05 -8.5% -0.12 -2.8% -0.53 -8.5% 0.11 3.7%

New

Haven-Bridgeport-StamfordWaterbury-Danbury, CT Maximum -0.68 -4.5% -0.09 -11.5% -0.05 -8.5% -0.12 -2.8% -0.53 -8.5% 0.11 3.7%

New

Haven-Bridgeport-StamfordWaterbury-Danbury, CT Average -0.68 -4.5% -0.09 -11.5% -0.05 -8.5% -0.12 -2.8% -0.53 -8.5% 0.11 3.7%

New York-Northern New

Jersey-Long Island,

NY-NJ-CT-PA Minimum -1.21 -7.6% -0.18 -21.2% -0.13 -15.1% -0.21 -4.8% -0.71 -11.6% 0.03 0.9%

New York-Northern New

Jersey-Long Island,

NY-NJ-CT-PA Maximum -1.21 -7.6% -0.18 -21.2% -0.13 -15.1% -0.21 -4.8% -0.71 -11.6% 0.03 0.9%

New York-Northern New

Jersey-Long Island,

NY-NJ-CT-PA Average -1.21 -7.6% -0.18 -21.2% -0.13 -15.1% -0.21 -4.8% -0.71 -11.6% 0.03 0.9%

Parkersburg-Marietta,

WV-OH Minimum -1.30 -8.3% -0.09 -12.2% -0.10 -15.9% -0.21 -5.6% -0.95 -11.3% 0.05 3.0%

Parkersburg-Marietta,

WV-OH Maximum -1.30 -8.3% -0.09 -12.2% -0.10 -15.9% -0.21 -5.6% -0.95 -11.3% 0.05 3.0%

Parkersburg-Marietta,

WV-OH Average -1.30 -8.3% -0.09 -12.2% -0.10 -15.9% -0.21 -5.6% -0.95 -11.3% 0.05 3.0%

Pittsburgh, PA Minimum -1.59 -8.5% -0.19 -18.4% -0.08 -11.9% -0.22 -4.4% -1.12 -12.5% 0.02 0.8%

Pittsburgh, PA Maximum -1.59 -8.5% -0.19 -18.4% -0.08 -11.9% -0.22 -4.4% -1.12 -12.5% 0.02 0.8%

Pittsburgh, PA Average -1.59 -8.5% -0.19 -18.4% -0.08 -11.9% -0.22 -4.4% -1.12 -12.5% 0.02 0.8%

St. Louis, MO-IL Minimum -1.01 -6.3% -0.15 -12.5% -0.06 -10.5% -0.22 -4.5% -0.59 -9.7% 0.02 0.7%

St. Louis, MO-IL Maximum -1.14 -7.1% -0.15 -13.5% -0.07 -11.3% -0.30 -6.4% -0.65 -10.5% 0.01 0.3%

St. Louis, MO-IL Average -1.08 -6.7% -0.15 -13.0% -0.06 -10.9% -0.26 -5.5% -0.62 -10.1% 0.02 0.5%

Steubenville-Weirton,

OH-WV Minimum -1.71 -10.5% -0.14 -16.9% -0.06 -12.2% -0.39 -10.1% -1.26 -15.0% 0.16 6.6%

Steubenville-Weirton,

OH-WV Maximum -1.84 -10.6% -0.15 -17.1% -0.08 -14.8% -0.42 -10.2% -1.36 -15.1% 0.14 6.3%

Steubenville-Weirton,

OH-WV Average -1.75 -10.6% -0.14 -17.0% -0.07 -13.8% -0.40 -10.2% -1.29 -15.1% 0.15 6.5%

Washington-Baltimore,

DC-MD-VA-WV Minimum -1.18 -7.3% -0.08 -9.4% -0.08 -12.1% -0.27 -5.4% -0.80 -12.1% 0.05 2.0%

Washington-Baltimore,

DC-MD-VA-WV Maximum -1.18 -7.3% -0.08 -9.4% -0.08 -12.1% -0.27 -5.4% -0.80 -12.1% 0.05 2.0%

Washington-Baltimore,

DC-MD-VA-WV Average -1.18 -7.3% -0.08 -9.4% -0.08 -12.1% -0.27 -5.4% -0.80 -12.1% 0.05 2.0%

York, PA Minimum -1.10 -7.3% -0.09 -13.8% -0.06 -11.5% -0.21 -5.3% -0.78 -11.6% 0.05 1.7%

York, PA Maximum -1.10 -7.3% -0.09 -13.8% -0.06 -11.5% -0.21 -5.3% -0.78 -11.6% 0.05 1.7%

York, PA Average -1.10 -7.3% -0.09 -13.8% -0.06 -11.5% -0.21 -5.3% -0.78 -11.6% 0.05 1.7%
L- 7

Talladega County, Alabama Minimum -0.94 -5.9% -0.10 -10.3% -0.05 -7.7% -0.09 -1.6% -0.73 -10.7% 0.04 3.0%

Talladega County, Alabama Maximum -0.94 -5.9% -0.10 -10.3% -0.05 -7.7% -0.09 -1.6% -0.73 -10.7% 0.04 3.0%

Talladega County, Alabama Average -0.94 -5.9% -0.10 -10.3% -0.05 -7.7% -0.09 -1.6% -0.73 -10.7% 0.04 3.0%

Floyd County, Georgia Minimum -1.29 -7.8% -0.19 -19.0% -0.06 -10.5% -0.14 -2.5% -0.93 -12.1% 0.02 1.7%

Floyd County, Georgia Maximum -1.29 -7.8% -0.19 -19.0% -0.06 -10.5% -0.14 -2.5% -0.93 -12.1% 0.02 1.7%

Floyd County, Georgia Average -1.29 -7.8% -0.19 -19.0% -0.06 -10.5% -0.14 -2.5% -0.93 -12.1% 0.02 1.7%

Hall County, Georgia Minimum -1.25 -8.3% -0.16 -20.3% -0.07 -12.5% -0.15 -2.8% -0.88 -12.6% 0.00 0.0%

Hall County, Georgia Maximum -1.25 -8.3% -0.16 -20.3% -0.07 -12.5% -0.15 -2.8% -0.88 -12.6% 0.00 0.0%

Hall County, Georgia Average -1.25 -8.3% -0.16 -20.3% -0.07 -12.5% -0.15 -2.8% -0.88 -12.6% 0.00 0.0%

Wilkinson County, Georgia Minimum -1.04 -6.3% -0.10 -11.6% -0.04 -5.9% -0.22 -3.6% -0.74 -10.4% 0.06 4.6%

Wilkinson County, Georgia Maximum -1.04 -6.3% -0.10 -11.6% -0.04 -5.9% -0.22 -3.6% -0.74 -10.4% 0.06 4.6%

Wilkinson County, Georgia Average -1.04 -6.3% -0.10 -11.6% -0.04 -5.9% -0.22 -3.6% -0.74 -10.4% 0.06 4.6%

Scioto County, Ohio Minimum -1.28 -7.3% -0.10 -12.2% -0.07 -10.8% -0.25 -5.2% -0.92 -10.7% 0.05 2.2%

Scioto County, Ohio Maximum -1.28 -7.3% -0.10 -12.2% -0.07 -10.8% -0.25 -5.2% -0.92 -10.7% 0.05 2.2%

Scioto County, Ohio Average -1.28 -7.3% -0.10 -12.2% -0.07 -10.8% -0.25 -5.2% -0.92 -10.7% 0.05 2.2%

Overall Minimum -0.68 -4.5% -0.08 -8.8% -0.03 -4.3% -0.07 -1.2% -0.53 -8.5% 0.16 6.6%

Overall Maximum -1.84 -10.6% -0.29 -21.8% -0.13 -19.0% -0.58 -10.6% -1.36 -15.1% 0.00 0.0%

Overall Average -1.21 -7.3% -0.14 -15.1% -0.07 -11.0% -0.22 -4.6% -0.84 -11.4% 0.05 2.1%
Technical Support Document for the 

Interstate Air Quality Rule

Air Quality Modeling Analyses

Appendix M

Projected Visibility Summaries for 20% Best and 

20% Worst Days at IMPROVE Monitoring Sites
M-1

Example Calculation of the Predicted Change in Visibility on the 20% Worst Days at Acadia National Park

The example shows the predicted improvement in visibility from 2001 to the 2015 IAQR control case

Day 1996

IMPROVE

Deciviews

 IMPROVE

bext IMPROVE

SO4 bext

IMPROVE

NO3 bext

IMPROVE

OMC bext

IMPROVE

EC bext

 IMPROVE

soil bext IMPROVE

coarse

bext

2015c

RRF

SO4 

2015c

RRF

NO3 

2015c

RRF

OMC 

2015c

RRF

EC 

2015c

RRF

soil 

2015c

RRF

coarse 

Day 1 21.66 87.27 46.45 14.53 9.10 3.87 0.46 2.86 1.04 1.01 0.85 0.77 0.96 1.04

Day 2 21.50 85.83 53.58 4.50 7.79 3.92 0.65 5.38 0.95 1.02 0.95 0.80 0.99 1.06

Day 3 23.54 105.31 67.83 11.24 8.32 4.26 0.30 3.35 0.58 0.73 0.83 0.70 1.04 1.07

Day 4 20.11 74.72 53.60 1.93 4.84 3.04 0.13 1.18 0.59 1.71 0.75 0.61 1.03 1.05

Day 5 21.85 88.93 51.43 7.38 10.18 1.43 0.25 8.26 0.62 1.84 0.81 0.62 1.09 1.09

Day 6 23.83 108.41 74.33 4.42 10.62 4.13 0.28 4.63 0.63 1.39 0.85 0.63 1.07 1.07

Day 7 24.69 118.16 80.05 5.64 12.08 7.95 0.47 1.98 0.63 1.01 0.77 0.58 1.09 1.08

Day 8 22.34 93.35 41.44 1.65 30.37 6.73 0.28 2.88 0.78 0.69 0.92 0.85 1.03 1.06

Day 9 22.47 94.56 59.46 4.35 12.62 3.66 0.29 4.19 0.92 0.64 0.83 0.64 1.06 1.08

Day 10 24.11 111.40 88.74 1.26 6.50 3.49 0.02 1.39 0.65 0.46 0.81 0.59 1.09 1.09

Day 11 32.94 269.47 235.95 1.29 15.09 6.52 0.09 0.54 0.59 0.74 0.80 0.64 1.07 1.08

Day 12 25.23 124.60 90.91 4.25 12.87 4.67 0.33 1.58 0.72 0.40 0.90 0.70 1.06 1.08

Day 13 30.50 211.16 179.59 3.26 11.20 5.42 0.07 1.62 0.57 1.26 0.77 0.62 1.08 1.07

Day 14 22.30 93.00 57.95 8.26 10.37 3.50 0.28 2.65 0.63 0.56 0.80 0.59 1.07 1.08

Day 15 24.07 111.05 77.79 3.03 13.68 3.47 0.23 2.85 0.78 1.05 0.90 0.67 1.04 1.06

Day 16 23.37 103.49 69.05 2.34 10.74 4.87 0.33 6.15 0.86 0.97 0.88 0.78 1.04 1.06

Day 17 20.27 75.91 54.19 1.77 4.66 2.45 0.16 2.69 1.00 0.91 0.94 0.96 0.97 1.04

Day 18 19.98 73.76 46.74 2.46 6.05 5.49 0.15 2.85 0.74 0.67 0.82 0.74 0.95 1.04

Day 19 22.15 91.58 65.68 2.83 6.14 4.43 0.11 2.38 0.77 1.15 0.84 0.71 0.97 1.03

Average

dv

23.52 Relative Reduction Factors are calculated from

REMSAD for each species based on the model 

1996 Observed values at the Acadia IMPROVE site predicted % reduction for each day.

(10 Mm-1 is added to each total bext value to account for Rayleigh scattering)

RRFs represent the predicted reduction from the

2001 base case to the 2015 IAQR control case.

An RRF of 0.85 indicates a 15% reduction.

2015c SO4

bext

2015c NO3

bext

2015c OMC

bext

2015c EC

bext

2015c soil

bext

2015c coarse

bext

2015c Total

bext

2015c Deciviews

48.37 14.70 7.74 2.99 0.44 2.99 87.23 21.66

50.86 4.61 7.44 3.14 0.64 5.73 82.41 21.09

39.67 8.25 6.90 2.98 0.31 3.58 71.69 19.70

31.76 3.29 3.62 1.87 0.13 1.24 51.91 16.47

31.86 13.61 8.23 0.89 0.27 9.03 73.89 20.00

46.93 6.13 9.04 2.59 0.31 4.96 79.96 20.79

50.46 5.70 9.30 4.64 0.51 2.14 82.74 21.13

32.36 1.14 27.98 5.72 0.29 3.06 80.56 20.86

54.68 2.78 10.50 2.33 0.31 4.50 85.10 21.41

57.47 0.58 5.24 2.06 0.02 1.51 76.89 20.40

138.81 0.96 12.05 4.17 0.10 0.58 166.67 28.13

65.89 1.69 11.58 3.28 0.35 1.71 94.49 22.46

101.73 4.11 8.64 3.37 0.07 1.74 129.67 25.62

36.71 4.63 8.26 2.08 0.30 2.86 64.84 18.69

60.92 3.17 12.27 2.34 0.24 3.01 91.96 22.19

59.35 2.28 9.44 3.81 0.34 6.52 91.74 22.16

54.08 1.61 4.36 2.35 0.15 2.79 75.33 20.19

34.74 1.64 4.96 4.06 0.15 2.97 58.51 17.67

50.31 3.25 5.14 3.16 0.10 2.46 74.43 20.07

Average dv 2015c 21.09

Reduction in dv from 2001-2015 control -2.43

The RRFs are multiplied by the base year bext values to get the 2015 control bext predictions.

The daily total bext values are converted to deciviews and then the deciview values are 

averaged across all days. The resultant average dv value for 2015c is subtracted from the 

observed value to get the predicted visibility improvement on the 20% worst days (-2.43 dv).
M-2

Projected Visibility Summaries for 20% Best Days at IMPROVE Monitoring Sites

IMPROVE

Site ID Site Name State

Base 2010

Improvement

from 2001

(dv)

IAQR Control

2010

Improvement

from 2001

(dv)

2010

Improvement

from IAQR

Only 

(dv)

Base 2015

Improvement

from 2001

(dv)

IAQR Control

2015

Improvement

from 2001

(dv)

2015

Improvement

from IAQR

Only 

(dv)

ACAD Acadia National Park Maine -0.25 -0.47 -0.22 -0.34 -0.56 -0.22

BADL Badlands National Park South Dakota -0.29 -0.35 -0.06 -0.33 -0.40 -0.07

BAND Bandelier National Monument New Mexico -0.28 -0.29 0.00 -0.39 -0.38 0.00

BIBE Big Bend National Park Texas -0.35 -0.35 -0.01 -0.37 -0.37 -0.01

BLIS Bliss State Park(TRPA) California -0.40 -0.41 -0.01 -0.54 -0.55 -0.01

BRCA Bryce Canyon National Park Colorado -0.30 -0.30 0.00 -0.39 -0.39 0.00

BRID Bridger Wilderness Wyoming -0.19 -0.19 0.00 -0.23 -0.23 0.00

BRIG Brigantine National Wildlife Refuge New Jersey -0.28 -0.77 -0.50 -0.27 -0.83 -0.56

CANY Canyonlands National Park Utah -0.17 -0.17 0.00 -0.18 -0.18 0.00

CHAS Chassahowitzka National Wildlife Florida -0.96 -1.91 -0.96 -1.12 -2.41 -1.29

CHIR Chiricahua National Monument Arizona -0.16 -0.16 0.00 -0.16 -0.17 0.00

CRLA Crater Lake National Park Oregon -0.30 -0.30 0.00 -0.38 -0.38 0.00

DOSO Dolly Sods /Otter Creek Wildernes West Virginia -0.43 -1.64 -1.22 -0.64 -1.98 -1.34

GICL Gila Wilderness New Mexico -0.17 -0.17 0.00 -0.19 -0.19 0.00

GLAC Glacier National Park Montana -0.48 -0.48 0.00 -0.58 -0.58 0.00

GRCA Grand Canyon- Hopi Point Arizona -0.24 -0.25 -0.01 -0.26 -0.27 -0.01

GRSA Great Sand Dunes National

Monument

Colorado -0.29 -0.29 0.00 -0.33 -0.33 0.00

GRSM Great Smoky Mountains National

Park

Tennessee -0.44 -1.21 -0.76 -0.58 -1.60 -1.02

GUMO Guadalupe Mountains National Park Texas -0.36 -0.40 -0.05 -0.40 -0.46 -0.05

JARB Jarbidge Wilderness Nevada -0.18 -0.18 0.00 -0.22 -0.22 0.00

JEFF Jefferson/James River Face

Wilderness

Virginia -0.26 -1.14 -0.88 -0.54 -1.49 -0.95

LAVO Lassen Volcanic National Park California -0.32 -0.32 0.00 -0.41 -0.41 0.00

LYBR Lye Brook Wilderness Vermont -0.34 -0.50 -0.16 -0.44 -0.59 -0.15

MACA Mammoth Cave National Park Kentucky -0.76 -1.43 -0.67 -0.95 -1.64 -0.69

MEVE Mesa Verde National Park Colorado -0.35 -0.35 0.00 -0.38 -0.38 0.00

MOOS Moosehorn NWR Maine -0.20 -0.37 -0.17 -0.26 -0.42 -0.16

MORA Mount Rainier National Park Washington -0.48 -0.48 0.00 -0.58 -0.58 0.00

MOZI Mount Zirkel Wilderness Colorado -0.18 -0.18 0.00 -0.19 -0.19 0.00

OKEF Okefenokee National Wildlife Refuge Georgia -0.54 -1.18 -0.64 -0.67 -1.46 -0.80

PEFO Petrified Forest National Park Arizona -0.27 -0.27 0.00 -0.29 -0.29 0.00

PINN Pinnacles National Monument California -0.65 -0.65 0.00 -0.82 -0.82 0.00

PORE Point Reyes National Seashore California -0.76 -0.76 0.00 -0.92 -0.92 0.00

REDW Redwood National Park California -0.22 -0.22 0.00 -0.24 -0.24 0.00

ROMA Cape Romain National Wildlife

Refuge

South

Carolina

-0.36 -0.95 -0.59 -0.42 -1.15 -0.73

SAGO San Gorgonio Wilderness California -0.36 -0.36 0.00 -0.43 -0.43 0.00

SEQU Sequoia National Park California -0.52 -0.52 0.00 -0.65 -0.65 0.00

SHEN Shenandoah National Park Virginia -0.23 -1.34 -1.10 -0.43 -1.56 -1.13

SHRO Shining Rock Wilderness North Carolina -0.24 -0.75 -0.51 -0.31 -0.98 -0.67

SIPS Sipsy Wilderness Alabama -0.57 -1.08 -0.51 -0.71 -1.26 -0.54

SOLA South Lake Tahoe California -0.78 -0.79 0.00 -1.07 -1.07 -0.01

THIS Three Sisters Wilderness Idaho -0.23 -0.23 0.00 -0.29 -0.29 0.00

TONT Tonto National Monument Arizona -0.23 -0.23 0.00 -0.24 -0.24 0.00

UPBU Upper Buffalo Wilderness Arkansas -0.55 -0.83 -0.28 -0.70 -1.06 -0.36

WEMI Weminuche Wilderness Colorado -0.28 -0.28 0.00 -0.35 -0.35 0.00

YOSE Yosemite National Park California -0.45 -0.45 0.00 -0.31 -0.31 0.00
M-3

Projected Visibility Summaries for 20% Worst Days at IMPROVE Monitoring Sites

IMPROVE

Site ID Site Name State

Base 2010

Improvement

from 2001

(dv)

IAQR Control

2010

Improvement

from 2001

(dv)

2010

Improvement

from IAQR

Only 

(dv)

Base 2015

Improvement

from 2001

(dv)

IAQR Control

2015

Improvement

from 2001

(dv)

2015

Improvement

from IAQR

Only 

(dv)

ACAD Acadia National Park Maine -0.97 -2.03 -1.06 -1.24 -2.43 -1.20

BADL Badlands National Park South Dakota -0.54 -0.95 -0.41 -0.73 -1.17 -0.44

BAND Bandelier National Monument New Mexico -0.56 -0.64 -0.08 -0.75 -0.85 -0.10

BIBE Big Bend National Park Texas -0.30 -0.34 -0.04 -0.33 -0.39 -0.06

BLIS Bliss State Park(TRPA) California -1.15 -1.15 0.00 -1.58 -1.58 0.00

BRCA Bryce Canyon National Park Colorado -0.73 -0.74 -0.01 -0.91 -0.92 -0.01

BRID Bridger Wilderness Wyoming -0.84 -0.85 -0.01 -1.01 -1.02 -0.01

BRIG Brigantine National Wildlife Refuge New Jersey -0.71 -2.24 -1.52 -1.03 -2.70 -1.67

CANY Canyonlands National Park Utah -0.57 -0.57 -0.01 -0.66 -0.67 -0.01

CHAS Chassahowitzka National Wildlife Florida -1.46 -3.05 -1.59 -1.70 -3.69 -1.98

CHIR Chiricahua National Monument Arizona -0.23 -0.25 -0.02 -0.23 -0.25 -0.02

CRLA Crater Lake National Park Oregon -1.34 -1.35 -0.01 -1.63 -1.65 -0.01

DOSO Dolly Sods /Otter Creek Wildernes West Virginia -1.36 -3.92 -2.56 -2.02 -4.62 -2.61

GICL Gila Wilderness New Mexico -0.58 -0.61 -0.03 -0.72 -0.76 -0.04

GLAC Glacier National Park Montana -0.70 -0.70 0.00 -0.87 -0.88 -0.01

GRCA Grand Canyon- Hopi Point Arizona -0.59 -0.62 -0.03 -0.67 -0.71 -0.04

GRSA Great Sand Dunes National

Monument

Colorado -0.65 -0.67 -0.02 -0.73 -0.76 -0.02

GRSM Great Smoky Mountains National

Park

Tennessee -1.38 -3.55 -2.17 -1.94 -4.52 -2.58

GUMO Guadalupe Mountains National Park Texas -0.42 -0.53 -0.11 -0.47 -0.60 -0.13

JARB Jarbidge Wilderness Nevada -0.90 -0.90 0.00 -1.14 -1.14 0.00

JEFF Jefferson/James River Face

Wilderness

Virginia -1.11 -2.98 -1.88 -1.75 -3.83 -2.07

LAVO Lassen Volcanic National Park California -1.03 -1.03 0.00 -1.26 -1.26 0.00

LYBR Lye Brook Wilderness Vermont -0.70 -1.77 -1.06 -0.95 -2.02 -1.07

MACA Mammoth Cave National Park Kentucky -1.88 -4.10 -2.22 -2.47 -5.08 -2.62

MEVE Mesa Verde National Park Colorado -0.79 -0.80 0.00 -0.88 -0.88 0.00

MOOS Moosehorn NWR Maine -0.77 -1.85 -1.09 -0.98 -2.12 -1.14

MORA Mount Rainier National Park Washington -1.67 -1.67 0.00 -1.89 -1.89 0.00

MOZI Mount Zirkel Wilderness Colorado -0.68 -0.69 -0.01 -0.76 -0.78 -0.02

OKEF Okefenokee National Wildlife Refuge Georgia -0.99 -2.32 -1.33 -1.27 -2.91 -1.64

PEFO Petrified Forest National Park Arizona -0.51 -0.54 -0.03 -0.58 -0.60 -0.02

PINN Pinnacles National Monument California -1.25 -1.26 -0.01 -1.67 -1.68 -0.02

PORE Point Reyes National Seashore California -1.43 -1.47 -0.04 -1.85 -1.90 -0.05

REDW Redwood National Park California -1.66 -1.66 0.00 -1.96 -1.96 0.00

ROMA Cape Romain National Wildlife

Refuge

South

Carolina

-0.51 -1.80 -1.29 -0.71 -2.36 -1.66

SAGO San Gorgonio Wilderness California -2.08 -2.08 0.00 -2.88 -2.88 0.00

SEQU Sequoia National Park California -1.63 -1.63 0.00 -2.25 -2.25 0.00

SHEN Shenandoah National Park Virginia -1.00 -3.43 -2.43 -1.62 -4.25 -2.63

SHRO Shining Rock Wilderness North Carolina -1.67 -3.70 -2.04 -2.21 -4.62 -2.41

SIPS Sipsy Wilderness Alabama -1.28 -3.29 -2.01 -1.86 -4.35 -2.49

SOLA South Lake Tahoe California -1.39 -1.39 0.00 -1.89 -1.89 0.00

THIS Three Sisters Wilderness Idaho -1.52 -1.52 0.00 -1.88 -1.88 0.00

TONT Tonto National Monument Arizona -0.68 -0.70 -0.02 -0.76 -0.79 -0.03

UPBU Upper Buffalo Wilderness Arkansas -0.57 -2.43 -1.85 -1.02 -3.13 -2.10

WEMI Weminuche Wilderness Colorado -0.72 -0.75 -0.02 -0.88 -0.90 -0.02

YOSE Yosemite National Park California -1.32 -1.32 0.00 -1.59 -1.59 0.0