Revised draft Federal Register notice and draft TSD for the rule titled " 

Regional Haze: Revisions to Provisions Governing Alternatives to 

Source-Specific Best Available Retrofit Technology (BART) Determinations, 

Limited SIP Disapprovals , and Federal Implementation Plans " RIN 

2060-AR05

Phil Lorang to: Kymn, Christine J. 12/14/2011 05:14 PM

Cc: Martha Keating, Rhea Jones

From: Phil Lorang/RTP/USEPA/US

To: "Kymn, Christine J." <Christine_J._Kymn@omb.eop.gov>

Cc: Martha Keating/RTP/USEPA/US@EPA, Rhea Jones/RTP/USEPA/US@EPA

Christine,

Attached are revised draft versions of the FR notice and TSD, addressing comments conveyed in the 

recent inter agency phone call. We appreciate the assistance given by these reviewers.

These versions are redlined to the one version of each of these you received earlier. I am eager to 

answer any questions you have.

I will not be in the office or reachable on Friday December 16. If you are calling on that day, please call 

Rhea Jones at 919-541-2940 or Martha Keating at 919-541-9407.

EO12866_RegionalHazeRevisions_2060-AR05_NPRM_20111019_REDLINE_12_14_11_version3.docx

BtB Modeling 2011 TSD draft final_v2_REDLINE_12-14-11.docx

Phil Lorang, Senior Policy Advisor

Air Quality Policy Division, OAQPS, EPA

919-541-5463
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6560-50-P

ENVIRONMENTAL PROTECTION AGENCY 

40 CFR Part 51

[EPA-HQ-OAR-2011-0729; FRL – ]

RIN 2060-AR05

Regional Haze: Revisions to Provisions Governing Alternatives to 

Source-Specific Best Available Retrofit Technology (BART) 

Determinations, Limited SIP Disapprovals, and Federal Implementation 

Plans 

AGENCY: Environmental Protection Agency (EPA).

ACTION: Proposed rule. 

SUMMARY: The EPA is proposing revisions to our rules that pertain to 

the regional haze program. In this action, the EPA is proposing that 

the trading program in the recently promulgated Transport Rule, also 

known as the Cross-State Air Pollution Rule, achieves 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. In this action, the EPA is also proposing a limited 

disapproval of the regional haze State Implementation Plans (SIPs) 

that have been submitted by Alabama, Florida, Georgia, Kentucky, 

Indiana, Iowa, Louisiana, Michigan, Mississippi, Missouri, North 

Carolina, Ohio, Pennsylvania, South Carolina and Texas. These states 

relied on requirements of the Clean Air Interstate Rule (CAIR) to 

satisfy certain regional haze requirements. To address deficiencies 

in all of the CAIR-dependent regional haze SIPs, in this action, the 
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EPA is proposing Federal Implementation Plans (FIPs) to replace 

reliance on the CAIR requirements in these SIPs with reliance on the 

Transport Rule as an alternative to BART. States are encouraged, at 

any time, to submit a revision to their regional haze SIP 

incorporating the requirements of the Transport Rule at which time we 

will withdraw the FIP we are proposing in this action.

DATES: Comments. Comments must be received on or before [INSERT DATE 

30 45 DAYS AFTER PUBLICATION IN THE FEDERAL REGISTER]. 

Public Hearings

ADDRESSES: Submit your comments, identified by Docket ID No. EPA-HQOAR-2011-0729, by one of the following methods:

. If anyone contacts us requesting a public hearing on 

or before [INSERT DATE 10 DAYS AFTER DATE OF PUBLICATION IN THE 

FEDERAL REGISTER]a public hearing will be held in Research Triangle 

Park, North Carolina on [INSERT DATE 15 DAYS AFTER DATE OF 

PUBLICATION IN THE FEDERAL REGISTER]., we will hold a public hearing. 

Please refer to SUPPLEMENTARY INFORMATION for additional information 

on the comment period and the public hearing. 

• www.regulations.gov. Follow the online instructions for 

submitting comments. Attention Docket ID No. EPA-HQ-OAR-2011-

0729.

• Email: a-and-r-docket@epa.gov. Attention Docket ID No. EPA-HQOAR-2011-0729.

• Fax: (202) 566-9744. Attention Docket ID No. EPA-HQ-OAR-2011-

0729.
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• Mail:

•

EPA Docket Center, EPA West (Air Docket), Attention Docket 

ID No. EPA-HQ-OAR-2011-0729, U.S. Environmental Protection 

Agency, Mailcode: 2822T, 1200 Pennsylvania Avenue, NW, 

Washington, D.C. 20460. Please include a total of two copies. 

Hand Delivery: U.S. Environmental Protection Agency, EPA West 

(Air Docket), 1301 Constitution Avenue, Northwest, Room 3334, 

Washington, D.C. 20004, Attention Docket ID No. EPA-HQ-OAR-2011-

0729. Such deliveries are only accepted during the Docket's 

normal hours of operation, and special arrangements should be 

made for deliveries of boxed information.

Instructions. Direct your comments to Docket ID No. EPA-HQ-OAR-2011-

0729. The EPA's policy is that all comments received will be included 

in the public docket without change and may be made available online 

at www.regulations.gov, including any personal information provided, 

unless the comment includes information claimed to be Confidential 

Business Information (CBI) or other information whose disclosure is 

restricted by statute. Do not submit information that you consider to 

be CBI or otherwise protected through www.regulations.gov or email. 

The www.regulations.gov website is an “anonymous access” system, 

which means the EPA will not know your identity or contact 

information unless you provide it in the body of your comment. If you 

send an email comment directly to the EPA without going through 

www.regulations.gov, your email address will be automatically 

captured and included as part of the comment that is placed in the 
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public docket and made available on the Internet. If you submit an 

electronic comment, the EPA recommends that you include your name and 

other contact information in the body of your comment and with any 

disk or CD-ROM you submit. If the EPA cannot read your comment due to 

technical difficulties and cannot contact you for clarification, the 

EPA may not be able to consider your comment. Electronic files should 

avoid the use of special characters, avoid any form of encryption, 

and be free of any defects or viruses. For additional information 

about the EPA's public docket, visit the EPA Docket Center homepage 

at www.epa.gov/epahome/dockets.htm.

Docket. All documents in the docket are listed in the 

www.regulations.gov index. Although listed in the index, some 

information is not publicly available, e.g., CBI or other information 

whose disclosure is restricted by statute. Certain other material, 

such as copyrighted material, will be publicly available only in hard 

copy. Publicly available docket materials are available either 

electronically in www.regulations.gov or in hard copy at the Air and 

Radiation Docket and Information Center, EPA/DC, EPA West Building, 

Room 3334, 1301 Constitution Ave., NW, Washington, D.C. The Public 

Reading Room is open from 8:30 a.m. to 4:30 p.m., Monday through 

Friday, excluding legal holidays. The telephone number for the Public 

Reading Room is (202) 566-1744, and the telephone number for the Air 

Docket is (202) 566-1742. 
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FOR FURTHER INFORMATION CONTACT: Ms. Martha Keating, Office of Air 

Quality Planning and Standards, Air Quality Policy Division, Mail 

code C539-04, Research Triangle Park, NC 27711, telephone (919) 541-

9407; fax number: 919-541-0824; email address:

keating.martha@epa.gov

SUPPLEMENTARY INFORMATION:

.

I. General Information

This proposed action does not directly regulate emission 

sources. It will affect state and local air pollution control 

agencies located within the geographic areas covered by the Transport 

Rule

A. Does this action apply to me?

1 and whose regional haze state implementation plan relied on 

CAIR2 as an alternative to BART for sulfur dioxide (SO2) and/or 

Nitrogen Oxide (NOX

These sources are in the following groups: 

)for electric generating units (EGUs) subject to 

BART requirements. Some of the EGUs located in such geographic areas 

may also be affected by the FIPs that may result from final 

rulemaking on this proposed action in that the final rule would allow 

states the option of not requiring them to meet source-specific BART 

emission limits to which they otherwise could be subject. 

 

1 See Federal Implementation Plans to Reduce Interstate Transport of 

Fine Particulate Matter and Ozone 76 FR 48208 (August 8, 2011).

2 See Rule to Reduce Interstate Transport of Fine Particulate Matter 

and Ozone (Clean Air Interstate Rule); Revisions to Acid Rain 

Program; Revisions to the NOx SIP Call; Final Rule, 70 FR 25162 (May 

12, 2005).
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Industry group SIC NAICS a b

Electric Services 492 221111, 221112, 221113, 221119, 

221121, 221122

a Standard Industrial Classification. b North American Industry Classification System.

B. What should I consider as I prepare my comments for the EPA?

1. Submitting CBI. Do not submit this information to the EPA

through www.regulations.gov or email. Clearly mark the part or all of 

the information that you claim to be CBI. For CBI information in a 

disk or CD ROM that you mail to the EPA, mark the outside of the disk 

or CD ROM as CBI and then identify electronically within the disk or 

CD ROM the specific information that is claimed as CBI. In addition 

to one complete version of the comment that includes information 

claimed as CBI, a copy of the comment that does not contain the 

information claimed to be CBI must be submitted for inclusion in the 

public docket. Information so marked will not be disclosed except in 

accordance with procedures set forth in 40 CFR Part 2.

2. Tips for Preparing Your Comments

• Identify the rulemaking by docket number and other identifying 

information (subject heading, 

. When submitting comments, 

remember to:

Federal Register date and page 

number).
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• Follow directions - The agency may ask you to respond to specific 

questions or organize comments by referencing a Code of Federal 

Regulations (CFR) part or section number.

• Explain why you agree or disagree; suggest alternatives and 

substitute language for your requested changes.

• Describe any assumptions and provide any technical information 

and/or data that you used.

• If you estimate potential costs or burdens, explain how you arrived 

at your estimate in sufficient detail to allow for it to be 

reproduced.

• Provide specific examples to illustrate your concerns, and suggest 

alternatives.

• Explain your views as clearly as possible, avoiding the use of 

profanity or personal threats.

• Make sure to submit your comments by the comment period deadline 

identified.

In addition to being available in the docket, an electronic copy 

of this notice will be posted at 

C. Where can I get a copy of this document and other related 

information?

http://www.epa.gov/ttn/oarpg/new.html under “Recent Actions.”

D. What information should I know about a possible public hearing? 
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To request that a public hearing be held or to register to speak 

at the hearing on this document, contact Ms. Pamela Long at (919) 

541-0641 before 5 p.m. on [INSERT DATE 15 10 DAYS AFTER DATE OF 

PUBLICATION IN THE FEDERAL REGISTER]. For updates and additional 

information on a public hearing, please check the EPA’s website at 

http://www.epa.gov/ttn/oarpg/new.html under “recent actions.”

The information presented in this notice is organized as 

follows:

E. How is this notice organized? 

I. General Information

 A. Does this action apply to me?

 B. What should I consider as I prepare my comments for the EPA?

1. Submitting CBI

2. Tips for Preparing Your Comments

 C. Where can I get a copy of this document and other related 

information?

 D. What information should I know about a public hearing?

 E. How is this notice organized? 

II. What action is the EPA proposing to take? 

III. What is the background for the EPA’s proposed action?

A. The Regional Haze Problem

B. Clean Air Act Requirements for Addressing Regional Haze

C. Alternative Measures In Lieu of BART 

1. Criteria for Comparing Visibility Progress of an Alternative 

Program to BART

2. What is the Relationship between BART and CAIR?

3. Remand of CAIR and Implications for State Regional Haze 

Implementation Plans

4. The Transport Rule and Regional Haze State Implementation 

Plans

IV. Proposed Determination that the Transport Rule is an Approvable 

Alternative to BART

A. Application of the Two-Pronged Test

B. Identification of Affected Class I Areas

C. Scenarios Examined

D. Emission Projections

E. Air Quality Modeling Results

F. Proposed Amendment to the Regional Haze Rule
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V. Proposed Limited Disapproval of Certain States’ Regional Haze 

SIPs

VI. Proposed FIPs

VII. Statutory and Executive Order

A. Executive Order 12866: Regulatory Planning and Review and 

Review

 Executive Order 13563: Improving Regulation and Regulatory

 Review

B. Paperwork Reduction Act

C. Regulatory Flexibility Act

D. Unfunded Mandates Reform Act

E. Executive Order 13132: Federalism

F. Executive Order 13175: Consultation and Coordination with 

 Indian Tribal Governments

G. Executive Order 13045: Protection of Children from 

 Environmental Health and Safety Risks

H. Executive Order 13211: Actions Concerning Regulations That 

 Significantly Affect Energy Supply, Distribution, or Use

I. National Technology Transfer and Advancement Act

J. Executive Order 12898: Federal Actions to Address 

 Environmental Justice in Minority Populations and 

 Low-Income Populations

II. What action is the EPA proposing to take?

In this action, the EPA is proposing to find that the trading 

programs in the Transport Rule3

 3 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)(finalized on December 15, 2011)for more details. 

For purposes of this proposed rule, the Transport Rule includes all 

of the states (28) included in the final Transport Rule and the 

supplemental.

achieve greater reasonable progress 

towards the national goal of achieving natural visibility conditions 

in mandatory Class I federal areas than source-specific BART in the 

states in which the Transport Rule applies. Specifically, we are 

proposing that the trading programs set out in the Transport Rule

Formatted: Indent: Left: 0", First line: 0"
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meet the requirements of an alternative program as prescribed in the 

regional haze rule (RHR) at 40 CFR 51.308(e)(3)and are proposing to 

revise the regional haze regulations at 40 CFR 51.308(e)(4) 

accordingly to allow states to substitute participation in the 

trading programs under the Transport Rule for source-specific BART.

In addition, we are also proposing to find that any approved SIPs 

revising or adopting the Transport Rule trading programs, which must 

control emissions at least as stringently as the Transport Rule FIPs,

will also meet the requirements for an alternative to BART for EGUs 

for the pollutants which the Transport Rule limits in that state.

In this action we are also proposing a limited disapproval of 

the regional haze SIPs that have been submitted by Alabama, Florida, 

Georgia, Kentucky, Indiana, Iowa, Louisiana, Michigan, Mississippi,

Missouri, North Carolina, Ohio, Pennsylvania, South Carolina and 

Texas. These states, fully consistent with the EPA’s regulations at 

the time, relied on CAIR requirements to satisfy the BART requirement 

and the requirement for a long-term strategy sufficient to achieve 

the state-adopted reasonable progress goals.4

 4 The states for which we are proposing limited disapproval in this 

action are those that both relied on CAIR to satisfy BART 

requirements and are now covered by the requirements of the Transport 

Rule, for which we have not already made such a proposal.

CAIR and the CAIR FIP 

requirements, however, will only remain in force to address emissions 

through the 2011 control period and thus CAIR cannot be relied upon 

in a SIP as a substitute for BART or as part of a long-term control 
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strategy. The EPA has already proposed limited disapproval of certain 

other state regional haze SIPs that relied on CAIR.5

In this action we are also proposing FIPs for all the states for 

which we have previously proposed limited disapproval and for all the 

states for which we are proposing a limited disapproval of their 

regional haze SIP in this action due to the change in status of CAIR. 

Regional haze SIPs were due in December 2007. For a number of the 

states identified above, we made a finding on January 15, 2009, that 

the states had failed to timely submit a regional haze SIP. Most of 

these states have subsequently submitted SIPs, but we have not yet 

acted on them. Under the CAA, the EPA is required to promulgate a FIP

within 2 years after finding that a state has failed to make a 

required submission or after disapproving a SIP in whole or in part, 

unless the state first adopts and we have fully approved a SIP. CAA § 

110(c)(1). Given these CAA requirements and the fact that the 

Transport Rule has now replaced CAIR, we consider it appropriate at 

this time to issue FIPs to address the deficiencies in the regional 

haze SIPs related to the termination of CAIR. Our adoption of these 

FIPs at this time avoids the near-term need for additional 

administrative steps on the part of these states. The proposed

regional haze FIPs also allow states the option of a less costly 

We plan to take 

final action on both groups of SIPs when this action is finalized. 

 5 The states for which the EPA has previously proposed limited 

disapproval of regional haze SIPs are Kentucky, Tennessee, Virginia 

and West Virginia.
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approach to meeting the regional haze requirements of the CAA since 

the proposed FIPs rely on the trading program already promulgated in 

the Transport Rule. We encourage states, at any time, to submit a 

revision to their regional haze SIP incorporating the requirements of 

the Transport Rule at which time we will withdraw the FIP we are 

proposing in this action. However, we anticipate that some states may 

choose to remain subject to the proposed FIP and not submit a SIP 

revision. Our proposed finding that the Transport Rule makes greater 

reasonable progress than BART for EGUs in these states will hold true 

regardless of whether a state chooses to submit a SIP revision under 

subpart 52.38 and 52.39 or remain subject to a FIP.

The proposed FIPs also address the requirements for the affected 

states’ long-term strategies for Regional Haze. The affected states 

originally set the reasonable progress goals in their SIPs based on 

the emission reductions expected to be achieved by CAIR, along with 

other emission reductions qualified for that purpose. Because overall 

EGU emission reductions from the Transport Rule are larger than the 

EGU reductions achieved by CAIR, we believe that the reasonable 

progress goals in the SIPs for the states addressed in this proposed 

action do not need to be revised by the states at this time or 

replaced by goals established by us via FIPs. The substitution of the 

Transport Rule for CAIR does not weaken any affected state’s longterm strategy. We intend to act on the reasonable progress goals and 

long-term strategies (including the proposed FIPs) and other 
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requirements of the RHR (monitoring, consultation with federal land 

managers, etc.) for each state in an individual notice at or after 

the time of the final rule for this action. 

III. What is the background for the EPA’s proposed action?

Regional haze is visibility impairment that is produced by a 

multitude of sources and activities which are located across a broad 

geographic area and emit fine particles (PM

A. The Regional Haze Problem

2.5) (e.g., sulfates, 

nitrates, organic carbon, elemental carbon, and soil dust), and their 

precursors (e.g., SO2, NOX, and in some cases, ammonia (NH3) and 

volatile organic compounds (VOC)). Fine particle precursors react in 

the atmosphere to form fine particulate matter, which impairs 

visibility by scattering and absorbing light. Visibility impairment 

reduces the clarity and alters the color of scenes, and reduces the 

distance at which one can see a scene. PM2.5

Data from the existing visibility monitoring network, the 

“Interagency Monitoring of Protected Visual Environments” (IMPROVE) 

monitoring network, show that visibility impairment caused by air 

pollution occurs virtually all the time at most national park and 

wilderness areas. The average visual range

can also cause serious 

health effects and mortality in humans and contributes to 

environmental effects such as acid deposition and eutrophication. 

6

 6 Visual range is the greatest distance at which a dark object can be 

viewed against the sky.

in many mandatory Class I 
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federal areas7 in the western United States is about 60 – 100 miles, or 

about one-half to two-thirds of the visual range that would exist 

without anthropogenic air pollution. In most of the eastern Class I 

areas of the United States, the average visual range is less than 20 

miles, or about one-fifth of the visual range that would exist under 

estimated natural conditions. 64 FR 35715 (July 1, 1999).

In section 169A of the 1977 Amendments to the CAA, Congress 

created a program for protecting visibility in the nation’s national 

parks and wilderness areas. This section of the CAA establishes as a 

national goal the “prevention of any future, and the remedying of any 

existing, impairment of visibility in mandatory Class I federal areas 

which impairment results from manmade air pollution.” On December 2, 

1980, the EPA promulgated regulations to address visibility 

impairment in Class I areas that is “reasonably attributable” to a 

B. Clean Air Act Requirements for Addressing Regional Haze 

 7 Areas designated as mandatory Class I federal areas consist of 

national parks exceeding 6000 acres, wilderness areas and national 

memorial parks exceeding 5000 acres, and all international parks that 

were in existence on August 7, 1977. 42 U.S.C. 7472(a). In accordance 

with section 169A of the CAA, EPA, in consultation with the 

Department of Interior, promulgated a list of 156 areas where 

visibility is identified as an important value. 44 FR 69122 (November 

30, 1979). The extent of a mandatory Class I area includes subsequent 

changes in boundaries, such as park expansions. 42 U.S.C. 7472(a). 

Although states and tribes may designate as Class I additional areas 

which they consider to have visibility as an important value, the 

requirements of the visibility program set forth in section 169A of 

the CAA apply only to “mandatory Class I Federal areas.” Each 

mandatory Class I Federal area is the responsibility of a “Federal 

Land Manager.” 42 U.S.C. 7602(i). When we use the term “Class I area” 

in this action, we mean a “mandatory Class I Federal area.”
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single source or small group of sources, i.e.

Congress added section 169B to the CAA in 1990 to address 

regional haze issues. The EPA promulgated the RHR to address regional 

haze on July 1, 1999 (64 FR 35713). The RHR revised the existing 

visibility regulations to integrate into the regulation provisions 

addressing regional haze impairment and established a comprehensive 

visibility protection program for Class I areas. The requirements for 

regional haze, found at 40 CFR 51.308 and 51.309, are included in the 

EPA’s visibility protection regulations at 40 CFR 51.300-309. The 

requirement to submit a regional haze SIP applies to all 50 states, 

the District of Columbia and the Virgin Islands. 40 CFR 51.308(b) 

requires states to submit the first implementation plan addressing 

regional haze visibility impairment no later than December 17, 2007. 

, “reasonably 

attributable visibility impairment”. 45 FR 80084. These regulations 

represented the first phase in addressing visibility impairment. The 

EPA deferred action on regional haze that emanates from a variety of 

sources until monitoring, modeling and scientific knowledge about the 

relationships between pollutants and visibility impairment were 

improved. 

Section 169A of the CAA and the EPA’s implementing regulations 

require states to establish long-term strategies for making 

reasonable progress towards the national goal of achieving natural 

visibility conditions in Class I areas. Implementation plans must 

also give specific attention to certain stationary sources.
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Specifically, section 169A(b)(2)(A) of the CAA requires states to 

revise their SIPs to contain such measures as may be necessary to 

make reasonable progress towards the natural visibility goal, 

including a requirement that certain categories of existing major 

stationary sources8 built between 1962 and 1977 procure, install, and 

operate the “Best Available Retrofit Technology” as determined by the 

state. Under the RHR, states are directed to conduct BART 

determinations for such “BART-eligible” sources that may be 

anticipated to cause or contribute to any visibility impairment in a 

Class I area. Rather than requiring source-specific BART controls, 

states also have the flexibility to adopt an emissions trading 

program or other alternative program as long as the alternative 

provides greater reasonable progress towards improving visibility 

than BART, as described below. 

1.

C. Alternative Measures In Lieu of BART

Criteria for determining if an alternative measure achieves 

greater reasonable progress than source-specific BART are set out in 

the RHR 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 

Criteria for Comparing Visibility Progress of an 

Alternative Program to BART

 8 The set of “major stationary sources” potentially subject to BART is 

listed in CAA section 169A(g)(7).
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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, then states are 

directed to conduct an air quality modeling study to determine 

differences in visibility between BART and the alternative program 

for each impacted Class I area for the worst and best 20 percent of 

days.9

- Visibility does not decline in any Class I area

The two-pronged visibility test would demonstrate “greater 

reasonable progress” under the alternative program if both of the 

following criteria are met:

10

- There is an overall improvement in visibility, determined by 

comparing the average differences between BART and the 

alternative over all affected Class I areas.

, and

The EPA’s authority to establish non-BART alternatives and the 

specific methodology outlined above havehas been judicially 

challenged and upheld twice, firmly establishing that the CAA allows 

states to substitute other programs for BART where the alternative 

achieves greater progress. In the first case, the court affirmed our 

interpretation of CAA 169A(b)(2) as allowing for alternatives to BART 

 9 While the RHR directs the state to conduct the air quality modeling 

study, as described in section III.C.2, EPA itself conducted such a 

study for CAIR and through a notice-and-comment rulemaking codified 

the conclusion that the stated criteria were met by adding specific 

provisions allowing the use of CAIR in lieu of source-specific BART. 

10 As explained in section VI.1, the “decline” is relative to modeled 

future baseline visibility conditions in the absence of any BART or 

alternative program control requirements.
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where those alternatives will result in greater reasonable progress 

than BART. Center for Energy and Economic Development v. EPA, 398 

f.3d 653, 660 (D.C. Cir. 2005) (“CEED”) (finding reasonable the EPA’s 

interpretation of CAA section 169(a)(2) as requiring BART only as 

necessary to make reasonable progress). In the second case, Utility 

Air Regulatory Group v. EPA

2.

, 471 F.3d 1333 (D.C. Cir. 2006), the 

court found EPA’s two-pronged visibility test to be a “reasonable 

notion of reasonable progress” and specifically upheld our 

determination that states could rely on CAIR, as discussed below, as 

an alternative program to BART for EGUs in the CAIR-affected states. 

In May 2005, the EPA published CAIR, which required 28 states 

and the District of Columbia to reduce emissions of SO

What is the relationship between BART and CAIR?

2 and NOX that 

significantly contribute to, or interfere with maintenance of, the 

1997 national ambient air quality standards (NAAQS) for fine 

particulates and/or ozone in any downwind state. The CAIR established

emission budgets for SO2 and NOX for states that contribute 

significantly to nonattainment in downwind states and required the 

significantly contributing states to submit SIP revisions that 

implemented these budgets. Because such SIP revisions were already 

overdue, CAIR also promulgated FIPs for the affected states 

establishing a cap and trade program for EGUs with opt-in provisions 

for other sources. States had the flexibility to subsequently adopt 

SIP revisions mirroring CAIR requirements or otherwise providing 
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emission reductions sufficient to address interference with 

attainment or maintenance of the NAAQS in other states. Many affected 

states adopted CAIR-mirroring SIPs, while others chose to remain 

under CAIR FIPs. 

As noted in Section III.C.1, the RHR allows states to implement 

an alternative program in lieu of BART so long as the alternative 

program has been demonstrated to achieve greater reasonable progress 

toward the national visibility goal than would BART. The EPA made 

just such a demonstration for CAIR in revisions to the regional haze 

program made in 2005. 70 FR 39104 (July 6, 2005). In those revisions, 

we amended our regulations to provide that states participating in 

the CAIR cap-and trade program under 40 CFR part 96 pursuant to an 

EPA-approved CAIR SIP or states that remain subject to the CAIR FIP 

in 40 CFR part 97 need not require affected BART-eligible EGUs to 

install, operate, and maintain BART for emissions of SO2 and NOX

As a result of our determination that CAIR was “better-thanBART” a number of states in the CAIR region, fully consistent with 

our regulations, designed their regional haze implementation plans to 

rely on the CAIR cap-and-trade program as an alternative to BART for 

EGU emissions of SO

. 40 

CFR 51.308(e)(4).

2 and NOX. These states also relied on CAIR as an 

element of a long-term strategy for achieving their reasonable 

progress goals. 
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3.

Following our determination in 2005 that CAIR was “better-thanBART” and the upholding of this determination by the court in 2006, 

the D.C. Circuit Court ruled on several petitions for review

challenging CAIR on various grounds. As a result of this litigation

the D.C. Circuit Court remanded CAIR to the EPA, but later decided 

not to vacate the rule.

Remand of CAIR and Implications for State Regional Haze 

Implementation Plans

11 The court thereby left CAIR and CAIR SIPs 

and FIPs in place in order to “temporarily preserve the environmental 

values covered by CAIR” until the EPA replaced it with a rule 

consistent with the court's opinion. 550 F.3d at 1178. The EPA 

replaced CAIR with the Transport Rule on August 8, 2011.12

Many states relied on CAIR as an alternative to BART for SO

The 

Transport Rule will take effect on January 1, 2012. The CAIR and the 

CAIR FIPs will remain in place to address emissions through the end 

of the 2011 control periods. 

2 and 

NOX

 11 See North Carolina v. EPA, 531 F.3d 896; modified by 550 F.3d 1176 

(D.C. Cir. 2008).

for subject EGUs, as allowed under the BART provisions at 40 CFR 

51.308(e)(4). These states also relied on the improvement in 

visibility expected to result from controls planned or already 

installed on sources in order to meet CAIR provisions in developing 

12 See Federal Implementation Plans to Reduce Interstate Transport of 

Fine Particulate Matter and Ozone 76 FR 48208 (August 8, 2011).
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their long-term visibility strategy. In addition, many states relied 

upon their own CAIR SIPs or the CAIR FIPs for their states as legal 

justification for these planned controls and consequently did not 

include separate enforceable measures in their long-term strategies 

(a required element of a regional haze SIP submission) to ensure 

these EGU reductions. These states also submitted demonstrations 

showing that no additional controls on EGUs beyond CAIR would be 

reasonable for the first 10-year implementation period of the 

regional haze program. 

Since states in the CAIR-affected region have based a number of 

required elements of their regional haze programs on CAIR, which has 

now been replaced by the Transport Rule, we cannot fully approve 

regional haze SIP revisions that have relied on CAIR for emission 

reduction measures. To date, we have proposed limited disapprovals 

for some states whose regional haze SIP revisions rely on CAIR (for 

example for the State of Tennessee, 76 FR 33662 (June 9, 2011)). We 

intend to take final action on those proposed limited disapprovals of 

SIPs when this action is finalized. However, there are other states 

whose regional haze SIP relied on CAIR but for which the EPA has not 

yet proposed to take action. In this action we are proposing a 

limited disapproval of the regional haze SIPs that have been 

submitted by Alabama, Florida, Georgia, Kentucky, Indiana, Iowa, 

Louisiana, Michigan, Mississippi, Missouri, North Carolina, Ohio, 

Pennsylvania, South Carolina and Texas. These states relied on CAIR 
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requirements to satisfy both the BART requirement and the requirement 

for a long-term strategy sufficient to achieve the state-adopted 

reasonable progress goals, and they are now covered by the Transport 

Rule requirements.

4.

The Transport Rule sunsets CAIR and the CAIR FIPs for control 

periods in 2012 and beyond. The Transport Rule requires 28 states in 

the eastern half of the United States to significantly improve air 

quality by reducing EGU SO

The Transport Rule and Regional Haze State Implementation 

Plans

2 and NOX emissions that cross state lines 

and contribute to ground-level ozone and/or fine particle pollution 

in other states. The rule allows air-quality-assured allowance 

trading among covered sources, utilizing an allowance market 

infrastructure modeled after existing allowance trading programs. The 

Transport Rule allows sources to trade emissions allowances with 

other sources within the same program (e.g., ozone season NOX

In developing the Transport Rule, we did not conduct any 

technical analysis to determine whether compliance with the Transport 

Rule would satisfy regional haze BART-related requirements. 

Accordingly, in the final Transport Rule, the EPA did not make a 

determination or establish any presumption that compliance with the 

) in the 

same or different states, while firmly constraining any emissions 

shifting that may occur by establishing an emission ceiling for each 

state.
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Transport Rule would satisfy BART-related requirements for EGUs. We 

have now completed such a technical analysis and it is the basis of 

this action in which we are proposing to find that in affected 

mandatory Class I federal areas, the Transport Rule achieves greater 

reasonable progress towards the national goal of achieving natural 

visibility conditions than source-specific BART. Specifically, we are 

proposing that participation by EGUs in the Transport Rule trading 

program set out in 40 CFR part 97 subparts AAAAA-DDDDD meets the 

requirements of an alternative program as prescribed in the regional 

haze rule (RHR) at § 51.308(e)(3), and we are proposing to revise the 

regional haze regulations at 40 CFR 51.308(e)(4) accordingly. The 

proposed determination in this action that participation in the 

Transport Rule trading program may substitute for BART applies only 

to EGUs in the states in the Transport Rule region and only to the 

pollutants subject to the requirements of the Transport Rule (i.e., 

SO2 and/or NOX). BART for Eemissions of other visibility impairing 

pollutants (e.g., primary PM2.5, NH3

Under the proposed revision to this section, a state in the 

Transport Rule region whose EGUs are subject to the requirements of 

the Transport Rule trading program only for annual NO

or VOC) must still be evaluated 

according to the RHR Guidelines. Non-EGU sources also remain subject 

to requirements of the RHR.

X or ozone season 

NOX would be allowed to rely on our proposed determination that the 

Transport Rule makes greater reasonable progress than source-specific 
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BART for NOX. Such a state would still need to address BART for SO2

In this action we are also proposing a FIP for those Transport 

Rule states for which we already have or now are proposing a limited 

disapproval due to the termination of CAIR. For these states, the 

proposed FIP would replace reliance on the CAIR requirements with 

reliance on the Transport Rule as an alternative to BART for SO

and other visibility impairing pollutants.

2 and 

NOX

We are proposing to leave unchanged the final sentence of 

section 51.308(e)(4) in the regional haze regulations. This language 

allows a state to address BART, when it is required based on 

reasonable attribution of visibility impairment at a Class I area to 

a particular source, by including a geographic enhancement in its 

SIP. For example, a geographic enhancement in the form of adjusted 

allocations at a BART-subject source might take the place of sourcespecific emission rate limits. Use of a geographic enhancement in the 

context of reasonable attribution of visibility impairment at a Class 

I area will be addressed in separate EPA or state actions on a caseby-case basis in accordance with 40 CFR 51.306.

emissions from EGUs and as a long-term strategy measure.

We are proposing to leave unchanged the language in the regional 

haze regulations at 40 CFR 51.308(e)(4)that allows states to retain 

the discretion to include geographic enhancements in their SIP to 
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accommodate the situation where BART is required based on reasonable 

attribution of visibility impairment at a Class I area.13

IV. Proposed Determination that the Transport Rule is an Approvable 

Alternative to BART

As described in section C.1., the two-pronged test for 

determining if an alternative program achieves greater reasonable 

progress than source-specific BART is set out in the RHR at 40 CFR

51.308(e)(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 reasonable progress than 

would source-specific BART. 

A. Application of the Two-Pronged Test

 

13 A geographic enhancement is a method, procedure, or process to 

allow a broad regional strategy, such as the Transport Rule cap and 

trade program, to accommodate BART for reasonable attributable 

impairment. For example, it could consist of a methodology for

adjusting allowance allocations at a source which is required to 

install BART controls. 
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In the case of the Transport Rule as an alternative to sourcespecific BART, the logical reference point for the first prong is 

visibility conditions as they are expected to be at the time the 

Transport Rule is implemented but in the absence of BART. This 

ensures that the predicted visibility differences are due to the

Transport Rule alternative and not to other extrinsic factors. For 

example, if large increases in wildfires are expected, due to 

accumulation of fuel from past forest management practices, a 

degradation of visibility from current conditions may be expected. It 

would be irrational to disapprove an alternative program as not 

meeting the first prong of the test because of a modeled degradation 

from current conditions, where that degradation is actually 

anticipated because of smoke from wildfires — sources which are not 

subject to the CAA BART provisions. By comparing the Transport Rule

alternative to future projected baseline conditions without any BART 

program, such extrinsic variables are accounted for. The future 

projected baseline also accounts for other non-Transport Rule 

constraints on EGU emissions including the Acid Rain Program, the NOx 

SIP Call, New Source Performance Standards, Title V permits, any 

state laws and consent order requiring emission reductions, and any 

other permanent and enforceable binding reduction commitments. We are 

thus able to ascertain (to the extent possible where future 

projections are concerned) whether visibility under the alternative 

would decline at any affected Class I area, all other things being 
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equal. Therefore, in applying the first prong of the test to the 

Transport Rule, we used a future (2014) projected baseline.14

To satisfy each prong of the test we examined visibility 

differences on both the worst and best 20 percent of days. Thus, 

under the first prong, visibility must not decline at any affected

Class I area on either the best 20 percent or the worst 20 percent 

days as a result of implementing the Transport Rule; and, under the 

second prong the 20 percent best and 20 percent worst days should be 

considered in determining whether the Transport Rule produces greater 

average improvement than source-specific BART over all affected Class 

I areas.

Similarly, in applying the second prong of the test, we assumed 

identical future conditions (the same as in the future 2014 baseline 

case) for non-EGU sources for both the source-specific BART scenario 

and the Transport Rule scenario.

B. Identification of Affected Class I Areas

In applying the two-pronged test to the Transport Rule, we first 

identified the Class I areas in the 48 contiguous states with 

sufficiently complete monitoring data available to support the 

analysis.15

 14 The 2014 baseline modeling for this analysis is identical to the 

Transport Rule 2014 baseline. The 2014 baseline does not include the 

Transport Rule, BART, or CAIR control programs.

There were 140 such Class I areas represented by 96 

15 The modeling used a 2005 base case projected to a 2014 future year. 

The modeling days for the analysis were based on the observed 20 

percent best and 20 percent worst days from 2005 at each IMPROVE 
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IMPROVE monitors; nine Class I areas were excluded that did not have 

sufficient historical ambient data from the IMPROVE monitoring 

program to support the technical analysis.16 After identifying these 

areas we then considered two possible approaches we could use to 

identify which of these areas are “affected” Class I areas in terms 

of the potential effect of the Transport Rule as an alternative 

control program to source-specific BART. In the first approach, we 

identified as affected Class I areas 60 mandatory Class I federal 

areas represented by 46 IMPROVE monitors located in 37 complete 

states and four partial states that are contained in the eastern 

portion of the Transport Rule modeling domain.17

In the Transport Rule, the determination of states that 

contribute significantly to downwind nonattainment and/or maintenance 

The second approach 

we considered was a national approach in which visibility impacts on 

140 Class I areas across the 48 contiguous states were evaluated.

 

site. Therefore, the analysis could not be completed for IMPROVE 

sites that did not have complete ambient data for 2005.

16 In the Regional Haze Program, there are 110 ambient monitoring 

sites which represent 155 Class I areas. Therefore, some monitors 

represent air quality at more than one Class I area. See Guidance for 

Tracking Progress under the Regional Haze Rule, U.S. EPA, EPA-454/B03-004, September 2003, which is found at: 

http://www.epa.gov/ttncaaa1/t1/memoranda/rh_tpurhr_gd.pdf. In our 

analysis we calculated visibility changes at each individual Class I 

area. Therefore, some IMPROVE monitors are counted more than once in 

the averaging of the visibility data. This does not affect the 

proposed finding that the Transport Rule is better than sourcespecific BART.

17 The “eastern” Transport Rule modeling grid used a horizontal 

resolution of 12 km. 
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focused on the 37 states that are fully contained in this eastern 

modeling domain. The eastern modeling domain also includes large 

parts of Montana, Wyoming, Colorado, and New Mexico. In the Transport 

Rule, EPA did not determine that Montana, Wyoming, Colorado, New 

Mexico or the six New England states were contributing to violations 

of the 1997 ozone NAAQS or the 1997 and 2006 PM2.5 NAAQS, or 

interfering with maintenance in downwind states and therefore they 

are not included in the Transport Rule program.18

The eastern Transport Rule modeling domain lies within a larger 

modeling domain which covers the lower 48 states and adjacent 

However, we included 

Class I areas located in these non-Transport Rule states and partial 

states in the first approach for identifying “affected areas”. It is 

conceivable that because of proximity, emissions from the Transport 

Rule states could impact any of the Class I areas in the eastern 

Transport Rule modeling domain. Specifically, in this first approach 

for identifying “affected areas” in the Transport Rule region we 

examined impacts on 27 Class I areas located within the Transport 

Rule states and 33 additional Class I areas located in non-Transport 

Rule states but within the eastern Transport Rule modeling domain, 

for a total of 60 Class I areas.

 18 The Transport Rule determined that the six New England states did 

not contribute to nonattainment or interfere with maintenance in 

downwind states. The Transport Rule did not make a determination 

whether Montana, Wyoming, Colorado, and New Mexico contribute to 

nonattainment or interfere with maintenance in neighboring states.
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portions of Canada and Mexico. In the Transport Rule, the results 

obtained with this national domain were used to calculate boundary 

conditions for the eastern Transport Rule region. The EPA did not use 

the national domain to investigate interstate contributions to 

nonattainment or interference with maintenance, in part because the 

air quality model structure for the national domain is less suitable 

for that type of use.19 In the second approach to identifying which 

areas are “affected” Class I areas we used data from the larger 

domain to estimate potential visibility impacts on Class I areas 

located to the west of the Transport Rule modeling region boundary. 

The additional 80 Class I areas under this national approach are in 

states or part of states that were not part of the eastern modeling 

domain for the Transport Rule, but were part of the western modeling 

domain.20

 19 The eastern modeling domain used a 12 km grid size, while the 

national modeling domain used a 36 km grid size. See Air Quality 

Modeling Final Rule Technical Support Document, U.S. EPA, June 2011, 

which is found at: 

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

In this approach, the eastern domain 12 km modeling results 

were used to calculate visibility changes in the 60 eastern Class I 

areas and the national domain 36 km modeling results were used to 

calculate visibility changes in the 80 western Class I areas. 

Consideration of this national region would encompass the possibility 

20 See Air Quality Modeling Final Rule Technical Support Document, 

U.S. EPA, June 2011, which is found at: 

http://www.epa.gov/airtransport/pdfs/AQModeling.pdf.
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that the Transport Rule might have the effect of increasing EGU 

emissions in the most western portion of the United States due to 

shifts in electricity generation or other market effects. In total, 

the national domain includes 140 Class I areas (including the 60 

contained within the Transport Rule region).

We request comment on whether the “affected Class I areas” 

should be considered to be the 60 Class I areas located in the 

Transport Rule eastern modeling domain, the larger set of 140 Class I 

areas in the larger national domain, or some other set. We note that 

given the modeling results presented in section VI.5, the choice 

between the 60 Class I areas or the 140 Class I areas does not affect 

our proposed conclusion that both prongs of the two-prong test are 

met.

The Transport Rule requires 28 states in the eastern half of the 

United States to reduce EGU SO

C. Scenarios Examined

2 and NOX emissions that cross state 

lines and contribute to ground-level ozone and fine particle 

pollution in other states. BART, on the other hand, is applicable 

nationwide and covers 26 industrial categories, including EGUs, of a 

certain vintage. In our comparison, we sought to determine whether 

the Transport Rule cap-and-trade program for EGUs will achieve 

greater reasonable progress than would BART for EGUs only. Therefore, 

we examined two relevant control scenarios. The first control 

scenario examined SO2 and NOX emissions from all EGUs nationwide after 
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the application of BART controls to all BART-eligible EGUs 

(“Nationwide BART”). In the second scenario, EGU SO2 and NOX

For the first prong of the test, the “Transport Rule + BARTelsewhere” scenario was compared to a 2014 future year base case

representing future emissions in the absence of either program (and 

in the absence of CAIR). The comparison to the 2014 future year “Base 

Case” allows the EPA to ensure that the Transport Rule would not 

cause degradation in visibility from conditions predicted for the 

year 2014 in the absence of the Transport Rule, BART and CAIR. 

emissions 

reductions attributable to the Transport Rule were applied in the 

Transport Rule region and BART controls were applied to all BARTeligible EGUS outside the Transport Rule region (“Transport Rule + 

BART-elsewhere”). The latter scenario reflects the fact that sourcespecific 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. 

For both the “Nationwide BART” scenario and the “Transport Rule

+ BART-elsewhere” scenario, we modeled the presumptive EGU BART 

limits for SO2 and NOX emission rates as specified in the BART 

Guidelines (Guidelines for BART Determinations Under the Regional 

Haze Rule, 70 FR 39104, July 6, 2005), unless an actual emission rate 

at a given unit with existing controls is lower. In the latter case, 
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we modeled the lower emission rates. In addition, we modeled the 

impacts of BART using stringent assumptions regarding the EGUs (or 

specific units at EGUs) that would be subject to BART. Specifically, 

we assumed that all BART-eligible EGUs were actually subject to BART 

requirements. We also assumed that presumptive BART limits would be 

applied to much smaller units. In this analysis we assumed the 

threshold for BART-eligibility was 100 MW for SO2 and 25 MW for NOX

and did not eliminate any sources based on their annual total 

emissions. (By comparison, the RHR BART Guidelines only apply 

presumptive limits to EGUs having a total generating capacity of 750 

MW and exempt BART-eligible units with the potential to emit less 

than 40 tons per year of either SO2 or NOX

The RHR BART Guidelines specify presumptive SO

.)

2 BART limits for 

an EGU with an existing scrubber as 95 percent scrubber control 

efficiency or 0.15 lbs/MMBtu. We used the National Electric Energy 

Data System (NEEDS), an EPA database of existing and plannedcommitted EGUs, to identify which BART-eligible units have existing 

scrubbers.21 The NEEDS also contains information on scrubber 

efficiency and emission rates. For scrubbed BART-eligible units we 

based our BART emission rate on a comparison of the emission rate 

listed for that unit in NEEDS to the presumptive SO2

 

21See 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.

emission rate. 
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That is, if the unit has at least a 95 percent efficient scrubber, 

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, we modeled that emission rate for the unit, even 

if the scrubber is less than 95 percent efficient. For BART-eligible 

units without existing scrubbers, we modeled an emission rate that 

reflected 95 percent control based on a new installation of a highly 

efficient scrubber. 

The RHR BART Guidelines specify presumptive limits for NOX 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,

we 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 (with the modifications about applicability as described 

above), we assumed installation of post-combustion controls that 

would meet the BART guidelines with year-round operation. In the 

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

are subject to the Transport Rule requirements during the ozone 
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season only.22 For these states, NOX controls were assumed to operate 

only during ozone season as required by the Transport Rule. The RHR 

BART Guidelines also specify presumptive limits for NOX based on coal 

type and boiler configuration. Table 1 summarizes the NOX emission 

limits we applied to BART-eligible units of 25 MW or greater. For 

units firing a coal blend, which the BART Guidelines do not address, 

we calculated a weighted presumptive NOX

Table 1. BART Presumptive NO

limit based on the percentage 

of each coal type fired. 

X 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

Certain EGUs in the analysis were constrained by emission limits 

other than presumptive limits due to a proposed or final regional 

haze SIP, a proposed or final regional haze FIP, a final consent 

decree, or state rules. These units and their emission limits are 

detailed in the Technical Support Document (TSD) for this proposed 

rule. (See Technical Support Document for Demonstration of the 

Transport Rule as a BART Alternative, Docket EPA-HQ-OAR-2011-0729.)

 22 States subject to the Transport Rule requirements during the ozone 

season only are Oklahoma, Arkansas, Louisiana, Mississippi and 

Florida.

D. Emission Projections
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To estimate emissions expected from the scenarios described in 

section IV.3, we used the Integrated Planning Model (IPM). 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 leastcost 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/.23 Table 2 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 nationwide24

Table 2. EGU SO

for both pollutants than source-specific “Nationwide BART” alone.

2 and NOX

(In thousands of tons per year)

Annual Emissions as Projected by IPM

2014 Base 

Case EGU 

Emissions

2014 

“Nationwide 

BART”

2014 

“Transport 

Rule + BARTelsewhere”

Additional 

reduction 

from 

“Transport 

Rule + BART-

 23 Extensive documentation of the IPM platform may be found at 

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

24 In the context of this action, when we refer to nationwide 

emissions or a nationwide analysis, we are referring to the 

contiguous 48 states.
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elsewhere”

(“Nationwide 

BART” minus 

“Transport 

Rule + BARTelsewhere”)

Nationwide 

SO 7,2007,160 2

3,8003,820 2,9002,918 900902

Nationwide 

NO 2,1001,946 X

1,9001,798 1,8001,756 10042

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

the “Transport Rule + BART-elsewhere” control scenario summarized in 

Table 2, which were used to arrive at the modeling results presented 

in section VI.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, 2011finalized on December 15, 2011.25

On October 14, 2011 the EPA issued a proposed notice that would 

increase NOX and SO2 budgets for certain states in accordance with 

revisions to certain unit-level input data. 76 FR 63860. Even if 

these proposed increases to state budgets are finalized, emissions of 

both NOX and SO2

 25 See 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 (76 FR 40662)(and

finalized on December 15, 2011) are included in the “Transport Rule + 

BART-elsewhere” control scenario.

in the Transport Rule states in the “Transport Rule + 

BART-elsewhere” control scenario will still be substantially below 

emissions in the “Base Case” scenario. Therefore, we believe that the 

modeling results in section VI.5 comparing these two scenarios based 

Formatted: Highlight

Comment [MarthaK1]: Confirm date with 

CAMD
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on the emissions from the final Transport Rule, showing that the 

first prong of the better-than-BART test is satisfied, are also 

sufficient for determining that the Transport Rule as modified by the 

proposed increases in the state budgets also would meet the first 

prong. 

Also, even if the proposed state budget increases are finalized, 

the “Transport Rule + BART-elsewhere” control scenario is still 

projected to result in about 3,00026,000 tons more NOX emission 

reductions than “Nationwide BART” and about 845,000821,000 tons more 

SO2 emission reductions than “Nationwide BART”. We believe the changes 

in the emissions differences between these two scenarios that would 

result if the proposed increases in state budgets are finalized are 

unlikely to affect the determination of whether “Transport Rule + 

BART-elsewhere” provides greater visibility improvement than 

“Nationwide BART” averaged across all affected Class I areas, as 

assessed by the second prong of two-pronged test. A sensitivity 

analysis that examines the impact of the proposed state budget 

increases on visibility improvement is presented in Appendix C of the 

TSD. We request comment on this aspect of our proposed determination. 

To assess the air quality metrics that are part of the two–

pronged test, we used the IPM emission projections summarized in 

Table 2 as inputs to an air quality model to determine the impact of 

“Transport Rule + BART-elsewhere” and “Nationwide BART” controls on 

E. Air Quality Modeling Results
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visibility in the affected Class I areas. To project air quality 

impacts we used the Comprehensive Air Quality Model with Extension 

(CAMx) version 5.3. The air quality modeling analysis and related 

analyses to project visibility improvement are described in more 

detail in the TSD for the Transport Rule.26 The base year meteorology 

used in the CAMx modeling was 2005. The base year IMPROVE ambient 

monitoring data for the years 2003-2007 were used to project 

visibility to 2014 and to compare the visibility improvements from 

the two control scenarios. The 2003-2007 IMPROVE data were used 

because these are the 5 years of data which straddle the base 2005 

modeling year. The post-processing calculations for visibility are 

consistent with the RHR tracking progress guidance27 and the regional 

haze air quality modeling guidance.28 The visibility projections for 

each Class I area are presented in the air quality modeling TSD.29

The cornerstone of our modeling process was the 2014 “Base Case”

modeling scenario, which contains emissions for 2014 based on 

 26 See Air Quality Modeling Final Rule Technical Support Document, 

U.S. EPA, June 2011, which is found at: 

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

27 See Guidance for Tracking Progress Under the Regional Haze Rule, 

U.S. EPA, EPA-454/B-03-004, September 2003, which is found at: 

http://www.epa.gov/ttncaaa1/t1/memoranda/rh_tpurhr_gd.pdf.

28 See 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-rhguidance.pdf.

29 See Technical Support Document for Demonstration of the Transport 

Rule as a BART Alternative, Docket EPA-HQ-OAR-2011-0729.
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predicted growth and existing emissions controls. We used modelpredicted 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” control 

strategy as well as the “Nationwide BART” strategy in 2014. 

We did two separate analyses to assess the potential visibility 

impacts of “Transport Rule + BART-elsewhere” and “Nationwide BART” 

controls on 60 Class I areas in the Transport Rule region and on 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 + BARTelsewhere” resulted in degradation of visibility at any affected 
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Class I area we compared the visibility impacts of “Transport Rule + 

BART-elsewhere” to base case 2014 visibility conditions. As described 

in detail in the TSD for this action, the “Transport Rule + BARTelsewhere” 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 modeling domain 

(i.e., when using the first approach to identifying affected areas), 

on either the 20 percent best or the 20 percent worst days. 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, Arizona, 

and Saguaro National Park, Arizona, on the 20 percent worst days.30

There was also a predicted degradation of 0.05 deciviews on the 20 

percent best days at Bryce Canyon National Park in Utah.31

 30 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).

While not 

part of the two-pronged test, we also compared the baseline scenario 

31 Changes 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. 
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to the “Nationwide BART” scenario. The analysis of the national 

region under the “Nationwide BART” control scenario 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 just noted).

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.32

In the CAMx output for 36 km grid cells in the vicinity of these 

four Class I areas, we observed that 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 + BARTelsewhere” case and the “Nationwide BART” case. The modeled nitrate 

concentrations in these cases ranged from 0.001 to 0.004 μg/m

Based on that 

investigation, we consider the visibility projections for the western 

portion of the national modeling domain that indicate potential 

degradation in 4 western Class I areas under the “Transport Rule + 

BART-elsewhere” 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 “Nationwide BART” 

scenario) will have on visibility in these areas.

3

 32 Appendix B of the The TSD in the docket for this action provides 

more information on this aspect of the CAMx modeling results.

, 
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averaged across the 20 percent worst or best days in 2005. 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 lead to a relatively large modeled 

percent increase in nitrate. As an example, on the worst days at Pine 

Mountain and Mazatzal the modeled nitrate concentration increased 

from 0.001 μg/m3 in the 2014 base case to 0.002 μg/m3

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. While IPM 

predicts modestly higher NO

in the 

“Transport Rule + BART-elsewhere” case. 

X emissions in some nearby states under the 

“Transport Rule + BART-elsewhere” scenario, the checkerboard pattern 

of nitrate differences in Arizona and southern Utah 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

 33 TAppendix B of the he TSD in the docket for this action provides 

more information on this issue.

Thus, we conclude that these positive 

and negative differences between very low nitrate concentrations are 

a modeling artifact attributable to the nitrate physics in CAMx for 
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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 illustrate how sensitive the predictions of degradation are 

to highly variable results on particular days, if the one day of the 

20 percent worst or best days with the largest increase in modeled 

nitrate concentration at each site is removed from consideration for 

that site, the apparent degradations no longer occur. We also note 

that although the increases in modeled nitrate concentrations are 

very small (ranging between 0.01 and 0.04 μg/m3 for the one day at 

each site just mentioned), the “relative response factor” method we 

used to combine CAMx output (representing future conditions) with 

IMPROVE monitoring data (representing historical conditions) greatly 

magnified these small increases in nitrate concentrations. The small 

increases in modeled nitrate are converted to relatively large 

percent increases in nitrate and then multiplied by actual ambient 

nitrate concentrations in the base period that are far higher than 

the concentrations predicted by CAMx. Thus, very small differences in 

concentrations of nitrate in the CAMx output that would have had no 

effect on calculated deciview values if used directly, nevertheless 

result in apparent degradations on the order of 0.1 to 0.26 deciviews 

after being combined with IMPROVE data. The EPA is investigating 

possible modifications to the software used to post-process CAMx 

output. These possible revisions are aimed at avoiding potentially 
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misleading results in situations such as the one observed near these

western Class I areas. We seek comment on an alternate methodology 

described in Appendix B of the TSD that attempts to address the 

effects of very low nitrate concentrations on visibility results. 

After considering the results of the first prong of the 

visibility test and examining the CAMx output in more detail as 

described above, we are confident that no degradation in the four 

western Class I areas will result from implementation of the 

Transport Rule trading programs in the Eastern eastern U.S. 

Consequently, we are proposing that the “Transport Rule + BARTelsewhere” 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). Details on the individual Class I area calculations can be 

found in the air quality modeling TSD. 

The second prong of the 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 + BART-elsewhere” 

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 
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average visibility improvement of the “Transport Rule + BARTelsewhere” alternative was greater than “Nationwide BART” on both the 

20 percent best and 20 percent worst days. Thus, the “Transport Rule

+ BART-elsewhere” alternative measure passed the second prong of the 

test, regardless of which way affected Class I areas are identified. 

A summary of the results of the second prong of the test for the 

Transport Rule and national regions under each control scenario is 

presented in Table 3.

Table 3. Average Visibility Improvement in 2015 v. 2015 Base Case 

(Deciviews)

“Transport Rule + 

BART-elsewhere” “Nationwide BART”

60 Class I Areas in the 

Eastern Transport Rule

Modeling Domain

20 percent Worst Days 1.6 0.7

20 percent Best Days 0.3 0.1

140 Class I Areas in the 

Western and Eastern Transport 

Rule Modeling Domains

20 percent Worst Days 0.7 0.5

20 percent Best Days 0.1 0.1

Based on our finding that the “Transport Rule + BART-elsewhere” 

control scenario passes the two-pronged test, we are proposing to 

determine that the Transport Rule trading programs will provide

greater progress towards Regional Haze goals than source-specific 

BART. This proposed determination applies only to EGUs in the 

F. Proposed Amendment to the Regional Haze Rule
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Transport Rule trading programs and only for the pollutants covered 

by the programs in each state. Accordingly, we propose to revise 40 

CFR 51.308(e)(3)(ii)(4) by essentially replacing the name of CAIR 

with the name of the Transport Rule. 

We are also proposing that a state that chooses to meet the 

emission reduction requirements of the Transport Rule by submitting a 

complete SIP revision substantively identical to the provisions of 

the EPA trading program that is approved as meeting the requirements 

of §52.38 and/or §52.39 also need not require BART-eligible EGUs in 

the state to install, operate, and maintain BART for the pollutants 

covered by such a trading program in the state.

We are preserving the language in the regional haze regulations 

at 40 CFR 51.308(e)(4)that allows states to include in their SIPs 

geographic enhancements to the alternative program to accommodate a 

situation where BART is required based on reasonable attribution of 

visibility impairment at a Class I area.

A number of the states for which we are proposing a FIP had 

previously failed to either submit a visibility SIP or had failed to 

submit a SIP that could be fully approved under the visibility 

regulations issued in 1980. See 45 FR 80084 (December 2, 1980). The 

proposed regulatory text is drafted to take account of this and is 

not intended to change the findings that have been made in the past 

with respect to the relevant states’ compliance with the requirements 

of visibility regulations found at 40 CFR 51.302-51.307.
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V. Proposed Limited Disapproval of Certain States’ Regional Haze 

SIPs

In this action, we are proposing a limited disapproval of the 

regional haze SIPs that have been submitted by Alabama, Florida, 

Georgia, Kentucky, Indiana, Iowa, Louisiana, Michigan, Mississippi, 

Missouri, North Carolina, Ohio, Pennsylvania, South Carolina and 

Texas. These states, fully consistent with the EPA’s regulations at 

the time, relied on CAIR requirements to satisfy the BART requirement 

and the requirement for a long-term strategy sufficient to achieve 

the state-adopted reasonable progress goals. 

We are not proposing to disapprove the reasonable progress 

targets for 2018 that are an element of the long-term strategies for 

these states. We made clear in the RHR that the reasonable progress 

goals are not mandatory standards in the sense of there being 

consequences if they are not met because there are inherent 

uncertainties in projecting future emissions and resulting visibility 

conditions. See 64 FR 35733. However, to assess whether current 

implementation strategies will be sufficient to meet the reasonable 

progress goals, the RHR requires a midcourse review by each state 

and, if necessary, a correction of the state’s regional haze plan. 

See 40 CFR 52.308(g). We anticipate that since the Transport Rule

will result in greater emission reductions overall than CAIR, that 

the need for such corrections will be unlikely. Based on the 

information currently before us, we believe that the substitution of 
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the Transport Rule for CAIR does not weaken any affected state’s 

long-term strategy, but we will assess the midcourse review of each 

state’s SIP to ensure that this is so. We intend to act on the 

reasonable progress goals and long-term strategy (including the 

proposed FIP) and other requirements of the RHR (BART determinations 

for non-EGU sources, monitoring, consultation with federal land 

managers, etc.) for each state in an individual notice separately 

from the final rule for this action. Those individual notices will 

constitute the final action (approval or disapproval) on those other 

elements of the SIP. 

The EPA has already proposed limited disapproval of regional 

haze SIPs that relied on CAIR that were submitted by Tennessee, 

Virginia and West Virginia. The remedy for the limited disapprovals 

previously proposed and those that are proposed in this action is 

FIPs as described in section VI.

VI. Proposed FIPs

In this action, we are proposing partial regional haze FIPs for 

states for which we already have or are now proposing limited 

disapprovals because of the termination of CAIR. These limited FIPs 

would satisfy the BART requirement and be a part of satisfying the 

requirement for a long-term strategy sufficient to achieve the stateadopted reasonable progress goals. The FIPs apply only to EGUs in the 

affected states and only to pollutants covered by the Transport Rule

programs in those states. For the reasons discussed in section V., 
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the proposed FIPs do not alter states’ reasonable progress goals or 

replace these goals. 

The proposed FIPs replace reliance on CAIR requirements with 

reliance on the Transport Rule as an alternative to BART for SO2 and 

NOX emissions from EGUs in the following states’ regional haze SIPs: 

Alabama, Georgia, Indiana, Iowa, Kentucky, Michigan, Missouri, North 

Carolina, Ohio, Pennsylvania, South Carolina, Tennessee, Texas, 

Virginia and West Virginia. The proposed FIPs replace reliance on 

CAIR requirements with reliance on the Transport Rule as an 

alternative to BART for NOX

Given the requirements of the CAA to promulgate a FIP after 

disapproving a SIP in whole or in part (CAA section 110(c)(1)), we

consider it appropriate at this time to propose to issue FIPs to

address the noted deficiencies in these states’ regional haze SIPs 

related to the termination of CAIR and the replacement of CAIR with 

the Transport Rule. A state may choose to submit a SIP or remain

subject to this FIP. The proposed regional haze FIPs rely on the 

trading programs set out in the FIPs promulgated by the EPA in August 

2011 in the Transport Rule to limit the interstate transport of NO

emissions from EGUs in the following 

states’ regional haze SIPs: Florida, Louisiana and Mississippi.

X

and SO2

VII. Statutory and Executive Order Reviews

.

A. Executive Order 12866: Regulatory Planning and Review and 

Executive Order 13563: Improving Regulation and Regulatory Review
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Under Executive Order 12866 (58 FR 51735, October 4, 1993), this 

action is a "significant regulatory action” because some may view it 

as raising novel legal or policy issues arising out of legal 

mandates, the President’s priorities, or the principles set forth in 

the Executive Order. Accordingly, the EPA submitted this action to 

the Office of Management and Budget (OMB) for review under Executive 

Orders 12866 and 13563 (76 FR 3821, January 21, 2011) and any changes 

made in response to OMB recommendations have been documented in the 

docket for this action.

This action does not impose an information collection burden 

under the provisions of the 

B. Paperwork Reduction Act

Paperwork Reduction Act, 44 U.S.C. 3501 

et seq. Burden is defined at 5 CFR 1320.3(b). This action does not 

include or require any information collection.

The Regulatory Flexibility Act (RFA) generally requires an 

agency to prepare a regulatory flexibility analysis of any rule 

subject to notice and comment rulemaking requirements under the 

Administrative Procedure Act or any other statute unless the agency 

certifies that the rule will not have a significant economic impact 

on a substantial number of small entities. Small entities include 

small businesses, small organizations, and small governmental 

jurisdictions.

C. Regulatory Flexibility Act

For purposes of assessing the impacts of this rule on small 
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entities, small entity is defined as: (1) A small business that is a 

small industrial entity as defined in the U.S. Small Business 

Administration (SBA) size standards. (See

After considering the economic impacts of this proposed rule on 

small entities, I certify that this action will not have a 

significant economic impact on a substantial number of small 

entities. This rule will not impose any requirements on small 

entities. Rather, this proposed rule would allow states to avoid 

regulating EGUs in new ways based on the current requirements of the 

Transport Rule and as such does not impose any new requirements on 

small entities. We continue to be interested in the potential impacts 

of the proposed rule on small entities and welcome comments on issues 

related to such impacts.

13 CFR 121.); (2) A 

governmental jurisdiction that is a government of a city, county, 

town, school district, or special district with a population of less 

than 50,000; and (3) A small organization that is any not-for-profit 

enterprise which is independently owned and operated and is not 

dominant in its field.

This action contains no federal mandates under the provisions of 

Title II of the Unfunded Mandates Reform Act of 1995 (UMRA, 2 U.S.C. 

1531 – 1538) for state, local, or tribal governments or the private 

sector. The action imposes no enforceable duty on any state, local, or 

D. Unfunded Mandates Reform Act
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tribal governments or the private sector. Therefore, this action is not 

subject to the requirements of sections 202 or 205 of the UMRA.

This action is also not subject to the requirements of section 

203 of UMRA because it contains no regulatory requirements that might 

significantly or uniquely affect small governments. This action 

merely interprets the statutory requirements that apply to states in 

preparing their SIPs and thus apply also to FIPs.

This action does not have federalism implications. It will not 

have substantial direct effects on the states, on the relationship 

between the national government and the states, or on the 

distribution of power and responsibilities among the various levels 

of government, as specified in Executive Order 13132. This action 

does not impose any new mandates on state or local governments.

E. Executive Order 13132: Federalism

Thus, Executive Order 13132 does not apply to this rule. 

In the spirit of Executive Order 13132 and consistent with EPA 

policy to promote communications between the EPA and state and local 

governments, the EPA is specifically soliciting comments on this 

proposed rule from state and local officials.

This rule does not have tribal implications, as specified in 

Executive Order 13175 (65 FR 67249, November 9, 2000). The rule does 

not have a substantial direct effect on one or more Indian tribes, 

F. Executive Order 13175: Consultation and Coordination with Indian 

Tribal Governments
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since there are no BART-eligible EGU sources on tribal lands in the 

Transport Rule region. In addition, the CAA does not provide for the 

inclusion of any tribal areas as mandatory Class I federal areas; 

thus, tribal areas are not subject to the requirements of the RHR. 

Furthermore, this proposed rule does not affect the relationship or 

distribution of power and responsibilities between the federal

government and Indian tribes. Thus, Executive Order 13175 does not 

apply to this action. The EPA specifically solicits additional 

comment on this proposed action from tribal officials.

The EPA interprets Executive Order 13045 (62 FR 19885, April 23, 

1997) as applying to those regulatory actions that concern health or 

safety risks, such that the analysis required under section

G. Executive Order 13045: Protection of Children from Environmental 

Health and Safety Risks 

5–501 of the Order has the potential to influence the regulation. 

This action is not subject to Executive Order 13045 because it does 

not involve decisions on environmental health or safety risks that

may disproportionately affect children. The EPA believes that the 

emissions reductions from the strategies in this rule will further 

improve air quality and will further improve children’s health.

H. Executive Order 13211: Actions Concerning Regulations That 

Significantly Affect Energy Supply, Distribution, or Use

This action is not a “significant energy action” as defined in 

Executive Order 13211 (66 FR 28355 (May 22, 2001)), because it is not 
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likely to have a significant adverse effect on the supply, 

distribution, or use of energy because it does not establish 

requirements that directly affect the general public and the public 

and private sectors. Rather, this proposed rule would allow states to 

avoid regulating EGUs in new ways based on the current requirements 

of the Transport Rule, and thus may avoid adverse effects that 

conceivably might result from such additional regulation of EGUs by 

states.

Section 12(d) of the National Technology Transfer and 

Advancement Act of 1995 ("NTTAA"), Public Law 104-113, section 12(d), 

(15 U.S.C. 272 note) directs the EPA to use voluntary consensus 

standards in its regulatory activities unless to do so would be 

inconsistent with applicable law or otherwise impractical. Voluntary 

consensus standards are technical standards (

I. National Technology Transfer and Advancement Act 

e.g., materials 

specifications, test methods, sampling procedures, and business 

practices) that are developed or adopted by voluntary consensus 

standards bodies. The NTTAA directs the EPA to provide Congress, 

through OMB, explanations when the Agency decides not to use 

available and applicable voluntary consensus standards. This 

rulemaking does not involve technical standards. Therefore, the EPA

is not considering the use of any voluntary consensus standards.

J. Executive Order 12898: Federal Actions to Address Environmental 

Justice in Minority Populations and Low-Income Populations
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Executive Order 12898 (EO) (59 FR 7629, February 16, 1994) 

establishes federal executive policy on environmental justice. Its 

main provision directs federal agencies, to the greatest extent 

practicable and permitted by law, to make environmental justice part 

of their mission by identifying and addressing, as appropriate, 

disproportionately high and adverse human health or environmental 

effects of their programs, policies, and activities on minority 

populations and low-income populations in the United States.

When considering the possible environmental justice impacts of 

this proposed rule, it is important to distinguish the set of 

scenarios on which the better-than-BART analysis described in this 

notice is based from the set of possible future situations that could 

come to pass based on the outcome of this rulemaking. The Transport 

Rule is in place and will remain in place regardless of the outcome 

of this rulemaking. If we finalize the proposed rule, a regional haze 

SIP or FIP for an affected state will be able to satisfy the BART

requirement for EGUs (for NOX only or for SO2 and NOX, depending on 

which Transport Rule programs applyies in that state) merely by 

formally incorporating the Transport Rule into the long-term strategy 

of the SIP.34

 34 Such action by a state would not preclude it from also including in 

the SIP source-specific emission limits for EGUs of its choosing.

If we do not adopt any rule establishing the Transport 

Rule as an alternative to BART, the EGUs in each affected state will 

still be required to participate in the cap-and-trade programs 
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established by the Transport Rule. In this case the SIP or FIP would 

also have to apply source-specific BART to all BART-eligible sources 

except any that are found not to be subject to BART due to minimal 

impacts on visibility or any that the state concludes should not be 

further controlled based on its consideration of existing controls, 

cost of additional controls, remaining lifetime of the unit, other 

non-air impacts and visibility impacts from controls. It is important 

to recognize that because of the nature of cap-and-trade programs, 

total state-wide emissions will not be very different, if at all, if 

the EPA were not to make a final determination that participation in 

the Transport Rule trading program satisfied the BART requirements. 

Any EGUs that would be required to comply with source-specific BART 

would generate tradable emission allowances that would find buyers 

among the other EGUs in the state. Thus, we expect that the outcome 

of this rule may change how a fixed amount of total emissions from 

EGUs is divided among EGUs in a given affected state. Because of the 

certainty of EGUs collectively meeting the Transport Rule emission 

caps, that fixed amount will generally be substantially less than 

historical total EGU emissions in a given state.

We have concluded that it is not practicable to perform an 

analysis which would attempt to predict exactly which EGUs would have 

higher and lower emissions under the two possible outcomes. We have, 

however, identified the locations of BART-eligible sources in 

Transport Rule-affected states to determine if there are high 
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percentages of minority or low-income populations living near such 

sources. These are the sources that conceivably could have higher 

emissions if we finalize the proposed rule than if we do not. An 

analysis of demographic data shows that the average percentage of 

African Americans living within a 3-mile radius of BART-eligible 

sources in Transport Rule-affected states is somewhat higher (six

percent) than the corresponding national average percentage. All 

other socio-demographic parameters evaluated are within two percent 

of the national average percentages, or below the national average 

percentages. The results of the demographic analysis are presented in 

the memorandum entitled “Review of Environmental Justice Impacts,” 

July 2011, a copy of which is available in the docket (EPA-HQ-OAR2011-0729). Strictly speaking, if we were not to finalize this rule 

and the states (or we, through FIPs) were to impose source-specific 

BART on these sources, other sources might increase their emissions 

under the cap-and-trading programs. Since we do not know which other 

sources might do so, we could not perform a similar demographic 

analysis on such other sources.

We do know that under the Transport Rule, ozone and PM2.5 air 

quality and health risks will be greatly reduced compared either to 

current conditions or to future conditions if there were no Transport 

Rule. In the Transport Rule, the EPA estimated the distribution of 

PM2.5 mortality risks according to race, income, and educational 

attainment before and after implementation of the Transport Rule. In 
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that analysis, we found that the Transport Rule market-based regional 

approach to reducing emissions of SO2 and NOX from EGUs provided the 

greatest PM2.5-related health benefits among populations: (1) most 

susceptible to air pollution impacts, regardless of race; (2) with 

lower levels of educational attainment; (3) living in counties with 

among the highest number of individuals living below the poverty 

line. The analysis also indicates that the Transport Rule, in 

conjunction with the implementation of existing or proposed rules, 

will reduce the disparity in risk between the highest-risk counties 

and the other 95 percent of counties for all races and educational 

levels. This analysis is presented in more detail in the Regulatory 

Impact Analysis for the Transport Rule which is available in the 

Transport Rule docket EPA-HQ-OAR-2009-0491 and from the main EPA 

webpage for the Transport Rule www.epa.gov/airtransport

The results of the Transport Rule analysis suggest that regional 

reductions in PM

. 

2.5 levels can produce significant human health 

benefits — particularly among populations most susceptible and 

vulnerable to PM2.5 impacts. PM2.5 air quality improvements that would 

be expected under implementation of source-specific BART may differ 

from the Transport Rule in terms of the emission reductions required 

at any given source, especially since states have the discretion to 

determine which BART-eligible sources to control and the level of 

control that is feasible. However, the results of the Transport Rule

assessment suggest that the regional Transport Rule approach provides 
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widespread health benefits especially among populations at greatest 

risk. 

List of Subjects

40 CFR Part 51

Administrative practice and procedure, Air pollution control, 

Incorporation by reference, Intergovernmental relations, Nitrogen 

oxides, Ozone, Particulate matter, Regional haze, Reporting and 

recordkeeping requirements, Sulfur dioxide.
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Page 59 of 72 - Regional Haze: Revisions to Provisions Governing 

Alternatives to Source-Specific Best Available Retrofit Technology 

(BART) Determinations, Limited SIP Disapprovals, and Federal 

Implementation Plans 

40 CFR Part 52

Administrative practice and procedure, Air pollution control, 

Incorporation by reference, Intergovernmental relations, Nitrogen 

oxides, Ozone, Particulate matter, Regional haze, Reporting and 

recordkeeping requirements, Sulfur dioxide.

___________________________________

Dated:

___________________________________

Lisa P. Jackson, 

Administrator.
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For the reasons set forth in the preamble, part 51 of chapter I of 

title 40 of the Code of Federal Regulations is proposed to be amended 

as follows:

PART 51-[AMENDED]

 1. The authority citation for part 51 continues to read as 

follows:

 Authority: 23 U.S.C. 101; 42 U.S.C. 7401-7671q

 2. Section 51.308 is amended by revising paragraph (e)(3)(ii)(4) 

to read as follows:

§51.308 Regional haze program requirements

 

(4) A State subject to a trading program established in accordance 

with §52.38 or §52.39 under a Transport Rule Federal Implementation 

Plan need not require BART-eligible fossil fuel-fired electric steam 

generating plants in the State to install, operate, and maintain BART 

for the pollutant covered by such trading program in the State. A 

State that chooses to meet the emission reduction requirements of the 

Transport Rule by submitting a SIP revision that establishes a 

trading program and is approved as meeting the requirements of §52.38 

or §52.39 also need not require BART-eligible fossil fuel-fired 

electric steam generating plants in the State to install, operate, 

and maintain BART for the pollutant covered by such trading program 

in the State. A State may adopt provisions, consistent with the 

requirements applicable to the State for a trading program 
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established in accordance with §52.38 or §52.39 under the Transport 

Rule Federal Implementation Plan or established under a SIP revision 

that is approved as meeting the requirements of §52.38 or §52.39, for 

a geographic enhancement to the program to address the requirement 

under §51.302(c) related to BART for reasonably attributable 

impairment from the pollutant covered by such trading program in that 

State. 

For the reasons set forth in the preamble, part 52 of chapter I of 

title 40 of the Code of Federal Regulations is amended as follows:

PART 52-[AMENDED]

1. The authority citation for part 52 continues to read as follows:

Authority: 42 U.S.C. 7401, et seq

Subpart B-Alabama

.

2. Section 52.61 is amended by revising paragraph (a) and adding new 

paragraphs (c) and (d) to read as follows: 

§52.61 Visibility protection.

(a) The requirements of section 169A of the Clean Air Act are not 

met because the plan does not include approvable measures for meeting 

the requirements of 40 CFR 51.302, 51.308(d)(3), and 51.308(e) for 

protection of visibility in mandatory Class I Federal areas.

*****

(c) Best Available Retrofit Technology for NOX. The requirements of 

40 CFR 51.308(e) with respect to emissions of NOX are satisfied by 

§52.54 for the sources subject to those requirements.
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(d) Best Available Retrofit Technology for SO2. The requirements of 

40 CFR 51.308(e) with respect to emissions of SO2

Subpart K-Florida

are satisfied by 

§52.55 for the sources subject to those requirements.

3. Section 52.534 is amended by revising paragraph (a) and adding 

new paragraph (c) as follows:

§52.534 Visibility protection.

(a) The requirements of section 169A of the Clean Air Act are not 

met because the plan does not include approvable measures for meeting 

the requirements of 40 CFR 51.305, 51.307, 51.308(d)(3), and 

51.308(e) for protection of visibility in mandatory Class I Federal 

areas.

*****

(c) Best Available Retrofit Technology for NOX. The requirements of 

40 CFR 51.308(e) with respect to emissions of NOX 

Subpart L-Georgia

are satisfied by 

§52.540 for the sources subject to those requirements.

4. Section 52.580 is added to read as follows:

§52.580 Visibility protection.

(a) The requirements of section 169A of the Clean Air Act are not 

met because the plan does not include approvable measures for meeting 

the requirements of 40 CFR 51.308(d)(3) and 51.308(e) for protection 

of visibility in mandatory Class I Federal areas.
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(b) Best Available Retrofit Technology for NOX. The requirements of 

40 CFR 51.308(e) with respect to emissions of NOX are satisfied by 

§52.584 with respect to emissions of NOX 

(c) Best Available Retrofit Technology for SO

for the sources subject to 

those requirements.

2. The requirements of 

40 CFR 51.308(e) with respect to emissions of SO2

Subpart P-Indiana

are satisfied by 

§52.585 for the sources subject to those requirements.

5. Section 52.791 is added to read as follows:

§52.791 Visibility protection.

a) The requirements of section 169A of the Clean Air Act are not met 

because the plan does not include approvable measures for meeting the 

requirements of 40 CFR 51.308(d)(3) and 51.308(e) for protection of 

visibility in mandatory Class I Federal areas.

(b) Best Available Retrofit Technology for NOX. The requirements of 

40 CFR 51.308(e) with respect to emissions of NOX

(c) Best Available Retrofit Technology for SO

are satisfied by 

§52.789 for the sources subject to those requirements.

2. The requirements of 

40 CFR 51.308(e) with respect to emissions of SO2

Subpart Q-Iowa

are satisfied by 

§52.790 for the sources subject to those requirements.

6. Section 52.842 is added to read as follows:

§52.842 Visibility protection.
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(a) The requirements of section 169A of the Clean Air Act are not 

met because the plan does not include approvable measures for meeting 

the requirements of 40 CFR 51.308(d)(3) and 51.308(e) for protection 

of visibility in mandatory Class I Federal areas.

(b) Best Available Retrofit Technology for NOX. The requirements of 

40 CFR 51.308(e) with respect to emissions of NOX

(c) Best Available Retrofit Technology for SO

are satisfied by 

§52.840 for the sources subject to those requirements.

2. The requirements of 

40 CFR 51.308(e) with respect to emissions of SO2

Subpart S-Kentucky

are satisfied by 

§52.841 for the sources subject to those requirements.

7. Section 52.936 is amended by revising paragraph (a) and adding 

new paragraphs (c) and (d) to read as follows: 

§52.936 Visibility protection.

(a) The requirements of section 169A of the Clean Air Act are not 

met because the plan does not include approvable measures for meeting 

the requirements of 40 CFR 51.307(b)and (c), 51.308(d)(3), and 

51.308(e) for protection of visibility in mandatory Class I Federal 

areas.

*****

(c) Best Available Retrofit Technology for NOX. The requirements of 

40 CFR 51.308(e) with respect to emissions of NOX are satisfied by 

§52.940 for the sources subject to those requirements.
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(d) Best Available Retrofit Technology for SO2. The requirements of 

40 CFR 51.308(e) with respect to emissions of SO2

Subpart T-Louisiana

are satisfied by 

§52.941 for the sources subject to those requirements.

8. Section 52.985 is added to read as follows:

§52.985 Visibility protection.

(a) The requirements of section 169A of the Clean Air Act are not 

met because the plan does not include approvable measures for meeting 

the requirements of 40 CFR 51.308(d)(3) and 51.308(e) for protection 

of visibility in mandatory Class I Federal areas.

(b) Best Available Retrofit Technology for NOX. The requirements of 

40 CFR 51.308(e) with respect to emissions of NOX

Subpart X-Michigan

are satisfied by 

§52.984 for the sources subject to those requirements.

9. Section 52.1183 is amended by revising paragraph (a) and adding 

new paragraphs (d) and (e) to read as follows: 

§52.1183 Visibility protection.

(a) The requirements of section 169A of the Clean Air Act are not 

met because the plan does not include approvable measures for meeting 

the requirements of 40 CFR 51.302, 51.305, 51.307, 51.308(d)(3), and 

51.308(e) for protection of visibility in mandatory Class I Federal 

areas.

*****
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(d) Best Available Retrofit Technology for NOX. The requirements of 

40 CFR 51.308(e) with respect to emissions of NOX

(e) Best Available Retrofit Technology for SO

are satisfied by 

§52.1186 for the sources subject to those requirements.

2. The requirements of 

40 CFR 51.308(e) with respect to emissions of SO2

Subpart Z-Mississippi

are satisfied by 

§52.1187 for the sources subject to those requirements.

10. Section 52.1279 is added to read as follows:

§52.1279 Visibility protection.

(a) The requirements of section 169A of the Clean Air Act are not 

met because the plan does not include approvable measures for meeting 

the requirements of 40 CFR 51.308(d)(3) and 51.308(e) for protection 

of visibility in mandatory Class I Federal areas.

(b) Best Available Retrofit Technology for NOX. The requirements of 

40 CFR 51.308(e) with respect to emissions of NOX

Subpart AA-Missouri

are satisfied by 

§52.1284 for the sources subject to those requirements.

11. Section 52.1339 is amended by revising paragraph (a) and adding 

new paragraphs (c) and (d) to read as follows:

§52.1339 Visibility protection.

(a) The requirements of section 169A of the Clean Air Act are not 

met because the plan does not include approvable measures for meeting 

the requirements of 40 CFR 51.302, 51.308(d)(3), and 51.308(e) for 

protection of visibility in mandatory Class I Federal areas.
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*****

(c) Best Available Retrofit Technology for NOX. The requirements of 

40 CFR 51.308(e) with respect to emissions of NOX

(d) Best Available Retrofit Technology for SO

are satisfied by 

§52.1236 for the sources subject to those requirements.

2. The requirements of 

40 CFR 51.308(e) with respect to emissions of SO2

Subpart II-North Carolina

are satisfied by 

§52.1327 for the sources subject to those requirements.

12. Section 52.1779 is added to read as follows:

§52.1779 Visibility protection.

(a) The requirements of section 169A of the Clean Air Act are not 

met because the plan does not include approvable measures for meeting 

the requirements of 40 CFR 51.308(d)(3) and 51.308(e) for protection 

of visibility in mandatory Class I Federal areas.

(b) Best Available Retrofit Technology for NOX. The requirements of 

40 CFR 51.308(e) with respect to emissions of NOX

(c) Best Available Retrofit Technology for SO

are satisfied by 

§52.1784 for the sources subject to those requirements.

2. The requirements of 

40 CFR 51.308(e) with respect to emissions of SO2

Subpart KK-Ohio

are satisfied by 

§52.1785 for the sources subject to those requirements.

13. Section 52.1886 is added to read as follows:

§52.1886 Visibility protection.
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(a) The requirements of section 169A of the Clean Air Act are not 

met because the plan does not include approvable measures for meeting 

the requirements of 40 CFR 51.308(d)(3) and 51.308(e) for protection 

of visibility in mandatory Class I Federal areas.

(b) Best Available Retrofit Technology for NOX. The requirements of 

40 CFR 51.308(e) with respect to emissions of NOX

(c) Best Available Retrofit Technology for SO

are satisfied by 

§52.1882 for the sources subject to those requirements.

2. The requirements of

40 CFR 51.308(e) with respect to emissions of SO2

Subpart NN-Pennsylvania

are satisfied by 

§52.1883 for the sources subject to those requirements.

14. Section 52.2042 is added to read as follows:

§52.2042 Visibility protection.

(a) The requirements of section 169A of the Clean Air Act are not 

met because the plan does not include approvable measures for meeting 

the requirements of 40 CFR 51.308(d)(3) and 51.308(e) for protection 

of visibility in mandatory Class I Federal areas.

(b) Best Available Retrofit Technology for NOX. The requirements of 

40 CFR 51.308(e) with respect to emissions of NOX

(c) Best Available Retrofit Technology for SO

are satisfied by 

§52.2040 for the sources subject to those requirements.

2. The requirements of 

40 CFR 51.308(e) with respect to emissions of SO2

Subpart PP-South Carolina

are satisfied by 

§52.2041 for the sources subject to those requirements.
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15. Section 52.2132 is amended by revising paragraph (a) and adding 

new paragraphs (d) and (e) to read as follows: 

§52.2132 Visibility protection.

(a) The requirements of section 169A of the Clean Air Act are not 

met because the plan does not include approvable measures for meeting 

the requirements of 40 CFR 51.302, 51.305, 51.308(d)(3), and 

51.308(e) for protection of visibility in mandatory Class I Federal 

areas.

*****

(d) Best Available Retrofit Technology for NOX. The requirements of 

40 CFR 51.308(e) with respect to emissions of NOX

(e) Best Available Retrofit Technology for SO

are satisfied by 

§52.2140 for the sources subject to those requirements.

2. The requirements of 

40 CFR 51.308(e) with respect to emissions of SO2

Subpart RR-Tennessee

are satisfied by 

§52.2141 for the sources subject to those requirements.

16. Section 52.2234 is added to read as follows:

§52.2234 Visibility protection.

(a) The requirements of section 169A of the Clean Air Act are not 

met because the plan does not include approvable measures for meeting 

the requirements of 40 CFR 51.308(d)(3) and 51.308(e) for protection 

of visibility in mandatory Class I Federal areas.
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(b) Best Available Retrofit Technology for NOX. The requirements of 

40 CFR 51.308(e) with respect to emissions of NOX

(c) Best Available Retrofit Technology for SO

are satisfied by 

§52.2240 for the sources subject to those requirements.

2. The requirements of 

40 CFR 51.308(e) with respect to emissions of SO2

Subpart SS-Texas

are satisfied by 

§52.2241 for the sources subject to those requirements.

17. Section 52.2304 is amended by revising paragraph (a) and adding 

new paragraphs (c) and (d) to read as follows: 

§52.2304 Visibility protection.

(a) The requirements of section 169A of the Clean Air Act are not 

met because the plan does not include approvable measures for meeting 

the requirements of 40 CFR 51.305, 51.308(d)(3), and 51.308(e) for 

protection of visibility in mandatory Class I Federal areas.

*****

(c) Best Available Retrofit Technology for NOX. The requirements of 

40 CFR 51.308(e) with respect to emissions of NOX

(d) Best Available Retrofit Technology for SO

are satisfied by 

§52.2283 for the sources subject to those requirements.

2. The requirements of 

40 CFR 51.308(e) with respect to emissions of SO2

Subpart VV-Virginia

are satisfied by 

§52.2284 for the sources subject to those requirements.

18. Section 52.2452 is amended by revising paragraph (a) and adding 

new paragraphs (d) and (e) to read as follows: 
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§52.2452 Visibility protection.

(a) The requirements of section 169A of the Clean Air Act are not 

met because the plan does not include approvable measures for meeting 

the requirements of 40 CFR 51.302, 51.305, 51.308(d)(3), and 

51.308(e) for protection of visibility in mandatory Class I Federal 

areas.

*****

(d) Best Available Retrofit Technology for NOX. The requirements of 

40 CFR 51.308(e) with respect to emissions of NOX

(e) Best Available Retrofit Technology for SO

are satisfied by 

§52.2440) for the sources subject to those requirements.

2. The requirements of 

40 CFR 51.308(e) with respect to emissions of SO2

Subpart XX-West Virginia

are satisfied by 

§52.2441 for the sources subject to those requirements.

19. Section 52.2533 is amended by revising paragraph (a) and adding 

new paragraphs (d) and (e) to read as follows: 

§52.2533 Visibility protection.

(a) The requirements of section 169A of the Clean Air Act are not 

met because the plan does not include approvable measures for meeting 

the requirements of 40 CFR 51.302, 51.305, 51.307, 51.308(d)(3), and 

51.308(e) for protection of visibility in mandatory Class I Federal 

areas.

*****
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(d) Best Available Retrofit Technology for NOX. The requirements of 

40 CFR 51.308(e) with respect to emissions of NOX

(e) Best Available Retrofit Technology for SO

are satisfied by 

§52.2540 for the sources subject to those requirements.

2. The requirements of 

40 CFR 51.308(e) with respect to emissions of SO2 are satisfied by 

§52.2541 for the sources subject to those requirements.
1

Technical Support Document for Demonstration of the Transport Rule as a 

BART Alternative

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

 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.

, and

2 The “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).
2

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

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

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
3

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 

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 

Guidelines3 specify 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 

 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.
4

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 

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.5 For 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

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

Louisiana, Mississippi and Florida.
5

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 

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.
6

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
7

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. 
8

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 nationwide7 for 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, 2011.8 State-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.

 6 Extensive 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 (and finalized on December 15, 2011) 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).
9

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 Massachusett

s

13.4 8.4 8.4

Nebraska9 73.1 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.

Formatted: Superscript

Formatted: Superscript

Formatted: Subscript
10

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.
11

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

 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 

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

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 
12

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.
13

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

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

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 

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).
14

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).

19 The 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).
15

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
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

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
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

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
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

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

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 
19

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:

New IMPROVE algorithm

Use model grid cells at (IMPROVE) monitor 

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

 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. 22 Extinction 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.
20

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

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 
21

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.

The apportionment of the total concentration of sulfate compounds into the 

concentrations of the small and large size fractions is accomplished using the following 
22

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).

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 

 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. 
23

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- 1st 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.
24

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

Degradati

on Test

20% Best 

Days

=[C] –

[A]*

No

Degradat

ion 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 Wilderness TN 12.2 27.0 11.4 23.3 -0.8 -3.7

La Garita Wilderness CO 2.3 9.5 2.3 9.4 -0.1 -0.1
25

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

Degradati

on Test

20% Best 

Days

=[C] –

[A]*

No

Degradat

ion Test

20% 

Worst 

Days

=[D] –

[B]*

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

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
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

Degradati

on Test

20% Best 

Days

=[C] –

[A]*

No

Degradat

ion Test

20% 

Worst 

Days

=[D] –

[B]*

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

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
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

Degradati

on Test

20% Best 

Days

=[C] –

[A]*

No

Degradat

ion Test

20% 

Worst 

Days

=[D] –

[B]*

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

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
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

Degradati

on Test

20% Best 

Days

=[C] –

[A]*

No

Degradat

ion Test

20% 

Worst 

Days

=[D] –

[B]*

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 Wilderness ID 3.6 14.8 3.6 14.8 0.0 0.0

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
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

Degradati

on Test

20% Best 

Days

=[C] –

[A]*

No

Degradat

ion Test

20% 

Worst 

Days

=[D] –

[B]*

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 

percent worst days.

25

 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.

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 
30

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 days 27 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 

“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 

 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. 
31

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/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 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 

 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
32

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 - 2nd 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.
33

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
34

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
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

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
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

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
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

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
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

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.
39

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.

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% 

 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”.
40

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.
41

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
42

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
43

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
44

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
45

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
46

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 Nelson 6 550 1982

Louisiana Rodemacher 2 523 1982

Maryland C P Crane 2 200 1963

Maryland Chalk Point LLC 1 341 1964

Maryland Chalk Point LLC 2 342 1965

Maryland Dickerson 3 182 1962

Maryland Herbert A Wagner 3 324 1966

Maryland Morgantown Generating Plant 1 624 1970

Maryland Morgantown Generating Plant 2 620 1971

Massachusetts Brayton Point 1 243 1963

Massachusetts Brayton Point 2 244 1964

Massachusetts Brayton Point 3 612 1969

Michigan Belle River 1 698 1984

Michigan Belle River 2 698 1985

Michigan Eckert Station 4 69.2 1964

Michigan Eckert Station 5 68.9 1968

Michigan Eckert Station 6 66.8 1970

Michigan Erickson Station 1 152 1973

Michigan Harbor Beach 1 103 1968

Michigan J H Campbell 1 260 1962

Michigan J H Campbell 2 355 1967

Michigan J H Campbell 3 825 1980

Michigan James De Young 4 20.5 1962

Michigan James De Young 5 27.0 1969

Michigan Monroe 1 770 1972

Michigan Monroe 2 785 1973

Michigan Monroe 3 795 1973

Michigan Monroe 4 775 1974

Michigan Presque Isle 5 88.0 1974

Michigan Presque Isle 6 88.0 1975

Michigan Presque Isle 7 85.0 1978

Michigan Presque Isle 8 85.0 1978

Michigan Presque Isle 9 85.0 1979

Michigan Shiras 2 19.5 1972

Michigan St Clair 7 440 1969

Michigan Trenton Channel 9A 536 1968
47

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
48

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.

Formatted: Superscript

Formatted: Superscript

Formatted: Subscript

Formatted: Superscript
49

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
50

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
51

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
52

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
53

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
54

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 

4th 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). 
55

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.
56

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/m3 in the 2014 base case to 0.002 μg/m3 in 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. 
57

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 

 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+ –SO42

− –NO3

− –Cl− –H2O aerosols, available at: http://www.atmos-chemphys.org/7/4639/2007/acp-7-4639-2007.pdf
58

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. 

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.
59

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.
60

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 4231

Mississippi N/A 2136

Michigan N/A 5228

Nebraska N/A 3559

New Jersey N/A 112

New York 3527 3485

Texas 70067 1375

Wisconsin 7757 2473

Total Increase 81351 22599

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)
61

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.
62

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. 
63

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.