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– Do Not Cite or Quote Technical Support Document (TSD) for the Final Transport Rule Docket ID No. EPA - HQ - OAR- 2009 - 0491



# Significant Contribution and State Emissions Budgets

Final Rule TSD

U.S. Environmental Protection Agency Office of Air and Radiation June 201 1

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– Do Not Cite or Quote This Technical Support Document (TSD) provides information that supports EPA’s anal ys is to quantify upwind state emissions that significantl y 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 VIin 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 a ssessment tool 2. Details on the construction of the air quality assessment tool 3. Description of the results of the anal ys is using AQAT for the final approach 4. Comparison between the air quality assessment tool estimates and CAMx air quality modelin g estimates A . Background on EPA’s Analysis to Quantify Emissions that Significantly Contribute or Interfere with Maintenance Section s V and VI of the final Transport Rule preamble describe EPA’s approach to identify upwind states’ emissions that signif icantly contribute to nonattainment or interfere with maintenance downwind with respect to the 1997 and 2006 PM 2.5 

## NAAQS and the 1997 ozone NAAQS . As described in the preamble, the approach uses air quality anal ys is to identify monitoring sites with proje cted nonattainment and maintenance problems for the PM 2.5 

## and ozone NAAQS as well as upwind states whose contributions to these monitoring sites exceed specified threshold amounts. See sections V.C and V.D in the preamble and the Air Quality Modeling Final Rule TSD for a detailed discussion of these air quality analyses. As described in 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 sign ificant contribution and interference with maintenance. The first step in the process identifies the emissions projected to remain in each state at ascending cost thresholds of emissions reductions. See section B in this TSD for discussion of the anal ys i s used in this step. 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 used in this step. Preamble section VI.D reviews the information gained from 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 .

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– Do Not Cite or Quote The determination was the outcome of first examining, state -by- state, emissions at different cost threshold s for the regulated pollutant. EPA started by examining cost thresholds of $500/ton for all covered pollutants in 2012. Then, as explained in preamble section VI, raising the SO 2 

## cost threshold to $2,300/ton for Group 1 states in 2014. Group 2 states remained at $500/ton in 2014 and each year thereafter because it was determined their contribution to nonattainment and interference with maintenance was eliminated at that point. All states remained at $500/ton for annual and ozone season NO x 

## for reasons explained in preamble section VI. Remaining emissions at these cost thresholds represent each state’s emissions after the removal of significant contribution to nonattainment and maintenance (or in the case of some states for ozone season NO x 

## , the progress towards that such removal). Cost thresholds are in 2007 dollars. 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 ) and on EPA’s website at [placeholder for website ]. Appendix C in this TSD describes these files. B. Electric Generating Unit Significant Contributi on Cost Analysis EPA used version 4.10 of the Integrated Planning Model (IPM) to anal yz e the annual SO 2 

## , annual NO x 

## , and ozone - season NO x 

## emissions reductions available from electric generating units ( EGUs ) at various cost levels in each upwind state. IP M is a multiregional, dynamic, deterministic linear programming model of the U.S. electric power sector that EPA uses to anal yz e cost and emissions impacts of environmental policies. S ee “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 ” in the docket listed above. EPA first modeled a base case EGU emissions scenario, i.e., a scenario absent any emission reduction requirements rel ated to the Transport Rule. The base case modeling includes the Title IV SO 2 

## cap and trade program; NO x 

## 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, th e base case does not include the Clean Air Interstate Rule (CAIR) , which will be replaced by this rule. Using IPM, EPA modeled the emissions that would occur within each state at ascending cost thresholds of emi ssions control . EPA designed a series of IPM runs that imposed increasing cost thresholds for reduction of SO 2 

## , annual NO x 

## , and ozone - season NO x 

## emissions and tabulated those projected emissions for each state at each cost level. EPA refers to these tabulations as “cost curves” in preamble section VI.B. 1 

## The 1 

### These projected state level emissions for each “cost threshold” run are presented in a several formats. In the “state emissions” excel workbook, there is a worksheet titled “all units” which shows aggregate emissions for all units in the state. The “all fossil > 25MW” worksheet is a subset of these units that shows emissions from units that are identified in NEEDs as having a capacity greater than 25 MW. The emissions in the “all fossil > 25 MW” worksheet are used to derive the budgets for each state at the appropriately determined cost threshold. The “fossil & biomass” worksheet reports total emissions from fossil - fired and biomass - fired units, and represents the state level emission total used in the AQAT analysis. These “fossil & biomass” emission totals are used as inputs for CAMx air quality modeling, which is why those

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– Do Not Cite or Quote remaining emissions at each cost threshold reflect that which remains after the state has made emission reductions that are available for cost that are less than the particular cost threshold. This part of the analysis applied cost thresholds to all fossil - fuel - fired EGUs with a capacity greater than 25 MW in each state included in the relevant Transport Rule control program . At all cost thresholds analyzed, emissions projected for covered states reflect the operation of all existing SO 2 

## and NO x 

## pollution controls on a year- round basis in states covered b y the Transport Rule for PM 2.5 

## and all existing NO x 

## pollution controls on an ozone - season basis in states covered b y the Transport Rule only for oz one. EPA first conducted this “cost curve” anal ys is for ozone - season NO x 

## . EPA imposed cost thresholds ranging from $500 per ton to $5,000 per ton of ozone - season NO x 

## . These cost thresholds were applied to the states covered by the final Transport Rul e 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 IP M -projected EGU emissions of ozone - season NO x 

## from the “Fos sil > 25 MW” units are shown at each cost threshold for 2012 and 2014 in Table B 1 . EPA then conducted cost curve anal ys is for annual NO x 

## , imposing cost thresholds ranging from $500 to $2,500 per ton in states covered in the final Transport Rule for PM 2.5 

## . The IPM -projected EGU emissions of annual NO x 

## from the “Fossil > 25 MW” units are shown at each cost threshol d for 2012 and 2014 in Table B - 2 . As explained in preamble section VI.D, EPA determined that $500/ton was the appropriate cost threshold for ozone - season NO x 

## control at all covered states in this rulemaking. EPA also determined that $500/ton was the appropriate cost threshold for annual NO x 

## control at all covered states in concert with varying degrees of SO 2 

## control to eliminate significant contribution and interference with maintenance of the PM 2.5 

## NAAQS. In line with these determinations, EPA conducted cost curve analysis for SO 2 

## while simultaneously

imposing cost thresholds of $500/ton for ozone - season NO x 

## in Transport Rule ozone states and $50 0/ton for annual NO x 

## in Transport Rule PM 2.5 

## states. While holding these ozone - season NO x 

## and annual NO x 

## cost thresholds constant, EPA examined different SO 2 

## cost thresholds. For SO 2 

## emissions , the lowest cost threshold that EPA modeled is $500 per ton starting in 2012 and for each ye ar thereafter. EPA did not examine higher cost thresholds for 2012 as higher costs induce advanced control retrofits that require a longer lead time for installation. EPA, however, did examine higher cost thresholds for SO 2 

## in 2014. Before doing so, EPA first used the Air Quality Assessment Tool (AQAT) to identify improvements in downwind air quality at $500 per ton. EPA determined that for 7 states, emission reductions at this $500 per ton threshold successfull y eliminated significant contribution and interference with maintenance at downwind receptors, as those receptors no longer had projected nonattainment and/or maintenance problems when emissions were limited b y a $500/ton cost level. These 7 states - Alabama, Georgia , Kansas,

emissions were used as inputs for AQAT. In the Transport Rule proposal Technical Support Document “Analysis to Quantify Significant Contributio n”, EPA stated that the “all units” emissions used in the AQAT analysis for the proposal and the emissions used in the CAMx air quality modeling were slightly different. EPA committed to determining the origin of the slight difference and removing it for the final Transport Rule. The emissions in the “Fossil & Biomass” correct this slight difference.

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– Do Not Cite or Quote Nebraska, Minnesota, South Carolina, and Texas

– are referred to as Group 2 states. Because their significant contribution and interference with maintenance was eliminated at this $500/ton threshold, higher SO 2 

## cost thresholds were not examined for these states in 2014. For all subsequent cost curve anal ys is, a constant $500/ton threshold was imposed on these states’ SO 2 

## emissions. For the remainder of the states covered for PM 2.5 

## , EPA examined escalating cost thresholds for SO 2 

## in 2014. EPA ex amined cost levels of $1,600/ton, $2,300/ton, $2,800/ton, and $3,300/ton as a representative sampling of points along the SO 2 

## 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 SO 2 

## emissions to approximately

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

pe of quantity

constraint was imposed, the marginal cost was modeled as 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. See Table Appendix A - 1 in Appendix A for a list of IPM runs. Inthese costing runs, EPA imposed the annual pollutant cost thresholds identified above on all states covered by the Transport Rule for PM 2.5 

## . EPA only imposed the ozone - season NO x 

## cost thresholds for those states that are covered only by the Transport Rule ozone - season NO x 

## program. Because of the time required to build advanced pollution controls, the model was prevented from installing any new post - combustion controls such as selective catal ytic reduction (SCR) or scrubbers in 2012, excepting committed controls already scheduled to come online at that time (and therefore also present in the base case). The modeling does include the addition or upgrading of NO x 

## combustion controls in 2012. In these cost curves with ascending SO 2 

## cost thresholds, cost thresholds for e ach pollutant (SO 2 

## , ozone - season NO x 

## , and annual NO x

## ) were anal yz ed simultaneously . This methodology

for the final rule’s anal ys is represents a technical improvement on the anal ys is used in the proposal, where cost thresholds for each pollutant were examined independentl y 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 SO 2 

## , annual NO x 

## , and ozone season NO x 

## policy requirements across the states covered b y the Transport Rule . Cost - effective actions taken to reduce annual NO x 

## , for example, may influence the cost of reducing SO 2 

## . The modeling of these final cost curves capture s these important economic interactions. EPA uses IPM to determine state level emissions at the different cost thresholds. At each cost threshold, IPM state emission totals from “All Fossil and B iomass” as well as from “All fossil > 25 MW” are reported . The “All Fossi l and Biomass” worksheet is meant to reflect total state EGU emissions used for subsequent air quality modeling. The “All Fossil > 25 MW” values 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 slightly

smaller as it i s a subset of the former. Table B - 6 shows the state - level SO 2 

## 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 also vary between these thresholds. 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

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– Do Not Cite or Quote 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 SO 2 

## emissions levels from “All F ossil and B iomass” at each of these cost thresholds analyzed were examined in AQAT to determine the impact on downwind air quality . Section VI.D of the 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 bi omass 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 state level emissions from the “All Fossil > 25 MW” worksheet to determine state budgets at the cost thresholds selected in the final rule. These state level emissions totals can be found for each of the costing runs in the Transport Rule Docket. Because the Transport Rule generally applies to fossil fuel - fired units greater than 25 MW, EPA uses emissions from the “All Fossil > 25 MW” worksheet as an appropriate reflection of emissions from covered Transport Rule units. These emissions are very close in magnitude to the state level emissions from the “Fossil and Biomass” units used for determining impacts on air quality, but are typicall y slightly lower as they

represent onl y the subset that is fossil and greater than 25 MW (i.e, potential covered Transport Rule units). Transport Rule applicability is explained in section VII.B of the preamble. The state level emissions for ozone - season NO x 

## , annual NOx, and SO 2 

## emissions from fossil units greater than 25 MW are shown in Tables B - 3 through B - 5 below. These tables show how state level emissions for each of these pollutants change as the c ost threshold is varied for Group 1 SO 2 

## states in the “final cost curves”. As explained in preamble section VI.D, EPA identif ied $2,300/ton as the appropriate cost threshold in Group 1 states for addressing significant contribution and interference with maintenance. EPA notes that the $2,300/ton cost threshold analysis simultaneously

applies all of the selected cost thresholds for defining significant contribution and interference with maintenance under the Transport Rule. As explained above, it impos es a $2 , 300/ton threshold on Group 1 state SO 2 

## emissions starting in 2014 (increased from a $500/ton threshold imposed in 2012), a $500/ton threshold on Group 2 SO 2 

## emissions, a $500/ton threshold on annual NO x 

## emissions in PM 2.5 

## states, and a $500/ton threshold on ozone - season NO x 

## in ozone states. Because the $2 , 300/ton analysis included all of these selected cost thresholds under the final Transport Rule, EPA used that IPM run’s projected state level emissions remaining in 2012 and 2014 from all fossil units greater than 25 MW as the basis for the state budgets in 2012 and 2014, respectivel y. Hence, the values in Tables B - 3 through B - 5 became the Transport Rule s tate budgets for covered EGUs, with minor exceptions noted below. This is an appropriate lev el to set state budgets as it reflects the remaining emissions at the state level after the emissions identified by EPA as significantly

contributing to nonattainment and interfering with maintenance are eliminated . In most cases, the remaining state lev el emissions from all fossil greater than 25 MW at these final cost thresholds became the state budget for EGUs. However, as explained in section VI .D of the preamble, no state’s 2014 budget may be larger than its

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– Do Not Cite or Quote 2012 budget for that pollutant; as a con sequence, some states’ 2014 budgets are equal to their 2012 emissions from this analysis. Additionally, there were five states whose ozone - season emissions in this analysis were not significantl y different from their base case projected emissions in 2012. EPA conducted a sensitivity anal ys is to confirm that if left uncapped, these states’ ozone - season emissions would rise as other states make Transport Rule - related emission reductions, due to shifts between states in electricity generation to meet demand. EPA is therefore setting these states’ ozone - season budgets equal to their 2012 base case emissions to eliminate these emission increases. Further explanation of this issue is provided in section VI.D of the Preamble. The IPM runs are listed in Table Appendix A - 1 in of this TSD. This table lists the name of each IP M run next to a description of the run. The runs themselves can be found in the rulemaking docket. In the preamble section VI.B, the emissions presented are rounded to the nearest thousand ton, and in section VI .D they 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 applied emissions results shown in Table B - 3 in the use of an air quality assessm ent tool (AQAT) to estimate the impact that the combined reductions available from upwind contributing states and the downwind state, at different cost-per- ton levels, would have on air quality at downwind monitor sites that had nonattainment and/or maintenance problems. In AQAT, the emissions at each cost -perton level, were taken directly from the IPM runs. Section C in this TSD describes EPA’s development and use of the AQAT and the results from our AQAT anal ys is. Section C also compares the AQAT results to those produced using the Comprehensive Air Quality Model with Extensions (CAMx) .

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– Do Not Cite or Quote Table B - 1 . 2012 & 2014 Ozone Season NO x 

## EGU Emissions* f or Each State at Various Pollution Control Cost Threshold s p er Ton o f Reduction (Tons).

State Base Case Emiss ion

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 27,0 29 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 Kentucky 37,588 35,296 36,687 34,95 7 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 Mississippi 10,161 11,212 10,164 11,212 10,153 11,212 9,106 9,592 New Jersey 3,440 3,668 3,448 3,66 9 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 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 15,33 9 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 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 Michigan 25,989 28,037 26,058 26,250 25,771 26,180 25,381 25,168 missouri 23,156 23,759 22,952 23,759 22,952 23,661 21,433 21,707 Oklahoma 31,415 31,723 21,574 22,059 20,998 21,328 20,009 19,456 Wisconsin 15,876 16,048 13,971 14,134 13,928 14,035 12,412 12,897 Total 

## 654,161 647,43 9 618,267 608,702 614,807 604,728 573,150 565,498

* 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 . Cost s are in 2007$.

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– Do Not Cite or Quote Table B - 2 . 2012 and 2014 Annual NO x 

## EGU Emissions* f or Each State at Various Pollution Control Cost Threshold s p er Ton o f Reduction (Tons). State Base Case Emission

Levels $500/ton $1,000/ton $2,500/ton 2012 2014 2012 2014 2012 2014 2012 2014 Alabama 82,005 74,937 78,468 71,685 77,859 71,670 75,292 70,060 Georgia 66,384 47,808 63,073 40,809 62,921 40,712 59,713 39,457 Illinoi s 51,969 54,661 48,150 50,541 48,160 50,237 48,665 49,385 Indiana 119,625 116,552 109,506 108,187 108,610 107,176 108,241 99,876 Iowa 42,563 44,614 38,262 39,539 37,875 39,247 36,647 37,319 Kansas 37,106 32,390 30,991 25,075 30,759 24,815 30,194 23,190 Kentucky 88,136 83,481 85,396 82,657 84,572 81,024 82,150 78,087 Maryland 16,602 17,444 16,590 17,444 16,496 17,409 16,380 17,396 Michigan 60,594 64,345 60,725 61,088 60,482 60,877 59,991 60,110 Minnesota 36,833 37,952 29,588 30,441 29,537 30,432 29,42 7 30,294 Missouri 53,199 54,528 52,892 54,411 52,827 54,103 50,799 51,036 Nebraska 42,985 43,410 26,481 26,741 26,108 26,374 25,497 20,611 New Jersey 7,391 7,858 7,398 7,866 7,264 7,867 7,124 7,740 New York 17,556 18,505 17,551 18,519 17,643 18,378 17, 317 18,290 North Carolina 51,902 46,130 52,021 45,755 51,584 45,617 50,856 43,777 Ohio 100,420 99,389 98,473 94,680 97,444 94,143 94,702 91,686 Pennsylvania 129,125 132,299 120,709 124,106 120,307 123,942 119,063 115,990 South Carolina 34,635 37,862 34 ,548 37,549 34,305 37,029 32,640 35,996 Tennessee 37,674 29,256 37,676 29,315 37,654 29,395 36,450 28,680 Texas 136,124 140,788 133,141 138,150 132,861 137,582 131,931 136,062 Virginia 34,567 35,798 33,490 34,785 33,178 34,642 32,416 27,610 West Virginia 61,792 64,182 61,702 64,102 61,560 63,831 59,906 60,555 Wisconsin 36,701 36,904 32,078 32,267 31,975 32,008 30,811 30,766 Total 1,345,888 1,321,09 3 1,268,907 1,235,710 1,261,982 1,228,509 1,236,210 1,173,972 * Source: Integrated Planning Model run by EPA, 2011 . See Appendix A for list anda description of these IPM runs. Emissions have been rounded to the nearest ton. Emissions shown for all fossil - fired units greater than 25 MW when only an ozone season cost constraint is applied to Transport Rule States. Cost s are in 2007$.

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– Do Not Cite or Quote Table B -3 . 2012 & 2014 Ozone Season NO x 

## EGU Emissions from all Fossil Units Greater than 25 MW

at Escalating SO 2 

## Cost Thresholds from Final Cost Curve Analysis (Tons). State Base Case $500 $1,600 $2,300 $2,800 $3,300 $1 0,000

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 1 1,207 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 14,90 8 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 1 4,946 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 654,161 647,439 612,3 48 600,802 611,565 589,319 610,731 585,429 610,174 583,803 609,910 580,839 567,009 547,167 * 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 NO x and ozone season NO x cost thresholds at $500/ton (all years), SO 2 Group 2 at $500/ton (all years), and SO 2 Group 1 (2012 - 2013) at $500/ton. The escalating cost thresholds identified in the column headers above only apply starting in 2014 for Group 1 SO 2 states. Costs are in 2007$

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– Do Not Cite or Quote Table B - 4 . 2012 & 2014 NO x 

## EGU Emissions from all Fossil Units Greater than 25 MW at Escalating SO 2 

## Cost Thresholds from Final Cost Curve Analysis ( T ons). State Base Case $500 $1,600 $2,3 00 $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 40,4 25 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 88,13 6 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 37,3 18 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 3 4,567 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 NO x and ozone season NO x cost thresholds at $500/ton (all years), SO 2 Group 2 at $500/ton (all years), and SO 2 Group 1 (2012 - 2013) at $500/ton. The escalating cost thresholds identified in the column headers above only apply starting in 2014 for Group 1 SO 2 states. Costs are in 2007$

## 12 Deliberative

– Do Not Cite or Quote Table B - 5 . 2012 & 2014 SO 2 

## EGU Emissions from all Fossil Units Greater than 25 MW at Escalating SO 2 

## 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 20 0,573 221,896 226,299 216,033 213,258 219,088 213,991 223,903 235,837 234,732 189,743 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 485,417 137,522 230,622 134,311 233,080 129,8 81 234,889 124,123 234,889 117,375 234,876 101,789 160,616 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 75,4 86 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 7 0,820 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 App endix A for list and description of these IPM runs. Emissions have been rounded to the nearest ton. These “final cost curve” runs have NO x and ozone season NO x cost thresholds at $500/ton (all years), SO 2 Group 2 at $500/ton (all years), and SO 2 Group 1 (2012 - 2013) at $500/ton. The escalating cost thresholds identified in the column headers above only apply starting in 2014 for Group 1 SO 2 states. Costs are in 2007$

## 13 Deliberative

– Do Not Cite or Quote Table B - 6 . 2012 & 2014 Transport Rule State SO 2 

## 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 23 6,172 235,074 190,078 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 4 0,892 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 68,5 41 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 3 0,123 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 1 148,60 6 126,048 122,063 109,612 139,232 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 1 495,463 509,650 281,2 72 296,596 281,681 166,402 280,938 114,431 279,934 109,658 281,058 103,929 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 43,5 85 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 NO x and ozone season NO x cost thresholds at $500/ton (all years), SO 2 Group 2 at $500/ton (all years), and SO 2 Group 1 (2012 -2 013) at $500/ton. The escalating cost thresholds identified in the column headers above only apply starting in 2014 for Grou p 1 SO 2 states. Costs are in 2007$

## 14

C. Analysis of Significant Contribution Using the Air Quality Assessment Tool In defining significant contribution to nonattainment and interference with maintenance using the multi - factor test (described in section VI.D of the preamble) based on both cost and air quality factors, a key quantitative input is the predicted downwind ambient air quality impacts of upwind EGU emission reductions under the SO 2 

## cost thresholds. Time and resource limitations (in particular the amount of time needed to set up, run the CAMx model, and anal yz e the results for a single model run) precluded the use of air qual ity modeling for all but a few emissions scenarios. Because EPA needed to evaluate emission reductions under several different SO 2 

## cost thresholds, it was not possible to use CAMx air quality modeling to evaluate all cases. EPA thus uses a simplified air quality assessment tool (AQAT) , to estimate the downwind air quality impacts from various different SO 2 

## cost thresholds. For the SO 2 

## cost thresholds, the state -by- state EGU emissions are projected using EPA’s IPM model under a given cost threshold of emis sion 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 anal yz e many

more SO 2 

## 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 o f the SO 2 

## cost threshold analyses; ● 

## Compare the AQ AT estimates and CAMx results of sulfate and total PM 2.5 

## for two emissions scenarios where CAMx modeling was performed (i.e., the 2014 base case and 2014 remedy); and ● 

## Depict the results of an analysis of emissions “leakage” for 2012 performed u sing AQAT. 1. Introduction: Use and development of the air quality assessment tool. AQAT was developed specificall y 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 PM 2.5 

## (i.e., AQAT was not used to estimate air quality changes due to emission changes in NO x

## ); ● 

## Calibration of AQAT’s predicted change in sulfate concentrations to change in SO 2 

## emissions using CAMx . This calibration is receptor- specific and is based on the changes in SO 2 

## emissions and resulting sulfate concentrations between the 2012 base case and an AQAT calibration scenario 2 

## in 2014 (for more details about this scenario, see the footnote and the brief description below) . 2 

### 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 NO x 

### for states proposed to be regulated for PM 2.5 

### and ozone

## 15

● 

## Use o f seasonal contributions, and seasonal relative response factors, in developing the relationship between upwind SO 2 

## reductions and downwind 24 - hour PM 2.5 

## concentrations; and ● 

## Application of these seasonal relative response factors to the CAMx modeled 2003 - 2007 24 - hour PM 2.5 

## values for the 2012 base case. This methodological change allows EPA to the recalculate the 98th percentile 24 - hour PM 2.5 

## concentration and estimates of the average and maximum design values at each SO 2 

## cost threshold for 2014. As described in section VI.B of the preamble, EPA determined that the $500/ton threshold for upwind annual and ozone - season NO x 

## control is appropriate for the final Transport Rule . Because this threshold corresponds to the NO x 

## control strategy modeled in the AQAT calibration scenario, EPA relied on CAMx modeling of this scenario for the ozone assessment of the final Transport Rule and did not create an ozone AQAT. Additionally, EPA relied on CAMx modeling of the AQAT calibration scenario for the nitrate estimate for the annual and 24 - hour PM 2.5 

## assessments for the final Transport Rule. Specificall y, EPA used this CAMx modeled nitrate estimate for each SO 2 

## cost threshold anal yz ed. EPA created and used two separate versions of AQAT ( annual and 24 - hour PM 2.5 

## ) to e stimate the impact of the upwind SO 2 

## emission reductions on downwind ambient sulfate concentrations for the two NAAQS, respectivel y . For both versions, the sulfate estimates were combined with CAMx estimates of nitrate and other pollutant species from the AQAT calibration scenario to estimate concentrations of total PM 2.5 

## for the two NAAQS, respectively. Most of the steps used construction of annual and 24 - hour PM 2.5 

## AQAT are the same . Consequently, when EPA refers to a single AQAT, the description appli es to both the annual and 24 - hour versions of the tool. S tep -bystep description s of the se 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 SO 2 

## emission reductions and reductions in downwind sulfate. For the purposes of developing and using AQAT to compare the air quality impacts of SO 2 

## emission reductions under various SO 2 

## cost thresholds , we assume that there is a relationship between changes in emissions and changes in sulfate contributions on a receptor-by- receptor basis. Specificall y, EPA assumes that within the range of total SO 2 

## emissions being considered (as defined by the SO 2 

## cost thresholds), a change in SO 2 

## emissions leads to a proportional change in downwind sulfate contributions. 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 scenario 2 

## . As described in the Air Quality Modeling Final Rule TSD, CAMx air quality modeling with state -by- state source - apportionment of emissions established a relationship between SO 2 

## emissions from each upwind state and the estimated air quality impact from that state to each downwind air quality monitor for the 2012 base case emission scenario. For example, from the output of the CAMx source apportionment modeling, we know the annual average sulfate contribution at a downwind monitor resulting from the specific SO 2 

## emissions in the 2012 base case from a particular upwind state. Similarly, we also know the sulfate contribution in each of the quarters in the year (resulting from th e quarterl y SO 2 

## emissions). 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 led to respectfully in the proposed Transport Rule; $500/ton for SO 2 

### in PM 2.5 

### Group 2 states from the proposed Transport Rule; and $2,000/ton for SO 2 

### in PM 2.5 

### Group 1 states f rom the proposed Transport Rule. Note that the geography and SO 2 

### cost thresholds for this scenario differ from the geography and SO 2 

### cost thresholds for the final Transport Rule. For more details on this scenario please refer to the Air Quality Modeling Final Rule TSD.

## 16

a 20% decrease in its downwind contribution. This relationship was then calibrated for use in the final AQAT by calculating the relationship between the relative change in PM 2.5 

## sulfate at each receptor using CAMx air quality modeling and the relative change in PM 2.5 

## sulfate at each receptor using AQAT based on emission reductions from the 2012 base case to the 2014 AQAT calibration scenario. This AQAT calibration scenario, as described further in the Air Quality Modeling Final Rule TSD, reflect ed SO 2 

## and NO x 

## emission reductions of similar stringency

and from the same geography as the Transport Rule proposal . Because of this relationship, it was possible to calibrate AQAT’s PM 2.5 

## sulfate response for use in assessing sulfate under various SO 2 

## cost thresholds. This is described further in section C.2 of this document. Using the example above, where a 20% reduction in emissions resulted in a 20% decrease in contribution, in “calibrated” AQAT, the 20% emission reduction leads to a 15% concentration reduction (a ratio derived directly from the emission reduction and concentration change from the 2012 base case to the 2014 AQAT calibration scenario). As was done for the proposal, AQAT applies a linear relationship 3 

## between reductions in upwind SO 2 

## emissions and air quality

improvements at downwind monitors. However, for the final Transport Rule, this relationship is now calibrated for the the range of emission reductions examined by EPA and no longer relies on an assumption that at zero upwind emissions there is zero downwind concentration to calculate the response. In the application of AQAT, we assume that the reduction of a ton of emissions of SO 2 

## 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 SO 2 

## emissions from the power sector is assumed to have the same downwind sulfate reduction as reduc ing one ton of SO 2 

## 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, the AQAT was developed based on modeling information that w as available because it was used in other parts of the Transport Rule. Developing these sector- specific factors would require sector-based source tagging, requiring significant additional air quality modeling resources to complete - resources already limited (as described above). 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 SO 2 

## 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 . Here, EPA presents comparisons of AQAT estimates and CAMx modeling results for sulfate and total PM 2.5 

## for th e 2014 base case and the 2014 final

3 

### 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 PM 2.5 

### . PM 2.5 

### concentration is comprised of several chemical species including related forms of particulate sulfate and particulate nitrate . The atmospheric chemical reactions that convert SO 2 

### to particulate sulfate are central to understanding the rel ationship 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 radic al (OH) concentrations, which depend indirectly on NO X 

### 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 H 2 

### O 2 

### and O 3 

### . During certain times and situations, such as the winter months when H2O2 concentrations may be low and SO 2 

### concentrations are high, the response in sulfate formation may be nonlinear. Some of the factors and reagents (among others) affecting the reactions include NH 3 

### , NO X 

### 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.

## 17

Transport Rule. Since, these CAMx modeling runs were not used in the development or calibration of AQAT , and span a wide range of EGU SO 2 

## emission levels, successful comparisons against these independent data sets indic ates that AQAT estimates for other SO 2 

## emission levels can be used . EPA finds that AQAT’s estimates of sulfate and total PM 2.5 

## design values are closel y correlated with the CAMx modeling results for both the 2014 base case and 2014 final Transport Rule. As shown in Figures C - 1, C - 2, and C - 3 , the slopes of the least squares linear regression equations are equal to 1.0 (+/- 0.02), with R2 

## values larger than 0.99 for all cases except annual PM 2.5 

## , where R2 

## is 0.95. As described above, and in more detail in C.2, EPA utilized CAMx modeling results from the AQAT calibration scenario for the estimates of nitrate and other (non- sulfate) PM 2.5 

## components for all SO 2 

## cost thresholds assessed including the AQAT simulation of the 2014 base case (base case SO 2 

## emissions from EGUs are described in section B ). Therefore, the nitrate and other PM 2.5 

## components in the AQAT base case are reflective of the response in these components to the emission reductions modeled in the AQAT calibration scenario . The AQAT 2014 base case was useful in providing a comparison point between AQAT and CAMx for sulfate and PM 2.5 

## . Additionally, the AQAT simulation of the 2014 base case was useful in providing a reference point for comparing the relative improvement in PM 2.5 

## design values under the SO 2 

## cost thresholds assessed. 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 C AMx 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, we link state -bystate modeling of SO 2 

## emission reductions (from IPM), available at different cost thresholds, with CAMx modeled SO 2 

## emissions to sulfate contributions in order to predict resulting air quality contributions to selected downwind receptors. Receptors were selected b y determining which monitors CAMx predicted to have non - attainment or maintenance problems with the annual or 24 - hour PM 2.5 

## standard for the 2012 base case. The reduction in sulfate contributions and resulting air quality improvement were then considered in a multi - factor test for defining significant contribution and interference with maintenance. In the analysis of a given receptor, onl y states that were “linked” to that receptor (i.e., contributed an air quality impact at or a bove 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) were assumed to make reductions from the base case emissions level. For a discussion of the 1 % threshold, see section V.D of the preamble. Specificall y, 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 SO 2 

## 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 ye ars (i.e., 2014) compared to the 2012 base case level due to EGU, mobile source, and other source - sector reductions. ● 

## The non - sulfate contribution under emissions modeled for the AQAT calibration scenario. The results of the anal ys is using AQAT can be found in section C.3 of this document.

## 18

(b) Data needed to construct AQAT for the final Transport Rule. Several data sources were needed 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 PM 2.5 

## AQAT. The uncalibrated versions of AQAT for annual and 24 - hour PM 2.5 

## were used to create AQAT estimates of sulfate response under SO 2 

## and NO x 

## emissions defined b y the AQAT calibration scenario. The datasets required to construct the annual and 24 - hour versions of AQAT included: the 2012 base case SO 2 

## emission inventories from all source sectors used in the source apportionment CAMx air quality modeling; the CAMx 2012 source app ortionment air quality modeling (contributions) for each upwind state to each downwind receptor; and the 2014 AQAT calibration scenario SO 2 

## emissions inventories from all source sectors. An additional dataset, 2014 sulfate results from CAMx for the AQAT c alibration scenario, allows EPA to compare the AQAT sulfate results of this scenario against the air quality modeling results, and develop calibration factors. These calibration factors were then used to create a “calibrated” AQAT. Finally, EGU SO 2 

## emiss ions (from IPM) at each cost threshold assessed provided the final dataset 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, respectivel y. 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 quarterl y time - scale. As described in section C.2.(c).5. of this TSD, f or estimating the design values in the 24 - hour PM 2.5 

## 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 Columbia4 

## , 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, Penns ylvania, 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 PM 2.5 

## concentrations from monitors within these states. AQAT does not quantify contributions from states outside the CAMx modeling domain. Therefore, the contributions and emissions from all other states were assumed to be invariant. (c) Detailed outline of the process for constructing the AQAT for the final approach. The annual and 24 - hour PM 2.5 

## versions of A QAT were created and used in a multi - step process. First, annual and 24 - hour PM 2.5 

## versions of AQAT were created specificall y for calibration. As described in the following paragraphs, the 24 - hour version of AQAT simulated each of four quarters in the ye ar to represent

4 

### Maryland was treated as a separate state in this analysis, rather than as combined with the District of Columbia. Its emissi ons were totaled separately and the changes in emissions occurring at different marginal costs were applied to upwind contributions from Maryland alone.

## 19

seasonal differences in SO 2 

## to sulfate formation. 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 PM 2.5 

## version of AQAT and on a quarterl y basis for the 24 - hour PM 2.5 

## version of AQAT. Next, the calibrated annual and 24 - hour versions of AQAT were used to evaluate the sulfate response of emission reduction s for each SO 2 

## cost threshold assessed. For the annual PM2.5 AQAT, at each cost threshold, the sulfate values were combined with other PM 2.5 

## constituents from the AQAT calibration scenario resulting in estimated annual PM2.5 design values. An additional step was necessary in the 24 - hour PM 2.5 

## AQAT to calculate design values, which was to project the adjusted sulfate change in each quarter to the representative modeled “days 5 

## ” in each quarter using relative response factors. For each day, the other PM 2.5 

## constituents were added using the estimates from the AQAT calibration scenario. For the 24 - hour AQAT, for each projected ye ar (2003 - 2007), the 98 th 

## percentile value was selected. The 98 th 

## percentile values were then used to predict 2014 design values f or 24 - hour PM 2.5 

## . This section describes the details behind these steps. One key difference between the way in which AQAT was used in the anal ysis for the proposed Transport Rule and the way it was used in the analysis for the final Transport Rule is the creation of 4 quarterl y specific components of the 24 - hour version of AQAT for estimating seasonal responses for 24 - hour PM 2.5 

## assessments. Following 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 PM 2.5 

## response to SO 2

## /NO x 

## emission reductions. EPA determined that creating 4 AQATs to assess the quarterl y response of downwind sulfate to upwind SO 2 

## red uctions would be beneficial in adequatel y accounting for seasonal differences in the relationship between emissions and sulfate formation. Quarters were determined based on calendar ye ar (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 quarterl y AQAT was based on quarterl y s pecific emissions and contributions from CAMx 2012 source apportionment modeling and quarterly specific calibration factors were developed (described later). As a “proof of concept”, EPA developed and evaluated quarterl y AQATs , using the air quality

modeling from the proposal . Following successful evaluation, EPA utilized the approach for the final Transport Rule. 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 SO 2 

## emission changes would more - closel y align with sulfate estimates from CAMx. To fill this role, EPA used the result s of IPM modeling of a control scenario with similar level and geographic distribution to the preferred remedy from the proposed Transport Rule. Selection of this AQAT calibration scenario was not an indication of the level of SO 2 

## reduction that would be achieved b y the final Transport Rule. This scenario only served to develop the calibration point s for AQAT which allowed EPA to reasonably assess the downwind impacts of SO 2 

## reductions both more and less stringent than the AQAT calibration scenario. In order to facilitate understanding of this process, EPA is including an example monitor for evaluation in this text: monitor number 261630033 in Way ne County, Michigan, with a 2012 base case predicted 24 - hour PM 2.5 

## average design value of 39.48 g/m 3 

## and maximum design value of 39.82 g/m 3



## . Additional details for all monitors can be found in the referenced tables in the docket. (1) Create uncalibrated annual and quarterl y AQAT s for calibration 5 

### 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.

## 20

To create the annual and quarterl y PM 2.5 

## AQATs for calibration, EPA used emissions and contributions to estimate the change in predicted sulfate due to SO 2 

## emission reductions under the AQAT calibration scenario. These “uncalibrated” AQATs are directly comparable to those from the proposed Transport Rule. First , EPA calculated annual and quarterl y state - level 2012 base case total SO 2 

## emissions from all source sectors. These emissions estimates were used for the CAMx 2012 source apportionment modeling. This emissions data is divided into multiple source sectors for the purposes of air quality modeling: power sector point (from IPM), non -power sector point, non -point, onroad, nonroad, C3 marine, alm, and fires (see the Air Quality

Modeling Final Rule TSD for additional details on the emissions inventories used in the CAMx air quality

modeling). The state - level total SO 2 

## emissions are the sum of emissions from all these source sectors. Next, EPA calculated the annual and quarterl y state - level 2014 total SO 2 

## emissions across all source sectors for the AQAT calibration scenario. EPA calculated the ratio of 2014 total SO 2 

## emissions for the AQAT calibration scenario to 2012 total SO 2 

## emissions for the 2012 base case for each state modeled in CAMx. More information on the emissions inventories can be found in preambl e section IV.C. The total emissions data and resulting ratios can be found in Table C - 1. For each monitor, the uncalibrated annual and quarterl y 2014 contribution of sulfate from each state for the AQAT calibration scenario is calculated b y subtracting the estimated change in concentration from the 2012 base case contribution. The 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 SO 2 

## 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 contributi ons (“BIOG”, ”OTHER”, “ICBC”, and “SOA”) are added to the annual and quarterl y total to account for sources of sulfate outside the CAMx modeling domain. The grand sulfate total from all the states and “other” contributions represents the total sulfate component of PM 2.5 

## estimated by uncalibrated AQAT for the AQAT calibration scenario. It is the ratio of the CAMx to AQAT sulfate components for this AQAT calibration scenario that becomes the constant calibration factor used in “calibrated” AQAT. Table C - 1 . 2012 Base Case and 2014 AQAT Calibration Scenario Ammonium Sulfate Contributions for Monitor Number 261630033 inWay ne County, Michigan, as well as Total SO 2 

## Emissions from all Source Sectors for Each State . State/Source 2012 Base Case

Quarter 2 Sulfate C ontributions ( g/m3 ) 2012 Base Case

Quarter 2 SO 2 Emissions (tons) 2014 AQAT

calibration Scenario Quarter 2 SO 2 Emissions (tons) Ratio of 2014 AQAT

calibration Scenario Emissions to 2012 Base Case SO 2 Emissions Estimated 2014 Contribution of Sulfate in Qua rter 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 0.0 8 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

## 21 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 PM 2.5 in Quarter 2 21.54 11.21 

## (2) Calibrate annual and quarterl y sulfate response in 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 AQATs to CAMx for the annual and quarterl y versions. First, the annual and quarterl y 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 b y either AQAT or CAMx for the AQAT calibration scenario from the respective 2012 total sulfate predicted b y CAMx for the 2012 base case. This difference was then divided by the 2012 total sulfate predicted b y 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 SO 2 

## reductions that EPA expected to be in the vicinity

of the final Transport Rule remedy. For 24 - hour PM 2.5 

## , the CAMx estimates of the 2012 base case and 2014 AQAT calibration scenario are presented b y ye ar as well as by quarter. Thus, 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 values. The AQAT and CAMX ammonium sulfate factors for 24 - hour PM 2.5 

## can be found in the “Dail y

## 22

PM Calibration Factors.xlsx ” excel workbook on worksheet “AQModeling Calib Factor DailyPM ” in column s BA and AJ, respectivel y. 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. For annual PM 2.5 

## , the CAMx estimates used in AQAT construction are represented as 5 - ye ar averages. The AQAT and CAMX ammonium sulfate factors for annual PM 2.5 

## can be found in the “Annual PM Calib Factors.xlsx ” excel workbook on worksheet “AQModeling Calib Factors Ann PM ” in columns AIand AE, respectivel y. 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. 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” seas ons are larger than they are for the “cool” seasons. 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 Way ne County, Michigan ( See Tabl e C - 1) for 24 - hour PM 2.5 

## . These Values are then Used to Create a Calibration Factor . 2012 Base Case Quarter 2 Sulfate Contributions ( g/m3 

### ) Estimated 2014

Contribution of Sulfate in Quarter 2 (uncalibrated AQAT) ( g/m3 

### ) Estimated Quarter 2

Reduction Divi ded by 2012 Base Case Concentration C AM x (Response For 2003 Projected to 2012 or 2014 ) 22.67 13.21 0.4172 AQAT (Response From The Average Contributions) 21.54 11.21 0.4795 Calibration Factor - Response Factor From C AM x Divided By Response Factor From AQ AT 0.8700 

## (3) Create calibrated annual and quarterl y AQAT s for cost threshold analysis Next, EPA created the annual and quarterl y PM 2.5 

## calibrated AQATs for cost threshold analysis. EPA used emissions, air quality sulfate contribution factors, and calibration factors to estimate the change in predicted sulfate due to SO 2 

## emission reductions under each cost threshold evaluated. First, as described in step 2, EPA calculated annual and quarterl y state - level 2012 base case total SO 2 

## emissions. Next, EPA calculated the annual and quarterl y state - level 2014 total SO 2 

## emissions across all source sectors for the cost thresholds. This total is the sum of IPM predicted SO 2 

## emissions from power sector point sources in 2014 and the predictions of 2014 base ca se SO 2 

## emissions from all other source sectors. Note, IPM estimates of SO 2 

## emissions are available annually onl y. In order to approximate the quarterly emissions needed for the 24 - hour

## 23

AQAT, EPA multiplied the annual emissions at each cost threshold for each state by the ratio of the state’s quarterl y 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 emissions for January, February, and March divided by the total annual emissions. EPA calculated the ratio of 2014 total SO 2 

## emissions for each cost threshold to 2012 total SO 2 

## 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 PM 2.5 

## under the AQAT calibration scenario can also be found in Table C - 1. For each cost threshold level anal yz ed, 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 anal yz ed) depending on whether the upwind state is “linked” to that receptor. States that are contributing above the air quality threshold (i.e., 1 percent contribution of total sulfate and nitrate for the annual and 24 - hour PM 2.5 

## AQAT) to the monitor, as well as the state containing the monitor, make SO 2 

## 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 b y multiplying the state specific 2012 base case sulfate contribution by the change in ratio of total SO 2 

## 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 b y 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 PM 2.5 

## , it is possible to estimate total PM 2.5 

## and to estimate design values. This process is described in the next two sections (4 and 5) for annual PM 2.5 

## and for 24 - hour PM 2.5 

## , respectively. (4) Calculating new annual PM 2.5 

## design values using the annual PM2.5 version of AQAT After estimating total sulfate in 2014 for each cost threshold, EPA estimated re sulting average and maximum design values for annual PM 2.5 

## by adding the total sulfate to the non - sulfate components of ambient PM 2.5 

## 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 PM 2.5 

## design values using quarterl y relative response factors in the 24 - hour version of AQAT ● 

## Calculate relative response factors as the ratio of calibrated AQAT predi cted total sulfate to 2012 CAMx modeled total sulfate ● 

## Calculate predicted 2014 total sulfate for all available CAMx modeled days (8 days per quarter per ye ar) by multiplying the 2012 CAMx modeled concentrations by the relative response factors ● 

## Add 2014 total nitrate and other PM 2.5 

## species from the 2014 CAMx modeling of the AQAT calibration scenario for each corresponding day ● 

## Calculate the 98th percentile day for each modeled year

## 24

● 

## Check the completeness and validity of each modeled year, keeping onl y the ye ars with monitoring data that met completeness criteria ● 

## Calculate average 2014 predicted DVs for each quantifiable 3 - ye ar period of projected historic monitoring data (2003 - 2005, 2004 - 2006, and 2005 - 2007) ● 

## Calculate final average DV as the average of quanti fiable 2014 predicted 3 - ye ar DVs ● 

## Calculate the maximum DV as the maximum of quantifiable 2014 predicted 3 - ye ar DVs The estimation of design values for the 24 - hour PM 2.5 

## standard is more complicated than it is for the annual PM 2.5 

## standard, because onl y th e 98 th 

## percentile day from each of the five ye ars contributes to the design value (and the particular day selected as the 98 th 

## percentile day can change at different cost threshold levels). After estimating average total sulfate in 2014 for each cost thre shold for each quarter, EPA developed relative response factors (RRF) for quarterl y sulfate concentrations and used these factors to calculate expected future sulfate concentrations for 32 selected modeled days for each of the 5 - ye ars (2003 - 2007) accounted for in the 2012 CAMx base case modeling. In other words, the “average” quarterl y responses were “mapped” to the 8 individual days in each quarter (32 days total per ye ar) for each of the 5 ye ars using the relative response factors. This was done b y multiplying the RRF by the 2012 base case sulfate value for each day . To calculate the 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” quarterl y sulfate contribution. 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 quarterl y RRF for that threshold to the 2012 base case ammonium sulfate values for each of the 32 days, for each of the 5 years, to estimate adjusted ammonium sulfate values. To these adjusted ammonium sulfate values, EPA added the concentrations from the other6 

## PM 2.5 

## components from the 2014 AQAT calibration scenario ( i.e., ammonium nitrate, elemental carbon, organic carbon, salt, and blank mass). The result is 32 PM 2.5 

## concentrations for each of the 5 years of analysis. The total concentration estimates (and adjusted ammonium sulfate values) for each monitor, ye ar, and day can be found in the “ dailyPM_al l_years_all_quarters... .xlsx “ workbooks. Next, we ranked the values for each ye ar and selected the 98 th 

## percentile for each ye ar for use in estimation of the 3 - ye ar 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 M odeling Final Rule TSD, and the modeling guidance document for state attainment demonstrations of the 24 - hour PM 2.5 

## ). The rank of the value that is the 98 th 

## percentile can be found in the “98thpercentilerank” worksheet in the “dail yP M_all ye ars_high_quarters.xlsx” workbook in column G. The 98 th 

## percentile value for each cost threshold level and for each ye ar can be found in the a ppropriate worksheet and columns Ithrough M in the “dail yP M_all ye ars_high_quarters.xlsx” workbook. Three valid consecutive yearl y 98 th 

## percentile values are needed to construct a design value. The completion codes for each potential design value 3 - ye ar time - period have values of 1, 2, 3, 4 or missing (0) for each design value period. Values of 1 or 2 indicate compete data and values of 3 or 4 indicate incomplete data. Missing values , or values equal to 0, were treated as incomplete periods.

6 

### By using nitrate from the AQAT calibration scenario, the estimate of nitrate is impacted by NOx reductions and SO 2 

### reductions which lead to nitrate replacement. The concentrations of elemental carbon, organic carbon, salt, and blank are nearly identi cal 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/m3 

### for organic carbon. By using these components of PM 2.5 

### as modeled in the 2014 AQAT calibration scenario, EPA is appropriately accounting for any changes in these components due to Transport Rule implementation.

## 25

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 maintena nce. The two air quality levels used were 15.05 g/m 3 

## and 35.5 g/m 3 

## to represent the 1997 and 2006 fine particulate matter (PM 2.5 

## ) NAAQS, respectivel y. 3. Description of the results of the anal ys is using AQAT for the final approach. This section describes the results of the anal ys is using the AQAT for the annual PM 2.5 

## , and 24 - hour PM 2.5

## NAAQS standards. In section C.2 of this TSD, we described the construction of the AQAT to estimate the air quality

impacts of various levels of EGU SO 2 

## emissions. The specific application of the tool is described in this section. For each identified receptor (identified based on nonattainment and maintenance problems in the CAMx modeled 2012 base case, as described above), EPA applied emissions reductions on a state -by- state basis. As described in section C.2of this TSD, for annual and 24 - hour PM 2.5 

## standards, SO 2 

## emissions reductions beyond the base case level for the year examined were applied to the state containing the receptor, as well as to upwind states contributing above the 1 percent air quality threshold to that particular receptor. For each receptor and at each cost threshold for SO 2 

## , we applied AQAT to estimate the resulting sulfate contributions, and resulting design values. For annual PM 2.5 

## in 2014, the estimated average and maximum PM2.5 

## design values ( g/m 3 

## ) for each identified receptor can be found in Table C - 3 and C - 4, respectivel y. The monitors are in order of decreasing 2012 base case maximum annual PM 2.5 

## design value. No monitors are estimate d to have remaining non attainment problems at the $2,300/ton SO 2 

## cost threshold. The only monitor that is estimated to have a remaining maintenance problem at the $2,300/ton SO 2 

## cost threshold is monitor number 420030064 , located in Allegheny (Liberty- Cl airton), Penns ylvania . As indicated in section VIII.B of the preamble, final air quality modeling of the Transport Rule indicates that the maintenance problem estimated by AQAT is resolved. For 24 - hour PM 2.5 

## in 2014, the estimated average and maximum air quality design values ( g/m 3 

## ) for each identified receptor can be found in Table C - 5 and C - 6, respectivel y. The monitors are in order of decreasing 2012 base case maximum 24 - hour PM2.5 

## design value. Using AQAT, a majority of the 24 - hour PM 2.5 

## 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 SO 2 

## emission reductions to achieve the NAAQS. The total number of estimated remaining nonattainment and maintenance receptors as a function of SO 2 

## 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 non- attainment problems are also estimated to have maintenance problems. For example, for the annual PM 2.5 

## 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 non - attainment and non - attainment or maintenance categories for the annual PM 2.5 

## columns. Also 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,

## 26

C - 4, C - 5, and C - 6 and noting the nonattainment area that they are associated with. Note that for the 24 - hour PM 2.5 

## 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 analysis, for these areas, EPA is using the annual PM 2.5 

## NAAQS nonattainment area designation. For example, for 24 - hour PM 2.5 

## , the receptors in Cook, IL and Lake, IN that are projected to be maintenance in the Transport Rule modeling are associated with their annual PM 2.5 

## nonattainment area designation (Chicago - Gary- Lake County, IL - IN ) since they

have not been designated for the 24 - hour PM 2.5

## NAAQS. 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 desi gn value for 24 - hour PM 2.5 

## standard. The reason is that the design value is composed of different days, possibly from different seasons, and that these days can change depending on the cost threshold examined. For example, in the base case level, the 98 th 

## percentile days which contribute to the design value could primarily be from “warm” seasons, which have high sulfate levels. At a higher cost level, the 98 th 

## 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 contribution of that sulfate. Lastl y, 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. Average and maximum design value estimates in 2014 for annual PM 2.5 

## and 24 - hour PM 2.5 

## c an be found in Tables C - 9 and C - 10 in section C.4 of this TSD. Air quality estimates were also made using CAMx and are also summarized in Tables C - 9 and C - 10 (see section C.4 of this TSD as well as the Air Quality Modeling Final Rule TSD for details). Additional comparisons between AQAT and CAMx estimates are shown in section C.4 of this TSD. Table C - 3 . Average Annual PM 2.5 

## DVs ( g/m 3 

## ) for SO 2 

## 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 17.5 3 15.78 15.28 15.03 14.97 14.91 14.69 390350038 Ohio Cuyahoga 15.99 15.68 14.10 13.77 13.60 13.52 13.46 13.22 10730023 Alabama Jefferson 16.15 15.60 14.33 14.38 14.31 14.31 14.38 14.15 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 13.22 390350060 Ohio Cuyahoga 15.67 15.34 13.75 13.42 13.25 13.17 13.11 12.87 390610014 Ohio Hamilton 15.76 15.39 13.29 12.93 12.75 12.67 12.59 12.27 390610042 Ohio Hamilton 15.40 1 5.07 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 12.99 10732003 Alabama Jefferson 15.16 14.68 13.55 13.58 13.52 13.51 13.57 13.36 390350045 Ohio Cuyahoga 15.14 14.83 13.23 12.90 12.73 12.65 12 .59 12.35 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 13.07 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 11.90 180970083 Indiana Marion 14.71 14.38 12.53 12.25 12.11 12.04 11.94 11.64 

## Table C - 4 . Maximum Annual PM 2.5 

## DVs ( g/m 3 

## ) for SO 2 

## 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 15.08

## 27

390350038 Ohio Cuyahoga 16.66 16.35 14.77 14.44 14.27 14.19 14.13 13.89 10730023 Alabama Jefferson 16.46 15.91 14.64 14.69 14.62 14.62 14.69 14.46 390618001 Ohio Hamilton 16.33 15.96 1 3.86 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 13.81 390350060 Ohio Cuyahoga 16.18 15.85 14.26 13.93 13.76 13.68 13.62 13.38 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 13.26 10732003 Alabama Jefferson 15.64 15.16 14.03 14.06 14.00 13.99 14.05 13.84 390350045 Ohio Cuyahoga 15.61 1 5.30 13.70 13.37 13.20 13.12 13.06 12.82 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 13.10 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 12.33 180970083 Indiana Marion 15.06 14.73 12.88 12.60 12.46 12.39 12.29 11.99 

## Table C - 5 . Average 24 - h our PM 2.5 

## DVs ( g/m 3 

## ) for SO 2 

## Cost Thresholds ($/ton) Assessed Using AQAT. Mo nitor 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 5.80 Avg. improvement from AQAT base case

– $500 receptors** 4.73 5.70 6.41 6.67 6.85 7.57 420030064 ** Pennsylvania Allegheny 56.71 54.34 47.57 46.36 45.54 45.37 45.23 44.72 420030093 ** Pennsylvania Allegheny 39.11 37.51 32.19 30.91 30 .25 30.12 29.96 29.36 390350038 ** Ohio Cuyahoga 39.46 37.95 34.18 33.73 33.51 33.43 33.36 32.97 261630016 ** Michigan Wayne 38.99 38.50 34.42 34.15 33.93 33.77 33.70 33.33 390350060 Ohio Cuyahoga 37.78 37.11 31.50 30.79 30.60 30.51 30.43 30.19 170311016 ** Illinois Cook 37.58 36.11 34.13 33.48 33.13 32.94 32.67 31.95 261630033 ** Michigan Wayne 39.48 39.01 36.31 35.59 35.00 34.65 34.43 33.49 180890022 ** Indiana Lake 34.94 34.04 32.79 32.47 32.38 32.29 32.16 31.85 540090011 West Virginia Brooke 37.57 36. 73 30.60 29.60 29.07 28.94 28.80 28.25 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 25.95 390811001 Ohio Jefferson 34.56 33.58 27.64 26.41 25.79 25.6 5 25.49 24.91 261630019 ** Michigan Wayne 37.34 36.86 35.27 35.09 34.93 34.82 34.77 34.52 390350065 Ohio Cuyahoga 34.91 33.50 27.65 26.61 26.11 25.95 25.81 25.26 170313301 Illinois Cook 34.97 33.60 31.11 30.72 30.54 30.40 30.24 29.70 420070014 Pennsylvania Beaver 36.21 34.84 29.28 28.10 27.59 27.48 27.36 26.94 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 31.24 550790026 Wisconsin Milwaukee 33.62 3 3.28 30.48 30.27 30.15 30.03 29.90 29.50 180970043 Indiana Marion 35.76 34.67 28.64 27.55 27.16 26.98 26.64 25.84 261470005 Michigan St Clair 36.23 35.61 33.35 33.01 32.78 32.67 32.59 32.27 550790043 Wisconsin Milwaukee 36.21 34.98 32.49 32.07 31.85 31. 70 31.53 31.19 180890026 Indiana Lake 34.08 33.00 30.91 30.65 30.52 30.42 30.30 30.05 180970081 Indiana Marion 35.85 33.70 28.44 27.66 27.35 27.21 26.93 26.20 180970066 Indiana Marion 35.73 34.49 29.22 28.45 28.13 27.96 27.65 26.95 171191007 Illinois M adison 36.59 34.59 29.92 29.48 29.32 29.13 28.88 28.13 550790010 Wisconsin Milwaukee 35.47 35.03 31.50 31.05 30.82 30.73 30.62 30.28 390170003 Ohio Butler 34.40 33.66 28.07 26.99 26.49 26.33 26.19 25.67 170316005 Illinois Cook 34.12 33.47 32.72 32.53 32 .41 32.31 32.18 31.80 420031008 Pennsylvania Allegheny 35.04 33.41 26.95 25.44 24.69 24.51 24.33 23.67 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 29.06 170310052 Illinois Cook 34.94 33.27 30.11 29.87 29.78 29.67 29.53 29.04 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 30.50 010732003 Alabama Jefferson 35.3 1 34.20 31.42 31.27 31.10 31.08 31.14 30.74 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 27.52 420031301 Pennsylvania Allegheny 33.95 32.45 27.16 25.87 25.21 25.06 24.91 24.28 391130032 Ohio Montgomery 33.68 32.19 24.40 23.37 23.15 23.05 22.95 22.60

## 28

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/m 3 at the $500 cost threshold in 2014. 

## Table C - 6 . Maximum 24 - hour PM 2.5 

## DVs ( g/m 3 

## ) for SO 2 

## Cost Thresholds ($/ton) Assessed Using AQAT. Monitor I dentification N umber State County CAMx 2012 Base Case ( g/m3 ) AQAT 2014 Maximum 24 - hour PM2.5 Design Valu es ( 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 6.08 Avg. improvement from AQAT base case

– $500 receptors** 3.27 3.86 4.22 4.37 4.50 4.99 420030064 ** Pennsylvania Allegheny 59.93 57.64 50.72 49.46 48.63 48.49 48.35 47.81 420030093 ** Pennsylvania Allegheny 44.40 42.63 36.85 35.50 34.80 34.66 34.49 33.84 390350038 ** Ohio Cuyahoga 41.84 40.37 35.93 35.58 35.41 35.33 35.29 34.90 261630016 ** M ichigan Wayne 41.28 40.77 36.20 35.88 35.65 35.49 35.42 35.08 390350060 Ohio Cuyahoga 40.85 39.90 33.69 33.23 33.04 32.94 32.86 32.60 170311016 ** Illinois Cook 40.44 39.05 37.40 36.85 36.54 36.35 36.10 35.47 261630033 ** Michigan Wayne 39.81 39.47 36.59 35.84 35.23 34.87 34.65 33.69 180890022 ** Indiana Lake 39.58 38.68 37.00 36.63 36.51 36.35 36.11 35.55 540090011 West Virginia Brooke 38.39 37.68 32.23 30.79 30.02 29.84 29.64 28.87 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 27.13 390811001 Ohio Jefferson 37.88 36.91 30.27 28.78 28.03 27.86 27.67 26.95 261630019 ** Michigan Wayne 37.83 37.29 36.20 36.01 35.83 35.72 35.66 35.41 390350065 Ohio Cuyah oga 37.67 36.41 28.79 27.60 27.00 26.80 26.64 26.06 170313301 Illinois Cook 37.67 36.26 33.36 33.01 32.84 32.71 32.55 32.11 420070014 Pennsylvania Beaver 37.42 35.99 30.46 29.27 28.70 28.58 28.46 28.03 420033007 Pennsylvania Allegheny 37.40 35.85 30.73 29.47 28.81 28.68 28.54 28.16 010730023 Alabama Jefferson 37.33 35.80 32.50 32.42 32.12 32.10 32.28 31.72 550790026 Wisconsin Milwaukee 37.24 36.72 33.54 33.32 33.21 33.09 32.96 32.57 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 33.01 550790043 Wisconsin Milwaukee 37.10 35.89 34.22 34.03 33.92 33.83 33.73 33.41 180890026 Indiana Lake 37.06 36.05 33.67 33.48 33.37 33.28 33.18 32.91 180970081 Indiana Marion 36.96 34.81 28.83 27.95 27.59 27.41 27.08 26.34 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 29.16 550790010 Wisconsin Milwaukee 36.71 36.56 33.47 33.25 3 3.13 33.04 32.94 32.62 390170003 Ohio Butler 36.59 36.03 28.71 27.76 27.33 27.17 27.01 26.52 170316005 Illinois Cook 36.42 35.87 35.09 34.90 34.82 34.71 34.59 34.20 420031008 Pennsylvania Allegheny 36.35 34.65 28.15 26.48 25.62 25.39 25.15 24.28 261610 008 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 31.69 170310052 Illinois Cook 36.07 34.06 30.62 30.41 30.31 30.21 30.08 29.71 421330008 Pennsylvania York 36.06 35.8 9 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 31.37 010732003 Alabama Jefferson 35.94 34.95 32.23 32.08 31.91 31.89 31.94 31.57 390618001 Ohio Hamilton 35.85 34.01 28.23 27.13 26.73 26.59 26.45 26.02 171190023 Illinois Madison 35.81 34.53 30.23 29.70 29.50 29.26 29.07 28.50 420031301 Pennsylvania Allegheny 35.65 33.91 28.05 26.67 26.15 26.04 25.92 25.44 391130032 Ohio Montgomery 35.61 33.81 25.99 24.94 24.62 24.48 24.31 23.80 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/m 3 at the $500 cost thresh old in 2014. 

## Table C - 7 . Relationship between the Monitor Receptors and Nonattainment Areas for the Annual PM 2.5 

## NAAQS. Monitor I dentification N umber State County CAMx 2012 Base Case Avg. DV CAMx 2012

Base Case Max. DV Area

## 29 ( g/m3 ) ( g/m3 ) 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 Illino is 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, I N 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, I N 

## Table C - 8 . Relationship between the Monitor Receptors and N onattainment Areas * for the 24 - hour PM 2.5 

## NAAQS. Monitor I dentification N umber 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 Il linois 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 - W V 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 390 350065 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, I N* 261470005 Michigan St Clair 36.23 37.14 Detroit - Ann Arbor, MI 5507 90043 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, I N* 180970066 Indiana Marion 35.73 36.92 Indianapolis, I N* 171191007 Illin ois 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, O H - KY - IN* 171190023 Illinois Madison 35.11 35.81 St. Louis, MO - IL*

## 30

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 PM 2.5 

## NAAQS. Consequently, only for purposes of this analysis, EPA associated the receptor with the area designated with respect to the annual PM 2.5 

## NAAQS. 

## 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. Consequentl y, a comparative anal ys is was done between the assess ment tool and the CAMx modeling for 2014 base case ammonium 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 anal ys is was also done with the estimates from the assessment tool. Examination of the comparison for the 2014 base shows strong correlations (nearl y one to one) between the estimated design values from AQAT and CAMx (Table C - 10 and Figure C - 1) Examination of the results of the CAMx modeling for 2014, implementing the remedy, shows that nearl y 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 PM 2.5 

## 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 (Tables C - 9 and C - 1 0, Figures C - 1 and C - 2). In addition, for the 24 - hour PM 2.5 

## standards, EPA conducted a detailed comparison of the sulfate estimates from AQAT and CAMx (relative to the 98 th 

## 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 PM 2.5 

## concentrations for the CAMx 98 th 

## percentile days, are contained in Appendix B, Tables B - 1 and B - 2. Table C - 9 . Average and Maximum Annual PM2.5 

## DVs ( g/m 3 

## ) in the 2014 Remedy Case Scenarios as M odeled in CAMx and as E stimated in Calibrated AQAT , for Receptors with Maximum DVs Greater than or Equal to 15.05 g/m 3 

## in the 2012 Base Case . Monitor Identification Number State County 2014 Remedy Scenario CAMx CAMx AQAT AQAT Difference (CAMxAQAT) Difference

(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 - 0.53 390 610014 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 39035004 5 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

## 31 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 

## Table C - 10 . Average and Maximum 24 - hour PM2.5 

## DVs ( g/m 3 

## ) in the 2014 B ase C ase and 2014 Remedy C ase S cenarios as M odeled in CAMx and as E stimated in AQAT. Monitor Identification Number State County 2014 Base Case Scenario 2014 Remedy Scenario

CAMx CAMx AQAT AQAT Differen ce (CAMxAQAT) Differe

nce (CAMx

- AQAT) CAMx CAMx AQAT AQAT Differe nce (CAMx

- AQAT) Differe

nce (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 I llino is 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 3 2.31 36.30 - 0.13 - 0.20 540090011 West

Vi rginia 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 Cuyahog a 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 Illino is 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 5 50790026 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 - 0.5 4 - 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 Illino is Madison 34.68 35.36 34.59 35.20 0.08 0.16 29.24 30.51 2 9.32 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 Illino is Cook 32.72 34.9 4 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 29.2 6 - 0.05 - 0.05 170312001 Illino is 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 Illino is 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 35.8 9 - 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 3906180 01 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 Illino is 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).

## 32

Figure C - 1 . Least squares linear regression plots showing correlations between estimated average and maximum design values ( g/m 3 

## ) for 24 - hour PM 2.5 

## 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/m 3 

## ) for annual PM 2.5 

## for CAMx and calibrated AQAT for the 2014 remedy .

## 33

*98 th 

## 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 e stimated sulfate ( g/m 3 

## ) for the 98 th 

## percentile 24 - hour PM 2.5 

## day for each ye ar * 

## 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 section s VI .C and XII.J.2.a. of the preamble for the final Transport Rule, EPA projects that some states not covered b y any of the fine particle control programs in the final Transport Rule may experience increases of SO 2 

## emissions greater than 5,000 tons compared to the base case. These states are Arkansas, Colorado, Louisiana, Montana, and Wyoming. Using AQAT, for the states with source contribution modeling (i. e., Arkansas and Louisiana), EPA estimated whether these SO 2 

## emission increases would result in these states exceeding the contribution thresholds. This was done by adding the “leakage” emissions to the 2012 base case emissions. As can be seen in the “ba se leakage_2012_threshold” worksheet in the “ annual PM25 AQAT.xlsx ” workbook, the estimated contributions from these states for annual PM 2.5 

## nonattainment and maintenance sites remain well below the 1% NAAQS threshold. A similar assessment was made for the 24 - hour PM 2.5

## NAAQS. EPA added the relative SO 2 

## emission increases to each of the quarterl y emission values for the 2012 base case. EPA then used the 24 - hour PM 2.5 

## AQAT and estimated the 2012 base case quarterl y contributions and the resulting design values for all monitors. EPA, then, examined the sulfate contributions from these states, finding that Arkansas had relativel y large contributions in the summer months to sites in Cook, IL (monitor 170311016 quarter 2); and in Lake, IN (monitors 180890022 and 180890026 in quarter 2). For the Cook, IL site, onl y one of the 98 th 

## percentile values is in the second quarter (2007). For the Lake county monitors 180890022 and 180890026 , none of the 98 th 

## 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, ALin quarter 1. In looking at when the 98 th 

## percentile days were in the years for

## 34

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.

## 35



# Appendix A: IPM Runs Used in Transport Rule Significant Contribution Analysis

## 36

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 Nam e Run Description TR_Base_Case_Final Base Case model run, which includes the national Title IV SO 2 

### cap - and -trade program; NO x 

### SIP Call regional ozone season cap - and -trade program; and settlements and state rules through Fall of 2010. This run represents c onditions without the proposed Transport Rule and without the rule it would replace (CAIR). TR_SO2_500_Final Im poses a marginal cost of $500 per ton of NO x 

### year round in annual states starting in 2012, $500 per ton of ozone season NO x 

### in ozone states starting in 2012, $500 per ton for SO 2 

### 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 Im poses a marginal cost of $500 per ton of NO x 

### year round in annual states starting in 2012, $500 per ton of ozone season NO x 

### in ozone states starting in 2012, $500 per ton for SO 2 

### year round in "Gro up 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 Im poses a marginal cost of $500 per ton of NO x 

### year round in annual states starting in 2012, $500 per ton of ozone season NO x 

### in ozone states starting in 2012, $500 per ton for SO 2 

### 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 Im poses a marginal cost of $500 per ton of NO x 

### year round in annual states starting in 2012, $500 per ton of ozone season NO x 

### in ozone states starting in 2012, $500 per ton for SO 2 

### 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.

## 37

TR_SO2_3300_Final Im poses a marginal cost of $500 per ton of NO x 

### year round in annual states starting in 2012, $500 per ton of ozone season NO x 

### in ozone states starting in 2012, $500 per ton for SO 2 

### 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 Im poses a marginal cost of $500 per ton of NO x 

### year round in annual states starting in 2012, $500 per ton of ozone season NO x 

### in ozone states starting in 201 2, $500 per ton for SO 2 

### year round in "Group 2" states starting in 2012. For SO 2 

### "Group 1" states, a cap of 2.41 million tons is imposed in 2012 and 2013, and a cap of 344,000 ton is im posed in 2014 and each year thereafter for SO 2 

### . 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 Im poses a marginal cost of $500 per ton of NO x 

### 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 NO x 

### reductions ) starting in 2012. Also forces dispatchable SCRs to run in the ozone season if located in this region TR_NOX_OS_1000_Final Im poses a marginal cost of $1,000 per ton of NO x 

### 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 NO x 

### reductions ) starting in 2012. Also forces dispatchable SCRs to run in the ozone season if located in this region TR_NOX_OS_5000_Final Im poses a marginal cost of $5,000 per ton of NO x 

### re duced 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 NO x 

### reductions ) starting in 2012. Also forces dispatchable SCRs to run in the ozone season if located in this region TR_NOX_500_Final Im poses a marginal cost of $500 per ton of NO x 

### 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 Im poses a marginal cost of $1,00 0 per ton of NO x 

### 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 Im poses a marginal cost of $2,500 per ton of NO x 

### reduced annually on each of 23 states in the annual region. Also forces SCRs to operate year round if located in this region

## 38



# Appendix B: Detailed Comparison of AQAT Estimates with CAMx Results

## 39

This Appendix contains tables with detailed comparisons of the sulfate and total PM 2.5 

## 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, respectivel y). The 98 th 

## percentile days were selected based on the days used to create the design value according to the CAMx results. That is, the 98 th 

## percentile day may have been different in AQAT. For this particular anal ys is, 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 PM 2.5 

## for 98 th 

## Percentile Days * for the 2014 Base Case . Future Date Monitor

Iden tification 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 20 040301 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 200704 22 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 2005091 3 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 390 350038 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 1 9.16 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 0.22 200 40904 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 42.7 9 - 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 Illino is Cook 40.44 8.66 8.64 0.02 38.12 38.72 - 0.60 20041229 170311016 Illino i s Cook 40.44 8.37 8.29 0.08 35.24 36.08 - 0.83 20050802 170311016 Illino is Cook 40.44 19.50 19.07 0.44 42.79 42.35 0.44 20061219 170311016 Illino is Cook 40.44 6.44 6.38 0.06 27.24 27.88 - 0.64 20070617 170311016 Illino is 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 5400 90011 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

## 40 20060309 2 61630019 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 20 030202 170313301 Illino is Cook 37.67 7.16 7.08 0.08 32.35 32.70 - 0.35 20040903 170313301 Illino is Cook 37.67 17.86 17.35 0.51 34.82 34.30 0.51 20050203 170313301 Illino is Cook 37.67 9.18 9.07 0.10 41.34 41.79 - 0.45 20060704 170313301 Illino is Cook 37.67 11.46 11.14 0.33 22.53 22.20 0.33 20060806 170313301 Illino is Cook 37.67 11.46 11.14 0.33 22.53 22.20 0.33 20071120 170313301 Illino is 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 27.4 9 - 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 2007 0524 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 4 20033007 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 24.3 0 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 2 0030813 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 2003 0913 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 9.3 5 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 200308 13 180970081 Indiana Marion 36.96 21.82 21.23 0.59 31.92 31.32 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 180970081 Indiana 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 Illino is Madison 36.83 12.14 11.83 0.31 35.89 39.65 - 3.76 20040218 171191007 Illino is Madison 36.83 10.51 10.24 0.27 31.15 34.40 - 3.26 20050808 171191007 Illino is Madison 36.83 26.21 25.45 0.76 39.05 38.28 0.76 20060429 171191007 Illino is Madison 36.83 13.2 1 13.07 0.13 32.56 32.51 0.05 20070617 171191007 Illino is 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 3 2.49 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 - 0.32 200 30301 390170003 Ohio Butler 36.59 15.51 14.95 0.56 35.99 38.23 - 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 18.6 8 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 Illino is Cook 36.42 7.88 7.89 0.00 32.58 33.06 - 0.48 20041229 170316005 Illino is Cook 36.42 9.16 9.06 0.10 35.38 36.53 - 1.15 2005122 1 170316005 Illino is Cook 36.42 9.55 9.45 0.10 36.86 38.07 - 1.20 20060117 170316005 Illino is Cook 36.42 5.91 5.92 0.00 24.56 24.92 - 0.36 20070530 170316005 Illino is Cook 36.42 10.69 10.41 0.28 31.40 31.16 0.24 20030807 420031008 Pennsylvania Allegheny 3 6.35 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 36.3 5 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

## 41 20030202 170312001 Illino is Cook 36.12 9.27 9.08 0.19 34.34 34.58 - 0.24 20040903 170312001 Illino is Cook 36.12 16.44 15.91 0.53 32.57 32.04 0.53 20050203 170312001 Illino is Cook 36.12 10.27 10.05 0.21 37.98 38.24 - 0.26 20060719 170312001 Illino is Co ok 36.12 11.94 11.56 0.38 23.80 23.41 0.39 20070530 170312001 Illino is Cook 36.12 14.77 14.40 0.37 31.12 30.76 0.36 20030316 170310052 Illino is Cook 36.07 8.17 8.13 0.04 31.22 31.69 - 0.47 20040904 170310052 Illino is Cook 36.07 16.15 15.79 0.37 31.64 31. 27 0.37 20051221 170310052 Illino is Cook 36.07 9.96 9.87 0.08 39.51 40.60 - 1.09 20060117 170310052 Illino is Cook 36.07 6.84 6.81 0.03 26.24 26.63 - 0.39 20070220 170310052 Illino is 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 0.0 7 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 Jef ferson 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 2005091 3 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 Illino is Madison 35.81 9.86 9.55 0.31 33.44 34.39 - 0.95 20040729 171190023 Illino is Madison 35.81 21.38 20.76 0.62 30.77 30.14 0.62 20050907 171190023 Illino is Madison 35.81 25.55 24.81 0.74 36.68 35.94 0.74 20060411 171190023 Illino is 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 98 th 

## percentile days were chosen based on CAMx. Table Appendix B - 2. Comparison of Sulfate and Total PM 2.5 

## for 98 th 

## 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 Al legheny 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 3903500 60 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 Illino is Cook 40.44 9.47 9.60 - 0.13 31.23 31.54 - 0.32 20050627 170311016 Illino is Cook 40.44 11.27 11.39 - 0.12 39.68 39.97 - 0.29 20060818 170311016 Illino is Cook 40.44 7.32 7.42 - 0.10 24.25 24.49 - 0.24 20070530 170311016 Illino is Cook 40.44 7.54 7.62 - 0.08 26.71 26.91 - 0.19 20030316 170311016 Illino is 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

## 42 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 39.8 1 4.07 4.17 - 0.10 28.44 28.70 - 0.26 20030304 261630033 Michigan Wayne 39.81 8.26 8.29 - 0.03 35.29 35.40 - 0.12 20030415 180890022 Indiana Lake 39.58 21.84 22.04 - 0.20 36.94 37.24 - 0.30 20041226 180890022 Indiana Lake 39.58 8.81 8.90 - 0.10 37.62 37.82 - 0. 19 20050116 180890022 Indiana Lake 39.58 8.86 8.86 0.00 33.76 33.83 - 0.07 20070310 180890022 Indiana Lake 39.58 7.48 7.48 0.00 28.58 28.64 - 0.06 20060123 180890022 Indiana Lake 39.58 6.21 6.21 0.00 23.83 23.88 - 0.05 20030626 540090011 West Virginia Bro oke 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 38.3 9 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 1 0.64 - 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 390 350045 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 2 0041117 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 1703133 01 Illino is Cook 37.67 12.27 12.36 - 0.09 36.65 36.87 - 0.22 20070617 170313301 Illino is Cook 37.67 9.40 9.47 - 0.07 28.19 28.35 - 0.17 20030214 170313301 Illino is Cook 37.67 6.19 6.19 0.00 31.42 31.48 - 0.06 20040227 170313301 Illino is Cook 37.67 5.84 5.84 0.00 29.68 29.74 - 0.06 20060306 170313301 Illino is Cook 37.67 4.22 4.22 0.00 21.58 21.62 - 0.04 20040608 420070014 Pennsylvania Beaver 37.42 10.19 10.39 - 0.20 23.91 24.41 - 0.49 20070602 420070014 Pennsylvania Beaver 37.42 9.08 9.25 - 0.18 21.36 21.80 - 0.4 4 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 4200 33007 Pennsylvania Allegheny 37.40 9.16 9.32 - 0.17 23.11 23.49 - 0.38 20030626 420033007 Pennsylvania Allegheny 37.40 10.68 10.83 - 0.15 40.46 40.86 - 0.40 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 Jeffer son 37.33 6.46 6.43 0.04 29.97 30.04 - 0.07 20070326 10730023 Alabama Jefferson 37.33 6.76 6.72 0.04 31.34 31.41 - 0.07 20060704 10730023 Alabama Jefferson 37.33 10.33 10.12 0.21 29.20 29.07 0.13 20050914 10730023 Alabama Jefferson 37.33 12.19 11.95 0.25 34.37 34.22 0.15 20061125 550790026 Wisconsin Milwaukee 37.24 6.05 6.12 - 0.07 35.65 35.85 - 0.20 20071211 550790026 Wisconsin Milwaukee 37.24 5.52 5.58 - 0.06 32.56 32.74 - 0.18 20050131 550790026 Wisconsin Milwaukee 37.24 6.99 6.99 0.00 30.64 30.72 - 0.07 20030307 550790026 Wisconsin Milwaukee 37.24 5.78 5.78 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 5 50790043 Wisconsin Milwaukee 37.10 8.88 8.89 - 0.01 39.14 39.22 - 0.08 20030316 550790043 Wisconsin Milwaukee 37.10 6.84 6.85 - 0.01 30.26 30.32 - 0.06 20071120 550790043 Wisconsin Milwaukee 37.10 5.50 5.50 0.00 30.83 30.91 - 0.09 20051224 550790043 Wisconsin Milwaukee 37.10 5.63 5.63 0.00 31.53 31.61 - 0.09 20050203 180890026 Indiana Lake 37.06 8.03 8.08 - 0.05 33.45 33.59 - 0.14 20040304 180890026 Indiana Lake 37.06 7.99 8.04 - 0.05 33.28 33.42 - 0.14 20030226 180890026 Indiana Lake 37.06 7.93 7.98 - 0.05 33.0 2 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 - 0.5 4 20060222 180970066 Indiana Marion 36.92 5.79 5.84 - 0.05 25.66 25.85 - 0.19

## 43 20040224 180970066 Indiana Marion 36.92 5.27 5.32 - 0.05 23.44 23.61 - 0.17 20050227 171191007 Illino is Madison 36.83 8.60 8.74 - 0.13 34.53 34.59 - 0.06 20040116 171191007 Illino i s Madison 36.83 6.67 6.77 - 0.10 26.89 26.93 - 0.04 20060126 171191007 Illino is Madison 36.83 6.35 6.45 - 0.10 25.62 25.66 - 0.04 20070220 171191007 Illino is Madison 36.83 6.05 6.14 - 0.09 24.43 24.47 - 0.04 20031114 171191007 Illino is 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 3901 70003 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 Illino is Cook 36.42 7.64 7.76 - 0.12 36.24 36.38 - 0.14 20041229 170316005 Illino is Cook 36.42 7.33 7.44 - 0.11 34.78 34.91 - 0.13 20070617 170316005 Illino is Cook 36.42 7.29 7.37 - 0.09 29.17 29.36 - 0.18 20030316 17 0316005 Illino is Cook 36.42 6.87 6.88 - 0.01 31.96 32.05 - 0.10 20060123 170316005 Illino is 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 Al legheny 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 Illino is Cook 36.12 12.30 12.41 - 0.11 32.55 32.76 - 0. 21 20070617 170312001 Illino is Cook 36.12 10.12 10.20 - 0.09 26.85 27.02 - 0.17 20050203 170312001 Illino is Cook 36.12 9.00 9.00 - 0.01 37.12 37.19 - 0.07 20040227 170312001 Illino is Cook 36.12 6.42 6.43 0.00 26.63 26.68 - 0.05 20060117 170312001 Illino is C ook 36.12 4.88 4.89 0.00 20.37 20.41 - 0.04 20041230 170310052 Illino is Cook 36.07 5.78 5.90 - 0.11 28.34 28.53 - 0.19 20061229 170310052 Illino is Cook 36.07 5.14 5.24 - 0.10 25.24 25.41 - 0.17 20050205 170310052 Illino is Cook 36.07 7.63 7.65 - 0.02 32.74 32. 86 - 0.11 20070308 170310052 Illino is Cook 36.07 7.31 7.34 - 0.02 31.42 31.53 - 0.11 20030226 170310052 Illino is 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 P ennsylvania 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 3 6.06 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 31.3 2 - 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 Illino is Madison 35.81 9.01 9.15 - 0.14 33.18 33.22 - 0.04 20030304 171190023 Illino is Madison 35. 81 8.41 8.54 - 0.13 30.99 31.03 - 0.04 20060222 171190023 Illino is Madison 35.81 6.75 6.85 - 0.10 24.96 25.00 - 0.03 20040424 171190023 Illino is 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 - 0.1 8 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 2 0050125 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 98 th 

## percentile days were chosen based on CAMx.

## 44



# Appendix C: Description of Excel Spreadsheet Data Files for Transport Rule

Significant Contribution Analysis

## 45

EPA placed the following Excel spreadsheet file in the Transport Rule docket and on EPA’s website at [placeholder for website ]: The annual and quarterly emissions for all AQAT simulations can be found in this file. AQAT_emissions.xlsx These files contain the 24 - hour PM2.5 2012 base case and 2014 AQAT Calibration Scenario contributions. QTR1_base_and_AQAT_calibration_scenario_contributions.xlsx QTR2_base_and_AQAT_calibration_scenario_contributions.xlsx QTR3_base_and_AQAT_calibration_scenario_contributions.xlsx QTR4_base_and_AQAT_calibration_scenario_contributions.xlsx The annual PM2.5 and 24 - hour PM2.5 calibration f actors can be found in the respective files. Annual PM Calib Factors.xlsx Daily PM Calibration Factors.xlsx These files contain the quarterl y contributions and calibrated Relative Response Factors (RRFs) for all 24 - hour PM2.5 simulations. dailyPM_adjuste d 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_adj usted 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_ad

justed sulfate contributions and RRF_2014_1600_remedy.xlsx dailyPM_adjusted sulfate contributions and RRF_2014_2300_remedy.xlsx dailyPM_adjusted sulfate contributions and RRF_2014_2800_remedy.xlsx These files contain the quarterl y contributions and calibrated RRFs for the variability assessments. The first four files assume that the "home" state (the state where the receptor is located) is also varying. The next four files in the list assume that the home state is held constant at the $2300/ton level. The number associated with "var" in the title notes the level of emissions variation above the level of the budget in the simulation. dailyPM_adjusted sulfate contributions and RRF_2014_2300CT_20var.xlsx dailyPM_adjusted sulfate contributions and RRF_2014_2 300CT_15var.xlsx dailyPM_adjusted sulfate contributions and RRF_2014_2300CT_10var.xlsx dailyPM_adjusted sulfate contributions and RRF_2014_2300CT_05var.xlsx dailyPM_adjusted sulfate contributions and RRF_2014_2300CT_20var_home_2300.xlsx dailyPM_adjusted sulfate contributions and RRF_2014_2300CT_15var_home_2300.xlsx dailyPM_adjusted sulfate contributions and RRF_2014_2300CT_10var_home_2300.xlsx dailyPM_adjusted sulfate contributions and RRF_2014_2300CT_05var_home_2300.xlsx This file contains a summary of th e estimated 98th percentile values and resulting average and maximum design values for all 24 - hour PM2.5 AQAT cost threshold level, variability anal ys es, and remedy simulations.

## 46

dailyPM_allyears_high_quarters.xlsx These files appl y 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_base_2014_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 These are the same as the files above, but were used in the variability anal ys is. The "home" state, containing the monitor was controlled at $2300/ton and als o increased with the variability level. The level of variability is noted in the name of the file. dailyPM_all_years_all_quarters_base_2300CT_20perc_whome.xlsx dailyPM_all_years_all_quarters_base_2300CT_15perc_whome.xlsx dailyPM_all_years_all_quarters_base_2300CT_10perc_whome.xlsx dailyPM_all_years_all_quarters_base_2300CT_05perc_whome.xlsx These are the same files as above, but the home state was held constant at the $2300/ton cost threshold level. dailyPM_all_years_all_quarters_base_2300CT_20perc_whomeat2300.xlsx dailyPM_all_years_all_quarters_base_2300CT_15perc_whomeat2300.xlsx dailyPM_all_years_all_quarters_base_2300CT_10perc_whomeat2300.xlsx dailyPM_all_years_all_quarters_base_2300CT_05perc_whomeat2300.xlsx The annualPM25 AQAT.xlsx file contains the base contributions, AQAT calibration scenario contributions, calibrated contributions, and estimated design values for all annual PM2.5 AQAT simulations. The AQAT vs. CAMx.xlsx file contains the 2014 base case and 2014 remedy comparisons for AQAT and CAMx.