# 1 Technical Support Document (TSD)

for the Final Transport Rule

Docket ID No. EPA

- HQ

- OAR

- 2009

- 0491



# Significant Contribution and State Emissions Budgets

Final Rule TSD

U.S. Environmental Protection Agency

Office of Air and Radiation

July 201 1

# 2 This Technical Support Document (TSD) provides information that supports

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

nonattainment or interfere with maintenance of the National Ambient Air Quality

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

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

as follows:

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

t o 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 analysis 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

to Nonattainment or Interfere with Maintenance

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

approach to identify upwind states’ emissions that significantly contribute to

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

2006 PM 2.5 

# NAAQS and the 1997 ozone NAAQS . As described in the preamble, the

approach uses air quality analysis to identify monit oring sites with projected

nonattainment and maintenance problems for the PM 2.5 

# and ozone NAAQS as well as

upwind states whose contributions to these monitoring sites meet or exceed specified

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

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

As described in TR preamble section VI, after identifying upwind

- to

- downwind

linkages based on air quality contribution thresholds, EPA uses a multi

- step p rocess to

quantify each state’s significant contribution to nonattainment and interference with

maintenance. The first step in the process identifies the emissions projected to remain in

each state at ascending cost thresholds of emissions reductions. Se e section B in this

TSD for discussion of this analysis. Next, the process uses an air quality assessment tool

(AQAT) to estimate the impact of the upwind state reductions on downwind state air

quality at different cost

- per

- ton levels

. See section C in t his TSD for discussion of the

development and use of the air quality assessment tool.

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

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

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

contribution to nonattainment and interference with maintenance . Cost thresholds were

# 3

applied specifically to the upwind states identified for each program as listed in the table

below.

Table A

- 1. Geography to Which Cost Thresholds Were Applied

SO2 Group 1 SO2 Group 2 Annual NOx Ozone Season NOx

Illinois Alabama Illinois 

# Alabama

Indiana Georgia Indiana 

# Arkansas

Iowa Kansas Iowa 

# Florida

Kentucky Minnesota Kentucky 

# Georgia

Ma ryland Nebraska Maryland 

# Illinois

Michigan South

Carolina Michigan 

# Indiana

Missouri Texas Missouri 

# Iowa *

New Jersey New Jersey 

# Kansas *

New York New York 

# Kentucky

North Carolina North

Carolina 

# Louisiana

Ohio Ohio 

# Maryland

Pennsylvania Penns ylvania 

# Michigan *

Tennessee Tennessee 

# Mississippi

Virginia Virginia 

# Missouri *

West Virginia West Virginia 

# New Jersey

Wisconsin Wisconsin 

# New York

Alabama 

# North Carolina

Georgia 

# Ohio

Kansas 

# Oklahoma *

Minnesota 

# Pennsylvania

Nebraska 

# South Carolina

South

Carolina 

# Tennessee

Texas 

# Texas

Virginia

West Virginia

Wisconsin *

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

proposed rulemaking for ozone

- s eason NO

x 

## emission reductions. See the preamble for details.



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

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

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

rulemaking ( Docket ID No. EPA

– HQ

– OAR

– 2009

– 0491 ). Appendix C in this TSD

describes these files.

# 4

B. Electric Generating Unit Significant Contribution Cost Analysis

EPA used its updated version 4.10 _FTr ansport of the Integrated Planning Model

(IPM) to analyze 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. IPM is a multiregional, dynamic, d eterministic linear programming model

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

environmental policies. S ee ―Documentation for EPA Base Case v.4.10 Using the

Integrated Planning Model‖ and ― Documentation Supp lement for EPA Base Case

v.4.10_FTransport

– Updates for Final Transport Rule ‖ for further description of the IPM

model.

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

any emission reduction requirements related to the Transp ort 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 sec tion V.B of the preamble, the base

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

this rule.

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

at ascending cost thresholds of emissions contr ol

. EPA designed a series of IPM runs

that imposed increasing cost thresholds for 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 TR preamble section VI.B. 1 

# The cost

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

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

. This part of the analysis applied cost thre sholds to all fossil

- fuel

- fired EGUs with

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

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

reflect the year

- round operation of all existing SO 2 

# and NO

x 

# pollution controls in states

covered by the Transport Rule for PM 2.5 

# and the ozone

- season operation of all existing

NO

x 

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

1 

## These projected state level emissions for each ―cost threshold‖ run are presented in a several formats. The

IPM analysis outputs available in the docket contain a ―state emi ssions‖ file for each analysis. The file

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

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

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

budgets for each state at the cost thresholds determined to eliminate significant contribution to

nonattainment or interference with maintenance in that upwind state, in an average year. The ―fossil &

biomass‖ worksheet reports total emissions from fossil

- fired and biomass

- fired units, and represents the

state level emission total used in the AQAT analysis. These ―fossil & biomass‖ emission tota ls are used as

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

Transport Rule proposal Technical Support Document ―Analysis to Quantify Significant Contribution‖,

EPA stated that the ―all units‖ emis sions used in the AQAT analysis for the proposal and the emissions

used in the CAMx air quality modeling were slightly different. EPA committed to determining the origin

of the slight difference and removing it for the final Transport Rule. In this final rule modeling, the

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

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

# 5

of the time required to build advanced pollution controls, the model was prevented from

building any new post

- combustion controls such as selective catalytic reduction (SCR) or

flue

- gas desulfurization (FGD) in 2012 in response to the cost thresholds. 2 

# The

modeling does allow the ad dition or upgrading of NO

x 

# combustion controls in 2012 (such

as Low NOx Burners (LNB))

. EPA first conducted this cost curve analysis 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 Rule for ozone

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

require ozone

- season reductions, as discussed in section III of the Transport Rule

preamble. The IPM

- projected EGU emissions of ozone

- season NO

x 

# from the ―Fossil >

25 MW‖ units are shown at each cost threshold for 2012 and 2014 in Table B

- 1. 3



# EPA then conducted cost curve analysis for annual 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 

# NAAQS. T he IPM

- projected EGU emissions of annual NO

x 

# from the ―Fossil > 25

MW‖ units are shown at each cost threshold for 2012 and 2014 in Table B

- 2.

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

the appropriate cost threshold for ozone

- season 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 covere d states in concert with varying degrees of SO 2 

# control to

eliminate significant contribution to nonattainment and interference with maintenance of

the PM 2.5 

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

for SO 2 

# while simulta neously imposing cost thresholds of $500/ton for ozone

- season NO

x 

# in Transport Rule ozone states and $500/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 differe nt SO 2 

# cost thresholds.

For SO 2 

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

starting in 2012 and for each year thereafter. EPA then used the Air Quality Assessment

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

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

successfully eliminated significant contribution to nonattainment and interference with

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

nonattainment and/or maintenance problems when emissions were limited by a $500/ton

cost level. These 7 states

- Alabama, Georgia, Kansas, Nebraska, Minnesota, South

Carolina, and Texas

– are referred to as Group 2 states. Because their signif icant

2 

## IPM results do include newly built 2012 post

- combustion pollution control retrofits in base case

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

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

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

in response to existing consent decree or state rule requirements.

3 

## EPA notes that while ozone

- season emissions generally decline as the c ost threshold increases, there are

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

lower cost threshold analyzed. This is related to the interconnected, interstate nature of the grid, and the

ability of generation to shift from a less efficienct/higher emitting source in one state to a more

efficient/lower emitting source in another state at higher cost levels. In other words, as multiple states

experience the higher cost threshold on ozone

- season NO X 

## , a region may minimize cost by dispatching

more generation from lower

- emitting

- rate units in a particular state that counterintuitively raise that state’s

total ozone

- season NO X 

## emissions, even as the regional ozone

- season NO X 

## emissions decline as a resul t.

# 6

contribution to nonattainment and interference with maintenance was eliminated at this

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

threshold on these states’ SO 2 

# emissions.

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

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

equipment, and higher cost thresholds may induce new advanced control retrofits that

require a longer lead time for insta llation. EPA, however, did examine higher cost

thresholds for SO 2 

# in 2014 for Group 1 states. EPA examined cost levels of $1,600/ton,

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

SO 2 

# cost curve explored at propo sal. 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 type of qua ntity constraint was imposed, the marginal cost projected

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

EPA refers to this scenario as the ―$10,000/ton‖ cost threshold scenario for the remainder

of this document an d through out the final Transport Rule materials. See Table Appendix

A

- 1 in Appendix A for a list of IPM analyses.

. In the cost curves with ascending SO 2 

# cost thresholds, cost thresholds for e ach

pollutant (SO 2 

# , ozone

- season NO

x 

# , and annual NO

x 

# ) were ana lyzed simultaneously. This

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

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

independently with no emission control cost assumed for th e 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 by the Transport Rule . Cost

- effective actions

taken to r educe 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.

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

state emission totals from ―All Fossil and Biomass‖ as well as from ―All fossil > 25

MW‖ are reported. The ―All Fossil and Biomass‖ totals are meant to reflect total state

EGU emissions used for subsequent air quality modeling. The ―All Fossil > 25 MW‖

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

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

as it is 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 may change as a consequence of

generation shifting between states in response to the increasing Group 1 cost threshold

. Changes in Group 2 state

- level emissions in this analysis reveal the interconnected nature

of the power sector and the fact that generation and fuel consumption patterns are not

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

vary in a given state based on decisions taken by EGUs in other states connected to the

same grid.

These resulting state SO 2 

# emissions levels from ―All Fossil and Biomass‖ at each

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

# 7

downwind air quality. Section VI.D of the TR preamble explains how EPA considered

the results of the cost and air quality analyses described in this TSD to determine the

appropriate set of cost thresholds for eliminating significant contribution to

nonattainment and interferenc e with maintenance. EPA used the emissions from all

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

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

EPA used the remaining state level emissions from the ―All Fossil > 25 MW‖ at the final

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

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

section VII.B of the prea mble. 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 and provided in TR Docket. These tables show how state level

emissions for each of these po llutants change as the cost 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 contribut ion to

nonattainment and interference with maintenance. EPA notes that the modeling of the

$2,300/ton cost threshold for Group 1 states includes simultaneously appli cation of all of

the selected cost thresholds for defining significant contribution to non attainment and

interference with maintenance under the Transport Rule. For example, it imposes a

$500/ton cost threshold for SO 2 

# , NO X 

# , and ozone

- season NO X 

# in TR states covered for

those emissions in 2012 and 2013. It imposes a $2,300/ton threshold on Gr oup 1 state

SO 2 

# emissions, a $500/ton threshold on Group 2 SO 2 

# emissions, a $500/ton threshold on

annual NO

x 

# emissions, and a $500/ton threshold on ozone

- season NO

x 

# in TR states

covered for those emissions in 2014 and each year thereafter. Because the $2, 300/ton

IPM analysis included all of these selected cost thresholds under the final Transport Rule,

EPA relied on that IPM analysis’s projected remaining state level emissions in 2012 and

2014 from all fossil units greater than 25 MW as the basis for the s tate budgets in those

years . Hence, the values in Tables B

- 3 through B

- 5 generally represent emissions levels

after the removal of significant contribution to nonattainment in an average year, and

therefore they are the appropriate basis for the state bud gets reflected in section VI.D of

the Transport Rule preamble . In most cases, these remaining emission levels from fossil

fired units greater than 25 MW become the state budget levels. There are few exceptions

and they are noted below and in the Transpor t Rule preamble

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

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

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

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

Group 2 state with a constant $500/ton thresholds in all years for SO 2 

# may have

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

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

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

Transport Rule by ensuring that budgets do not increase between 2 012 and 2014.

Additionally, there were five states whose 2012 ozone

- season emissions in this analysis

were not significantly different from their 2012 base case projected emissions . EPA

conducted a sensitivity analysis to confirm that if left uncapped, th ese states’ ozone

-

# 8

season emissions would increase as other states make Transport Rule

- related emission

reductions, due to emissions leakage . EPA is therefore setting these states’ ozone

- season

budgets equal to their 2012 base case emissions to prevent suc h emission increases.

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



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

in Section VIII.A of the Transport Rule preamble. These were ba sed on post

- modeling

quality assurance checks that found discrepancies in the SO 2 

# pollution control technology

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

commenter

- provided data and independent third party proprietary sources. These

discrepancies affected the 2012 SO 2 

# budgets for Michigan and Kentucky and the 2012

and 2014 SO 2 

# budgets for New York.

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

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

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

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

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

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

and Kentucky do not reflect operation of those controls.

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

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

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

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

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

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

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

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

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

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

incorporated finalized var iability limits, which were higher than those originally modeled

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

Regulatory Impact Analysis.

The IPM runs performed for the cost analys es are listed in Table Appendix A

- 1

in of this TSD. This table lists the name of each IPM run next to a description of the run.

The output files of these model runs can be found in the rulemaking docket. In the TR

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

threshold s analyzed are presented rounded to the nearest thousand ton s

. The bugets given

in section VI.D of the TR preamble (which are based off tables B

- 3 through B

- 5 below)

are presented rounded to the nearest ton. In Tables B

- 1 throug h B

- 6 the emissions are

presented rounded to the nearest ton.

As noted above, EPA used the emissions shown in Table B

- 6 as input s to the air

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

available from states covered un der the Transport Rule, at different cost

- per

- ton levels,

4 

## EPA made these 2012 adjustments for ozone

- season NO X 

## budgets given that the 2012 compliance

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

EPA made a similar review for SO 2 

## and annual NO X 

## budge ts in 2014 (which is coordinated with the April

2015 maximum attainment deadline), and found that no such adjustments were necessary.

# 9

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

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

EPA’s development and use of AQAT and the results from our AQAT analysis. Section

C also compares the AQAT results to those produced using the Comprehensive Air

Quality Model with Extensions (CAMx)

.

# 10

Table B

- 1

. 2012 & 2014 Ozone Season NO

x 

# EGU Emissions* f or Each State a t

Variou s Pollution Control Cost Threshold s p er Ton o f Reduction (Tons).

State Base Case Emission

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

2012 2014 2012 2014 2012 2014 2012 2014

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

Arkansas 15,037 1 6,644 14,995 16,565 14,944 16,432 13,969 14,970

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

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

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

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

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

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

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

Louisiana 1 3,433 13,924 13,435 13,910 13,451 13,910 13,301 13,728

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

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

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

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

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

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

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

Ohio 42,2 74 41,327 42,302 40,493 41,863 40,375 38,437 38,348

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

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

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

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

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

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

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

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

Total 

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

* Source: Integrated Planning Model run by EPA, 2011

. See Appendix A for list and description of the se IPM runs. Emissions have

been rounded to the nearest ton. Emissions shown for all fossil

- fired units greater than 25 MW when only an ozone season cost

constraint is applied to Transport Rule States. Costs are in 2007$.

# 11

Table B

- 2 . 2012 and 2014 Annual NO

x 

# EGU Emissions* f or Each State a t 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 71,68 5 77,859 71,670 75,292 70,060

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

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

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

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

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

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

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

Michigan 60,594 64,3 45 60,725 61,088 60,482 60,877 59,991 60,110

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

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

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

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

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

North

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

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

Pennsy lvania 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. Costs are in 2007$.

# 12 Table B

- 3

. 2012 & 2014 Ozone Season NO

x 

# EGU Emissions from all Fossil Units Greater than 25 MW

at Escalating SO 2 

# Cost Thre sholds from Final Cost Curve Analysis (Tons).

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

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

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

Arkansas 15,037 16,644 15,087 16,652 15,087 16,759 15,0 87 16,794 15,087 16,794 15,087 16,794 16,690 16,867

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

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

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

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

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

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

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

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

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

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

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

Missouri 23,15 6 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 44,18 6 48,207

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

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

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

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

West Virginia 26,464 27,099 25,418 27,014 25,434 24,475 25,2 83 23,291 25,092 23,655 25,092 24,364 17,932 22,778

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

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

* Source: Integrated Planning Model run by EPA, 2011

. See Ap pendix 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 Gro up 1

SO 2 states. Costs are in 2007$

# 13 Table B

- 4

. 2012 & 2014 NO

x 

# EGU Emissions from all Fossil Units Greater than 25 MW

at E scalating SO 2 

# Cost Thresholds from Final Cost Curve Analysis ( T ons).

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

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

Alabama 82,005 74,937 73,772 70,582 73,127 71,787 7 2,691 71,962 72,748 72,033 72,538 72,088 80,949 72,410

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

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

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

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

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

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

Maryland 16,602 17,444 16,634 17,364 16,634 16,604 16,633 1 6,574 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 30,98 6 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 41,0 49 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 11 0,275

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

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

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

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

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

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

Total 1,345,888 1,321,093 1,248,871 1,217,391 1,248,070 1,190,494 1,245,869 1,179,952 1,2 42,762 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 cos t 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 $500/ton. The escalating cost thresholds identified in the column headers above only apply starting in 2014 for Gro up 1 SO 2 states. Costs are in 2007$

# 14 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

20 12 2014 2012 2014 2012 2014 2012 2014 2012 2014 2012 2014 2012 2014

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

Georgia 405,933 169,702 157,474 94,105 158,455 94,142 158,527 95,2 31 159,484 95,484 158,022 94,946 174,898 97,942

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

Indiana 776,359 711,265 285,584 245,191 294,517 178,525 285,424 161,111 285,099 152,95 4 282,070 120,532 159,737 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

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

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

Michigan 252,411 265,611 194,537 2 06,173 194,537 188,646 194,537 143,995 194,537 105,223 194,537 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 5,37 4 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 871,401 831,64 8 311,386 293,727 325,562 174,809 310,230 137,077 309,272 123,021 308,557 114,919 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 82,9 93 92,761 84,431 99,853 88,620 103,371 89,183 104,311 89,180 104,462 106,928 104,924

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

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

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

West Virginia 535,586 497,398 146,239 157,335 148,095 121,751 146,174 75,668 144,20 6 74,373 143,472 71,505 47,973 55,246

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

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

* Source: Integrated Planning Model run by EPA, 2011

. See Appendix A for list and description of these IPM runs. Emissions have been rounded to the nearest ton. These ―final cos t curve‖ runs have NO

x and ozone se ason

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$

# 15 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 201 4

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

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

Illinois 1 48 9,140 141,606 235,127 138,815 237,585 134,386 239,393 128,997 239,393 122,249 239,381 106,945 165,772 40,892

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

Iowa 1 127,102 133,083 1 02,989 117,830 112,450 83,661 112,450 81,137 112,334 72,460 112,334 50,664 64,589 23,429

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

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

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

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

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

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

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

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

New York 1 56,461 42,887 26,00 6 23,181 26,041 22,618 25,960 14,404 25,735 13,399 25,735 12,342 20,095 10,588

North

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

Ohio 1 882,559 851,199 327,015 313,193 341,192 202,4 43 325,375 166,691 324,417 153,471 323,702 145,431 130,251 98,812

Pennsylvani

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

South

Carolina 2 186,355 213,281 85,479 96,504 86,917 103,596 9 1,106 107,114 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 2 446,006 453,332 244,613 281,298 244,613 282,066 244,287 284,132 242,4 14 281,721 240,305 281,721 282,685 242,905

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

West

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

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

Total 7,015,727 6,220,607 3,315,495 3,108,724 3,413,731 2,597,726 3,389,078 2,326,289 3,383,625 2,168,226 3,379,807 2,0 35,357 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 ―fin al 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 Gro up

1 SO 2 states. Costs are i n 2007$

# 16

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

EPA has defined significant contribution to nonattainment and interference with

maintenance using a multi

- factor test (described in section VI.D of the preamble) which i s based

on both cost and air quality factors

. A key quantitative input for determining the amount of

significant contribution is the predicted downwind ambient air quality impacts of upwind EGU

emission reductions under the SO 2 

# and NO X 

# cost thresholds. Ti me and resource limitations (in

particular the amount of time needed to set up, run the CAMx model, and analyze the results for

a single model run) precluded the use of air quality modeling for all but a few emissions

scenarios. Because EPA needed to eval uate emission reductions under several different SO 2 

# cost

thresholds, it was not possible to use CAMx air quality modeling to evaluate all cases. 5



# EPA t hus used a simplified air quality assessment tool (AQAT) to estimate the downwind

air quality impacts fr om the 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 emission

reductions (see section B of this TSD for details about the IPM model runs and for the e mission

projections). The air quality impacts of these cost thresholds are then estimated using AQAT.

The simplified tool allows the Agency to analyze many more SO 2 

# cost thresholds than would

otherwise be possible. The remainder of section C of this docu ment will:

● 

# Present an introduction and overview of AQAT;

● 

# Describe the construction of AQAT;

● 

# Provide the results o f the SO 2 

# cost threshold analyses;

● 

# Compar e the AQAT estimates and CAMx results for sulfate and total PM 2.5 

# for two

emissions scenarios where C AMx modeling was performed (i.e., the 2014 base case

and 2014 remedy); and

● 

# Describe the results of an analysis of emissions ―leakage‖ for 2012 performed u sing

AQAT.

1. Introduction: D evelopment of the air quality assessment tool.

AQAT was developed spec ifically 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.

Significa nt 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 e mission 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 betwee n the 2012 base

5 

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

Sector Variability Final Rule TSD).

# 17 case and an AQAT calibration scenario 6 

# in 2014 (for more details about this scenario,

see the footnote and the brief description below)

.

● 

# Use of seasonal contributions, and seasonal relative response factors, in developing

the relationship b etween SO 2 

# reductions and 24

- hour PM 2.5 

# concentrations; and

Application of these seasonal relative response factors to estimate annual and 24

- hour

PM2.5 average and maximum design values for the cost levels analyzed

.

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

threshold for upwind annual and ozone

- season 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 u sed the CAMx modeling from this scenario directly rather than

develop an AQAT for ozone. Additionally, EPA used the nitrate predictions from the CAMx

modeling of the AQAT calibration scenario to calculate the nitrate component of annual and 24

-

hour PM 2.5 

# design values at the various SO 2 

# cost thresholds analyzed for the final Transport

Rule. EPA created and used two separate versions of AQAT ( annual and 24

- hour PM 2.5 

# ) to

estimate the impact of the SO 2 

# emission reductions on ambient sulfate concentrations f or the two

NAAQS, respectively

. For both versions of AQAT , the sulfate estimates were combined with

CAMx estimates of nitrate and other pollutant species calculated from the AQAT calibration

scenario to estimate concentrations of total PM 2.5



# . Most of the steps used in the construction of

the annual and 24

- hour PM 2.5 

# versions of AQAT are the same . Consequently, when EPA refers

to a single AQAT, the description applies to both the annual and 24

- hour versions of the tool.

S tep

- by

- step 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 reduction s in 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 determine the relationship between changes in emissions and changes in sulfate contributions

on a receptor

- by

- receptor basis. Specifically, as a start EPA assumed that within the range of

total 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 contrib utions. This proportional

relationship was then modified using calibration factors based on air quality modeling, as

described below.

Within AQAT, t he relationships between upwind emissions and downwind air quality are

defined using the 2012 base case con tribution air quality modeling and a 2014 AQAT calibration

scenario 6 

# . As described in the Air Quality Modeling Final Rule TSD, CAMx state

- by

- state

source

- apportionment modeling was used to quantify the contributio ns to sulfate at PM2.5

monitoring sites due to SO 2 

# emissions from each upwind state for the 2012 base case emission

scenario. For example, from the output of the CAMx source apportionment modeling, we know

6 

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

scenario. This scenario was created prior to the development of AQAT for the final Transport Rule and it’s EGU

emission s 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 f or PM 2.5 

## and ozone 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 from

the proposed Transport Rule. Note that the geography and SO 2 

## cos t thresholds for this scenario differ from the

geography and SO 2 

## cost thresholds for the final Transport Rule.

# 18

the annual average sulfate contribution at a dow nwind 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 s for each quarter of the year (January

— March, April

— June , July

— September , and October

— December )

. In AQAT, we associate a change in emissions from that

upwind state with a particular change in its downwind contribution. In ―uncalibrated‖ AQAT,

for example, we assume that a 20% decrease in the upwind state’s emissions leads to a 20%

decrease in its downwind contribution. This relationship is calibrated using emission reductions

from the 2012 base case to the 2014 AQAT calibration scenario by calculating the relationship

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

relative change in sulfate at each receptor using AQAT. This AQAT calibration scenario as

described further in the Air Quality Modeling Final Rule TSD, reflect ed SO 2 

# and NO

x 

# emission

reductions of similar stringency and from the same geogr aphy as the Transport Rule proposal

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

sulfate under various SO 2 

# cost thresholds. This is described further in section C.2 of this

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

decrease in contribution, using ―calibrated‖ AQAT may yield a 15% reduction in concentration

from the 20% reduction in emission s (a s derived directly from the emission reduction and

concentration change from the 2012 base case to the 2014 AQAT calibration scenario).

F or the proposal AQAT was applie d assuming a linear relationship 7 

# between reductions

in upwind SO 2 

# emissions and air quality improvements at downwind monitors and that this

linearity h eld to the point that zero SO 2 

# emissions yield zero sulfate formation . However, for the

final Transport Rule, this relationship is now calibrated for the the range of emission reductions

examined by EPA

. In the application of AQAT, we assume that the red uction 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 w as reduced. For example, reducing one ton of SO 2 

# emissions from the

power sector is assumed to have the same downwind sulfate reduction as reducing one ton of

SO 2 

# emissions of from the mobile source sector. Commenters on the proposed Transport Rule

sugge sted that EPA develop sector

- specific contribution factors for use in AQAT. However,

7 

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

the relationship between emissi ons 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 atmospheri c chemical reactions that convert SO 2 

## to particulate sulfate

are central to understanding the relationship between emissions and particulate formation. Both gas

- phase and

aqueous

- phase processes can be important in the formation of particulates.

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

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

on hydroxyl radical (OH) concentrati ons, 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, sunligh t 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 wa s 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 effec ts of transport and diffusion, and the uneven

geographic distribution of sources and controls across a state.

# 19

relying on the contribution from all sources is reasonable for the analysis of EGU costs and

reductions because total SO 2 

# emissions in 2012 are d ominated by emissions fro m EGUs

. While less rigorous than the air quality models used for attainment demonstrations, EPA

has established that AQAT is a cost

- effective tool for estimating the downwind sulfate

reductions due to upwind SO 2 

# emission reductions for the air quality inpu t to the multi

- factor test

for the final Transport Rule

. The evidence substantiating this is found in section C.4 in this TSD

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

who prefer to access the results of the a nalysis using the AQAT tool are directed to section C.3.

Comparisons between AQAT and the CAMx modeling for the 2014 base case and the 2014

remedy can be found in section C.4

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

(a) Overvi ew of AQAT.

This section describes the step

- by

- step development process for AQAT. In AQAT, EPA

link s state

- by

- state SO 2 

# emission reductions (from IPM) with CAMx modeled sulfate

contributions in order to predict PM2.5 concentrations at different cost thres holds at monitoring

sites with projected nonattainment and/or maintenance problems in the 2012 base case . The

reduction in sulfate contributions and resulting air quality improvement were then considered in

a multi

- factor test for defining significant con tribution to nonattainment and interference with

maintenance. In the analysis for a given receptor, emissions were reduced in only those upwind

states that were ―linked‖ to that receptor (i.e., contributed an air quality impact at or above the 1

percent

- - of the NAAQS standard

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

receptor (regardless of that state’s contribution)

. For a discussion of the 1% threshold, see

section V.D of the preamble.

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

● 

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

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

containing the receptor.

● 

# The sulfate contribution un der 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 years (i.e., 2014) compared to the 2012 base case

level due to EGU, mobile sour ce, and other source

- sector reductions.

● 

# The non

- sulfate concentrations under emissions modeled for the 2014 AQAT

calibration scenario.

The results of the AQAT analysis for each cost threshold can be found in section C.3 of

this document.

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

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

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

versions of annual and 24

- hour PM 2.5 

# AQAT. The uncalibrated annual and 24

- hour PM 2.5

# 20

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

# and NO

x 

# emissions defined by the AQAT calibration scenario. The datasets required to construct the

annual and 24

- hour versions of AQAT included: the 2012 base case SO 2 

# emission inventories

from all source sectors used in the source apportionment CAMx air quality modeling; the CAMx

2012 contributions for each upwind state to each downwind receptor; and the 2014 AQA T

calibration scenario SO 2 

# emissions inventories from all source sectors. An additional dataset,

2014 sulfate concentrations from CAMx for the AQAT calibration scenario, was used to

compare the AQAT

- estimated sulfate concentrations for this scenario to th e corresponding air

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

changes in SO 2 

# emissions in AQAT with the response predicted by CAMx. . These calibration

factors were then used to create a ―calibrated‖ AQAT . Finally, EGU SO 2 

# emissions (from IPM)

at each cost threshold were used to generate AQAT air quality results using calibrated AQAT.

The base case emissions inventories for 2012 and 2014, as well as the CAMx 2012 source

apportionment air quality modeling results are discussed in preamble sections V.C and V.D,

respectively. The EGU emissions for each cost threshold (projected using IPM) including the

base case are listed in Table B

- 6 and described in section B of this TSD. To construct the

annual PM2.5 v ersion of AQAT, the emissions and CAMx air quality modeling estimates were

at an annual time

- scale. To construct the 24

- hour PM2.5 version of AQAT, both the emissions

and CAMx air quality modeling estimates were at a quarterly time

- scale.

As described in section C.2.(c).5. of this TSD, 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 th e

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 Columbia 8 

# , Florida, Georgia, Illinois,

Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Michigan,

Minnesota, Mississippi, Missouri, Nebraska, New Hampshire, New Jersey, New York, North

Carolina , North Dakota, Ohio, Oklahoma, Pennsylvania, Rhode Island, South Carolina, South

Dakota, Tennessee, Texas, Vermont, Virginia, West Virginia, and Wisconsin. Thus, in AQAT,

these states had the possibility of making reductions in emissions leading to chang es 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

used for the Transport Rule (see the Air Quality Modeling Final Rule Technical Support

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

evaluating SO 2 

# cost thresholds

.

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

The annual and 24

- hour PM 2.5 

# versions of AQAT were created and used in a multi

- step

process. First, annual and 24

- hour PM 2.5 

# versions of AQAT were created specifically fo r

calibration. As described in the following paragraphs, t he 24

- hour version of AQAT simulated

8 

## Maryland was treated as a separate state in this analysis, rather than as combined with the District of Columbia. Its

emissions were totaled se parately and the changes in emissions occurring at different marginal costs were applied to

upwind contributions from Maryland alone.

# 21

each of four quarters in the a to represent seasonal differences in the response of sulfate to

reductions in emissions of SO 2



# . Next, the relative sulfate respo nse 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 quarterly 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

reductions for each SO 2 

# cost threshold assessed. For the annual PM2.5 AQAT, at each cost

threshold, the sul fate 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 final design values, which was to project the

adjusted sulfate change in each quarter to the representative modeled ―days 9 

# ‖ 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 year,

the 98 th 

# percentile value was selected. The 98 th 

# percentile values were then used to predict 2014

design values for 24

- hour PM 2.5 

# . This section describes the details behind these steps. 10



# As summarized above, o ne key d ifference between the 24

- hour PM 2.5 

# AQAT used in the

analysis for the proposed and final Transport Rule was the refinement of the SO 2 

# to sulfate

response

, by season. For the 24

- hour PM 2.5

, 

# version of A QAT used for the final Transport Rule

, 4 quarterly

- sp ecific components for estimating seasonal responses for 24

- hour PM 2.5 

# assessments

were created

. In response to public comment on the CAMx air quality estimates from the

proposal and the comparison with the AQAT estimates from the proposal, EPA conducted

f urther analysis that demonstrated seasonal differences in the PM 2.5 

# response to SO 2 

# /NO

x 

# emission reductions. EPA determined that creating 4 quarterly components to the 24

- hour PM 2.5 

# AQAT to assess the quarterly response of downwind sulfate to upwind SO 2 

# r eductions would be

beneficial in adequately accounting for seasonal differences in the relationship between

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

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

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

quarterly component of the 24

- hour PM 2.5 

# AQAT was based on quarterly

- specific emissions

, quarterly

- specific contributions from the CAMx 2012 base case state

- by

- state source

apportionment modeling

, and quarterly sulfate values from the 2012 base case and 2014 AQAT

calibration scenario s

. Consequently, EPA developed quarterly

- specific calibration factors , and

used these to estimate quar terly

- specific relative response factors and resulting sulfate

concentrations for each cost threshold level of SO 2 

# emissions ( these steps are described later in

this TSD ).

The AQAT calibration scenario played a key role in calibrat ing 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

- closely align with sulfate estimates from CAMx. To fill this

role, EPA used the results of IPM modeling of a control scenario 6 

# with similar level and

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

9 

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

year. 32 days were mapped to each year over the 2003

- 2007 time frame and projected to the 2012 or 2014 year.

10 

## Details on procedures for calculating av era g e and max imum design values can be found in the Air Quality

Modeling Final Rule T echnical Support Document

.

# 22

this AQAT calibration scenario was not an indication of the level of SO 2 

# reduction that would be

achieved by 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 the calibration proce ss, EPA is including an

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

Michigan, with a 2012 base case predicted 24

- hour 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 24

- hour PM2.5 versions of AQAT for calibration

To create the annual and the 24

- hour PM 2.5 

# version s of A QAT for calibration, EPA used

emis sions and contributions to estimate the change in predicted sulfate due to SO 2 

# emission

reductions under the AQAT calibration scenario relative to the 2012 base case . These

―uncalibrated‖ versions of AQAT are directly comparable to those from the proposed Transport

Rule (with the exception that for the final rule the 24

- hour PM2.5 AQAT is constructed using

quarterly components rather than annual)

. First, EPA calculated annual and quarterly state

- level 2012 base case total SO 2 

# emissions

from all source se ctors. 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

p oint, onroad, nonroad, C3 marine, alm, and fires (see the Emissions Inventory Final Rule TSD

for additional details on the emissions inventories used in the CAMx air quality modeling). The

state

- level total SO 2 

# emissions are the sum of emissions from all these source sectors. Next,

EPA calculated the annual and quarterly 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 t o 2012 total SO 2 

# emissions for the 2012 base case for each

state modeled in CAMx. This was done on an annual basis for the annual PM 2.5 

# version of

AQAT and on a quarterly basis for the 24

- hour PM 2.5 

# version of AQAT. More information on

the emissions inve ntories can be found in preamble section IV.C. The total emissions data and

resulting ratios for quarter 2 of the 24

- hour PM 2.5 

# version of AQAT can be found in Table C

- 1.

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

each state for the AQAT calibration scenario is calculated by subtracting the estimated

―uncalibrated‖ change in concentration from the 2012 base case contribution. The

―uncalibrated‖ change in concentration is found by multiplying the 2012 base case sulfate

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

is calculated as 1 minus the ratio of total SO 2 

# emissions in the AQAT calibration scenario to the

2012 base case scenario. When the change in conc entration 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 tota l sulfate

contribution from the states in the CAMx modeling domain. Finally, ―other‖ modeled sulfate

contributions (―BIOG‖, ‖OTHER‖, ―ICBC‖, and ―SOA‖) are added to the annual and quarterly

total to account for sources of sulfate outside the CAMx modeling domain. The total sulfate

from all the states and ―other‖ contributions represents the total sulfate component of PM 2.5

# 23

estimated by uncalibrated AQAT for the AQAT calibration scenario. It is the ratio of the CAMx

to AQAT sulfate components for this AQ AT calibration scenario that becomes the constant

calibration factor used in ―calibrated‖ AQAT.

# 24

Table C

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

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

SO 2 

# Emissions from all Source

- Sectors for Each State

.

State/Source 2012 Base Case

Quarter 2 Sulfate

Contributions

( g/m 3 ) 2012 Base Case

Quarter 2 SO 2 Emissions (tons) 2014 AQAT

calibration Scenario

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

c alibration Scenario

Emissions to 2012 Base

Case SO 2 Emissions for

Quarter 2 Estimated 2014

Contribution of Sulfate

in Quarter 2

(uncalibrated AQAT)

( g/m 3 )

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

D E 0.01 2,440 2,145 0.88 0.01

DC 0.00 499 485 0.97 0.00

FL 0.06 60,947 63,051 1.03 0.06

GA 0.21 128,332 46,991 0.37 0.08

IL 1.21 141,050 58,995 0.42 0.51

IN 3.09 223,451 77,561 0.35 1.07

IA 0.12 48,675 34,918 0.72 0.08

KS 0.06 26,869 24,901 0.93 0.05

KY 1.89 135,520 42,304 0.31 0.59

LA 0.23 59,724 58,568 0.98 0.22

ME 0.00 5,967 4,744 0.79 0.00

MD 0.11 29,347 25,558 0.87 0.10

MA 0.01 10,663 10,850 1.02 0.01

MI 3.93 85,280 60,550 0.71 2.79

MN 0.03 26,684 22,243 0.83 0.03

MS 0.06 15,408 15,954 1. 04 0.06

MO 0.86 114,219 71,433 0.63 0.54

NE 0.02 19,586 18,871 0.96 0.02

NH 0.00 2,747 3,482 1.27 0.00

NJ 0.02 11,115 7,292 0.66 0.01

NY 0.12 51,969 33,122 0.64 0.08

NC 0.14 55,881 34,548 0.62 0.09

ND 0.03 28,083 28,235 1.01 0.03

OH 3.56 237,608 66 ,535 0.28 1.00

OK 0.08 39,479 38,672 0.98 0.08

PA 0.80 149,123 57,087 0.38 0.30

RI 0.00 1,316 1,315 1.00 0.00

SC 0.03 58,121 36,711 0.63 0.02

SD 0.01 9,341 9,282 0.99 0.01

TN 0.73 100,713 38,227 0.38 0.28

TX 0.18 174,356 184,266 1.06 0.19

VT 0.00 1 ,469 1,473 1.00 0.00

VA 0.15 42,859 31,049 0.72 0.11

WV 1.02 140,798 29,254 0.21 0.21

WI 0.09 49,290 28,540 0.58 0.05

BIOG 0.00 1 0.00

OTHER 0.91 1 0.91

ICBC 1.14 1 1.14

SOA 0.00 1 0.00

Total Sulfate

Component of

PM 2.5 in

Quarter 2 21.54 11.21

# 25

(2) Calibrate annual and quarterly sulfate response in the annual and 24

- hour PM 2.5 

# versions of

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

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

scenario was used to calibrate the the annual and 24

- hour versions of AQAT to CAMx. First, the

annual and quarterly changes in sulfate predicted by AQAT and CAMx relative to 2012 base

case concentrations were calculated for each monitor. To calculate this for AQAT and CAMx

independently, EPA subtracted the 2014 total sulfate estimated by either AQAT or CAMx for the

AQAT calibration scenario from the respective 2012 total sulfate predicted by CAMx for the

2012 base case. This difference was t hen divided by the 2012 total sulfate predicted by CAMx

for the 2012 base case (see Table C

- 2 for an example calculation). The calculation of these

monitor

- specific calibration factors provided EPA with the ability to align the sulfate response

predicted by AQAT to the sulfate response predicted by CAMx at a level of SO 2 

# reductions that

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

. For 24

- hour PM 2.5 

# , the CAMx estimates of the 2012 base case and 2014 AQAT

calibration scenar io are presented by year as well as by quarter. Thus, in the CAMx estimates,

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

- 2007 projected to the future

year). In contrast, the estimates from AQAT are the average of the five yearly values , since the

AQAT values are derived from the ―average‖ quarterly contributions . The AQAT and CAMX

ammonium sulfate factors for 24

- hour PM 2.5 

# can be found in the ― Daily PM Calibration

Factors.xlsx ‖ excel workbook on worksheet ― AQModeling Calib Factor DailyPM ‖ in columns

BA and AJ, respectively. The calibration factor is the ratio of the CAMx response factor divided

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

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

monitoring site.

For annual PM 2.5 

# , the CAMx estimates used in AQAT construction are represented as 5

- year averages. The AQAT and CAMx ammonium sulfate factors for annual PM 2.5 

# can be found

in the ―Annua l PM Calib Factors.xlsx ‖ excel workbook on worksheet ― AQModeling Calib

Factor s Ann PM ‖ in columns AI and AE, respectively. The calibration factor is the ratio of the

CAMx response factor divided by the uncalibrated AQAT response factor. This calibration

factor can be found in column AK of the aforementioned excel worksheet. There is a single

calibration factor, representing an annual value, for each monitoring site.

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

t he ―warm‖ seasons (i.e., 2nd and 3rd calendar quarters) were greater than during the ―cool‖

seasons (i.e., 1st and 4th quarters). Consequently, the calibration factors for the ―warm‖ seasons

are larger than they are for the ―cool‖ seasons.

# 26

Table C

- 2. To tal Estimated Sulfate Contributions in the 2012 Base Case and 2014 AQAT

Calibration Scenario from CAMx and Uncalibrated AQAT for Monitor Number 261630033 in

Wayne County, Michigan ( See Table C

- 1) for 24

- hour PM 2.5



# . These Values are then Used to

Create a C alibration Factor

.

2012 Base Case

Quarter 2 Sulfate

Concentration

( g/m 3 

## ) Estimated 2014

Concentration of Sulfate

in Quarter 2

(uncalibrated AQAT)

( g/m 3 

## ) Estimated Quarter 2

Reduction Divided by

2012 Base Case

Concentration

C AM x * 22.67 13.21 0.4172

AQA T * 21.54 11.21 0.4795

Calibration Factor

- Response Factor From

C AMx Divided By

Response Factor From

AQAT 0.8700



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

each projected year, 2003

- 2007). In AQAT, there is just have a single ―average‖ value per

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

(3) Create calibrated versions of annual and 24

- hour AQAT for cost threshold analysis

Next, EPA created the calibrated versions of ann ual and 24

- hour PM 2.5 

# AQAT for the

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

calibration factors to estimate the change in predicted sulfate due to SO 2 

# emission reductions

under each cost threshold evaluate d. First, as described in step 2, EPA calculated annual and

quarterly state

- level 2012 base case total SO 2 

# emissions , for the annual and 24

- hour PM 2.5 

# versions of AQAT, respectively . Next, EPA calculated the annual and quarterly 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 case SO 2 

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

# emissions are

available annually only. In order to approximate the quarterly emissions needed for the quarterly

components of the 24

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

threshold for each state by the ratio o f the state’s quarterly to annual emissions for the power

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

quarter one is the sum of the EGU SO 2 

# emissions for January, February, and March divided by

the total annual EGU SO 2 

# emissions. Finally, 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

prea mble 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 analyzed, on a receptor

- by

- receptor basis, the emissions

reductions for each upwind state are associated with one of two cost threshold levels (either the

base case emissions level or the particular threshold cost level being analyzed) depending on

# 27

whether the upwind state is ―linked‖ to that receptor or if the receptor is located within the state

. States that are contributing above the respective air quality threshold 11 

# (i.e., greater than or equal

to 1 percent contribution of total sulfate and nitrate for the annual and 24

- hour PM 2.5 

# ) 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 ca lculated

by 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 ch ange in sulfate, calculated by adding up the

change in contributions from all states

, is multiplied by the calibration factor. This calibrated

change in sulfate is then subtracted from the total sulfate from the 2012 base case modeling,

resulting in the ― calibrated‖ average total sulfate. The 2012 base case sulfate includes the

contributions from all upwind states as well as the ―other‖ sulfate contributions. When this

―calibrated‖ sulfate is combined with the other components of PM 2.5 

# , it is possible to estimate

total PM 2.5 

# . Th e process of estimating design values is described in the next two sections (4 and

5) for annual PM 2.5 

# and for 24

- hour PM 2.5 

# , respectively.

(4) Calculate 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 average and

maximum design values for annual PM 2.5 

# by adding the total sulfate to the non

- sulfate

components of PM 2.5 

# from the CAMx modeling of the 2014 AQAT calibration scenario. Th e

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 quarterly relative response factors in the

24

- hour version of AQAT

● 

# Calculate quarte rly relative response factors as the ratio of calibrated AQAT

predicted total sulfate to 2012 CAMx modeled total sulfate

● 

# Calculate predicted 2014 total sulfate for all available CAMx modeled days (8 days

per quarter per year) by multiplying the 2012 CAMx m odeled concentrations by the

quarterly 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 98 th 

# percentile day for each modeled year

● 

# Check the completeness and validity of each modeled year, keeping only the years

with monitoring data that met completeness criteria

● 

# Calculate average 2014 predicted DVs for each quantifiable 3

- year period of

projected historic monitoring data (2003

- 2005, 2004

- 2006, and 2005

- 2007)

11 

## For the 24

- hour version of AQAT, the assessment of the 1% contributions to the threshold are based on the

contributions to the average design value from the 2012 base case, and not on the average quarterly contributions.

# 28

● 

# Calculate final average DV as the average of quantifiable 2014 predicted 3

- year DVs

● 

# Calculate the maximum DV as the maximum of quantifiable 2014 predicted 3

- year

DVs

The estimation of design values for the 24

- hour PM 2.5 

# standard is more complicated than

it is for the annual PM 2.5 

# standard, because only the 98 th 

# percentile day from each of the five

years contributes to the design value (and the particular day selected as the 98 th 

# percentile day

can change at different cost thresho ld levels). After estimating average total sulfate in 2014 for

each cost threshold for each quarter, EPA developed relative response factors (RRF) for

quarterly sulfate concentrations and used these factors to calculate expected future sulfate

concentrati ons for 32 selected modeled days for each of the 5

- years accounted for in the 2012

CAMx base case modeling (2003

- 2007) . In other words, the ―average‖ quarterly responses were

―mapped‖ to the 8 individual days in each quarter (32 days total per year) for e ach of the 5 years

using the relative response factors. This was done by multiplying the appropriate quarterly RRF

by the 2012 base case sulfate value for each day.

For each monitor, t o calculate the quarterly relative response factors, EPA took the

―aver age‖ calibrated quarterly sulfate contribution for the cost threshold level and divided it by

the 2012 base case ―average‖ quarterly sulfate contribution. For each monitor, t here is a single

RRF for each quarter, with the same RRF applied equally to all 5 years.

For each cost threshold level evaluated, EPA multiplied the appropriate quarterly RRF

for that threshold to the 2012 base case sulfate values for each of the 32 days, for each of the 5

years, to estimate adjusted sulfate values. To these adjusted sulfate values, EPA added the

concentrations from the other 12 

# PM 2.5 

# components from the 2014 AQAT calibration scenario

(i.e., 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 sulfate values) for each monitor, year, and day can be found in the

― dailyPM_al l_years_all_quarters... .xlsx ― workbooks.

Next, we ranked the values for each year and selected the 98 th 

# percentile for each year for

use in estimation of the 3

- year design values. The particular rank of the value selected depended

on the sampling frequency of the monitor (for more details see section V.C.2.b (2) of the

preamble, the Air Quality Modeling Final Rule TSD, a nd 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

―dailyPM_allyears_high_quarters.xlsx‖ workbook in colu mn G.

For each monitor, t he 98 th 

# percentile value for each cost threshold level and for each year

can be found in the appropriate worksheet and columns I through M in the

―dailyPM_allyears_high_quarters.xlsx‖ workbook. Three valid consecutive yearly 98 th



# percentile values are needed to construct a design value. The completion codes for each potential

design value 3

- year time

- period have values of 1, 2, 3, 4 or missing (0) for each design value

12 

## By using nitrate from the A QAT 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 identical in the 2014 base case and AQ AT calibration case CAMx modeling. The largest difference

in concentration between the two modeled scenarios was 0.02 g/m 3 

## 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.

# 29

period. Values of 1 or 2 indicate compete data and values of 3 or 4 indicate incomplete data.

Missing values , or values equal to 0, were treated as incomplete periods.

The average design value was calculated as the average of all valid design values, while

the maximum design value was calculated as the maximum avai lable valid design value.

As the cost threshold value increased, the estimated average and maximum design values

at each receptor decreased. In AQAT, the estimated value of the average design value was used

to estimate whether the location will be out of attainment, while the estimated maximum design

value was used to estimate whether the location will be out of maintenance. The two air quality

levels used were 15.05 g/m 3 

# and 35.5 g/m 3 

# to represent the 1997 and 2006 fine particulate

matter (PM 2.5 

# ) NAAQS, respectively.

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

This section describes the results of the cost threshold analysis using the annual and 24

-

hour versions of AQAT for the annual PM 2.5 

# , and 24

- hour PM 2.5 

# NAAQS standards. In section

C.2 of this TSD, we described the construction of the annual and 24

- hour versions of AQAT to

estimate the air quality impacts of various level s of EGU SO 2 

# emissions.

For annual PM 2.5 

# in 2014, the average and maximum PM 2.5 

# design values ( g/m 3 

# )

estimated using AQAT for each identified receptor for each cost threshold level can be found in

Table C

- 3 and C

- 4, respectively. The monitors are in order of decreasing 2012 base case

maximum annual PM 2.5 

# design value. No monitors are estimated to have remaining

nonattainment 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 C ounty

, Pennsylv ania (Liberty

- Clairton nonattainment

area ). As indicated in section VIII.B of the preamble, final air quality modeling of the Transport

Rule remedy scenario using CAMx indicates that the maintenance problem estimated by AQAT

is resolved.

For 24

- hour PM 2.5 

# in 2014, the estimated average and maximum air quality design values

( g/m 3 

# ) estimated using AQAT for each identified receptor for each cost threshold level can be

found in Table C

- 5 and C

- 6, respectively. The monitors are in order of decreasing 2012 bas e

case maximum 24

- hour PM 2.5 

# design value. Based on applying 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 receptor s are projected to

require substantial additional SO 2 

# emission reductions to eliminate significant contribution to

nonattainment and interference with maintenance of the standard

. The total number of estimated nonattainment and maintenance receptors as a function of

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 becaus e the maximum design value

(maintenance) is always equal to or greater than the average design value (nonattainment), all

receptors that are estimated to have nonattainment problems are also estimated to have

maintenance problems. For example, for the ann ual 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 account s for the value of 1 in both the nonattainment and

nonattainment or maintenance categories for the annual PM 2.5 

# columns (see appendix D for a

# 30

description about how the monitors were associated with nonattainment areas to crea t e this table

in the preamble)

. In the assessment of air quality using the calibrated AQAT, it is difficult to estimate the

relative contributions of particular upwind states contributing to a particular estimated design

value for 24

- hour PM 2.5 

# standard. The reason is that the design value is calculated using

projections for different days, possibly from different seasons, and the rank of these days can

change depending on the emissions changes from each cost threshold examined. For example,

in the base case, the 98 th 

# percentile day s which contribute to the design value could primarily be

from ―warm‖ seasons, which have high sulfate levels. At a higher cost threshold level, the 98 th



# percentile day could shift to a ―cool‖ season, which has a lower sulfate level. Consequently, this

c an confound the interpretation of the change in sulfate as well as change in the relative upwind

contribution s of particular states to design values

. Lastly, once the budgets for the final Transport Rule were established (based on the

results of the mult i

- factor test) and IPM was used to model compliance with the final rule, it was

possible to estimate air quality concentrations at each downwind receptor using AQAT for the

final rule remedy scenario . Average and maximum design value estimates in 2014 for annual

PM 2.5 

# and 24

- hour PM 2.5 

# can be found in Tables C

- 7 and C

- 8 in section C.4 of this TSD. CAMx

was run for this same set of emissions and the air quality results from this run are also

summarized in Tables C

- 7 and C

- 8 (see section C.4 of this TSD). A dditional 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/m 3 ) AQAT 2014 Average Annual PM2.5 Design Values ( g/m 3 ).

Base

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

Avg. improvement from AQAT base case

– 2012 base case

receptors 1.60 1.87 2.02 2.10 2.15 2.42

420030064 Pennsylvania Allegheny 17.94 17. 53 15.78 15.28 15.03 14.97 14.91 14.70

390350038 Ohio Cuyahoga 15.99 15.68 14.10 13.77 13.60 13.52 13.46 13.23

10730023 Alabama Jefferson 16.15 15.60 14.33 14.38 14.31 14.31 14.38 14.06

390618001 Ohio Hamilton 16.01 15.64 13.54 13.18 13.01 12.93 12.85 1 2.53

261630033 Michigan Wayne 15.73 15.44 14.35 14.12 13.87 13.69 13.61 13.25

390350060 Ohio Cuyahoga 15.67 15.34 13.75 13.42 13.25 13.17 13.11 12.88

390610014 Ohio Hamilton 15.76 15.39 13.29 12.93 12.75 12.67 12.59 12.27

390610042 Ohio Hamilton 15.40 15.07 12.97 12.61 12.44 12.36 12.28 11.98

171191007 Illinois Madison 15.46 14.85 13.83 13.64 13.56 13.43 13.31 13.00

10732003 Alabama Jefferson 15.16 14.68 13.55 13.58 13.52 13.51 13.57 13.29

390350045 Ohio Cuyahoga 15.14 14.83 13.23 12.90 12.73 12.65 1 2.59 12.36

180970081 Indiana Marion 14.86 14.52 12.68 12.40 12.26 12.19 12.09 11.79

131210039 Georgia Fulton 15.07 14.29 13.35 13.24 13.20 13.18 13.17 13.05

390617001 Ohio Hamilton 14.74 14.40 12.30 11.93 11.76 11.68 11.60 11.28

390350065 Ohio Cuyahoga 14.67 14.38 12.79 12.45 12.28 12.20 12.14 11.91

180970083 Indiana Marion 14.71 14.38 12.53 12.25 12.11 12.04 11.94 11.64

# 31

Table C

- 4

. Maximum Annual PM 2.5 

# DVs ( g/m 3 

# ) for SO 2 

# Cost Thresholds ($/ton) Assessed

Using AQAT.

Monitor

identification

number Sta te County CAMx 2012

Base Case

( g/m 3 ) AQAT 2014 Maximum Annual PM2.5 Design Values ( g/m 3 ).

Base

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

Avg. improvement from AQAT base case

– 2012 base case

receptors 1.60 1.87 2.02 2.10 2.15 2.42

420030064 Pennsylvania Allegheny 18.33 17.92 16.17 15.67 15.42 15.36 15.30 15.09

390350038 Ohio Cuyahoga 16.66 16.35 14.77 14.44 14.27 14.19 14.13 13.90

10730023 Alabama Jefferson 16.46 15.91 14.64 14.69 14.62 14.62 14.69 14.37

390618001 Ohio Hamilton 16.33 15.96 13.86 13.50 13.33 13.25 13.17 12.85

261630033 Michigan Wayne 16.32 16.03 14.94 14.71 14.46 14.28 14.20 13.84

390350060 Ohio Cuyahoga 16.18 15.85 14.26 13.93 13.76 13.68 13.62 13.39

390610014 Ohio Hamilton 15.98 15.61 13.51 13.15 12.97 12.89 12.81 12.49

390610042 Ohio Hamilton 15.77 15.44 13.34 12.98 12.81 12.73 12.65 12.35

171191007 Illinois Madison 15.73 15.12 14.10 13.91 13.83 13.70 13.58 13.27

10732003 Alabama Jefferson 15.64 15.16 14.03 14.06 14.00 13.99 14.05 13.77

390350045 Ohio Cuyahoga 15.61 15.30 13.70 13.37 13.20 13.12 13.06 12.83

180970081 Indiana Marion 15.16 14.82 12.98 12.70 12.56 12.49 12.39 12.09

131210039 Georgia Fulton 15.10 14.32 13.38 13.27 13.23 13.21 13.20 13.08

390617001 Ohio Hamilton 15.10 14.76 12.66 12.29 12.12 12.04 11.9 6 11.64

390350065 Ohio Cuyahoga 15.10 14.81 13.22 12.88 12.71 12.63 12.57 12.34

180970083 Indiana Marion 15.06 14.73 12.88 12.60 12.46 12.39 12.29 11.99

# 32

Table C

- 5 . Average 24

- h ou r 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/m 3 ) AQAT 2014 Average 24

- hour PM2.5 Design Values ( g/m 3 ).

Base

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

Avg. improvement from AQAT base case

– 2012 base case

receptors 4.09 4 .77 5.09 5.22 5.35 5. 79

Avg. improvement from AQAT base case

– $500 receptors** 4.73 5.70 6.41 6.67 6.85 7. 5 4

420030064 ** Pennsylvania Allegheny 56.71 54.34 47.57 46.36 45.54 45.37 45.23 44.73

420030093 ** Pennsylvania Allegheny 39.11 37.51 32.19 30.91 30.25 30.12 29.96 29.37

390350038 ** Ohio Cuyahoga 39.46 37.95 34.18 33.73 33.51 33.43 33.36 32.99

261630016 ** Michigan Wayne 38.99 38.50 34.42 34.15 33.93 33.77 33.70 33.36

390350060 Ohio Cuyahoga 37.78 37.11 31.50 30.79 30.60 30.51 30.43 30.20

170311 016 ** Illinois Cook 37.58 36.11 34.13 33.48 33.13 32.94 32.67 31.98

261630033 ** Michigan Wayne 39.48 39.01 36.31 35.59 35.00 34.65 34.43 33.55

180890022 ** Indiana Lake 34.94 34.04 32.79 32.47 32.38 32.29 32.16 31.86

540090011 West Virginia Brooke 37.57 36.73 30.60 29.60 29.07 28.94 28.80 28.27

420710007 ** Pennsylvania Lancaster 35.98 35.54 35.19 35.02 34.95 34.94 34.93 34.88

390350045 Ohio Cuyahoga 34.80 33.63 27.69 26.61 26.30 26.20 26.15 25.96

390811001 Ohio Jefferson 34.56 33.58 27.64 26.41 25.79 2 5.65 25.49 24.92

261630019 ** Michigan Wayne 37.34 36.86 35.27 35.09 34.93 34.82 34.77 34.54

390350065 Ohio Cuyahoga 34.91 33.50 27.65 26.61 26.11 25.95 25.81 25.28

170313301 Illinois Cook 34.97 33.60 31.11 30.72 30.54 30.40 30.24 29.73

420070014 Pennsy lvania Beaver 36.21 34.84 29.28 28.10 27.59 27.48 27.36 26.95

420033007 Pennsylvania Allegheny 32.40 30.98 26.27 25.31 24.88 24.80 24.71 24.48

010730023 Alabama Jefferson 36.96 35.43 31.93 31.86 31.61 31.60 31.74 31.06

550790026 Wisconsin Milwaukee 33.6 2 33.28 30.48 30.27 30.15 30.03 29.90 29.54

180970043 Indiana Marion 35.76 34.67 28.64 27.55 27.16 26.98 26.64 25.83

261470005 Michigan St Clair 36.23 35.61 33.35 33.01 32.78 32.67 32.59 32.29

550790043 Wisconsin Milwaukee 36.21 34.98 32.49 32.07 31.85 31.70 31.53 31.24

180890026 Indiana Lake 34.08 33.00 30.91 30.65 30.52 30.42 30.30 30.07

180970081 Indiana Marion 35.85 33.70 28.44 27.66 27.35 27.21 26.93 26.19

180970066 Indiana Marion 35.73 34.49 29.22 28.45 28.13 27.96 27.65 26.95

171191007 Illinoi s Madison 36.59 34.59 29.92 29.48 29.32 29.13 28.88 28.16

550790010 Wisconsin Milwaukee 35.47 35.03 31.50 31.05 30.82 30.73 30.62 30.32

390170003 Ohio Butler 34.40 33.66 28.07 26.99 26.49 26.33 26.19 25.68

170316005 Illinois Cook 34.12 33.47 32.72 32.53 32.41 32.31 32.18 31.84

420031008 Pennsylvania Allegheny 35.04 33.41 26.95 25.44 24.69 24.51 24.33 23.69

261610008 Michigan Washtenaw 35.05 34.93 29.40 28.71 28.54 28.47 28.42 28.18

170312001 Illinois Cook 33.62 32.33 29.84 29.68 29.58 29.48 29.37 29.0 8

170310052 Illinois Cook 34.94 33.27 30.11 29.87 29.78 29.67 29.53 29.07

421330008 Pennsylvania York 33.38 33.11 31.60 31.21 31.03 31.00 30.96 30.83

261630015 Michigan Wayne 35.55 34.42 32.23 31.53 31.10 30.93 30.85 30.52

010732003 Alabama Jefferson 3 5.31 34.20 31.42 31.27 31.10 31.08 31.14 30.59

390618001 Ohio Hamilton 35.29 33.57 27.63 26.51 26.11 25.96 25.77 25.40

171190023 Illinois Madison 35.11 33.58 29.23 28.69 28.49 28.26 28.07 27.54

420031301 Pennsylvania Allegheny 33.95 32.45 27.16 25.87 25 .21 25.06 24.91 24.30

391130032 Ohio Montgomery 33.68 32.19 24.40 23.37 23.15 23.05 22.95 22.60

420030116 Pennsylvania Allegheny 35.59 33.88 27.97 26.86 26.34 26.23 26.08 25.57

** Identify receptors that have maximum design values greater than or equal to 35.5 g/m 3 at the $500 cost threshold in 2014.

# 33

Table C

- 6

. Maximum 24

- h ou r 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/m 3 ) AQAT 2014 Maximum 24

- hour PM2.5 Design Val ues ( g/m 3 ).

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. 0 7

Avg. improvement from AQAT base case

– $500 receptors** 3.27 3.86 4.22 4.37 4.50 4. 9 6

420030064 ** Pennsylvania Allegheny 59.93 57.64 50.72 49.46 48.63 48.49 48.35 47.82

420030093 ** Pennsylvania Allegheny 44.40 42.63 36.85 35.50 34.80 34.66 34.49 33.85

390350038 ** Ohio Cuyahoga 41.84 40.37 35.93 35.58 35.41 35.33 35.29 34.93

261630016 ** Michigan Wayne 41.28 40.77 36.20 35.88 35.65 35.49 35.42 35.10

390350060 Ohio Cuyahoga 40.85 39.90 33.69 33.23 33.04 32.94 32.86 32.61

170311016 ** Illinois Cook 40.44 39.05 37.40 36.85 36.54 36.35 36.10 35.50

261630033 ** Michigan Wayne 39.81 39.47 36.59 35.84 35.23 34.87 34.65 33.75

180890022 ** Indiana Lake 39.58 38.68 37.00 36.63 36.51 36.35 36.11 35.57

540090011 West Virginia Brooke 38.39 37.68 32.23 30.79 30.02 29.84 29.64 28.89

420710007 ** Pennsylvania Lancaster 38.37 37.82 37.43 37.25 37.18 37.17 37.15 37.10

390350045 Ohio Cuyahoga 38.13 36.65 29.48 28.11 27.60 27.43 27.35 27.14

390811001 Ohio Jefferson 37.88 36.91 30.27 28.78 28.03 27.86 27.67 26.96

261630019 ** Michigan Wayne 37.83 37.29 36.20 36.01 35.83 35.72 35.66 35.43

390350065 Ohio Cuya hoga 37.67 36.41 28.79 27.60 27.00 26.80 26.64 26.08

170313301 Illinois Cook 37.67 36.26 33.36 33.01 32.84 32.71 32.55 32.14

420070014 Pennsylvania Beaver 37.42 35.99 30.46 29.27 28.70 28.58 28.46 28.04

420033007 Pennsylvania Allegheny 37.40 35.85 30.73 29.47 28.81 28.68 28.54 28.17

010730023 Alabama Jefferson 37.33 35.80 32.50 32.42 32.12 32.10 32.28 31.52

550790026 Wisconsin Milwaukee 37.24 36.72 33.54 33.32 33.21 33.09 32.96 32.61

180970043 Indiana Marion 37.20 36.09 29.00 28.09 27.82 27.70 27.46 2 6.83

261470005 Michigan St Clair 37.14 36.57 34.16 33.59 33.38 33.29 33.24 33.02

550790043 Wisconsin Milwaukee 37.10 35.89 34.22 34.03 33.92 33.83 33.73 33.46

180890026 Indiana Lake 37.06 36.05 33.67 33.48 33.37 33.28 33.18 32.93

180970081 Indiana Mari on 36.96 34.81 28.83 27.95 27.59 27.41 27.08 26.33

180970066 Indiana Marion 36.92 35.62 30.40 29.52 29.13 28.93 28.54 27.69

171191007 Illinois Madison 36.83 35.20 31.19 30.85 30.66 30.42 30.10 29.18

550790010 Wisconsin Milwaukee 36.71 36.56 33.47 33.25 33.13 33.04 32.94 32.66

390170003 Ohio Butler 36.59 36.03 28.71 27.76 27.33 27.17 27.01 26.53

170316005 Illinois Cook 36.42 35.87 35.09 34.90 34.82 34.71 34.59 34.23

420031008 Pennsylvania Allegheny 36.35 34.65 28.15 26.48 25.62 25.39 25.15 24.29

26161 0008 Michigan Washtenaw 36.32 35.38 30.20 29.50 29.33 29.25 29.20 28.95

170312001 Illinois Cook 36.12 34.95 32.71 32.49 32.33 32.22 32.07 31.71

170310052 Illinois Cook 36.07 34.06 30.62 30.41 30.31 30.21 30.08 29.74

421330008 Pennsylvania York 36.06 35. 89 34.55 34.12 33.91 33.88 33.84 33.69

261630015 Michigan Wayne 36.00 34.81 33.04 32.35 31.99 31.82 31.74 31.39

010732003 Alabama Jefferson 35.94 34.95 32.23 32.08 31.91 31.89 31.94 31.43

390618001 Ohio Hamilton 35.85 34.01 28.23 27.13 26.73 26.59 26.45 26.03

171190023 Illinois Madison 35.81 34.53 30.23 29.70 29.50 29.26 29.07 28.52

420031301 Pennsylvania Allegheny 35.65 33.91 28.05 26.67 26.15 26.04 25.92 25.45

391130032 Ohio Montgomery 35.61 33.81 25.99 24.94 24.62 24.48 24.31 23.79

420030116 Penns ylvania 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 thres hold in 2014.

# 34

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

estimates

.

As the AQAT was being developed for the final Transport Rule, it was possible to

evaluate the estimates from the tool with the model predictions from CAMx for the 2014 base

case scenario. This case was independently modeled in CAMx. The estimates were not used in

the development or calibration of the AQAT. Consequently, a comparative analysis was done

between the assessment tool an d the CAMx modeling for 2014 base case sulfate estimates as

well as the resulting design value estimates. Additionally, when the CAMx air quality modeling

of the final remedy (2014 control case) was available, a corresponding comparative analysis was

also done with the estimates from the assessment tool.

Examination of the comparison for the 2014 base shows strong correlations (nearly one

to one) between the estimated design values from AQAT and CAMx (Table C

- 10 and Figure C

1)

Examination of the results of the CAMx modeling for 2014, implementing the remedy,

shows that nearly all of the air quality monitoring locations of interest are estimated to be

brought into attainment and maintenance for both the 24

- hour and annual 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 for the intended purpose (Tables C

- 7 and C

- 8 , 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. Appendix

E shows updated estimate s for the final Transport Rule remedy in comparison with Final

Remedy Sensitivity (where the variability limits were increased).

# 35

Table C

- 7 . Average and Maximum Annual PM 2.5 

# 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

Avg. DV CAMx

Max. DV AQAT

Avg. DV AQAT

Max. DV Difference

,

Avg. DV

(CAM x

AQAT) Difference

,

Max. DV

(CAMx

AQAT)

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 Je fferson 13.94 14.21 13.89 14.20 0.05 0.01

390618001 Ohio Hamilton 12.73 12.99 12.96 13.28

- 0.23

- 0.29

261630033 Michigan Wayne 13.59 14.08 13.77 14.36

- 0.18

- 0.28

390350060 Ohio Cuyahoga 12.70 13.14 13.16 13.67

- 0.46

- 0.53

390610014 Ohio Hamilton 12.47 12.63 12.70 12.92

- 0.23

- 0.29

390610042 Ohio Hamilton 12.16 12.47 12.36 12.73

- 0.20

- 0.26

171191007 Illinois Madison 13.28 13.51 13.39 13.66

- 0.11

- 0.15

010732003 Alabama Jefferson 13.11 13.53 13.13 13.61

- 0.02

- 0.08

390350045 Ohio Cuyahoga 12.15 12.5 3 12.64 13.11

- 0.49

- 0.58

180970081 Indiana Marion 12.01 12.27 12.24 12.54

- 0.23

- 0.27

131210039 Georgia Fulton 12.99 13.02 13.07 13.10

- 0.08

- 0.08

390617001 Ohio Hamilton 11.48 11.80 11.71 12.07

- 0.23

- 0.27

390350065 Ohio Cuyahoga 11.69 12.03 12.19 12 .62

- 0.50

- 0.59

180970083 Indiana Marion 11.86 12.16 12.09 12.44

- 0.23

- 0.28

# 36

Table C

- 8 . Average and Maximum 24

- hour PM 2.5 

# DVs ( g/m 3 

# ) in the 2014 B ase C ase and 2014

R emedy C ase S cenarios as M odeled in CAMx and as E stimated in AQAT.

Monitor

Identificati on

Number State County 2014 Base Case Scenario 2014 Remedy Scenario

CAMx

Avg.

DV CAMx

Max.

DV AQAT

Avg.

DV AQAT

Max.

DV Differen

ce , Avg.

DV

(CAMx

AQAT) Differe

nce , Max.

DV

(CAMx

- AQAT) CAMx

Avg.

DV CAMx

Max.

DV AQAT

Avg.

DV AQAT

Max.

DV Differe

nce , Avg.

DV

(CAMx

- AQAT) Differe

nce , Max.

DV

(CAMx

- AQAT)

avg. of 6 sites* 38.89 41.49 39.23 41.88

- 0.34

- 0.38 35.52 38.05 35.72 38.27

- 0.20

- 0.22

avg. of 8 sites** 38.73 41.01 39.04 41.39

- 0.32

- 0.38 35.01 37.17 35.32 37.50

- 0.31

- 0.33

Avg. of all 2012 base case 34.79 36.70 35.05 36.96

- 0.26

- 0.27 29.53 31.17 29.82 31.48

- 0.29

- 0.31

420030064 Pennsylvania Allegheny 54.14 57.51 54.34 57.64

- 0.20

- 0.14 45.03 48.09 45.45 48.52

- 0.42

- 0.43

420030093 Pennsylvania Allegheny 37.53 42.57 37.51 42.63 0.03

- 0.06 29.44 33 .76 29.88 34.28

- 0.44

- 0.52

390350038 Ohio Cuyahoga 38.24 40.57 37.95 40.37 0.29 0.21 32.64 34.55 33.46 35.39

- 0.82

- 0.84

261630016 Michigan Wayne 37.94 40.17 38.50 40.77

- 0.56

- 0.60 33.72 35.43 33.88 35.61

- 0.16

- 0.18

390350060 Ohio Cuyahoga 36.78 39.7 6 37.11 39.90

- 0.33

- 0.14 29.82 32.20 30.51 32.94

- 0.69

- 0.74

170311016 Illinois Cook 35.89 38.72 36.11 39.05

- 0.22

- 0.33 32.69 36.16 32.95 36.40

- 0.26

- 0.24

261630033 Michigan Wayne 38.22 38.52 39.01 39.47

- 0.79

- 0.95 34.31 34.50 34.74 34.95

- 0.43

- 0.45

180890022 Indiana Lake 33.77 38.31 34.04 38.68

- 0.27

- 0.37 32.18 36.10 32.31 36.30

- 0.13

- 0.20

540090011 West

Virginia Brooke 36.20 37.04 36.73 37.68

- 0.53

- 0.63 28.39 29.11 28.83 29.63

- 0.44

- 0.52

420710007 Pennsylvania Lancaster 35.31 37.60 35.54 37. 82

- 0.24

- 0.22 34.77 36.97 34.87 37.08

- 0.10

- 0.11

390350045 Ohio Cuyahoga 33.78 37.08 33.63 36.65 0.14 0.43 25.51 26.61 26.23 27.43

- 0.72

- 0.82

390811001 Ohio Jefferson 33.16 36.45 33.58 36.91

- 0.43

- 0.46 25.14 27.30 25.57 27.76

- 0.43

- 0.46

261630019 M ichigan Wayne 36.31 36.65 36.86 37.29

- 0.55

- 0.64 34.71 35.57 34.87 35.74

- 0.16

- 0.17

390350065 Ohio Cuyahoga 33.77 36.57 33.50 36.41 0.26 0.16 25.15 25.94 25.95 26.81

- 0.80

- 0.87

170313301 Illinois Cook 33.49 36.17 33.60 36.26

- 0.11

- 0.09 30.23 32.58 30 .35 32.70

- 0.12

- 0.12

420070014 Pennsylvania Beaver 34.57 35.73 34.84 35.99

- 0.27

- 0.26 27.00 28.09 27.39 28.49

- 0.39

- 0.40

420033007 Pennsylvania Allegheny 30.95 35.71 30.98 35.85

- 0.03

- 0.14 24.54 28.30 24.78 28.63

- 0.24

- 0.33

010730023 Alabama Jeffer son 35.69 36.01 35.43 35.80 0.26 0.21 31.14 31.63 31.10 31.57 0.04 0.06

550790026 Wisconsin Milwaukee 32.39 35.99 33.28 36.72

- 0.88

- 0.73 29.96 32.95 30.08 33.10

- 0.12

- 0.15

180970043 Indiana Marion 34.35 35.73 34.67 36.09

- 0.32

- 0.36 26.67 27.42 27.13 2 7.76

- 0.46

- 0.34

261470005 Michigan St Clair 35.06 36.01 35.61 36.57

- 0.54

- 0.56 32.28 32.94 32.67 33.29

- 0.39

- 0.35

550790043 Wisconsin Milwaukee 34.57 35.41 34.98 35.89

- 0.41

- 0.48 31.69 33.83 31.80 33.92

- 0.11

- 0.09

180890026 Indiana Lake 32.82 35.82 33.00 36.05

- 0.19

- 0.23 30.36 33.25 30.49 33.39

- 0.13

- 0.14

180970081 Indiana Marion 34.12 35.18 33.70 34.81 0.42 0.37 26.90 27.04 27.30 27.54

- 0.40

- 0.50

180970066 Indiana Marion 34.21 35.34 34.49 35.62

- 0.28

- 0.28 27.67 28.63 28.10 29.11

- 0.43

- 0.48

171191007 Illinois Madison 34.68 35.36 34.59 35.20 0.08 0.16 29.24 30.51 29.32 30.64

- 0.08

- 0.13

550790010 Wisconsin Milwaukee 34.08 35.44 35.03 36.56

- 0.95

- 1.12 30.76 33.05 30.83 33.13

- 0.07

- 0.08

390170003 Ohio Butler 33.01 35.42 33.66 36.03

- 0.65

- 0. 61 26.17 26.97 26.47 27.29

- 0.30

- 0.32

170316005 Illinois Cook 32.72 34.94 33.47 35.87

- 0.75

- 0.93 31.90 34.32 32.02 34.45

- 0.12

- 0.13

420031008 Pennsylvania Allegheny 33.28 34.49 33.41 34.65

- 0.13

- 0.16 24.00 24.85 24.47 25.38

- 0.47

- 0.53

261610008 Mic higan Washtenaw 33.93 35.01 34.93 35.38

- 1.00

- 0.37 28.42 29.21 28.47 29.26

- 0.05

- 0.05

170312001 Illinois Cook 32.46 34.96 32.33 34.95 0.13 0.01 29.41 32.09 29.50 32.21

- 0.09

- 0.12

170310052 Illinois Cook 33.20 34.12 33.27 34.06

- 0.07 0.06 29.54 30.06 2 9.69 30.20

- 0.15

- 0.14

421330008 Pennsylvania York 32.65 35.36 33.11 35.89

- 0.46

- 0.53 30.81 33.68 30.92 33.79

- 0.11

- 0.11

261630015 Michigan Wayne 34.20 34.69 34.42 34.81

- 0.22

- 0.12 30.80 31.67 31.02 31.91

- 0.22

- 0.24

010732003 Alabama Jefferson 34.27 34.92 34.20 34.95 0.07

- 0.03 30.59 31.39 30.62 31.46

- 0.03

- 0.07

390618001 Ohio Hamilton 33.51 33.92 33.57 34.01

- 0.07

- 0.09 25.60 26.29 25.96 26.64

- 0.36

- 0.35

171190023 Illinois Madison 32.86 33.63 33.58 34.53

- 0.72

- 0.90 28.33 29.33 28.41 29.41

- 0.08

- 0.08

420031301 Pennsylvania Allegheny 32.38 33.87 32.45 33.91

- 0.07

- 0.04 24.58 25.49 24.96 25.85

- 0.38

- 0.36

391130032 Ohio Montgomer

y 32.15 33.93 32.19 33.81

- 0.04 0.12 22.85 24.24 23.09 24.54

- 0.24

- 0.30

420030116 Pennsylvania Allegheny 33.87 33.87 33.88 33.88

- 0.02

- 0.02 25.67 25.67 26.13 26.13

- 0.46

- 0.46

*The six sites are Allegheny, PA (64); Lancaster, PA (07); Wayne, MI (16 and 19); Cook, IL (16); and Lake, IN (22).

**The eight sites include the six sites listed above as well as Cuyahoga, OH (38) and Wayne, MI (33).

# 37

Figure C

- 1 . Least squares linear regression plots showing correlations between estimated

average and maximum design values ( g/m 3 

# ) for 24

- h our 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 a nd maximum design values ( g/m 3 

# ) for annual PM 2.5 

# for CAMx and calibrated AQAT

for the 2014 remedy.

# 38

*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 estimated sulfate

( g/m 3 

# ) for the 98 th 

# percentile 24

- h our PM 2.5 

# day for each year * 

# for CAMx and calibrated AQAT

for the 2014 base case (left panel) and 2014 remedy (right panel ).

5. Using the AQAT to estimate contributions in 2012 resulting from ―leakage‖ of emissions to

states not included in one or more of the programs for the Transport Rule

.

As described in section s VI.C and XII.J.2.a. of the preamble for the final Transport Rule,

EPA projects that some states not covered by any of the fine particle control programs in the

final Transport Rule may experience increases of SO 2 

# emissions greater than 5,000 tons

compared to the base case. These states are Arkansas, Color ado, Louisiana, Montana, and

Wyoming. Using both the annual and 24

- hour versions of AQAT, for the states with source

contribution modeling (i.e., Arkansas and Louisiana), EPA estimated whether these SO 2



# emission increases would result in contributions fro m these states that are greater than or equal to

the contribution thresholds. This was done by adding the ―leakage‖ emissions to the 2012 base

case emissions. As can be seen in the ―base leakage_2012_threshold ‖ worksheet in the ― annual

PM25 AQAT.xlsx ‖ wo rkbook, 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 quarterly emission values for the 2012 base case. EPA

then used the 24

- hour PM 2.5 

# AQAT and estimated the 2012 base case quarterly contributions and

the resulting design values for all monitors. EPA, then, examined the sulfate contributions f rom

these states, finding that Arkansas had relatively large contributions in the summer months to

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

# 39

180890026 in quarter 2). For the Cook, IL site, only one of the 98 t h 

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



# percentile days were in the years for 2003

- 2007, for monitor 10730023 Jefferson, AL, the values

did not occur in quarter 1. EPA concludes that LA does not exceed the 1% contribution

threshold.

# 40



# Appendix A: IPM Runs Used in Transport Rule Significant

Contribution Analysis

# 41

Table A

- 1 lists IPM runs used in the significant contribution analysis. The IP M runs can

be found in the docket for this rulemaking ( Docket ID No. EPA

- HQ

- OAR

- 2009

- 0491 ).

Table Appendix A

- 1. IPM Runs Used in Transport Rule Significant Contribution Analysis

Run Name Run Description

TR_Base_Case_Final Base Case model run, which inclu des 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 conditions without the proposed Transport Rule

and without the rule it would replace (CAIR).

TR_SO2_500_Final Imposes a marginal cost of $500 per ton of 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 s tarting in 2012, $500 per ton for Group 1 states in

2012 and 2013, $500 per ton for Group 1 states in 2014 and each

year thereafter. Also forces all SCR and FGD to operate for

relevant time period if in a TR covered state.

TR_SO2_1600_Final Imposes a mar ginal 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, $1,600 per ton for Group 1 states in 2014 and each

year thereafter. Also forces all SCR and FGD to operate for

relevant time period if in a TR covered state.

TR_SO2_2300_Final Imposes a marginal cost of $500 per ton of 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 a nd each

year thereafter. Also forces all SCR and FGD to operate for

relevant time period if in a TR covered state.

TR_SO2_2800_Final Imposes a marginal cost of $500 per ton of 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.

# 42

TR_SO2_3300_Final Imposes 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 fo r 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 Imposes 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 201 2. 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 imposed 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 to n for Group 1 states in 2012 and 2013,

$3,300 per ton for Group 1 states in 2014 and each year thereafter.

Also forces all SCR and FGD to operate for relevant time period if

in a TR covered state.

TR_NOX_OS_500_Final Imposes a marginal cost of $500 per t on 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 Imposes 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 

## r eductions ) starting in 2012. Also forces dispatchable

SCRs to run in the ozone season if located in this region

TR_NOX_OS_5000_Final Imposes a marginal cost of $5,000 per ton of NO

x 

## reduced in the

ozone season on each of 26 ozone states (including the si x 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 Imposes a marginal cost of $500 per to n 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 Imposes a marginal cost of $1,000 per ton of NO

x 

## reduced annually

on each of 23 states in the annual r egion. Also forces SCRs to

operate year round if located in this region

TR_NOX_2500_Final Imposes a marginal cost of $2,500 per ton of NO

x 

## reduced annually

on each of 23 states in the annual region. Also forces SCRs to

operate year round if located in t his region

TR_Remedy_Final * Models the air quality

- assured trading final remedy described in the

Transport Rule preamble with variability limits ranging from 10 to

20%

# 43

TR_Remedy_Sensitivity_with_final_variability_limits Models the air quality

- assured tr ading final remedy described in the

Transport Rule preamble with variability limits of 18% for SO2 and

Annual NOx, and 21% for ozone

- season NOx and other technical

corrections discussed in Section VIII.A of the preamble



# * These projected impacts of this f inal rule do not reflect minor technical corrections to SO2 budgets in

three states (KY, MI, and NY). These projections also assumed preliminary variability limits that were

smaller than the variabilty limits finalized in this rule. EPA conducted sensitiv ity analysis confirming that

these differences do not meaningfully alter any of the Agency's findings or conclusions based on the

projected cost, benefit, and air quality impacts presented for the final Transport Rule. The results of this

sensitivity analy sis are presented in Appendix F in the final Transport Rule RIA. The

TR_Remedy_Sensitivity_with_final_variability_limits run below includes such corrections, and the

subsequent AQAT analysis on this run suggest no difference in air quality impacts (see di scussion earlier

in TSD)

# 44



# Appendix B: Detailed Comparison of AQAT Estimates with CAMx

Results

# 45

This Appendix contains tables with detailed comparisons of the sulfate and total 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, respectively). 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 d ifferent in AQAT. For this particular analysis,

whatever day the ―Future Date‖ was selected based on the CAMx estimates was the day selected

from the AQAT results. Consequently, the AQAT to CAMx design value comparison (presented

in table C

- 10 in this TS D) 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

Identification

Number State County 2012 Base

Case

Max. DV Sulf ate Total PM2.5

CAMx AQAT Difference

(CAMx

AQAT)

, Sulfate CAMx AQAT Difference

(CAMx

AQAT)

, Total

PM2.5

20031031 420030064 Pennsylvania Allegheny 59.93 15.15 15.22

- 0.07 56.44 56.51

- 0.07

20040301 420030064 Pennsylvania Allegheny 59.93 15.32 15.11 0.21 57.78 58.91

- 1.13

20050914 420030064 Pennsylvania Allegheny 59.93 30.25 29.99 0.25 58.31 58.04 0.26

20060618 420030064 Pennsylvania Allegheny 59.93 22.04 22.50

- 0.46 47.30 47.75

- 0.46

20070422 420030064 Pennsylvania Allegheny 59.93 21.32 21.76

- 0.4 4 45.76 46.20

- 0.44

20030626 420030093 Pennsylvania Allegheny 44.40 18.15 18.37

- 0.22 47.08 47.29

- 0.22

20040608 420030093 Pennsylvania Allegheny 44.40 15.11 15.29

- 0.18 39.27 39.45

- 0.18

20050913 420030093 Pennsylvania Allegheny 44.40 19.20 19.00 0.20 41.35 41.14 0.21

20060710 420030093 Pennsylvania Allegheny 44.40 14.03 13.89 0.15 30.37 30.21 0.15

20070524 420030093 Pennsylvania Allegheny 44.40 10.48 10.61

- 0.13 27.40 27.52

- 0.12

20030821 390350038 Ohio Cuyahoga 41.84 25.34 24.82 0.52 40.83 40.31 0. 52

20040607 390350038 Ohio Cuyahoga 41.84 25.06 24.91 0.14 36.78 36.63 0.14

20050913 390350038 Ohio Cuyahoga 41.84 27.39 26.83 0.56 44.10 43.54 0.57

20060818 390350038 Ohio Cuyahoga 41.84 19.56 19.16 0.40 31.63 31.23 0.40

20070906 390350038 Ohio Cuyaho ga 41.84 21.18 20.75 0.43 34.21 33.78 0.44

20030318 261630016 Michigan Wayne 41.28 11.05 10.92 0.13 41.57 42.52

- 0.95

20040730 261630016 Michigan Wayne 41.28 20.55 20.33 0.21 32.35 32.13 0.22

20040904 261630016 Michigan Wayne 41.28 20.55 20.33 0.21 32.3 5 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 39035006 0 Ohio Cuyahoga 40.85 25.89 25.36 0.53 38.69 38.16 0.53

20040218 390350060 Ohio Cuyahoga 40.85 10.82 10.59 0.24 38.40 39.53

- 1.12

20051004 390350060 Ohio Cuyahoga 40.85 16.51 16.29 0.21 42.19 42.79

- 0.61

20060529 390350060 Ohio Cuyahoga 40.85 15.15 15.0 6 0.09 28.02 27.93 0.09

20070921 390350060 Ohio Cuyahoga 40.85 21.98 21.53 0.45 32.92 32.47 0.45

20030316 170311016 Illinois Cook 40.44 8.66 8.64 0.02 38.12 38.72

- 0.60

20041229 170311016 Illinois Cook 40.44 8.37 8.29 0.08 35.24 36.08

- 0.83

20050802 17 0311016 Illinois Cook 40.44 19.50 19.07 0.44 42.79 42.35 0.44

20061219 170311016 Illinois Cook 40.44 6.44 6.38 0.06 27.24 27.88

- 0.64

20070617 170311016 Illinois Cook 40.44 12.48 12.15 0.34 31.56 31.28 0.27

20030301 261630033 Michigan Wayne 39.81 9.69 9 .58 0.11 36.63 37.44

- 0.81

20040608 261630033 Michigan Wayne 39.81 21.83 21.75 0.08 33.43 33.35 0.08

20050206 261630033 Michigan Wayne 39.81 11.98 11.84 0.14 45.17 46.17

- 1.00

20061213 261630033 Michigan Wayne 39.81 7.50 7.59

- 0.10 36.95 38.88

- 1.93

20 070524 261630033 Michigan Wayne 39.81 20.26 20.19 0.07 31.06 30.99 0.07

20030301 180890022 Indiana Lake 39.58 11.72 11.70 0.02 40.77 41.06

- 0.29

20041226 180890022 Indiana Lake 39.58 10.70 10.77

- 0.08 38.80 39.69

- 0.89

20050113 180890022 Indiana Lake 39 .58 10.14 10.13 0.01 35.36 35.61

- 0.25

20060123 180890022 Indiana Lake 39.58 6.97 6.96 0.01 24.45 24.62

- 0.17

20070921 180890022 Indiana Lake 39.58 17.57 17.15 0.42 30.58 30.16 0.42

20031229 540090011 West Virginia Brooke 38.39 9.56 9.71

- 0.15 36.44 36. 76

- 0.32

20040212 540090011 West Virginia Brooke 38.39 16.98 16.70 0.28 39.36 40.62

- 1.25

20050419 540090011 West Virginia Brooke 38.39 24.93 25.34

- 0.41 35.33 35.74

- 0.41

20060222 540090011 West Virginia Brooke 38.39 14.11 13.87 0.23 32.78 33.82

- 1.04

20070828 540090011 West Virginia Brooke 38.39 20.39 20.39 0.00 39.07 39.05 0.02

20030313 420710007 Pennsylvania Lancaster 38.37 11.56 11.41 0.15 44.04 44.43

- 0.39

20041009 420710007 Pennsylvania Lancaster 38.37 4.43 4.52

- 0.10 30.06 30.00 0.06

20050209 420710007 Pennsylvania Lancaster 38.37 10.14 10.01 0.13 38.69 39.03

- 0.34

20060330 420710007 Pennsylvania Lancaster 38.37 8.57 8.46 0.11 32.78 33.07

- 0.29

20070301 420710007 Pennsylvania Lancaster 38.37 8.35 8.24 0.11 31.94 32.22

- 0.28

20030702 3903500 45 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

200509 13 390811001 Ohio Jefferson 37.88 17.19 17.19 0.00 34.81 34.79 0.02

20060827 390811001 Ohio Jefferson 37.88 12.75 12.74 0.00 25.94 25.92 0.01

20070804 390811001 Ohio Jefferson 37.88 13.30 13.30 0.00 27.03 27.02 0.01

20030801 261630019 Michigan Wayne 37. 83 20.28 20.05 0.23 32.86 32.62 0.24

20040304 261630019 Michigan Wayne 37.83 7.56 7.44 0.12 29.72 30.26

- 0.53

20050206 261630019 Michigan Wayne 37.83 12.12 11.93 0.19 47.37 48.23

- 0.86

# 46 20060309 261630019 Michigan Wayne 37.83 8.35 8.22 0.13 32.80 33.39

- 0.59

20070921 261630019 Michigan Wayne 37.83 16.46 16.27 0.19 26.75 26.56 0.20

20030316 390350065 Ohio Cuyahoga 37.67 11.97 11.71 0.26 36.18 37.43

- 1.25

20040924 390350065 Ohio Cuyahoga 37.67 22.44 21.99 0.46 32.32 31.86 0.46

20050627 390350065 Ohio Cu yahoga 37.67 25.62 25.47 0.15 41.22 41.07 0.15

20060710 390350065 Ohio Cuyahoga 37.67 16.38 16.05 0.34 23.73 23.39 0.34

20070921 390350065 Ohio Cuyahoga 37.67 22.20 21.75 0.45 31.98 31.52 0.46

20030202 170313301 Illinois Cook 37.67 7.16 7.08 0.08 32.35 32.70

- 0.35

20040903 170313301 Illinois Cook 37.67 17.86 17.35 0.51 34.82 34.30 0.51

20050203 170313301 Illinois Cook 37.67 9.18 9.07 0.10 41.34 41.79

- 0.45

20060704 170313301 Illinois Cook 37.67 11.46 11.14 0.33 22.53 22.20 0.33

20060806 170313301 Ill inois Cook 37.67 11.46 11.14 0.33 22.53 22.20 0.33

20071120 170313301 Illinois Cook 37.67 7.36 7.32 0.05 30.35 31.31

- 0.96

20030822 420070014 Pennsylvania Beaver 37.42 15.45 15.51

- 0.06 27.44 27.49

- 0.05

20040711 420070014 Pennsylvania Beaver 37.42 18.8 8 18.95

- 0.07 33.42 33.48

- 0.06

20050627 420070014 Pennsylvania Beaver 37.42 23.28 23.74

- 0.47 39.87 40.33

- 0.46

20061128 420070014 Pennsylvania Beaver 37.42 8.14 8.13 0.01 33.91 34.09

- 0.18

20070524 420070014 Pennsylvania Beaver 37.42 17.11 17.45

- 0.34 29.43 29.77

- 0.34

20030626 420033007 Pennsylvania Allegheny 37.40 18.77 19.16

- 0.39 48.80 49.19

- 0.39

20040924 420033007 Pennsylvania Allegheny 37.40 18.46 18.31 0.15 32.63 32.48 0.16

20050627 420033007 Pennsylvania Allegheny 37.40 9.78 9.99

- 0.20 25.6 9 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 Alaba ma Jefferson 37.33 14.70 14.51 0.19 35.19 35.00 0.19

20031009 550790026 Wisconsin Milwaukee 37.24 5.20 5.21

- 0.01 25.52 25.92

- 0.40

20040110 550790026 Wisconsin Milwaukee 37.24 6.79 6.73 0.05 24.30 25.54

- 1.24

20050203 550790026 Wisconsin Milwaukee 37.2 4 10.23 10.15 0.08 36.38 38.25

- 1.87

20061125 550790026 Wisconsin Milwaukee 37.24 7.52 7.53

- 0.01 36.69 37.27

- 0.58

20070220 550790026 Wisconsin Milwaukee 37.24 9.80 9.73 0.07 34.89 36.68

- 1.79

20030813 180970043 Indiana Marion 37.20 22.54 21.93 0.61 33 .45 32.83 0.62

20040720 180970043 Indiana Marion 37.20 18.80 18.29 0.51 27.99 27.47 0.52

20050203 180970043 Indiana Marion 37.20 11.59 11.23 0.36 39.74 41.35

- 1.61

20060725 180970043 Indiana Marion 37.20 22.30 21.69 0.61 33.10 32.49 0.61

20070726 18097 0043 Indiana Marion 37.20 23.14 22.51 0.63 34.33 33.70 0.63

20030608 261470005 Michigan St Clair 37.14 21.35 21.04 0.31 31.27 30.97 0.30

20040903 261470005 Michigan St Clair 37.14 18.69 18.47 0.22 28.00 27.77 0.23

20051004 261470005 Michigan St Clair 37 .14 8.97 9.03

- 0.07 42.74 44.20

- 1.46

20060222 261470005 Michigan St Clair 37.14 8.48 8.35 0.13 34.81 35.29

- 0.48

20070921 261470005 Michigan St Clair 37.14 20.37 20.13 0.25 30.47 30.22 0.25

20030913 550790043 Wisconsin Milwaukee 37.10 19.39 19.02 0.36 32.92 32.64 0.27

20040906 550790043 Wisconsin Milwaukee 37.10 20.64 20.25 0.39 35.01 34.72 0.29

20051224 550790043 Wisconsin Milwaukee 37.10 6.47 6.52

- 0.05 31.98 32.51

- 0.53

20060329 550790043 Wisconsin Milwaukee 37.10 10.86 10.81 0.05 39.24 41.14

- 1.9 0

20071211 550790043 Wisconsin Milwaukee 37.10 6.83 6.89

- 0.06 33.76 34.31

- 0.56

20030202 180890026 Indiana Lake 37.06 10.11 10.06 0.05 36.79 37.46

- 0.67

20040304 180890026 Indiana Lake 37.06 9.35 9.30 0.05 34.06 34.68

- 0.62

20050802 180890026 Indiana Lake 37.06 17.33 16.97 0.36 36.60 36.24 0.37

20060719 180890026 Indiana Lake 37.06 12.00 11.75 0.25 25.49 25.23 0.26

20071120 180890026 Indiana Lake 37.06 8.42 8.51

- 0.09 29.63 30.13

- 0.49

20030813 180970081 Indiana Marion 36.96 21.82 21.23 0.59 31.92 3 1.32 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 I ndiana Marion 36.96 20.42 20.24 0.18 33.55 33.37 0.18

20030726 180970066 Indiana Marion 36.92 23.32 22.68 0.63 34.78 34.14 0.64

20040608 180970066 Indiana Marion 36.92 16.60 16.45 0.15 28.16 28.01 0.15

20050203 180970066 Indiana Marion 36.92 11.63 11.27 0.36 39.40 41.01

- 1.62

20060818 180970066 Indiana Marion 36.92 21.40 20.82 0.58 31.96 31.38 0.59

20070617 180970066 Indiana Marion 36.92 20.50 20.32 0.18 34.67 34.48 0.18

20030316 171191007 Illinois Madison 36.83 12.14 11.83 0.31 35.89 39.65

- 3.76

200 40218 171191007 Illinois Madison 36.83 10.51 10.24 0.27 31.15 34.40

- 3.26

20050808 171191007 Illinois Madison 36.83 26.21 25.45 0.76 39.05 38.28 0.76

20060429 171191007 Illinois Madison 36.83 13.21 13.07 0.13 32.56 32.51 0.05

20070617 171191007 Illinois Madison 36.83 12.84 12.71 0.13 31.66 31.61 0.05

20030202 550790010 Wisconsin Milwaukee 36.71 8.38 8.34 0.04 29.59 31.06

- 1.47

20040905 550790010 Wisconsin Milwaukee 36.71 19.66 19.30 0.37 32.77 32.49 0.28

20050203 550790010 Wisconsin Milwaukee 36.71 9. 78 9.74 0.04 34.48 36.20

- 1.71

20060329 550790010 Wisconsin Milwaukee 36.71 10.30 10.25 0.04 36.26 38.07

- 1.80

20070530 550790010 Wisconsin Milwaukee 36.71 18.16 18.28

- 0.12 35.57 35.89

- 0.32

20030301 390170003 Ohio Butler 36.59 15.51 14.95 0.56 35.99 3 8.23

- 2.24

20040924 390170003 Ohio Butler 36.59 23.08 22.70 0.38 30.19 29.80 0.38

20050203 390170003 Ohio Butler 36.59 17.30 16.67 0.63 40.07 42.58

- 2.50

20060710 390170003 Ohio Butler 36.59 18.68 18.37 0.31 24.52 24.21 0.31

20070524 390170003 Ohio But ler 36.59 19.80 19.78 0.02 31.48 31.45 0.02

20030316 170316005 Illinois Cook 36.42 7.88 7.89 0.00 32.58 33.06

- 0.48

20041229 170316005 Illinois Cook 36.42 9.16 9.06 0.10 35.38 36.53

- 1.15

20051221 170316005 Illinois Cook 36.42 9.55 9.45 0.10 36.86 38.07

- 1.20

20060117 170316005 Illinois Cook 36.42 5.91 5.92 0.00 24.56 24.92

- 0.36

20070530 170316005 Illinois Cook 36.42 10.69 10.41 0.28 31.40 31.16 0.24

20030807 420031008 Pennsylvania Allegheny 36.35 19.65 19.60 0.05 33.83 33.78 0.05

20040608 420031008 Pennsylvania Allegheny 36.35 21.08 21.36

- 0.28 36.45 36.74

- 0.28

20050624 420031008 Pennsylvania Allegheny 36.35 19.15 19.41

- 0.26 33.17 33.43

- 0.26

20060818 420031008 Pennsylvania Allegheny 36.35 17.70 17.66 0.04 30.53 30.49 0.05

20070828 420031008 Pe nnsylvania 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 Washtena w 36.32 12.78 12.58 0.20 45.91 47.04

- 1.13

20061225 261610008 Michigan Washtenaw 36.32 5.00 5.06

- 0.06 26.56 29.19

- 2.62

20070906 261610008 Michigan Washtenaw 36.32 16.15 15.85 0.30 28.65 28.35 0.31

# 47 20030202 170312001 Illinois Cook 36.12 9.27 9.08 0.19 34.34 34.58

- 0.24

20040903 170312001 Illinois Cook 36.12 16.44 15.91 0.53 32.57 32.04 0.53

20050203 170312001 Illinois Cook 36.12 10.27 10.05 0.21 37.98 38.24

- 0.26

20060719 170312001 Illinois Cook 36.12 11.94 11.56 0.38 23.80 23.41 0.39

20070530 17031 2001 Illinois Cook 36.12 14.77 14.40 0.37 31.12 30.76 0.36

20030316 170310052 Illinois Cook 36.07 8.17 8.13 0.04 31.22 31.69

- 0.47

20040904 170310052 Illinois Cook 36.07 16.15 15.79 0.37 31.64 31.27 0.37

20051221 170310052 Illinois Cook 36.07 9.96 9.87 0.08 39.51 40.60

- 1.09

20060117 170310052 Illinois Cook 36.07 6.84 6.81 0.03 26.24 26.63

- 0.39

20070220 170310052 Illinois Cook 36.07 8.73 8.68 0.04 33.33 33.83

- 0.50

20030301 421330008 Pennsylvania York 36.06 16.99 16.83 0.17 42.70 43.70

- 1.00

2004022 4 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 Pennsylv ania York 36.06 13.39 13.26 0.13 33.74 34.53

- 0.79

20030301 261630015 Michigan Wayne 36.00 7.71 7.62 0.09 28.82 29.47

- 0.66

20040608 261630015 Michigan Wayne 36.00 19.87 19.80 0.07 30.54 30.47 0.07

20050913 261630015 Michigan Wayne 36.00 26.59 26.31 0.2 8 42.00 41.72 0.28

20060222 261630015 Michigan Wayne 36.00 8.44 8.34 0.10 31.52 32.23

- 0.72

20070530 261630015 Michigan Wayne 36.00 18.75 18.68 0.07 28.85 28.78 0.07

20030910 10732003 Alabama Jefferson 35.94 11.52 11.11 0.40 29.68 29.27 0.40

20040817 1 0732003 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 Jeffers on 35.94 12.21 11.78 0.43 31.42 30.99 0.43

20030301 390618001 Ohio Hamilton 35.85 9.87 9.61 0.26 31.77 32.92

- 1.14

20040720 390618001 Ohio Hamilton 35.85 17.50 17.31 0.20 27.48 27.28 0.20

20050913 390618001 Ohio Hamilton 35.85 26.68 26.38 0.30 41.62 41. 32 0.30

20060908 390618001 Ohio Hamilton 35.85 19.02 18.81 0.21 29.83 29.61 0.22

20070906 390618001 Ohio Hamilton 35.85 19.34 19.12 0.22 30.31 30.09 0.22

20031114 171190023 Illinois Madison 35.81 9.86 9.55 0.31 33.44 34.39

- 0.95

20040729 171190023 Illi nois Madison 35.81 21.38 20.76 0.62 30.77 30.14 0.62

20050907 171190023 Illinois Madison 35.81 25.55 24.81 0.74 36.68 35.94 0.74

20060411 171190023 Illinois Madison 35.81 10.48 10.37 0.11 28.83 28.79 0.04

20030821 420031301 Pennsylvania Allegheny 35.65 24.28 24.21 0.07 40.13 40.06 0.08

20040912 420031301 Pennsylvania Allegheny 35.65 18.39 18.34 0.05 30.52 30.47 0.06

20050419 420031301 Pennsylvania Allegheny 35.65 14.97 15.23

- 0.26 30.96 31.22

- 0.26

20061110 420031301 Pennsylvania Allegheny 35.65 6.83 6.86

- 0.03 31.27 31.32

- 0.05

20070807 420031301 Pennsylvania Allegheny 35.65 21.02 20.96 0.06 34.81 34.75 0.07

20030826 391130032 Ohio Montgomery 35.61 26.88 26.61 0.27 35.23 34.95 0.28

20040903 391130032 Ohio Montgomery 35.61 20.29 20.08 0.21 26.71 26. 50 0.21

20050203 391130032 Ohio Montgomery 35.61 4.90 4.75 0.14 39.86 41.17

- 1.31

20060710 391130032 Ohio Montgomery 35.61 18.89 18.70 0.19 24.91 24.71 0.19

20070602 391130032 Ohio Montgomery 35.61 20.63 20.47 0.16 31.34 31.18 0.16

20030821 420030116 P ennsylvania 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

Identif ication

Number State County 2012 Base

Case

Max. DV Sulfate Total PM2.5

CAMx AQAT Difference

(CAMx

AQAT)

, Sulfate CAMx AQAT Difference

(CAMx

AQAT)

, Total

PM2.5

20050418 420030064 Pennsylvania Allegheny 59.93 15.80 16.01

- 0.22 47.16 47.68

- 0.52

20030 324 420030064 Pennsylvania Allegheny 59.93 9.93 10.09

- 0.15 50.84 51.30

- 0.46

20041222 420030064 Pennsylvania Allegheny 59.93 10.33 10.44

- 0.11 46.29 46.59

- 0.30

20061128 420030064 Pennsylvania Allegheny 59.93 9.25 9.34

- 0.10 41.48 41.75

- 0.27

20071031 420030064 Pennsylvania Allegheny 59.93 8.33 8.42

- 0.09 37.41 37.65

- 0.24

20030626 420030093 Pennsylvania Allegheny 44.40 9.44 9.65

- 0.21 37.96 38.57

- 0.61

20040608 420030093 Pennsylvania Allegheny 44.40 7.86 8.03

- 0.17 31.68 32.19

- 0.51

20050913 4200300 93 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

- 0.1 6 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 26 1630016 Michigan Wayne 41.28 9.73 9.77

- 0.04 41.38 41.51

- 0.13

20030221 261630016 Michigan Wayne 41.28 9.18 9.22

- 0.04 39.06 39.19

- 0.12

20060330 261630016 Michigan Wayne 41.28 7.72 7.75

- 0.03 32.92 33.02

- 0.10

20051004 390350060 Ohio Cuyahoga 40.85 10. 79 11.11

- 0.32 36.64 37.61

- 0.97

20030130 390350060 Ohio Cuyahoga 40.85 7.08 7.20

- 0.12 30.60 31.23

- 0.63

20040310 390350060 Ohio Cuyahoga 40.85 6.79 6.91

- 0.12 29.38 29.98

- 0.60

20070310 390350060 Ohio Cuyahoga 40.85 5.56 5.66

- 0.10 24.14 24.64

- 0.49

20060309 390350060 Ohio Cuyahoga 40.85 5.17 5.26

- 0.09 22.49 22.95

- 0.46

20040903 170311016 Illinois Cook 40.44 9.47 9.60

- 0.13 31.23 31.54

- 0.32

20050627 170311016 Illinois Cook 40.44 11.27 11.39

- 0.12 39.68 39.97

- 0.29

20060818 170311016 Illinois Cook 40.44 7.32 7.42

- 0.10 24.25 24.49

- 0.24

20070530 170311016 Illinois Cook 40.44 7.54 7.62

- 0.08 26.71 26.91

- 0.19

20030316 170311016 Illinois Cook 40.44 7.61 7.61 0.00 37.60 37.68

- 0.08

20050627 261630033 Michigan Wayne 39.81 20.76 21.08

- 0.32 38.11 38. 86

- 0.75

# 48 20061213 261630033 Michigan Wayne 39.81 5.22 5.34

- 0.12 36.30 36.63

- 0.33

20041117 261630033 Michigan Wayne 39.81 4.17 4.27

- 0.10 29.10 29.37

- 0.26

20071226 261630033 Michigan Wayne 39.81 4.07 4.17

- 0.10 28.44 28.70

- 0.26

20030304 261630033 Mi chigan 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 3 3.76 33.83

- 0.07

20070310 180890022 Indiana Lake 39.58 7.48 7.48 0.00 28.58 28.64

- 0.06

20060123 180890022 Indiana Lake 39.58 6.21 6.21 0.00 23.83 23.88

- 0.05

20030626 540090011 West Virginia Brooke 38.39 16.21 16.52

- 0.31 28.97 29.66

- 0.69

20040702 54 0090011 West Virginia Brooke 38.39 10.20 10.40

- 0.20 29.26 29.75

- 0.50

20070828 540090011 West Virginia Brooke 38.39 9.82 10.01

- 0.19 28.20 28.68

- 0.48

20051112 540090011 West Virginia Brooke 38.39 5.98 6.09

- 0.11 29.12 29.48

- 0.36

20061110 540090011 We st Virginia Brooke 38.39 5.38 5.48

- 0.10 26.27 26.60

- 0.32

20041009 420710007 Pennsylvania Lancaster 38.37 3.70 3.74

- 0.04 29.12 29.21

- 0.10

20030313 420710007 Pennsylvania Lancaster 38.37 10.63 10.64

- 0.01 43.55 43.66

- 0.11

20050209 420710007 Pennsylva nia Lancaster 38.37 9.32 9.33

- 0.01 38.26 38.36

- 0.10

20060330 420710007 Pennsylvania Lancaster 38.37 7.88 7.89

- 0.01 32.41 32.50

- 0.08

20070301 420710007 Pennsylvania Lancaster 38.37 7.67 7.68 0.00 31.58 31.66

- 0.08

20041229 390350045 Ohio Cuyahoga 38. 13 7.73 7.96

- 0.23 26.40 27.61

- 1.21

20050305 390350045 Ohio Cuyahoga 38.13 7.45 7.58

- 0.13 27.56 28.20

- 0.64

20030319 390350045 Ohio Cuyahoga 38.13 6.98 7.11

- 0.12 25.89 26.49

- 0.60

20070310 390350045 Ohio Cuyahoga 38.13 6.23 6.34

- 0.11 23.16 23.69

- 0. 53

20060222 390350045 Ohio Cuyahoga 38.13 6.06 6.17

- 0.11 22.54 23.06

- 0.52

20040702 390811001 Ohio Jefferson 37.88 9.67 9.85

- 0.19 29.82 30.31

- 0.49

20030814 390811001 Ohio Jefferson 37.88 8.57 8.73

- 0.16 26.48 26.92

- 0.44

20050913 390811001 Ohio Jeff erson 37.88 8.28 8.44

- 0.16 25.62 26.04

- 0.42

20060117 390811001 Ohio Jefferson 37.88 7.38 7.51

- 0.14 21.68 22.08

- 0.40

20070804 390811001 Ohio Jefferson 37.88 6.41 6.53

- 0.12 19.93 20.26

- 0.33

20041117 261630019 Michigan Wayne 37.83 3.86 3.96

- 0.10 27. 56 27.76

- 0.21

20050206 261630019 Michigan Wayne 37.83 10.63 10.65

- 0.03 46.77 46.95

- 0.18

20060309 261630019 Michigan Wayne 37.83 7.32 7.34

- 0.02 32.39 32.51

- 0.12

20030304 261630019 Michigan Wayne 37.83 6.83 6.85

- 0.02 30.24 30.36

- 0.12

20070217 2616 30019 Michigan Wayne 37.83 4.94 4.96

- 0.01 22.03 22.11

- 0.08

20050627 390350065 Ohio Cuyahoga 37.67 15.41 15.73

- 0.33 30.29 31.33

- 1.03

20030702 390350065 Ohio Cuyahoga 37.67 13.52 13.77

- 0.24 23.93 24.96

- 1.04

20070906 390350065 Ohio Cuyahoga 37.67 12. 98 13.22

- 0.23 23.00 23.99

- 0.99

20040212 390350065 Ohio Cuyahoga 37.67 6.42 6.53

- 0.11 23.60 24.15

- 0.55

20060210 390350065 Ohio Cuyahoga 37.67 5.61 5.71

- 0.10 20.69 21.17

- 0.48

20050627 170313301 Illinois Cook 37.67 12.27 12.36

- 0.09 36.65 36.87

- 0.22

20070617 170313301 Illinois Cook 37.67 9.40 9.47

- 0.07 28.19 28.35

- 0.17

20030214 170313301 Illinois Cook 37.67 6.19 6.19 0.00 31.42 31.48

- 0.06

20040227 170313301 Illinois Cook 37.67 5.84 5.84 0.00 29.68 29.74

- 0.06

20060306 170313301 Illinois Cook 3 7.67 4.22 4.22 0.00 21.58 21.62

- 0.04

20040608 420070014 Pennsylvania Beaver 37.42 10.19 10.39

- 0.20 23.91 24.41

- 0.49

20070602 420070014 Pennsylvania Beaver 37.42 9.08 9.25

- 0.18 21.36 21.80

- 0.44

20061128 420070014 Pennsylvania Beaver 37.42 5.47 5.59

- 0.13 31.18 31.55

- 0.37

20051127 420070014 Pennsylvania Beaver 37.42 5.11 5.23

- 0.12 29.19 29.53

- 0.34

20031105 420070014 Pennsylvania Beaver 37.42 4.17 4.27

- 0.10 23.93 24.21

- 0.28

20040924 420033007 Pennsylvania Allegheny 37.40 9.16 9.32

- 0.17 23.11 2 3.49

- 0.38

20030626 420033007 Pennsylvania Allegheny 37.40 10.68 10.83

- 0.15 40.46 40.86

- 0.40

20050627 420033007 Pennsylvania Allegheny 37.40 5.57 5.65

- 0.08 21.34 21.55

- 0.21

20061125 420033007 Pennsylvania Allegheny 37.40 1.58 1.60

- 0.02 24.46 24.59

- 0.13

20071208 420033007 Pennsylvania Allegheny 37.40 1.37 1.39

- 0.01 21.26 21.37

- 0.11

20031115 10730023 Alabama Jefferson 37.33 6.47 6.45 0.01 27.57 27.66

- 0.09

20040225 10730023 Alabama Jefferson 37.33 6.46 6.43 0.04 29.97 30.04

- 0.07

20070326 10730 023 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 3 7.24 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

2 0060818 180970043 Indiana Marion 37.20 11.97 12.25

- 0.29 22.70 23.16

- 0.46

20030313 180970043 Indiana Marion 37.20 5.77 5.82

- 0.05 25.92 26.11

- 0.19

20040227 180970043 Indiana Marion 37.20 5.53 5.58

- 0.05 24.86 25.04

- 0.18

20051004 261470005 Michigan St Clair 37.14 5.51 5.67

- 0.17 40.08 40.84

- 0.76

20070524 261470005 Michigan St Clair 37.14 13.75 13.87

- 0.12 23.60 24.09

- 0.49

20060222 261470005 Michigan St Clair 37.14 7.39 7.42

- 0.03 34.24 34.36

- 0.11

20030307 261470005 Michigan St Clair 37.14 6.30 6. 33

- 0.03 29.28 29.38

- 0.10

20040325 261470005 Michigan St Clair 37.14 5.25 5.27

- 0.02 24.50 24.58

- 0.08

20040906 550790043 Wisconsin Milwaukee 37.10 11.32 11.38

- 0.06 25.66 25.85

- 0.20

20060329 550790043 Wisconsin Milwaukee 37.10 8.88 8.89

- 0.01 39.14 3 9.22

- 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

200502 03 180890026 Indiana Lake 37.06 8.03 8.08

- 0.05 33.45 33.59

- 0.14

20040304 180890026 Indiana Lake 37.06 7.99 8.04

- 0.05 33.28 33.42

- 0.14

20030226 180890026 Indiana Lake 37.06 7.93 7.98

- 0.05 33.02 33.16

- 0.14

20070220 180890026 Indiana Lake 37.06 6.10 6.14

- 0.04 25.54 25.64

- 0.10

20060123 180890026 Indiana Lake 37.06 5.71 5.74

- 0.03 23.90 24.00

- 0.10

20050910 180970081 Indiana Marion 36.96 17.37 17.78

- 0.41 32.00 32.66

- 0.66

20070617 180970081 Indiana Marion 36.96 11.14 11.50

- 0.36 24.00 24.63

- 0.63

20030130 180970081 Indiana Marion 36.96 5.95 6.00

- 0.05 25.86 26.05

- 0.19

20060306 180970081 Indiana Marion 36.96 5.78 5.84

- 0.05 25.14 25.33

- 0.18

20040224 180970081 Indiana Marion 36.96 5.28 5.33

- 0.05 23.01 23.18

- 0.17

20050910 180970066 Indiana Mar ion 36.92 16.90 17.31

- 0.40 32.11 32.77

- 0.65

20070803 180970066 Indiana Marion 36.92 14.77 15.12

- 0.35 28.12 28.70

- 0.57

20030801 180970066 Indiana Marion 36.92 13.85 14.18

- 0.33 26.40 26.93

- 0.54

20060222 180970066 Indiana Marion 36.92 5.79 5.84

- 0.05 25.66 25.85

- 0.19

# 49 20040224 180970066 Indiana Marion 36.92 5.27 5.32

- 0.05 23.44 23.61

- 0.17

20050227 171191007 Illinois Madison 36.83 8.60 8.74

- 0.13 34.53 34.59

- 0.06

20040116 171191007 Illinois Madison 36.83 6.67 6.77

- 0.10 26.89 26.93

- 0.04

2006012 6 171191007 Illinois Madison 36.83 6.35 6.45

- 0.10 25.62 25.66

- 0.04

20070220 171191007 Illinois Madison 36.83 6.05 6.14

- 0.09 24.43 24.47

- 0.04

20031114 171191007 Illinois Madison 36.83 6.50 6.54

- 0.04 30.13 30.25

- 0.11

20060329 550790010 Wisconsin Mil waukee 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 5.5 9 5.60

- 0.01 24.19 24.24

- 0.05

20071211 550790010 Wisconsin Milwaukee 36.71 5.57 5.57 0.00 31.17 31.26

- 0.09

20050203 390170003 Ohio Butler 36.59 12.09 12.27

- 0.18 37.83 38.18

- 0.35

20070530 390170003 Ohio Butler 36.59 11.14 11.31

- 0.17 23.14 23.56

- 0.4 3

20040924 390170003 Ohio Butler 36.59 10.97 11.10

- 0.14 17.90 18.21

- 0.31

20030318 390170003 Ohio Butler 36.59 7.38 7.49

- 0.11 23.28 23.49

- 0.21

20060222 390170003 Ohio Butler 36.59 6.30 6.39

- 0.09 19.95 20.13

- 0.18

20051221 170316005 Illinois Cook 36 .42 7.64 7.76

- 0.12 36.24 36.38

- 0.14

20041229 170316005 Illinois Cook 36.42 7.33 7.44

- 0.11 34.78 34.91

- 0.13

20070617 170316005 Illinois Cook 36.42 7.29 7.37

- 0.09 29.17 29.36

- 0.18

20030316 170316005 Illinois Cook 36.42 6.87 6.88

- 0.01 31.96 32.05

- 0 .10

20060123 170316005 Illinois Cook 36.42 5.10 5.10

- 0.01 23.84 23.92

- 0.07

20040608 420031008 Pennsylvania Allegheny 36.35 11.00 11.24

- 0.24 26.03 26.61

- 0.58

20050627 420031008 Pennsylvania Allegheny 36.35 10.04 10.26

- 0.22 23.81 24.35

- 0.53

2003082 1 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 2616 10008 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 Wash tenaw 36.32 2.55 2.63

- 0.08 22.45 22.46

- 0.01

20040304 261610008 Michigan Washtenaw 36.32 5.96 5.99

- 0.03 25.67 25.77

- 0.11

20030403 170312001 Illinois Cook 36.12 12.30 12.41

- 0.11 32.55 32.76

- 0.21

20070617 170312001 Illinois Cook 36.12 10.12 10.20

- 0. 09 26.85 27.02

- 0.17

20050203 170312001 Illinois Cook 36.12 9.00 9.00

- 0.01 37.12 37.19

- 0.07

20040227 170312001 Illinois Cook 36.12 6.42 6.43 0.00 26.63 26.68

- 0.05

20060117 170312001 Illinois Cook 36.12 4.88 4.89 0.00 20.37 20.41

- 0.04

20041230 17031 0052 Illinois Cook 36.07 5.78 5.90

- 0.11 28.34 28.53

- 0.19

20061229 170310052 Illinois Cook 36.07 5.14 5.24

- 0.10 25.24 25.41

- 0.17

20050205 170310052 Illinois Cook 36.07 7.63 7.65

- 0.02 32.74 32.86

- 0.11

20070308 170310052 Illinois Cook 36.07 7.31 7.34

- 0.02 31.42 31.53

- 0.11

20030226 170310052 Illinois Cook 36.07 6.77 6.79

- 0.02 29.11 29.21

- 0.10

20071208 421330008 Pennsylvania York 36.06 4.92 5.00

- 0.09 29.97 30.07

- 0.10

20030301 421330008 Pennsylvania York 36.06 14.24 14.33

- 0.08 41.07 41.20

- 0.14

20041123 421330008 Pennsylvania York 36.06 4.45 4.53

- 0.08 27.17 27.26

- 0.09

20050206 421330008 Pennsylvania York 36.06 11.35 11.41

- 0.07 32.81 32.92

- 0.11

20060216 421330008 Pennsylvania York 36.06 9.23 9.28

- 0.05 26.77 26.86

- 0.09

20051004 261630015 Michigan Wayne 36.00 5.94 6.09

- 0.14 36.16 36.50

- 0.34

20041229 261630015 Michigan Wayne 36.00 4.53 4.63

- 0.11 27.65 27.91

- 0.26

20060222 261630015 Michigan Wayne 36.00 7.40 7.43

- 0.03 31.22 31.32

- 0.10

20030214 261630015 Michigan Wayne 36.00 6.73 6.76

- 0.03 28.46 28.55

- 0.09

20070325 261630015 Michigan Wayne 36.00 5.32 5.34

- 0.02 22.58 22.65

- 0.07

20061210 10732003 Alabama Jefferson 35.94 6.17 6.24

- 0.07 31.19 31.44

- 0.26

20070815 10732003 Alabama Jefferson 35.94 7.88 7.76 0.12 27.22 27.18 0.04

200 30415 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 3 5.85 13.46 13.74

- 0.27 29.11 29.64

- 0.53

20070527 390618001 Ohio Hamilton 35.85 9.58 9.76

- 0.18 22.95 23.35

- 0.40

20030316 390618001 Ohio Hamilton 35.85 5.98 6.14

- 0.15 25.39 25.65

- 0.26

20040218 390618001 Ohio Hamilton 35.85 5.74 5.88

- 0.15 24.37 24.62

- 0.25

20060222 390618001 Ohio Hamilton 35.85 5.41 5.55

- 0.14 23.00 23.23

- 0.24

20050227 171190023 Illinois Madison 35.81 9.01 9.15

- 0.14 33.18 33.22

- 0.04

20030304 171190023 Illinois Madison 35.81 8.41 8.54

- 0.13 30.99 31.03

- 0.04

20060222 171190023 I llinois Madison 35.81 6.75 6.85

- 0.10 24.96 25.00

- 0.03

20040424 171190023 Illinois Madison 35.81 6.03 6.08

- 0.05 23.84 23.99

- 0.15

20070906 420031301 Pennsylvania Allegheny 35.65 11.73 11.91

- 0.18 26.27 26.71

- 0.44

20030221 420031301 Pennsylvania Alleg heny 35.65 8.65 8.81

- 0.17 28.93 29.33

- 0.40

20060710 420031301 Pennsylvania Allegheny 35.65 10.34 10.50

- 0.16 23.22 23.61

- 0.39

20050624 420031301 Pennsylvania Allegheny 35.65 8.37 8.53

- 0.15 24.57 24.92

- 0.35

20040512 420031301 Pennsylvania Allegheny 35.65 7.82 7.96

- 0.14 22.97 23.30

- 0.32

20030624 391130032 Ohio Montgomery 35.61 13.74 13.97

- 0.23 26.72 27.21

- 0.49

20070530 391130032 Ohio Montgomery 35.61 11.68 11.88

- 0.20 22.80 23.21

- 0.42

20050125 391130032 Ohio Montgomery 35.61 2.34 2.37

- 0.03 25 .66 25.87

- 0.21

20040131 391130032 Ohio Montgomery 35.61 1.84 1.87

- 0.02 20.36 20.53

- 0.17

20060309 391130032 Ohio Montgomery 35.61 1.74 1.76

- 0.02 19.22 19.37

- 0.16

20051004 420030116 Pennsylvania Allegheny 35.59 8.27 8.46

- 0.19 24.05 24.45

- 0.40

2004 0608 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.

# 50



# Appendix C: Description of E xcel Spreadsheet Data Files for

Transport Rule Significant Contribution Analysis

# 51

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

.

The annual and quarterly emissions for all AQAT simulations can be found in this file.

AQAT_ emissions.xlsx

These files contain the 24

- hour PM2.5 2012 base case and 2014 AQAT Calibration Scenario

contributions.

QTR1_base_and_AQAT_calibration_scenario_contributions.xlsx

QTR2_base_and_AQAT_calibration_scenario_contributions.xlsx

QTR3_base_and_AQAT_ calibration_scenario_contributions.xlsx

QTR4_base_and_AQAT_calibration_scenario_contributions.xlsx

The annual PM2.5 and 24

- hour PM2.5 calibration factors can be found in the respective files.

Annual PM Calib Factors.xlsx

Daily PM Calibration Factors.xlsx

These files contain the quarterly contributions and calibrated Relative Response Factors (RRFs)

for all 24

- hour PM2.5 simulations.

dailyPM_adjusted sulfate contributions and RRF_2014_base.xlsx

dailyPM_adjusted sulfate contributions and RRF_2012_base_wlea kage.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_500CT.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_1600CT.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_2300CT.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_280 0CT.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_3300CT.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_10000CT.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_1600_remedy.xlsx

dailyPM_adjusted sulfate contributions and RRF_20 14_2300_remedy.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_ 100 00_remedy.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_final_remedy_sensitivity.xlsx

These files contain the quarterly contributions and calibrated RRFs for the variabi lity

assessments. The files in the list assume that the home state is held constant at the $2300/ton

level. The number associated with "var" in the title notes the level of emissions variation above

the level of the budget in the simulation.

dailyPM_adju sted sulfate contributions and RRF_2014_2300CT_2 1 var_home_2300.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_2300CT_20var_home_2300.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_2300CT_ 18 var_home_2300.xlsx

dailyPM_adjusted sulfate con tributions and RRF_2014_2300CT_15var_home_2300.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_2300CT_10var_home_2300.xlsx

dailyPM_adjusted sulfate contributions and RRF_2014_2300CT_05var_home_2300.xlsx

# 52

This file contains a summary of the estimat ed 98th percentile values and resulting average and

maximum design values for all 24

- hour PM2.5 AQAT cost threshold level, variability analyses,

and remedy simulations.

dailyPM_allyears_high_quarters.xlsx

These files apply the RRFs to each of the 32 days per year for each of the 5 years of available

receptor estimates. The result is the estimated 24

- hour PM2.5 concentration for that day. The

98th percentile day is also identified in these files. They are all in 2014 unless otherwise

specified in the tit le of the file.

dailyPM_all_years_all_quarters_base.xlsx

dailyPM_all_years_all_quarters_base_500CT.xlsx

dailyPM_all_years_all_quarters_base_1600CT.xlsx

dailyPM_all_years_all_quarters_base_2300CT.xlsx

dailyPM_all_years_all_quarters_base_2800CT.xlsx

dailyPM _all_years_all_quarters_base_3300CT.xlsx

dailyPM_all_years_all_quarters_base_10000CT.xlsx

dailyPM_all_years_all_quarters_base_2012_leakage.xlsx

dailyPM_all_years_all_quarters_1600_remedy.xlsx

dailyPM_all_years_all_quarters_2300_remedy.xlsx

dailyPM_all_year s_all_quarters_10000_remedy.xlsx

dailyPM_all_years_all_quarters_ final_remedy_sensitivity .xlsx

These are the same files as above, but were used in the variability analysis. T he home state was

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

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

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

dailyPM_all_years_all_quarters_base_2300CT_2 1 perc_whome at2300.xlsx

dailyPM_all_years_all_quarters_base_2300CT_20perc_whomeat2300.xlsx

dailyPM_all_years_all_quarters_base_2300CT_ 18 perc_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 al l annual PM2.5 AQAT

simulations.

The AQAT vs. CAMx.xlsx file contains the 2014 base case and 2014 remedy comparisons for

AQAT and CAMx.

# 53



# Appendix D: Description of Relationship Assumed between

Monitor Location and Nonattainment Areas

In Tabl e VI.C

- 2 of the preamble is a list of the number of projected nonattainment and

maintenance areas. These were counted using the number of receptors from Tables C

- 3, C

- 4, C

5, and C

- 6 in this TSD and noting the nonattainment area that they are associated w ith (found in

Tables Appendix D

- 1 and Appendix D

- 2). 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 purpo ses of the final Transport Rule summary

of results in Table VI.C

- 2 of the preamble , 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 projec ted 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.

Table Appendix D

- 1

. Relationship b etween 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

( g/m 3 ) CAMx 2012

Base Case

Max. DV

( g/m 3 ) Area

420030064 Pennsylvania Allegheny 17.94 18.33 Liberty

- C lairton, 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, M I

390350060 Ohio Cuyahoga 15.67 16.18 Cleveland

- Akron

- Lorain, OH

390610014 Ohio Hamilton 15.76 15.98 Cincinnati

- Hamilton, OH

- KY

- IN

390610042 Ohio Hamilton 15.40 15.77 Cincinnati

- Hamilton, OH

- KY

- IN

171191007 Illinois Madison 15.46 15.73 St. Louis, MO

- IL

10732003 Alabama Jefferson 15.16 15.64 Birmingham, AL

390350045 Ohio Cuyahoga 15.14 15.61 Cleveland

- Akron

- Lorain, OH

180970081 Indiana Marion 14.86 15.16 Indianapolis, IN

131210039 Georgia Fulton 15.07 15.10 Atlanta, GA

390617001 Ohio Hamilton 14.74 15.10 Cincinnati

- Hamilton, OH

- KY

- IN

390350065 Ohio Cuyahoga 14.67 15.10 Cleveland

- Akron

- Lorain, OH

180970083 Indiana Marion 14.71 15.06 Indianapolis, IN

# 54

Table Appendix D

- 2

. Relationship between the Monitor Receptors and Nonattainment Areas*

for the 24

- h ou r PM 2.5 

# NAAQS.

Monitor

I dentification

N umber State County CAMx 2012

Base Case

Avg. DV

( g/m 3 ) CAMx 2012

Base Case

Max. DV

( g/m 3 ) Area

420030064 Pennsylvania Allegheny 56.71 59.93 Liberty

- Clairton, PA

420030093 Pennsylvania Allegheny 39.11 44.40 Pitt sburgh

- Beaver Valley, PA

390350038 Ohio Cuyahoga 39.46 41.84 Cleveland

- Akron

- Lorain, OH

261630016 Michigan Wayne 38.99 41.28 Detroit

- Ann Arbor, MI

390350060 Ohio Cuyahoga 37.78 40.85 Cleveland

- Akron

- Lorain, OH

170311016 Illinois Cook 37.58 40.44 Chicag o

- Gary

- Lake County, IL

- IN*

261630033 Michigan Wayne 39.48 39.81 Detroit

- Ann Arbor, MI

180890022 Indiana Lake 34.94 39.58 Chicago

- Gary

- Lake County, IL

- IN*

540090011 West Virginia Brooke 37.57 38.39 Steubenville

- Weirton, OH

- WV

420710007 Pennsylvania Lanc aster 35.98 38.37 Lancaster, PA

390350045 Ohio Cuyahoga 34.80 38.13 Cleveland

- Akron

- Lorain, OH

390811001 Ohio Jefferson 34.56 37.88 Steubenville

- Weirton, OH

- WV

261630019 Michigan Wayne 37.34 37.83 Detroit

- Ann Arbor, MI

390350065 Ohio Cuyahoga 34.91 37. 67 Cleveland

- Akron

- Lorain, OH

170313301 Illinois Cook 34.97 37.67 Chicago

- Gary

- Lake County, IL

- IN*

420070014 Pennsylvania Beaver 36.21 37.42 Pittsburgh

- Beaver Valley, PA

420033007 Pennsylvania Allegheny 32.40 37.40 Liberty

- Clairton, PA

10730023 Alabama Jefferson 36.96 37.33 Birmingham, AL

550790026 Wisconsin Milwaukee 33.62 37.24 Milwaukee

- Racine, WI

180970043 Indiana Marion 35.76 37.20 Indianapolis, IN*

261470005 Michigan St Clair 36.23 37.14 Detroit

- Ann Arbor, MI

550790043 Wisconsin Milwaukee 36.2 1 37.10 Milwaukee

- Racine, WI

180890026 Indiana Lake 34.08 37.06 Chicago

- Gary

- Lake County, IL

- IN*

180970081 Indiana Marion 35.85 36.96 Indianapolis, IN*

180970066 Indiana Marion 35.73 36.92 Indianapolis, IN*

171191007 Illinois Madison 36.59 36.83 St. Lo uis, 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 36.3 5 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 Pennsylvan ia York 33.38 36.06 Harrisburg

- Lebanon

- Carlisle, PA

261630015 Michigan Wayne 35.55 36.00 Detroit

- Ann Arbor, MI

10732003 Alabama Jefferson 35.31 35.94 Birmingham, AL

390618001 Ohio Hamilton 35.29 35.85 Cincinnati

- Hamilton, OH

- KY

- IN*

171190023 Illinois M adison 35.11 35.81 St. Louis, MO

- IL*

420031301 Pennsylvania Allegheny 33.95 35.65 Pittsburgh

- Beaver Valley, PA

391130032 Ohio Montgomery 33.68 35.61 Dayton

- Springfield, OH*

420030116 Pennsylvania Allegheny 35.59 35.59 Pittsburgh

- Beaver Valley, PA



## * Ind icates 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.

# 55



# Appendix E: Comparison of Estimated 24

- hour PM 2.5 

# Design

Values for the Final Transport Rule Remedy and Final Remedy

Sensitivity

. 13



# This appendix contains the AQAT estimates for the final Transport Rule remedy that was

modeled in CAMx as well as the Fin al Remedy Sensitivity . The comparison of these two

estimates show the air quality effects of the larger variability limits for SO 2 

# on PM 2.5



# concentrations.

13 

## ― Final Remedy Sensitivity‖ models the air quality

- assured trading final remedy described in the Transport Rule

preamble with variability limits of 18% for SO 2 

## and Annual NO

x 

## , and 21% for ozone

- season NO

x 

## . See run

description in Appendix Table A

- 1.

# 56

Table Appendix E

- 1 . Average and Maximum PM 2.5 

# DVs ( g/m 3 

# ) in 2014 for the Final Transport

Rule Re medy (as modeled in AQAT) and for Final Remedy Sensitivity

.

Site ID State County Average DV Difference in

air quality

between

Final

Remedy

Sensitivity

and the

remedy for

Avg. DV. Maximum DV Difference in

air quality

between

Final

Remedy

Sensitivity

and th e

remedy for

Max DV.

remedy Final

Remedy

Sensitivity remedy Final

Remedy

Sensitivity

420030064 Pennsylvania Allegheny 45.45 45.59 0.14 48.52 48.65 0.12

420030093 Pennsylvania Allegheny 29.88 30.02 0.14 34.28 34.43 0.15

390350038 Ohio Cuyahoga 33.4 6 33.52 0.06 35.39 35.44 0.05

261630016 Michigan Wayne 33.88 33.94 0.06 35.61 35.67 0.06

390350060 Ohio Cuyahoga 30.51 30.56 0.06 32.94 33.00 0.06

170311016 Illinois Cook 32.95 33.06 0.11 36.40 36.50 0.10

261630033 Michigan Wayne 34.74 34.90 0.15 34.95 35.11 0.16

180890022 Indiana Lake 32.31 32.34 0.03 36.30 36.36 0.06

540090011 West Virginia Brooke 28.83 28.95 0.12 29.63 29.80 0.17

420710007 Pennsylvania Lancaster 34.87 34.88 0.01 37.08 37.09 0.01

390350045 Ohio Cuyahoga 26.23 26.29 0.05 27.43 27.5 0 0.07

390811001 Ohio Jefferson 25.57 25.71 0.14 27.76 27.93 0.17

261630019 Michigan Wayne 34.87 34.92 0.04 35.74 35.79 0.05

390350065 Ohio Cuyahoga 25.95 26.07 0.12 26.81 26.96 0.15

170313301 Illinois Cook 30.35 30.39 0.04 32.70 32.73 0.04

420070014 Pennsylvania Beaver 27.39 27.47 0.09 28.49 28.58 0.09

420033007 Pennsylvania Allegheny 24.78 24.87 0.09 28.63 28.77 0.14

010730023 Alabama Jefferson 31.10 31.12 0.02 31.57 31.59 0.02

550790026 Wisconsin Milwaukee 30.08 30.10 0.02 33.10 33.12 0.02

18097 0043 Indiana Marion 27.13 27.27 0.14 27.76 27.86 0.10

261470005 Michigan St Clair 32.67 32.73 0.06 33.29 33.33 0.04

550790043 Wisconsin Milwaukee 31.80 31.85 0.05 33.92 33.94 0.02

180890026 Indiana Lake 30.49 30.52 0.03 33.39 33.42 0.03

180970081 India na Marion 27.30 27.41 0.11 27.54 27.67 0.13

180970066 Indiana Marion 28.10 28.21 0.11 29.11 29.25 0.15

171191007 Illinois Madison 29.32 29.34 0.03 30.64 30.67 0.03

550790010 Wisconsin Milwaukee 30.83 30.86 0.03 33.13 33.16 0.02

390170003 Ohio Butler 26 .47 26.60 0.13 27.29 27.39 0.10

170316005 Illinois Cook 32.02 32.05 0.02 34.45 34.47 0.02

420031008 Pennsylvania Allegheny 24.47 24.63 0.15 25.38 25.57 0.18

261610008 Michigan Washtenaw 28.47 28.51 0.04 29.26 29.30 0.04

170312001 Illinois Cook 29.50 29 .53 0.03 32.21 32.25 0.04

170310052 Illinois Cook 29.69 29.70 0.02 30.20 30.21 0.02

421330008 Pennsylvania York 30.92 30.95 0.03 33.79 33.83 0.04

261630015 Michigan Wayne 31.02 31.10 0.08 31.91 31.98 0.07

010732003 Alabama Jefferson 30.62 30.65 0.03 31 .46 31.48 0.03

390618001 Ohio Hamilton 25.96 26.09 0.14 26.64 26.74 0.11

171190023 Illinois Madison 28.41 28.44 0.03 29.41 29.44 0.03

420031301 Pennsylvania Allegheny 24.96 25.10 0.14 25.85 25.96 0.11

391130032 Ohio Montgomery 23.09 23.15 0.06 24.54 24 .62 0.08

420030116 Pennsylvania Allegheny 26.13 26.26 0.12 26.13 26.26 0.12