Supplemental Air Quality Modeling Technical Support Document (TSD) for the Clean Air 

Interstate Rule (CAIR), May, 2004. 

I. Introduction 

This document provides additional background and analyses supporting EPA’s proposed 

determination that compliance with the proposed CAIR, if achieved by power plants under the 

model cap-and-trade programs, would satisfy the best available retrofit technology (BART) 

requirements for those sources as a “better than BART” alternative. Section III.E of the 

supplemental proposal (SNPR) of the Clean Air Interstate Rule (CAIR) discusses this proposed 

determination. The EPA’s supporting assessment is consistent with the requirements established 

under the regional haze rule (64 FR 3714, July 1, 1999), and under the proposed Guidelines for 

Best Available Retrofit Technology (BART) Determinations (66 FR 38108, July 20, 2001, and 

69 FR 25184, May 5, 2004). Section II of this document discusses and compares the emissions 

projections for reductions expected under the proposed CAIR and under the BART requirements. 

Section III of this document explains the projections of visibility impacts at Class I areas used to 

support the “better than BART” determination in the CAIR proposed rule. 

II. Emissions Projections Used for CAIR Analysis 

A. Overview of Emission Projections 

As discussed in the SNPR preamble, in performing the “Better-than-BART” analysis we 

would ideally use air quality modeling based on emissions projections for the scenario where the 

proposed CAIR is in effect only in the proposed CAIR region and source-specific BART is in 

effect in the rest of the country.1

 We would compare the visibility impacts of this scenario to 

existing visibility conditions to determine whether the proposed CAIR resulted in a degradation 

of visibility at any Class I area. We would also compare these visibility impacts with the 

visibility impacts of nationwide BART implementation, to assess whether the proposed CAIR 

would result in greater average visibility improvement than nationwide BART. These 

comparisons should be made for the year in which source-specific BART would be fully 

implemented (2014). 

As noted in the SNPR preamble, currently available modeling runs approximated, but did 

not exactly match the scenarios described above. Specifically, emissions projections for BART 

are currently available only on a nationwide basis, and only for EGUs larger than 250 MW. 

The available emissions projections reflecting implementation of the proposed CAIR are based 

on nationwide SO22

 and 323

 state NOx emissions reduction requirements for all EGUs, without 

1 As discussed in the SNPR preamble, we applied the two-pronged visibility test, rather than the simpler 

overall emissions reductions test, because our modeling showed a potential for a different geographic distribution of 

emissions reductions under the proposed CAIR cap and trade program than under source-specific BART. 

2

 SO2 emissions for this mod eling were base d on a 2 .86:1 retireme nt ratio of Title IV a llowances. 

3

 31 entire states and a portion of one state (eastern Texas). 
BART being in effect outside the proposed CAIR region. 

We believe that, despite the differences in the geographic scope of the proposed CAIR 

emission reductions requirements as modeled and as proposed, our CAIR modeling reasonably 

approximates the expected emissions under the proposed CAIR. Similarly, we believe that the 

emissions projections we used to represent BART implementation reasonably approximate 

emissions under BART as ideally modeled. The rest of this section summarizes the emissions 

projections that EPA used in this analysis; explains why EPA believes that they represent 

reasonable approximations of the ideal scenarios; and explains qualitatively what EPA believes 

would be the impact of further refining the emissions projections. 

B. CAIR EGU Emissions Projections

EGU emissions were projected using the Integrated Planning Model. A full description 

of the Integrated Planning Model as well as the assumptions for the Base Case can be found at: 

http://www.epa.gov/airmarkets/epa-ipm/ . The emissions projections for the proposed CAIR 

were described in the January 2004 CAIR analysis. In that analysis, EPA analysts simulated 

nationwide cap on SO2 emissions and a 32 State regionwide cap on NOx emissions.By contrast, 

the CAIR as proposed would achieve SO2 emission reductions in an eastern region of 28 States 

and the District of Columbia, rather than nationwide. Similarly, while the proposed CAIR would 

achieve NOx reductions in 29 States and the District of Columbia, the modeling scenarios EPA 

analyzed also included NOx reductions in Vermont, New Hampshire, Maine and Rhode Island, 

which were not required under the proposed CAIR. Conversely, NOx reductions in Kansas and 

the western half of Texas are required by the CAIR proposal but were not included in the 

CAIRscenario as modeled. 

 However, as noted in the January 30, 2004 notice of proposed rulemaking (NPRM), and 

illustrated further in the tables below, the SO2 and NOx reduction requirements simulated in this 

analysis provide a very close estimate to the reductions that would be expected under the CAIR 

as proposed. 

The State-by-State emissions under the base case and the proposed CAIR as analyzed are 

presented in Table 1 below. Table 2 summarizes total emissions in proposed CAIR and NonCAIR States under the base case and under the proposed CAIR, and shows the resulting emission 

reductions expected under the proposed CAIR in each region. 

Page 2 of 27 
)

4

SO2 NOx SO2 NOx SO2 NOx SO2 NOx 

416 129 334 47.8 86.0 47.8 85.8 

Arkansas 123 53 78 10.7 17.8 10.7 17.8 

6 5 5 70.4 81.0 70.4 81.0 

Delaware 48 11 35 - -

0 0 0

 230 171 174 17.7 38.5 17.0 38.5 

600 153 197 96.3 56.6 96.3 56.8 

Illinois 534 179 258 17.3 40.7 17.5 41.1 

Indiana 523 242 327 

Iowa 160 87 146 48.2 76.1 48.2 76.3 

Kansas* 65 102 60 171.2 80.2 67.2 84.5 

133.0 86.6 131.9 87.2 

Kentucky 357 199 282 15.2 13.5 15.2 13.5 

Louisiana 113 50 80 - -

Maryland 230 62 40 41.5 12.3 16.4 

16 12 10 31.4 69.2 31.4 69.2 

Michigan 384 126 379 94 Vermont†† - - - -

87 105 73 42 25.5 25.4 

74 45 43 15 46.0 89.0 44.5 89.0 

Missouri 307 141 279 69 

New Jersey 38 30 20 14 Total 762.0 781.9 613.9 790.7 

197 66 101 53 * modeling of the NOx cap did not include Kansas and the 

North Carolina 141 62 141 54 

1,025 256 290 97 

806 213 170 77 2

South Carolina 196 66 145 31 

Tennessee 310 103 192 32 

Texas* 487 200 365 159 

185 57 116 33 

485 148 139 36 

176 97 168 56 

Total 8,319 3,169 4,646 1,457 

Table II-1: Projected 2015 EGU Emissions under Base Case and CAIR (as analyzed (1000 tons) 

CA IR-region State Ba se Ca se “CAIR” Non-C AIR re gion State Ba se Ca se “CAIR” 

Alabama 59 Arizona

 9 California

Connecticut† 5 Colorado

 9 Idaho 1.2 1.2 

District Of Columbia 0 Maine†† 2.6 1.9 2.6 1.9 

Florida 54 Montana

Georgia 52 Nebraska

 95 Nevada

 74 New Hampshire†† 7.3 3.8 5.6 3.1 

 35 New Mexico 

 101 North Dakota**

Oklahoma

 53 Oregon

 15 Rhode Island†† 2.0 2.0 

 25 South Dakota** 2.2 

Massachusetts 11 Utah

Minnesota Washington 5.4 5.4 

Mississippi Wyoming

New York

Western half of Texas 

Ohio **Modeling the CAIR rule as actually proposed would not likely 

show significant SO reductions in North Dakota and South 

Dakota, because they would not be within the CAIR region. 

Pennsylvania

†Connecticut is required under the proposed CAIR to control 

NOx in the summertime 

Virginia †† Our CAIR modeling included NOx emission reduction 

requirements for ME, NH, RI, and VT which are not required by 

the CAIR proposal. 

West Virginia

Wisconsin

4

 The scenario we used to represent the CAIR differed slightly from the actual CAIR proposal, as explained in the text and noted in the Table notes 

above. 

Page 3 of 27 
Table II-2. Summary of Projected 2015 SO2 and NOx Emissions Totals in proposed CAIR 

and non-CAIR States under the CAIR as analyzed 5

 (1000 tons). 

SO2 NOx SO2 NOx SO2 NOx 

Total in CAIR 

States

 8,319 3,169 4,646 1,457 3,673 1,712 

Total in non-CAIR 762 782 614 791 148 – 9 

Total Reductions 3,821 1,704 

Ba se Ca se CAIR as analyzed Emission Reductions 

(Base Case – CAIR) 

states 

When we compare the total emissions after proposed CAIR implementation to the 

baseline, both in the non-CAIR States and in the CAIR states, we note that of the 3,822,000 tons 

of SO2 reductions, only 149,000 tons of reductions, or less than 4%, occurred in non-CAIR 

states. For NOx, all of the reductions occurred in the CAIR States, with a 9,000 ton increase in 

non-CAIR states (about 1% over base case). Because these differences are small relative to the 

overall reductions, they do not affect the validity of the Better-than-BART analysis. 

C. BART EGU Emissions Projections

 BART is applicable to all fossil-fuel fired steam electric plants that have the potential to 

emit more than 250 tons of any pollutant contributing to regional haze, that were not in operation 

by August 7, 1962, and for which construction began by August 7, 1977. (BART also applies to 

25 other source categories, but our analysis considered only EGUs, because only EGUs are 

eligible for participation in the proposed base CAIR model cap and trade program). 

5

 As explained in text and noted in Table 1, the assumptions in the CAIR as analyzed differed slightly from 

CAIR as proposed. 

Page 4 of 27 
Table II-3: State-by-state EGU Emissions Projections under BART (as analyzed)6

 in 2015 

(1000 tons). 

CA IR-Reg ion State 2015EGU Emissions Non-C AIR R egion State 2015 EGU Emissions 

Under “BART” Under “BART” 

SO2 NOx SO2 NOx 

Alabama 371 89 Arizona 47.8 86.2 

Arkansas 31 36 California 17.3 19.6 

Connecticut 3 5 Colorado 49.0 64.3 

Delaware 52 10 Idaho - 1.2 

District Of Columbia - 0 Maine 3.2 2.1 

Florida 194 144 Montana 21.8 38.5 

Georgia 224 41 Nebraska 69.0 44.4 

Illinois 317 112 Nevada 17.1 30.2 

Indiana 579 148 New Hampshire 8.8 3.9 

Iowa 185 81 New Mexico 48.6 76.5 

North Dakota 118.4 49.7 

Kansas 70 56 Oklahoma 42.2 57.9 

Kentucky 346 99 Oregon 15.2 13.5 

Louisiana 72 43 Rhode Island - 2.0 

Maryland 144 27 South Dakota 5.6 4.7 

Massachusetts 17 12 Utah 31.5 69.4 

Michigan 182 103 Vermont - 0.0 

Minnesota 91 77 Washington 6.0 16.1 

Mississippi 94 36 Wyoming 50.0 90.5 

Missouri 158 110 Total 551.5 670.7 

New Jersey 61 14 

New York 227 63 

*Connecticut is a CAIR region state for purposes of 

summertime NO x only 

North Carolina 146 64 

Ohio 948 138 

Pennsylvania 396 110 

South Carolina 167 37 

Tennessee 418 53 

Texas 343 204 

Virginia 141 40 

West Virginia 323 65 

Wisconsin 162 95 

Total 6,460 2,110 

The BART emissions projections used in the “better than BART” analysis were 

developed for EPA’s April, 15, 2004 BART proposal. The modeling of EGUs for that rule 

included controls on BART-eligible EGUs larger than 250 MW. There are 302 BART-eligible 

units of greater than 250 MW, as listed in Table A-1 in the Appendix, that emit about 85% of 

both the SO2 and the NOx emitted by all BART eligible EGUs. The EPA’s modeling of the 

BART proposal projects emissions reductions of approximately 3.2 million tons of SO2 and 1.2 

million tons of NOx in 2015 from these larger EGUs. However, States would also be required 

6

 BART control assumptions were applied only to BART-eligible EGUs larger than 250MW , as explained 

in text. 

Page 5 of 27 
to make BART determinations for BART eligible units smaller than 250 MW. Therefore, it is 

likely that the BART rule would actually achieve greater reductions than currently modeled, 

although it is uncertain as to the extent of reductions achievable from these smaller EGUs. 

We can gain an idea of the upper bound of potential reductions if all of the smaller EGUs 

install BART. Nationwide there are between 130 and 166 units that are of a size between 25 

MW and 250 MW in size that would otherwise meet the BART criteria7

. In 2001 these units 

emitted between 742,000 and 806,000 tons of SO2 and between 287,000 and 341,000 tons of 

NOx. These emissions provide an upper bound to the amount of emissions reductions possible 

from these units under BART, if one assumed that all of the units were reduced to zero. 

For this analysis, we used a modeling scenario for CAIR that does not reflect the effect of 

BART requirements in the western States. As a result, we believe the results suggesting a small 

difference in visibility improvements between the BART and CAIR scenarios in visibility 

improvement in a few Class I areas is an artifact of the available emissions scenarios used for 

modeling and does not accurately reflect the effect of the combination of CAIR coupled with the 

BART requirements in western States. We will develop this more accurate modeling scenario 

when we redo the air quality modeling in developing a final rule. 

Table 4 shows how these emissions are distributed between CAIR and non-CAIR States. 

As can be seen, the large portion of these emissions are from units located in the CAIR region. 

Table II-4: SO2 and NOx emissions from BART eligible EGUs between 

25 MW and 250 MW (1,000 tons) 

Area NOx SO2 

CAIR Region 228 to 265 647 to 694 

non-CAIR Region 59 to 76 96 to 112 

Total 287 to 341 742 to 807 

Source : Acid Ra in Datab ase 

As discussed in the SNPR preamble, for SO2 the proposed CAIR would achieve 1.6 

million tons more reductions than BART in 2015, and 2.6 million tons more by 2020. For NOx, 

the proposed CAIR would result in about 500,000 tons more emissions reductions than BART in 

both 2015 and 2020. These differences are about twice the level of total emissions from the 

BART-eligible EGUs of 25-250 MW. Therefore, even if all SO2 and NOx emissions from 

BART-eligible EGUs between 25 and 250 MW were to be reduced to zero, the proposed CAIR 

would still result in about 800,000 to 1.6 million tons more SO2 reductions, and about 250,000 

7

The range reflects different assumptions regarding the BART eligibility of units with on-line dates after 

1977. The higher number of units reflects the inclusion of all coal units that went on line through 1985. The lower 

number reflects only units that came on-line through 1977. BART only applies to those units that began actual 

construction before August 7, 1977. W e utilized a range because without an extensive review of permitting and 

con struction history, it is no t app arent w hen m any po tentially B AR T-e ligible so urces com men ced cons truction . 

States are currently engaged in such a review. 

Page 6 of 27 
tons more NOx reductions, than source-specific BART. 

D. Comparing BART and CAIR Projections 

1. Year of CAIR-to-BART Comparison

 The better-than-BART analysis is based on emissions projections for the year 2015, 

because that is the year for which the air quality modeling was performed indeveloping the CAIR 

proposal. This year occurs when the projected difference between the proposed CAIR emissions 

and the BART emissions are near their minimum. Emissions reductions from the proposed 

CAIR are projected to be greater than for a national BART strategy in all other years, except for 

2014. Since BART does not require or provide incentives for reductions before 2014, or 2013 at 

the earliest8

, the proposed CAIR is expected to show greater emissions reductions than the BART 

requirements in the years leading up to full implementation of the BART requiements. Similarly, 

emissions in the BART case will grow after 2015 with the growth of the EGU sector (and lack of 

cap), while they would be expected to decline after 2015 in the CAIR case, and continue to be 

constrained by the cap in future years under proposed CAIR. In 2014, the likely first year of the 

BART program, emissions for the two programs should be similar to the emissions EPA is 

projecting for 2015. 

2. Effect of not including BART (outside the CAIR region) in the CAIR emissions projections

Because SO2 reductions under BART in the non-CAIR states were greater (by about 

65,000 tons) than the modeled reductions for the proposed CAIR in those same states,9

 we note 

that EPA’s proposed policy approach – CAIR combined with BART in the non-CAIR region – 

would lead to greater reductions than was modeled for the proposed CAIR by itself. 

With respect to NOx, our modeling of emissions reductios from the proposed CAIR 

included reductions in areas not actually covered by the CAIR proposal (VT, NH, ME, RH). 

However, as can be seen in Table II-1, total NOx emissions for these four states are very small 

(7,700 tons total or 1% of base case NOx emissions for all non-CAIR states) and our CAIR 

modeling projected only 700 tons of emissions reductions from this level (all from NH). Our 

modeling of CAIR emissions reductions also excluded some areas that are covered by the CAIR 

proposal (KS, west TX). When we include the proposed CAIR NOx emission reduction 

requirements for KS and west TX, we anticipate that the additional NOx reductions from these 

states will be greater than the reductions from the four New England states, which were 

8

 States tha t deve lop a trading pro gram or oth er me asure s in lieu of B AR T h ave un til 201 8 to fully 

imple men t the pro gram . Ho weve r, our b etter-than -BA RT analysis co mpa res C AIR to sou rce-sp ecific B AR T, n ot to 

yet-to-be developed trading or other alternatives. If in fact some states opt for cap and trade programs or other 

alternatives in lieu of BAR T, on a 201 8 schedule, BART reductions would be even further into the future than CAIR 

redu ctions. 

9 Total SO2 emissions for non-CAIR states are 571,100 tons under the propo sed CAIR (see T able II-1) and 

506 ,200 ton s under B ART (see Ta ble II-3). The difference is 64,80 0 tons. 

Page 7 of 27 
incorrectly attributed to the CAIR in our modeling. 

Aside from these differences in the region where NOx emissions were modeled, the 

CAIR modeling understated expected NOx reductions on a national basis since it did not reflect 

any NOx reductions outside of the 32 state region. In fact, eligible units in those states would be 

required to reduce emissions under BART. Therefore, as with SO2, we note that implementation 

of the proposed CAIR, in conjunction with BART in the non-CAIR region, will lead to greater 

nationwide emissions reductions than implementation of BART nationally. 

As explained in section III below, the visibility projections based on CAIR alone – 

without BART implementation in the non-CAIR region – satisfy the better-than-BART test. 

Inclusion of BART reductions in the non-CAIR region would only increase emissions reductions 

and result in greater visibility improvement. Therefore, the lack of western (non-CAIR region) 

BART emissions reductions in our CAIR-scenario projections does not affect the better-thanBART conclusions. 

III. Air Quality Analysis 

A. Air quality modeling to determine future visibility 

Introduction 

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

the proposed finding in the CAIR supplemental proposal that compliance with the proposed 

CAIR model trading rule by BART-eligible sources would result in greater visibility 

improvement that source-specific BART.. 

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

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

future year scenarios. The IAQR Air Quality Modeling Technical Support Document (NPRAQMTSD)10 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 

postprocessing of model results and calculation of visibility and visibility metrics. 

1. Overview of the Modeling Process 

We completed numerous modeling runs and postprocessing calculations to determine the 

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

such visibility impacts allows comparison of the effects of compliance with BART compared 

with compliance with the proposed CAIR model rule. 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 development of the 2015 base case, 

10 U.S. EPA, Technical Support Doc ument for the Interstate Air Quality Rule - Air Quality Modeling 

Ana lyses. Jan uary 2 004 . Do cket nu mbe r OA R-20 03-0 053 -016 2. 

Page 8 of 27 
which contains emissions for 2015 based on predicted growth and existing emissions controls. 

We used modeled PM concentrations to estimate visibility impairment at Class I areas. We then 

used the model-predicted changes in visibility impairment along with the observed current 

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

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

changes, to the current visibility values to estimate future visibility. The projected visibility 

values were based on emissions changes between the 2001 “proxy” inventory and the 2015 

inventory. 

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

visibility improvements at each Class I area by modeling the CAIR control strategy as well as the 

BART strategy in 2015. 

2. Methodology 

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

modeling approach that was used in the January 2004 proposal to develop base and future year 

predictions of PM2.5 values. As in the January 2004 proposal, we used the REMSAD model to 

predict base and future PM2.5 levels. We used the REMSAD predicted PM2.5 components to 

estimate future year changes in visibility at Class I areas. Details of the application of REMSAD, 

including model performance, can be found in the NPR-AQMTSD. That modeling approach is 

described in detail in the January 2004 proposal preamble, 64 FR 4593-4596, 4607-4609, 4635­

4639 (January 30, 2004) and the NPR-AQMTSD (pp. 10-22, 37-42, and 57-63) and familiarity 

with that detailed description is assumed for present purposes. 

As described in the NPR-AQMTSD, we performed a 1996 Base Year simulation to 

examine the ability of the modeling system to replicate observed concentrations of PM and its 

precursors. We then performed simulations using a 2001 “proxy” emissions inventory. The 

2001 “proxy” inventory was created for the purpose of modeling 2001, which represents the most 

recent year for which modeling is practicable. We followed the 2001 modeling with a simulation 

for a future-year base case scenario for 2015. The future-year base case scenario included 

emissions resulting from growth and emissions controls required under Federal and State law. 

We then quantified the impacts of the CAIR and BART controls on visibility impairment by 

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

results of the CAIR and BART control strategy model runs. 

PM

We quantified visibility impacts in this manner at the 44 Class I areas for which ambient 

2.5 data for 1996 exists. Since the base year meteorology used in the REMSAD modeling is 

from 1996, ambient data from 1996 is needed to be able to apply the model results. 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 1996, 

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 

collecting ambient PM2.5 data at Class I areas, but only 44 of these sites have complete data for 

1996. 

These 44 sites are scattered throughout the country and represent all of the IMPROVE 

Page 9 of 27 
defined regional visibility areas11 except the Boundary Waters (Northern Great Lakes) region. Of 

the 44 sites, 15 are in the East12 and 29 are in the West, where the bulk of the Class I areas are 

located. 

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. For this proposal, the calculation of baseline 

visibility values for each Class I area generally followed the procedures detailed in the Guidance 

for Tracking Progress.13 The baseline visibility on the 20% worst days at each Class I area was 

calculated using the default IMPROVE visibility equation14. The daily deciview values were 

ranked for each Class I area for 1996. 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 III-1 shows the current (1998-200215) estimated visibility impairment (in 

deciviews) at the 44 Class I areas on the 20% worst days and 20% best days at each area16. Each 

IMPROVE site had 1-5 years of complete data available for the analysis. The number of years of 

complete data for each site is listed in the table. 

Table III-1. Current visibility (1998-2002) on the 20% best days and 20% worst days, at 44 

IMPROVE sites 

Class I Area 

(IMP RO VE Site) 

IMPROVE 

Site 

Identifier 

State Number of 

Years of 

Complete 

Data 

1998-2002 

Baseline 

Visibility (in dv) 

20% W orst Days 

1998-2002 

Baseline 

Visibility (in dv) 

20% B est Days 

Acadia National Park ACAD Maine 5 22.7 8.4 

11 IMP ROV E: Spatial and Seasonal Patterns and Temporal V ariability of Haze and its Constituents in the 

United States: Report III (May 2000). 

12 The East is defined as the part of the country that is east of 100 degrees longitude. 

13 U.S. EPA, Guidance for Tracking Progress Under the Regional Haze Rule (Tracking Progress Guidance) 

(September 200 3). 

14 Tracking Progress Guidance, page 3-10. 

15 Analyses unde r the Region al Haze rule(including B ART analyses) will use a five-year visibility base 

period of 2000-2004. For this analysis, we used visibility data from the most recently available five year period 

(19 98-2 002 ). 

16 The best and worst day calculations for the current visibility used the ambient data from 1998-20 02. The 

best an d wo rst mo deling days fo r each Class I a rea we re ide ntified b ased on the 199 6 am bient d ata. 

Page 10 of 27 
Class I Area 

(IMPROVE Site) 

IMPROVE 

Site 

Identifier 

State Number of 

Years of 

Complete 

Data 

1998-2002 

Baseline 

Visibility (in dv) 

20% W orst Days 

1998-2002 

Baseline 

Visibility (in dv) 

20% Best Days 

Badlands National Park BADL South Dakota 5 17.3 7.1 

Bandelier National Monument BAND New Mexico 5 13.2 6.3 

Big Bend National Park BIBE Texas 4 18.4 7.7 

Bliss State Park (Desolation) BLIS California 3 12.9 3.5 

Bryce Canyon National Park BRCA Utah 5 12.0 4.1 

Bridger Wilderness BRID Wyoming 5 11.5 3.8 

Brigantine N ational Wildlife 

Refuge 

BRIG New Jersey 4 27.6 13.6 

Canyonlands National Park CANY Utah 5 12.0 5.3 

Chassahowitzka National 

Wildlife 

CHAS Florida 4 25.7 16.4 

Chiricahua National 

Monument 

CHIR Arizona 5 13.9 5.9 

Crater Lake National Park CRLA Oregon 3 14.1 3.2 

Dolly Sods /Otter Creek 

Wildernes 

DOSO West Virginia 5 27.6 13.0 

Gila W ilderness GICL New Mexico 4 13.5 5.1 

Glacier National Park GLAC Montana 4 19.5 7.3 

Grand Canyon- Hopi Point GRCA Arizona 3 12.0 4.1 

Great Sand Dunes National 

Monument 

GRSA Colorado 5 13.1 5.7 

Great Smoky Mountains 

National Park 

GRSM Tennessee 5 29.5 14.2 

Guadalupe Mountains National 

Park 

GUMO Texas 5 17.6 7.2 

Jarbidge Wilderness JARB Nevada 3 12.6 3.0 

Jefferson/James River Face 

Wilderness 

JEFF Virginia 1 28.3 15.8 

Lassen Volcanic National Park LAVO California 5 14.8 3.3 

Lye Brook W ilderness LYBR Vermont 4 23.9 6.6 

Mammoth Cave National Park MACA Kentucky 4 30.2 16.5 

Mesa Verde National Park MEVE Colorado 5 12.8 5.5 

Moosehorn NWR MOOS Maine 5 21.4 8.6 

Mount Rainier National Park MORA Washington 5 18.9 4.9 

Mount Zirkel W ilderness MOZI Colorado 4 11.7 4.4 

Okefenokee N ational Wildlife 

Refuge 

OKEF Georgia 5 26.4 15.5 

Page 11 of 27 
Class I Area 

(IMPROVE Site) 

IMPROVE 

Site 

Identifier 

State Number of 

Years of 

Complete 

Data 

1998-2002 

Baseline 

Visibility (in dv) 

20% W orst Days 

1998-2002 

Baseline 

Visibility (in dv) 

20% Best Days 

Petrified Forest National Park PEFO Arizona 5 13.5 6.3 

Pinnacles National Monument PINN Califo rnia 3 19.1 8.8 

Point Reyes National Seashore PORE Califo rnia 2 20.2 8.6 

Redwoo d National Park REDW Califo rnia 5 16.5 5.0 

Cape Romain National 

W ildlife Refuge 

ROMA Sou th 

Carolina 

4 25.9 13.8 

San G orgonio W ilderness SAGO Califo rnia 4 21.5 6.8 

Sequoia National Park SEQU Califo rnia 3 23.5 8.8 

Shenandoah N ational Park SHEN Virg inia 4 27.6 12.2 

Shining Ro ck W ilderness SHRO No rth 

Carolina 

1 29.7 7.8 

Sipsy W ilderness SIPS Alabama 4 28.7 16.3 

Thre e Sisters W ilderness TH IS Idaho 5 15.7 2.8 

Tonto N ational Monument TONT Arizona 3 14.7 7.4 

Up per B uffalo W ilderness UPBU Arkansas 5 25.5 12.2 

W eminuche W ilderness WEMI Colorado 4 11.6 4.4 

Yosemite National Park YOSE California 5 17.6 4.0 

4. Projection of Future Year Visibility Levels 

Future year levels of visibility impairment were estimated by applying relative changes in 

model predicted visibility to current measurements of ambient data. As with forecasting future 

year design values for PM2.5, the approach for forecasting future visibility impairment used the 

model predictions in a relative way to project current visibility levels to 2015. The modeling 

portion of this approach uses the annual simulations for 2001 emissions and the 2015 Base Case 

emissions scenario. As described below, the predictions from these runs were used to calculate 

relative reduction factors (RRFs) which were then applied to current visibility values17. The 

approach we followed is consistent with the procedures in the draft regional haze air quality 

modeling guidance18. 

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 

17 An example calculation is included in Appendix M of the NPR-AQMTSD. 

18 U.S. EPA, Draft Guidance for Demonstrating Attainment of Air Quality Goals for PM2 .5 and Regional 

Haze. January 2001. 

Page 12 of 27 
nitrate, organic carbon mass, elemental carbon, crustal mass and coarse mass. Consistent with 

the IMPROVE procedures, sulfate is assumed to be in the form of ammonium sulfate. Nitrate is 

assumed to in the form of ammonium nitrate. Measured organic carbon concentrations are 

multiplied by 1.4 to derive total organic mass. Crustal PM2.5 mass is calculated using the 

IMPROVE crustal formula. Coarse mass is defined as the difference between PM10 and PM2.5. 

The procedure for calculating future year regional haze values is similar to the “Speciated 

Modeled Attainment Test” (SMAT) that was used to calculate future year PM2.5 design values in 

the January 2004 proposal. The following is a brief summary of those steps. Additional details 

on the SMAT procedure are provided in the NPR-AQMTSD (Appendix E). 

Step 1. Calculate mean light extinction19 on the 20% worst and best days for each of the 

six components of regional haze. This is done by using the default IMPROVE equation applied 

to IMPROVE ambient measurements. 

Step 2. For each of the 20% worst and best days20, calculate the ratio of future (e.g., 2015) 

to current (i.e., 2001) predictions for each component specie. The result is a component-specific 

RRF (e.g., assume that 2001 predicted sulfate extinction for a particular location is 50 Mm-1 and 

the 2015 Base extinction is 40 Mm-1, then the RRF for sulfate is 0.8). 

Step 3. For each component specie, multiply the current daily component light extinction 

(step 1) by the component-specific daily RRF obtained in step 2. This produces an estimated 

future mean light extinction value for each component, for each of the 20% worst(best) days 

(e.g., sulfate extinction of 50 Mm-1 x 0.8 = future sulfate extinction of 40 Mm-1). 

Step 4. Sum the daily component extinction values to get total daily light extinction21 and 

convert extinction to daily average deciviews. 

Step 5. Compute the future mean deciview values for the 20% best and worst days by 

averaging the daily deciview values. 

The results of this analysis are discussed in the next section below. 

B. Air Quality Modeling of Proposed Emissions Reductions

 Introduction 

19 Light extinction is measured in units of inverse megameters (Mm-1). 

20 The model predicted RRFs are calculated on the 20% best and worst days from 1996 based on the 1996 

ambient IMP ROV E data. 

21 A value of 10 Mm-1 is added to each daily value of bext to account for Rayleigh scattering. 

Page 13 of 27 
In this section we describe the air quality modeling performed to determine the projected 

impacts on visibility impairment of the CAIR regional SO2 and NOx emissions reductions, as 

well as air quality modeling of the BART program. The visibility improvements from the 

proposed CAIR strategy were compared to the BART visibility improvements as part of the 

“better-than-BART” test. 

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 (CAIR) trading program with existing visibility conditions. 

Under the second prong, overall visibility, as measured by the average improvement at all 

affected Class I areas, must be better under the trading program than under source-specific 

BART. The future year air quality modeling results were used to make this demonstration. 

1. Modeling of the CAIR and BART strategies for 2015 

The PM and visibility modeling platform described above was used by EPA to model the 

impacts of the proposed EGU SO2 and NOx controls on visibility impairment. Modeling for 

visibility was performed for 2015 to assess the expected effects of the CAIR and BART controls 

on projected visibility impairment (compared to the 2001 base year). 

The modeled effects of the emissions reductions on visibility are expressed in terms of 

expected future visibility impairment on the 20% best and worst days (in deciviews). Smaller 

numbers represent better visibility. 

Table III-2 shows the projected visibility on the 20% best days at each Class I area in the 

2015 baseline and from the CAIR and BART control strategies. Visibility impairment is shown 

for the 20% best days for the current (1998-2002) baseline, the 2015 baseline, and the CAIR and 

BART strategies in 2015. Also shown is the average visibility (on the 20% best days) for the 44 

Class I areas and the 15 Eastern Class I areas. 

Page 14 of 27 
Table III-2. Projected visibility for the 2015 baseline and the 2015 CAIR and BART (as 

analyzed)22 strategies on the 20% best days, at 44 IMPROVE sites. 

Class I Areas 

(IMPROVE Site) 

State 1998-2002 

Baseline 

Visibility 

(dv) 

2015 

Baseline 

Visibility 

(dv) 

2015 

“CAIR” 

Control 

Case 

Visibility 

(dv) 

2015 

“BART” 

Control 

Case 

Visibility 

(dv) 

Acadia National Park Maine 8.4 8.1 7.8 8.0 

Brigantine National Wildlife Refuge New Jersey 13.6 13.4 12.8 13.1 

Chassahowitzka N ational Wildlife Florida 16.4 15.2 14.0 13.9 

Dolly Sods /Otter Creek W ilderness West Virginia 13.0 12.4 11.1 11.8 

Great Smoky M ountains National Park Tennessee 14.2 13.7 12.6 13.0 

Jefferson/James River Face Wilderness Virginia 15.8 15.3 14.3 14.8 

Lye Brook W ilderness Vermont 6.6 6.2 6.0 6.1 

Mammoth Cave National Park Kentucky 16.5 15.5 14.8 15.2 

Moosehorn NWR Maine 8.6 8.3 8.2 8.2 

Okefenokee National Wildlife Refuge Georgia 15.5 14.9 14.1 14.4 

Cape Romain National Wildlife Refuge South Carolina 13.8 13.4 12.7 13.0 

Shenandoah National Park Virginia 12.2 11.7 10.6 11.3 

Shining Rock W ilderness North Carolina 7.8 7.5 6.9 7.2 

Sipsy W ilderness Alabama 16.3 15.6 15.1 15.2 

Upper Buffalo Wilderness Arkansas 12.2 11.5 11.2 11.1 

Average Visibility (15 Eastern Class I areas) 12.7 12.2 11.5 11.8 

Badlands National Park South Dakota 7.1 6.7 6.7 6.7 

Bandelier National Monument New Mexico 6.3 5.9 5.9 5.9 

Big Bend National Park Texas 7.7 7.4 7.3 7.3 

Bliss State Park (Desolation) California 3.5 3.0 3.0 3.0 

Bryce Canyon National Park Utah 4.1 3.7 3.7 3.7 

Bridger Wilderness Wyoming 3.8 3.5 3.5 3.5 

Canyonlands National Park Utah 5.3 5.1 5.1 5.1 

Chiricahua National Monument Arizona 5.9 5.7 5.7 5.7 

Crater Lake National Park Oregon 3.2 2.8 2.8 2.8 

Gila W ilderness New Mexico 5.1 4.9 4.9 4.9 

Glacier National Park Montana 7.3 6.7 6.7 6.7 

Grand Canyon- Hopi Point Arizona 4.1 3.9 3.8 3.8 

22 See section II.B above for discussion of differences between the CAIR as analyzed and as proposed, and 

see section II.C. above for discussion of differences between BART as analyzed and the maximum potential 

reductions available from BART as proposed. 

Page 15 of 27 
Class I Areas 

(IMPROVE Site) 

State 1998-2002 

Baseline 

Visibility 

(dv) 

2015 

Baseline 

Visibility 

(dv) 

2015 

“CAIR” 

Control 

Case 

Visibility 

(dv) 

2015 

“BART” 

Control 

Case 

Visibility 

(dv) 

Great Sand D unes National Monum ent Colorado 5.7 5.4 5.4 5.4 

Guadalupe M ountains National Park Texas 7.2 6.8 6.7 6.7 

Jarbidge W ilderness Nevada 3.0 2.8 2.8 2.8 

Lassen Volcanic National Park Califo rnia 3.3 2.9 2.9 2.9 

Mesa V erde National Park Colorado 5.5 5.1 5.1 5.1 

Mo unt Rainier National Park Washington 4.9 4.3 4.3 4.3 

Mount Zirkel W ilderness Colorado 4.4 4.2 4.2 4.2 

Petrified Forest National Park Arizona 6.3 6.0 6.0 6.0 

Pinnacles National Monument Califo rnia 8.8 7.9 7.9 7.9 

Point Reyes National Seashore California 8.6 7.7 7.7 7.7 

Redwoo d National Park California 5.0 4.8 4.8 4.8 

San G orgonio W ilderness California 6.8 6.4 6.4 6.4 

Sequoia National Park California 8.8 8.1 8.1 8.1 

Three Sisters W ilderness Idaho 2.8 2.5 2.5 2.5 

Tonto N ational Monument Arizona 7.4 7.2 7.2 7.2 

W eminuche W ilderness Colorado 4.4 4.0 4.0 4.0 

Yosemite National Park Califo rnia 4.0 3.7 3.7 3.7 

Average Visibility (all 44 C lass I areas) 8.0 7.5 7.3 7.4 

The modeling results show that the proposed CAIR cap-and-trade programs will not 

result in degradation of visibility on the 20% best days, compared to existing visibility conditions 

(or the 2015 baseline), at any of the 44 Class I areas considered. In each of the 44 areas – the 13 

within the proposed CAIR region and the 31 outside of it – visibility is expected to improve 

(compared to current visibility) or at worst remain unchanged (compared to 2015 baseline 

visibility). 

For Class I areas in the proposed CAIR region, our analysis indicates that proposed CAIR 

emissions reductions in the East produce greater visibility improvements than source-specific 

BART. Specifically, for the 15 Eastern Class I areas analyzed, the average visibility 

improvement (on the 20 percent best days) expected solely as a result of the CAIR is 0.7 

deciviews (dv), and the average degree of improvement predicted for source-specific BART is 

0.4 dv.

Similarly, on a national basis, the visibility modeling shows that for the 44 class I areas 

evaluated, the average visibility improvement, on the 20 percent best days, in 2015 was 0.2 dv 

under the proposed CAIR cap-and-trade programs, and 0.1 dv under the source-specific BART 

approach 

Page 16 of 27 
We note that for western Class I areas the projection of greater visibility improvement 

under BART than under CAIR is an artifact of the available emissions scenarios used for the 

modeling. Because our CAIR scenario did not include BART reductions in the non-CAIR 

region, the modeling naturally shows western Class I areas seeing more improvement under a 

nationwide BART scenario than they do under the CAIR scenario used, with emissions 

reductions only in the east. This will be resolved when we re-do the air quality modeling using a 

CAIR scenario which includes BART in the non-CAIR region. 

Table III-3 shows the projected visibility on the 20% worst days at each Class I area in the 

2015 baseline and from the CAIR and BART control strategies. Visibility impairment is shown 

for the 20% worst days for the current (1998-2002) baseline, the 2015 baseline, and the CAIR 

and BART strategies in 2015. Also shown is the average visibility (on the 20% worst days) for 

the 44 Class I areas and the 15 Eastern Class I areas. 

Table III-3- Projected visibility for the 2015 baseline and the 2015 CAIR and BART (as 

analyzed)23 strategies on the 20% worst days, at 44 IMPROVE sites. 

Class I Area 

(IMPROVE Site) 

State 1998-2002 

Baseline 

Visibility 

(dv) 

2015 

Baseline 

Visibility 

(dv) 

2015 

“CAIR” 

Control 

Case 

Visibility 

(dv) 

2015 

“BART” 

Control 

Case 

Visibility 

(dv) 

Acadia National Park Maine 22.7 21.5 20.3 21.0 

Brigantine National Wildlife Refuge New Jersey 27.6 26.5 24.8 25.8 

Chassahowitzka N ational Wildlife Florida 25.7 24.0 22.0 22.0 

Dolly Sods /Otter Creek W ilderness West Virginia 27.6 25.6 23.0 24.6 

Great Smoky Mountains National Park Tennessee 29.5 27.6 25.0 26.5 

Jefferson/James River Face W ilderness Virginia 28.3 26.6 24.5 25.6 

Lye Brook W ilderness Vermont 23.9 22.9 21.9 22.4 

Mammoth Cave National Park Kentucky 30.2 27.7 25.1 26.9 

Moosehorn NWR Maine 21.4 20.4 19.3 19.9 

Okefenokee National Wildlife Refuge Georgia 26.4 25.1 23.5 24.2 

Cape Romain National Wildlife Refuge South Carolina 25.9 25.2 23.5 24.3 

Shenandoah National Park Virginia 27.6 26.0 23.4 24.9 

Shining Rock Wilderness North Carolina 29.7 27.5 25.1 26.3 

Sipsy W ilderness Alabama 28.7 26.9 24.4 25.8 

Upper Buffalo Wilderness Arkansas 25.5 24.5 22.4 23.1 

Average Visibility (15 Eastern Class I areas) 26.7 25.2 23.2 24.2 

23 See section II.B above for discussion of differences between CAIR as analyzed and as proposed, and see 

section II.C. above for discussion of differences between BART as analyzed and the maximum potential reductions 

available from BART as proposed. 

Page 17 of 27 
Class I Area 

(IMPROVE Site) 

State 1998-2002 

Baseline 

Visibility 

(dv) 

2015 

Baseline 

Visibility 

(dv) 

2015 

“CAIR” 

Control 

Case 

Visibility 

(dv) 

2015 

“BART” 

Control 

Case 

Visibility 

(dv) 

Badlands National Park South Dakota 17.3 16.6 16.2 16.1 

Bandelier National Monument New Mexico 13.2 12.5 12.4 12.3 

Big Bend National Park Texas 18.4 18.0 18.0 18.0 

Bliss State Park (Desolation) California 12.9 11.4 11.4 11.4 

Bryce Canyon National Park Utah 12.0 11.1 11.1 11.0 

Bridger Wilderness Wyoming 11.5 10.4 10.4 10.4 

Canyonlands National Park Utah 12.0 11.3 11.3 11.3 

Chiricahua National Monument Arizona 13.9 13.6 13.6 13.6 

Crater Lake National Park Oregon 14.1 12.4 12.4 12.4 

Gila Wilderness New Mexico 13.5 12.8 12.8 12.8 

Glacier National Park Montana 19.5 18.6 18.6 18.6 

Grand Canyon- Hopi Point Arizona 12.0 11.3 11.3 11.3 

Great Sand Dunes National Monument Colorado 13.1 12.4 12.3 12.2 

Guadalupe M ountains National Park Texas 17.6 17.1 17.0 16.9 

Jarbidge Wilderness Nevada 12.6 11.4 11.4 11.4 

Lassen Volcanic National Park California 14.8 13.5 13.5 13.5 

Mesa Verde National Park Colorado 12.8 12.0 12.0 11.9 

Mount Rainier National Park Washington 18.9 17.0 17.0 16.9 

Mount Zirkel W ilderness Colorado 11.7 10.9 10.9 10.8 

Petrified Forest National Park Arizona 13.5 12.9 12.9 12.9 

Pinnacles National Monument California 19.1 17.5 17.5 17.5 

Point Reyes National Seashore California 20.2 18.4 18.3 18.4 

Redwood National Park California 16.5 14.5 14.5 14.5 

San Gorgonio Wilderness California 21.5 18.6 18.6 18.6 

Sequoia National Park California 23.5 21.3 21.3 21.3 

Three Sisters Wilderness Idaho 15.7 13.8 13.8 13.8 

Tonto National Monument Arizona 14.7 14.0 13.9 13.9 

Weminuche Wilderness Colorado 11.6 10.7 10.7 10.7 

Yosemite National Park California 17.6 16.0 16.0 16.0 

Average Visibility (all 44 C lass I areas) 19.2 18.0 17.3 17.6 

The modeling results show that the CAIR cap-and- trade program will not result in 

degradation of visibility on the 20% worst days, compared to existing visibility conditions (or the 

2015 baseline), at any of the 44 Class I areas considered. In each of the 44 areas – the 13 within 

Page 18 of 27 
the proposed CAIR region and the 31 outside of it – visibility is expected to improve (compared 

to current visibility) or at worst remain unchanged (compared to 2015 baseline visibility). Based 

on these results, we believe the CAIR impact on emissions passes the first prong of the twopronged “better-than-BART” test by not causing degradation of visibility at any Class I area on 

the 20% best or worst visibility days. 

For Class I areas in the proposed CAIR region, our analysis indicates that proposed CAIR 

emissions reductions in the East produce significantly greater visibility improvements than 

source-specific BART. For the 15 Eastern Class I areas analyzed, the average visibility 

improvement (on the 20 percent worst days) expected solely as a result of the CAIR is 2.0 

deciviews (dv), and the average degree of improvement predicted for source-specific BART is 

1.0 dv. Therefore, the proposed CAIR is substantially better than BART – indeed, the proposed 

CAIR provides more than twice the visibility improvement benefits – for Eastern Class I areas. 

Similarly, on a national basis, the visibility modeling shows that for the 44 class I areas 

evaluated, the average visibility improvement, on the 20 percent worst days, in 2015 was 0.7 dv 

under the proposed CAIR cap-and-trade programs, but only 0.4 dv under the source-specific 

BART approach. Based on these results, the proposed CAIR passes the second prong of the 

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 proposed CAIR compared to BART on the 

20% best and 20% worst visibility days. 

2. Better-than-BART Test 

We believe the impact of the proposed CAIR on emissions passes the first prong of the 

two-pronged “better-than-BART” test by not causing degradation of visibility at any Class I area 

on either the 20% best or 20% worst visibility days. The CAIR also passes the second prong of 

the better-than-BART visibility test based on the expectation that, on average, in both the Eastern 

Class I areas and nationally, visibility improvement is greater under the proposed CAIR 

compared to BART on the 20% best and 20% worst days. We therefore believe that these 

results, in combination with the emissions analysis in Section II, demonstrate that the both 

prongs of the better-than-BART test are met. 

Page 19 of 27 
Appendix 

The list of units below reflects the 302 BART-eligible coal-fired generating units larger than 250 

MW and for which controls were presumed in the IPM modeling. EPA has estimated that these 

units had gone online after August 7, 1962, but began construction before August 7, 1977 

Table A-1: 


Units that were Presumed to be BART-eligible, Requiring Controls, for purposes of


Modeling Emissions. 


STATE UNITID Online 

Year 

Nameplate 

Capacity [1] 

FACILITY_NAME 

MN Allen S King 1 1968 598 

OH Avon Lake Power Plant 12 1970 680 

IN Bailly 8 1968 422 

IL Baldwin 1 1970 623 

IL Baldwin 2 1973 635 

IL Baldwin 3 1975 635 

AL Barry 4 1969 404 

AL Barry 5 1971 789 

NC Belews Creek 1 1974 1080 

NC Belews Creek 2 1975 1080 

MI Belle River 1 1984 698 

MI Belle River 2 1985 698 

FL Big Bend BB01 1970 446 

FL Big Bend BB02 1973 446 

FL Big Bend BB03 1976 446 

TX Big Brown 1 1971 593 

TX Big Brown 2 1972 593 

LA Big Cajun 2 2B1 1980 559 

LA Big Cajun 2 2B2 1981 559 

KY Big Sandy BSU1 1963 281 

KY Big Sandy BSU2 1969 816 

SD Big Stone 1 1975 456 

GA Bowen 1BLR 1971 700 

GA Bowen 2BLR 1972 700 

GA Bowen 3BLR 1974 880 

GA Bowen 4BLR 1975 880 

MA Brayton Point 3 1969 643 

CT Bridgeport Harbor Station BHB3 1968 400 

PA Bruce Mansfield 1 1976 914 

PA Bruce Mansfield 2 1977 914 

PA Bruce Mansfield 3 1980 914 

Page 20 of 27 
STATE UNITID Online 

Year 

Nameplate 

Capacity [1] 

FACILITY_NAME 

PA Brunner Island 2 1965 405 

PA Brunner Island 3 1969 790 

TN Bull Run 1 1967 950 

KY Cane Run 6 1969 272 

OH Cardinal 1 1967 615 

OH Cardinal 2 1967 615 

OH Cardinal 3 1977 650 

IN Cayuga 1 1970 531 

IN Cayuga 2 1972 531 

WA Centralia BW21 1972 730 

WA Centralia BW22 1973 730 

MD Chalk Point 1 1964 364 

MD Chalk Point 2 1965 364 

VA Chesterfield 5 1964 359 

VA Chesterfield 6 1969 694 

PA Cheswick 1 1970 565 

AZ Cholla 3 1980 289 

AZ Cholla 2 1978 289 

AZ Cholla 4 1981 414 

MN Clay Boswell 3 1973 365 

NC Cliffside 5 1972 571 

ND Coal Creek 1 1979 506 

ND Coal Creek 2 1981 506 

IL Coffeen 01 1965 389 

IL Coffeen 02 1972 617 

AL Colbert 5 1965 550 

WI Columbia 1 1975 512 

WI Columbia 2 1978 512 

CO Comanche (470) 1 1973 350 

CO Comanche (470) 2 1975 350 

PA Conemaugh 1 1970 936 

PA Conemaugh 2 1971 936 

OH Conesville 4 1973 842 

OH Conesville 5 1976 444 

OH Conesville 6 1978 444 

AZ Coronado Generating Station U1B 1979 411 

AZ Coronado Generating Station U2B 1980 411 

IA Council Bluffs 3 1978 726 

CO Craig C1 1980 446 

CO Craig C2 1979 446 

FL Crist Electric Generating Plant 6 1970 370 

FL Crist Electric Generating Plant 7 1973 578 

Page 21 of 27 
STATE UNITID Online 

Year 

Nameplate 

Capacity [1] 

FACILITY_NAME 

FL Crystal River 1 1966 441 

FL Crystal River 2 1969 524 

FL Crystal River 4 1982 739 

FL Crystal River 5 1984 739 

TN Cumberland 1 1973 1300 

TN Cumberland 2 1973 1300 

MS Daniel Electric Generating 1 1977 500 

Plant 

MS Daniel Electric Generating 2 1981 500 

Plant 

WY Dave Johnston BW44 1972 360 

IL Duck Creek 1 1976 441 

AL E C Gaston 5 1974 952 

IL E D Edwards 2 1968 281 

IL E D Edwards 3 1972 364 

KY E W Brown 3 1971 446 

OH Eastlake 5 1972 680 

KY Elmer Smith 2 1974 265 

IN F B Culley Generating Station 3 1973 265 

FL F J Gannon GB06 1967 414 

AR Flint Creek 1 1978 558 

WV Fort Martin 1 1967 576 

WV Fort Martin 2 1968 576 

NM Four Corners 3 1964 253 

NM Four Corners 4 1969 818 

NM Four Corners 5 1970 818 

OH Gen J M Gavin 1 1974 1300 

OH Gen J M Gavin 2 1975 1300 

WI Genoa 1 1969 346 

IA George Neal North 2 1972 349 

IA George Neal North 3 1975 550 

NE Gerald Gentleman Station 1 1979 681 

NE Gerald Gentleman Station 2 1982 681 

KY Ghent 2 1977 556 

KY Ghent 1 1974 557 

IN Gibson 1 1976 668 

IN Gibson 2 1975 668 

IN Gibson 3 1978 668 

IN Gibson 4 1979 668 

AL Gorgas 10 1972 789 

AL Greene County 2 1966 269 

AL Greene County 1 1965 299 

Page 22 of 27 
STATE UNITID Online 

Year 

Nameplate 

Capacity [1] 

FACILITY_NAME 

KY H L Spurlock 1 1977 305 

KY H L Spurlock 2 1981 508 

GA Hammond 4 1970 500 

IN Harding Street Station (EW 70 1973 471 

Stout) 

GA Harllee Branch 1 1965 250 

GA Harllee Branch 2 1967 319 

GA Harllee Branch 3 1968 481 

GA Harllee Branch 4 1969 490 

TX Harrington Station 061B 1976 360 

WV Harrison 1 1972 684 

WV Harrison 2 1973 684 

WV Harrison 3 1974 684 

PA Hatfields Ferry 1 1969 576 

PA Hatfields Ferry 2 1970 576 

PA Hatfields Ferry 3 1971 576 

IL Havana 9 1978 488 

CO Hayden H2 1976 275 

MD Herbert a Wagner 3 1966 359 

PA Homer City 1 1969 660 

PA Homer City 2 1969 660 

PA Homer City 3 1977 692 

NJ Hudson 2 1968 660 

UT Huntington 2 1974 446 

UT Huntington 1 1977 446 

AR Independence 1 1983 850 

MI J H Campbell 1 1962 265 

MI J H Campbell 2 1967 385 

MI J H Campbell 3 1980 871 

OH J M Stuart 1 1971 610 

OH J M Stuart 2 1970 610 

OH J M Stuart 3 1972 610 

OH J M Stuart 4 1974 610 

TX J T Deely 1 1977 446 

TX J T Deely 2 1978 446 

AL James H Miller Jr 1 1978 706 

AL James H Miller Jr 2 1985 706 

KS Jeffrey Energy Center 1 1978 720 

KS Jeffrey Energy Center 2 1980 720 

WY Jim Bridger BW71 1974 561 

WY Jim Bridger BW72 1975 561 

WY Jim Bridger BW73 1976 561 

Page 23 of 27 
STATE UNITID Online 

Year 

Nameplate 

Capacity [1] 

FACILITY_NAME 

WY Jim Bridger BW74 1979 561 

WV John E Amos 1 1971 816 

WV John E Amos 2 1972 816 

WV John E Amos 3 1973 1300 

IL Joliet 29 71 1965 660 

IL Joliet 29 72 1965 660 

IL Joliet 29 81 1965 660 

IL Joliet 29 82 1965 660 

PA Keystone 1 1967 936 

PA Keystone 2 1968 936 

IL Kincaid 1 1967 660 

IL Kincaid 2 1968 660 

NC L V Sutton 3 1972 447 

KS La Cygne 2 1977 685 

KS La Cygne 1 1973 893 

MO Labadie 1 1970 574 

MO Labadie 2 1971 574 

MO Labadie 3 1972 621 

MO Labadie 4 1973 621 

OH Lake Shore 18 1962 256 

IA Lansing 4 1977 275 

WY Laramie River 3 1982 570 

WY Laramie River 1 1980 570 

WY Laramie River 2 1981 570 

KS Lawrence Energy Center 5 1971 458 

NC Lee 3 1962 252 

ND Leland Olds 2 1975 440 

NC Marshall 1 1965 350 

NC Marshall 2 1966 350 

NC Marshall 3 1969 648 

NC Marshall 4 1970 648 

TX Martin Lake 1 1977 793 

TX Martin Lake 2 1978 793 

IN Merom 1SG1 1983 540 

IN Merom 2SG1 1982 540 

NH Merrimack 2 1968 346 

OH Miami Fort 7 1975 557 

OH Miami Fort 8 1978 558 

IN Michigan City 12 1974 540 

KY Mill Creek 1 1972 356 

KY Mill Creek 2 1974 356 

KY Mill Creek 3 1978 463 

Page 24 of 27 
STATE UNITID Online 

Year 

Nameplate 

Capacity [1] 

FACILITY_NAME 

KY Mill Creek 4 1982 544 

ND Milton R Young B1 1970 257 

ND Milton R Young B2 1977 477 

WV Mitchell 1 1971 816 

WV Mitchell 2 1971 816 

PA Mitchell 33 1963 299 

NV Mohave 1 1971 818 

NV Mohave 2 1971 818 

MI Monroe 1 1971 817 

MI Monroe 4 1974 817 

MI Monroe 2 1973 823 

MI Monroe 3 1973 823 

TX Monticello 1 1974 593 

TX Monticello 2 1975 593 

TX Monticello 3 1978 793 

PA Montour 2 1973 819 

PA Montour 1 1972 823 

MD Morgantown 1 1970 626 

MD Morgantown 2 1971 626 

WV Mount Storm Power Station 3 1973 522 

WV Mount Storm Power Station 1 1965 570 

WV Mount Storm Power Station 2 1966 570 

WV Mountaineer (1301) 1 1980 1300 

OH Muskingum River 5 1968 615 

OK Muskogee 4 1977 572 

OK Muskogee 5 1978 572 

WY Naughton 3 1971 326 

AZ Navajo Generating Station 1 1974 803 

AZ Navajo Generating Station 2 1975 803 

AZ Navajo Generating Station 3 1976 803 

MO New Madrid 1 1972 600 

MO New Madrid 2 1977 600 

IL Newton 1 1977 617 

IL Newton 2 1982 617 

OK Northeastern 3313 1979 473 

OK Northeastern 3314 1980 473 

KY Paradise 3 1970 1150 

KY Paradise 1 1963 704 

KY Paradise 2 1963 704 

IN Petersburg 1 1967 253 

IN Petersburg 2 1969 471 

IN Petersburg 3 1977 574 

Page 25 of 27 
STATE UNITID Online 

Year 

Nameplate 

Capacity [1] 

FACILITY_NAME 

WV Pleasants 1 1979 684 

WV Pleasants 2 1980 684 

PA Portland 2 1962 255 

IL Powerton 51 1972 893 

IL Powerton 52 1972 893 

IL Powerton 61 1975 893 

IL Powerton 62 1975 893 

IN R M Schahfer 14 1976 540 

IN R M Schahfer 15 1979 556 

NC Roxboro 1 1966 411 

NC Roxboro 2 1968 657 

NC Roxboro 3A 1973 745 

NC Roxboro 3B 1973 745 

NC Roxboro 4A 1980 745 

NC Roxboro 4B 1980 745 

MO Rush Island 1 1976 621 

MO Rush Island 2 1977 621 

TX Sam Seymour 1 1979 615 

TX Sam Seymour 2 1980 615 

NM San Juan 2 1973 350 

NM San Juan 1 1976 361 

NM San Juan 3 1979 534 

NM San Juan 4 1982 534 

GA Scherer 1 1982 818 

GA Scherer 2 1984 818 

MN Sherburne County 1 1976 660 

MN Sherburne County 2 1977 660 

MO Sibley 3 1969 419 

MO Sioux 1 1967 550 

MO Sioux 2 1968 550 

OK Sooner 1 1979 569 

OK Sooner 2 1980 569 

WI South Oak Creek 7 1965 318 

WI South Oak Creek 8 1967 324 

MI St. Clair 7 1969 545 

IN State Line Generating Station 4 1962 389 

(IN) 

IN Tanners Creek U4 1964 580 

MO Thomas Hill MB2 1969 285 

MI Trenton Channel 9A 1968 536 

TX W A Parish WAP5 1977 734 

TX W A Parish WAP6 1978 734 

Page 26 of 27 
STATE UNITID Online 

Year 

Nameplate 

Capacity [1] 

FACILITY_NAME 

OH W H Sammis 5 1967 318 

OH W H Sammis 6 1969 623 

OH W H Sammis 7 1971 623 

IN Wabash River 6 1968 387 

OH Walter C Beckjord 6 1969 461 

GA Wansley (6052) 1 1976 865 

GA Wansley (6052) 2 1978 865 

IN Warrick 4 1970 323 

SC Wateree WAT1 1970 386 

SC Wateree WAT2 1971 386 

IL Waukegan 8 1962 355 

TX Welsh 1 1977 558 

TX Welsh 2 1980 558 

TX Welsh 3 1982 558 

AR White Bluff 1 1980 850 

AR White Bluff 2 1981 850 

AL Widows Creek 8 1965 550 

IL Will County 4 1963 598 

SC Williams WIL1 1973 633 

SC Winyah 1 1975 315 

SC Winyah 2 1977 315 

IL Wood River 5 1964 388 

WY Wyodak BW91 1978 362 

[1] Nameplate capacity of generator connected to boiler 

Page 27 of 27