Technical Support Document for Demonstration of the Transport 

Rule as a BART Alternative

U.S. Environmental Protection Agency

Office of Air Quality Planning and Standards

Research Triangle Park, NC 27711

December 2011

Docket Number EPA-HQ-OAR-2011-0729
1

Table of Contents

1.0 Introduction................................................................... ...........2

2.0 Emission Projections Used for Transport Rule Is Better-than-BART 

Analysis...................................................................................3

2.1 Eligible BART Units and Modeling Criteria.................................4

2.2 Emission Projections............................................................9

3.0 Air Quality Modeling to Determine Future Visibility..............................12

3.1 Overview of the Modeling Process...........................................12

3.2 Overview of the Future Year Visibility Projections.......................13

3.3 Calculation of Base Year (Current) Visibility Levels........................15

3.4 Projection of Future Year Visibility Level..................................20

3.5 Details of the Visibility Projection Methodology...........................21 

3.6 Modeling Analysis of the Better than BART Two Pronged Test........23

3.6.1 Better than BART Test- 1

st Prong................................24

3.6.2 Better than BART Test - 2

nd Prong..............................33

3.6.3 Summary of Better than BART Test............................41

Appendix A EGUs that were assumed to BART-subject for the purpose of this 

analysis..............................................................................................42

Appendix B Analysis of Visibility Degradation at Four Class I Areas................55

Appendix C Sensitivity Analysis Accounting for Proposed Increases in Certain 

Transport Rule State Budgets..................................................................61
2

1.0 Introduction

This Technical Support Document (TSD) provides details on the air quality modeling 

analysis performed in support of EPA’s proposed determination that the trading programs in the 

Transport Rule1, also known as the Cross-State Air Pollution Rule, achieve greater reasonable 

progress towards the national goal of achieving natural visibility conditions in Class I areas than 

source-specific Best Available Retrofit Technology (BART) in those states covered by the 

Transport Rule. 

The Transport Rule requires 28 states in the eastern half of the United States to reduce 

electric generating unit (EGU) sulfur dioxide (SO2) and nitrogen dioxide (NOX) emissions that 

cross state lines and contribute to ground-level ozone and/or fine particle pollution in downwind

states. BART, on the other hand, is applicable nationwide and covers 26 industrial categories, 

including EGUs of a certain vintage. The analysis described in this TSD sought to determine 

whether the Transport Rule air quality assured allowance trading program for EGUs will achieve 

greater visibility improvement than would BART for EGUs only. 

Criteria for determining if a BART alternative (such as the Transport Rule trading programs) 

achieves greater reasonable progress than source-specific BART are set out in the Regional Haze 

Rule at § 51.308(e)(3). The “better-than-BART” test may be satisfied as follows: If the 

distribution of emissions is not substantially different than under BART, and the alternative 

measure results in greater emission reductions, then the alternative measure may be deemed to 

achieve greater reasonable progress. If the distribution of emissions is significantly different 

under the alternative, then an air quality modeling study is conducted to determine differences in 

visibility between BART and the alternative program for each affected Class I area for the worst 

and best 20 percent of days. The two-pronged visibility test would demonstrate “greater 

reasonable progress” under the alternative program if both of the following criteria are met:

- Visibility does not decline in any Class I area2, and

- There is an overall improvement in visibility, determined by comparing the average 

differences between BART and the alternative over all affected Class I areas.

 

1

See Federal Implementation Plans to Reduce Interstate Transport of Fine Particulate Matter and Ozone, 

76 FR 48208 (August 8, 2011) and Federal Implementation Plans for Iowa, Kansas, Michigan, Missouri, 

Oklahoma, and Wisconsin To Reduce Interstate Transport of Ozone, 76 FR 40662 (July 11, 2011) for 

more details. For the purposes of the proposed Transport Rule is Better-than-BART rule and for the 

analysis described in this TSD, the Transport Rule includes all of the states (28) included in the final

Transport Rule and the supplemental notice.

2The “decline” is relative to modeled future base case visibility conditions in the absence of any BART or 

alternative program control requirements (including CAIR and the Transport Rule).
3

The underlying purpose of both prongs of the test is to assess whether visibility conditions at 

Class I areas would be better with the alternative program in place than they would without it. 

The first prong ensures that the alternative program will not cause a decline in visibility at any 

affected Class I area. It addresses the possibility that the alternative program might cause local 

changes in emissions that could result in localized visibility degradation. The second prong 

ensures that the program results in improvements in average visibility across all affected Class I 

areas as compared to adopting source-specific BART. Together, these tests ensure that the 

alternative program provides for greater visibility improvement than would source-specific 

BART. 

Since the distribution of emissions under the Transport Rule programs is substantially 

different than the distribution of emissions that might result from source-specific BART, an air 

quality modeling study was conducted to determine whether visibility improvement resulting 

from implementation of the Transport Rule meets the two-pronged visibility test for 

demonstrating “greater reasonable progress” than source-specific BART.

Section 2.0 of this document describes the development of the emission projections that are 

the basis for the air quality and visibility modeling in the Transport Rule Is “Better-than –

BART” analysis. Section 2.1 describes the underlying inventory of BART units and the 

modeling criteria used to constrain each control scenario. National and state-specific emission 

projections from the Integrated Planning Model (IPM) are described in section 2.2. Section 3.0

of this TSD describes the air quality modeling performed for this analysis including the modeling 

process and methodology, and calculation of visibility impacts for each control scenario.

Projected visibility impacts for each control scenario are shown for each mandatory Class I 

Federal area (for which there were sufficient data). Appendix A lists the inventory of BARTeligible units used in the “Nationwide BART” control scenario. Appendix B provides a detailed 

analysis of the effect of very low nitrate concentrations on visibility results at several Class I 

areas, and Appendix C describes the potential visibility impacts of proposed revisions to the 

NOx and SO2 budgets for certain states subject to the Transport Rule. .

2.0 Emission Projections Used for Transport Rule Is Better-than-BART Analysis

The air quality modeling conducted for this analysis is based on emission projections for 

two control scenarios. One scenario represents “Nationwide BART” and the other represents 

“Transport Rule + BART-elsewhere”. The “Nationwide BART” scenario examined SO2 and 

NOX emissions from all EGUs nationwide after the application of BART controls to all BARTeligible EGUs. In the “Transport Rule + BART-elsewhere” scenario, EGU SO2 and NOX

emissions reductions attributable to the Transport Rule were applied in the 28-state Transport 

Rule region and BART controls were applied to all BART-eligible EGUS outside the Transport 

Rule region that are not subject to Transport Rule requirements. The latter scenario reflects the 
4

fact that source-specific BART would remain a regional haze SIP element outside the Transport 

Rule region. In order to more accurately project the Transport Rule emissions, it is necessary to 

assume EGU BART controls outside the Transport Rule region to account for potential load and 

emission shifting among EGUs.

2.1 Eligible BART Units and Modeling Criteria

The first step in developing the control scenarios was to identify EGUs in the contiguous 

48 states that are eligible for BART controls and for which EPA has developed presumptive 

emission limits. These criteria limited the analysis to coal-fired EGUs for which construction 

commenced by August 7, 1977 and that were not in operation prior to August 7, 1962. The 

impacts of BART were modeled using stringent assumptions regarding the EGUs (or specific 

units at EGUs) that would be subject to BART. The Regional Haze rule (RHR) BART 

Guidelines3specify that presumptive limits for NOx and SO2 should be applied to EGUs having 

a total generating capacity of 750 MW. The Guidelines also exempt from controls BARTeligible units with the potential to emit less than 40 tons per year of either NOx or SO2. In this 

analysis it was assumed that all BART-eligible EGUs were subject to BART requirements to 

ensure that controls that states might choose to require on sources not subject to presumptive 

BART were accounted for in this analysis. It was also assumed that presumptive BART limits 

would be applied to much smaller units. In this analysis it was assumed that the threshold for 

BART-eligibility was 100 MW for SO2 and 25 MW for NOX and no source was eliminated 

based on their annual total emissions. Appendix A lists the EGUs that were assumed to be 

BART-subject for the purpose of this analysis.

For both the “Nationwide BART” scenario and the “Transport Rule + BART-elsewhere” 

scenario, the modeled emission rates were the presumptive EGU BART limits for SO2 and NOX

as specified in the BART Guidelines, unless an actual emission rate at a given unit with existing 

controls is lower. In the latter case, the lower emission rates were modeled. Version 410 of the 

National Electric Energy Data System (NEEDS), an EPA database of existing and plannedcommitted EGUs, was used to identify emission rates for each EGU.4

For SO2, the RHR BART Guidelines specify presumptive BART limits for an EGU with 

an existing scrubber as 95 percent scrubber control efficiency or 0.15 lbs/MMBtu. The NEEDS 

was used to identify which BART-eligible units have existing scrubbers, the scrubber efficiency 

at these units and the controlled SO2 emission rate. For scrubbed BART-eligible units the 

modeled SO2 emission rate was based on a comparison of the SO2 emission rate listed for that 

 

3 Guidelines for BART Determinations under the Regional Haze Rule, 70 FR 39104, July 6, 2005.

4

See The NEEDS User Guide: http://www.epa.gov/airmarkets/progsregs/epaipm/CSAPR/docs/Guide_to_NEEDSv410.pdf which is found at 

http://www.epa.gov/airmarkets/progsregs/epa-ipm/transport.html.
5

unit in NEEDS to the presumptive SO2 limits. For an EGU with a scrubber operating at 95 

percent or higher efficiency, the emission rate being achieved at that control efficiency was 

modeled for that unit even if the emission rate was higher than 0.15 lbs/MMBtu. Conversely, if 

an emission rate of 0.15 lbs/MMBtu or lower is being achieved, that emission rate was modeled 

for that unit, even if the scrubber is less than 95 percent efficient. For BART-eligible units 

without existing scrubbers, an emission rate was modeled that reflected 95 percent control based 

on a new installation of a highly efficient scrubber. 

For NOx, the RHR BART Guidelines specify presumptive limits based on coal type and 

boiler configuration. The BART guidelines also specify that existing NOX controls must be 

operated year round. For the source-specific “Nationwide BART” scenario and for the 

“elsewhere” EGUs in the “Transport Rule + BART-elsewhere” scenario, it was assumed that any 

BART-subject unit with existing NOX controls in the future baseline case would retain at least 

those controls and would be required to operate them year round. If the existing NOX controls in 

the future baseline case did not meet the presumptive BART limits, installation of postcombustion controls (i.e., selective catalytic reduction (SCR) or selective non-catalytic reduction 

(SNCR)) that would meet the BART guidelines with year-round operation was assumed. In the 

“Transport Rule + BART-elsewhere” scenario, there are 5 states that are subject to the Transport 

Rule requirements during the ozone season only.5For these states, NOX controls were assumed 

to operate only during ozone season as required by the Transport Rule. 

Table 2-1 summarizes the NOX emission limits that were applied to BART-eligible units 

of 25 MW or greater. For units firing a coal blend, which the BART Guidelines do not address, a 

weighted presumptive NOX limit based on the percentage of each coal type fired was calculated 

and modeled. 

Table 2-1. BART Presumptive NOX Limits by Boiler Configuration and Coal Type 

(lbs/MMBtu)

Bituminous Subbituminous Lignite

Dry bottom wall-fired 0.39 0.23 0.29

Tangential-fired 0.28 0.15 0.17

Cell burners 0.40 0.45 Not applicable

Dry turbo-fired 0.32 0.23 Not applicable

Wet bottom tangential-fired 0.62 Not applicable Not applicable

Cyclone 0.10 0.10 0.10

BART emission limits modeled for certain EGUs were different from presumptive limits if the 

EGU has a configuration for which presumptive limits have not been established, or if the EGU 

has a different emission limit prescribed by a proposed or finalized State Implementation Plan, a 

 

5

States subject to the Transport Rule requirements during the ozone season only are Oklahoma, Arkansas, 

Louisiana, Mississippi and Florida.
6

Federal Implementation Plan, a final consent decree, or by state rule. These units and the 

emission rates modeled for these units are detailed in Table 2-2. 

Note that all but a few of the EGUs listed in Table 2-2 are in states that are not covered 

by the Transport Rule. Hence, the assumptions about their BART emission rates cannot affect 

the outcome of the second prong of the “better than BART” test because the emission rates for 

these EGUs are the same in both the “nationwide BART” and Transport Rule + BARTelsewhere” air quality analyses that are compared in the second prong of the visibility test.
7

Table 2-2. Modeled Emission Rates other than Presumptive Limits for Specific EGUs* 

EGU Name State ORIS Plant 

Code/CAMD 

UnitID

Modeled NOx 

Emission Rate 

(lbs/MMBtu)

Modeled SO2

Emission Rate 

(lbs/MMBtu)

Comment

R.M. Heskett B ND 2790/1875 0.20 FBC unit with no presumptive limits established. Used the 

“general” NOx limit used for this size boiler in the 2005 

RHR BART Guidelines.

Marion IL 976/9173 0.20 FBC unit with no presumptive limits established. Used the 

“general” NOx limit used for this size boiler in the 2005 

RHR BART Guidelines.

Monticello TX 6147/2809 0.14 Wall-fired, wet bottom firing a 50/50 lignite/subbituminous 

blend. Emission rate per Memo from Rob LaCount re: New 

NOx requirements in the State of Texas (SB7)

Big Bend FL 645/467

645/468

645/469

0.12 Consent decree

Bridgeport CT 568/373 0.14 (ozone season)

0.15 (non-ozone 

season)

0.33 State alternative BART demonstration.

Navajo Tribal 4941/2662

4941/2663

4941/2664

0.24 NOx limit beginning 2009 per voluntary permit limit.

San Juan NM 2451/1596

2451/1597

2451/1598

2451/1599

0.05 0.15 Approved SIP (NOx)

Final FIP (SO2)

Muskogee OK 2952/2005

2952/2006

0.06 Proposed FIP

Sooner OK 6095/2773

6095/2774

0.06 Proposed FIP

Northeastern OK 2963/2019

2963/2020

0.06 Proposed FIP

Valmont CO 477/309 ------ ------- Shutting down in 2017.

Howard Down NJ 50397/3655 ------ ------- Converted to No. 6 fuel oil in 2010.

Edge Moor DE 593/383 0.15 in 2009

0.125 in 2012

0.37 in 2009

0.26 in 2012

Delaware state regulation 1146

Indian River DE 594/388

594/389

0.15 in 2009

0.125 in 2012

0.37 in 2009

0.26 in 2012

Delaware state regulation 1146

Reid Gardner NV 2324/1401 0.20 0.15 Proposed SIP
8

2324/1402

Reid Gardner NV 2324/1403 0.28 0.15 Proposed SIP

Four Corners Tribal 2442/1581

2442/1582

2442/1583

----- ----- Shutdown by 12/31/2014 (proposed FIP).

Four Corners Tribal 2442/1584

2442/1585

0.098 Unit 1584 installing NOx controls by the end of 2017 and 

Unit 1585 installing NOx controls by end of 2018 (proposed 

FIP). 

KPC&L La Cygne KS 1241/819

1241/820

0.13 weighted 

average of emissions 

from both units

0.10 weighted 

average of 

emissions from 

both units

State consent decree.

Jeffrey Energy Center KS 6068/2734

6068/2735

0.15 0.15 State consent decree.

Boardman OR 6106/2778 0.23 beginning July 

1, 2011

0.4 by 2014 Interim SO2 limits must be met in 2014. NOx limit must be 

met beginning July 1, 2011. Facility shutdown in 2020.

*Where an emission rate is not listed, the presumptive NOx or SO2 emission rate for that EGU configuration was modeled. 
9

2.2 Emission Projections

To estimate emissions expected from the control scenarios described in section 2.1, the 

Integrated Planning Model6(IPM) was used. The IPM is a multi-regional, dynamic, 

deterministic linear programming model of the electric power sector. It is used extensively by 

the EPA to support regulatory activities. The IPM provides forecasts of least-cost capacity 

expansion, electricity dispatch, and emission control strategies for meeting electricity demand 

subject to environmental, transmission, dispatch, and reliability constraints. The IPM was used 

in this case to evaluate the emissions impacts of the described scenarios limiting the emissions of 

SO2 and NOX from EGUs. This analysis used the most recently updated IPM platform which is 

documented at http://www.epa.gov/crossstaterule/. Table 2-3 presents the annual emissions for 

each policy scenario as projected by the IPM. As shown by the numbers in the far right column, 

“Transport Rule + BART-elsewhere” achieved greater emission reductions nationwide7for both 

pollutants than source-specific “Nationwide BART” alone.

Table 2-3. National EGU SO2 and NOX Annual Emissions as Projected by IPM (in thousands of 

tons per year)

2014 Base Case 

EGU Emissions

2014 

“Nationwide 

BART”

2014 “Transport 

Rule + BARTelsewhere”

Additional reduction from 

“Transport Rule + BARTelsewhere” (“Nationwide 

BART” minus “Transport 

Rule + BART-elsewhere”)

National Annual 

SO2

7,160 3,820 2,918 902

National Annual 

NOX

2,089 1,798 1,756 42

The IPM projections of annual NOX and SO2 emissions from EGUs for the “Transport 

Rule + BART-elsewhere” control scenario summarized in Table 2-3, which were used to arrive 

at the modeling results presented in section 3.5, are based on the state budgets prescribed in the 

final Transport Rule published on August 8, 2011 and the supplemental proposal published on 

July 11, 2011.8State-specific emission projections for the 2014 base case and each control 

scenario are shown for SO2 emissions in Table 2-4 and for NOx emissions in Table 2-5.

 

6Extensive documentation of the IPM platform may be found at 

http://www.epa.gov/airmarkets/progsregs/epa-ipm/transport.html.

7

In the context of this analysis, when we refer to nationwide emissions or a nationwide analysis, we are 

referring to the contiguous 48 states.

8

Federal Implementation Plans to Reduce Interstate Transport of Fine Particulate Matter and Ozone 76 

FR 48208 (August 8, 2011). The ozone season state budgets for the states affected by the supplemental 

proposal published on July 11, 2001 are included in the “Transport Rule + BART-elsewhere” control 

scenario (Federal Implementation Plans for Iowa, Kansas, Michigan, Missouri, Oklahoma, and Wisconsin 

To Reduce Interstate Transport of Ozone, 76 FR 40662).
10

Table 2-4. 2014 Annual SO2 Emissions in the Transport Rule Region and Non-Transport Rule 

Region States for the 2014 base case and each Control Scenario.

Transport 

Region 

States

2014 

Base 

Case

Nationwide 

BART 

Transport 

Rule + 

BARTelsewhere

NonTransport 

Region States

2014 

Base 

Case

Nationwide 

BART

Transport 

Rule + 

BARTelsewhere

Alabama 417.3 181.5 168.5 Arkansas 99.4 38.1 39.7

Georgia 170.3 151.4 93.6 Arizona 35.6 24.6 24.6

Illinois 141.6 116.1 133.7 California 7.3 7.4 7.3

Indiana 727.8 454.5 202.9 Colorado 62.1 22.4 26.6

Iowa 133.1 43.9 83.8 Connecticut 3.8 1.9 1.9

Kansas 69.8 19.8 49.1 Delaware 2.2 2.1 2.1

Kentucky 488.0 164.9 125.4 Florida 143.6 77.5 79.8

Maryland 42.9 36.5 28.6 Idaho 0.2 0.2 0.2

Michigan 269.4 141.4 174.3 Louisiana 118.2 81.2 92.6

Minnesota 70.9 56.5 50.1 Maine 2.4 2.6 2.4

Missouri 390.3 107.9 181.8 Massachusetts 13.4 8.4 8.4

Nebraska973.1 32.9 71.2 Mississippi 31.0 16.3 17.8

New Jersey 38.9 26.1 6.1 Montana 15.4 5.9 6.7

New York 42.9 42.1 18.8 Nevada 14.4 14.8 14.8

North 

Carolina

126.0 130.9 74.0 New 

Hampshire

6.5 4.7 5.2

Ohio 851.2 546.7 190.0 New Mexico 11.9 10.8 11.7

Pennsylvania 509.6 222.6 134.5 North Dakota 103.6 37.7 37.0

South 

Carolina

213.3 114.0 101.5 Oklahoma 138.0 41.4 41.4

Tennessee 284.5 321.3 66.7 Oregon 11.3 0.2 0.2

Texas 453.3 139.3 266.6 Rhode Island 0 0.0 0.0

Virginia 77.3 78.7 53.9 South Dakota 29.7 2.4 2.5

West Virginia 498.5 165.1 90.4 Utah 33.5 32.7 32.1

Wisconsin 130.5 52.1 51.4 Vermont 0.3 0.3 0.3

Washington 3.4 3.4 3.4

Total 6,220.50 3,346.20 2,416.90 Wyoming 51.8 37.2 42.9

Total 939.00 474.20 501.60

 

9 Gerald Gentleman Unit 2 was inadvertently omitted from the inventory of BART-eligible units. SO2 emissions for 

Nebraska would be approximately 12,000 tons less under the Nationwide BART scenario if Gerald Gentleman Unit 

2 had been properly included in the Nationwide BART control scenario; however we do not expect that this 

difference in emissions would change the outcome of our analysis. 
11

Table 2- 5. 2014 Annual NOx Emissions in the Transport Rule Region and Non-Transport Rule

Region States for the 2014 base case and each Control Scenario10

Transport 

Region States

2014 

Base 

Case

Nationwide 

BART 

Transport 

Rule + 

BARTelsewhere

Non-Transport 

Region States

2014 

Base 

Case

Nationwide 

BART

Transport 

Rule + 

BARTelsewhere

Alabama 76.0 74.1 70.3 Arkansas 36.3 26.2 36.9

Georgia 49.4 45.4 41.8 Arizona 35.6 26.0 26.0

Illinois 55.3 49.3 49.6 California 26.9 27.0 27.1

Indiana 117.8 107.8 111.5 Colorado 49.4 46.9 48.0

Iowa 48.4 41.8 42.4 Connecticut 2.9 2.7 2.5

Kansas 32.6 22.6 24.4 Delaware 1.7 1.8 1.7

Kentucky 83.5 75.4 78.0 Florida 100.6 81.0 75.7

Maryland 17.6 15.6 16.8 Idaho 0.6 0.6 0.6

Michigan 67.7 64.7 62.3 Louisiana 31.6 31.7 31.7

Minnesota 41.5 34.1 35.1 Maine 5.4 6.2 5.4

Missouri 57.3 50.3 51.2 Massachusetts 7.0 7.2 7.1

Nebraska 45.0 30.2 28.1 Mississippi 26.3 19.7 19.5

New Jersey 8.0 8.1 7.8 Montana 19.4 13.4 13.5

New York 21.7 21.8 20.5 Nevada 14.1 13.8 13.9

North Carolina 49.3 52.2 45.4 New Hampshire 5.1 3.1 3.0

Ohio 104.1 96.9 90.1 New Mexico 64.7 13.0 13.0

Pennsylvania 134.1 122.3 118.4 North Dakota 53.3 43.9 43.9

South Carolina 39.0 34.3 37.0 Oklahoma 67.0 44.6 44.3

Tennessee 29.3 34.7 20.5 Oregon 9.6 2.3 2.3

Texas 142.1 139.4 139.5 Rhode Island 0.4 0.4 0.5

Virginia 40.5 41.9 39.1 South Dakota 14.3 2.5 2.6

West Virginia 64.8 65.7 53.2 Utah 67.4 61.7 62.2

Wisconsin 40.7 36.5 34.5 Vermont 0.5 0.5 0.5

Washington 13.3 10.0 10.0

Total 1,365.70 1,265.10 1,217.50 Wyoming 70.2 46.5 46.5

Total 723.60 532.70 538.40

 

10 Due to limitations in the IPM model, NOx emissions from a small number of units were underestimated in the 

“National BART” run. At the national level, the NOx emissions that were underestimated account for about 0.22% 

of national emissions which is not significant for the scope of this analysis.
12

3.0 Air Quality Modeling to Determine Future Visibility 

In this section we describe the photochemical air quality modeling performed to support 

the finding that compliance with the Transport rule for EGUs will result in greater visibility 

improvement in Federal Class I areas than source-specific BART. 

This section also includes technical information on the air quality model applied in 

support of the proposed rule, and the procedures for projecting regional haze for future year 

scenarios. The Transport Rule Air Quality Modeling - Final Rule Technical Support Document 

(TR-AQMTSD)11 contains more detailed information on the air quality modeling aspects of this 

rule. This technical support document provides additional information, including further details 

on the post-processing of model results and calculation for visibility and visibility metrics.

3.1 Overview of the Modeling Process

We completed numerous modeling runs and post-processing calculations to determine the 

impacts of emissions and emissions control strategies on visibility in Class I areas. Determining 

such visibility impacts allows for the comparison of the effects of compliance with a 

“Nationwide BART” program (for EGUs) to the final Transport Rule, as represented by the 

“Transport rule + BART-elsewhere” scenario. We detail these calculations and the modeling 

process in subsequent sections, following a brief description of the overall process.

The cornerstone of our modeling process was the 2014 base case modeling scenario, 

which contains emissions for 2014 based on predicted growth and existing emissions controls.12 

We used model-predicted changes in visibility impairment along with the observed base year 

visibility values to estimate future visibility impairment at each Class I area. We applied the 

relative predicted change in visibility (expressed as a percent) from the model, due to emissions 

changes, to the base year visibility values to estimate future visibility. The projected visibility 

values were based on emissions changes between the 2005 base year inventory and the 2014

inventory. 

After we established the future year 2014 base case visibility values, we calculated 

estimated visibility improvements at each Class I area by modeling the “Transport Rule + 

BART-elsewhere”

13 control strategy as well as the “nationwide BART” strategy in 2014. 

 

11 U.S. EPA, Air Quality Modeling - Final Rule Technical Support Document (for the Cross State Air Pollution 

Rule). June 2011. Docket number EPA-HQ-OAR-2009- 0491 available at 

http://www.epa.gov/airtransport/pdfs/AQModeling.pdf

12This is the same 2014 base case model run used for the Transport Rule. It is a “no CAIR” scenario which assumes 

that CAIR is not in place in 2014. The basis for this assumption is explained in further detail in the Transport Rule 

final rule preamble.

13The Transport Rule + BART elsewhere strategy is the Transport Rule applied in the region where it is in effect 
13

3.2 Overview of the Future Year Visibility Projections

In general, we estimated base and future year visibility impairment using the same 

modeling approach that was used in the July 2011 Transport Rule final rule modeling to develop 

base and future year predictions of PM2.5 concentrations and visibility impairment. In the final

Transport Rule modeling we used the CAMx model (version 5.30) to predict PM2.5 levels. We 

used the CAMx predicted PM2.5 components to estimate future year changes in visibility at Class 

I areas. Details of the application of CAMx for the final Transport Rule, including model 

performance, can be found in Section V of the Transport Rule NFR preamble and the TRAQMTSD. Familiarity with that detailed description is assumed for present purposes. 

As described in the final TR-AQMTSD, we performed a 2005 Base Year model 

simulation to examine the ability of the modeling system to replicate observed concentrations of 

PM and its precursors. We also modeled a future-year base case scenario for 2014. The futureyear base case scenario included emissions resulting from growth and emissions controls 

required under Federal and State law.

14 We completed two additional model runs for this 

analysis; the “nationwide BART” run and the “Transport Rule + BART-elsewhere” run. We 

quantified the impacts of the Transport Rule and BART controls on visibility impairment by 

comparing the results of the future-year 2014 base case model runs with the results of the 

“Transport Rule + BART-elsewhere” and the “nationwide BART” control strategy model runs.

Table 3-1 lists the four CAMx model runs that were used to post-process the visibility results for 

this proposed rule.

Table 3-1. CAMx model scenarios used for the Better than BART analysis

Model Scenario

Modeling Completed 

and Documented in the 

Final Transport rule

Modeling Completed 

and Documented for the 

Better than BART rule

2005 base case X

2014 base case X

2014 Nationwide BART X

2014 Transport Rule + 

BART-elsewhere X

Figure 3-1 shows the CAMx modeling domain used for the Transport Rule Better- thanBART model runs. As shown, there is a Continental U.S. 36km domain (the large black box) 

with a nested Eastern U.S. 12km domain (outlined in red). Visibility results were calculated 

 

and BART applied in the rest of the contiguous U.S., as previously defined. 

14 For the Transport Rule base case modeling, we assumed that CAIR was not in place and only included EGU 

controls that were otherwise required by Federal or State law or regulations.
14

using the 12km modeling results for all Class I areas inside of the 12km domain. The 36km 

modeling results were used for all other Class I areas in the West, outside of the 12km domain.

Figure 3-1. CAMx 12km and 36km resolution modeling domains

We quantified visibility impacts at the Class I areas which have complete IMPROVE 

ambient data for 2005 or are represented by IMPROVE monitors with complete data. Since the 

base year meteorology used in the CAMx modeling is from 2005, ambient data from 2005 is 

needed to be able to identify the best and worst observed visibility days. It is necessary to know 

which days make up the 20 percent best and worst days so that the model outputs can be 

calculated on the same days. For a Class I area without ambient data in 2005, there is no way to 

match up the model predicted changes in visibility with the ambient data from the 20 percent 

best and worst days. There are currently 110 IMPROVE monitoring sites (representing 155

Class I areas15) collecting ambient PM2.5 data at Class I areas. Of these 110 sites, 104 are in the 

Continental U.S. Of the 104 sites in the Continental U.S., 96 have complete data for 2005. 

These 96 sites represent 140 Class I areas.16

 

The 140 Class I area sites are scattered throughout the country and represent all of the 

IMPROVE defined regional visibility areas.

17 Of the 140 Class I areas, 60 are in the Eastern 

12km modeling domain and 80 are in the West, outside of the 12km domain, but inside the 36km 

 

15 There are 156 Class I areas, but one area (Bering Sea) is not represented by an IMPROVE monitor. 

16 The matching of sites to monitors is taken from the Guidance for Tracking Progress Under the Regional 

Haze Rule, EPA-454/B-03-004, September 2003.

17 IMPROVE: Spatial and Seasonal Patterns and Temporal Variability of Haze and its Constituents in the

United States: Report III (May 2000).
15

modeling domain. 

 

3.3 Calculation of Base Year (Current) Visibility Levels

Base year (current) visibility values at Class I areas were needed to determine the starting 

point for calculating future year visibility improvements. For the purpose of evaluating visibility 

for the “better than BART” analysis, visibility impairment was calculated for the 20% worst days 

and the 20% best days at each Class I area. The calculation of baseline visibility values for each 

Class I area followed the procedures detailed in the Guidance for Tracking Progress.18 The 

baseline visibility on the 20% worst days at each Class I area was calculated using the “new” 

IMPROVE visibility equation (IMPROVE, 2006). The IMPROVE data was pre-processed19 for 

Regional Haze calculations and reformatted to MATS format (EPA, 2010). The daily deciview

values were calculated and ranked for each Class I area for each of five years for period 2003-

2007.

20

 The 20% highest deciview values were identified as the 20% worst days for the year. A 

similar procedure was followed to get the 20% best days in each Class I area. Table 3-2 shows 

the base year (2003-2007) estimated visibility impairment (in deciviews) at the 140 Class I areas 

on the 20% worst days and 20% best days at each area. 

Table 3-2. Base Year Visibility (2003-2007) on the 20% Best Days and 20% Worst Days, at 140

IMPROVE Sites

Class I Area IMPROVE 

Representative 

Site

IMPROVE 

Site 

Identifier

State 2003-

2007

Baseline 

Visibility 

(in dv)

20% Best 

Days

2003-2007

Baseline 

Visibility 

(in dv)

20% 

Worst 

Days

Acadia NP ACAD1 ACAD ME 8.3 22.8

Agua Tibia Wilderness AGTI1 AGTI CA 8.4 22.4

Alpine Lake Wilderness SNPA1 ALLA WA 5.1 17.0

Anaconda-Pintler Wilderness SULA1 ANAC MT 2.4 17.1

Ansel Adams Wilderness 

(Minarets) KAIS1 ANAD CA 1.9 15.2

Arches NP CANY1 ARCH UT 3.0 10.8

Badlands NP BADL1 BADL SD 6.7 16.8

Mount Baldy Wilderness BALD1 BALD AZ 2.9 11.3

 

18 U.S. EPA, Guidance for Tracking Progress under the Regional Haze Rule (Tracking Progress 

Guidance) (September 2003).

19The IMPROVE data are available at: http://views.cira.colostate.edu/web/DataWizard/ 

20The base case modeling year for this analysis (and for the Transport Rule) was 2005. Therefore, for this 

analysis, we used visibility data from the five year period centered about the 2005 base year (2003-2007).
16

Class I Area IMPROVE 

Representative 

Site

IMPROVE 

Site 

Identifier

State 2003-

2007

Baseline 

Visibility 

(in dv)

20% Best 

Days

2003-2007

Baseline 

Visibility 

(in dv)

20% 

Worst 

Days

Bandelier NM BAND1 BAND NM 4.6 12.0

Big Bend NP BIBE1 BIBE TX 5.7 17.2

Black Canyon of the Gunnison 

NM WEMI1 BLCA CO 2.8 10.0

Bosque del Apache BOAP1 BOAP NM 6.1 13.8

Bob Marshall Wilderness MONT1 BOMA MT 3.3 16.1

Boundary Waters Canoe Area BOWA1 BOWA MN 5.9 20.2

Bryce Canyon NP BRCA1 BRCA UT 2.4 11.6

Bridger Wilderness BRID1 BRID WY 1.7 10.7

Brigantine BRIG1 BRIG NJ 14.3 28.7

Cabinet Mountains Wilderness CABI1 CABI MT 3.3 14.3

Caney Creek Wilderness CACR1 CACR AR 11.8 26.7

Canyonlands NP CANY1 CANY UT 3.0 10.8

Capitol Reef NP CAPI1 CAPI UT 3.2 10.9

Caribou Wilderness LAVO1 CARI CA 2.5 13.7

Carlsbad Caverns NP GUMO1 CAVE TX 5.6 16.5

Chiricahua NM CHIR1 CHIR AZ 4.6 12.9

Chiricahua Wilderness CHIR1 CHIW AZ 4.6 12.9

Cohutta Wilderness COHU1 COHU GA 13.8 30.5

Crater Lake NP CRLA1 CRLA OR 1.6 13.2

Craters of the Moon NM CRMO1 CRMO ID 3.7 14.1

Cucamonga Wilderness SAGA1 CUCA CA 4.5 18.4

Desolation Wilderness BLIS1 DESO CA 2.3 12.9

Diamond Peak Wilderness CRLA1 DIPE OR 1.6 13.2

Dolly Sods Wilderness DOSO1 DOSO WV 11.2 29.9

Eagle Cap Wilderness STAR1 EACA OR 3.6 17.3

Eagles Nest Wilderness WHRI1 EANE CO 0.9 8.8

Emigrant Wilderness YOSE1 EMIG CA 3.4 16.9

Everglades NP EVER1 EVER FL 12.3 22.5

Fitzpatrick Wilderness BRID1 FITZ WY 1.7 10.7

Flat Tops Wilderness WHRI1 FLTO CO 0.9 8.8

Galiuro Wilderness CHIR1 GALI AZ 4.6 12.9

Gates of the Mountains 

Wilderness GAMO1 GAMO MT 1.4 11.9

Gearhart Mountain Wilderness CRLA1 GEMO OR 1.6 13.2

Gila Wilderness GICL1 GICL NM 3.1 13.1
17

Class I Area IMPROVE 

Representative 

Site

IMPROVE 

Site 

Identifier

State 2003-

2007

Baseline 

Visibility 

(in dv)

20% Best 

Days

2003-2007

Baseline 

Visibility 

(in dv)

20% 

Worst 

Days

Glacier NP GLAC1 GLAC MT 7.1 19.6

Glacier Peak Wilderness NOCA1 GLPE WA 3.0 13.3

Goat Rocks Wilderness WHPA1 GORO WA 1.6 12.7

Grand Canyon NP GRCA2 GRCA AZ 2.2 11.9

Great Gulf Wilderness GRGU1 GRGU NH 7.0 21.4

Great Sand Dunes NM GRSA1 GRSA CO 3.9 11.8

Great Smoky Mountains NP GRSM1 GRSM TN 13.2 30.6

Grand Teton NP YELL2 GRTE WY 2.2 11.2

Guadalupe Mountains NP GUMO1 GUMO TX 5.6 16.5

Hells Canyon Wilderness HECA1 HECA OR 5.0 19.0

Hercules-Glades Wilderness HEGL1 HEGL MO 13.0 27.0

Hoover Wilderness HOOV1 HOOV CA 1.5 11.6

Isle Royale NP ISLE1 ISLE MI 6.6 21.3

Jarbidge Wilderness JARB1 JARB NV 2.4 12.3

James River Face Wilderness JARI1 JARI VA 14.0 28.9

John Muir Wilderness KAIS1 JOMU CA 1.9 15.2

Joshua Tree NM JOSH1 JOSH CA 5.7 18.9

Joyce-Kilmer-Slickrock 

Wilderness GRSM1 JOYC TN 13.2 30.6

Kaiser Wilderness KAIS1 KAIS CA 1.9 15.2

Kalmiopsis Wilderness KALM1 KALM OR 6.4 16.4

Kings Canyon NP SEQU1 KICA CA 8.2 23.7

Lava Beds NM LABE1 LABE CA 3.0 14.1

La Garita Wilderness WEMI1 LAGA CO 2.8 10.0

Lassen Volcanic NP LAVO1 LAVO CA 2.5 13.7

Linville Gorge Wilderness LIGO1 LIGO NC 11.3 29.7

Lostwood LOST1 LOST ND 8.0 19.6

Lye Brook Wilderness LYBR1 LYBR VT 5.8 24.1

Maroon Bells-Snowmass 

Wilderness WHRI1 MABE CO 0.9 8.8

Mammoth Cave NP MACA1 MACA KY 16.5 32.0

Mazatzal Wilderness IKBA1 MAZA AZ 5.4 14.0

Medicine Lake MELA1 MELA MT 6.6 18.2

Mesa Verde NP MEVE1 MEVE CO 3.7 12.1

Mission Mountains Wilderness MONT1 MIMO MT 3.3 16.1

Mount Hood Wilderness MOHO1 MOHO OR 1.8 14.7
18

Class I Area IMPROVE 

Representative 

Site

IMPROVE 

Site 

Identifier

State 2003-

2007

Baseline 

Visibility 

(in dv)

20% Best 

Days

2003-2007

Baseline 

Visibility 

(in dv)

20% 

Worst 

Days

Mount Jefferson Wilderness THSI1 MOJE OR 2.7 15.8

Mokelumne Wilderness BLIS1 MOKE CA 2.3 12.9

Mountain Lakes Wilderness CRLA1 MOLA OR 1.6 13.2

Moosehorn MOOS1 MOOS ME 8.6 21.2

Mount Rainier NP MORA1 MORA WA 5.1 17.1

Mount Washington Wilderness THSI1 MOWA OR 2.7 15.8

Mount Zirkel Wilderness MOZI1 MOZI CO 1.2 9.7

North Absaroka Wilderness NOAB1 NOAB WY 1.7 11.3

North Cascades NP NOCA1 NOCA WA 3.0 13.3

Okefenokee OKEF1 OKEF GA 14.9 27.2

Olympic NP OLYM1 OLYM WA 5.7 15.8

Otter Creek Wilderness DOSO1 OTCR WV 11.2 29.9

Pasayten Wilderness PASA1 PASA WA 2.6 15.4

Pecos Wilderness WHPE1 PECO NM 1.4 9.6

Petrified Forest NP PEFO1 PEFO AZ 5.0 13.6

Pine Mountain Wilderness IKBA1 PIMO AZ 5.4 14.0

Pinnacles NM PINN1 PINN CA 8.3 17.9

Point Reyes NS PORE1 PORE CA 9.7 22.4

Presidential Range-Dry River 

Wilderness GRGU1 PRRA NH 7.0 21.4

San Rafael Wilderness RAFA1 RAFA CA 5.9 19.4

Rawah Wilderness MOZI1 RAWA CO 1.2 9.7

Red Rock Lakes YELL2 REDR WY 2.2 11.2

Redwood NP REDW1 REDW CA 5.8 18.6

Roosevelt Campobello 

International Park MOOS1 ROCA ME 8.6 21.2

Cape Romain ROMA1 ROMA SC 15.1 27.4

Rocky Mountain NP ROMO1 ROMO CO 2.3 12.9

Salt Creek SACR1 SACR NM 7.9 18.3

San Gabriel Wilderness SAGA1 SAGA CA 4.5 18.4

San Gorgonio Wilderness SAGO1 SAGO CA 5.0 21.4

Saguaro NM SAGU1 SAGU AZ 7.0 14.4

San Jacinto Wilderness SAGO1 SAJA CA 5.0 21.4

San Pedro Parks Wilderness SAPE1 SAPE NM 1.5 10.4

Sawtooth Wilderness SAWT1 SAWT ID 3.7 15.0

Scapegoat Wilderness MONT1 SCAP MT 3.3 16.1
19

Class I Area IMPROVE 

Representative 

Site

IMPROVE 

Site 

Identifier

State 2003-

2007

Baseline 

Visibility 

(in dv)

20% Best 

Days

2003-2007

Baseline 

Visibility 

(in dv)

20% 

Worst 

Days

Selway-Bitterroot Wilderness SULA1 SELW MT 2.4 17.1

Seney SENE1 SENE MI 7.1 25.1

Sequoia NP SEQU1 SEQU CA 8.2 23.7

Shenandoah NP SHEN1 SHEN VA 10.2 29.4

Shining Rock Wilderness SHRO1 SHRO NC 7.3 28.5

Sierra Ancha Wilderness SIAN1 SIAN AZ 5.7 14.5

Sipsey Wilderness SIPS1 SIPS AL 15.3 29.9

South Warner Wilderness LABE1 SOWA CA 3.0 14.1

Strawberry Mountain Wilderness STAR1 STMO OR 3.6 17.3

Superstition Wilderness TONT1 SUPE AZ 6.0 14.2

Sycamore Canyon Wilderness SYCA1 SYCA AZ 5.3 15.5

Teton Wilderness YELL2 TETO WY 2.2 11.2

Three Sisters Wilderness THSI1 THIS OR 2.7 15.8

Thousand Lakes Wilderness LAVO1 THLA CA 2.5 13.7

Theodore Roosevelt NP THRO1 THRO ND 7.1 17.9

UL Bend ULBE1 ULBE MT 4.4 15.5

Upper Buffalo Wilderness UPBU1 UPBU AR 12.0 27.0

Ventana Wilderness PINN1 VENT CA 8.3 17.9

Voyageurs NP VOYA2 VOYA MN 6.8 19.6

Washakie Wilderness NOAB1 WASH WY 1.7 11.3

West Elk Wilderness WHRI1 WEEL CO 0.9 8.8

Weminuche Wilderness WEMI1 WEMI CO 2.8 10.0

White Mountain Wilderness WHIT1 WHIT NM 3.4 13.0

Mount Adams Wilderness WHPA1 WHPA WA 1.6 12.7

Wheeler Peak Wilderness WHPE1 WHPE NM 1.4 9.6

Wind Cave NP WICA1 WICA SD 4.9 16.0

Wichita Mountains WIMO1 WIMO OK 9.9 23.6

Wolf Island OKEF1 WOLF GA 14.9 27.2

Yellowstone NP YELL2 YELL WY 2.2 11.2

Yosemite NP YOSE1 YOSE CA 3.4 16.9
20

3.4 Projection of Future Year Visibility Level

The visibility impacts of the Transport Rule and nationwide BART SO2 and NOX

emissions reductions were examined in terms of the projected improvements in visibility on the 

20 percent best and worst visibility days at the Class I areas. We quantified visibility impacts for 

the 140 Class I areas (listed in Table 3-2) in the Continental U.S. modeling domain which have 

complete IMPROVE ambient data for 2005 or are represented by IMPROVE monitors with 

complete data.

Visibility values for the 2014 base case and 2014 Transport Rule and nationwide BART 

scenarios were calculated using the regional haze methodology outlined in section 6 of the 

photochemical modeling guidance.

21 This photochemical modeling guidance recommends the 

calculation of future year changes in visibility in a similar manner to the calculation of changes 

in PM2.5 design values. The regional haze methodology for calculating future year visibility 

impairment is codified in EPA’s MATS software, which was used to post-process the modeling 

runs for this analysis. This software can be found at:

(http://www.epa.gov/scram001/modelingapps_mats.htm).

In calculating visibility impairment, the extinction coefficient values22 are made up of 

individual component species (sulfate, nitrate, organics, etc). The predicted change in visibility 

(on the 20 percent best and worst days) is calculated as the modeled percent change in the mass 

for each of the PM2.5 species and for coarse mass (on the 20% best and worst observed days) 

multiplied by the observed concentrations. The future mass of each component species is 

converted to extinction and then daily species extinction coefficients are summed to get a daily 

total extinction value (including Rayleigh scattering). The daily extinction coefficients are 

converted to deciviews and averaged across all 20 percent best or worst days. In this way, we 

calculate an average change in deciviews from the base case to a future case at each IMPROVE 

site. Subtracting the 2014 “Transport Rule + BART-elsewhere” scenario deciview values from 

the corresponding 2014 base case deciview values gives an estimate of the visibility benefits in 

Class I areas that are expected to occur from “Transport Rule + BART-elsewhere” . Likewise, 

subtracting the 2014 “nationwide BART” scenario deciview values from the corresponding 2014 

base case deciview values gives an estimate of the visibility benefits in Class I areas that are 

expected to occur from “nationwide BART”.

The following options were chosen in MATS for calculating the future year visibility values for 

the rule:

 

21 Guidance on the Use of Models and Other Analyses for Demonstrating Attainment of Air Quality 

Goals for Ozone, PM2.5, and Regional Haze, U.S. EPA, EPA-454/B-07-002, April 2007, which is found 

at: http://www.epa.gov/scram001/guidance/guide/final-03-pm-rh-guidance.pdf.

22Extinction coefficient is in units of inverse megameters (Mm-1

).It is a measure of how much light is 

absorbed or scattered as it passes through a medium. Light extinction is commonly used as a measure of 

visibility impairment in the regional haze program.
21

New IMPROVE algorithm

Use model grid cells at (IMPROVE) monitor 

Temporal adjustment at monitor- 3x3 for 12km grid, (1x1 for 36km grid)

Start monitor year- 2003

End monitor year- 2007

Base model year 2005

Minimum years required for a valid monitor- 3

The “base model year” was chosen as 2005 because it is the base case meteorological 

year for the final Transport Rule modeling. The start and end years were chosen as 2003 and 

2007 because that is the 5 year period which is centered on the base model year of 2005. These 

choices are consistent with using a 5 year base period for regional haze calculations. 

3.5 Details of the Visibility Projection Methodology

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

estimate changes expected to occur in each major PM species that are used to estimate visibility 

impairment on the 20% best and worst days. These species are ammonium sulfate, ammonium 

nitrate, organic carbon mass, elemental carbon, crustal mass, sea salt, and coarse mass. Dayspecific observations for mass associated with SO4, NO3, OC, EC, soil, and CM, are used to 

calculate bext for each day. The appropriate month- and area-specific climatological relative 

humidity adjustment factor(s) (f(rh)) are used to calculate extinction values. Total bext for all 

components are converted to deciviews for each day to get a daily deciview value. 

The steps in the visibility impairment calculation are detailed below: 

Step 1. Calculate the average baseline deciviews for the 20% of days with worst and the 

20% days with best visibility.

For each of the 5 years in the base period, order all days from worst (highest deciview 

value) to best (lowest deciview value) visibility. For each year, note the 20% of days with worst 

and the 20% of days with best visibility. Calculate the arithmetic mean deciview value for the 

identified 20% worst- and best visibility days in each year. Average the resulting 5 yearly mean 

deciview values reflecting worst visibility. Average the 5 yearly mean deciview values 

reflecting mean visibility on the days with best visibility. 

Step 2. Estimate relative response factors (RRF) for each component of PM2.5 and for 

coarse mass (CM).

This is done using the modeled species concentrations on the measured best and worst 

days in 2005 at each IMPROVE site. Take the arithmetic mean concentration for each PM2.5
22

component (and coarse mass) computed at the Class I area monitoring site from the 2014 future 

emissions case and divide this by the corresponding arithmetic mean concentration for each 

component obtained with base year (2005) emissions. The resulting quotients are the 

component-specific RRF’s. A separate set of RRF values are calculated for the “worst” and 

“best” visibility days identified in step 1. The RRFs are calculated using the identified 20% best 

and 20% worst monitored days at each Class I area. 

Step 3. Using the RRFs and ambient data, calculate future year daily concentration data 

for the best and worst days.

Multiply the relative response factors derived in Step 2 times measured daily 

concentration data for each component of PM2.5 and CM to get future daily estimates of species 

concentrations for PM2.5 components and CM on “worst visibility” and “best visibility” days. 

These multiplications produce future concentration estimates for SO4, NO3, OC, EC, Soil and 

CM for each of the previously selected “worst” and “best” visibility days. This calculation is 

performed for each best and worst day for the five year period using an RRF for each PM 

component (a separate set of RRFs for the best days and the worst days)

.

Step 4. Use the information developed in Step 3 to compute future year daily bext values 

for the best and worst days.

Use the future year concentration data calculated in step 3 to calculate future year daily 

bext values for each PM component for each of the best and worst days for the five year period. 

This is accomplished by applying the “new” IMPROVE visibility algorithm shown below:

 bext  2.2 x fs(RH) x [Small Sulfate] + 4.8 x fL(RH) x [Large Sulfate] 

+ 2.4 x fs(RH) x [Small Nitrate] + 5.1 x fL(RH) x [Large Nitrate]

+ 2.8 x {Small Organic Mass] + 6.1 x [Large Organic Mass]

+ 10 x [Elemental Carbon]

+ 1 x [Fine Soil]

+ 1.7 x fss(RH) x [Sea Salt]

+ 0.6 x [Coarse Mass]

+ Rayleigh Scattering (site specific)

The total sulfate, nitrate and organic carbon compound concentrations are each split into 

two fractions, representing small and large size distributions of those components. The organic 

mass concentration used in the new algorithm is 1.8 times the organic carbon mass 

concentration, changed from 1.4 times carbon mass concentration used for input into the old 

IMPROVE algorithm. Site-specific Rayleigh scattering is calculated for the elevation and 

annual average temperature of each IMPROVE monitoring site.
23

The apportionment of the total concentration of sulfate compounds into the 

concentrations of the small and large size fractions is accomplished using the following 

equations.

[Large Sulfate] = [Total Sulfate]/20 ug/m3 x [Total Sulfate], for [Total Sulfate] < 20 ug/m3

[Large Sulfate] = [Total Sulfate], for [Total Sulfate]  20 ug/m3

[Small Sulfate] = [Total Sulfate] - [Large Sulfate]

The same equations are used to apportion total nitrate and total organic mass 

concentrations into the small and large size fractions.

Sea salt is calculated as 1.8 x [Chloride] or 1.8 x [Chlorine] if the chloride measurement 

is below detection limits, missing or invalid. The algorithm also uses three new water growth 

adjustment terms. They are for use with the small size distribution and the large size distribution 

sulfate and nitrate compounds and for sea salt (fS(RH), fL(RH) and fSS(RH) respectively).

23

Step 5. Use the daily total bext values from step 4 to calculate future mean deciview 

values for the best and worst days.

The total daily bext for each day is converted to deciviews. This gives a future year daily 

deciview value for each of the best and worst days at each site. 

Next, compute the arithmetic mean future deciview value for the “worst” and “best” 

visibility days for each year. This leads to 5 future estimated mean deciview values for the 

“worst” and 5 future estimated mean deciview values for the “best” visibility days. Compute the 

arithmetic mean of the 5 mean values for deciviews on the “worst” days, and the arithmetic mean 

of the 5 mean deciview values estimated for the “best” visibility days. 

3.6 Modeling Analysis of the Better than BART Two Pronged Test

The visibility improvements from the Transport Rule + BART-elsewhere strategy were 

compared to the nationwide BART visibility improvements, in accordance with the two-pronged 

test for “better-than-BART” determinations set out in the Regional Haze Rule at § 51.308(e)(3).

 

23 Default values for site specific Rayleigh scattering and the f(rh) terms were supplied in the IMPROVE data 

available at on the VIEWS website. 
24

The better-than-BART test is a two pronged test. Under the first prong, visibility must 

not decline at any Class I area, as determined by comparing the predicted visibility impacts at 

each affected Class I area under the “Transport Rule + BART-elsewhere” scenario with future 

baseline (2014) visibility conditions. Under the second prong, overall visibility as measured by 

the average improvement at all affected Class I areas must be better under the “Transport Rule + 

BART-elsewhere” scenario than under “Nationwide BART”. The future year air quality 

modeling results were used to make this demonstration. 

3.6.1 Better than BART Test- 1

st Prong

We did two separate analyses to assess the potential visibility impacts of “Transport Rule 

+ BART-elsewhere” and “Nationwide BART” controls. We analyzed one set of results (“first 

approach”) for the 60 Class I areas in the Transport Rule region (the Eastern 12km modeling 

domain) and a second set of results (“second approach”) for the 140 Class I areas in the 

contiguous 48 states (referred to as the national region). For both visibility scenarios we 

quantified the visibility impacts on the 20 percent best and 20 percent worst visibility days for 

the 2014 future-year base case, the “Transport Rule + BART-elsewhere” scenario, and the 

“Nationwide BART” control scenario. Under the first prong of the test, visibility cannot degrade 

at any affected Class I area. To determine if “Transport Rule + BART-elsewhere” resulted in 

degradation of visibility at any affected Class I area we compared the visibility impacts of 

“Transport Rule + BART-elsewhere” to base case 2014 visibility conditions. 

Table 3-3 shows the projected visibility on the 20% best days and 20% worst days at each 

Class I area in the 2014 baseline and from the “Transport Rule + BART-elsewhere” control 

strategy. Under the degradation test (1st prong), there should be no degradation from the 

Transport Rule scenario compared to the 2014 baseline. The “no degradation test” column shows 

the results of subtracting the 2014 baseline visibility values from the 2014 Transport Rule + 

BART-elsewhere values. In order to pass this test, all values in the degradation test column 

should be zero or negative.24 

 

24All differences that are < 0.05 dv were rounded down to 0.0 and are considered to be no degradation. In Tables 3-3 

and 3-4, these sites are identified with an * next to the 0.0 in the no degradation-test column. Calculating visibility 

changes to the nearest tenth of a deciview (rather than the nearest hundredth) is consistent with the practice for 

implementing the reasonable progress goals under the Regional Haze rule. (See, e.g., Guidelines for Tracking 

Reasonable Progress, sample calculations in sections 1.8 and 1.11). Therefore, for purposes of the better-than-BART 

test, we have assumed that changes in visibility of less than 0.05 dv should not be considered degradation.
25

Table 3-3. Projected visibility and visibility change for the 2014 baseline and the 2014 Transport 

+ BART-elsewhere 20% best and worst days, at 140 Class I areas.

Class I Area

(IMPROVE Site)

State [A]

2014 

Baseline 

Visibility

20% Best 

Days (dv)

[B]

2014 

Baseline 

Visibility

20% Worst 

Days (dv)

[C]

2014 TR + 

BARTElsewhere 

Case 

Visibility 

20% Best 

Days (dv)

[D]

2014 TR + 

BARTElsewhere 

Case 

Visibility 

20% Worst 

Days (dv)

No

Degradation 

Test

20% Best 

Days

=[C] – [A]*

No

Degradation 

Test

20% Worst 

Days

=[D] – [B]*

Eastern Class I areas (12km modeling domain)

Acadia NP ME 8.0 20.1 7.9 19.0 0.0 -1.1

Badlands NP SD 6.3 16.0 6.2 15.4 -0.1 -0.6

Bandelier NM NM 4.2 11.1 4.1 10.8 -0.1 -0.3

Big Bend NP TX 5.4 16.3 5.2 15.2 -0.2 -1.1

Black Canyon of the 

Gunnison NM CO 2.3 9.5 2.3 9.4 -0.1 -0.1

Bosque del Apache NM 5.6 13.0 5.5 12.5 -0.1 -0.6

Boundary Waters 

Canoe Area MN 5.8 18.8 5.7 17.6 -0.1 -1.2

Brigantine NJ 13.2 25.4 12.9 22.8 -0.4 -2.5

Caney Creek 

Wilderness AR 11.3 24.4 10.9 21.2 -0.4 -3.2

Carlsbad Caverns 

NP TX 5.2 15.5 5.1 14.7 -0.1 -0.9

Cohutta Wilderness GA 12.9 26.6 12.1 22.8 -0.8 -3.8

Dolly Sods 

Wilderness WV 10.3 27.1 9.2 21.4 -1.1 -5.7

Eagles Nest 

Wilderness CO 0.4 8.3 0.4 8.2 0.0 -0.1

Everglades NP FL 11.5 20.4 11.1 19.3 -0.3 -1.0

Flat Tops 

Wilderness CO 0.4 8.3 0.4 8.2 0.0 -0.1

Great Gulf 

Wilderness NH 6.7 19.2 6.6 17.4 -0.1 -1.8

Great Sand Dunes 

NM CO 3.5 11.3 3.5 11.1 -0.1 -0.2

Great Smoky 

Mountains NP TN 12.2 27.0 11.4 23.3 -0.8 -3.7

Guadalupe 

Mountains NP TX 5.2 15.5 5.1 14.7 -0.1 -0.9

Hercules-Glades 

Wilderness MO 12.2 25.2 11.6 22.7 -0.6 -2.5

Isle Royale NP MI 6.4 19.9 6.3 18.9 -0.1 -1.0

James River Face 

Wilderness VA 12.9 25.8 12.1 21.6 -0.9 -4.2

Joyce-KilmerSlickrock TN 12.2 27.0 11.4 23.3 -0.8 -3.7
26

Class I Area

(IMPROVE Site)

State [A]

2014 

Baseline 

Visibility

20% Best 

Days (dv)

[B]

2014 

Baseline 

Visibility

20% Worst 

Days (dv)

[C]

2014 TR + 

BARTElsewhere 

Case 

Visibility 

20% Best 

Days (dv)

[D]

2014 TR + 

BARTElsewhere 

Case 

Visibility 

20% Worst 

Days (dv)

No

Degradation 

Test

20% Best 

Days

=[C] – [A]*

No

Degradation 

Test

20% Worst 

Days

=[D] – [B]*

Wilderness

La Garita 

Wilderness CO 2.3 9.5 2.3 9.4 -0.1 -0.1

Linville Gorge 

Wilderness NC 10.3 26.0 9.6 21.8 -0.7 -4.3

Lostwood ND 7.9 18.8 7.8 18.3 -0.1 -0.5

Lye Brook 

Wilderness VT 5.5 20.7 5.4 18.2 -0.1 -2.6

Maroon BellsSnowmass 

Wilderness CO 0.4 8.3 0.4 8.2 0.0 -0.1

Mammoth Cave NP KY 15.3 29.5 14.1 24.4 -1.2 -5.1

Medicine Lake MT 6.5 17.7 6.5 17.4 0.0 -0.3

Mesa Verde NP CO 3.2 11.4 3.1 11.1 -0.1 -0.3

Moosehorn ME 8.4 19.0 8.4 18.0 0.0* -1.0

Mount Zirkel 

Wilderness CO 1.0 9.2 1.0 9.2 0.0 -0.1

North Absaroka 

Wilderness WY 1.5 11.1 1.5 11.0 0.0 0.0

Okefenokee GA 13.9 24.1 13.0 21.6 -0.9 -2.5

Otter Creek 

Wilderness WV 10.3 27.1 9.2 21.4 -1.1 -5.7

Pecos Wilderness NM 1.0 9.0 1.0 8.9 -0.1 -0.2

Presidential RangeDry River 

Wilderness NH 6.7 19.2 6.6 17.4 -0.1 -1.8

Rawah Wilderness CO 1.0 9.2 1.0 9.2 0.0 -0.1

Roosevelt 

Campobello 

International Park ME 8.4 19.0 8.4 18.0 0.0* -1.0

Cape Romain SC 13.6 24.0 12.9 21.2 -0.7 -2.9

Rocky Mountain NP CO 2.0 12.2 1.9 12.1 0.0 -0.1

Salt Creek NM 7.3 17.1 7.2 16.4 -0.1 -0.7

San Pedro Parks 

Wilderness NM 1.2 9.9 1.0 9.6 -0.2 -0.3

Seney MI 6.9 23.3 6.8 21.7 -0.1 -1.6

Shenandoah NP VA 9.0 26.2 8.2 21.2 -0.8 -5.0

Shining Rock 

Wilderness NC 6.3 24.8 5.7 21.1 -0.7 -3.8

Sipsey Wilderness AL 14.5 26.5 13.6 22.8 -0.9 -3.7
27

Class I Area

(IMPROVE Site)

State [A]

2014 

Baseline 

Visibility

20% Best 

Days (dv)

[B]

2014 

Baseline 

Visibility

20% Worst 

Days (dv)

[C]

2014 TR + 

BARTElsewhere 

Case 

Visibility 

20% Best 

Days (dv)

[D]

2014 TR + 

BARTElsewhere 

Case 

Visibility 

20% Worst 

Days (dv)

No

Degradation 

Test

20% Best 

Days

=[C] – [A]*

No

Degradation 

Test

20% Worst 

Days

=[D] – [B]*

Theodore Roosevelt 

NP ND 6.8 17.0 6.8 16.7 0.0 -0.3

UL Bend MT 4.2 15.2 4.2 15.2 0.0 -0.1

Upper Buffalo 

Wilderness AR 11.3 24.7 10.8 22.2 -0.5 -2.5

Voyageurs NP MN 6.6 18.4 6.5 17.4 -0.1 -1.0

Washakie 

Wilderness WY 1.5 11.1 1.5 11.0 0.0 0.0

West Elk 

Wilderness CO 0.4 8.3 0.4 8.2 0.0 -0.1

Weminuche 

Wilderness CO 2.3 9.5 2.3 9.4 -0.1 -0.1

White Mountain 

Wilderness NM 3.1 12.3 3.0 11.7 -0.1 -0.6

Wheeler Peak 

Wilderness NM 1.0 9.0 1.0 8.9 -0.1 -0.2

Wind Cave NP SD 4.6 15.1 4.6 14.8 0.0 -0.3

Wichita Mountains OK 9.1 21.7 8.9 20.2 -0.2 -1.6

Wolf Island GA 13.9 24.1 13.0 21.6 -0.9 -2.5

Western Class I Areas (36km Modeling Domain)

Agua Tibia 

Wilderness CA 7.9 20.2 7.8 20.2 0.0 0.0

Alpine Lake 

Wilderness WA 4.6 15.8 4.6 15.7 0.0* 0.0

Anaconda-Pintler 

Wilderness MT 2.3 16.8 2.3 16.8 0.0 0.0

Ansel Adams 

Wilderness 

(Minarets) CA 1.7 14.6 1.7 14.6 0.0 0.0

Arches NP UT 2.6 9.8 2.6 9.6 -0.1 -0.2

Mount Baldy 

Wilderness AZ 2.5 10.9 2.4 10.9 -0.1 0.0*

Bob Marshall 

Wilderness MT 3.1 15.8 3.1 15.8 0.0 -0.1

Bryce Canyon NP UT 2.4 10.8 2.4 10.7 0.1# -0.1

Bridger Wilderness WY 1.6 10.4 1.6 10.3 0.0 -0.1

Cabinet Mountains 

Wilderness MT 3.0 14.0 3.0 13.9 0.0* -0.1

Canyonlands NP UT 2.6 9.8 2.6 9.6 -0.1 -0.2

Capitol Reef NP UT 2.9 10.3 2.9 10.2 0.0 -0.2
28

Class I Area

(IMPROVE Site)

State [A]

2014 

Baseline 

Visibility

20% Best 

Days (dv)

[B]

2014 

Baseline 

Visibility

20% Worst 

Days (dv)

[C]

2014 TR + 

BARTElsewhere 

Case 

Visibility 

20% Best 

Days (dv)

[D]

2014 TR + 

BARTElsewhere 

Case 

Visibility 

20% Worst 

Days (dv)

No

Degradation 

Test

20% Best 

Days

=[C] – [A]*

No

Degradation 

Test

20% Worst 

Days

=[D] – [B]*

Caribou Wilderness CA 2.3 13.2 2.3 13.2 0.0 0.0

Chiricahua NM AZ 4.4 12.4 4.4 12.4 -0.1 -0.1

Chiricahua 

Wilderness AZ 4.4 12.4 4.4 12.4 -0.1 -0.1

Crater Lake NP OR 1.5 12.7 1.5 12.7 0.0 0.0

Craters of the Moon 

NM ID 3.5 13.7 3.5 13.6 0.0 0.0

Cucamonga 

Wilderness CA 3.9 16.4 3.9 16.4 0.0 0.0

Desolation 

Wilderness CA 2.1 12.4 2.1 12.4 0.0 0.0

Diamond Peak 

Wilderness OR 1.5 12.7 1.5 12.7 0.0 0.0

Eagle Cap 

Wilderness OR 3.5 16.9 3.4 16.9 0.0 0.0*

Emigrant 

Wilderness CA 3.1 16.2 3.1 16.2 0.0 0.0

Fitzpatrick 

Wilderness WY 1.6 10.4 1.6 10.3 0.0 -0.1

Galiuro Wilderness AZ 4.4 12.4 4.4 12.4 -0.1 -0.1

Gates of the 

Mountains 

Wilderness MT 1.2 11.6 1.2 11.6 0.0 -0.1

Gearhart Mountain 

Wilderness OR 1.5 12.7 1.5 12.7 0.0 0.0

Gila Wilderness NM 2.9 13.0 2.9 12.9 0.0 -0.2

Glacier NP MT 6.8 19.1 6.8 19.0 0.0 0.0

Glacier Peak 

Wilderness WA 2.9 12.6 2.9 12.6 0.0 0.0

Goat Rocks 

Wilderness WA 1.5 11.9 1.5 11.9 0.0* -0.1

Grand Canyon NP AZ 1.9 11.0 1.9 10.9 0.0* -0.1

Grand Teton NP WY 2.0 10.9 2.0 10.9 0.0 0.0

Hells Canyon 

Wilderness OR 4.8 18.3 4.7 18.3 0.0 0.0

Hoover Wilderness CA 1.3 11.2 1.3 11.2 0.0 0.0

Jarbidge Wilderness NV 2.3 12.0 2.3 12.0 0.0 0.0

John Muir 

Wilderness CA 1.7 14.6 1.7 14.6 0.0 0.0

Joshua Tree NM CA 5.5 17.3 5.5 17.3 0.0 0.0

Kaiser Wilderness CA 1.7 14.6 1.7 14.6 0.0 0.0
29

Class I Area

(IMPROVE Site)

State [A]

2014 

Baseline 

Visibility

20% Best 

Days (dv)

[B]

2014 

Baseline 

Visibility

20% Worst 

Days (dv)

[C]

2014 TR + 

BARTElsewhere 

Case 

Visibility 

20% Best 

Days (dv)

[D]

2014 TR + 

BARTElsewhere 

Case 

Visibility 

20% Worst 

Days (dv)

No

Degradation 

Test

20% Best 

Days

=[C] – [A]*

No

Degradation 

Test

20% Worst 

Days

=[D] – [B]*

Kalmiopsis 

Wilderness OR 6.1 16.1 6.1 16.1 0.0 0.0

Kings Canyon NP CA 7.6 21.5 7.6 21.5 0.0 0.0

Lava Beds NM CA 2.9 13.5 2.9 13.5 0.0 0.0

Lassen Volcanic NP CA 2.3 13.2 2.3 13.2 0.0 0.0

Mazatzal 

Wilderness AZ 5.0 12.9 5.0 13.2 0.0 0.2#

Mission Mountains 

Wilderness MT 3.1 15.8 3.1 15.8 0.0 -0.1

Mount Hood 

Wilderness OR 1.6 13.9 1.6 13.8 0.0 -0.1

Mount Jefferson 

Wilderness OR 2.6 15.4 2.6 15.3 0.0 0.0

Mokelumne 

Wilderness CA 2.1 12.4 2.1 12.4 0.0 0.0

Mountain Lakes 

Wilderness OR 1.5 12.7 1.5 12.7 0.0 0.0

Mount Rainier NP WA 4.8 16.0 4.7 15.9 0.0 -0.1

Mount Washington 

Wilderness OR 2.6 15.4 2.6 15.3 0.0 0.0

North Cascades NP WA 2.9 12.6 2.9 12.6 0.0 0.0

Olympic NP WA 5.4 15.1 5.4 15.0 0.0 0.0

Pasayten Wilderness WA 2.4 14.8 2.4 14.7 0.0 -0.1

Petrified Forest NP AZ 4.4 13.4 4.3 13.4 0.0 0.0

Pine Mountain 

Wilderness AZ 5.0 12.9 5.0 13.2 0.0 0.2#

Pinnacles NM CA 7.8 16.5 7.8 16.5 0.0 0.0

Point Reyes NS CA 9.0 21.2 9.0 21.2 0.0 0.0

San Rafael 

Wilderness CA 5.3 18.2 5.3 18.2 0.0 0.0

Red Rock Lakes WY 2.0 10.9 2.0 10.9 0.0 0.0

Redwood NP CA 5.8 18.0 5.8 18.0 0.0 0.0

San Gabriel 

Wilderness CA 3.9 16.4 3.9 16.4 0.0 0.0

San Gorgonio 

Wilderness CA 4.6 19.7 4.6 19.7 0.0 0.0

Saguaro NM AZ 6.5 13.4 6.3 13.7 -0.2 0.3#

San Jacinto 

Wilderness CA 4.6 19.7 4.6 19.7 0.0 0.0

Sawtooth ID 3.6 14.8 3.6 14.8 0.0 0.0
30

Class I Area

(IMPROVE Site)

State [A]

2014 

Baseline 

Visibility

20% Best 

Days (dv)

[B]

2014 

Baseline 

Visibility

20% Worst 

Days (dv)

[C]

2014 TR + 

BARTElsewhere 

Case 

Visibility 

20% Best 

Days (dv)

[D]

2014 TR + 

BARTElsewhere 

Case 

Visibility 

20% Worst 

Days (dv)

No

Degradation 

Test

20% Best 

Days

=[C] – [A]*

No

Degradation 

Test

20% Worst 

Days

=[D] – [B]*

Wilderness

Scapegoat 

Wilderness MT 3.1 15.8 3.1 15.8 0.0 -0.1

Selway-Bitterroot 

Wilderness MT 2.3 16.8 2.3 16.8 0.0 0.0

Sequoia NP CA 7.6 21.5 7.6 21.5 0.0 0.0

Sierra Ancha 

Wilderness AZ 5.1 14.0 5.0 14.0 -0.1 0.0

South Warner 

Wilderness CA 2.9 13.5 2.9 13.5 0.0 0.0

Strawberry 

Mountain 

Wilderness OR 3.5 16.9 3.4 16.9 0.0 0.0*

Superstition 

Wilderness AZ 5.5 13.5 5.5 13.4 0.0 -0.1

Sycamore Canyon 

Wilderness AZ 4.8 15.0 4.8 14.9 0.0 -0.1

Teton Wilderness WY 2.0 10.9 2.0 10.9 0.0 0.0

Three Sisters 

Wilderness OR 2.6 15.4 2.6 15.3 0.0 0.0

Thousand Lakes 

Wilderness CA 2.3 13.2 2.3 13.2 0.0 0.0

Ventana Wilderness CA 7.8 16.5 7.8 16.5 0.0 0.0

Mount Adams 

Wilderness WA 1.5 11.9 1.5 11.9 0.0* -0.1

Yellowstone NP WY 2.0 10.9 2.0 10.9 0.0 0.0

Yosemite NP CA 3.1 16.2 3.1 16.2 0.0 0.0

Notes: Values in the “No Degradation Test” columns are derived by subtracting values in appropriate columns 

before those have been rounded to the nearest 0.1 dv, and then rounding the result. Thus apparent discrepancies are 

due to rounding. Negative numbers indicate visibility improvement.

* The result at these Class I areas was a degradation of less than 0.05 deciviews, which when rounded equals 0.0. 

# The default visibility test at these areas resulted in visibility degradation. See discussion below and Appendix B 

for more information on why we do not believe that visibility degradation will actually occur in these areas.

The modeling results show that the “Transport Rule + BART-elsewhere” alternative 

passed this first prong in the Transport Rule region by not causing visibility degradation at any of 

the 60 affected Class I areas in the eastern Transport Rule 12km modeling domain (i.e., when 

using the first approach to identifying affected areas), on either the 20 percent best or the 20 
31

percent worst days.

25 In the national region (i.e., when using the second approach to identifying 

affected areas), the “Transport Rule + BART-elsewhere” alternative was also predicted to not 

cause visibility degradation at any affected Class I area on either the 20 percent best of the 20 

percent worst days, with a few exceptions. The exceptions were predicted average degradations 

of 0.23, 0.23, and 0.26 deciviews, respectively, at Pine Mountain Wilderness, Arizona, Mazatzal 

Wilderness, Arizona26, and Saguaro National Park, Arizona, on the 20 percent worst days27 and a 

predicted degradation of 0.05 deciviews on the 20 percent best days at Bryce Canyon National 

Park in Utah.28 Table 3-4 shows the visibility results (with two significant digits) at these four 

Class I areas for both the “Transport Rule + BART-elsewhere” and the “Nationwide BART” 

scenario.

Table 3-4. Class I areas with predicted visibility degradation using the default visibility 

calculations

Class I Area 

Name

IMPROVE 

ID

Representative 

Site State

TR+BARTelsewhere 

20% Best 

days

TR+BARTelsewhere 

20% Worst 

days

Nationwide 

BART 20% 

Best Days

Nationwide 

BART 20% 

Worst Days

Bryce Canyon 

NP BRCA BRCA1 UT 0.05 -0.08 0.06 -0.10

Mazatzal 

Wilderness MAZA IKBA1 AZ -0.02 0.23 -0.02 0.23

Pine Mountain 

Wilderness PIMO IKBA1 AZ -0.02 0.23 -0.02 0.23

Saguaro NM SAGU SAGU1 AZ -0.24 0.26 -0.19 -0.09

The fact that unexpected degradations at some western Class I areas were predicted for 

the “Nationwide BART” scenario as well as the “Transport Rule + BART-elsewhere” scenario 

led us to investigate the CAMx modeling output in more detail. Based on that investigation, we 

consider the visibility projections for the western portion of the national modeling domain that 

indicate potential degradation in four western Class I areas under the “Transport Rule + BARTelsewhere” scenario compared to the “Base Case” scenario to be anomalous results that do not 

indicate the true effects that the “Transport Rule + BART-elsewhere” scenario (or the 

 

25 Two Class I areas had degradation of < 0.05 dv on the 20% best days and are denoted in the table with a “*”. 

These values were rounded to zero and are considered to be no degradation.

26 While not part of the two-pronged test, we also compared the baseline scenario to the “Nationwide BART” 

scenario. The analysis of the national region under the “Nationwide BART” control scenario also projected a 

degradation of 0.23 deciviews on the 20 percent worst days at Pine Mountain Wilderness and Mazatzal Wilderness,

the same as the “Transport Rule + BART-elsewhere” result.

27 The results for Pine Mountain and Mazatzal were the same because they are both represented by the 

same IMPROVE monitoring site (Ike’s Backbone, IKBA).

28Changes in visibility were rounded to the nearest 0.1 deciviews. Therefore, any changes that were less 

than 0.05 were rounded down and treated as zero. Any changes that were 0.05 or greater were rounded up 

and treated as potential degradation. 
32

“Nationwide BART” scenario) will have on visibility in these areas.

All four of the Class I areas are located in the western portion of the 36km modeling 

domain (outside of the 12km modeling domain). In the CAMx output for the grid cells in the 

vicinity of these four Class I areas, we observed that modeled concentrations of nitrate were very 

low on most of the 20 percent worst days (and 20 percent best days at Bryce Canyon) in all of 

the modeling scenarios, including the 2005 and 2014 base cases as well as the “Transport Rule + 

BART-elsewhere” and “Nationwide BART” cases. Notably, the 2005 base case modeled

concentrations were generally a small fraction of monitored ambient nitrate concentrations at the 

IMPROVE sites for the four Class I areas. In the cases where degradation was calculated, a very 

small increase in modeled nitrate was observed on several of the worst or best modeled days. 

This lead to a relatively large modeled percent increase in nitrate. Since the modeled visibility 

test is based on relative changes in PM species, the very small increase in nitrate has a relatively 

large impact on the final results. 

 

Further examination of the days when these nitrate increases occur reveals a seemingly

random pattern of very small increases and decreases that appear unrelated to EGU emissions 

changes. This nitrate modeling issue appears similar to a previously noted nitrate chemistry 

stability issue when modeled concentrations are very small and relative humidity is very low. 29

Thus, we conclude that these positive and negative differences between very low nitrate 

concentrations are a modeling artifact attributable to the ISORROPIA nitrate mechanism in 

CAMx for the conditions that apply in this geographic area on these days, and are not reasonable 

predictors of the true relative effects on visibility of the emission control scenarios. 

To further analyze the cause of the visibility degradation, we performed an analysis to 

illustrate how sensitive the predictions of degradation are to highly variable results on particular 

days. For each of these four Class I areas, we removed one day of the 20 percent worst or best 

days with the largest increase in modeled nitrate concentration at from the calculation of 2014 

visibility in the “Transport Rule + BART-elsewhere” control case. When the single day is 

removed, the predicted visibility degradations no longer occur.

Another possible method to address this modeling issue is to set a minimum 

concentration threshold for use in calculating RRFs for all PM species. Under this approach if

the average concentration across all best or worst days at each site is below the minimum 

threshold, then the RRF would be set to 1.0. If a minimum threshold of 0.01 ug/m3is used, the 

degradation no longer occurs at any Class I area. Based on this information, the EPA is 

 

29Tong 2005, Numerical instability in the Community Multiscale Air Quality Model and its impacts on aerosol and 

ozone simulations, available at: http://www.princeton.edu/~mauzeral/papers/CMAQ_instability%20submission.pdf 

and Bhave 2004, CMAQ Aerosol Module Development: Recent Enhancements and Future Plans, available at: 

http://www.cmascenter.org/conference/2004/abstracts/Model%20Development/bhave_abstract.pdf
33

considering whether to implement a minimum threshold concentration as part of the Regional 

Haze calculations. The minimum threshold would be built into the MATS software used to postprocess photochemical outputs. These possible revisions are aimed at avoiding potentially 

misleading results in situations such as the one observed near these Western Class I areas. 

Based on these detailed analyses, we are confident that no degradation in the four western 

Class I areas will result from implementation of the Transport Rule trading program in the 

Eastern U.S.(or from BART). Consequently, we are proposing that the “Transport Rule + 

BART-elsewhere” control scenario passes the first prong of the visibility test considering 

affected Class I areas located in both the Transport Rule region (first approach) and the national 

region (second approach). Additional details on the analysis of this issue, including the 

sensitivity analyses where we removed individual days and set a minimum concentration 

threshold for the RRF calculation can be found in Appendix B. 

3.6.2 Better than BART Test - 2

nd Prong

The second prong of the Better than BART test assesses whether the “Transport Rule + 

BART-elsewhere” scenario results in greater average visibility improvement at affected Class I 

areas compared to the “Nationwide BART” scenario. To determine if “Transport Rule + BARTelsewhere” achieved greater average visibility improvement, we compared the visibility impacts 

of “Transport Rule + BART-elsewhere” at the Class I areas to visibility impacts predicted at 

these same areas after implementation of “Nationwide BART”.

In the Transport Rule region (first approach) and the national region (second approach), 

the average visibility improvement should be larger under the “Transport Rule + BARTelsewhere” scenario compared to the “nationwide BART” scenario. Table 3-5 shows the detailed 

results of the 2nd prong analysis. In order to provide detailed information the modeled visibility 

change is shown individually for all 140 Class I areas. However, under the greater improvement 

test, it is acceptable if some Class I areas show greater improvement under the nationwide BART 

case, as long as the average improvement under the “Transport+ BART-elsewhere” case is 

larger.
34

Table 3-5- Projected visibility for the 2014 base case and the visibility change in 2014 for the 

“Transport Rule+ BART-elsewhere” and the “nationwide BART” scenarios on the 20% best and 

worst days at 140 IMPROVE Sites (including the average change for 60 and 140 areas).

Class I Areas

(IMPROVE Site)

State 2014 

Base 

Case

Visibility

20% Best 

Days (dv)

2014 Base 

Case

Visibility

20% 

Worst 

Days (dv)

TR – 2014 

Base Case 

20% Best 

Days

TR – 2014 

Base Case

20% 

Worst 

Days

BART –

2014 Base 

Case

20% Best 

Days

BART –

2014 

Base 

Case

20% 

Worst 

Days

Acadia NP ME 8.0 20.1 0.0 -1.1 0.0 -0.8

Badlands NP SD 6.3 16.0 -0.1 -0.6 -0.1 -0.7

Bandelier NM NM 4.2 11.1 -0.1 -0.3 -0.1 -0.4

Big Bend NP TX 5.4 16.3 -0.2 -1.1 -0.2 -1.0

Black Canyon of the 

Gunnison NM CO 2.3 9.5 -0.1 -0.1 -0.1 -0.1

Bosque del Apache NM 5.6 13.0 -0.1 -0.6 -0.1 -0.6

Boundary Waters 

Canoe Area MN 5.8 18.8 -0.1 -1.2 -0.1 -1.0

Brigantine NJ 13.2 25.4 -0.4 -2.5 -0.2 -1.6

Caney Creek 

Wilderness AR 11.3 24.4 -0.4 -3.2 -0.6 -2.2

Carlsbad Caverns NP TX 5.2 15.5 -0.1 -0.9 -0.1 -0.8

Cohutta Wilderness GA 12.9 26.6 -0.8 -3.8 -0.5 -2.3

Dolly Sods 

Wilderness WV 10.3 27.1 -1.1 -5.7 -0.8 -3.2

Eagles Nest 

Wilderness CO 0.4 8.3 0.0 -0.1 0.0 -0.1

Everglades NP FL 11.5 20.4 -0.3 -1.0 -0.3 -0.7

Flat Tops Wilderness CO 0.4 8.3 0.0 -0.1 0.0 -0.1

Great Gulf 

Wilderness NH 6.7 19.2 -0.1 -1.8 -0.1 -1.3

Great Sand Dunes 

NM CO 3.5 11.3 -0.1 -0.2 -0.1 -0.2

Great Smoky 

Mountains NP TN 12.2 27.0 -0.8 -3.7 -0.7 -2.0

Guadalupe 

Mountains NP TX 5.2 15.5 -0.1 -0.9 -0.1 -0.8

Hercules-Glades 

Wilderness MO 12.2 25.2 -0.6 -2.5 -0.8 -1.7

Isle Royale NP MI 6.4 19.9 -0.1 -1.0 -0.2 -0.9

James River Face 

Wilderness VA 12.9 25.8 -0.9 -4.2 -0.5 -2.1

Joyce-KilmerSlickrock Wilderness TN 12.2 27.0 -0.8 -3.7 -0.7 -2.0
35

Class I Areas

(IMPROVE Site)

State 2014 

Base 

Case

Visibility

20% Best 

Days (dv)

2014 Base 

Case

Visibility

20% 

Worst 

Days (dv)

TR – 2014 

Base Case 

20% Best 

Days

TR – 2014 

Base Case

20% 

Worst 

Days

BART –

2014 Base 

Case

20% Best 

Days

BART –

2014 

Base 

Case

20% 

Worst 

Days

La Garita Wilderness CO 2.3 9.5 -0.1 -0.1 -0.1 -0.1

Linville Gorge 

Wilderness NC 10.3 26.0 -0.7 -4.3 -0.5 -2.3

Lostwood ND 7.9 18.8 -0.1 -0.5 -0.1 -0.5

Lye Brook 

Wilderness VT 5.5 20.7 -0.1 -2.6 -0.1 -1.7

Maroon BellsSnowmass 

Wilderness CO 0.4 8.3 0.0 -0.1 0.0 -0.1

Mammoth Cave NP KY 15.3 29.5 -1.2 -5.1 -0.9 -2.8

Medicine Lake MT 6.5 17.7 0.0 -0.3 0.0 -0.3

Mesa Verde NP CO 3.2 11.4 -0.1 -0.3 -0.1 -0.3

Moosehorn ME 8.4 19.0 0.0* -1.0 0.0* -0.8

Mount Zirkel 

Wilderness CO 1.0 9.2 0.0 -0.1 0.0 -0.1

North Absaroka 

Wilderness WY 1.5 11.1 0.0 0.0 0.0 0.0

Okefenokee GA 13.9 24.1 -0.9 -2.5 -0.7 -1.7

Otter Creek 

Wilderness WV 10.3 27.1 -1.1 -5.7 -0.8 -3.2

Pecos Wilderness NM 1.0 9.0 -0.1 -0.2 -0.1 -0.2

Presidential RangeDry River 

Wilderness NH 6.7 19.2 -0.1 -1.8 -0.1 -1.3

Rawah Wilderness CO 1.0 9.2 0.0 -0.1 0.0 -0.1

Roosevelt 

Campobello 

International Park ME 8.4 19.0 0.0* -1.0 0.0* -0.8

Cape Romain SC 13.6 24.0 -0.7 -2.9 -0.4 -1.9

Rocky Mountain NP CO 2.0 12.2 0.0 -0.1 0.0 -0.1

Salt Creek NM 7.3 17.1 -0.1 -0.7 -0.2 -0.7

San Pedro Parks 

Wilderness NM 1.2 9.9 -0.2 -0.3 -0.2 -0.4

Seney MI 6.9 23.3 -0.1 -1.6 0.0 -1.5

Shenandoah NP VA 9.0 26.2 -0.8 -5.0 -0.6 -3.0

Shining Rock 

Wilderness NC 6.3 24.8 -0.7 -3.8 -0.5 -2.1

Sipsey Wilderness AL 14.5 26.5 -0.9 -3.7 -0.9 -2.1

Theodore Roosevelt 

NP ND 6.8 17.0 0.0 -0.3 0.0 -0.4
36

Class I Areas

(IMPROVE Site)

State 2014 

Base 

Case

Visibility

20% Best 

Days (dv)

2014 Base 

Case

Visibility

20% 

Worst 

Days (dv)

TR – 2014 

Base Case 

20% Best 

Days

TR – 2014 

Base Case

20% 

Worst 

Days

BART –

2014 Base 

Case

20% Best 

Days

BART –

2014 

Base 

Case

20% 

Worst 

Days

UL Bend MT 4.2 15.2 0.0 -0.1 0.0 -0.1

Upper Buffalo 

Wilderness AR 11.3 24.7 -0.5 -2.5 -0.6 -1.4

Voyageurs NP MN 6.6 18.4 -0.1 -1.0 -0.1 -0.8

Washakie Wilderness WY 1.5 11.1 0.0 0.0 0.0 0.0

West Elk Wilderness CO 0.4 8.3 0.0 -0.1 0.0 -0.1

Weminuche 

Wilderness CO 2.3 9.5 -0.1 -0.1 -0.1 -0.1

White Mountain 

Wilderness NM 3.1 12.3 -0.1 -0.6 -0.2 -0.5

Wheeler Peak 

Wilderness NM 1.0 9.0 -0.1 -0.2 -0.1 -0.2

Wind Cave NP SD 4.6 15.1 0.0 -0.3 -0.1 -0.4

Wichita Mountains OK 9.1 21.7 -0.2 -1.6 -0.2 -1.2

Wolf Island GA 13.9 24.1 -0.9 -2.5 -0.7 -1.7

Eastern Class I Areas 

Average

(60 Areas) 

-0.3 -1.6 -0.2 -1.0

Agua Tibia 

Wilderness CA 7.9 20.2 0.0 0.0 0.0 0.0

Alpine Lake 

Wilderness WA 4.6 15.8 0.0* 0.0 0.0* 0.0

Anaconda-Pintler 

Wilderness MT 2.3 16.8 0.0 0.0 0.0 0.0

Ansel Adams 

Wilderness 

(Minarets) CA 1.7 14.6 0.0 0.0 0.0 0.0

Arches NP UT 2.6 9.8 -0.1 -0.2 -0.1 -0.2

Mount Baldy 

Wilderness AZ 2.5 10.9 -0.1 0.0* -0.1 -0.2

Bob Marshall 

Wilderness MT 3.1 15.8 0.0 -0.1 0.0 -0.1

Bryce Canyon NP UT 2.4 10.8 0.1# -0.1 0.1# -0.1

Bridger Wilderness WY 1.6 10.4 0.0 -0.1 0.0 -0.1

Cabinet Mountains 

Wilderness MT 3.0 14.0 0.0* -0.1 0.0* -0.1

Canyonlands NP UT 2.6 9.8 -0.1 -0.2 -0.1 -0.2

Capitol Reef NP UT 2.9 10.3 0.0 -0.2 0.0 -0.2

Caribou Wilderness CA 2.3 13.2 0.0 0.0 0.0 0.0
37

Class I Areas

(IMPROVE Site)

State 2014 

Base 

Case

Visibility

20% Best 

Days (dv)

2014 Base 

Case

Visibility

20% 

Worst 

Days (dv)

TR – 2014 

Base Case 

20% Best 

Days

TR – 2014 

Base Case

20% 

Worst 

Days

BART –

2014 Base 

Case

20% Best 

Days

BART –

2014 

Base 

Case

20% 

Worst 

Days

Chiricahua NM AZ 4.4 12.4 -0.1 -0.1 -0.1 -0.2

Chiricahua 

Wilderness AZ 4.4 12.4 -0.1 -0.1 -0.1 -0.2

Crater Lake NP OR 1.5 12.7 0.0 0.0 0.0 0.0

Craters of the Moon 

NM ID 3.5 13.7 0.0 0.0 0.0 0.0

Cucamonga 

Wilderness CA 3.9 16.4 0.0 0.0 0.0 0.0

Desolation 

Wilderness CA 2.1 12.4 0.0 0.0 0.0 0.0

Diamond Peak 

Wilderness OR 1.5 12.7 0.0 0.0 0.0 0.0

Eagle Cap 

Wilderness OR 3.5 16.9 0.0 0.0* 0.0 0.0*

Emigrant Wilderness CA 3.1 16.2 0.0 0.0 0.0 0.0

Fitzpatrick 

Wilderness WY 1.6 10.4 0.0 -0.1 0.0 -0.1

Galiuro Wilderness AZ 4.4 12.4 -0.1 -0.1 -0.1 -0.2

Gates of the 

Mountains 

Wilderness MT 1.2 11.6 0.0 -0.1 0.0* 0.0

Gearhart Mountain 

Wilderness OR 1.5 12.7 0.0 0.0 0.0 0.0

Gila Wilderness NM 2.9 13.0 0.0 -0.2 0.0 -0.3

Glacier NP MT 6.8 19.1 0.0 0.0 0.0 0.0

Glacier Peak 

Wilderness WA 2.9 12.6 0.0 0.0 0.0 0.0

Goat Rocks 

Wilderness WA 1.5 11.9 0.0* -0.1 0.0* -0.1

Grand Canyon NP AZ 1.9 11.0 0.0* -0.1 0.0 -0.1

Grand Teton NP WY 2.0 10.9 0.0 0.0 0.0 0.0

Hells Canyon 

Wilderness OR 4.8 18.3 0.0 0.0 0.0 0.0

Hoover Wilderness CA 1.3 11.2 0.0 0.0 0.0 0.0

Jarbidge Wilderness NV 2.3 12.0 0.0 0.0 0.0 0.0

John Muir 

Wilderness CA 1.7 14.6 0.0 0.0 0.0 0.0

Joshua Tree NM CA 5.5 17.3 0.0 0.0 0.1# 0.0

Kaiser Wilderness CA 1.7 14.6 0.0 0.0 0.0 0.0

Kalmiopsis OR 6.1 16.1 0.0 0.0 0.0 -0.1
38

Class I Areas

(IMPROVE Site)

State 2014 

Base 

Case

Visibility

20% Best 

Days (dv)

2014 Base 

Case

Visibility

20% 

Worst 

Days (dv)

TR – 2014 

Base Case 

20% Best 

Days

TR – 2014 

Base Case

20% 

Worst 

Days

BART –

2014 Base 

Case

20% Best 

Days

BART –

2014 

Base 

Case

20% 

Worst 

Days

Wilderness

Kings Canyon NP CA 7.6 21.5 0.0 0.0 0.0 0.0

Lava Beds NM CA 2.9 13.5 0.0 0.0 0.0 0.0

Lassen Volcanic NP CA 2.3 13.2 0.0 0.0 0.0 0.0

Mazatzal Wilderness AZ 5.0 12.9 0.0 0.2# 0.0 0.2#

Mission Mountains 

Wilderness MT 3.1 15.8 0.0 -0.1 0.0 -0.1

Mount Hood 

Wilderness OR 1.6 13.9 0.0 -0.1 0.0 -0.1

Mount Jefferson 

Wilderness OR 2.6 15.4 0.0 0.0 0.0 0.0

Mokelumne

Wilderness CA 2.1 12.4 0.0 0.0 0.0 0.0

Mountain Lakes 

Wilderness OR 1.5 12.7 0.0 0.0 0.0 0.0

Mount Rainier NP WA 4.8 16.0 0.0 -0.1 0.0 -0.1

Mount Washington 

Wilderness OR 2.6 15.4 0.0 0.0 0.0 0.0

North Cascades NP WA 2.9 12.6 0.0 0.0 0.0 0.0

Olympic NP WA 5.4 15.1 0.0 0.0 0.0 0.0

Pasayten Wilderness WA 2.4 14.8 0.0 -0.1 0.0 -0.1

Petrified Forest NP AZ 4.4 13.4 0.0 0.0 -0.1 -0.2

Pine Mountain 

Wilderness AZ 5.0 12.9 0.0 0.2# 0.0 0.2#

Pinnacles NM CA 7.8 16.5 0.0 0.0 0.0 0.0

Point Reyes NS CA 9.0 21.2 0.0 0.0 0.0 0.0

San Rafael 

Wilderness CA 5.3 18.2 0.0 0.0 0.0 0.0

Red Rock Lakes WY 2.0 10.9 0.0 0.0 0.0 0.0

Redwood NP CA 5.8 18.0 0.0 0.0 0.0 0.0

San Gabriel 

Wilderness CA 3.9 16.4 0.0 0.0 0.0 0.0

San Gorgonio 

Wilderness CA 4.6 19.7 0.0 0.0 0.0 0.0

Saguaro NM AZ 6.5 13.4 -0.2 0.3# -0.2 -0.1

San Jacinto 

Wilderness CA 4.6 19.7 0.0 0.0 0.0 0.0

Sawtooth Wilderness ID 3.6 14.8 0.0 0.0 0.0 0.0

Scapegoat 

Wilderness MT 3.1 15.8 0.0 -0.1 0.0 -0.1
39

Class I Areas

(IMPROVE Site)

State 2014 

Base 

Case

Visibility

20% Best 

Days (dv)

2014 Base 

Case

Visibility

20% 

Worst 

Days (dv)

TR – 2014 

Base Case 

20% Best 

Days

TR – 2014 

Base Case

20% 

Worst 

Days

BART –

2014 Base 

Case

20% Best 

Days

BART –

2014 

Base 

Case

20% 

Worst 

Days

Selway-Bitterroot 

Wilderness MT 2.3 16.8 0.0 0.0 0.0 0.0

Sequoia NP CA 7.6 21.5 0.0 0.0 0.0 0.0

Sierra Ancha 

Wilderness AZ 5.1 14.0 -0.1 0.0 0.0 0.0

South Warner 

Wilderness CA 2.9 13.5 0.0 0.0 0.0 0.0

Strawberry Mountain 

Wilderness OR 3.5 16.9 0.0 0.0* 0.0 0.0*

Superstition 

Wilderness AZ 5.5 13.5 0.0 -0.1 0.0 -0.1

Sycamore Canyon 

Wilderness AZ 4.8 15.0 0.0 -0.1 0.0 -0.1

Teton Wilderness WY 2.0 10.9 0.0 0.0 0.0 0.0

Three Sisters 

Wilderness OR 2.6 15.4 0.0 0.0 0.0 0.0

Thousand Lakes 

Wilderness CA 2.3 13.2 0.0 0.0 0.0 0.0

Ventana Wilderness CA 7.8 16.5 0.0 0.0 0.0 0.0

Mount Adams 

Wilderness WA 1.5 11.9 0.0* -0.1 0.0* -0.1

Yellowstone NP WY 2.0 10.9 0.0 0.0 0.0 0.0

Yosemite NP CA 3.1 16.2 0.0 0.0 0.0 0.0

All Class I Areas Average

(140 Areas)

-0.1 -0.7 -0.1 -0.5

Notes: Negative numbers indicate visibility improvement.

* The result at these Class I areas was a degradation of less than 0.05 deciviews, which when rounded equals 0.0. 

# The default visibility test at these areas resulted in visibility degradation. See Appendix B for more information on 

why we do not believe that visibility degradation will actually occur in these areas. The values shown in this table 

were used (before rounding) to calculate the eastern and national averages. 

A summary of the results of the second prong of the test for the Transport Rule and 

national regions under each control scenario is shown below in Table 3-6.
40

Table 3-6. Summary of average visibility results for Eastern and nationwide areas.

Transport 

Rule – 2014 

Base Case 

20% Best Days

Transport Rule 

– 2014 Base 

Case

20% Worst 

Days

Nationwide 

BART – 2014 

Base Case

20% Best 

Days

Nationwide 

BART – 2014 

Base Case

20% Worst 

Days

Eastern Class I Areas 

Average

(60 Areas) 

-0.3 -1.6 -0.2 -1.0

All Class I Areas Average

(140 Areas)

-0.1 -0.7 -0.1 -0.5

For the 60 Eastern Class I areas analyzed, the average visibility improvement from 2014 

baseline (on the 20 percent worst days) expected as a result of the Transport Rule + BARTelsewhere is 1.6 deciviews (dv), and the average degree of improvement from 2014 baseline 

predicted for nationwide BART is 1.0 dv. The average visibility improvement from 2014 

baseline on the 20 percent best days expected as a result of the Transport Rule + BARTelsewhere is 0.3 deciviews (dv), and the average degree of improvement from 2014 baseline 

predicted for nationwide BART is 0.2 dv. Therefore, the Transport Rule makes substantially 

more improvement than nationwide BART in the East.

Similarly, for Class I Areas nationwide, the visibility modeling shows that for the 140 

class I areas evaluated, the average visibility improvement, on the 20 percent worst days in 2014 

was 0.7 dv under the Transport Rule, and 0.5 dv under the nationwide BART approach. The 

average visibility improvement, on the 20 percent best days in 2014 was 0.1 dv for “Transport 

Rule + BART-elsewhere” and 0.1 dv under “nationwide BART”

30

.

Based on these results, the Transport Rule scenario passes the second prong of the 

proposed better-than-BART test based on the fact that, on average, in both the Eastern Class I 

areas and nationally, visibility improvement is greater under the Transport Rule + BARTelsewhere scenario compared to nationwide BART on the 20% best and 20% worst visibility 

days.

 

30 Before rounding to the tenths digit, “Transport Rule +BART-elsewhwere” has a visibility improvement of 0.13 

dv compared to 0.11 dv for “nationwide BART”.
41

3.6.3 Summary of Better than BART Test

The Transport Rule scenario passes the first prong of the two-pronged better-than-BART 

test by not causing degradation of visibility at any Class I area on either the 20% best or 20% 

worst visibility days. The Transport Rule also passes the second prong of the better-than-BART 

test based on the expectation that :(a) for the Eastern Class I areas visibility improvement is 

greater on average for the both the 20% best and 20% worst days under the Transport Rule 

compared to nationwide BART, and (b) for all Class I areas nationally, visibility improvement is 

essentially equal on the best 20% days, and greater on the worst 20% days.31

 

31 Under the Regional Haze Rule, reasonable progress goals are defined in terms of improvement on the worst 20% 

days, and no degradation on the 20% best days. The proposed better-than-BART test does not specify whether each 

prong should apply on both best and worst days. For purposes of this analysis, as previously noted, we have 

considered both the best days and the worst days under both the no-degradation prong and the overall-improvement 

prong, without determining that all subsequent better-than-BART demonstrations must do so. Similarly, the fact that 

we have considered all Class I areas nationwide in part of our analysis does not imply that any future better-thanBART demonstration must necessarily consider all Class I areas in the nation to be “impacted” by the particular 

BART-alternative under consideration.
42

Appendix A

EGUs that were assumed to BART-subject for the purpose of this analysis

State Name Plant Name Unit ID Capacity 

(MW)

On Line 

Year

Alabama Barry 4 362 1969

Alabama Barry 5 750 1971

Alabama Charles R Lowman 1 86.0 1969

Alabama Charles R Lowman 2 238 1979

Alabama Charles R Lowman 3 238 1980

Alabama Colbert 5 459 1965

Alabama E C Gaston 4 256 1962

Alabama E C Gaston 5 861 1974

Alabama Gorgas 10 690 1972

Alabama Greene County 1 254 1965

Alabama Greene County 2 243 1966

Alabama James H Miller Jr 2 687 1985

Alabama James H Miller Jr 1 684 1978

Alabama Widows Creek 8 464 1965

Arizona Apache Station 2 175 1979

Arizona Apache Station 3 175 1979

Arizona Cholla 2 275 1978

Arizona Cholla 3 271 1980

Arizona Cholla 4 380 1981

Arizona Coronado U1B 395 1979

Arizona Coronado U2B 390 1980

Arizona H Wilson Sundt Generating Station 4 156 1967

Arizona Navajo 1 750 1974

Arizona Navajo 2 750 1975

Arizona Navajo 3 750 1976

Arkansas Flint Creek 1 528 1978

Arkansas White Bluff 1 815 1980

Arkansas White Bluff 2 825 1981

Colorado Cherokee 3 165 1962

Colorado Cherokee 4 388 1968

Colorado Comanche 1 366 1973

Colorado Comanche 2 370 1975

Colorado Craig C1 428 1980

Colorado Craig C2 428 1979

Colorado Hayden H1 205 1965

Colorado Hayden H2 300 1976
43

State Name Plant Name Unit ID Capacity 

(MW)

On Line 

Year

Colorado Martin Drake 5 46.0 1962

Colorado Martin Drake 6 77.0 1968

Colorado Martin Drake 7 131 1974

Colorado Pawnee 1 505 1981

Colorado Ray D Nixon 1 208 1980

Colorado Valmont 5 199 1964

Connecticut Bridgeport Station BHB3 372 1968

Delaware Edge Moor 4 174 1966

Delaware Indian River Generating Station 3 153 1970

Delaware Indian River Generating Station 4 405 1980

Florida Big Bend BB01 391 1970

Florida Big Bend BB02 391 1973

Florida Big Bend BB03 364 1976

Florida Crist 6 302 1970

Florida Crist 7 472 1973

Florida Crystal River 1 379 1966

Florida Crystal River 2 491 1969

Florida Crystal River 4 722 1982

Florida Crystal River 5 721 1984

Florida Deerhaven Generating Station B2 228 1981

Florida Lansing Smith 1 162 1965

Florida Lansing Smith 2 195 1967

Georgia Bowen 1BLR 713 1971

Georgia Bowen 2BLR 718 1972

Georgia Bowen 3BLR 902 1974

Georgia Bowen 4BLR 929 1975

Georgia Hammond 4 510 1970

Georgia Harllee Branch 1 266 1965

Georgia Harllee Branch 2 325 1967

Georgia Harllee Branch 3 509 1968

Georgia Harllee Branch 4 507 1969

Georgia Jack McDonough MB1 258 1963

Georgia Jack McDonough MB2 259 1964

Georgia Kraft 3 102 1965

Georgia McIntosh 1 157 1979

Georgia Scherer 1 837 1982

Georgia Scherer 2 843 1984

Georgia Wansley 1 891 1976

Georgia Wansley 2 892 1978

Georgia Yates Y6BR 352 1974
44

State Name Plant Name Unit ID Capacity 

(MW)

On Line 

Year

Georgia Yates Y7BR 355 1974

Illinois Baldwin Energy Complex 1 624 1970

Illinois Baldwin Energy Complex 2 629 1973

Illinois Baldwin Energy Complex 3 629 1975

Illinois Coffeen 01 340 1965

Illinois Coffeen 02 560 1972

Illinois Dallman 31 86.0 1968

Illinois Dallman 32 87.0 1972

Illinois Dallman 33 199 1978

Illinois Duck Creek 1 335 1976

Illinois E D Edwards 2 273 1968

Illinois E D Edwards 3 364 1972

Illinois Havana 9 487 1978

Illinois Joliet 29 71 259 1965

Illinois Joliet 29 72 259 1965

Illinois Joliet 29 81 259 1965

Illinois Joliet 29 82 259 1965

Illinois Kincaid Generation LLC 1 584 1967

Illinois Kincaid Generation LLC 2 584 1968

Illinois Marion 4 170 1978

Illinois Marion 123 120 2003

Illinois Newton 1 557 1977

Illinois Newton 2 569 1982

Illinois Pearl Station 1A 22.0 1967

Illinois Powerton 51 385 1972

Illinois Powerton 52 385 1972

Illinois Powerton 61 385 1975

Illinois Powerton 62 385 1975

Illinois Waukegan 8 361 1962

Illinois Will County 4 510 1963

Illinois Wood River 5 383 1964

Indiana A B Brown 1 245 1979

Indiana Bailly 7 160 1962

Indiana Bailly 8 320 1968

Indiana Cayuga 1 479 1970

Indiana Cayuga 2 473 1972

Indiana Crawfordsville 6 12.6 1965

Indiana Dean H Mitchell 11 110 1970

Indiana F B Culley 2 90.0 1966

Indiana F B Culley 3 270 1973
45

State Name Plant Name Unit ID Capacity 

(MW)

On Line 

Year

Indiana Frank E Ratts 1SG1 122 1970

Indiana Frank E Ratts 2SG1 121 1970

Indiana Gibson 1 630 1975

Indiana Gibson 2 628 1975

Indiana Gibson 3 628 1978

Indiana Gibson 4 622 1979

Indiana Harding Street 70 435 1973

Indiana Jasper 2 1 14.0 1968

Indiana Logansport 6 22.0 1964

Indiana Merom 1SG1 507 1983

Indiana Merom 2SG1 493 1982

Indiana Michigan City 12 469 1974

Indiana Petersburg 1 232 1967

Indiana Petersburg 2 435 1969

Indiana Petersburg 3 540 1977

Indiana R M Schahfer 14 431 1976

Indiana R M Schahfer 15 472 1979

Indiana State Line 4 303 1962

Indiana Tanners Creek U4 500 1964

Indiana Wabash River 6 318 1968

Indiana Warrick 2 136 1964

Indiana Warrick 3 136 1965

Indiana Warrick 4 300 1970

Indiana Whitewater Valley 2 62.8 1973

Iowa Ames Electric Services Power Plant 7 33.0 1968

Iowa Burlington 1 209 1968

Iowa Fair Station 2 41.0 1967

Iowa George Neal North 1 135 1964

Iowa George Neal North 2 300 1972

Iowa George Neal North 3 515 1975

Iowa George Neal South 4 632 1979

Iowa Lansing 4 261 1977

Iowa Milton L Kapp 2 211 1967

Iowa Muscatine Plant #1 8 35.0 1969

Iowa Ottumwa 1 673 1981

Iowa Pella 6 15.0 1974

Iowa Pella 7 12.8 1964

Iowa Prairie Creek 4 125 1967

Iowa Streeter Station 6 18.9 1963

Iowa Streeter Station 7 35.8 1973
46

State Name Plant Name Unit ID Capacity 

(MW)

On Line 

Year

Iowa Walter Scott Jr. Energy Center 3 690 1978

Kansas Jeffrey Energy Center 1 730 1978

Kansas Jeffrey Energy Center 2 730 1980

Kansas La Cygne 1 724 1973

Kansas La Cygne 2 682 1977

Kansas Lawrence Energy Center 5 366 1971

Kansas Nearman Creek N1 229 1981

Kansas Quindaro 1 72.0 1965

Kansas Quindaro 2 111 1971

Kansas Tecumseh Energy Center 10 129 1962

Kentucky Big Sandy BSU1 260 1963

Kentucky Big Sandy BSU2 800 1969

Kentucky Cane Run 4 155 1962

Kentucky Cane Run 5 168 1966

Kentucky Cane Run 6 240 1969

Kentucky Cooper 1 116 1965

Kentucky Cooper 2 225 1969

Kentucky E W Brown 2 160 1963

Kentucky E W Brown 3 422 1971

Kentucky East Bend 2 600 1981

Kentucky Elmer Smith 1 132 1964

Kentucky Elmer Smith 2 261 1974

Kentucky Ghent 1 475 1973

Kentucky Ghent 2 469 1977

Kentucky Ghent 3 478 1981

Kentucky H L Spurlock 1 315 1977

Kentucky H L Spurlock 2 509 1981

Kentucky HMP&L Station Two Henderson H1 153 1973

Kentucky HMP&L Station Two Henderson H2 159 1974

Kentucky Kenneth C Coleman C1 150 1969

Kentucky Kenneth C Coleman C2 150 1970

Kentucky Kenneth C Coleman C3 155 1971

Kentucky Mill Creek 1 303 1972

Kentucky Mill Creek 2 301 1974

Kentucky Mill Creek 3 391 1978

Kentucky Mill Creek 4 477 1982

Kentucky Paradise 1 616 1963

Kentucky Paradise 2 602 1963

Kentucky Paradise 3 977 1970

Kentucky R D Green G1 231 1979
47

State Name Plant Name Unit ID Capacity 

(MW)

On Line 

Year

Kentucky R D Green G2 233 1981

Kentucky Robert A Reid R1 65.0 1966

Louisiana Big Cajun 2 2B1 580 1981

Louisiana Big Cajun 2 2B1 580 1981

Louisiana R S Nelson6 550 1982

Louisiana Rodemacher2 523 1982

Maryland C P Crane2 200 1963

Maryland Chalk Point LLC1 341 1964

Maryland Chalk Point LLC2 342 1965

Maryland Dickerson3 182 1962

Maryland Herbert A Wagner3 324 1966

Maryland Morgantown Generating Plant1 624 1970

Maryland Morgantown Generating Plant2 620 1971

Massachusetts Brayton Point1 243 1963

Massachusetts Brayton Point2 244 1964

Massachusetts Brayton Point3 612 1969

Michigan Belle River1 698 1984

Michigan Belle River2 698 1985

Michigan Eckert Station4 69.2 1964

Michigan Eckert Station5 68.9 1968

Michigan Eckert Station6 66.8 1970

Michigan Erickson Station1 152 1973

Michigan Harbor Beach1 103 1968

Michigan J H Campbell1 260 1962

Michigan J H Campbell2 355 1967

Michigan J H Campbell3 825 1980

Michigan James De Young4 20.5 1962

Michigan James De Young5 27.0 1969

Michigan Monroe1 770 1972

Michigan Monroe2 785 1973

Michigan Monroe3 795 1973

Michigan Monroe4 775 1974

Michigan Presque Isle5 88.0 1974

Michigan Presque Isle6 88.0 1975

Michigan Presque Isle7 85.0 1978

Michigan Presque Isle8 85.0 1978

Michigan Presque Isle9 85.0 1979

Michigan Shiras2 19.5 1972

Michigan St Clair7 440 1969

Michigan Trenton Channel 9A 536 1968
48

State Name Plant Name Unit ID Capacity 

(MW)

On Line 

Year

Minnesota Allen S King 1 610 1968

Minnesota Austin Northeast NEPP 29.3 1971

Minnesota Clay Boswell 3 351 1973

Minnesota Clay Boswell 4 525 1980

Minnesota Hibbing 1 10.2 1965

Minnesota Hibbing 2 10.2 1965

Minnesota Hibbing 3 10.2 1965

Minnesota Hoot Lake 3 84.0 1964

Minnesota Sherburne County 1 762 1976

Minnesota Sherburne County 2 752 1977

Minnesota Silver Bay Power BLR2 69.0 1963

Minnesota Silver Lake 3 24.3 1962

Minnesota Silver Lake 4 59.2 1969

Minnesota Taconite Harbor Energy Center 3 68.0 1967

Mississippi Henderson H3 17.9 1967

Mississippi Jack Watson 4 230 1968

Mississippi Jack Watson 5 476 1973

Mississippi R D Morrow 1 180 1978

Mississippi R D Morrow 2 180 1978

Mississippi Victor J Daniel Jr 1 514 1977

Mississippi Victor J Daniel Jr 2 514 1981

Missouri Asbury 1 213 1970

Missouri Blue Valley 3 51.0 1965

Missouri Iatan 1 651 1980

Missouri James River Power Station 4 56.0 1964

Missouri James River Power Station 5 97.0 1970

Missouri Labadie 1 597 1970

Missouri Labadie 2 594 1971

Missouri Labadie 3 612 1972

Missouri Labadie 4 612 1973

Missouri Lake Road 6 97.4 1967

Missouri Marshall 5 16.0 1967

Missouri Montrose 3 176 1964

Missouri New Madrid 1 580 1972

Missouri New Madrid 2 580 1977

Missouri Rush Island 1 604 1976

Missouri Rush Island 2 604 1977

Missouri Sibley 2 53.9 1962

Missouri Sibley 3 401 1969

Missouri Sikeston Power Station 1 233 1981
49

State Name Plant Name Unit ID Capacity 

(MW)

On Line 

Year

Missouri Sioux 1 497 1967

Missouri Sioux 2 497 1968

Missouri Southwest Power Station 1 178 1976

Missouri Thomas Hill MB1 175 1966

Missouri Thomas Hill MB2 275 1969

Montana Colstrip 1 307 1975

Montana Colstrip 2 307 1976

Montana J E Corette Plant 2 158 1968

Nebraska Gerald Gentleman32 1 665 1979

Nebraska Lon Wright 7 20.0 1963

Nebraska Lon Wright 8 85.0 1976

Nebraska Nebraska City 1 646 1979

Nebraska North Omaha 4 138 1963

Nebraska North Omaha 5 224 1968

Nebraska Sheldon 1 105 1968

Nebraska Whelan Energy Center 1 77.0 1981

Nevada Reid Gardner 1 110 1965

Nevada Reid Gardner 2 110 1968

Nevada Reid Gardner 3 110 1976

New Hampshire Merrimack 2 320 1968

New Jersey PSEG Hudson Generating Station 2 608 1967

New Mexico Four Corners 1 170 1963

New Mexico Four Corners 2 170 1963

New Mexico Four Corners 3 220 1964

New Mexico Four Corners 4 760 1969

New Mexico Four Corners 5 760 1970

New Mexico San Juan 1 322 1976

New Mexico San Juan 2 320 1973

New Mexico San Juan 3 495 1979

New Mexico San Juan 4 506 1982

North Carolina Asheville 1 191 1964

North Carolina Asheville 2 185 1971

North Carolina Belews Creek 1 1115 1974

North Carolina Belews Creek 2 1115 1975

North Carolina Cliffside 5 550 1972

North Carolina L V Sutton 3 403 1972

 

32 Gerald Gentleman Unit 2 was inadvertently omitted from the BART inventory. See Table 2-4 for information on 

how this omission potentially affected total SO2 emissions the “Nationwide BART” control scenario. Total NOx 

emissions would not have been impacted.
50

State Name Plant Name Unit ID Capacity 

(MW)

On Line 

Year

North Carolina Lee 3 248 1962

North Carolina Marshall 1 378 1965

North Carolina Marshall 2 378 1966

North Carolina Marshall 3 657 1969

North Carolina Marshall 4 657 1970

North Carolina Roxboro 1 369 1966

North Carolina Roxboro 2 671 1968

North Carolina Roxboro 3A 353 1973

North Carolina Roxboro 3B 353 1973

North Carolina Roxboro 4A 349 1980

North Carolina Roxboro 4B 349 1980

North Dakota Coal Creek 1 554 1979

North Dakota Coal Creek 2 560 1981

North Dakota Leland Olds 1 221 1966

North Dakota Leland Olds 2 448 1975

North Dakota Milton R Young B1 250 1970

North Dakota Milton R Young B2 455 1977

North Dakota R M Heskett B2 75.5 1963

North Dakota Stanton 1 130 1967

Ohio Avon Lake 12 616 1970

Ohio Bay Shore 3 142 1963

Ohio Bay Shore 4 215 1968

Ohio Cardinal 1 600 1967

Ohio Cardinal 2 600 1967

Ohio Cardinal 3 630 1977

Ohio Conesville 3 165 1962

Ohio Conesville 4 780 1973

Ohio Conesville 5 375 1976

Ohio Conesville 6 375 1978

Ohio Dover 4 15.2 1968

Ohio Eastlake 5 597 1972

Ohio General James M Gavin 1 1310 1974

Ohio General James M Gavin 2 1300 1975

Ohio Hamilton 8 32.8 1964

Ohio Hamilton 9 51.0 1974

Ohio J M Stuart 1 597 1971

Ohio J M Stuart 2 597 1970

Ohio J M Stuart 3 597 1972

Ohio J M Stuart 4 597 1974

Ohio Lake Road 11 20.0 1967
51

State Name Plant Name Unit ID Capacity 

(MW)

On Line 

Year

Ohio Lake Shore 18 245 1962

Ohio Miami Fort 7 500 1975

Ohio Miami Fort 8 500 1978

Ohio Muskingum River 5 585 1968

Ohio Orrville 13 23.0 1971

Ohio Shelby Municipal Light Plant 1 12.0 1968

Ohio Shelby Municipal Light Plant 2 12.0 1968

Ohio St Marys 6 9.00 1967

Ohio W H Sammis 4 180 1962

Ohio W H Sammis 5 300 1967

Ohio W H Sammis 6 600 1969

Ohio W H Sammis 7 600 1971

Ohio Walter C Beckjord 5 238 1962

Ohio Walter C Beckjord 6 414 1969

Oklahoma Muskogee 4 511 1977

Oklahoma Muskogee 5 522 1978

Oklahoma Northeastern 3313 450 1979

Oklahoma Northeastern 3314 450 1980

Oklahoma Sooner 1 535 1979

Oklahoma Sooner 2 540 1980

Oregon Boardman 1SG 585 1980

Pennsylvania Bruce Mansfield 1 830 1976

Pennsylvania Bruce Mansfield 2 830 1977

Pennsylvania Bruce Mansfield 3 830 1979

Pennsylvania Cheswick 1 580 1970

Pennsylvania Conemaugh 1 850 1970

Pennsylvania Conemaugh 2 850 1971

Pennsylvania Hatfields Ferry Power Station 1 530 1969

Pennsylvania Hatfields Ferry Power Station 2 530 1970

Pennsylvania Hatfields Ferry Power Station 3 530 1971

Pennsylvania Homer City Station 1 620 1969

Pennsylvania Homer City Station 2 614 1970

Pennsylvania Homer City Station 3 650 1977

Pennsylvania Keystone 1 850 1967

Pennsylvania Keystone 2 850 1968

Pennsylvania Mitchell Power Station 33 277 1963

Pennsylvania New Castle 5 138 1964

Pennsylvania Portland 2 243 1962

Pennsylvania PPL Brunner Island 2 387 1965

Pennsylvania PPL Brunner Island 3 754 1981
52

State Name Plant Name Unit ID Capacity 

(MW)

On Line 

Year

Pennsylvania PPL Montour 1 761 1971

Pennsylvania PPL Montour 2 757 1973

South Carolina Canadys Steam CAN1 105 1962

South Carolina Canadys Steam CAN2 116 1964

South Carolina Canadys Steam CAN3 175 1967

South Carolina Dolphus M Grainger 1 85.0 1966

South Carolina Dolphus M Grainger 2 85.0 1966

South Carolina Jefferies 3 153 1970

South Carolina Jefferies 4 153 1970

South Carolina Wateree WAT1 350 1970

South Carolina Wateree WAT2 350 1971

South Carolina Williams WIL1 615 1973

South Carolina Winyah 1 295 1975

South Carolina Winyah 2 295 1977

South Dakota Big Stone 1 470 1975

Tennessee Bull Run 1 881 1967

Tennessee Cumberland 1 1232 1973

Tennessee Cumberland 2 1233 1973

Texas Big Brown 1 575 1971

Texas Big Brown 2 575 1972

Texas Coleto Creek 1 632 1980

Texas Fayette Power Project 1 598 1979

Texas Fayette Power Project 2 598 1980

Texas Harrington 061B 347 1976

Texas Harrington 062B 347 1978

Texas Harrington 063B 347 1980

Texas J T Deely 1 385 1977

Texas J T Deely 2 385 1978

Texas Martin Lake 1 750 1977

Texas Martin Lake 2 750 1978

Texas Martin Lake 3 750 1979

Texas Monticello 1 565 1974

Texas Monticello 2 565 1975

Texas Monticello 3 750 1978

Texas Sandow 4 545 1981

Texas W A Parish WAP5 645 1977

Texas W A Parish WAP6 650 1978

Texas W A Parish WAP7 565 1980

Texas Welsh 1 528 1977

Texas Welsh 2 528 1980
53

State Name Plant Name Unit ID Capacity 

(MW)

On Line 

Year

Utah Hunter 1 430 1978

Utah Hunter 2 430 1980

Utah Huntington 1 445 1977

Utah Huntington 2 450 1974

Virginia Chesapeake 4 217 1962

Virginia Chesterfield 5 310 1964

Virginia Chesterfield 6 658 1969

Washington Transalta Centralia Generation BW21 703 1972

Washington Transalta Centralia Generation BW22 703 1973

West Virginia Fort Martin Power Station 1 552 1967

West Virginia Fort Martin Power Station 2 555 1968

West Virginia Harrison Power Station 1 652 1972

West Virginia Harrison Power Station 2 642 1973

West Virginia Harrison Power Station 3 651 1974

West Virginia John E Amos 1 800 1971

West Virginia John E Amos 2 800 1972

West Virginia John E Amos 3 1300 1973

West Virginia Mitchell 1 800 1971

West Virginia Mitchell 2 800 1971

West Virginia Mountaineer 1 1300 1980

West Virginia Mt Storm 1 524 1965

West Virginia Mt Storm 2 524 1966

West Virginia Mt Storm 3 521 1973

West Virginia Pleasants Power Station 1 639 1979

West Virginia Pleasants Power Station 2 639 1980

Wisconsin Capitol Heat and Power 1 0.9 1963

Wisconsin Capitol Heat and Power 2 1 1964

Wisconsin Columbia 1 555 1975

Wisconsin Columbia 2 559 1978

Wisconsin Edgewater 4 321 1969

Wisconsin Genoa 1 356 1969

Wisconsin John P Madgett B1 398 1978

Wisconsin Manitowoc 7 18.0 1964

Wisconsin Menasha B24 14.5 1964

Wisconsin Nelson Dewey 2 111 1962

Wisconsin Pleasant Prairie 1 617 1980

Wisconsin Pulliam 8 133 1964

Wisconsin South Oak Creek 7 298 1965

Wisconsin South Oak Creek 8 312 1967

Wisconsin Valley 1 70.0 1968
54

State Name Plant Name Unit ID Capacity 

(MW)

On Line 

Year

Wisconsin Valley 2 70.0 1968

Wisconsin Valley 3 70.0 1969

Wisconsin Valley 4 70.0 1969

Wisconsin Weston 3 338 1981

Wyoming Dave Johnston BW43 220 1964

Wyoming Dave Johnston BW44 330 1972

Wyoming Jim Bridger BW71 530 1974

Wyoming Jim Bridger BW72 530 1975

Wyoming Jim Bridger BW73 530 1976

Wyoming Jim Bridger BW74 530 1979

Wyoming Laramie River Station 1 565 1980

Wyoming Laramie River Station 2 570 1981

Wyoming Naughton 1 160 1963

Wyoming Naughton 2 210 1968

Wyoming Naughton 3 330 1971

Wyoming Neil Simpson 5 14.6 1969

Wyoming Wyodak BW91 335 1978
55

Appendix B

Analysis of Visibility Degradation at Four Class I Areas

The default post-processing to calculate changes in visibility from the Transport Rule and 

nationwide BART (compared to the 2014 base case) showed apparent visibility degradation at 

four Class I areas, as shown in table B-1 below.

Table B-1. Visibility change for the 2014 “Transport Rule + BART-elsewhere” case compared 

to the 2014 base case (positive numbers represent degradation).

Class I Area Name

IMPROVE 

ID

Representative 

IMPROVE 

Site State

Visibility 

Change 

TR+BARTelsewhere 

20% Best 

days

Visibility 

Change 

TR+BARTelsewhere 

20% Worst 

days

Bryce Canyon NP BRCA BRCA1 UT 0.05 -0.08

Mazatzal Wilderness MAZA IKBA1 AZ -0.02 0.23

Pine Mountain 

Wilderness PIMO IKBA1 AZ -0.02 0.23

Saguaro NM SAGU SAGU1 AZ -0.24 0.26

All four of the Class I areas are located in the western portion of the 36km modeling 

domain (outside of the 12km modeling domain). In the CAMx output for the grid cells in the 

vicinity of these four Class I areas, we observed that in most cases, modeled concentrations of 

nitrate were very low on the 20 percent worst days (and 20 percent best days at Bryce Canyon) in 

both the “Transport Rule + BART-elsewhere” case and the “Nationwide BART” case. The 

modeled nitrate concentrations in these cases ranged from 0.001 to 0.004 μg/m3, averaged across 

the 20 percent worst or best days in 2005. As an example, Figure B-1 shows the daily average 

nitrate concentration for September 4 (the figure shows the modeled concentration for September 

4

th in 2014), which is one of the measured 20% worst days at Saguaro National Monument. It 

can be seen that the modeled nitrate concentration in much of Arizona and large parts of the 

West are very small (the gray color is concentrations of < 0.0001 ug/m3). 
56

Figure B-1. Modeled daily average particulate nitrate concentration in the 2014 base case 

scenario.

Notably, the modeled concentrations were generally a small fraction of monitored 

ambient nitrate concentrations at the IMPROVE sites for the four Class I areas. In the cases 

where degradation was calculated, a very small increase in modeled nitrate was observed on 

several of the worst or best modeled days. This led to a relatively large modeled percent increase 

in nitrate. Table B-2 shows the modeled daily average nitrate concentration on a single best or 

worst day at each of the four Class 1 areas. Among the 20% best or worst days at each of these 

Class I areas, these particular days had the largest percentage increase in nitrate. At each class I 

area, there were between 22 and 25 days that went into the best or worst days calculations.
57

Table B-2. Modeled nitrate concentrations on the single best or worst day with the largest 

modeled nitrate increase between the 2014 base case and the 2014 Transport Rule scenario.

Class I Area State Date

Best 

or 

Worst 

Day?

Modeled 

Nitrate 

Concentration 

2014 Base 

Case (ug/m3)

Modeled Nitrate 

Concentration 

2014 Transport 

Rule + BARTElsewhere 

(ug/m3)

% 

Nitrate 

Increase

Mazatzal Wilderness AZ 9/10/2005 Worst 0.0003 0.0196 6533%

Pine Mountain Wilderness AZ 9/10/2005 Worst 0.0003 0.0196 6533%

Bryce Canyon NP UT 12/09/2005 Best 0.0047 0.015 319%

Saguaro NM AZ 9/04/2005 Worst 0.0096 0.0497 518%

The large percentage increase in nitrate on a single day at Matzatzal and Pine Mountain 

has a large influence on the average nitrate RRF that is used in the visibility calculation. The 

average nitrate concentration (averaged over 23 days) on the worst days at Pine Mountain and 

Mazatzal increased from 0.001 μg/m3in the 2014 base case to 0.002 μg/m3in the “Transport 

Rule + BART-elsewhere” case. This is a very small increase that would not lead to noticeable 

changes in visibility; however it is a large percentage increase (100%). Since the modeled 

visibility test is based on relative changes in PM species, the very small increase in nitrate has a 

large impact on the final results. 

 

Further examination of the days when these nitrate increases occur reveals a somewhat 

random pattern of very small increases and decreases that appear unrelated to EGU emissions 

changes. Figure B-2 shows the relative change in nitrate on September 4th between the 2014 

base case and 2014 Transport Rule +BART-elsewhere case. 
58

Figure B-2. Nitrate relative response factor on September 4th between the 2014 base case and 

the 2014 Transport Rule scenario (values > 1 are increases in nitrate in the control scenario).

While IPM predicts modestly higher NOX emissions in some nearby states under the 

“Transport Rule + BART-elsewhere” scenario, the checkerboard pattern of nitrate increases and 

decreases in the desert Southwest (among other areas) show no logical connection to these 

modestly higher emissions. This nitrate modeling issue appears similar to a previously noted 

nitrate chemistry stability issue when modeled concentrations are very small and relative 

humidity is very low.33 The instability is related to the ISORROPIA nitrate partitioning 

mechanism that is used in CAMx to solve the partitioning of nitrate between the gas and particle 

phase. The same partitioning mechanism is used in both the CAMx and CMAQ models. An 

updated version of ISORROPIA (ISOROPPIA II) has been developed and reportedly has solved 

most of the instability issues encountered with the current version.

34

 

Thus, we conclude that these positive and negative differences between very low nitrate 

 

33 Tong 2005, Numerical instability in the Community Multiscale Air Quality Model and its impacts on aerosol and 

ozone simulations, available at: http://www.princeton.edu/~mauzeral/papers/CMAQ_instability%20submission.pdf. 

And Bhave 2004, CMAQ Aerosol Module Development: Recent Enhancements and Future Plans, available at: 

http://www.cmascenter.org/conference/2004/abstracts/Model%20Development/bhave_abstract.pdf

34 Fountoukis 2007, ISORROPIA II: a computationally efficient thermodynamic equilibrium model for K

+

–Ca2+–

Mg2+–NH4

+

–Na+–SO4

2

− –NO3

−

–Cl−–H2O aerosols, available at: http://www.atmos-chemphys.org/7/4639/2007/acp-7-4639-2007.pdf
59

concentrations are a modeling artifact attributable to the ISORROPIA nitrate mechanism in 

CAMx for the conditions that apply in this geographic area on these days, and are not reasonable 

predictors of the true relative effects on visibility of the emission control scenarios. 

As a comparison, we also examined the sulfate relative concentration on September 4th. Figure 

B-3 shows the relative change in sulfate on September 4th between the 2014 base case and 2014 

Transport Rule +BART-elsewhere case. Note the smooth spatial pattern of sulfate relative 

changes.

Figure B-3. Sulfate relative response factor on September 4th between the 2014 base case and 

the 2014 Transport Rule scenario (values < 1 are decreases in sulfate in the control scenario).

To further analyze the cause of the visibility degradation, we performed an analysis to 

illustrate how sensitive the predictions of degradation are to highly variable results on particular 

days. For each of these four Class I areas, we removed the one day (the day at each site listed in 

table B-2) of the 20 percent worst or best days with the largest increase in modeled nitrate 

concentration. When the single day is removed, the apparent visibility degradations no longer 

occur. Table B-3 shows the default results with all days and the results when the single day is 

removed from the analysis.
60

Table B-3. Visibility results with all days and with one day removed from the calculations.

Class I area

IMPROVE 

site

Visibility 

change 

default

Visibility 

change with 

one day 

removed

Number of 

modeled days in 

the best or 

worst days 

calculation

Bryce Canyon NP BRCA 0.05 (best) -0.01 22

Mazatzal Wilderness MAZA 0.23 (worst) -0.01 23

Pine Mountain 

Wilderness PIMO 0.23 (worst) -0.01 23

Saguaro NM SAGU 0.26 (worst) -0.06 25

The large percentage increase in nitrate can also be accounted for in the post-processing 

of the model results by setting a minimum concentration RRF threshold when calculating RRFs 

for all PM species. If the average species concentration across all best or worst days at each site 

is below the minimum threshold, then the RRF for that particular species would be set to 1.0. To 

test this, the regional haze post-processing software (MATS) was run again with a minimum 

average concentration threshold of 0.01 ug/m3. Table B-4 shows the results of this analysis.

Table B-4. Change in visibility between the 2014 base case and the 2014 Transport Rule 

scenario when a minimum RRF threshold (0.01 ug/m3) is set in the post-processing software.

Class I area

IMPROVE 

site

Visibility 

Change 

TR1+BART 

best days

Visiblity 

Change 

TR1+BART 

worst days

Bryce Canyon NP BRCA -0.01 -0.08

Mazatzal Wilderness MAZA -0.02 -0.01

Pine Mountain 

Wilderness PIMO -0.02 -0.01

Saguaro NM SAGU -0.24 -0.06

As can be seen from Table B-4, if a minimum RRF threshold of 0.01 ug/m3 is used, the 

degradation no longer occurs at any Class I area. Based on this information, the EPA is 

considering whether to implement a minimum threshold concentration as part of the default 

Regional Haze calculations. The minimum threshold would be built into the MATS software 

used to post-process photochemical outputs. These possible revisions are aimed at avoiding 

potentially misleading results in situations such as the one observed near these particular 

Western Class I areas. 

Based on these additional analyses of the visibility modeling results, we are confident 

that no degradation in the four western Class I areas will result from implementation of the 

Transport Rule trading program in the Eastern U.S.
61

Appendix C

Sensitivity Analysis Accounting for Proposed Increases in Certain Transport 

Rule State Budgets 

The Better than BART modeling analysis for this proposed rule was completed using the 

emissions totals from the final Transport Rule, and also assuming that the July 11, 2011 proposal 

regarding ozone-season NOx emissions in five states will be finalized.35 Recently, EPA 

proposed36 to make adjustments to the EGU SO2 and NOx budgets in several states. In all cases, 

the budgets are proposed to be increased. Table C-1 summarizes the proposed budget increases.

Table C-1. Proposed increases in Transport Rule 2014 SO2 and NOx Budgets

State 2014 SO2

Budget 

Increase (tons)

2014 NOx 

Budget Increase 

(tons)

Louisiana N/A 4,231

Mississippi N/A 2,136

Michigan N/A 5,228

Nebraska N/A 3,559

New Jersey N/A 112

New York 3,527 3,485

Texas 70,067 1,375

Wisconsin 7,757 2,473

Total Increase 81,351 22,599

The proposed SO2 and NOx budget increases are relatively modest compared to the total 

modeled reductions from the Transport Rule. The modeled SO2 reductions from the Transport 

Rule are ~3.8 million tons per year and NOx reductions are ~125,000 tons per year. In addition, 

considering the proposed budget increases, the “Transport Rule +BART-elsewhere” control 

scenario is still projected to result in about 26,000 tons more NOx emission reductions than 

“nationwide BART” and about 821,000 tons more SO2 emission reductions than “nationwide 

BART”. The only increase in emissions that is large enough to potentially affect the Better than 

BART analysis is the SO2 budget increase in Texas. This is due to the relatively large increase in 

Texas SO2 compared to other states, and the fact that SO2 emissions account for the majority of 

the PM2.5 and visibility impacts from the Transport Rule. Therefore, we completed a more 

detailed sensitivity analysis of the potential impact from the Texas SO2 budget increase. 

 

35 The air quality modeling conducted for this analysis included in the CSAPR scenario the states 

addressed in the July 11 proposal. These states are Iowa, Kansas, Michigan, Nebraska, Minnesota, and 

Oklahoma.

36 Revisions to the Federal Implementation Plans to Reduce Interstate Transport of Fine Particulate 

Matter and Ozone, 76 FR 63860 (October 14, 2011)
62

Table C-2 shows the Texas SO2 emissions as modeled in the Better than BART analysis 

and the emissions that would occur if the state budget is increased as proposed.

Table C-2. Texas 2014 SO2 emissions and decreases compared to the 2014 modeled base 

case

2014 Base Case 

SO2 Emissions

2014 TR+ 

BARTelsewhere SO2

Emissions

(estimate from 

IPM used in air 

quality 

modeling)

2014 Proposed 

Budget Increase

SO2 Emissions 

Decrease from 

TR (as modeled)

SO2 Emissions 

Decrease from 

TR with 

Increased 

Budget

453,332 266,627 70,067 -186,705 -116,638

Table C-2 shows that the modeled SO2 reduction in Texas is ~186,000 tons per year. 

After accounting for the proposed increase in the SO2 budget for Texas, the SO2 emissions 

reduction is still ~116,000 tons per year. This would reduce the total SO2 reduction from the 

Transport Rule from ~3.8 million tons per year to ~3.7 million tons per year. 

It can be seen from Table C-2 that even with the increase in the SO2 budget for Texas, 

there is still a large SO2 reduction in Texas, compared to the 2014 base case and the impact on 

the total net SO2 reduction from the Transport Rule is minimal (< 2% decrease in SO2

reductions).

Given this information, we would still expect major visibility improvements at Class I 

areas in and around Texas from the Transport Rule. However, in order to better inform the 

process, we performed a very conservative sensitivity analysis to provide a limiting upper 

estimate of the worst case impact of increases in Texas SO2 allowances. 

Sensitivity Analysis of Better than BART with Increased Texas SO2 Allowances 

We identified the most likely “affected” Class I areas for Texas and then assumed that 

these areas would get zero benefit from the Transport Rule. Table C-3 shows the 9 Class I areas 

in and near Texas that were identified as the affected areas.

37 We identified these areas by 

looking at the regional haze analysis conducted by the CENRAP regional planning organization 

in support of the development of regional haze SIPs in Texas and surrounding states. 

 

37 Affected Class I areas were identified by examining consultation letters from states surrounding Texas which 

were included in the Texas regional haze SIP submissions. The letters are included in Appendix 4-3 available here: 

http://www.tceq.texas.gov/airquality/sip/bart/haze_appendices.html. We included 6 areas which had a > 2 Mm-1

impact from Texas. These areas had much higher visibility impacts from Texas compared to other analyzed Class I 

areas. We also included the 3 Class I areas that are in Texas or are represented by an IMPROVE monitor that is 

located in Texas.
63

Table C-3. Class I areas most affected by Texas emissions and the modeled visibility 

improvement from the Transport Rule + BART-elsewhere case.

Class I Area Name State

TR+BARTelsewhere 

20% Best 

days

TR+BARTelsewhere 20% 

Worst days

Big Bend NP TX -0.2 -1.1

Caney Creek Wilderness AR -0.4 -3.2

Carlsbad Caverns NP TX -0.1 -0.9

Guadalupe Mountains NP TX -0.1 -0.9

Hercules-Glades Wilderness MO -0.6 -2.5

Salt Creek NM -0.1 -0.7

Upper Buffalo Wilderness AR -0.5 -2.5

White Mountain Wilderness NM -0.1 -0.6

Wichita Mountains OK -0.2 -1.6

For this analysis, we assumed that the visibility benefits shown in Table C-3 would 

become zero as a result of the allowance adjustment for SO2 in Texas. This is a very 

conservative assumption because there would still be 116,000 tons per year of SO2 reductions in 

Texas and this also assumes that there would be no visibility benefits in these 9 Class I areas 

from the ~3.6 million tons of SO2 reductions from the other Transport Rule states. This is not a 

realistic assumption, but one that we use here in order to be conservative. 

Sensitivity Analysis Results

We recalculated the average visibility changes at the 60 Eastern and 140 nationwide 

Class I areas in the Better than BART analysis, assuming that the visibility change at the 9 Class 

I areas in Table C-3 would be zero. The first prong of the Better than BART test would still be 

passed because there will be no degradation in visibility at any Class I areas on either the 20% 

best or 20% worst days. Table C-4 below shows the average visibility results and the recalculated average visibility results for the Texas sensitivity case. 
64

Table C-4. Average visibility improvement on the 20% best and 20% worst days in the Better 

than BART analysis and the Texas sensitivity analysis.

“Transport Rule + 

BARTelsewhere”

“Transport Rule + 

BART-elsewhere” 

Texas Sensitivity Case

“Nationwide BART”

60 Class I Areas in the Eastern 

Transport Rule Modeling 

Domain

20 percent Worst Days 1.6 1.3 1.0

20 percent Best Days 0.3 0.3 0.2

140 Class I Areas in the Western 

and Eastern Transport Rule 

Modeling Domains

20 percent Worst Days 0.7 0.6 0.5

20 percent Best Days 0.1 0.1 0.1

Table C-4 shows that the even with the very conservative assumptions in this analysis, 

the Transport Rule+ BART-elsewhere case is still, on average, better than (or equal to) 

nationwide BART on both the 20% best and 20% worst days at the 60 Eastern Class I areas and 

at the 140 nationwide Class I areas.