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

July 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

to Nonattainment 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 meet or 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 to nonattainment 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. Cost thresholds were 
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applied specifically to the upwind states identified for each program as listed in the table 

below.

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 cost 

curves report the remaining emissions at each cost threshold after the state has made 

emission reductions that are available up to the particular cost threshold analyzed. 

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 year-round operation of all existing SO2 and NOx pollution controls in states 

covered by the Transport Rule for PM2.5 and the ozone-season operation of all existing 

NOx pollution controls in states covered by the Transport Rule only for ozone. Because 

 

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 cost thresholds determined to eliminate significant contribution to 

nonattainment or interference with maintenance in that upwind state, in an average year. 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. In this final rule modeling, the 

emissions reported in the ―Fossil & Biomass‖ worksheets were used as emission inputs in both AQAT and 

CAMx modeling of the scenarios that were analyzed with both models.
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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 B-1.3

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 to nonattainment 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 then 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 to nonattainment and interference with 

maintenance at downwind receptors, as those receptors no longer had projected 

nonattainment and/or 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 

 

2

IPM results do include newly built 2012 post-combustion pollution control retrofits in base case 

modeling, cost curve runs, and remedy runs. These 2012 retrofits do not reflect any controls installed in 

response to the Transport Rule, but instead represent those that are already announced and/or under 

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

in response to existing consent decree or state rule requirements.

3 EPA notes that while ozone-season emissions generally decline as the cost threshold increases, there are 

instances where a state may see a small increase in emissions at a higher cost threshold compared to a 

lower cost threshold analyzed. This is related to the interconnected, interstate 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. In other words, as multiple states 

experience the higher cost threshold on ozone-season NOX, a region may minimize cost by dispatching 

more generation from lower-emitting-rate units in a particular state that counterintuitively raise that state’s 

total ozone-season NOX emissions, even as the regional ozone-season NOX emissions decline as a result. 
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contribution to nonattainment and interference with maintenance was eliminated at this 

$500/ton threshold, all subsequent cost curve analysis applied a constant $500/ton 

threshold on these states’ SO2 emissions.

EPA did not examine other cost thresholds in any states for 2012, as $500 per ton 

is a reasonably representative cost threshold to incentivize operation of existing control 

equipment, and higher cost thresholds may induce new 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. 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 

A-1 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 between states in response to the increasing 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 
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downwind air quality. Section VI.D of the TR preamble explains how EPA considered 

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 to 

nonattainment 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 to nonattainment 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 to nonattainment in an average year, and

therefore they 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 fossilfired 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 covered by the 

Transport Rule 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-
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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 emissions to prevent such emission increases. 

Further explanation of this issue is provided in section VI.D of the Preamble.4 

There were also a few budget adjustments made based on technical corrections described 

in Section VIII.A of the Transport Rule preamble. These were based on post-modeling 

quality assurance checks that found discrepancies in the SO2 pollution control technology

modeled for specific units in Kentucky, Michigan, and New York that did not match

commenter-provided data and independent third party proprietary sources. These 

discrepancies affected the 2012 SO2 budgets for Michigan and Kentucky and the 2012 

and 2014 SO2 budgets for New York.

Modeling of the Transport Rule showed scrubbers operating in 2012 on units at 

Monroe in Michigan and at Big Sandy in Kentucky, whereas commenters had informed 

the Agency that these units would not have scrubbers online by that time. As a result, 

EPA made technical corrections to the 2012 SO2 budgets in Michigan and Kentucky to 

reflect unscrubbed emissions from those units (taken from base case modeling of those 

units’ unscrubbed operation). Therefore, the corrected 2012 SO2 budgets in Michigan 

and Kentucky do not reflect operation of those controls.

Modeling of the Transport Rule also showed scrubbers operating in 2012 and in 

2014 on units at Dunkirk and at Huntley in New York. However, public comments 

showed that these units operate dry sorbent injection, not scrubbers, which would yield a 

lower SO2 removal than what was modeled at those units. As a result, EPA made 

technical corrections to the 2012 and 2014 SO2 budgets in New York to reflect a revised 

SO2 removal rate at those units consistent with the technology reported by commenters 

for those units. Therefore, the corrected 2012 and 2014 budgets in New York now reflect 

operation of the controls reported by commenters at the affected units.

EPA conducted a sensitivity analysis to reflect the impact of these technical 

corrections on projections for the Transport Rule remedy. That analysis also 

incorporated finalized variability limits, which were higher than those originally modeled 

in the main analysis. The results of this analysis are presented in Appendix F of the 

Regulatory Impact Analysis.

The IPM runs 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 output files of these model runs 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 tons. 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 inputs 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, 

 

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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would have on air quality at downwind monitors that were identified as nonattainment 

and/or maintenance for purposes of the Transport Rule. Section C in this TSD describes 

EPA’s development and use of 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).
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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.5

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 between the 2012 base 

 

5

For similar reasons, EPA used AQAT to assess the air quality impacts of variability in emissions. (See the Power 

Sector Variability Final Rule TSD).
17

case and an AQAT calibration scenario6in 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.

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 

scenario6. 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 

 

6

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

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 relationship7between reductions 

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

 

7 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 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.
19

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 to nonattainment 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.

● 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
20

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 states: 

Alabama, Arkansas, Connecticut, Delaware, District of Columbia8, 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 

 

8 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.
21

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 ―days9‖ 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.10

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 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 scenario6 with similar level and 

geographic distribution to the preferred remedy from the proposed Transport Rule. Selection of 

 

9 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.

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

Modeling Final Rule Technical Support Document.
22

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/m3

and 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). 

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, nonpoint, 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
23

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.

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 
27

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 threshold11 (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

● 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)

 

11 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

● 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 other12 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.

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 

 

12 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

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 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 
30

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. 

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
31

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 Montgomery 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 C1)

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 Washtenaw 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

Montgomer

y

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 large contributions in the summer months to 

sites in Cook, IL (monitor 170311016 quarter 2); and in Lake, IN (monitors 180890022 and
39

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 capand-trade program; NOx SIP Call regional ozone season cap-andtrade 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

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
46

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

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
47

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.

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
48

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 0.0033.76 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 Cook37.67 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 23.1123.49-0.38

20030626 420033007 Pennsylvania Allegheny 37.40 10.68 10.83-0.15 40.46 40.86-0.40

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 Milwaukee37.24 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 5.78 0.00 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 Michigan St 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 6.30 6.33-0.03 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 39.1439.22-0.08

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
49

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 Cook 36.42 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

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.
50

Appendix C: Description of Excel Spreadsheet Data Files for 

Transport Rule Significant Contribution Analysis 
51

EPA placed the following Excel spreadsheet file in the Transport Rule docket.

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_10000_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_21var_home_2300.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_2300CT_20var_home_2300.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_2300CT_18var_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
52

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 were used in the variability analysis. The home state was 

controlled and held constant at the $2300/ton cost threshold level. States that were linked to the 

particular receptor were controlled at the $2300/ton cost threshold level plust the variability 

limit. The level of variability is noted in the name of the file.

dailyPM_all_years_all_quarters_base_2300CT_21perc_whomeat2300.xlsx

dailyPM_all_years_all_quarters_base_2300CT_20perc_whomeat2300.xlsx

dailyPM_all_years_all_quarters_base_2300CT_18perc_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.
53

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, C5, 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
54

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.
55

Appendix E: Comparison of Estimated 24-hour PM2.5 Design 

Values for the Final Transport Rule Remedy and Final Remedy 

Sensitivity.

13

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.

 

13― 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 ozone-season NOx. See run 

description in Appendix Table A-1.
56

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 

Sensitivity

remedy

Final 

Remedy 

Sensitivity

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

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