1

Technical Support Document (TSD)

for the Final Transport Rule

Docket ID No. EPA-HQ-OAR-2009-0491

Significant Contribution and State Emissions Budgets

Final Rule TSD

U.S. Environmental Protection Agency

Office of Air and Radiation

June 2011
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This Technical Support Document (TSD) provides information that supports 

EPA’s analysis to quantify upwind state emissions that significantly contribute to 

nonattainment or interfere with maintenance of the National Ambient Air Quality

Standards (NAAQS) in downwind states in the final Transport Rule. The analysis is 

described in detail in section VI in the preamble to the final rule. This TSD is organized 

as follows:

A. Background on EPA’s Analysis to Quantify Emissions that Significantly Contribute 

to Nonattainment or Interfere with Maintenance

B. Electric Generating Unit Significant Contribution Cost Analysis

C. Analysis of Significant Contribution Using the Air Quality Assessment Tool (AQAT)

1. Introduction: Use and development of the air quality assessment tool

2. Details on the construction of the air quality assessment tool

3. Description of the results of the analysis using AQAT for the final approach

4. Comparison between the air quality assessment tool estimates and CAMx air 

quality modeling estimates

A. Background on EPA’s Analysis to Quantify Emissions that Significantly Contribute 

or Interfere with Maintenance

Sections V and VI of the final Transport Rule (TR) preamble describe EPA’s 

approach to identify upwind states’ emissions that significantly contribute to 

nonattainment or interfere with maintenance downwind with respect to the 1997 and 

2006 PM2.5 NAAQS and the 1997 ozone NAAQS. As described in the preamble, the 

approach uses air quality analysis to identify monitoring sites with projected 

nonattainment and maintenance problems for the PM2.5 and ozone NAAQS as well as 

upwind states whose contributions to these monitoring sites exceed specified threshold 

amounts. See sections V.C and V.D in the TR preamble and the Air Quality Modeling 

Final Rule TSD for a detailed discussion of these air quality analyses.

As described in TR preamble section VI, after identifying upwind-to-downwind 

linkages based on air quality contribution thresholds, EPA uses a multi-step process to 

quantify each state’s significant contribution and interference with maintenance. The 

first step in the process identifies the emissions projected to remain in each state at 

ascending cost thresholds of emissions reductions. See section B in this TSD for 

discussion of this analysis. Next, the process uses an air quality assessment tool (AQAT) 

to estimate the impact of the upwind state reductions on downwind state air quality at 

different cost-per-ton levels. See section C in this TSD for discussion of the development 

and use of the air quality assessment tool.

TR preamble section VI.D reviews the cost and air quality impact analyses 

referenced in preamble sections VI.B and VI.C and explains EPA’s determination of the 

resulting specific cost thresholds that are used to quantify each state’s significant 

contribution to nonattainment and interference with maintenance. The determination was 
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the outcome of first examining, state-by-state, emissions at different cost thresholds for 

the regulated pollutants SO2 and NOX. EPA started by examining cost thresholds of 

$500/ton for these two pollutants in 2012. Then, as explained in TR preamble section VI, 

raising the SO2 cost threshold to $2,300/ton for Group 1 states in 2014. Group 2 states 

remained at $500/ton in 2014 and each year thereafter because it was determined their 

contribution to nonattainment and interference with maintenance was eliminated at that 

point. All states remained at $500/ton for annual and ozone season NOx for reasons 

explained in TR preamble section VI. Remaining emissions at these cost thresholds 

represent each state’s emissions after the removal of significant contribution to 

nonattainment and maintenance (or in the case of some states for ozone season NOx, the 

progress towards such removal). Cost thresholds are in 2007 dollars and were applied 

accordingly to the states listed in the table below.
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Table A-1. Geography to Which Cost Thresholds Were Applied

SO2 Group 1 SO2 Group 2 Annual NOx Ozone Season NOx

Illinois Alabama Illinois Alabama

Indiana Georgia Indiana Arkansas

Iowa Kansas Iowa Florida

Kentucky Minnesota Kentucky Georgia

Maryland Nebraska Maryland Illinois

Michigan

South 

Carolina Michigan Indiana

Missouri Texas Missouri Iowa*

New Jersey New Jersey Kansas*

New York New York Kentucky

North Carolina

North 

Carolina Louisiana

Ohio Ohio Maryland

Pennsylvania Pennsylvania Michigan*

Tennessee Tennessee Mississippi

Virginia Virginia Missouri*

West Virginia West Virginia New Jersey

Wisconsin Wisconsin New York

Alabama North Carolina

Georgia Ohio

Kansas Oklahoma*

Minnesota Pennsylvania

Nebraska South Carolina

South 

Carolina Tennessee

Texas Texas

Virginia

West Virginia

Wisconsin*

*Indicates a state (IA, KS, MI, OK, WI, and MO) that is included in the supplemental notice of 

proposed rulemaking for ozone-season NOx emission reductions. See the preamble for details.

A set of Excel spreadsheet files containing AQAT data supporting the final 

Transport Rule’s determination of emissions that constitute significant contribution to 

nonattainment and interference with maintenance is available in the docket for this 

rulemaking (Docket ID No. EPA–HQ–OAR–2009–0491). Appendix C in this TSD 

describes these files.
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B. Electric Generating Unit Significant Contribution Cost Analysis

EPA used its updated version 4.10_FTransport of the Integrated Planning Model 

(IPM) to analyze the annual SO2, annual NOx, and ozone-season NOx emissions 

reductions available from electric generating units (EGUs) at various cost levels in each 

upwind state. IPM is a multiregional, dynamic, deterministic linear programming model 

of the U.S. electric power sector that EPA uses to analyze cost and emissions impacts of 

environmental policies. See “Documentation for EPA Base Case v.4.10 Using the 

Integrated Planning Model” and “Documentation Supplement for EPA Base Case 

v.4.10_FTransport – Updates for Final Transport Rule” for further description of the IPM 

model.

EPA first modeled a base case EGU emissions scenario (i.e., a scenario absent 

any emission reduction requirements related to the Transport Rule). The base case 

modeling includes the Title IV SO2 cap and trade program; NOx SIP Call regional ozone 

season cap and trade program; settlements; and state and federal rules as listed in the IPM 

documentation referenced above. As explained in section V.B of the preamble, the base 

case does not include the Clean Air Interstate Rule (CAIR), which will be replaced by 

this rule.

Using IPM, EPA then modeled the emissions that would occur within each state 

at ascending cost thresholds of emissions control. EPA designed a series of IPM runs 

that imposed increasing cost thresholds for SO2, annual NOx, and ozone-season NOx

emissions and tabulated those projected emissions for each state at each cost level. EPA 

refers to these tabulations as “cost curves” in TR preamble section VI.B.1 The remaining 

emissions at each cost threshold reflect that which remains after the state has made 

emission reductions that are available for cost that are less than the particular cost 

threshold. 

This part of the analysis applied cost thresholds to all fossil-fuel-fired EGUs with 

a capacity greater than 25 MW in each state covered under the relevant Transport Rule 

control program. At all cost thresholds analyzed, emissions projected for covered states 

reflect the operation of all existing SO2 and NOx pollution controls on a year-round basis 

in states covered by the Transport Rule for PM2.5 and all existing NOx pollution controls 

on an ozone-season basis in states covered by the Transport Rule only for ozone. As 

described in the Transport Rule preamble, EPA did not believe it would be appropriate 

for sources in “linked” states to not operate existing controls, and therefore forced them 

 

1 These projected state level emissions for each “cost threshold” run are presented in a several formats. The 

IPM analysis outputs available in the docket contain a “state emissions” file for each analysis. The file 

contains three worksheets, the first is titled “all units” which shows aggregate emissions for all units in the 

state. The second is titled “all fossil > 25MW” and shows emissions for a subset of these units that have a 

capacity greater than 25 MW. The emissions in the “all fossil > 25 MW” worksheet are used to derive the 

budgets for each state at the appropriately determined cost threshold. The “fossil & biomass” worksheet 

reports total emissions from fossil-fired and biomass-fired units, and represents the state level emission 

total used in the AQAT analysis. These “fossil & biomass” emission totals are used as inputs for CAMx air 

quality modeling, which is why those emissions were used as inputs for AQAT. In the Transport Rule 

proposal Technical Support Document “Analysis to Quantify Significant Contribution”, EPA stated that the 

“all units” emissions used in the AQAT analysis for the proposal and the emissions used in the CAMx air 

quality modeling were slightly different. EPA committed to determining the origin of the slight difference 

and removing it for the final Transport Rule. The emissions in the “Fossil & Biomass” correct this slight 

difference.
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to operate in each of these costing runs. Because of the time required to build advanced 

pollution controls, the model was prevented from building any new post-combustion 

controls such as selective catalytic reduction (SCR) or flue-gas desulfurization (FGD) in 

2012 in response to the cost thresholds.2 The modeling does allow the addition or 

upgrading of NOx combustion controls in 2012 (such as Low NOx Burners (LNB)).

EPA first conducted this “cost curve” analysis for ozone-season NOx. EPA 

imposed cost thresholds ranging from $500 per ton to $5,000 per ton of ozone-season 

NOx. These cost thresholds were applied to the states covered by the final Transport Rule 

for ozone control as well as to the states for which EPA is issuing a supplemental 

proposal to require ozone-season reductions, as discussed in section III of the Transport 

Rule preamble. The IPM-projected EGU emissions of ozone-season NOx from the 

“Fossil > 25 MW” units are shown at each cost threshold for 2012 and 2014 in Table B1.3

EPA then conducted cost curve analysis for annual NOx, imposing cost thresholds 

ranging from $500 to $2,500 per ton in states covered in the final Transport Rule for 

PM2.5 NAAQS. The IPM-projected EGU emissions of annual NOx from the “Fossil > 25 

MW” units are shown at each cost threshold for 2012 and 2014 in Table B-2.

As explained in TR preamble section VI.D, EPA determined that $500/ton was 

the appropriate cost threshold for ozone-season NOx control at all covered states in this 

rulemaking. EPA also determined that $500/ton was the appropriate cost threshold for 

annual NOx control at all covered states in concert with varying degrees of SO2 control to 

eliminate significant contribution and interference with maintenance of the PM2.5

NAAQS. In line with these determinations, EPA conducted cost curve analysis for SO2

while simultaneously imposing cost thresholds of $500/ton for ozone-season NOx in 

Transport Rule ozone states and $500/ton for annual NOx in Transport Rule PM2.5 states. 

While holding these ozone-season NOx and annual NOx cost thresholds constant, EPA 

examined different SO2 cost thresholds.

For SO2 emissions, the lowest cost threshold that EPA examined was $500 per ton 

starting in 2012 and for each year thereafter. EPA did not examine higher cost thresholds 

for 2012 as higher costs induce advanced control retrofits that require a longer lead time 

for installation. EPA, however, did examine higher cost thresholds for SO2 in 2014 for 

Group 1 states. Before doing so, EPA first used the Air Quality Assessment Tool 

(AQAT) to identify improvements in downwind air quality at $500 per ton. EPA 

determined that for 7 states, emission reductions at this $500 per ton threshold 

successfully eliminated significant contribution and interference with maintenance at

downwind receptors, as those receptors no longer had projected nonattainment and/or 

 

2 While there are no advanced controls built in 2012 modeling in response to cost thresholds or TR policy 

signals, EPA notes that there are newly built 2012 control retrofits, in its base case modeling, costing runs, 

and subsequent remedy runs. However, these control retrofits represent those that are already announced 

and/or under construction and expected to be online by 2012, or controls that were built in the base case in 

response to consent decree or state rule requirements.

3 EPA notes that while the TR ozone-season emissions generally come down as states move higher on the 

cost curve, there are instances where a state may see a small increase in emissions as cost increase. This is 

related to the interconnected nature of the grid, and the ability of generation to shift from a less 

efficienct/higher emitting source in one state to a more efficient/lower emitting source in another state at 

higher cost levels. As described later, when determining state budgets, EPA takes steps to ensure that no 

state’s 2012 ozone-season budget is greater than its base case emissions.
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maintenance problems when emissions were limited by a $500/ton cost level. These 7 

states - Alabama, Georgia, Kansas, Nebraska, Minnesota, South Carolina, and Texas –

are referred to as Group 2 states. Because their significant contribution and interference 

with maintenance was eliminated at this $500/ton threshold, higher SO2 cost thresholds 

were not examined for these states in 2014. For all subsequent cost curve analysis, a 

constant $500/ton threshold was imposed on these states’ SO2 emissions.

For the remainder of the states covered for PM2.5, EPA examined increasing cost 

thresholds for SO2 in 2014. EPA examined cost levels of $1,600/ton, $2,300/ton, 

$2,800/ton, and $3,300/ton as a representative sampling of points along the SO2 cost 

curve explored at proposal. To assess the upper bounds of the cost curve, EPA examined 

a very stringent scenario by restricting each Group 1 state’s 2014 SO2 emissions to 

approximately 30% of that state’s emissions modeled at the $3,300/ton level in 2014. 

When this type of quantity constraint was imposed, the marginal cost projected by the 

model to reach such emission levels was approximately $10,000/ton, and therefore EPA 

refers to this scenario as the “$10,000/ton” cost threshold scenario for the remainder of 

this document and throughout the final Transport Rule materials. See Table Appendix A1 in Appendix A for a list of IPM analyses.. 

In the cost curves with ascending SO2 cost thresholds, cost thresholds for each 

pollutant (SO2, ozone-season NOx, and annual NOx) were analyzed simultaneously. This 

methodology for the final rule’s analysis represents a technical improvement on the 

analysis used in the proposal, where cost thresholds for each pollutant were examined 

independently with no emission control cost assumed for the other two pollutants (see 

Appendix Table A-1). The final rule’s cost curves reflect a price signal for all pollutants 

for which that state is covered. This finalized approach better captures the real-world 

interactions between simultaneous SO2, annual NOx, and ozone season NOx policy

requirements across the states covered by the Transport Rule. Cost-effective actions 

taken to reduce annual NOx, for example, may influence the cost of reducing SO2. The 

modeling of these final cost curves captures these important economic interactions.

At each cost threshold examined with the IPM model, the model outputs include 

state emission totals from “All Fossil and Biomass” as well as from “All fossil > 25 

MW” are reported. The “All Fossil and Biomass” totals are meant to reflect total state 

EGU emissions used for subsequent air quality modeling. The “All Fossil > 25 MW” 

totals represent an approximation of emissions from EGUs subject to the Transport Rule. 

These two state level totals are very close in value. The later is generally slightly lower 

as it is a subset of the former. Table B-6 shows the state-level SO2 emissions from fossil 

and biomass units as the Group 1 2014 cost threshold is varied in these final cost curve 

runs. Note that although the Group 1 cost threshold is the only cost threshold that 

changes, emission levels in some Group 2 states may change as a consequence of 

generation shifting resulting from the higher Group 1 cost threshold. Changes in Group 2 

state-level emissions in this analysis reveal the interconnected nature of the power sector 

and the fact that generation and fuel consumption patterns are not independently

determined inside each state. As a result, emission levels from EGUs may vary in a 

given state based on decisions taken by EGUs in other states connected to the same grid.

These resulting state SO2 emissions levels from “All Fossil and Biomass” at each 

of these cost thresholds analyzed were examined in AQAT to determine the impact on 

downwind air quality. Section VI.D of the TR preamble explains how EPA considered 
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the results of the cost and air quality analyses described in this TSD to determine the 

appropriate set of cost thresholds for eliminating significant contribution to 

nonattainment and interference with maintenance. EPA used the emissions from all 

fossil and biomass EGUs in its air quality modeling to capture the impact of all upwind 

EGU emissions on downwind receptors as explained in section C of this document.

EPA used the remaining state level emissions from the “All Fossil > 25 MW” at the final 

cost threshold levels to determine state budgets, as this set of units generally reflect those 

EGUs covered by the Transport Rule. Transport Rule applicability is explained in 

section VII.B of the preamble. The state level emissions for ozone-season NOx, annual 

NOx, and SO2 emissions from fossil units greater than 25 MW are shown in Tables B-3 

through B-5 below and provided in TR Docket. These tables show how state level 

emissions for each of these pollutants change as the cost threshold is varied for Group 1 

SO2 states in the “final cost curves”. 

 As explained in preamble section VI.D, EPA identified $2,300/ton as the 

appropriate cost threshold in Group 1 states for addressing significant contribution and 

interference with maintenance. EPA notes that the modeling of the $2,300/ton cost 

threshold for Group 1 states includes simultaneously application of all of the selected cost

thresholds for defining significant contribution and interference with maintenance under 

the Transport Rule. For example, it imposes a $500/ton cost threshold for SO2, NOX, and 

ozone-season NOX in TR states covered for those emissions in 2012 and 2013. It

imposes a $2,300/ton threshold on Group 1 state SO2 emissions, a $500/ton threshold on 

Group 2 SO2 emissions, a $500/ton threshold on annual NOx emissions, and a $500/ton 

threshold on ozone-season NOx in TR states covered for those emissions in 2014 and

each year thereafter. Because the $2,300/ton IPM analysis included all of these selected 

cost thresholds under the final Transport Rule, EPA relied on that IPM analysis’s 

projected remaining state level emissions in 2012 and 2014 from all fossil units greater 

than 25 MW as the basis for the state budgets in those years. Hence, the values in Tables 

B-3 through B-5 generally represent emissions levels after the removal of significant 

contribution and therefore are the appropriate basis for the state budgets reflected in 

section VI.D of the Transport Rule preamble. In most cases, these remaining emission 

levels from fossil-fired units greater than 25 MW become the state budget levels. There 

are few exceptions and they are noted below and in the Transport Rule preamble. 

As explained in section VI.D of the TR preamble, no state’s 2014 budget may be 

larger than its 2012 budget for that pollutant. In instances where the above approach 

would have yielded a higher state budget in 2014 relative to its 2012 state budget, than 

2014 budgets were set equal to the state’s 2012 budget for that pollutant. For instance, a 

Group 2 state with a constant $500/ton thresholds in all years for SO2 may have 

experienced upward pressure on its 2014 emissions due to emissions leakage from Group 

1 states that had a higher cost threshold simultaneously imposed starting in year 2014. 

However, the steps above prevent such emission increases in states by ensuring that 

budgets do not increase between 2012 and 2014. Additionally, there were five states 

whose 2012 ozone-season emissions in this analysis were not significantly different from 

their 2012 base case projected emissions. EPA conducted a sensitivity analysis to 

confirm that if left uncapped, these states’ ozone-season emissions would increase as 

other states make Transport Rule-related emission reductions, due to emissions leakage. 

EPA is therefore setting these states’ ozone-season budgets equal to their 2012 base case 
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emissions to prevent such emission increases. Further explanation of this issue is 

provided in section VI.D of the Preamble.4 There were also several adjustments made 

based on technical corrections described in Section VIII.A of the Transport Rule 

preamble.

The IPM run analyses performed for the cost analyses are listed in Table 

Appendix A-1 in of this TSD. This table lists the name of each IPM run next to a 

description of the run. The runs themselves can be found in the rulemaking docket. In 

the TR preamble section VI.B, the emissions that reflect state emissions under particular

cost thresholds analyzed are presented rounded to the nearest thousand ton. The bugets 

given in section VI.D of the TR preamble (which are based off tables B-3 through B-5 

below) are presented rounded to the nearest ton. In Tables B-1 through B-6 the 

emissions are presented rounded to the nearest ton.

As noted above, EPA used the emissions shown in Table B-6 as input to the air 

quality assessment tool (AQAT) to estimate the impact that the combined reductions 

available from states covered under the Transport Rule, at different cost-per-ton levels, 

would have on air quality at downwind monitors that had nonattainment and/or 

maintenance issues. In AQAT, the EGU emissions at each cost-per-ton level, were taken 

directly from the IPM runs. Section C in this TSD describes EPA’s development and use 

of the AQAT and the results from our AQAT analysis. Section C also compares the 

AQAT results to those produced using the Comprehensive Air Quality Model with 

Extensions (CAMx).

 

4 EPA made these 2012 adjustments for ozone-season NOX budgets given that the 2012 compliance 

deadline is coordinated with the June 2013 maximum attainment deadline for ozone nonattainment areas. 

EPA made a similar review for SO2 and annual NOX budgets in 2014 (which is coordinated with the April 

2015 maximum attainment deadline), and found that no such adjustments were necessary.
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Table B-1. 2012 & 2014 Ozone Season NOx EGU Emissions* for Each State at 

Various Pollution Control Cost Thresholds per Ton of Reduction (Tons).

State

Base Case Emission 

Levels $500/ton $1,000/ton $5,000/ton

2012 2014 2012 2014 2012 2014 2012 2014

Alabama 34,074 31,365 34,203 31,372 33,951 31,393 30,831 29,824

Arkansas 15,037 16,644 14,995 16,565 14,944 16,432 13,969 14,970

Florida 41,646 45,993 27,069 29,607 27,029 29,122 24,277 26,866

Georgia 29,106 19,293 28,185 18,331 28,033 18,323 25,413 17,569

Illinois 21,371 22,043 21,266 21,961 21,313 21,859 20,844 21,505

Indiana 46,877 46,086 46,123 46,471 46,190 46,174 42,769 41,374

Iowa 18,307 19,440 16,526 17,082 16,308 16,996 15,227 15,776

Kansas 16,126 13,967 13,502 10,849 13,502 10,730 12,030 9,506

Kentucky 37,588 35,296 36,687 34,957 36,221 34,573 33,548 32,483

Louisiana 13,433 13,924 13,435 13,910 13,451 13,910 13,301 13,728

Maryland 7,179 7,540 7,238 7,540 7,235 7,540 6,983 7,293

Michigan 25,989 28,037 26,058 26,250 25,771 26,180 25,381 25,168

Mississippi 10,161 11,212 10,164 11,212 10,153 11,212 9,106 9,592

Missouri 23,156 23,759 22,952 23,759 22,952 23,661 21,433 21,707

New Jersey 3,440 3,668 3,448 3,669 3,407 3,668 3,361 3,648

New York 8,336 9,031 8,329 9,035 8,420 8,910 8,039 8,525

North Carolina 22,902 20,169 22,904 20,182 22,642 19,997 21,240 18,949

Ohio 42,274 41,327 42,302 40,493 41,863 40,375 38,437 38,348

Oklahoma 31,415 31,723 21,574 22,059 20,998 21,328 20,009 19,456

Pennsylvania 52,895 54,217 52,626 54,134 52,444 53,842 49,279 49,444

South Carolina 15,145 16,586 15,108 16,351 14,946 15,958 13,594 14,745

Tennessee 15,505 12,141 15,512 12,126 15,486 12,126 14,715 11,613

Texas 64,711 65,492 63,081 64,341 62,872 64,448 60,419 62,453

Virginia 15,148 15,339 14,662 15,299 14,599 15,116 12,543 13,575

West Virginia 26,464 27,099 26,350 27,014 26,151 26,819 23,988 24,485

Wisconsin 15,876 16,048 13,971 14,134 13,928 14,035 12,412 12,897

Total 654,161 647,439 618,267 608,702 614,807 604,728 573,150 565,498

*Source: Integrated Planning Model run by EPA, 2011. See Appendix A for list and description of these IPM runs. Emissions have 

been rounded to the nearest ton. Emissions shown for all fossil-fired units greater than 25 MW when only an ozone season cost 

constraint is applied to Transport Rule States. Costs are in 2007$. 
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Table B-2. 2012 and 2014 Annual NOx EGU Emissions* for Each State at Various 

Pollution Control Cost Thresholds per Ton of Reduction (Tons).

State Base Case Emission 

Levels $500/ton $1,000/ton $2,500/ton

2012 2014 2012 2014 2012 2014 2012 2014

Alabama 82,005 74,937 78,468 71,685 77,859 71,670 75,292 70,060

Georgia 66,384 47,808 63,073 40,809 62,921 40,712 59,713 39,457

Illinois 51,969 54,661 48,150 50,541 48,160 50,237 48,665 49,385

Indiana 119,625 116,552 109,506 108,187 108,610 107,176 108,241 99,876

Iowa 42,563 44,614 38,262 39,539 37,875 39,247 36,647 37,319

Kansas 37,106 32,390 30,991 25,075 30,759 24,815 30,194 23,190

Kentucky 88,136 83,481 85,396 82,657 84,572 81,024 82,150 78,087

Maryland 16,602 17,444 16,590 17,444 16,496 17,409 16,380 17,396

Michigan 60,594 64,345 60,725 61,088 60,482 60,877 59,991 60,110

Minnesota 36,833 37,952 29,588 30,441 29,537 30,432 29,427 30,294

Missouri 53,199 54,528 52,892 54,411 52,827 54,103 50,799 51,036

Nebraska 42,985 43,410 26,481 26,741 26,108 26,374 25,497 20,611

New Jersey 7,391 7,858 7,398 7,866 7,264 7,867 7,124 7,740

New York 17,556 18,505 17,551 18,519 17,643 18,378 17,317 18,290

North 

Carolina 51,902 46,130 52,021 45,755 51,584 45,617 50,856 43,777

Ohio 100,420 99,389 98,473 94,680 97,444 94,143 94,702 91,686

Pennsylvania 129,125 132,299 120,709 124,106 120,307 123,942 119,063 115,990

South 

Carolina 34,635 37,862 34,548 37,549 34,305 37,029 32,640 35,996

Tennessee 37,674 29,256 37,676 29,315 37,654 29,395 36,450 28,680

Texas 136,124 140,788 133,141 138,150 132,861 137,582 131,931 136,062

Virginia 34,567 35,798 33,490 34,785 33,178 34,642 32,416 27,610

West 

Virginia 61,792 64,182 61,702 64,102 61,560 63,831 59,906 60,555

Wisconsin 36,701 36,904 32,078 32,267 31,975 32,008 30,811 30,766

Total 1,345,888 1,321,093 1,268,907 1,235,710 1,261,982 1,228,509 1,236,210 1,173,972

*Source: Integrated Planning Model run by EPA, 2011. See Appendix A for list anda description of these IPM runs. Emissions have 

been rounded to the nearest ton. Emissions shown for all fossil-fired units greater than 25 MW when only an ozone season cost 

constraint is applied to Transport Rule States. Costs are in 2007$. 
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Table B-3. 2012 & 2014 Ozone Season NOx EGU Emissions from all Fossil Units Greater than 25 MW

at Escalating SO2 Cost Thresholds from Final Cost Curve Analysis (Tons).

State

Base Case $500 $1,600 $2,300 $2,800 $3,300 $10,000 

2012 2014 2012 2014 2012 2014 2012 2014 2012 2014 2012 2014 2012 2014

Alabama 34,074 31,365 32,285 30,954 32,091 31,481 31,746 31,499 31,749 31,509 31,749 31,513 35,056 31,624 

Arkansas 15,037 16,644 15,087 16,652 15,087 16,759 15,087 16,794 15,087 16,794 15,087 16,794 16,690 16,867 

Florida 41,646 45,993 27,888 29,657 27,825 29,925 27,825 29,894 27,825 29,700 27,825 29,700 29,034 30,143 

Georgia 29,106 19,293 27,949 18,184 27,948 18,259 27,944 18,279 27,878 18,444 27,878 18,449 29,784 18,320 

Illinois 21,371 22,043 21,208 21,791 21,212 21,589 21,208 21,383 21,208 21,222 21,202 21,010 19,936 19,536 

Indiana 46,877 46,086 47,788 46,249 47,348 46,734 47,351 46,175 47,357 45,774 47,365 45,482 44,011 42,999 

Iowa 18,307 19,440 16,532 17,135 16,532 16,848 16,532 16,207 16,532 16,174 16,532 16,172 14,055 14,570 

Kansas 16,126 13,967 13,536 10,590 13,536 10,709 13,536 10,998 13,536 11,164 13,536 11,207 14,116 11,392 

Kentucky 37,588 35,296 36,204 34,515 36,204 32,952 36,167 32,674 36,178 32,729 36,178 31,650 33,623 24,214 

Louisiana 13,433 13,924 13,581 13,925 13,582 13,861 13,614 13,897 13,509 13,998 13,513 14,015 13,898 14,204 

Maryland 7,179 7,540 7,285 7,540 7,285 7,276 7,284 7,248 7,164 7,141 7,164 7,141 6,781 6,911 

Michigan 25,989 28,037 25,757 26,032 25,752 25,550 25,752 24,727 25,752 24,427 25,752 24,566 23,955 22,388 

Mississippi 10,161 11,212 10,644 11,244 10,644 11,345 10,644 11,345 10,642 11,345 10,642 11,345 11,385 11,486 

Missouri 23,156 23,759 22,762 23,299 22,762 22,136 22,762 21,073 22,762 20,679 22,762 20,072 18,284 17,430 

New Jersey 3,440 3,668 3,377 3,684 3,377 3,661 3,382 3,652 3,383 3,646 3,383 3,646 4,396 3,287 

New York 8,336 9,031 8,358 9,045 8,357 9,029 8,331 9,032 8,359 9,030 8,359 9,028 8,214 8,983 

North Carolina 22,902 20,169 22,241 19,707 22,209 18,454 22,168 18,455 22,172 18,442 22,172 18,104 17,657 16,767 

Ohio 42,274 41,327 40,114 39,081 40,136 36,890 40,063 37,792 39,907 37,674 39,867 36,758 27,779 29,813 

Oklahoma 31,415 31,723 21,836 22,063 21,835 22,110 21,835 22,110 21,859 22,110 21,840 22,110 21,822 22,321 

Pennsylvania 52,895 54,217 52,207 53,407 52,242 52,251 52,201 51,912 52,166 51,755 52,150 51,689 44,186 48,207 

South Carolina 15,145 16,586 14,165 15,711 14,050 15,696 13,909 16,060 13,943 16,181 13,943 16,224 16,673 16,400 

Tennessee 15,505 12,141 14,908 9,700 14,908 8,443 14,908 8,016 14,908 8,016 14,908 8,019 10,585 8,803 

Texas 64,711 65,492 63,010 64,369 63,042 64,432 63,043 64,450 63,043 64,462 62,856 64,464 63,872 64,547 

Virginia 15,148 15,339 14,437 15,387 14,449 14,823 14,452 15,250 14,458 14,930 14,452 14,946 11,721 13,712 

West Virginia 26,464 27,099 25,418 27,014 25,434 24,475 25,283 23,291 25,092 23,655 25,092 24,364 17,932 22,778 

Wisconsin 15,876 16,048 13,771 13,867 13,718 13,631 13,704 13,216 13,705 12,802 13,703 12,371 11,564 9,465 

Total 654,161 647,439 612,348 600,802 611,565 589,319 610,731 585,429 610,174 583,803 609,910 580,839 567,009 547,167

*Source: Integrated Planning Model run by EPA, 2011. See Appendix A for list and description of these IPM runs. Emissions have been rounded to the nearest ton. These “final cost curve” runs have NOx and ozone season NOx cost 

thresholds at $500/ton (all years), SO2 Group 2 at $500/ton (all years), and SO2 Group 1 (2012-2013) at $500/ton. The escalating cost thresholds identified in the column headers above only apply starting in 2014 for Group 1 SO2 states. 

Costs are in 2007$
13

Table B-4. 2012 & 2014 NOx EGU Emissions from all Fossil Units Greater than 25 MW 

at Escalating SO2 Cost Thresholds from Final Cost Curve Analysis (Tons).

State Base Case $500 $1,600 $2,300 $2,800 $3,300 $10,000 

2012 2014 2012 2014 2012 2014 2012 2014 2012 2014 2012 2014 2012 2014

Alabama 82,005 74,937 73,772 70,582 73,127 71,787 72,691 71,962 72,748 72,033 72,538 72,088 80,949 72,410

Georgia 66,384 47,808 61,601 40,349 62,014 40,425 62,010 40,540 61,948 40,706 61,421 40,742 66,945 40,743

Illinois 51,969 54,661 47,890 50,293 47,874 49,495 47,872 48,478 47,874 48,282 47,869 48,171 43,323 44,244

Indiana 119,625 116,552 110,396 107,081 109,790 109,291 109,726 108,424 109,642 107,305 109,592 106,426 101,584 99,243

Iowa 42,563 44,614 38,335 39,549 38,335 38,762 38,335 37,498 38,288 36,709 38,288 36,637 32,212 34,101

Kansas 37,106 32,390 30,714 24,379 30,714 24,782 30,714 25,560 30,714 25,779 30,714 25,811 32,312 26,063

Kentucky 88,136 83,481 85,200 81,786 85,124 77,999 85,086 77,238 85,034 76,974 84,905 73,977 72,916 56,152

Maryland 16,602 17,444 16,634 17,364 16,634 16,604 16,633 16,574 16,513 16,330 16,513 16,330 15,633 15,906

Michigan 60,594 64,345 60,200 60,541 60,193 59,135 60,193 57,812 60,193 57,677 60,193 57,562 55,437 51,034

Minnesota 36,833 37,952 29,573 30,377 29,571 31,021 29,572 31,345 29,573 31,354 29,529 31,350 30,986 31,818

Missouri 53,199 54,528 52,373 53,633 52,373 50,742 52,374 48,717 52,374 47,277 52,374 46,505 42,689 39,797

Nebraska 42,985 43,410 26,444 26,546 26,440 26,739 26,440 26,739 26,478 26,739 26,478 26,739 26,489 26,822

New Jersey 7,391 7,858 7,245 7,903 7,245 7,851 7,266 7,825 7,257 7,800 7,263 7,795 9,477 7,025

New York 17,556 18,505 17,536 18,547 17,534 18,531 17,543 18,549 17,569 18,544 17,574 18,542 17,119 17,951

North Carolina 51,902 46,130 50,960 44,897 51,020 41,916 50,587 41,553 50,586 41,049 50,587 40,040 39,839 37,982

Ohio 100,420 99,389 92,500 91,476 92,822 86,866 92,703 87,493 92,555 87,358 92,382 84,866 64,064 69,029

Pennsylvania 129,125 132,299 119,984 123,299 120,031 120,528 119,986 119,194 119,799 118,829 119,788 118,853 100,823 110,275

South Carolina 34,635 37,862 33,143 36,191 32,856 36,355 32,498 36,821 32,531 37,110 32,532 37,318 38,093 37,705

Tennessee 37,674 29,256 36,208 23,458 36,208 20,381 35,703 19,337 34,092 19,329 33,596 19,343 23,995 20,743

Texas 136,124 140,788 133,596 138,268 133,671 138,358 133,595 138,410 132,835 138,413 132,223 138,415 136,850 138,400

Virginia 34,567 35,798 33,133 35,607 33,156 34,790 33,242 34,903 33,246 34,606 33,011 34,704 26,351 31,083

West Virginia 61,792 64,182 59,606 63,625 59,622 56,738 59,472 54,582 59,280 55,301 59,280 56,565 40,804 52,565

Wisconsin 36,701 36,904 31,828 31,640 31,716 31,398 31,628 30,398 31,633 29,207 31,533 28,090 26,042 21,663

Total 1,345,888 1,321,093 1,248,871 1,217,391 1,248,070 1,190,494 1,245,869 1,179,952 1,242,762 1,174,711 1,240,183 1,166,869 1,124,932 1,082,754

*Source: Integrated Planning Model run by EPA, 2011. See Appendix A for list and description of these IPM runs. Emissions have been rounded to the nearest ton. These “final cost curve” runs have NOx and ozone season NOx

cost thresholds at $500/ton (all years), SO2 Group 2 at $500/ton (all years), and SO2 Group 1 (2012-2013) at $500/ton. The escalating cost thresholds identified in the column headers above only apply starting in 2014 for Group 1 SO2

states. Costs are in 2007$
14

Table B-5. 2012 & 2014 SO2 EGU Emissions from all Fossil Units Greater than 25 MW 

at Escalating SO2 Cost Thresholds from Final Cost Curve Analysis (Tons).

State Base Case $500 $1,600 $2,300 $2,800 $3,300 $10,000 

2012 2014 2012 2014 2012 2014 2012 2014 2012 2014 2012 2014 2012 2014

Alabama 455,503 417,009 210,559 200,573 221,896 226,299 216,033 213,258 219,088 213,991 223,903 235,837 234,732 189,743

Georgia 405,933 169,702 157,474 94,105 158,455 94,142 158,527 95,231 159,484 95,484 158,022 94,946 174,898 97,942

Illinois 485,417 137,522 230,622 134,311 233,080 129,881 234,889 124,123 234,889 117,375 234,876 101,789 160,616 35,735

Indiana 776,359 711,265 285,584 245,191 294,517 178,525 285,424 161,111 285,099 152,954 282,070 120,532 159,737 69,382

Iowa 121,663 127,354 97,556 112,000 107,085 77,765 107,085 75,184 106,969 66,507 106,969 44,711 56,120 12,852

Kansas 68,490 69,767 41,528 55,250 41,528 57,372 41,528 60,811 41,528 61,193 41,528 61,360 45,235 45,465

Kentucky 520,531 487,990 176,229 160,567 185,426 126,374 189,335 106,284 189,830 102,868 191,235 88,755 75,486 45,958

Maryland 49,942 42,926 30,123 32,187 30,123 28,288 30,120 28,203 30,072 25,712 30,072 23,609 25,048 18,368

Michigan 252,411 265,611 194,537 206,173 194,537 188,646 194,537 143,995 194,537 105,223 194,537 93,569 115,742 23,884

Minnesota 64,524 66,268 41,981 43,336 41,981 45,191 41,981 45,638 41,981 45,628 41,880 45,618 43,119 44,257

Missouri 375,771 381,939 194,109 212,349 207,466 173,022 207,466 165,941 207,466 109,378 207,466 83,546 138,781 21,387

Nebraska 70,754 71,821 65,054 68,214 65,052 70,223 65,052 70,223 65,079 70,223 65,079 70,223 65,220 66,051

New Jersey 26,346 38,857 5,583 7,069 5,583 7,008 5,574 6,611 5,554 6,506 5,554 6,469 5,374 4,602

New York 51,243 40,416 20,550 20,657 20,578 20,037 20,497 11,823 20,515 10,928 20,515 9,871 14,917 8,105

North Carolina 144,554 120,441 117,658 103,780 134,827 60,725 136,881 57,620 136,942 48,683 136,942 40,047 35,412 30,440

Ohio 871,401 831,648 311,386 293,727 325,562 174,809 310,230 137,077 309,272 123,021 308,557 114,919 99,078 65,201

Pennsylvania 493,206 507,360 278,972 294,283 279,394 164,089 278,651 112,021 277,647 107,249 278,771 101,520 75,867 74,761

South Carolina 184,045 209,538 82,993 92,761 84,431 99,853 88,620 103,371 89,183 104,311 89,180 104,462 106,928 104,924

Tennessee 324,372 284,463 143,276 82,154 150,768 63,323 148,150 58,833 144,319 58,810 142,874 58,802 65,994 24,360

Texas 445,715 452,978 244,281 280,938 244,281 281,706 243,954 283,743 242,082 281,325 239,973 281,325 282,288 242,508

Virginia 80,889 64,917 70,810 58,969 70,820 50,806 70,820 35,057 70,758 33,380 69,647 31,563 18,870 15,963

West Virginia 535,586 497,398 146,239 157,335 148,095 121,751 146,174 75,668 144,206 74,373 143,472 71,505 47,973 55,246

Wisconsin 131,199 124,862 79,833 51,443 79,664 47,172 79,480 40,126 79,508 37,515 79,066 33,727 55,015 13,805

Total 6,935,854 6,122,052 3,226,937 3,007,372 3,325,149 2,487,007 3,301,008 2,211,952 3,296,008 2,052,637 3,292,188 1,918,705 2,102,450 1,310,939

*Source: Integrated Planning Model run by EPA, 2011. See Appendix A for list and description of these IPM runs. Emissions have been rounded to the nearest ton. These “final cost curve” runs have NOx and ozone season NOx

cost thresholds at $500/ton (all years), SO2 Group 2 at $500/ton (all years), and SO2 Group 1 (2012-2013) at $500/ton. The escalating cost thresholds identified in the column headers above only apply starting in 2014 for Group 1 SO2

states. Costs are in 2007$
15

Table B-6. 2012 & 2014 Transport Rule State SO2 EGU Emission Total Used in AQAT Modeling (Tons)

Base Case $500 $1,600 $2,300 $2,800 $3,300 $10,000 

State Group 2012 2014 2012 2014 2012 2014 2012 2014 2012 2014 2012 2014 2012 2014

Alabama 2 455,825 417,340 210,886 200,905 222,223 226,634 216,360 213,593 219,414 214,326 224,230 236,172 235,074 190,078

Georgia 2 406,279 170,288 157,838 94,691 158,820 94,745 158,891 95,834 159,849 96,087 158,386 95,549 175,457 98,523

Illinois 1 489,140 141,606 235,127 138,815 237,585 134,386 239,393 128,997 239,393 122,249 239,381 106,945 165,772 40,892

Indiana 1 789,116 727,786 299,438 262,386 308,439 196,258 299,346 179,539 299,021 171,784 295,991 139,546 175,756 89,307

Iowa 1 127,102 133,083 102,989 117,830 112,450 83,661 112,450 81,137 112,334 72,460 112,334 50,664 64,589 23,429

Kansas 2 68,541 69,819 41,587 55,308 41,587 57,432 41,587 60,870 41,587 61,252 41,587 61,419 45,295 45,524

Kentucky 1 520,546 488,006 176,244 160,582 185,441 126,390 189,350 106,299 189,845 102,883 191,251 88,770 75,502 45,973

Maryland 1 49,942 42,926 30,123 32,187 30,123 28,288 30,120 28,203 30,072 25,712 30,072 23,609 25,048 18,368

Michigan 1 255,038 269,434 197,385 210,163 197,384 192,884 197,380 148,232 197,380 109,506 197,380 97,932 120,259 29,350

Minnesota 2 67,816 70,937 45,321 47,720 45,300 49,589 45,300 50,213 45,300 50,203 45,199 50,193 46,972 49,281

Missouri 1 383,314 390,287 201,504 221,689 214,803 182,508 214,803 175,480 214,861 118,917 214,861 93,085 149,341 41,805

Nebraska 2 71,905 73,073 66,204 69,466 66,203 71,475 66,203 71,475 66,230 71,475 66,230 71,475 66,371 67,303

New Jersey 1 26,346 38,857 5,583 7,069 5,583 7,008 5,574 6,611 5,554 6,506 5,554 6,469 5,374 4,602

New York 1 56,461 42,887 26,006 23,181 26,041 22,618 25,960 14,404 25,735 13,399 25,735 12,342 20,095 10,588

North 

Carolina 1 148,606 126,048 122,063 109,612 139,232 66,643 141,263 63,577 141,311 54,717 141,311 46,081 40,187 36,326

Ohio 1 882,559 851,199 327,015 313,193 341,192 202,443 325,375 166,691 324,417 153,471 323,702 145,431 130,251 98,812

Pennsylvani

a 1 495,463 509,650 281,272 296,596 281,681 166,402 280,938 114,431 279,934 109,658 281,058 103,929 78,272 77,170

South 

Carolina 2 186,355 213,281 85,479 96,504 86,917 103,596 91,106 107,114 91,669 108,055 91,666 108,660 109,715 109,122

Tennessee 1 324,377 284,468 143,281 82,159 150,773 63,328 148,155 58,838 144,324 58,815 142,879 58,807 66,001 24,366

Texas 2 446,006 453,332 244,613 281,298 244,613 282,066 244,287 284,132 242,414 281,721 240,305 281,721 282,685 242,905

Virginia 1 92,468 77,256 83,019 71,505 83,029 63,367 83,029 47,639 82,772 45,962 81,661 44,145 31,527 28,545

West 

Virginia 1 536,695 498,507 147,349 158,445 149,205 122,860 147,284 76,778 145,315 75,483 144,582 72,615 49,083 56,356

Wisconsin 1 135,828 130,538 85,168 57,418 85,110 53,147 84,925 46,205 84,895 43,585 84,453 39,797 60,984 19,431

Total 7,015,727 6,220,607 3,315,495 3,108,724 3,413,731 2,597,726 3,389,078 2,326,289 3,383,625 2,168,226 3,379,807 2,035,357 2,219,608 1,448,054

*Source: Integrated Planning Model run by EPA, 2011. See Appendix A for list and description of these IPM runs. Emissions are shown for all fossil and biomass units. These “final cost curve” runs have NOx and ozone season 

NOx cost thresholds at $500/ton (all years), SO2 Group 2 at $500/ton (all years), and SO2 Group 1 (2012-2013) at $500/ton. The escalating cost thresholds identified in the column headers above only apply starting in 2014 for Group 

1 SO2 states. Costs are in 2007$
16

C. Analysis of Significant Contribution Using the Air Quality Assessment Tool

EPA has defined significant contribution to nonattainment and interference with 

maintenance using a multi-factor test (described in section VI.D of the preamble) which 

is based on both cost and air quality factors. A key quantitative input for determining the 

amount of significant contribution is the predicted downwind ambient air quality impacts 

of upwind EGU emission reductions under the SO2 and NOX cost thresholds. Time and 

resource limitations (in particular the amount of time needed to set up, run the CAMx 

model, and analyze the results for a single model run) precluded the use of air quality

modeling for all but a few emissions scenarios. Because EPA needed to evaluate 

emission reductions under several different SO2 cost thresholds, it was not possible to use 

CAMx air quality modeling to evaluate all cases.

EPA thus used a simplified air quality assessment tool (AQAT) to estimate the 

downwind air quality impacts from the SO2 cost thresholds. For the SO2 cost thresholds, 

the state-by-state EGU emissions are projected using EPA’s IPM model under a given 

cost threshold of emission reductions (see section B of this TSD for details about the IPM 

model runs and for the emission projections). The air quality impacts of these cost 

thresholds are then estimated using AQAT. The simplified tool allows the Agency to 

analyze many more SO2 cost thresholds than would otherwise be possible. The remainder 

of section C of this document will:

● Present an introduction and overview of AQAT;

● Describe the construction of AQAT;

● Provide the results of the SO2 cost threshold analyses;

● Compare the AQAT estimates and CAMx results for sulfate and total PM2.5

for two emissions scenarios where CAMx modeling was performed (i.e., the 

2014 base case and 2014 remedy); and

● Describe the results of an analysis of emissions “leakage” for 2012 performed 

using AQAT.

1. Introduction: Development of the air quality assessment tool.

AQAT was developed specifically for use in the Transport Rule significant 

contribution analysis. EPA described AQAT in detail in the proposed Transport Rule and 

took comment on the tool. For this final rule, EPA refined both the construction and 

application of AQAT. Significant changes made since proposal and in response to 

comments include: 

● Reliance on CAMx modeling for the evaluation of downwind ozone 

concentrations and the nitrate component of ambient PM2.5 (i.e., AQAT was 

not used to estimate air quality changes due to emission changes in NOx);

● Calibration of AQAT’s predicted change in sulfate concentrations to change 

in SO2 emissions using CAMx. This calibration is receptor-specific and is 

based on the changes in SO2 emissions and resulting sulfate concentrations 
17

between the 2012 base case and an AQAT calibration scenario5in 2014 (for 

more details about this scenario, see the footnote and the brief description 

below).

● Use of seasonal contributions, and seasonal relative response factors, in 

developing the relationship between SO2 reductions and 24-hour PM2.5

concentrations; and 

Application of these seasonal relative response factors to estimate annual and 

24-hour PM2.5 average and maximum design values for the cost levels 

analyzed. 

As described in section VI.B of the preamble, EPA determined that the $500/ton 

threshold for upwind annual and ozone-season NOx control is appropriate for the final 

Transport Rule. Because this threshold corresponds to the NOx control strategy modeled 

in the AQAT calibration scenario, EPA used the CAMx modeling from this scenario 

directly rather than develop an AQAT for ozone. Additionally, EPA used the nitrate 

predictions from the CAMx modeling of the AQAT calibration scenario to calculate the 

nitrate component of annual and 24-hour PM2.5 design values at the various SO2 cost 

thresholds analyzed for the final Transport Rule. EPA created and used two separate 

versions of AQAT (annual and 24-hour PM2.5) to estimate the impact of the SO2 emission 

reductions on ambient sulfate concentrations for the two NAAQS, respectively. For both 

versions of AQAT, the sulfate estimates were combined with CAMx estimates of nitrate 

and other pollutant species calculated from the AQAT calibration scenario to estimate 

concentrations of total PM2.5. Most of the steps used in the construction of the annual 

and 24-hour PM2.5 versions of AQAT are the same. Consequently, when EPA refers to a 

single AQAT, the description applies to both the annual and 24-hour versions of the tool. 

Step-by-step descriptions of these tools are found in section C.2 of this document. Where 

differences in the construction of the tools are present, the differences are described.

A critical factor in AQAT is the establishment of a relationship between SO2

emission reductions and reductions in sulfate. For the purposes of developing and using 

AQAT to compare the air quality impacts of SO2 emission reductions under various SO2

cost thresholds, we determine the relationship between changes in emissions and changes 

in sulfate contributions on a receptor-by-receptor basis. Specifically, as a start EPA 

assumed that within the range of total SO2 emissions being considered (as defined by the 

SO2 cost thresholds), a change in SO2 emissions leads to a proportional change in 

downwind sulfate contributions. This proportional relationship was then modified using 

calibration factors based on air quality modeling, as described below.

 

5

An integral input to the creation and use of AQAT was CAMx air quality modeling of the 

AQAT calibration scenario. This scenario was created prior to the development of AQAT for the final 

Transport Rule and it’s EGU emissions modeling reflects the geography and cost thresholds from the 

preferred remedy of the proposed Transport Rule. Specifically, this scenario uses IPM to model cost 

thresholds of $500/ton for annual and ozone-season NOx for states proposed to be regulated for PM2.5 and 

ozone respectfully in the proposed Transport Rule; $500/ton for SO2 in PM2.5 Group 2 states from the 

proposed Transport Rule; and $2,000/ton for SO2 in PM2.5 Group 1 states from the proposed Transport 

Rule. Note that the geography and SO2 cost thresholds for this scenario differ from the geography and SO2

cost thresholds for the final Transport Rule. 
18

Within AQAT, the relationships between upwind emissions and downwind air 

quality are defined using the 2012 base case contribution air quality modeling and a 2014 

AQAT calibration scenario5. As described in the Air Quality Modeling Final Rule TSD, 

CAMx state-by-state source-apportionment modeling was used to quantify the 

contributions to sulfate at PM2.5 monitoring sites due to SO2 emissions from each 

upwind state for the 2012 base case emission scenario. For example, from the output of 

the CAMx source apportionment modeling, we know the annual average sulfate 

contribution at a downwind monitor resulting from the specific SO2 emissions in the 

2012 base case from a particular upwind state. Similarly, we also know the sulfate 

contributions for each quarter of the year (January—March, April—June , July—

September , and October—December). In AQAT, we associate a change in emissions 

from that upwind state with a particular change in its downwind contribution. In

“uncalibrated” AQAT, for example, we assume that a 20% decrease in the upwind state’s 

emissions leads to a 20% decrease in its downwind contribution. This relationship is 

calibrated using emission reductions from the 2012 base case to the 2014 AQAT 

calibration scenario by calculating the relationship between the relative change in sulfate 

at each receptor using CAMx air quality modeling and the relative change in sulfate at 

each receptor using AQAT. This AQAT calibration scenario as described further in the 

Air Quality Modeling Final Rule TSD,reflected SO2 and NOx emission reductions of 

similar stringency and from the same geography as the Transport Rule proposal. Using

this relationship, it was possible to calibrate AQAT’s sulfate response for use in assessing 

sulfate under various SO2 cost thresholds. This is described further in section C.2 of this 

document. For the example above, where a 20% reduction in emissions resulted in a 

20% decrease in contribution, using “calibrated” AQAT may yield a 15% reduction in 

concentration from the 20% reduction in emissions (as derived directly from the emission 

reduction and concentration change from the 2012 base case to the 2014 AQAT 

calibration scenario).

For the proposal AQAT was applied assuming a linear relationship6between 

reductions in upwind SO2 emissions and air quality improvements at downwind monitors

 

6 As described in the proposed Transport Rule Analysis to Quantify Significant Contribution TSD, 

understanding the relationship between emissions and air quality involves looking at some of the chemical 

reactions involved in the formation of PM2.5. PM2.5 concentration is comprised of several chemical species 

including related forms of particulate sulfate and particulate nitrate. The atmospheric chemical reactions 

that convert SO2 to particulate sulfate are central to understanding the relationship between emissions and 

particulate formation. Both gas-phase and aqueous-phase processes can be important in the formation of 

particulates. 

In both phases, the reaction is presumably dependent on complex effects from oxidants, possibly 

leading to a nonlinear response in sulfate formation (particularly for the aqueous phase). In the gas phase, 

the reaction depends on hydroxyl radical (OH) concentrations, which depend indirectly on NOX and VOC 

concentrations, as well as sunlight intensity. In the aqueous phase, the rate of formation in solution is 

dependent on oxidants in solution such as H2O2 and O3. During certain times and situations, such as the 

winter months when H2O2 concentrations may be low and SO2 concentrations are high, the response in 

sulfate formation may be nonlinear. Some of the factors and reagents (among others) affecting the 

reactions include NH3, NOX and VOC concentrations, sunlight intensity, and temperature. (Atmospheric 

Chemistry and Physics: From Air Pollution to Climate Change (2nd Edition). 2006. John H. Seinfeld & 

Spyros N. Pandis. Published by John Wiley & Sons, Inc., Hoboken, New Jersey).

The air quality assessment tool was not designed or intended to account for the non-linear 

relationships between emissions and air quality. In contrast to the assessment tool, the CAMx modeling 
19

and that this linearity held to the point that zero SO2 emissions yield zero sulfate 

formation. However, for the final Transport Rule, this relationship is now calibrated for 

the the range of emission reductions examined by EPA. 

In the application of AQAT, we assume that the reduction of a ton of emissions of 

SO2 from the upwind state has an equivalent air quality effect downwind (on an air 

quality impact per ton basis), regardless of source sector or the location of the particular 

emission source within the state where the ton was reduced. For example, reducing one 

ton of SO2 emissions from the power sector is assumed to have the same downwind 

sulfate reduction as reducing one ton of SO2 emissions of from the mobile source sector. 

Commenters on the proposed Transport Rule suggested that EPA develop sector-specific 

contribution factors for use in AQAT. However, relying on the contribution from all 

sources is reasonable for the analysis of EGU costs and reductions because total SO2

emissions in 2012 are dominated by emissions from EGUs.

While less rigorous than the air quality models used for attainment 

demonstrations, EPA has established that AQAT is a cost-effective tool for estimating the 

downwind sulfate reductions due to upwind SO2 emission reductions for the air quality 

input to the multi-factor test for the final Transport Rule. The evidence substantiating 

this is found in section C.4 in this TSD. 

Section C.2, below, is a technical explanation of the construction of AQAT. 

Readers who prefer to access the results of the analysis using the AQAT tool are directed 

to section C.3. Comparisons between AQAT and the CAMx modeling for the 2014 base 

case and the 2014 remedy can be found in section C.4

2. Details on the construction of the air quality assessment tool.

(a) Overview of AQAT.

This section describes the step-by-step development process for AQAT. In 

AQAT, EPA links state-by-state SO2 emission reductions (from IPM) with CAMx 

modeled sulfate contributions in order to predict PM2.5 concentrations at different cost 

thresholds at monitoring sites with projected nonattainment and/or maintenance problems 

in the 2012 base case.. The reduction in sulfate contributions and resulting air quality

improvement were then considered in a multi-factor test for defining significant 

contribution and interference with maintenance. In the analysis for a given receptor, 

emissions were reduced in only those upwind states that were “linked” to that receptor 

(i.e., contributed an air quality impact at or above the 1 percent -- of the NAAQS 

standard -- air quality threshold) as well as the state that contained that receptor 

(regardless of that state’s contribution). For a discussion of the 1% threshold, see section 

V.D of the preamble. 

Specifically, the key estimates from AQAT for each receptor are:

● The sulfate contribution as a function of emissions at each cost threshold, for 

each upwind state that is contributing above the 1 percent air quality threshold 

and the state containing the receptor.

 

explicitly accounts for interactions and nonlinearities in the atmospheric reactions, the effects of transport 

and diffusion, and the uneven geographic distribution of sources and controls across a state.
20

● The sulfate contribution under base case SO2 emissions, for each upwind state 

that is not above the 1 percent air quality threshold for that receptor. These 

base level emissions may be reduced in future years (i.e., 2014) compared to 

the 2012 base case level due to EGU, mobile source, and other source-sector 

reductions.

● The non-sulfate concentrations under emissions modeled for the 2014 AQAT 

calibration scenario.

The results of the AQAT analysis for each cost threshold can be found in section 

C.3 of this document.

(b) Data used to construct AQAT for the final Transport Rule.

Several data sources were used to construct the calibrated AQAT for the final 

Transport Rule. Three data sources provide the necessary initial information to construct 

the uncalibrated versions of annual and 24-hour PM2.5 AQAT. The uncalibrated annual 

and 24-hour PM2.5 versions of AQAT were used to create AQAT estimates of sulfate 

response under SO2 and NOx emissions defined by the AQAT calibration scenario. The 

datasets required to construct the annual and 24-hour versions of AQAT included: the 

2012 base case SO2 emission inventories from all source sectors used in the source 

apportionment CAMx air quality modeling; the CAMx 2012 contributions for each 

upwind state to each downwind receptor; and the 2014 AQAT calibration scenario SO2

emissions inventories from all source sectors. An additional dataset, 2014 sulfate 

concentrations from CAMx for the AQAT calibration scenario, was used to compare the 

AQAT-estimated sulfate concentrations for this scenario to the corresponding air quality

modeling results, and develop calibration factors to align the response of sulfate to 

changes in SO2 emissions in AQAT with the response predicted by CAMx.. These 

calibration factors were then used to create a “calibrated” AQAT. Finally, EGU SO2

emissions (from IPM) at each cost threshold were used to generate AQAT air quality

results using calibrated AQAT. The base case emissions inventories for 2012 and 2014, 

as well as the CAMx 2012 source apportionment air quality modeling results are 

discussed in preamble sections V.C and V.D, respectively. The EGU emissions for each 

cost threshold (projected using IPM) including the base case are listed in Table B-6 and 

described in section B of this TSD. To construct the annual PM2.5 version of AQAT, the 

emissions and CAMx air quality modeling estimates were at an annual time-scale. To 

construct the 24-hour PM2.5 version of AQAT, both the emissions and CAMx air quality

modeling estimates were at a quarterly time-scale.

As described in section C.2.(c).5. of this TSD, for estimating the design values in 

the 24-hour PM2.5 version of AQAT, an additional data set was necessary, the PM2.5 

components (including sulfate) for 8 days in each quarter for each year between 2003-

2007 projected to the 2012 base case and the 2014 AQAT calibration scenario case.

As described in the Air Quality Modeling Final Rule TSD and section V.D of the 

preamble, the air quality contributions and emissions were modeled for the following 38 
21

states: Alabama, Arkansas, Connecticut, Delaware, District of Columbia7, Florida, 

Georgia, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, 

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

New Jersey, New York, North Carolina, North Dakota, Ohio, Oklahoma, Pennsylvania, 

Rhode Island, South Carolina, South Dakota, Tennessee, Texas, Vermont, Virginia, West 

Virginia, and Wisconsin. Thus, in AQAT, these states had the possibility of making 

reductions in emissions leading to changes in air quality contributions at the downwind 

receptors. Additionally, due to the modeling domain, AQAT is only able to estimate 

changes in PM2.5 concentrations from monitors within these states. AQAT does not 

quantify contributions from states outside the CAMx modeling domain used for the 

Transport Rule (see the Air Quality Modeling Final Rule Technical Support Document). 

Therefore, the contributions and emissions from all other states were not varied in 

evaluating SO2 cost thresholds. 

(c) Detailed outline of the process for constructing and utilizing AQAT for the final 

approach.

The annual and 24-hour PM2.5 versions of AQAT were created and used in a 

multi-step process. First, annual and 24-hour PM2.5 versions of AQAT were created 

specifically for calibration. As described in the following paragraphs, the 24-hour 

version of AQAT simulated each of four quarters in the a to represent seasonal 

differences in the response of sulfate to reductions in emissions of SO2. Next, the relative 

sulfate response from AQAT was calibrated to the sulfate response from CAMx using the 

change in emissions from the 2012 base case to the 2014 AQAT calibration scenario. 

This was done on an annual basis for the annual PM2.5 version of AQAT and on a 

quarterly basis for the 24-hour PM2.5 version of AQAT. Next, the calibrated annual and 

24-hour versions of AQAT were used to evaluate the sulfate response of emission 

reductions for each SO2 cost threshold assessed. For the annual PM2.5 AQAT, at each 

cost threshold, the sulfate values were combined with other PM2.5 constituents from the 

AQAT calibration scenario resulting in estimated annual PM2.5 design values. An 

additional step was necessary in the 24-hour PM2.5 AQAT to calculate final design 

values, which was to project the adjusted sulfate change in each quarter to the 

representative modeled “days

8

” in each quarter using relative response factors. For each 

day, the other PM2.5 constituents were added using the estimates from the AQAT 

calibration scenario. For the 24-hour AQAT, for each projected year, the 98th percentile 

value was selected. The 98th percentile values were then used to predict 2014 design 

values for 24-hour PM2.5. This section describes the details behind these steps.9

As summarized above, one key difference between the 24-hour PM2.5 AQAT used 

in the analysis for the proposed and final Transport Rule was the refinement of the SO2 to 

 

7 Maryland was treated as a separate state in this analysis, rather than as combined with the District of 

Columbia. Its emissions were totaled separately and the changes in emissions occurring at different 

marginal costs were applied to upwind contributions from Maryland alone.

8 As described in section C.2.(c).5 of this TSD, 8 days were simulated in each quarter, for a total of 32 

days per year. 32 days were mapped to each year over the 2003-2007 time frame and projected to the 2012 

or 2014 year.

9 Details on procedures for calculating average and maximum design values can be found in the Air Quality

Modeling Final Rule Technical Support Document.
22

sulfate response, by season. For the 24-hour PM2.5, version of AQAT used for the final 

Transport Rule, 4 quarterly-specific components for estimating seasonal responses for 

24-hour PM2.5 assessments were created. In response to public comment on the CAMx 

air quality estimates from the proposal and the comparison with the AQAT estimates 

from the proposal, EPA conducted further analysis that demonstrated seasonal 

differences in the PM2.5 response to SO2/NOx emission reductions. EPA determined that 

creating 4 quarterly components to the 24-hour PM2.5 AQAT to assess the quarterly 

response of downwind sulfate to upwind SO2 reductions would be beneficial in 

adequately accounting for seasonal differences in the relationship between emissions and 

sulfate formation. Quarters were determined based on calendar year (i.e. January, 

February, and March were quarter 1; April, May, and June were quarter 2; July, August, 

and September were quarter 3; and October, November, and December were quarter 4). 

Each quarterly component of the 24-hour PM2.5 AQAT was based on quarterly-specific 

emissions, quarterly-specific contributions from the CAMx 2012 base case state-by-state 

source apportionment modeling, and quarterly sulfate values from the 2012 base case and 

2014 AQAT calibration scenarios. Consequently, EPA developed quarterly-specific 

calibration factors, and used these to estimate quarterly-specific relative response factors

and resulting sulfate concentrations for each cost threshold level of SO2 emissions (these 

steps are described later in this TSD). 

The AQAT calibration scenario played a key role in calibrating AQAT for use in 

the final Transport Rule. The intent of this scenario was to create a calibration point 

within the range of all emission reductions examined by EPA using AQAT. This 

calibration point was used to create site-specific calibration factors so that the response of 

sulfate concentrations to upwind SO2 emission changes would more-closely align with 

sulfate estimates from CAMx. To fill this role, EPA used the results of IPM modeling of 

a control scenario5 with similar level and geographic distribution to the preferred remedy

from the proposed Transport Rule. Selection of this AQAT calibration scenario was not 

an indication of the level of SO2 reduction that would be achieved by the final Transport 

Rule. This scenario only served to develop the calibration points for AQAT which 

allowed EPA to reasonably assess the downwind impacts of SO2 reductions both more 

and less stringent than the AQAT calibration scenario. 

In order to facilitate understanding of the calibration process, EPA is including an 

example monitor for evaluation in this text: monitor number 261630033 in Wayne 

County, Michigan, with a 2012 base case predicted 24-hour PM2.5 average design value 

of 39.48 g/m3and maximum design value of 39.82 g/m3. Additional details for all 

monitors can be found in the referenced tables in the docket. 

(1) Create uncalibrated annual and 24-hour PM2.5 versions of AQAT for calibration

To create the annual and the 24-hour PM2.5 versions of AQAT for calibration, 

EPA used emissions and contributions to estimate the change in predicted sulfate due to 

SO2 emission reductions under the AQAT calibration scenario relative to the 2012 base 

case. These “uncalibrated” versions of AQAT are directly comparable to those from the 

proposed Transport Rule (with the exception that for the final rule the 24-hour PM2.5 

AQAT is constructed using quarterly components rather than annual). 
23

First, EPA calculated annual and quarterly state-level 2012 base case total SO2

emissions from all source sectors. These emissions estimates were used for the CAMx 

2012 source apportionment modeling. This emissions data is divided into multiple 

source sectors for the purposes of air quality modeling: power sector point (from IPM), 

non-power sector point, non-point, onroad, nonroad, C3 marine, alm, and fires (see the 

Emissions Inventory Final Rule TSD for additional details on the emissions inventories 

used in the CAMx air quality modeling). The state-level total SO2 emissions are the sum 

of emissions from all these source sectors. Next, EPA calculated the annual and quarterly 

state-level 2014 total SO2 emissions across all source sectors for the AQAT calibration 

scenario. EPA calculated the ratio of 2014 total SO2 emissions for the AQAT calibration 

scenario to 2012 total SO2 emissions for the 2012 base case for each state modeled in 

CAMx. This was done on an annual basis for the annual PM2.5 version of AQAT and on 

a quarterly basis for the 24-hour PM2.5 version of AQAT. More information on the 

emissions inventories can be found in preamble section IV.C. The total emissions data 

and resulting ratios for quarter 2 of the 24-hour PM2.5 version of AQAT can be found in 

Table C-1.

For each monitor, the uncalibrated annual and quarterly 2014 contribution of 

sulfate from each state for the AQAT calibration scenario is calculated by subtracting the 

estimated “uncalibrated” change in concentration from the 2012 base case contribution. 

The “uncalibrated” change in concentration is found by multiplying the 2012 base case 

sulfate contribution by the difference in the ratio of emissions. The difference in the ratio 

of emissions is calculated as 1 minus the ratio of total SO2 emissions in the AQAT 

calibration scenario to the 2012 base case scenario. When the change in concentration is 

subtracted from the base case contribution, the net result is the uncalibrated estimated 

sulfate contribution from each state for the AQAT calibration scenario.

For each monitor, these state-level contributions are then summed to estimate 

total sulfate contribution from the states in the CAMx modeling domain. Finally, “other” 

modeled sulfate contributions (“BIOG”, ”OTHER”, “ICBC”, and “SOA”) are added to 

the annual and quarterly total to account for sources of sulfate outside the CAMx 

modeling domain. The total sulfate from all the states and “other” contributions 

represents the total sulfate component of PM2.5 estimated by uncalibrated AQAT for the 

AQAT calibration scenario. It is the ratio of the CAMx to AQAT sulfate components for 

this AQAT calibration scenario that becomes the constant calibration factor used in 

“calibrated” AQAT.
24

Table C-1. 2012 Base Case and 2014 AQAT Calibration Scenario Ammonium Sulfate 

Contributions for Monitor Number 261630033 inWayne County, Michigan, as well as 

Total SO2 Emissions from all Source-Sectors for Each State.

State/Source

2012 Base Case 

Quarter 2 Sulfate 

Contributions 

(g/m3)

2012 Base 

Case 

Quarter 2 

SO2

Emissions 

(tons)

2014 AQAT 

calibration 

Scenario Quarter 

2 SO2 Emissions 

(tons)

Ratio of 2014 

AQAT calibration 

Scenario Emissions 

to 2012 Base Case 

SO2 Emissions for 

Quarter 2

Estimated 2014 

Contribution of 

Sulfate in 

Quarter 2 

(uncalibrated 

AQAT) (g/m3)

AL 0.50 133,175 84,803 0.64 0.32

AR 0.14 30,280 34,229 1.13 0.16

CT 0.00 4,599 4,628 1.01 0.00

DE 0.01 2,440 2,145 0.88 0.01

DC 0.00 499 485 0.97 0.00

FL 0.06 60,947 63,051 1.03 0.06

GA 0.21 128,332 46,991 0.37 0.08

IL 1.21 141,050 58,995 0.42 0.51

IN 3.09 223,451 77,561 0.35 1.07

IA 0.12 48,675 34,918 0.72 0.08

KS 0.06 26,869 24,901 0.93 0.05

KY 1.89 135,520 42,304 0.31 0.59

LA 0.23 59,724 58,568 0.98 0.22

ME 0.00 5,967 4,744 0.79 0.00

MD 0.11 29,347 25,558 0.87 0.10

MA 0.01 10,663 10,850 1.02 0.01

MI 3.93 85,280 60,550 0.71 2.79

MN 0.03 26,684 22,243 0.83 0.03

MS 0.06 15,408 15,954 1.04 0.06

MO 0.86 114,219 71,433 0.63 0.54

NE 0.02 19,586 18,871 0.96 0.02

NH 0.00 2,747 3,482 1.27 0.00

NJ 0.02 11,115 7,292 0.66 0.01

NY 0.12 51,969 33,122 0.64 0.08

NC 0.14 55,881 34,548 0.62 0.09

ND 0.03 28,083 28,235 1.01 0.03

OH 3.56 237,608 66,535 0.28 1.00

OK 0.08 39,479 38,672 0.98 0.08

PA 0.80 149,123 57,087 0.38 0.30

RI 0.00 1,316 1,315 1.00 0.00

SC 0.03 58,121 36,711 0.63 0.02

SD 0.01 9,341 9,282 0.99 0.01

TN 0.73 100,713 38,227 0.38 0.28

TX 0.18 174,356 184,266 1.06 0.19

VT 0.00 1,469 1,473 1.00 0.00

VA 0.15 42,859 31,049 0.72 0.11

WV 1.02 140,798 29,254 0.21 0.21

WI 0.09 49,290 28,540 0.58 0.05

BIOG 0.00 1 0.00

OTHER 0.91 1 0.91

ICBC 1.14 1 1.14

SOA 0.00 1 0.00

Total Sulfate 

Component 

of PM2.5 in 

Quarter 2

21.54 11.21
25

(2) Calibrate annual and quarterly sulfate response in the annual and 24-hour PM2.5

versions of AQAT using CAMx modeling of 2012 base and 2014 AQAT calibration 

scenario

Next, the estimate of the monitor specific sulfate responses under the AQAT 

calibration scenario was used to calibrate the the annual and 24-hour versions of AQAT 

to CAMx. First, the annual and quarterly changes in sulfate predicted by AQAT and 

CAMx relative to 2012 base case concentrations were calculated for each monitor. To 

calculate this for AQAT and CAMx independently, EPA subtracted the 2014 total sulfate 

estimated by either AQAT or CAMx for the AQAT calibration scenario from the 

respective 2012 total sulfate predicted by CAMx for the 2012 base case. This difference 

was then divided by the 2012 total sulfate predicted by CAMx for the 2012 base case (see 

Table C-2 for an example calculation). The calculation of these monitor-specific 

calibration factors provided EPA with the ability to align the sulfate response predicted 

by AQAT to the sulfate response predicted by CAMx at a level of SO2 reductions that 

EPA expected to be within the range of all emission reductions examined by EPA.

For 24-hour PM2.5, the CAMx estimates of the 2012 base case and 2014 AQAT 

calibration scenario are presented by year as well as by quarter. Thus, in the CAMx 

estimates, for each quarter, there are five values (one for each year from 2003-2007 

projected to the future year). In contrast, the estimates from AQAT are the average of the 

five yearly values, since the AQAT values are derived from the “average” quarterly 

contributions. The AQAT and CAMX ammonium sulfate factors for 24-hour PM2.5 can 

be found in the “Daily PM Calibration Factors.xlsx” excel workbook on worksheet 

“AQModeling Calib Factor DailyPM” in columns BA and AJ, respectively. The 

calibration factor is the ratio of the CAMx response factor divided by the uncalibrated 

AQAT response factor. This calibration factor can be found in column BC of the 

aforementioned excel worksheet. There is one calibration factor for each quarter, for 

each monitoring site.

For annual PM2.5, the CAMx estimates used in AQAT construction are 

represented as 5-year averages. The AQAT and CAMx ammonium sulfate factors for 

annual PM2.5 can be found in the “Annual PM Calib Factors.xlsx” excel workbook on 

worksheet “AQModeling Calib Factors Ann PM” in columns AI and AE, respectively. 

The calibration factor is the ratio of the CAMx response factor divided by the 

uncalibrated AQAT response factor. This calibration factor can be found in column AK 

of the aforementioned excel worksheet. There is a single calibration factor, representing 

an annual value, for each monitoring site.

Generally, for similar emission reductions, the sulfate reductions predicted by

CAMx for the “warm” seasons (i.e., 2nd and 3rd calendar quarters) were greater than 

during the “cool” seasons (i.e., 1st and 4th quarters). Consequently, the calibration 

factors for the “warm” seasons are larger than they are for the “cool” seasons.
26

Table C-2. Total Estimated Sulfate Contributions in the 2012 Base Case and 2014 

AQAT Calibration Scenario from CAMx and Uncalibrated AQAT for Monitor Number 

261630033 in Wayne County, Michigan (See Table C-1) for 24-hour PM2.5. These 

Values are then Used to Create a Calibration Factor.

2012 Base Case 

Quarter 2 Sulfate 

Concentration

(g/m3)

Estimated 2014 

Concentration of Sulfate 

in Quarter 2 

(uncalibrated AQAT) 

(g/m3)

Estimated Quarter 2 

Reduction Divided by 

2012 Base Case 

Concentration

CAMx* 22.67 13.21 0.4172

AQAT* 21.54 11.21 0.4795

Calibration Factor -

Response Factor From 

CAMx Divided By 

Response Factor From 

AQAT 0.8700

* As described above in this section, in CAMx, there are estimated 5 values per quarter 

(one for each projected year, 2003-2007). In AQAT, there is just have a single “average” 

value per quarter. The CAMx value shown is the projected 2003 quarter 2 value.

(3) Create calibrated versions of annual and 24-hour AQAT for cost threshold analysis

Next, EPA created the calibrated versions of annual and 24-hour PM2.5 AQAT for 

the cost threshold analysis. EPA used emissions, air quality sulfate contribution factors, 

and calibration factors to estimate the change in predicted sulfate due to SO2 emission 

reductions under each cost threshold evaluated. First, as described in step 2, EPA 

calculated annual and quarterly state-level 2012 base case total SO2 emissions, for the 

annual and 24-hour PM2.5 versions of AQAT, respectively. Next, EPA calculated the 

annual and quarterly state-level 2014 total SO2 emissions across all source sectors for the 

cost thresholds. This total is the sum of IPM predicted SO2 emissions from power sector 

point sources in 2014 and the predictions of 2014 base case SO2 emissions from all other 

source sectors. Note, IPM estimates of SO2 emissions are available annually only. In 

order to approximate the quarterly emissions needed for the quarterly components of the 

24-hour version of AQAT, EPA multiplied the annual emissions at each cost threshold 

for each state by the ratio of the state’s quarterly to annual emissions for the power sector 

from SMOKE modeling of the AQAT calibration scenario. For example, the ratio for 

quarter one is the sum of the EGU SO2 emissions for January, February, and March 

divided by the total annual EGU SO2 emissions. Finally, EPA calculated the ratio of 

2014 total SO2 emissions for each cost threshold to 2012 total SO2 emissions for the 2012 

base case for each state modeled in CAMx. More information on the emissions 

inventories can be found in preamble section IV.C. This emissions data and resulting 

ratios for the second quarter for 24-hour PM2.5 under the AQAT calibration scenario can 

also be found in Table C-1.
27

For each cost threshold level analyzed, on a receptor-by-receptor basis, the 

emissions reductions for each upwind state are associated with one of two cost threshold 

levels (either the base case emissions level or the particular threshold cost level being 

analyzed) depending on whether the upwind state is “linked” to that receptor or if the 

receptor is located within the state. States that are contributing above the respective air 

quality threshold10 (i.e., greater than or equal to 1 percent contribution of total sulfate and 

nitrate for the annual and 24-hour PM2.5) to the monitor, as well as the state containing 

the monitor, make SO2 emissions reductions available at the particular threshold level. 

The emissions for all other states are at the base case level.

For each monitor, the predicted 2014 contribution of sulfate from each state is 

calculated by multiplying the state specific 2012 base case sulfate contribution by the 

change in ratio of total SO2 emissions (either the cost threshold level or the base case 

level depending on whether the state is linked). For each receptor, the total change in 

sulfate, calculated by adding up the change in contributions from all states, is multiplied 

by the calibration factor. This calibrated change in sulfate is then subtracted from the 

total sulfate from the 2012 base case modeling, resulting in the “calibrated” average total 

sulfate. The 2012 base case sulfate includes the contributions from all upwind states as 

well as the “other” sulfate contributions. When this “calibrated” sulfate is combined with 

the other components of PM2.5, it is possible to estimate total PM2.5. The process of 

estimating design values is described in the next two sections (4 and 5) for annual PM2.5

and for 24-hour PM2.5, respectively.

(4) Calculate new annual PM2.5 design values using the annual PM2.5 version of AQAT

After estimating total sulfate in 2014 for each cost threshold, EPA estimated 

average and maximum design values for annual PM2.5 by adding the total sulfate to the 

non-sulfate components of PM2.5 from the CAMx modeling of the 2014 AQAT 

calibration scenario. The non-sulfate components added in this step were ammonium 

nitrate, elemental carbon, organic carbon, salt, and blank mass. The resulting sum is the 

estimated average design value. To estimate the maximum design value, EPA took the 

difference between the average and maximum design value for the 2012 base case, and 

added this difference to the 2014 average design value.

(5) Calculating new 24-hour PM2.5 design values using quarterly relative response factors

in the 24-hour version of AQAT

● Calculate quarterly relative response factors as the ratio of calibrated AQAT 

predicted total sulfate to 2012 CAMx modeled total sulfate

● Calculate predicted 2014 total sulfate for all available CAMx modeled days (8 

days per quarter per year) by multiplying the 2012 CAMx modeled 

concentrations by the quarterly relative response factors

 

10 For the 24-hour version of AQAT, the assessment of the 1% contributions to the threshold are based on 

the contributions to the average design value from the 2012 base case, and not on the average quarterly 

contributions. 
28

● Add 2014 total nitrate and other PM2.5 species from the 2014 CAMx modeling 

of the AQAT calibration scenario for each corresponding day

● Calculate the 98th percentile day for each modeled year

● Check the completeness and validity of each modeled year, keeping only the 

years with monitoring data that met completeness criteria

● Calculate average 2014 predicted DVs for each quantifiable 3-year period of 

projected historic monitoring data (2003-2005, 2004-2006, and 2005-2007)

● Calculate final average DV as the average of quantifiable 2014 predicted 3-

year DVs

● Calculate the maximum DV as the maximum of quantifiable 2014 predicted 

3-year DVs

The estimation of design values for the 24-hour PM2.5 standard is more 

complicated than it is for the annual PM2.5 standard, because only the 98th percentile day

from each of the five years contributes to the design value (and the particular day selected 

as the 98th percentile day can change at different cost threshold levels). After estimating 

average total sulfate in 2014 for each cost threshold for each quarter, EPA developed 

relative response factors (RRF) for quarterly sulfate concentrations and used these factors 

to calculate expected future sulfate concentrations for 32 selected modeled days for each 

of the 5-years accounted for in the 2012 CAMx base case modeling (2003-2007). In 

other words, the “average” quarterly responses were “mapped” to the 8 individual days in 

each quarter (32 days total per year) for each of the 5 years using the relative response 

factors. This was done by multiplying the appropriate quarterly RRF by the 2012 base 

case sulfate value for each day.

For each monitor, to calculate the quarterly relative response factors, EPA took 

the “average” calibrated quarterly sulfate contribution for the cost threshold level and 

divided it by the 2012 base case “average” quarterly sulfate contribution. For each 

monitor, there is a single RRF for each quarter, with the same RRF applied equally to all 

5 years.

For each cost threshold level evaluated, EPA multiplied the appropriate quarterly 

RRF for that threshold to the 2012 base case sulfate values for each of the 32 days, for 

each of the 5 years, to estimate adjusted sulfate values. To these adjusted sulfate values, 

EPA added the concentrations from the other11 PM2.5 components from the 2014 AQAT 

calibration scenario (i.e., nitrate, elemental carbon, organic carbon, salt, and blank mass). 

The result is 32 PM2.5 concentrations for each of the 5 years of analysis. The total 

concentration estimates (and adjusted sulfate values) for each monitor, year, and day can 

be found in the “dailyPM_all_years_all_quarters....xlsx “ workbooks.

 

11 By using nitrate from the AQAT calibration scenario, the estimate of nitrate is impacted by NOx 

reductions and SO2 reductions which lead to nitrate replacement. The concentrations of elemental carbon, 

organic carbon, salt, and blank are nearly identical in the 2014 base case and AQAT calibration case CAMx 

modeling. The largest difference in concentration between the two modeled scenarios was 0.02 g/m3for 

organic carbon. By using these components of PM2.5 as modeled in the 2014 AQAT calibration scenario, 

EPA is appropriately accounting for any changes in these components due to Transport Rule 

implementation. 
29

Next, we ranked the values for each year and selected the 98th percentile for each 

year for use in estimation of the 3-year design values. The particular rank of the value 

selected depended on the sampling frequency of the monitor (for more details see section 

V.C.2.b (2) of the preamble, the Air Quality Modeling Final Rule TSD, and the modeling 

guidance document for state attainment demonstrations of the 24-hour PM2.5). The rank 

of the value that is the 98th percentile can be found in the “98thpercentilerank” worksheet 

in the “dailyPM_allyears_high_quarters.xlsx” workbook in column G.

For each monitor, the 98th percentile value for each cost threshold level and for 

each year can be found in the appropriate worksheet and columns I through M in the 

“dailyPM_allyears_high_quarters.xlsx” workbook. Three valid consecutive yearly 98th

percentile values are needed to construct a design value. The completion codes for each 

potential design value 3-year time-period have values of 1, 2, 3, 4 or missing (0) for each 

design value period. Values of 1 or 2 indicate compete data and values of 3 or 4 indicate 

incomplete data. Missing values, or values equal to 0, were treated as incomplete 

periods.

The average design value was calculated as the average of all valid design values, 

while the maximum design value was calculated as the maximum available valid design 

value.

As the cost threshold value increased, the estimated average and maximum design 

values at each receptor decreased. In AQAT, the estimated value of the average design 

value was used to estimate whether the location will be out of attainment, while the 

estimated maximum design value was used to estimate whether the location will be out of 

maintenance. The two air quality levels used were 15.05 g/m3and 35.5 g/m3to 

represent the 1997 and 2006 fine particulate matter (PM2.5) NAAQS, respectively. 

3. Description of the results of the analysis using AQAT for the final approach.

This section describes the results of the cost threshold analysis using the annual 

and 24-hour versions of AQAT for the annual PM2.5, and 24-hour PM2.5NAAQS 

standards. In section C.2 of this TSD, we described the construction of the annual and 

24-hour versions of AQAT to estimate the air quality impacts of various levels of EGU 

SO2 emissions. 

For annual PM2.5 in 2014, the average and maximum PM2.5 design values (g/m3) 

estimated using AQAT for each identified receptor for each cost threshold level can be 

found in Table C-3 and C-4, respectively. The monitors are in order of decreasing 2012 

base case maximum annual PM2.5 design value. No monitors are estimated to have 

remaining nonattainment problems at the $2,300/ton SO2 cost threshold. The only

monitor that is estimated to have a remaining maintenance problem at the $2,300/ton SO2

cost threshold is monitor number 420030064, located in Allegheny County, Pennsylvania

(Liberty-Clairton nonattainment area). As indicated in section VIII.B of the preamble, 

final air quality modeling of the Transport Rule remedy scenario using CAMx indicates 

that the maintenance problem estimated by AQAT is resolved.

For 24-hour PM2.5 in 2014, the estimated average and maximum air quality design 

values (g/m3) estimated using AQAT for each identified receptor for each cost threshold 

level can be found in Table C-5 and C-6, respectively. The monitors are in order of 

decreasing 2012 base case maximum 24-hour PM2.5 design value. Based on applying 
30

AQAT, a majority of the 24-hour PM2.5 receptors are estimated to have their 

nonattainment and maintenance problems resolved at the $500/ton cost threshold in 2014. 

However, a number of receptors are projected to require substantial additional SO2

emission reductions to eliminate significant contribution to nonattainment and 

interference with maintenance of the standard.

The total number of estimated nonattainment and maintenance receptors as a 

function of SO2 cost threshold is summarized in Table VI.C-2 of the preamble and can be 

assessed using Tables C-3, C-4, C-5, and C-6. At each cost threshold, receptors are 

counted if their estimated design value is greater than the NAAQS. Note that because the 

maximum design value (maintenance) is always equal to or greater than the average 

design value (nonattainment), all receptors that are estimated to have nonattainment 

problems are also estimated to have maintenance problems. For example, for the annual 

PM2.5 standard, at a cost threshold of $500/ton, the average and maximum design values 

for receptor number 420030064 located in Allegheny, PA are estimated to exceed the 

level of the NAAQS. In Table VI.C-2 in the preamble, this monitoring site accounts for 

the value of 1 in both the nonattainment and nonattainment or maintenance categories for 

the annual PM2.5 columns (see appendix D for a description about how the monitors were 

associated with nonattainment areas to create this table in the preamble). 

In the assessment of air quality using the calibrated AQAT, it is difficult to 

estimate the relative contributions of particular upwind states contributing to a particular 

estimated design value for 24-hour PM2.5 standard. The reason is that the design value is 

calculated using projections for different days, possibly from different seasons, and the

rank of these days can change depending on the emissions changes from each cost 

threshold examined. For example, in the base case, the 98th percentile days which 

contribute to the design value could primarily be from “warm” seasons, which have high 

sulfate levels. At a higher cost threshold level, the 98th percentile day could shift to a 

“cool” season, which has a lower sulfate level. Consequently, this can confound the 

interpretation of the change in sulfate as well as change in the relative upwind 

contributions of particular states to design values.

Lastly, once the budgets for the final Transport Rule were established (based on 

the results of the multi-factor test) and IPM was used to model compliance with the final 

rule, it was possible to estimate air quality concentrations at each downwind receptor 

using AQAT for the final rule remedy scenario. Average and maximum design value 

estimates in 2014 for annual PM2.5 and 24-hour PM2.5can be found in Tables C-7 and C-8 

in section C.4 of this TSD. CAMx was run for this same set of emissions and the air 

quality results from this run are also summarized in Tables C-7 and C-8 (see section C.4 

of this TSD). Additional comparisons between AQAT and CAMx estimates are shown in 

section C.4 of this TSD. 
31

Table C-3. Average Annual PM2.5 DVs (g/m3) for SO2 Cost Thresholds ($/ton) Assessed Using AQAT.

Monitor 

Identification 

Number

State County

CAMx 2012 

Base Case 

(g/m3)

AQAT 2014 Average Annual PM2.5 Design Values (g/m3).

Base 

Case $500 $1,600 $2,300 $2,800 $3,300 $10,000

Avg. improvement from AQAT base case – 2012 base case receptors 1.60 1.87 2.02 2.10 2.15 2.42

420030064 Pennsylvania Allegheny 17.94 17.53 15.78 15.28 15.03 14.97 14.91 14.70

390350038 Ohio Cuyahoga 15.99 15.68 14.10 13.77 13.60 13.52 13.46 13.23

10730023 Alabama Jefferson 16.15 15.60 14.33 14.38 14.31 14.31 14.38 14.06

390618001 Ohio Hamilton 16.01 15.64 13.54 13.18 13.01 12.93 12.85 12.53

261630033 Michigan Wayne 15.73 15.44 14.35 14.12 13.87 13.69 13.61 13.25

390350060 Ohio Cuyahoga 15.67 15.34 13.75 13.42 13.25 13.17 13.11 12.88

390610014 Ohio Hamilton 15.76 15.39 13.29 12.93 12.75 12.67 12.59 12.27

390610042 Ohio Hamilton 15.40 15.07 12.97 12.61 12.44 12.36 12.28 11.98

171191007 Illinois Madison 15.46 14.85 13.83 13.64 13.56 13.43 13.31 13.00

10732003 Alabama Jefferson 15.16 14.68 13.55 13.58 13.52 13.51 13.57 13.29

390350045 Ohio Cuyahoga 15.14 14.83 13.23 12.90 12.73 12.65 12.59 12.36

180970081 Indiana Marion 14.86 14.52 12.68 12.40 12.26 12.19 12.09 11.79

131210039 Georgia Fulton 15.07 14.29 13.35 13.24 13.20 13.18 13.17 13.05

390617001 Ohio Hamilton 14.74 14.40 12.30 11.93 11.76 11.68 11.60 11.28

390350065 Ohio Cuyahoga 14.67 14.38 12.79 12.45 12.28 12.20 12.14 11.91

180970083 Indiana Marion 14.71 14.38 12.53 12.25 12.11 12.04 11.94 11.64

Table C-4. Maximum Annual PM2.5 DVs (g/m3) for SO2 Cost Thresholds ($/ton) Assessed Using AQAT.

Monitor 

identification 

number

State County

CAMx 2012 

Base Case 

(g/m3)

AQAT 2014 Maximum Annual PM2.5 Design Values (g/m3).

Base 

Case $500 $1,600 $2,300 $2,800 $3,300 $10,000

Avg. improvement from AQAT base case – 2012 base case receptors 1.60 1.87 2.02 2.10 2.15 2.42

420030064 Pennsylvania Allegheny 18.33 17.92 16.17 15.67 15.42 15.36 15.30 15.09

390350038 Ohio Cuyahoga 16.66 16.35 14.77 14.44 14.27 14.19 14.13 13.90

10730023 Alabama Jefferson 16.46 15.91 14.64 14.69 14.62 14.62 14.69 14.37

390618001 Ohio Hamilton 16.33 15.96 13.86 13.50 13.33 13.25 13.17 12.85

261630033 Michigan Wayne 16.32 16.03 14.94 14.71 14.46 14.28 14.20 13.84

390350060 Ohio Cuyahoga 16.18 15.85 14.26 13.93 13.76 13.68 13.62 13.39

390610014 Ohio Hamilton 15.98 15.61 13.51 13.15 12.97 12.89 12.81 12.49

390610042 Ohio Hamilton 15.77 15.44 13.34 12.98 12.81 12.73 12.65 12.35

171191007 Illinois Madison 15.73 15.12 14.10 13.91 13.83 13.70 13.58 13.27

10732003 Alabama Jefferson 15.64 15.16 14.03 14.06 14.00 13.99 14.05 13.77

390350045 Ohio Cuyahoga 15.61 15.30 13.70 13.37 13.20 13.12 13.06 12.83

180970081 Indiana Marion 15.16 14.82 12.98 12.70 12.56 12.49 12.39 12.09

131210039 Georgia Fulton 15.10 14.32 13.38 13.27 13.23 13.21 13.20 13.08

390617001 Ohio Hamilton 15.10 14.76 12.66 12.29 12.12 12.04 11.96 11.64

390350065 Ohio Cuyahoga 15.10 14.81 13.22 12.88 12.71 12.63 12.57 12.34

180970083 Indiana Marion 15.06 14.73 12.88 12.60 12.46 12.39 12.29 11.99
32

Table C-5. Average 24-hour PM2.5 DVs (g/m3) for SO2 Cost Thresholds ($/ton) 

Assessed Using AQAT.

Monitor 

Identification 

Number

State County

CAMx 

2012 Base 

Case 

(g/m3)

AQAT 2014 Average 24-hour PM2.5 Design Values (g/m3).

Base 

Case $500 $1,600 $2,300 $2,800 $3,300 $10,000

Avg. improvement from AQAT base case – 2012 base case

receptors 4.09 4.77 5.09 5.22 5.35 5.79

Avg. improvement from AQAT base case – $500 

receptors** 4.73 5.70 6.41 6.67 6.85 7.54

420030064** Pennsylvania Allegheny 56.71 54.34 47.57 46.36 45.54 45.37 45.23 44.73

420030093** Pennsylvania Allegheny 39.11 37.51 32.19 30.91 30.25 30.12 29.96 29.37

390350038** Ohio Cuyahoga 39.46 37.95 34.18 33.73 33.51 33.43 33.36 32.99

261630016** Michigan Wayne 38.99 38.50 34.42 34.15 33.93 33.77 33.70 33.36

390350060 Ohio Cuyahoga 37.78 37.11 31.50 30.79 30.60 30.51 30.43 30.20

170311016** Illinois Cook 37.58 36.11 34.13 33.48 33.13 32.94 32.67 31.98

261630033** Michigan Wayne 39.48 39.01 36.31 35.59 35.00 34.65 34.43 33.55

180890022** Indiana Lake 34.94 34.04 32.79 32.47 32.38 32.29 32.16 31.86

540090011 West Virginia Brooke 37.57 36.73 30.60 29.60 29.07 28.94 28.80 28.27

420710007** Pennsylvania Lancaster 35.98 35.54 35.19 35.02 34.95 34.94 34.93 34.88

390350045 Ohio Cuyahoga 34.80 33.63 27.69 26.61 26.30 26.20 26.15 25.96

390811001 Ohio Jefferson 34.56 33.58 27.64 26.41 25.79 25.65 25.49 24.92

261630019** Michigan Wayne 37.34 36.86 35.27 35.09 34.93 34.82 34.77 34.54

390350065 Ohio Cuyahoga 34.91 33.50 27.65 26.61 26.11 25.95 25.81 25.28

170313301 Illinois Cook 34.97 33.60 31.11 30.72 30.54 30.40 30.24 29.73

420070014 Pennsylvania Beaver 36.21 34.84 29.28 28.10 27.59 27.48 27.36 26.95

420033007 Pennsylvania Allegheny 32.40 30.98 26.27 25.31 24.88 24.80 24.71 24.48

010730023 Alabama Jefferson 36.96 35.43 31.93 31.86 31.61 31.60 31.74 31.06

550790026 Wisconsin Milwaukee 33.62 33.28 30.48 30.27 30.15 30.03 29.90 29.54

180970043 Indiana Marion 35.76 34.67 28.64 27.55 27.16 26.98 26.64 25.83

261470005 Michigan St Clair 36.23 35.61 33.35 33.01 32.78 32.67 32.59 32.29

550790043 Wisconsin Milwaukee 36.21 34.98 32.49 32.07 31.85 31.70 31.53 31.24

180890026 Indiana Lake 34.08 33.00 30.91 30.65 30.52 30.42 30.30 30.07

180970081 Indiana Marion 35.85 33.70 28.44 27.66 27.35 27.21 26.93 26.19

180970066 Indiana Marion 35.73 34.49 29.22 28.45 28.13 27.96 27.65 26.95

171191007 Illinois Madison 36.59 34.59 29.92 29.48 29.32 29.13 28.88 28.16

550790010 Wisconsin Milwaukee 35.47 35.03 31.50 31.05 30.82 30.73 30.62 30.32

390170003 Ohio Butler 34.40 33.66 28.07 26.99 26.49 26.33 26.19 25.68

170316005 Illinois Cook 34.12 33.47 32.72 32.53 32.41 32.31 32.18 31.84

420031008 Pennsylvania Allegheny 35.04 33.41 26.95 25.44 24.69 24.51 24.33 23.69

261610008 Michigan Washtenaw 35.05 34.93 29.40 28.71 28.54 28.47 28.42 28.18

170312001 Illinois Cook 33.62 32.33 29.84 29.68 29.58 29.48 29.37 29.08

170310052 Illinois Cook 34.94 33.27 30.11 29.87 29.78 29.67 29.53 29.07

421330008 Pennsylvania York 33.38 33.11 31.60 31.21 31.03 31.00 30.96 30.83

261630015 Michigan Wayne 35.55 34.42 32.23 31.53 31.10 30.93 30.85 30.52

010732003 Alabama Jefferson 35.31 34.20 31.42 31.27 31.10 31.08 31.14 30.59

390618001 Ohio Hamilton 35.29 33.57 27.63 26.51 26.11 25.96 25.77 25.40

171190023 Illinois Madison 35.11 33.58 29.23 28.69 28.49 28.26 28.07 27.54

420031301 Pennsylvania Allegheny 33.95 32.45 27.16 25.87 25.21 25.06 24.91 24.30

391130032 Ohio

Montgomer

y

33.68 32.19 24.40 23.37 23.15 23.05 22.95 22.60

420030116 Pennsylvania Allegheny 35.59 33.88 27.97 26.86 26.34 26.23 26.08 25.57

** Identify receptors that have maximum design values greater than or equal to 35.5 g/m3at the $500 cost threshold in 2014.
33

Table C-6. Maximum 24-hour PM2.5 DVs (g/m3) for SO2 Cost Thresholds ($/ton) 

Assessed Using AQAT.

Monitor 

Identification 

Number

State County

CAMx 

2012 

Base 

Case 

(g/m3)

AQAT 2014 Maximum 24-hour PM2.5 Design Values (g/m3).

Base 

Case $500 $1,600 $2,300 $2,800 $3,300 $10,000

Avg. improvement from AQAT base case – 2012 base case

receptors* 4.28 4.98 5.33 5.46 5.60 6.07

Avg. improvement from AQAT base case – $500 receptors** 3.27 3.86 4.22 4.37 4.50 4.96

420030064** Pennsylvania Allegheny 59.93 57.64 50.72 49.46 48.63 48.49 48.35 47.82

420030093** Pennsylvania Allegheny 44.40 42.63 36.85 35.50 34.80 34.66 34.49 33.85

390350038** Ohio Cuyahoga 41.84 40.37 35.93 35.58 35.41 35.33 35.29 34.93

261630016** Michigan Wayne 41.28 40.77 36.20 35.88 35.65 35.49 35.42 35.10

390350060 Ohio Cuyahoga 40.85 39.90 33.69 33.23 33.04 32.94 32.86 32.61

170311016** Illinois Cook 40.44 39.05 37.40 36.85 36.54 36.35 36.10 35.50

261630033** Michigan Wayne 39.81 39.47 36.59 35.84 35.23 34.87 34.65 33.75

180890022** Indiana Lake 39.58 38.68 37.00 36.63 36.51 36.35 36.11 35.57

540090011 West Virginia Brooke 38.39 37.68 32.23 30.79 30.02 29.84 29.64 28.89

420710007** Pennsylvania Lancaster 38.37 37.82 37.43 37.25 37.18 37.17 37.15 37.10

390350045 Ohio Cuyahoga 38.13 36.65 29.48 28.11 27.60 27.43 27.35 27.14

390811001 Ohio Jefferson 37.88 36.91 30.27 28.78 28.03 27.86 27.67 26.96

261630019** Michigan Wayne 37.83 37.29 36.20 36.01 35.83 35.72 35.66 35.43

390350065 Ohio Cuyahoga 37.67 36.41 28.79 27.60 27.00 26.80 26.64 26.08

170313301 Illinois Cook 37.67 36.26 33.36 33.01 32.84 32.71 32.55 32.14

420070014 Pennsylvania Beaver 37.42 35.99 30.46 29.27 28.70 28.58 28.46 28.04

420033007 Pennsylvania Allegheny 37.40 35.85 30.73 29.47 28.81 28.68 28.54 28.17

010730023 Alabama Jefferson 37.33 35.80 32.50 32.42 32.12 32.10 32.28 31.52

550790026 Wisconsin Milwaukee 37.24 36.72 33.54 33.32 33.21 33.09 32.96 32.61

180970043 Indiana Marion 37.20 36.09 29.00 28.09 27.82 27.70 27.46 26.83

261470005 Michigan St Clair 37.14 36.57 34.16 33.59 33.38 33.29 33.24 33.02

550790043 Wisconsin Milwaukee 37.10 35.89 34.22 34.03 33.92 33.83 33.73 33.46

180890026 Indiana Lake 37.06 36.05 33.67 33.48 33.37 33.28 33.18 32.93

180970081 Indiana Marion 36.96 34.81 28.83 27.95 27.59 27.41 27.08 26.33

180970066 Indiana Marion 36.92 35.62 30.40 29.52 29.13 28.93 28.54 27.69

171191007 Illinois Madison 36.83 35.20 31.19 30.85 30.66 30.42 30.10 29.18

550790010 Wisconsin Milwaukee 36.71 36.56 33.47 33.25 33.13 33.04 32.94 32.66

390170003 Ohio Butler 36.59 36.03 28.71 27.76 27.33 27.17 27.01 26.53

170316005 Illinois Cook 36.42 35.87 35.09 34.90 34.82 34.71 34.59 34.23

420031008 Pennsylvania Allegheny 36.35 34.65 28.15 26.48 25.62 25.39 25.15 24.29

261610008 Michigan Washtenaw 36.32 35.38 30.20 29.50 29.33 29.25 29.20 28.95

170312001 Illinois Cook 36.12 34.95 32.71 32.49 32.33 32.22 32.07 31.71

170310052 Illinois Cook 36.07 34.06 30.62 30.41 30.31 30.21 30.08 29.74

421330008 Pennsylvania York 36.06 35.89 34.55 34.12 33.91 33.88 33.84 33.69

261630015 Michigan Wayne 36.00 34.81 33.04 32.35 31.99 31.82 31.74 31.39

010732003 Alabama Jefferson 35.94 34.95 32.23 32.08 31.91 31.89 31.94 31.43

390618001 Ohio Hamilton 35.85 34.01 28.23 27.13 26.73 26.59 26.45 26.03

171190023 Illinois Madison 35.81 34.53 30.23 29.70 29.50 29.26 29.07 28.52

420031301 Pennsylvania Allegheny 35.65 33.91 28.05 26.67 26.15 26.04 25.92 25.45

391130032 Ohio Montgomery 35.61 33.81 25.99 24.94 24.62 24.48 24.31 23.79

420030116 Pennsylvania Allegheny 35.59 33.88 27.97 26.86 26.34 26.23 26.08 25.57

* Used in Table VI.C-1 of the preamble

** Used in Table VI.D-1 of the preamble, Identify receptors that have maximum design values greater than or equal to 35.5 g/m3at 

the $500 cost threshold in 2014.
34

4. Comparison between the air quality assessment tool estimates and CAMx air quality

modeling estimates.

As the AQAT was being developed for the final Transport Rule, it was possible to 

evaluate the estimates from the tool with the model predictions from CAMx for the 2014 

base case scenario. This case was independently modeled in CAMx. The estimates 

were not used in the development or calibration of the AQAT. Consequently, a 

comparative analysis was done between the assessment tool and the CAMx modeling for 

2014 base case sulfate estimates as well as the resulting design value estimates. 

Additionally, when the CAMx air quality modeling of the final remedy (2014 control 

case) was available, a corresponding comparative analysis was also done with the 

estimates from the assessment tool. 

Examination of the comparison for the 2014 base shows strong correlations 

(nearly one to one) between the estimated design values from AQAT and CAMx (Table 

C-10 and Figure C-1)

Examination of the results of the CAMx modeling for 2014, implementing the 

remedy, shows that nearly all of the air quality monitoring locations of interest are 

estimated to be brought into attainment and maintenance for both the 24-hour and annual 

PM2.5 standards (see sections VI.C and VI.D of the preamble). Qualitatively, these 

results are quite similar to those from the assessment tool. Quantitatively, the results are 

also very similar, demonstrating that the calibrated AQAT was adequate for the intended 

purpose (Tables C-7 and C-8, Figures C-1 and C-2).

In addition, for the 24-hour PM2.5 standards, EPA conducted a detailed 

comparison of the sulfate estimates from AQAT and CAMx (relative to the 98th

percentile days selected according to CAMx) for both the 2014 base case and 2014

remedy case. The comparison is shown graphically for sulfate in Figure C-3. The sulfate 

estimates, as well as the PM2.5 concentrations for the CAMx 98th percentile days, are 

contained in Appendix B, Tables B-1 and B-2. Appendix E shows updated estimates for 

the final Transport Rule remedy in comparison with Final Remedy Sensitivity (where the 

variability limits were increased).
35

Table C-7. Average and Maximum Annual PM2.5 DVs (g/m3) in the 2014 Remedy Case 

Scenarios as Modeled in CAMx and as Estimated in Calibrated AQAT, for Receptors 

with Maximum DVs Greater than or Equal to 15.05 g/m3in the 2012 Base Case.

Monitor 

Identification 

Number

State County 2014 Remedy Scenario

CAMx

Avg. DV

CAMx

Max. DV

AQAT

Avg. DV

AQAT

Max. DV

Difference, 

Avg. DV

(CAMxAQAT)

Difference, 

Max. DV

(CAMxAQAT)

Avg. of all 2012 base case 12.74 13.05 12.98 13.36 -0.24 -0.30

420030064 Pennsylvania Allegheny 14.62 14.95 14.86 15.25 -0.24 -0.30

390350038 Ohio Cuyahoga 12.99 13.54 13.51 14.18 -0.52 -0.64

010730023 Alabama Jefferson 13.94 14.21 13.89 14.20 0.05 0.01

390618001 Ohio Hamilton 12.73 12.99 12.96 13.28 -0.23 -0.29

261630033 Michigan Wayne 13.59 14.08 13.77 14.36 -0.18 -0.28

390350060 Ohio Cuyahoga 12.70 13.14 13.16 13.67 -0.46 -0.53

390610014 Ohio Hamilton 12.47 12.63 12.70 12.92 -0.23 -0.29

390610042 Ohio Hamilton 12.16 12.47 12.36 12.73 -0.20 -0.26

171191007 Illinois Madison 13.28 13.51 13.39 13.66 -0.11 -0.15

010732003 Alabama Jefferson 13.11 13.53 13.13 13.61 -0.02 -0.08

390350045 Ohio Cuyahoga 12.15 12.53 12.64 13.11 -0.49 -0.58

180970081 Indiana Marion 12.01 12.27 12.24 12.54 -0.23 -0.27

131210039 Georgia Fulton 12.99 13.02 13.07 13.10 -0.08 -0.08

390617001 Ohio Hamilton 11.48 11.80 11.71 12.07 -0.23 -0.27

390350065 Ohio Cuyahoga 11.69 12.03 12.19 12.62 -0.50 -0.59

180970083 Indiana Marion 11.86 12.16 12.09 12.44 -0.23 -0.28
36

Table C-8. Average and Maximum 24-hour PM2.5 DVs (g/m3) in the 2014 Base Case 

and 2014 Remedy Case Scenarios as Modeled in CAMx and as Estimated in AQAT.

Monitor 

Identification 

Number

State County 2014 Base Case Scenario 2014 Remedy Scenario

CAMx

Avg. 

DV

CAMx

Max. 

DV

AQAT

Avg. 

DV

AQAT

Max. 

DV

Differen

ce, Avg. 

DV

(CAMxAQAT)

Differe

nce, 

Max. 

DV

(CAMx

-

AQAT)

CAMx

Avg. 

DV

CAMx

Max. 

DV

AQAT

Avg. 

DV

AQAT

Max. 

DV

Differe

nce, 

Avg.

DV

(CAMx

-

AQAT)

Differe

nce, 

Max. 

DV

(CAMx

-

AQAT)

avg. of 6 sites* 38.89 41.49 39.23 41.88 -0.34 -0.38 35.52 38.05 35.72 38.27 -0.20 -0.22

avg. of 8 sites** 38.73 41.01 39.04 41.39 -0.32 -0.38 35.01 37.17 35.32 37.50 -0.31 -0.33

Avg. of all 2012 base case 34.79 36.70 35.05 36.96 -0.26 -0.27 29.53 31.17 29.82 31.48 -0.29 -0.31

420030064 Pennsylvania Allegheny 54.14 57.51 54.34 57.64 -0.20 -0.14 45.03 48.09 45.45 48.52 -0.42 -0.43

420030093 Pennsylvania Allegheny 37.53 42.57 37.51 42.63 0.03 -0.06 29.44 33.76 29.88 34.28 -0.44 -0.52

390350038 Ohio Cuyahoga 38.24 40.57 37.95 40.37 0.29 0.21 32.64 34.55 33.46 35.39 -0.82 -0.84

261630016 Michigan Wayne 37.94 40.17 38.50 40.77 -0.56 -0.60 33.72 35.43 33.88 35.61 -0.16 -0.18

390350060 Ohio Cuyahoga 36.78 39.76 37.11 39.90 -0.33 -0.14 29.82 32.20 30.51 32.94 -0.69 -0.74

170311016 Illinois Cook 35.89 38.72 36.11 39.05 -0.22 -0.33 32.69 36.16 32.95 36.40 -0.26 -0.24

261630033 Michigan Wayne 38.22 38.52 39.01 39.47 -0.79 -0.95 34.31 34.50 34.74 34.95 -0.43 -0.45

180890022 Indiana Lake 33.77 38.31 34.04 38.68 -0.27 -0.37 32.18 36.10 32.31 36.30 -0.13 -0.20

540090011

West 

Virginia Brooke 36.20 37.04 36.73 37.68 -0.53 -0.63 28.39 29.11 28.83 29.63 -0.44 -0.52

420710007 Pennsylvania Lancaster 35.31 37.60 35.54 37.82 -0.24 -0.22 34.77 36.97 34.87 37.08 -0.10 -0.11

390350045 Ohio Cuyahoga 33.78 37.08 33.63 36.65 0.14 0.43 25.51 26.61 26.23 27.43 -0.72 -0.82

390811001 Ohio Jefferson 33.16 36.45 33.58 36.91 -0.43 -0.46 25.14 27.30 25.57 27.76 -0.43 -0.46

261630019 Michigan Wayne 36.31 36.65 36.86 37.29 -0.55 -0.64 34.71 35.57 34.87 35.74 -0.16 -0.17

390350065 Ohio Cuyahoga 33.77 36.57 33.50 36.41 0.26 0.16 25.15 25.94 25.95 26.81 -0.80 -0.87

170313301 Illinois Cook 33.49 36.17 33.60 36.26 -0.11 -0.09 30.23 32.58 30.35 32.70 -0.12 -0.12

420070014 Pennsylvania Beaver 34.57 35.73 34.84 35.99 -0.27 -0.26 27.00 28.09 27.39 28.49 -0.39 -0.40

420033007 Pennsylvania Allegheny 30.95 35.71 30.98 35.85 -0.03 -0.14 24.54 28.30 24.78 28.63 -0.24 -0.33

010730023 Alabama Jefferson 35.69 36.01 35.43 35.80 0.26 0.21 31.14 31.63 31.10 31.57 0.04 0.06

550790026 Wisconsin Milwaukee 32.39 35.99 33.28 36.72 -0.88 -0.73 29.96 32.95 30.08 33.10 -0.12 -0.15

180970043 Indiana Marion 34.35 35.73 34.67 36.09 -0.32 -0.36 26.67 27.42 27.13 27.76 -0.46 -0.34

261470005 Michigan St Clair 35.06 36.01 35.61 36.57 -0.54 -0.56 32.28 32.94 32.67 33.29 -0.39 -0.35

550790043 Wisconsin Milwaukee 34.57 35.41 34.98 35.89 -0.41 -0.48 31.69 33.83 31.80 33.92 -0.11 -0.09

180890026 Indiana Lake 32.82 35.82 33.00 36.05 -0.19 -0.23 30.36 33.25 30.49 33.39 -0.13 -0.14

180970081 Indiana Marion 34.12 35.18 33.70 34.81 0.42 0.37 26.90 27.04 27.30 27.54 -0.40 -0.50

180970066 Indiana Marion 34.21 35.34 34.49 35.62 -0.28 -0.28 27.67 28.63 28.10 29.11 -0.43 -0.48

171191007 Illinois Madison 34.68 35.36 34.59 35.20 0.08 0.16 29.24 30.51 29.32 30.64 -0.08 -0.13

550790010 Wisconsin Milwaukee 34.08 35.44 35.03 36.56 -0.95 -1.12 30.76 33.05 30.83 33.13 -0.07 -0.08

390170003 Ohio Butler 33.01 35.42 33.66 36.03 -0.65 -0.61 26.17 26.97 26.47 27.29 -0.30 -0.32

170316005 Illinois Cook 32.72 34.94 33.47 35.87 -0.75 -0.93 31.90 34.32 32.02 34.45 -0.12 -0.13

420031008 Pennsylvania Allegheny 33.28 34.49 33.41 34.65 -0.13 -0.16 24.00 24.85 24.47 25.38 -0.47 -0.53

261610008 Michigan

Washtena

w

33.93 35.01 34.93 35.38 -1.00 -0.37 28.42 29.21 28.47 29.26 -0.05 -0.05

170312001 Illinois Cook 32.46 34.96 32.33 34.95 0.13 0.01 29.41 32.09 29.50 32.21 -0.09 -0.12

170310052 Illinois Cook 33.20 34.12 33.27 34.06 -0.07 0.06 29.54 30.06 29.69 30.20 -0.15 -0.14

421330008 Pennsylvania York 32.65 35.36 33.11 35.89 -0.46 -0.53 30.81 33.68 30.92 33.79 -0.11 -0.11

261630015 Michigan Wayne 34.20 34.69 34.42 34.81 -0.22 -0.12 30.80 31.67 31.02 31.91 -0.22 -0.24

010732003 Alabama Jefferson 34.27 34.92 34.20 34.95 0.07 -0.03 30.59 31.39 30.62 31.46 -0.03 -0.07

390618001 Ohio Hamilton 33.51 33.92 33.57 34.01 -0.07 -0.09 25.60 26.29 25.96 26.64 -0.36 -0.35

171190023 Illinois Madison 32.86 33.63 33.58 34.53 -0.72 -0.90 28.33 29.33 28.41 29.41 -0.08 -0.08

420031301 Pennsylvania Allegheny 32.38 33.87 32.45 33.91 -0.07 -0.04 24.58 25.49 24.96 25.85 -0.38 -0.36

391130032 Ohio

Montgome

ry

32.15 33.93 32.19 33.81 -0.04 0.12 22.85 24.24 23.09 24.54 -0.24 -0.30

420030116 Pennsylvania Allegheny 33.87 33.87 33.88 33.88 -0.02 -0.02 25.67 25.67 26.13 26.13 -0.46 -0.46

*The six sites are Allegheny, PA (64); Lancaster, PA (07); Wayne, MI (16 and 19); Cook, IL (16); and Lake, IN (22). 

**The eight sites include the six sites listed above as well as Cuyahoga, OH (38) and Wayne, MI (33).
37

Figure C-1. Least squares linear regression plots showing correlations between estimated 

average and maximum design values (g/m3) for 24-hour PM2.5 for CAMx and calibrated 

AQAT for the 2014 base case (left panel) and 2014 remedy (right panel). 

 

Figure C-2. Least squares linear regression plots showing correlations between estimated 

average and maximum design values (g/m3) for annual PM2.5 for CAMx and calibrated 

AQAT for the 2014 remedy.
38

*98th percentile day chosen by CAMx, with the matching day from AQAT selected for 

comparison.

Figure C-3. Least squares linear regression plots showing correlations between estimated 

sulfate (g/m3) for the 98th percentile 24-hour PM2.5 day for each year*for CAMx and 

calibrated AQAT for the 2014 base case (left panel) and 2014 remedy (right panel). 

5. Using the AQAT to estimate contributions in 2012 resulting from “leakage” of 

emissions to states not included in one or more of the programs for the Transport Rule.

As described in sections VI.C and XII.J.2.a. of the preamble for the final 

Transport Rule, EPA projects that some states not covered by any of the fine particle 

control programs in the final Transport Rule may experience increases of SO2 emissions 

greater than 5,000 tons compared to the base case. These states are Arkansas, Colorado, 

Louisiana, Montana, and Wyoming. Using both the annual and 24-hour versions of 

AQAT, for the states with source contribution modeling (i.e., Arkansas and Louisiana), 

EPA estimated whether these SO2 emission increases would result in contributions from 

these states that are greater than or equal to the contribution thresholds. This was done 

by adding the “leakage” emissions to the 2012 base case emissions. As can be seen in the 

“base leakage_2012_threshold” worksheet in the “annual PM25 AQAT.xlsx” workbook, 

the estimated contributions from these states for annual PM2.5 nonattainment and 

maintenance sites remain well below the 1% NAAQS threshold.

A similar assessment was made for the 24-hour PM2.5NAAQS. EPA added the 

relative SO2 emission increases to each of the quarterly emission values for the 2012 base 

case. EPA then used the 24-hour PM2.5 AQAT and estimated the 2012 base case 

quarterly contributions and the resulting design values for all monitors. EPA, then, 

examined the sulfate contributions from these states, finding that Arkansas had relatively 
39

large contributions in the summer months to sites in Cook, IL (monitor 170311016 

quarter 2); and in Lake, IN (monitors 180890022 and 180890026 in quarter 2). For the 

Cook, IL site, only one of the 98thpercentile values is in the second quarter (2007). For 

the Lake county monitors 180890022 and 180890026, none of the 98th percentile values 

are in the second quarter. Consequently, EPA concludes that Arkansas’ contribution is 

unlikely to go above the 1% contribution threshold. Similarly, Louisiana had relatively 

large contributions to Jefferson, AL in quarter 1. In looking at when the 98th percentile 

days were in the years for 2003-2007, for monitor 10730023 Jefferson, AL, the values 

did not occur in quarter 1. EPA concludes that LA does not exceed the 1% contribution 

threshold.
40

Appendix A: IPM Runs Used in Transport Rule Significant 

Contribution Analysis 
41

Table A-1 lists IPM runs used in the significant contribution analysis. The IPM 

runs can be found in the docket for this rulemaking (Docket ID No. EPA-HQ-OAR-2009-

0491).

Table Appendix A-1. IPM Runs Used in Transport Rule Significant Contribution 

Analysis

Run Name Run Description

TR_Base_Case_Final Base Case model run, which includes the national Title IV SO2

cap-and-trade program; NOx SIP Call regional ozone season 

cap-and-trade program; and settlements and state rules through 

Fall of 2010. This run represents conditions without the 

proposed Transport Rule and without the rule it would replace 

(CAIR).

TR_SO2_500_Final Imposes a marginal cost of $500 per ton of NOx year round in 

annual states starting in 2012, $500 per ton of ozone season NOx

in ozone states starting in 2012, $500 per ton for SO2 year round 

in "Group 2" states starting in 2012, $500 per ton for Group 1 

states in 2012 and 2013, $500 per ton for Group 1 states in 2014 

and each year thereafter. Also forces all SCR and FGD to 

operate for relevant time period if in a TR covered state.

TR_SO2_1600_Final Imposes a marginal cost of $500 per ton of NOx year round in 

annual states starting in 2012, $500 per ton of ozone season NOx

in ozone states starting in 2012, $500 per ton for SO2 year round 

in "Group 2" states starting in 2012, $500 per ton for Group 1 

states in 2012 and 2013, $1,600 per ton for Group 1 states in 

2014 and each year thereafter. Also forces all SCR and FGD to 

operate for relevant time period if in a TR covered state.

TR_SO2_2300_Final Imposes a marginal cost of $500 per ton of NOx year round in 

annual states starting in 2012, $500 per ton of ozone season NOx

in ozone states starting in 2012, $500 per ton for SO2 year round 

in "Group 2" states starting in 2012, $500 per ton for Group 1 

states in 2012 and 2013, $2,300 per ton for Group 1 states in 

2014 and each year thereafter. Also forces all SCR and FGD to 

operate for relevant time period if in a TR covered state.

TR_SO2_2800_Final Imposes a marginal cost of $500 per ton of NOx year round in 

annual states starting in 2012, $500 per ton of ozone season NOx

in ozone states starting in 2012, $500 per ton for SO2 year round 

in "Group 2" states starting in 2012, $500 per ton for Group 1 

states in 2012 and 2013, $2,800 per ton for Group 1 states in 

2014 and each year thereafter. Also forces all SCR and FGD to 

operate for relevant time period if in a TR covered state.
42

TR_SO2_3300_Final Imposes a marginal cost of $500 per ton of NOx year round in 

annual states starting in 2012, $500 per ton of ozone season NOx

in ozone states starting in 2012, $500 per ton for SO2 year round 

in "Group 2" states starting in 2012, $500 per ton for Group 1 

states in 2012 and 2013, $3,300 per ton for Group 1 states in 

2014 and each year thereafter. Also forces all SCR and FGD to 

operate for relevant time period if in a TR covered state.

TR_SO2_10,000_Final Imposes a marginal cost of $500 per ton of NOx year round in 

annual states starting in 2012, $500 per ton of ozone season NOx

in ozone states starting in 2012, $500 per ton for SO2 year round 

in "Group 2" states starting in 2012. For SO2 "Group 1" states, a 

cap of 2.41 million tons is imposed in 2012 and 2013, and a cap 

of 344,000 ton is imposed in 2014 and each year thereafter for 

SO2. These caps were designed to reflect a 70% reduction from 

levels observed in the $500 per ton for Group 1 states in 2012 

and 2013, $3,300 per ton for Group 1 states in 2014 and each 

year thereafter. Also forces all SCR and FGD to operate for 

relevant time period if in a TR covered state.

TR_NOX_OS_500_Final Imposes a marginal cost of $500 per ton of NOx reduced in the 

ozone season on each of 26 ozone states (including the six states

for which EPA is issuing a supplemental proposal to require 

ozone season NOx reductions) starting in 2012. Also forces 

dispatchable SCRs to run in the ozone season if located in this 

region

TR_NOX_OS_1000_Final Imposes a marginal cost of $1,000 per ton of NOx reduced in the 

ozone season on each of 26 ozone states (including the six states

for which EPA is issuing a supplemental proposal to require 

ozone season NOx reductions) starting in 2012. Also forces 

dispatchable SCRs to run in the ozone season if located in this 

region

TR_NOX_OS_5000_Final Imposes a marginal cost of $5,000 per ton of NOx reduced in the 

ozone season on each of 26 ozone states (including the six states

for which EPA is issuing a supplemental proposal to require 

ozone season NOx reductions) starting in 2012. Also forces 

dispatchable SCRs to run in the ozone season if located in this 

region

TR_NOX_500_Final Imposes a marginal cost of $500 per ton of NOx reduced 

annually on each of 23 states in the annual region. Also forces 

SCRs to operate year round if located in this region

TR_NOX_1000_Final Imposes a marginal cost of $1,000 per ton of NOx reduced 

annually on each of 23 states in the annual region. Also forces 

SCRs to operate year round if located in this region

TR_NOX_2500_Final Imposes a marginal cost of $2,500 per ton of NOx reduced 

annually on each of 23 states in the annual region. Also forces 

SCRs to operate year round if located in this region

TR_Remedy_Final* Models the air quality-assured trading final remedy described in 

the Transport Rule preamble with variability limits ranging from 

10 to 20%
43

TR_Remedy_Sensitivity_with_final_variability_limits Models the air quality-assured trading final remedy described in 

the Transport Rule preamble with variability limits of 18% for 

SO2 and Annual NOx, and 21% for ozone-season NOx and 

other technical corrections discussed in Section VIII.A of the 

preamble

*These projected impacts of this final rule do not reflect minor technical corrections to SO2 

budgets in three states (KY, MI, and NY). These projections also assumed preliminary variability 

limits that were smaller than the variabilty limits finalized in this rule. EPA conducted sensitivity

analysis confirming that these differences do not meaningfully alter any of the Agency's findings 

or conclusions based on the projected cost, benefit, and air quality impacts presented for the final 

Transport Rule. The results of this sensitivity analysis are presented in Appendix F in the final 

Transport Rule RIA. The TR_Remedy_Sensitivity_with_final_variability_limits run below includes 

such corrections, and the subsequent AQAT analysis on this run suggest no difference in air 

quality impacts (see discussion earlier in TSD)
44

Appendix B: Detailed Comparison of AQAT Estimates with 

CAMx Results 
45

This Appendix contains tables with detailed comparisons of the sulfate and total 

PM2.5 for the AQAT estimates compared with CAMx for the 2014 base case and 2014 

final remedy (Table Appendix B-1 and Table Appendix B-2, respectively). The 98th

percentile days were selected based on the days used to create the design value according 

to the CAMx results. That is, the 98th percentile day may have been different in AQAT. 

For this particular analysis, whatever day the “Future Date” was selected based on the 

CAMx estimates was the day selected from the AQAT results. Consequently, the AQAT 

to CAMx design value comparison (presented in table C-10 in this TSD) could have been 

based on different subset of days from AQAT.

Table Appendix B-1. Comparison of Sulfate and Total PM2.5 for 98th Percentile Days* 

for the 2014 Base Case.

Future 

Date

Monitor 

Identification 

Number

State County

2012 Base 

Case 

Max. DV

Sulfate Total PM2.5

CAMx AQAT

Difference 

(CAMxAQAT), 

Sulfate

CAMx AQAT

Difference 

(CAMxAQAT), Total 

PM2.5

20031031 420030064 Pennsylvania Allegheny 59.93 15.15 15.22 -0.07 56.44 56.51 -0.07

20040301 420030064 Pennsylvania Allegheny 59.93 15.32 15.11 0.21 57.78 58.91 -1.13

20050914 420030064 Pennsylvania Allegheny 59.93 30.25 29.99 0.25 58.31 58.04 0.26

20060618 420030064 Pennsylvania Allegheny 59.93 22.04 22.50 -0.46 47.30 47.75 -0.46

20070422 420030064 Pennsylvania Allegheny 59.93 21.32 21.76 -0.44 45.76 46.20 -0.44

20030626 420030093 Pennsylvania Allegheny 44.40 18.15 18.37 -0.22 47.08 47.29 -0.22

20040608 420030093 Pennsylvania Allegheny 44.40 15.11 15.29 -0.18 39.27 39.45 -0.18

20050913 420030093 Pennsylvania Allegheny 44.40 19.20 19.00 0.20 41.35 41.14 0.21

20060710 420030093 Pennsylvania Allegheny 44.40 14.03 13.89 0.15 30.37 30.21 0.15

20070524 420030093 Pennsylvania Allegheny 44.40 10.48 10.61 -0.13 27.40 27.52 -0.12

20030821 390350038 Ohio Cuyahoga 41.84 25.34 24.82 0.52 40.83 40.31 0.52

20040607 390350038 Ohio Cuyahoga 41.84 25.06 24.91 0.14 36.78 36.63 0.14

20050913 390350038 Ohio Cuyahoga 41.84 27.39 26.83 0.56 44.10 43.54 0.57

20060818 390350038 Ohio Cuyahoga 41.84 19.56 19.16 0.40 31.63 31.23 0.40

20070906 390350038 Ohio Cuyahoga 41.84 21.18 20.75 0.43 34.21 33.78 0.44

20030318 261630016 Michigan Wayne 41.28 11.05 10.92 0.13 41.57 42.52 -0.95

20040730 261630016 Michigan Wayne 41.28 20.55 20.33 0.21 32.35 32.13 0.22

20040904 261630016 Michigan Wayne 41.28 20.55 20.33 0.21 32.35 32.13 0.22

20050202 261630016 Michigan Wayne 41.28 12.40 12.26 0.14 46.60 47.67 -1.07

20060330 261630016 Michigan Wayne 41.28 8.80 8.70 0.10 33.21 33.97 -0.76

20070906 261630016 Michigan Wayne 41.28 18.37 18.18 0.19 28.98 28.78 0.19

20030702 390350060 Ohio Cuyahoga 40.85 25.89 25.36 0.53 38.69 38.16 0.53

20040218 390350060 Ohio Cuyahoga 40.85 10.82 10.59 0.24 38.40 39.53 -1.12

20051004 390350060 Ohio Cuyahoga 40.85 16.51 16.29 0.21 42.19 42.79 -0.61

20060529 390350060 Ohio Cuyahoga 40.85 15.15 15.06 0.09 28.02 27.93 0.09

20070921 390350060 Ohio Cuyahoga 40.85 21.98 21.53 0.45 32.92 32.47 0.45

20030316 170311016 Illinois Cook 40.44 8.66 8.64 0.02 38.12 38.72 -0.60

20041229 170311016 Illinois Cook 40.44 8.37 8.29 0.08 35.24 36.08 -0.83

20050802 170311016 Illinois Cook 40.44 19.50 19.07 0.44 42.79 42.35 0.44

20061219 170311016 Illinois Cook 40.44 6.44 6.38 0.06 27.24 27.88 -0.64

20070617 170311016 Illinois Cook 40.44 12.48 12.15 0.34 31.56 31.28 0.27

20030301 261630033 Michigan Wayne 39.81 9.69 9.58 0.11 36.63 37.44 -0.81

20040608 261630033 Michigan Wayne 39.81 21.83 21.75 0.08 33.43 33.35 0.08

20050206 261630033 Michigan Wayne 39.81 11.98 11.84 0.14 45.17 46.17 -1.00

20061213 261630033 Michigan Wayne 39.81 7.50 7.59 -0.10 36.95 38.88 -1.93

20070524 261630033 Michigan Wayne 39.81 20.26 20.19 0.07 31.06 30.99 0.07

20030301 180890022 Indiana Lake 39.58 11.72 11.70 0.02 40.77 41.06 -0.29

20041226 180890022 Indiana Lake 39.58 10.70 10.77 -0.08 38.80 39.69 -0.89

20050113 180890022 Indiana Lake 39.58 10.14 10.13 0.01 35.36 35.61 -0.25

20060123 180890022 Indiana Lake 39.58 6.97 6.96 0.01 24.45 24.62 -0.17

20070921 180890022 Indiana Lake 39.58 17.57 17.15 0.42 30.58 30.16 0.42

20031229 540090011 West Virginia Brooke 38.39 9.56 9.71 -0.15 36.44 36.76 -0.32

20040212 540090011 West Virginia Brooke 38.39 16.98 16.70 0.28 39.36 40.62 -1.25

20050419 540090011 West Virginia Brooke 38.39 24.93 25.34 -0.41 35.33 35.74 -0.41

20060222 540090011 West Virginia Brooke 38.39 14.11 13.87 0.23 32.78 33.82 -1.04

20070828 540090011 West Virginia Brooke 38.39 20.39 20.39 0.00 39.07 39.05 0.02

20030313 420710007 Pennsylvania Lancaster 38.37 11.56 11.41 0.15 44.04 44.43 -0.39

20041009 420710007 Pennsylvania Lancaster 38.37 4.43 4.52 -0.10 30.06 30.00 0.06

20050209 420710007 Pennsylvania Lancaster 38.37 10.14 10.01 0.13 38.69 39.03 -0.34

20060330 420710007 Pennsylvania Lancaster 38.37 8.57 8.46 0.11 32.78 33.07 -0.29

20070301 420710007 Pennsylvania Lancaster 38.37 8.35 8.24 0.11 31.94 32.22 -0.28

20030702 390350045 Ohio Cuyahoga 38.13 26.24 25.70 0.54 38.50 37.96 0.54

20040304 390350045 Ohio Cuyahoga 38.13 10.89 10.65 0.24 33.29 34.43 -1.14

20050913 390350045 Ohio Cuyahoga 38.13 26.89 26.34 0.55 39.44 38.89 0.55

20060210 390350045 Ohio Cuyahoga 38.13 8.22 8.04 0.18 25.25 26.11 -0.86

20070921 390350045 Ohio Cuyahoga 38.13 20.41 20.00 0.42 30.07 29.65 0.42

20031126 390811001 Ohio Jefferson 37.88 13.51 13.72 -0.21 34.02 34.46 -0.44

20040702 390811001 Ohio Jefferson 37.88 20.06 20.06 0.00 40.54 40.52 0.02

20050913 390811001 Ohio Jefferson 37.88 17.19 17.19 0.00 34.81 34.79 0.02

20060827 390811001 Ohio Jefferson 37.88 12.75 12.74 0.00 25.94 25.92 0.01

20070804 390811001 Ohio Jefferson 37.88 13.30 13.30 0.00 27.03 27.02 0.01

20030801 261630019 Michigan Wayne 37.83 20.28 20.05 0.23 32.86 32.62 0.24
46

20040304 261630019 Michigan Wayne 37.83 7.56 7.44 0.12 29.72 30.26-0.53

20050206 261630019 Michigan Wayne 37.83 12.12 11.93 0.19 47.37 48.23-0.86

20060309 261630019 Michigan Wayne 37.83 8.35 8.22 0.13 32.80 33.39-0.59

20070921 261630019 Michigan Wayne 37.83 16.46 16.27 0.19 26.75 26.56 0.20

20030316 390350065 Ohio Cuyahoga 37.67 11.97 11.71 0.26 36.18 37.43-1.25

20040924 390350065 Ohio Cuyahoga 37.67 22.44 21.99 0.46 32.32 31.86 0.46

20050627 390350065 Ohio Cuyahoga 37.67 25.62 25.47 0.15 41.22 41.07 0.15

20060710 390350065 Ohio Cuyahoga 37.67 16.38 16.05 0.34 23.73 23.39 0.34

20070921 390350065 Ohio Cuyahoga 37.67 22.20 21.75 0.45 31.98 31.52 0.46

20030202 170313301 Illinois Cook 37.67 7.16 7.08 0.08 32.35 32.70-0.35

20040903 170313301 Illinois Cook 37.67 17.86 17.35 0.51 34.82 34.30 0.51

20050203 170313301 Illinois Cook 37.67 9.18 9.07 0.10 41.34 41.79-0.45

20060704 170313301 Illinois Cook 37.67 11.46 11.14 0.33 22.53 22.20 0.33

20060806 170313301 Illinois Cook 37.67 11.46 11.14 0.33 22.53 22.20 0.33

20071120 170313301 Illinois Cook 37.67 7.36 7.32 0.05 30.35 31.31-0.96

20030822 420070014 Pennsylvania Beaver 37.42 15.45 15.51-0.06 27.44 27.49-0.05

20040711 420070014 Pennsylvania Beaver 37.42 18.88 18.95-0.07 33.42 33.48-0.06

20050627 420070014 Pennsylvania Beaver 37.42 23.28 23.74-0.47 39.87 40.33-0.46

20061128 420070014 Pennsylvania Beaver 37.42 8.14 8.13 0.01 33.91 34.09-0.18

20070524 420070014 Pennsylvania Beaver 37.42 17.11 17.45-0.34 29.43 29.77-0.34

20030626 420033007 Pennsylvania Allegheny 37.40 18.77 19.16-0.39 48.80 49.19-0.39

20040924 420033007 Pennsylvania Allegheny 37.40 18.46 18.31 0.15 32.63 32.48 0.16

20050627 420033007 Pennsylvania Allegheny 37.40 9.78 9.99-0.20 25.69 25.89-0.20

20060710 420033007 Pennsylvania Allegheny 37.40 16.58 16.44 0.14 29.36 29.22 0.14

20070804 420033007 Pennsylvania Allegheny 37.40 16.21 16.07 0.14 28.71 28.57 0.14

20031103 10730023 Alabama Jefferson 37.33 9.29 9.33-0.03 31.67 31.74-0.07

20040723 10730023 Alabama Jefferson 37.33 15.32 14.89 0.42 34.09 33.66 0.42

20050914 10730023 Alabama Jefferson 37.33 18.25 17.74 0.51 40.51 40.01 0.51

20061216 10730023 Alabama Jefferson 37.33 9.49 9.52-0.03 32.32 32.39-0.07

20070521 10730023 Alabama Jefferson 37.33 14.70 14.51 0.19 35.19 35.00 0.19

20031009 550790026 Wisconsin Milwaukee 37.24 5.20 5.21-0.01 25.52 25.92-0.40

20040110 550790026 Wisconsin Milwaukee 37.24 6.79 6.73 0.05 24.30 25.54-1.24

20050203 550790026 Wisconsin Milwaukee 37.24 10.23 10.15 0.08 36.38 38.25-1.87

20061125 550790026 Wisconsin Milwaukee 37.24 7.52 7.53-0.01 36.69 37.27-0.58

20070220 550790026 Wisconsin Milwaukee 37.24 9.80 9.73 0.07 34.89 36.68-1.79

20030813 180970043 Indiana Marion 37.20 22.54 21.93 0.61 33.45 32.83 0.62

20040720 180970043 Indiana Marion 37.20 18.80 18.29 0.51 27.99 27.47 0.52

20050203 180970043 Indiana Marion 37.20 11.59 11.23 0.36 39.74 41.35-1.61

20060725 180970043 Indiana Marion 37.20 22.30 21.69 0.61 33.10 32.49 0.61

20070726 180970043 Indiana Marion 37.20 23.14 22.51 0.63 34.33 33.70 0.63

20030608 261470005 Michigan St Clair 37.14 21.35 21.04 0.31 31.27 30.97 0.30

20040903 261470005 Michigan St Clair 37.14 18.69 18.47 0.22 28.00 27.77 0.23

20051004 261470005 Michigan St Clair 37.14 8.97 9.03-0.07 42.74 44.20-1.46

20060222 261470005 Michigan St Clair 37.14 8.48 8.35 0.13 34.81 35.29-0.48

20070921 261470005 Michigan St Clair 37.14 20.37 20.13 0.25 30.47 30.22 0.25

20030913 550790043 Wisconsin Milwaukee 37.10 19.39 19.02 0.36 32.92 32.64 0.27

20040906 550790043 Wisconsin Milwaukee 37.10 20.64 20.25 0.39 35.01 34.72 0.29

20051224 550790043 Wisconsin Milwaukee 37.10 6.47 6.52-0.05 31.98 32.51-0.53

20060329 550790043 Wisconsin Milwaukee 37.10 10.86 10.81 0.05 39.24 41.14-1.90

20071211 550790043 Wisconsin Milwaukee 37.10 6.83 6.89-0.06 33.76 34.31-0.56

20030202 180890026 Indiana Lake 37.06 10.11 10.06 0.05 36.79 37.46-0.67

20040304 180890026 Indiana Lake 37.06 9.35 9.30 0.05 34.06 34.68-0.62

20050802 180890026 Indiana Lake 37.06 17.33 16.97 0.36 36.60 36.24 0.37

20060719 180890026 Indiana Lake 37.06 12.00 11.75 0.25 25.49 25.23 0.26

20071120 180890026 Indiana Lake 37.06 8.42 8.51-0.09 29.63 30.13-0.49

20030813 180970081 Indiana Marion 36.96 21.82 21.23 0.59 31.9231.32 0.60

20040912 180970081 Indiana Marion 36.96 19.19 18.67 0.52 28.14 27.61 0.53

20050627 180970081 Indiana Marion 36.96 25.22 24.99 0.22 41.31 41.09 0.22

20060818 180970081 Indiana Marion 36.96 20.97 20.40 0.57 30.69 30.12 0.57

20070617 180970081Indiana Marion 36.96 20.42 20.24 0.18 33.55 33.37 0.18

20030726 180970066 Indiana Marion 36.92 23.32 22.68 0.63 34.78 34.14 0.64

20040608 180970066 Indiana Marion 36.92 16.60 16.45 0.15 28.16 28.01 0.15

20050203 180970066 Indiana Marion 36.92 11.63 11.27 0.36 39.40 41.01-1.62

20060818 180970066 Indiana Marion 36.92 21.40 20.82 0.58 31.96 31.38 0.59

20070617 180970066 Indiana Marion 36.92 20.50 20.32 0.18 34.67 34.48 0.18

20030316 171191007 Illinois Madison 36.83 12.14 11.83 0.31 35.89 39.65-3.76

20040218 171191007 Illinois Madison 36.83 10.51 10.24 0.27 31.15 34.40-3.26

20050808 171191007 Illinois Madison 36.83 26.21 25.45 0.76 39.05 38.28 0.76

20060429 171191007 Illinois Madison 36.83 13.21 13.07 0.13 32.56 32.51 0.05

20070617 171191007 Illinois Madison 36.83 12.84 12.71 0.13 31.66 31.61 0.05

20030202 550790010 Wisconsin Milwaukee 36.71 8.38 8.34 0.04 29.59 31.06-1.47

20040905 550790010 Wisconsin Milwaukee 36.71 19.66 19.30 0.37 32.77 32.49 0.28

20050203 550790010 Wisconsin Milwaukee 36.71 9.78 9.74 0.04 34.48 36.20-1.71

20060329 550790010 Wisconsin Milwaukee 36.71 10.30 10.25 0.04 36.26 38.07-1.80

20070530 550790010 Wisconsin Milwaukee 36.71 18.16 18.28-0.12 35.57 35.89-0.32

20030301 390170003 Ohio Butler 36.59 15.51 14.95 0.56 35.9938.23-2.24

20040924 390170003 Ohio Butler 36.59 23.08 22.70 0.38 30.19 29.80 0.38

20050203 390170003 Ohio Butler 36.59 17.30 16.67 0.63 40.07 42.58-2.50

20060710 390170003 Ohio Butler 36.59 18.68 18.37 0.31 24.52 24.21 0.31

20070524 390170003 Ohio Butler 36.59 19.80 19.78 0.02 31.48 31.45 0.02

20030316 170316005 Illinois Cook 36.42 7.88 7.89 0.00 32.58 33.06-0.48

20041229 170316005 Illinois Cook 36.42 9.16 9.06 0.10 35.38 36.53-1.15

20051221 170316005 Illinois Cook 36.42 9.55 9.45 0.10 36.86 38.07-1.20

20060117 170316005 Illinois Cook 36.42 5.91 5.92 0.00 24.56 24.92-0.36

20070530 170316005 Illinois Cook 36.42 10.69 10.41 0.28 31.40 31.16 0.24

20030807 420031008 Pennsylvania Allegheny 36.35 19.65 19.60 0.05 33.83 33.78 0.05

20040608 420031008 Pennsylvania Allegheny 36.35 21.08 21.36-0.28 36.45 36.74-0.28

20050624 420031008 Pennsylvania Allegheny 36.35 19.15 19.41-0.26 33.17 33.43-0.26

20060818 420031008 Pennsylvania Allegheny 36.35 17.70 17.66 0.04 30.53 30.49 0.05

20070828 420031008 Pennsylvania Allegheny 36.35 18.70 18.65 0.04 32.22 32.17 0.05

20030418 261610008 Michigan Washtenaw 36.32 24.35 24.13 0.21 32.39 32.18 0.21

20041229 261610008 Michigan Washtenaw 36.32 5.03 5.10-0.06 26.73 29.37-2.64

20050206 261610008 Michigan Washtenaw 36.32 12.78 12.58 0.20 45.91 47.04-1.13
47

20061225 261610008 Michigan Washtenaw 36.32 5.00 5.06 -0.06 26.56 29.19 -2.62

20070906 261610008 Michigan Washtenaw 36.32 16.15 15.85 0.30 28.65 28.35 0.31

20030202 170312001 Illinois Cook 36.12 9.27 9.08 0.19 34.34 34.58 -0.24

20040903 170312001 Illinois Cook 36.12 16.44 15.91 0.53 32.57 32.04 0.53

20050203 170312001 Illinois Cook 36.12 10.27 10.05 0.21 37.98 38.24 -0.26

20060719 170312001 Illinois Cook 36.12 11.94 11.56 0.38 23.80 23.41 0.39

20070530 170312001 Illinois Cook 36.12 14.77 14.40 0.37 31.12 30.76 0.36

20030316 170310052 Illinois Cook 36.07 8.17 8.13 0.04 31.22 31.69 -0.47

20040904 170310052 Illinois Cook 36.07 16.15 15.79 0.37 31.64 31.27 0.37

20051221 170310052 Illinois Cook 36.07 9.96 9.87 0.08 39.51 40.60 -1.09

20060117 170310052 Illinois Cook 36.07 6.84 6.81 0.03 26.24 26.63 -0.39

20070220 170310052 Illinois Cook 36.07 8.73 8.68 0.04 33.33 33.83 -0.50

20030301 421330008 Pennsylvania York 36.06 16.99 16.83 0.17 42.70 43.70 -1.00

20040224 421330008 Pennsylvania York 36.06 11.23 11.12 0.11 28.39 29.05 -0.66

20050814 421330008 Pennsylvania York 36.06 26.28 26.01 0.28 35.00 34.72 0.28

20060216 421330008 Pennsylvania York 36.06 11.01 10.90 0.11 27.83 28.48 -0.65

20070226 421330008 Pennsylvania York 36.06 13.39 13.26 0.13 33.74 34.53 -0.79

20030301 261630015 Michigan Wayne 36.00 7.71 7.62 0.09 28.82 29.47 -0.66

20040608 261630015 Michigan Wayne 36.00 19.87 19.80 0.07 30.54 30.47 0.07

20050913 261630015 Michigan Wayne 36.00 26.59 26.31 0.28 42.00 41.72 0.28

20060222 261630015 Michigan Wayne 36.00 8.44 8.34 0.10 31.52 32.23 -0.72

20070530 261630015 Michigan Wayne 36.00 18.75 18.68 0.07 28.85 28.78 0.07

20030910 10732003 Alabama Jefferson 35.94 11.52 11.11 0.40 29.68 29.27 0.40

20040817 10732003 Alabama Jefferson 35.94 12.37 11.94 0.43 31.84 31.40 0.43

20050623 10732003 Alabama Jefferson 35.94 10.58 10.63 -0.05 37.80 37.85 -0.05

20060201 10732003 Alabama Jefferson 35.94 7.09 7.13 -0.04 35.12 35.26 -0.14

20070805 10732003 Alabama Jefferson 35.94 12.21 11.78 0.43 31.42 30.99 0.43

20030301 390618001 Ohio Hamilton 35.85 9.87 9.61 0.26 31.77 32.92 -1.14

20040720 390618001 Ohio Hamilton 35.85 17.50 17.31 0.20 27.48 27.28 0.20

20050913 390618001 Ohio Hamilton 35.85 26.68 26.38 0.30 41.62 41.32 0.30

20060908 390618001 Ohio Hamilton 35.85 19.02 18.81 0.21 29.83 29.61 0.22

20070906 390618001 Ohio Hamilton 35.85 19.34 19.12 0.22 30.31 30.09 0.22

20031114 171190023 Illinois Madison 35.81 9.86 9.55 0.31 33.44 34.39 -0.95

20040729 171190023 Illinois Madison 35.81 21.38 20.76 0.62 30.77 30.14 0.62

20050907 171190023 Illinois Madison 35.81 25.55 24.81 0.74 36.68 35.94 0.74

20060411 171190023 Illinois Madison 35.81 10.48 10.37 0.11 28.83 28.79 0.04

20030821 420031301 Pennsylvania Allegheny 35.65 24.28 24.21 0.07 40.13 40.06 0.08

20040912 420031301 Pennsylvania Allegheny 35.65 18.39 18.34 0.05 30.52 30.47 0.06

20050419 420031301 Pennsylvania Allegheny 35.65 14.97 15.23 -0.26 30.96 31.22 -0.26

20061110 420031301 Pennsylvania Allegheny 35.65 6.83 6.86 -0.03 31.27 31.32 -0.05

20070807 420031301 Pennsylvania Allegheny 35.65 21.02 20.96 0.06 34.81 34.75 0.07

20030826 391130032 Ohio Montgomery 35.61 26.88 26.61 0.27 35.23 34.95 0.28

20040903 391130032 Ohio Montgomery 35.61 20.29 20.08 0.21 26.71 26.50 0.21

20050203 391130032 Ohio Montgomery 35.61 4.90 4.75 0.14 39.86 41.17 -1.31

20060710 391130032 Ohio Montgomery 35.61 18.89 18.70 0.19 24.91 24.71 0.19

20070602 391130032 Ohio Montgomery 35.61 20.63 20.47 0.16 31.34 31.18 0.16

20030821 420030116 Pennsylvania Allegheny 35.59 15.73 15.51 0.22 34.86 34.63 0.23

*the 98th percentile days were chosen based on CAMx.
48

Table Appendix B-2. Comparison of Sulfate and Total PM2.5 for 98th Percentile Days* 

for the 2014 Final Remedy.

Future 

Date

Monitor 

Identification 

Number

State County

2012 Base 

Case 

Max. DV

Sulfate Total PM2.5

CAMx AQAT

Difference 

(CAMxAQAT), 

Sulfate

CAMx AQAT

Difference 

(CAMxAQAT), Total 

PM2.5

20050418 420030064 Pennsylvania Allegheny 59.93 15.80 16.01 -0.22 47.16 47.68 -0.52

20030324 420030064 Pennsylvania Allegheny 59.93 9.93 10.09 -0.15 50.84 51.30 -0.46

20041222 420030064 Pennsylvania Allegheny 59.93 10.33 10.44 -0.11 46.29 46.59 -0.30

20061128 420030064 Pennsylvania Allegheny 59.93 9.25 9.34 -0.10 41.48 41.75 -0.27

20071031 420030064 Pennsylvania Allegheny 59.93 8.33 8.42 -0.09 37.41 37.65 -0.24

20030626 420030093 Pennsylvania Allegheny 44.40 9.44 9.65 -0.21 37.96 38.57 -0.61

20040608 420030093 Pennsylvania Allegheny 44.40 7.86 8.03 -0.17 31.68 32.19 -0.51

20050913 420030093 Pennsylvania Allegheny 44.40 9.81 9.94 -0.13 31.65 32.08 -0.43

20070524 420030093 Pennsylvania Allegheny 44.40 5.45 5.57 -0.12 22.14 22.49 -0.35

20060710 420030093 Pennsylvania Allegheny 44.40 7.17 7.27 -0.09 23.28 23.59 -0.31

20070524 390350038 Ohio Cuyahoga 41.84 14.76 15.07 -0.31 25.64 26.55 -0.91

20041115 390350038 Ohio Cuyahoga 41.84 7.18 7.39 -0.21 32.28 33.71 -1.43

20050206 390350038 Ohio Cuyahoga 41.84 10.43 10.61 -0.18 38.13 39.04 -0.91

20030220 390350038 Ohio Cuyahoga 41.84 9.08 9.24 -0.16 33.27 34.06 -0.79

20060222 390350038 Ohio Cuyahoga 41.84 7.61 7.74 -0.13 27.97 28.63 -0.66

20041117 261630016 Michigan Wayne 41.28 4.47 4.58 -0.11 25.88 26.12 -0.24

20071120 261630016 Michigan Wayne 41.28 3.91 4.01 -0.09 22.71 22.92 -0.21

20050206 261630016 Michigan Wayne 41.28 9.73 9.77 -0.04 41.38 41.51 -0.13

20030221 261630016 Michigan Wayne 41.28 9.18 9.22 -0.04 39.06 39.19 -0.12

20060330 261630016 Michigan Wayne 41.28 7.72 7.75 -0.03 32.92 33.02 -0.10

20051004 390350060 Ohio Cuyahoga 40.85 10.79 11.11 -0.32 36.64 37.61 -0.97

20030130 390350060 Ohio Cuyahoga 40.85 7.08 7.20 -0.12 30.60 31.23 -0.63

20040310 390350060 Ohio Cuyahoga 40.85 6.79 6.91 -0.12 29.38 29.98 -0.60

20070310 390350060 Ohio Cuyahoga 40.85 5.56 5.66 -0.10 24.14 24.64 -0.49

20060309 390350060 Ohio Cuyahoga 40.85 5.17 5.26 -0.09 22.49 22.95 -0.46

20040903 170311016 Illinois Cook 40.44 9.47 9.60 -0.13 31.23 31.54 -0.32

20050627 170311016 Illinois Cook 40.44 11.27 11.39 -0.12 39.68 39.97 -0.29

20060818 170311016 Illinois Cook 40.44 7.32 7.42 -0.10 24.25 24.49 -0.24

20070530 170311016 Illinois Cook 40.44 7.54 7.62 -0.08 26.71 26.91 -0.19

20030316 170311016 Illinois Cook 40.44 7.61 7.61 0.00 37.60 37.68 -0.08

20050627 261630033 Michigan Wayne 39.81 20.76 21.08 -0.32 38.11 38.86 -0.75

20061213 261630033 Michigan Wayne 39.81 5.22 5.34 -0.12 36.30 36.63 -0.33

20041117 261630033 Michigan Wayne 39.81 4.17 4.27 -0.10 29.10 29.37 -0.26

20071226 261630033 Michigan Wayne 39.81 4.07 4.17 -0.10 28.44 28.70 -0.26

20030304 261630033 Michigan Wayne 39.81 8.26 8.29 -0.03 35.29 35.40 -0.12

20030415 180890022 Indiana Lake 39.58 21.84 22.04 -0.20 36.94 37.24 -0.30

20041226 180890022 Indiana Lake 39.58 8.81 8.90 -0.10 37.62 37.82 -0.19

20050116 180890022 Indiana Lake 39.58 8.86 8.86 0.00 33.76 33.83 -0.07

20070310 180890022 Indiana Lake 39.58 7.48 7.48 0.00 28.58 28.64 -0.06

20060123 180890022 Indiana Lake 39.58 6.21 6.21 0.00 23.83 23.88 -0.05

20030626 540090011 West Virginia Brooke 38.39 16.21 16.52 -0.31 28.97 29.66 -0.69

20040702 540090011 West Virginia Brooke 38.39 10.20 10.40 -0.20 29.26 29.75 -0.50

20070828 540090011 West Virginia Brooke 38.39 9.82 10.01 -0.19 28.20 28.68 -0.48

20051112 540090011 West Virginia Brooke 38.39 5.98 6.09 -0.11 29.12 29.48 -0.36

20061110 540090011 West Virginia Brooke 38.39 5.38 5.48 -0.10 26.27 26.60 -0.32

20041009 420710007 Pennsylvania Lancaster 38.37 3.70 3.74 -0.04 29.12 29.21 -0.10

20030313 420710007 Pennsylvania Lancaster 38.37 10.63 10.64 -0.01 43.55 43.66 -0.11

20050209 420710007 Pennsylvania Lancaster 38.37 9.32 9.33 -0.01 38.26 38.36 -0.10

20060330 420710007 Pennsylvania Lancaster 38.37 7.88 7.89 -0.01 32.41 32.50 -0.08

20070301 420710007 Pennsylvania Lancaster 38.37 7.67 7.68 0.00 31.58 31.66 -0.08

20041229 390350045 Ohio Cuyahoga 38.13 7.73 7.96 -0.23 26.40 27.61 -1.21

20050305 390350045 Ohio Cuyahoga 38.13 7.45 7.58 -0.13 27.56 28.20 -0.64

20030319 390350045 Ohio Cuyahoga 38.13 6.98 7.11 -0.12 25.89 26.49 -0.60

20070310 390350045 Ohio Cuyahoga 38.13 6.23 6.34 -0.11 23.16 23.69 -0.53

20060222 390350045 Ohio Cuyahoga 38.13 6.06 6.17 -0.11 22.54 23.06 -0.52

20040702 390811001 Ohio Jefferson 37.88 9.67 9.85 -0.19 29.82 30.31 -0.49

20030814 390811001 Ohio Jefferson 37.88 8.57 8.73 -0.16 26.48 26.92 -0.44

20050913 390811001 Ohio Jefferson 37.88 8.28 8.44 -0.16 25.62 26.04 -0.42

20060117 390811001 Ohio Jefferson 37.88 7.38 7.51 -0.14 21.68 22.08 -0.40

20070804 390811001 Ohio Jefferson 37.88 6.41 6.53 -0.12 19.93 20.26 -0.33

20041117 261630019 Michigan Wayne 37.83 3.86 3.96 -0.10 27.56 27.76 -0.21

20050206 261630019 Michigan Wayne 37.83 10.63 10.65 -0.03 46.77 46.95 -0.18

20060309 261630019 Michigan Wayne 37.83 7.32 7.34 -0.02 32.39 32.51 -0.12

20030304 261630019 Michigan Wayne 37.83 6.83 6.85 -0.02 30.24 30.36 -0.12

20070217 261630019 Michigan Wayne 37.83 4.94 4.96 -0.01 22.03 22.11 -0.08

20050627 390350065 Ohio Cuyahoga 37.67 15.41 15.73 -0.33 30.29 31.33 -1.03

20030702 390350065 Ohio Cuyahoga 37.67 13.52 13.77 -0.24 23.93 24.96 -1.04

20070906 390350065 Ohio Cuyahoga 37.67 12.98 13.22 -0.23 23.00 23.99 -0.99

20040212 390350065 Ohio Cuyahoga 37.67 6.42 6.53 -0.11 23.60 24.15 -0.55

20060210 390350065 Ohio Cuyahoga 37.67 5.61 5.71 -0.10 20.69 21.17 -0.48

20050627 170313301 Illinois Cook 37.67 12.27 12.36 -0.09 36.65 36.87 -0.22

20070617 170313301 Illinois Cook 37.67 9.40 9.47 -0.07 28.19 28.35 -0.17

20030214 170313301 Illinois Cook 37.67 6.19 6.19 0.00 31.42 31.48 -0.06

20040227 170313301 Illinois Cook 37.67 5.84 5.84 0.00 29.68 29.74 -0.06

20060306 170313301 Illinois Cook 37.67 4.22 4.22 0.00 21.58 21.62 -0.04

20040608 420070014 Pennsylvania Beaver 37.42 10.19 10.39 -0.20 23.91 24.41 -0.49

20070602 420070014 Pennsylvania Beaver 37.42 9.08 9.25 -0.18 21.36 21.80 -0.44

20061128 420070014 Pennsylvania Beaver 37.42 5.47 5.59 -0.13 31.18 31.55 -0.37

20051127 420070014 Pennsylvania Beaver 37.42 5.11 5.23 -0.12 29.19 29.53 -0.34

20031105 420070014 Pennsylvania Beaver 37.42 4.17 4.27 -0.10 23.93 24.21 -0.28

20040924 420033007 Pennsylvania Allegheny 37.40 9.16 9.32 -0.17 23.11 23.49 -0.38

20030626 420033007 Pennsylvania Allegheny 37.40 10.68 10.83 -0.15 40.46 40.86 -0.40
49

20050627 420033007 Pennsylvania Allegheny 37.40 5.57 5.65-0.08 21.34 21.55-0.21

20061125 420033007 Pennsylvania Allegheny 37.40 1.58 1.60-0.02 24.46 24.59-0.13

20071208 420033007 Pennsylvania Allegheny 37.40 1.37 1.39-0.01 21.26 21.37-0.11

20031115 10730023 Alabama Jefferson 37.33 6.47 6.45 0.01 27.57 27.66-0.09

20040225 10730023 Alabama Jefferson 37.33 6.46 6.43 0.04 29.97 30.04-0.07

20070326 10730023 Alabama Jefferson 37.33 6.76 6.72 0.04 31.34 31.41-0.07

20060704 10730023 Alabama Jefferson 37.33 10.33 10.12 0.21 29.20 29.07 0.13

20050914 10730023 Alabama Jefferson 37.33 12.19 11.95 0.25 34.37 34.22 0.15

20061125 550790026 Wisconsin Milwaukee 37.24 6.05 6.12-0.07 35.65 35.85-0.20

20071211 550790026 Wisconsin Milwaukee 37.24 5.52 5.58-0.06 32.56 32.74-0.18

20050131 550790026 Wisconsin Milwaukee 37.24 6.99 6.99 0.00 30.64 30.72-0.07

20030307 550790026 Wisconsin Milwaukee 37.24 5.78 5.780.00 25.40 25.46-0.06

20040110 550790026 Wisconsin Milwaukee 37.24 5.51 5.51 0.00 24.27 24.32-0.06

20050910 180970043 Indiana Marion 37.20 16.70 17.10-0.40 31.49 32.13-0.64

20070617 180970043 Indiana Marion 37.20 11.68 12.06-0.38 24.60 25.26-0.66

20060818 180970043 Indiana Marion 37.20 11.97 12.25-0.29 22.70 23.16-0.46

20030313 180970043 Indiana Marion 37.20 5.77 5.82-0.05 25.92 26.11-0.19

20040227 180970043 Indiana Marion 37.20 5.53 5.58-0.05 24.86 25.04-0.18

20051004 261470005 MichiganSt Clair 37.14 5.51 5.67-0.17 40.08 40.84-0.76

20070524 261470005 Michigan St Clair 37.14 13.75 13.87-0.12 23.60 24.09-0.49

20060222 261470005 Michigan St Clair 37.14 7.39 7.42-0.03 34.24 34.36-0.11

20030307 261470005 Michigan St Clair 37.14 6.306.33-0.03 29.28 29.38-0.10

20040325 261470005 Michigan St Clair 37.14 5.25 5.27-0.02 24.50 24.58-0.08

20040906 550790043 Wisconsin Milwaukee 37.10 11.32 11.38-0.06 25.66 25.85-0.20

20060329 550790043 Wisconsin Milwaukee 37.10 8.88 8.89-0.01 39.14 39.22-0.08

20030316 550790043 Wisconsin Milwaukee 37.10 6.84 6.85-0.01 30.26 30.32-0.06

20071120 550790043 Wisconsin Milwaukee 37.10 5.50 5.50 0.00 30.83 30.91-0.09

20051224 550790043 Wisconsin Milwaukee 37.10 5.63 5.63 0.00 31.53 31.61-0.09

20050203 180890026 Indiana Lake 37.06 8.03 8.08-0.05 33.45 33.59-0.14

20040304 180890026 Indiana Lake 37.06 7.99 8.04-0.05 33.28 33.42-0.14

20030226 180890026 Indiana Lake 37.06 7.93 7.98-0.05 33.02 33.16-0.14

20070220 180890026 Indiana Lake 37.06 6.10 6.14-0.04 25.54 25.64-0.10

20060123 180890026 Indiana Lake 37.06 5.71 5.74-0.03 23.90 24.00-0.10

20050910 180970081 Indiana Marion 36.96 17.37 17.78-0.41 32.00 32.66-0.66

20070617 180970081 Indiana Marion 36.96 11.14 11.50-0.36 24.00 24.63-0.63

20030130 180970081 Indiana Marion 36.96 5.95 6.00-0.05 25.86 26.05-0.19

20060306 180970081 Indiana Marion 36.96 5.78 5.84-0.05 25.14 25.33-0.18

20040224 180970081 Indiana Marion 36.96 5.28 5.33-0.05 23.01 23.18-0.17

20050910 180970066 Indiana Marion 36.92 16.90 17.31-0.40 32.11 32.77-0.65

20070803 180970066 Indiana Marion 36.92 14.77 15.12-0.35 28.12 28.70-0.57

20030801 180970066 Indiana Marion 36.92 13.85 14.18-0.33 26.40 26.93-0.54

20060222 180970066 Indiana Marion 36.92 5.79 5.84-0.05 25.66 25.85-0.19

20040224 180970066 Indiana Marion 36.92 5.27 5.32-0.05 23.44 23.61-0.17

20050227 171191007 Illinois Madison 36.83 8.60 8.74-0.13 34.53 34.59-0.06

20040116 171191007 Illinois Madison 36.83 6.67 6.77-0.10 26.89 26.93-0.04

20060126 171191007 Illinois Madison 36.83 6.35 6.45-0.10 25.62 25.66-0.04

20070220 171191007 Illinois Madison 36.83 6.05 6.14-0.09 24.43 24.47-0.04

20031114 171191007 Illinois Madison 36.83 6.50 6.54-0.04 30.13 30.25-0.11

20060329 550790010 Wisconsin Milwaukee 36.71 8.42 8.43-0.01 36.17 36.25-0.07

20050131 550790010 Wisconsin Milwaukee 36.71 7.39 7.40-0.01 31.83 31.89-0.06

20030202 550790010 Wisconsin Milwaukee 36.71 6.85 6.86-0.01 29.52 29.58-0.06

20040223 550790010 Wisconsin Milwaukee 36.71 5.59 5.60-0.01 24.19 24.24-0.05

20071211 550790010 Wisconsin Milwaukee 36.71 5.57 5.57 0.00 31.17 31.26-0.09

20050203 390170003 Ohio Butler 36.59 12.09 12.27-0.18 37.83 38.18-0.35

20070530 390170003 Ohio Butler 36.59 11.14 11.31-0.17 23.14 23.56-0.43

20040924 390170003 Ohio Butler 36.59 10.97 11.10-0.14 17.90 18.21-0.31

20030318 390170003 Ohio Butler 36.59 7.38 7.49-0.11 23.28 23.49-0.21

20060222 390170003 Ohio Butler 36.59 6.30 6.39-0.09 19.95 20.13-0.18

20051221 170316005 Illinois Cook36.42 7.64 7.76-0.12 36.24 36.38-0.14

20041229 170316005 Illinois Cook 36.42 7.33 7.44-0.11 34.78 34.91-0.13

20070617 170316005 Illinois Cook 36.42 7.29 7.37-0.09 29.17 29.36-0.18

20030316 170316005 Illinois Cook 36.42 6.87 6.88-0.01 31.96 32.05-0.10

20060123 170316005 Illinois Cook 36.42 5.10 5.10-0.01 23.84 23.92-0.07

20040608 420031008 Pennsylvania Allegheny 36.35 11.00 11.24-0.24 26.03 26.61-0.58

20050627 420031008 Pennsylvania Allegheny 36.35 10.04 10.26-0.22 23.81 24.35-0.53

20030821 420031008 Pennsylvania Allegheny 36.35 9.99 10.15-0.16 24.71 25.19-0.48

20070310 420031008 Pennsylvania Allegheny 36.35 6.69 6.81-0.12 24.20 24.56-0.36

20060222 420031008 Pennsylvania Allegheny 36.35 6.01 6.12-0.11 21.80 22.12-0.33

20051004 261610008 Michigan Washtenaw 36.32 3.74 3.86-0.12 32.73 32.74-0.02

20031009 261610008 Michigan Washtenaw 36.32 3.34 3.45-0.11 29.24 29.26-0.01

20061225 261610008 Michigan Washtenaw 36.32 3.11 3.22-0.10 27.33 27.34-0.01

20071226 261610008 Michigan Washtenaw 36.32 2.55 2.63-0.08 22.45 22.46-0.01

20040304 261610008 Michigan Washtenaw 36.32 5.96 5.99-0.03 25.67 25.77-0.11

20030403 170312001 Illinois Cook 36.12 12.30 12.41-0.11 32.55 32.76-0.21

20070617 170312001 Illinois Cook 36.12 10.12 10.20-0.09 26.85 27.02-0.17

20050203 170312001 Illinois Cook 36.12 9.00 9.00-0.01 37.12 37.19-0.07

20040227 170312001 Illinois Cook 36.12 6.42 6.43 0.00 26.63 26.68-0.05

20060117 170312001 Illinois Cook 36.12 4.88 4.89 0.00 20.37 20.41-0.04

20041230 170310052 Illinois Cook 36.07 5.78 5.90-0.11 28.34 28.53-0.19

20061229 170310052 Illinois Cook 36.07 5.14 5.24-0.10 25.24 25.41-0.17

20050205 170310052 Illinois Cook 36.07 7.63 7.65-0.02 32.74 32.86-0.11

20070308 170310052 Illinois Cook 36.07 7.31 7.34-0.02 31.42 31.53-0.11

20030226 170310052 Illinois Cook 36.07 6.77 6.79-0.02 29.11 29.21-0.10

20071208 421330008 Pennsylvania York 36.06 4.92 5.00-0.09 29.97 30.07-0.10

20030301 421330008 Pennsylvania York 36.06 14.24 14.33-0.08 41.07 41.20-0.14

20041123 421330008 Pennsylvania York 36.06 4.45 4.53-0.08 27.17 27.26-0.09

20050206 421330008 Pennsylvania York 36.06 11.35 11.41-0.07 32.81 32.92-0.11

20060216 421330008 Pennsylvania York 36.06 9.23 9.28-0.05 26.77 26.86-0.09

20051004 261630015 Michigan Wayne 36.00 5.94 6.09-0.14 36.16 36.50-0.34

20041229 261630015 Michigan Wayne 36.00 4.53 4.63-0.11 27.65 27.91-0.26

20060222 261630015 Michigan Wayne 36.00 7.40 7.43-0.03 31.22 31.32-0.10

20030214 261630015 Michigan Wayne 36.00 6.73 6.76-0.03 28.46 28.55-0.09

20070325 261630015 Michigan Wayne 36.00 5.32 5.34-0.02 22.58 22.65-0.07
50

20061210 10732003 Alabama Jefferson 35.94 6.17 6.24 -0.07 31.19 31.44 -0.26

20070815 10732003 Alabama Jefferson 35.94 7.88 7.76 0.12 27.22 27.18 0.04

20030415 10732003 Alabama Jefferson 35.94 5.24 5.09 0.14 25.94 25.93 0.01

20050921 10732003 Alabama Jefferson 35.94 9.82 9.68 0.14 33.82 33.77 0.05

20040610 10732003 Alabama Jefferson 35.94 5.90 5.74 0.16 29.17 29.16 0.02

20050910 390618001 Ohio Hamilton 35.85 13.46 13.74 -0.27 29.11 29.64 -0.53

20070527 390618001 Ohio Hamilton 35.85 9.58 9.76 -0.18 22.95 23.35 -0.40

20030316 390618001 Ohio Hamilton 35.85 5.98 6.14 -0.15 25.39 25.65 -0.26

20040218 390618001 Ohio Hamilton 35.85 5.74 5.88 -0.15 24.37 24.62 -0.25

20060222 390618001 Ohio Hamilton 35.85 5.41 5.55 -0.14 23.00 23.23 -0.24

20050227 171190023 Illinois Madison 35.81 9.01 9.15 -0.14 33.18 33.22 -0.04

20030304 171190023 Illinois Madison 35.81 8.41 8.54 -0.13 30.99 31.03 -0.04

20060222 171190023 Illinois Madison 35.81 6.75 6.85 -0.10 24.96 25.00 -0.03

20040424 171190023 Illinois Madison 35.81 6.03 6.08 -0.05 23.84 23.99 -0.15

20070906 420031301 Pennsylvania Allegheny 35.65 11.73 11.91 -0.18 26.27 26.71 -0.44

20030221 420031301 Pennsylvania Allegheny 35.65 8.65 8.81 -0.17 28.93 29.33 -0.40

20060710 420031301 Pennsylvania Allegheny 35.65 10.34 10.50 -0.16 23.22 23.61 -0.39

20050624 420031301 Pennsylvania Allegheny 35.65 8.37 8.53 -0.15 24.57 24.92 -0.35

20040512 420031301 Pennsylvania Allegheny 35.65 7.82 7.96 -0.14 22.97 23.30 -0.32

20030624 391130032 Ohio Montgomery 35.61 13.74 13.97 -0.23 26.72 27.21 -0.49

20070530 391130032 Ohio Montgomery 35.61 11.68 11.88 -0.20 22.80 23.21 -0.42

20050125 391130032 Ohio Montgomery 35.61 2.34 2.37 -0.03 25.66 25.87 -0.21

20040131 391130032 Ohio Montgomery 35.61 1.84 1.87 -0.02 20.36 20.53 -0.17

20060309 391130032 Ohio Montgomery 35.61 1.74 1.76 -0.02 19.22 19.37 -0.16

20051004 420030116 Pennsylvania Allegheny 35.59 8.27 8.46 -0.19 24.05 24.45 -0.40

20040608 420030116 Pennsylvania Allegheny 35.59 8.95 9.12 -0.17 26.74 27.25 -0.51

20030821 420030116 Pennsylvania Allegheny 35.59 7.42 7.58 -0.16 26.23 26.70 -0.47

*the 98th percentile days were chosen based on CAMx.
51

Appendix C: Description of Excel Spreadsheet Data Files for 

Transport Rule Significant Contribution Analysis 
52

EPA placed the following Excel spreadsheet file in the Transport Rule docket and 

on EPA’s website at [placeholder for website]:

The annual and quarterly emissions for all AQAT simulations can be found in this file.

AQAT_emissions.xlsx

These files contain the 24-hour PM2.5 2012 base case and 2014 AQAT Calibration 

Scenario contributions.

QTR1_base_and_AQAT_calibration_scenario_contributions.xlsx

QTR2_base_and_AQAT_calibration_scenario_contributions.xlsx

QTR3_base_and_AQAT_calibration_scenario_contributions.xlsx

QTR4_base_and_AQAT_calibration_scenario_contributions.xlsx

The annual PM2.5 and 24-hour PM2.5 calibration factors can be found in the respective 

files. 

Annual PM Calib Factors.xlsx

Daily PM Calibration Factors.xlsx

These files contain the quarterly contributions and calibrated Relative Response Factors 

(RRFs) for all 24-hour PM2.5 simulations.

dailyPM_adjusted sulfate contributions and RRF_2014_base.xlsx

dailyPM_adjusted sulfate contributions and RRF_2012_base_wleakage.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_500CT.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_1600CT.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_2300CT.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_2800CT.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_3300CT.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_10000CT.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_1600_remedy.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_2300_remedy.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_2800_remedy.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_final_remedy_sensitivity.xlsx

These files contain the quarterly contributions and calibrated RRFs for the variability 

assessments. The files in the list assume that the home state is held constant at the 

$2300/ton level. The number associated with "var" in the title notes the level of 

emissions variation above the level of the budget in the simulation.

dailyPM_adjusted sulfate contributions and RRF_2014_2300CT_20var_home_2300.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_2300CT_15var_home_2300.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_2300CT_10var_home_2300.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_2300CT_05var_home_2300.xlsx
53

This file contains a summary of the estimated 98th percentile values and resulting 

average and maximum design values for all 24-hour PM2.5 AQAT cost threshold level, 

variability analyses, and remedy simulations.

dailyPM_allyears_high_quarters.xlsx

These files apply the RRFs to each of the 32 days per year for each of the 5 years of 

available receptor estimates. The result is the estimated 24-hour PM2.5 concentration for 

that day. The 98th percentile day is also identified in these files. They are all in 2014 

unless otherwise specified in the title of the file.

dailyPM_all_years_all_quarters_base.xlsx

dailyPM_all_years_all_quarters_base_500CT.xlsx

dailyPM_all_years_all_quarters_base_1600CT.xlsx

dailyPM_all_years_all_quarters_base_2300CT.xlsx

dailyPM_all_years_all_quarters_base_2800CT.xlsx

dailyPM_all_years_all_quarters_base_3300CT.xlsx

dailyPM_all_years_all_quarters_base_10000CT.xlsx

dailyPM_all_years_all_quarters_base_2012_leakage.xlsx

dailyPM_all_years_all_quarters_1600_remedy.xlsx

dailyPM_all_years_all_quarters_2300_remedy.xlsx

dailyPM_all_years_all_quarters_10000_remedy.xlsx

dailyPM_all_years_all_quarters_final_remedy_sensitivity.xlsx

These are the same files as above, but the home state was held constant at the $2300/ton 

cost threshold level.

dailyPM_all_years_all_quarters_base_2300CT_20perc_whomeat2300.xlsx

dailyPM_all_years_all_quarters_base_2300CT_15perc_whomeat2300.xlsx

dailyPM_all_years_all_quarters_base_2300CT_10perc_whomeat2300.xlsx

dailyPM_all_years_all_quarters_base_2300CT_05perc_whomeat2300.xlsx

The annualPM25 AQAT.xlsx file contains the base contributions, AQAT calibration 

scenario contributions, calibrated contributions, and estimated design values for all 

annual PM2.5 AQAT simulations.

The AQAT vs. CAMx.xlsx file contains the 2014 base case and 2014 remedy

comparisons for AQAT and CAMx.
54

Appendix D: Description of Relationship Assumed 

between Monitor Location and Nonattainment Areas

In Table VI.C-2 of the preamble is a list of the number of projected nonattainment 

and maintenance areas. These were counted using the number of receptors from Tables 

C-3, C-4, C-5, and C-6 in this TSD and noting the nonattainment area that they are 

associated with (found in Tables Appendix D-1 and Appendix D-2). Note that for the 24-

hour PM2.5 standard, some areas with the receptors identified as having potential 

nonattainment and/or maintenance issues have not been designated as being 

nonattainment. For purposes of the final Transport Rule summary of results in Table 

VI.C-2 of the preamble, for these areas, EPA is using the annual PM2.5 NAAQS 

nonattainment area designation. For example, for 24-hour PM2.5, the receptors in Cook, 

IL and Lake, IN that are projected to be maintenance in the Transport Rule modeling are 

associated with their annual PM2.5nonattainment area designation (Chicago-Gary-Lake 

County, IL-IN) since they have not been designated for the 24-hour PM2.5NAAQS. 

Table Appendix D-1. Relationship between the Monitor Receptors and Nonattainment 

Areas for the Annual PM2.5 NAAQS.

Monitor 

Identification 

Number

State County

CAMx 2012 

Base Case

Avg. DV 

(g/m3)

CAMx 2012 

Base Case

Max. DV 

(g/m3)

Area

420030064 Pennsylvania Allegheny 17.94 18.33 Liberty-Clairton, PA

390350038 Ohio Cuyahoga 15.99 16.66 Cleveland-Akron-Lorain, OH

10730023 Alabama Jefferson 16.15 16.46 Birmingham, AL

390618001 Ohio Hamilton 16.01 16.33 Cincinnati-Hamilton, OH-KY-IN

261630033 Michigan Wayne 15.73 16.32 Detroit-Ann Arbor, MI

390350060 Ohio Cuyahoga 15.67 16.18 Cleveland-Akron-Lorain, OH

390610014 Ohio Hamilton 15.76 15.98 Cincinnati-Hamilton, OH-KY-IN

390610042 Ohio Hamilton 15.40 15.77 Cincinnati-Hamilton, OH-KY-IN

171191007 Illinois Madison 15.46 15.73 St. Louis, MO-IL

10732003 Alabama Jefferson 15.16 15.64 Birmingham, AL

390350045 Ohio Cuyahoga 15.14 15.61 Cleveland-Akron-Lorain, OH

180970081 Indiana Marion 14.86 15.16 Indianapolis, IN

131210039 Georgia Fulton 15.07 15.10 Atlanta, GA

390617001 Ohio Hamilton 14.74 15.10 Cincinnati-Hamilton, OH-KY-IN

390350065 Ohio Cuyahoga 14.67 15.10 Cleveland-Akron-Lorain, OH

180970083 Indiana Marion 14.71 15.06 Indianapolis, IN
55

Table Appendix D-2. Relationship between the Monitor Receptors and Nonattainment 

Areas* for the 24-hour PM2.5 NAAQS.

Monitor 

Identification 

Number

State County

CAMx 2012 

Base Case

Avg. DV 

(g/m3)

CAMx 2012 

Base Case

Max. DV 

(g/m3)

Area

420030064 Pennsylvania Allegheny 56.71 59.93 Liberty-Clairton, PA

420030093 Pennsylvania Allegheny 39.11 44.40 Pittsburgh-Beaver Valley, PA

390350038 Ohio Cuyahoga 39.46 41.84 Cleveland-Akron-Lorain, OH

261630016 Michigan Wayne 38.99 41.28 Detroit-Ann Arbor, MI

390350060 Ohio Cuyahoga 37.78 40.85 Cleveland-Akron-Lorain, OH

170311016 Illinois Cook 37.58 40.44 Chicago-Gary-Lake County, IL-IN*

261630033 Michigan Wayne 39.48 39.81 Detroit-Ann Arbor, MI

180890022 Indiana Lake 34.94 39.58 Chicago-Gary-Lake County, IL-IN*

540090011 West Virginia Brooke 37.57 38.39 Steubenville-Weirton, OH-WV

420710007 Pennsylvania Lancaster 35.98 38.37 Lancaster, PA

390350045 Ohio Cuyahoga 34.80 38.13 Cleveland-Akron-Lorain, OH

390811001 Ohio Jefferson 34.56 37.88 Steubenville-Weirton, OH-WV

261630019 Michigan Wayne 37.34 37.83 Detroit-Ann Arbor, MI

390350065 Ohio Cuyahoga 34.91 37.67 Cleveland-Akron-Lorain, OH

170313301 Illinois Cook 34.97 37.67 Chicago-Gary-Lake County, IL-IN*

420070014 Pennsylvania Beaver 36.21 37.42 Pittsburgh-Beaver Valley, PA

420033007 Pennsylvania Allegheny 32.40 37.40 Liberty-Clairton, PA

10730023 Alabama Jefferson 36.96 37.33 Birmingham, AL

550790026 Wisconsin Milwaukee 33.62 37.24 Milwaukee-Racine, WI

180970043 Indiana Marion 35.76 37.20 Indianapolis, IN*

261470005 Michigan St Clair 36.23 37.14 Detroit-Ann Arbor, MI

550790043 Wisconsin Milwaukee 36.21 37.10 Milwaukee-Racine, WI

180890026 Indiana Lake 34.08 37.06 Chicago-Gary-Lake County, IL-IN*

180970081 Indiana Marion 35.85 36.96 Indianapolis, IN*

180970066 Indiana Marion 35.73 36.92 Indianapolis, IN*

171191007 Illinois Madison 36.59 36.83 St. Louis, MO-IL*

550790010 Wisconsin Milwaukee 35.47 36.71 Milwaukee-Racine, WI

390170003 Ohio Butler 34.40 36.59 Cincinnati-Hamilton, OH-KY-IN*

170316005 Illinois Cook 34.12 36.42 Chicago-Gary-Lake County, IL-IN*

420031008 Pennsylvania Allegheny 35.04 36.35 Pittsburgh-Beaver Valley, PA

261610008 Michigan Washtenaw 35.05 36.32 Detroit-Ann Arbor, MI

170312001 Illinois Cook 33.62 36.12 Chicago-Gary-Lake County, IL-IN*

170310052 Illinois Cook 34.94 36.07 Chicago-Gary-Lake County, IL-IN*

421330008 Pennsylvania York 33.38 36.06 Harrisburg-Lebanon-Carlisle, PA

261630015 Michigan Wayne 35.55 36.00 Detroit-Ann Arbor, MI

10732003 Alabama Jefferson 35.31 35.94 Birmingham, AL

390618001 Ohio Hamilton 35.29 35.85 Cincinnati-Hamilton, OH-KY-IN*

171190023 Illinois Madison 35.11 35.81 St. Louis, MO-IL*

420031301 Pennsylvania Allegheny 33.95 35.65 Pittsburgh-Beaver Valley, PA

391130032 Ohio Montgomery 33.68 35.61 Dayton-Springfield, OH*

420030116 Pennsylvania Allegheny 35.59 35.59 Pittsburgh-Beaver Valley, PA

* Indicates that the receptor is not associated with a designated nonattainment area for the 24-

hour PM2.5 NAAQS. Consequently, only for purposes of this analysis, EPA associated the 

receptor with the area designated with respect to the annual PM2.5 NAAQS.
56

Appendix E: Comparison of Estimated 24-hour PM2.5 

Design Values for the Final Transport Rule Remedy and Final 

Remedy SensitivityFinal Remedy Sensitivity.

12

This appendix contains the AQAT estimates for the final Transport Rule remedy that was 

modeled in CAMx as well as the Final Remedy Sensitivity. The comparison of these 

two estimates show the air quality effects of the larger variability limits for SO2 on PM2.5 

concentrations.

Table Appendix E-1. Average and Maximum PM2.5 DVs (g/m3) in 2014 for the Final 

Transport Rule Remedy (as modeled in AQAT) and for Final Remedy Sensitivity.

Site ID State County Average DV

Difference 

in air 

quality 

between 

Final 

Remedy 

Sensitivity

and the 

remedy for 

Avg. DV. 

Maximum DV Difference 

in air 

quality 

between 

Final 

Remedy 

Sensitivity

and the 

remedy for 

Max DV.

remedy

Final 

Remedy 

Sensitivi

ty

remedy

Final 

Remedy 

Sensitivi

ty

420030064 Pennsylvania Allegheny 45.45 45.59 0.14 48.52 48.65 0.12

420030093 Pennsylvania Allegheny 29.88 30.02 0.14 34.28 34.43 0.15

390350038 Ohio Cuyahoga 33.46 33.52 0.06 35.39 35.44 0.05

261630016 Michigan Wayne 33.88 33.94 0.06 35.61 35.67 0.06

390350060 Ohio Cuyahoga 30.51 30.56 0.06 32.94 33.00 0.06

170311016 Illinois Cook 32.95 33.06 0.11 36.40 36.50 0.10

261630033 Michigan Wayne 34.74 34.90 0.15 34.95 35.11 0.16

180890022 Indiana Lake 32.31 32.34 0.03 36.30 36.36 0.06

540090011 West Virginia Brooke 28.83 28.95 0.12 29.63 29.80 0.17

420710007 Pennsylvania Lancaster 34.87 34.88 0.01 37.08 37.09 0.01

390350045 Ohio Cuyahoga 26.23 26.29 0.05 27.43 27.50 0.07

390811001 Ohio Jefferson 25.57 25.71 0.14 27.76 27.93 0.17

261630019 Michigan Wayne 34.87 34.92 0.04 35.74 35.79 0.05

390350065 Ohio Cuyahoga 25.95 26.07 0.12 26.81 26.96 0.15

170313301 Illinois Cook 30.35 30.39 0.04 32.70 32.73 0.04

420070014 Pennsylvania Beaver 27.39 27.47 0.09 28.49 28.58 0.09

420033007 Pennsylvania Allegheny 24.78 24.87 0.09 28.63 28.77 0.14

010730023 Alabama Jefferson 31.10 31.12 0.02 31.57 31.59 0.02

550790026 Wisconsin Milwaukee 30.08 30.10 0.02 33.10 33.12 0.02

180970043 Indiana Marion 27.13 27.27 0.14 27.76 27.86 0.10

261470005 Michigan St Clair 32.67 32.73 0.06 33.29 33.33 0.04

550790043 Wisconsin Milwaukee 31.80 31.85 0.05 33.92 33.94 0.02

180890026 Indiana Lake 30.49 30.52 0.03 33.39 33.42 0.03

180970081 Indiana Marion 27.30 27.41 0.11 27.54 27.67 0.13

180970066 Indiana Marion 28.10 28.21 0.11 29.11 29.25 0.15

 

12“ Final Remedy Sensitivity” models the air quality-assured trading final remedy described in the 

Transport Rule preamble with variability limits of 18% for SO2 and Annual NOx, and 21% for ozoneseason NOx. See run description in Appendix Table A-1.
57

171191007 Illinois Madison 29.32 29.34 0.03 30.64 30.67 0.03

550790010 Wisconsin Milwaukee 30.83 30.86 0.03 33.13 33.16 0.02

390170003 Ohio Butler 26.47 26.60 0.13 27.29 27.39 0.10

170316005 Illinois Cook 32.02 32.05 0.02 34.45 34.47 0.02

420031008 Pennsylvania Allegheny 24.47 24.63 0.15 25.38 25.57 0.18

261610008 Michigan Washtenaw 28.47 28.51 0.04 29.26 29.30 0.04

170312001 Illinois Cook 29.50 29.53 0.03 32.21 32.25 0.04

170310052 Illinois Cook 29.69 29.70 0.02 30.20 30.21 0.02

421330008 Pennsylvania York 30.92 30.95 0.03 33.79 33.83 0.04

261630015 Michigan Wayne 31.02 31.10 0.08 31.91 31.98 0.07

010732003 Alabama Jefferson 30.62 30.65 0.03 31.46 31.48 0.03

390618001 Ohio Hamilton 25.96 26.09 0.14 26.64 26.74 0.11

171190023 Illinois Madison 28.41 28.44 0.03 29.41 29.44 0.03

420031301 Pennsylvania Allegheny 24.96 25.10 0.14 25.85 25.96 0.11

391130032 Ohio Montgomery 23.09 23.15 0.06 24.54 24.62 0.08

420030116 Pennsylvania Allegheny 26.13 26.26 0.12 26.13 26.26 0.12