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.1We 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 SO22and 323state NOx emissions reduction requirements for all EGUs, without

1As 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.

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

331 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 SO2and 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 arepresented 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 94Vermont†† - - - - 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 14Total 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

2

806 213 170 77 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 59Arizona 9California Connecticut† 5Colorado 9Idaho 1.2 1.2 District Of Columbia 0Maine†† 2.6 1.9 2.6 1.9 Florida 54Montana Georgia 52Nebraska 95Nevada 74 New Hampshire†† 7.3 3.8 5.6 3.1 35New Mexico 101North Dakota** Oklahoma 53Oregon 15 Rhode Island†† 2.0 2.0 25South Dakota** 2.2 Massachusetts 11Utah

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 SOreductions 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 emissionreduction requirements for ME, NH, RI, and VT which are not required by the CAIR proposal. West Virginia Wisconsin

4The 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 27Table II-2. Summary of Projected 2015 SO2and NOx Emissions Totals in proposed CAIR and non-CAIR States under the CAIR as analyzed5(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 SO2reductions, 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).

5As explained in text and noted in Table 1, the assumptions in the CAIR as analyzed differed slightly from CAIR as proposed. Page 4 of 27Table II-3: State-by-state EGU Emissions Projections under BART (as analyzed)6in 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 89Arizona 47.8 86.2 Arkansas 31 36 California 17.3 19.6 Connecticut 3 5Colorado 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 41Nebraska 69.0 44.4 Illinois 317 112Nevada 17.1 30.2 Indiana 579 148New Hampshire 8.8 3.9

Iowa 185 81New 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 43Rhode Island - 2.0 Maryland 144 27 South Dakota 5.6 4.7 Massachusetts 17 12Utah 31.5 69.4 Michigan 182 103Vermont - 0.0 Minnesota 91 77Washington 6.0 16.1 Mississippi 94 36Wyoming 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 SO2and 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 SO2and 1.2 million tons of NOx in 2015 from these larger EGUs. However, States would also be required

6BART control assumptions were applied only to BART-eligible EGUs larger than 250MW , as explained in text. Page 5 of 27to 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: SO2and 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 SO2the 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 SO2and 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 SO2reductions, and about 250,000

7The 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 27tons 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 SO2reductions 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,9we 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

8States 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.

9Total SO2emissions 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 27incorrectly 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 forfuture year scenarios. The IAQR Air Quality Modeling Technical Support Document (NPRAQMTSD)10contains 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,

10U.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 27which 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 PM2.5values. As in the January 2004 proposal, we used the REMSAD model to predict base and future PM 2.5levels. We used the REMSAD predicted PM 2.5components 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 inthe 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

2.5data 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 27defined regional visibility areas11except the Boundary Waters (Northern Great Lakes) region. Of the 44 sites, 15 are in the East12and 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.13The baseline visibility on the 20% worst days at each Class I area was calculated using the default IMPROVE visibility equation14. The daily deciviewvalueswere 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

11IMP ROV E: Spatial and Seasonal Patterns and Temporal V ariability of Haze and its Constituents in the United States: Report III (May 2000).

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

13U.S.EPA, Guidance for Tracking Progress Under the Regional Haze Rule (Tracking Progress Guidance) (September 200 3).

14Tracking Progress Guidance, page 3-10.

15Analyses 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 ).

16The 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 27Class 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 MonumentBAND 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 ParkLAVO California 5 14.8 3.3 Lye Brook W ilderness LYBR Vermont 4 23.9 6.6
Mammoth Cave National ParkMACA 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 27Class 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 ParkPEFO Arizona 5 13.5 6.3
Pinnacles National MonumentPINN Califo rnia 3 19.1 8.8 Point Reyes National SeashorePORE 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 ambientdata. 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

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

18U.S.EPA, Draft Guidance for Demonstrating Attainment of Air Quality Goals for PM2 .5 and Regional Haze. January 2001. Page 12 of 27nitrate, 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 PM2.5mass is calculated using the IMPROVE crustal formula. Coarse mass is defined as the difference between PM10and 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 PM2.5design 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 extinction19on 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-1and 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-1x 0.8 = future sulfate extinction of 40 Mm-1).
Step 4. Sum the daily component extinction values to get total daily light extinction21and 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

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

20The model predicted RRFs are calculated on the 20% best and worst days from 1996 based on the 1996 ambient IMP ROV E data.

21A value of 10 Mm-1is added to each daily value of bext to account for Rayleigh scattering. Page 13 of 27In this section we describe the air quality modeling performed to determine the projected impacts on visibility impairment of the CAIR regional SO2and 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 SO2and 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 27Table III-2

.Projected visibility for the 2015 baseline and the 2015 CAIR and BART (as analyzed)22strategies 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 ParkTennessee 14.2 13.7 12.6 13.0
Jefferson/James River Face WildernessVirginia 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 RefugeSouth 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 Iareas) 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

22See 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 27Class 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 entColorado 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 0.4dv.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 27We 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)23strategies 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 ParkTennessee 29.5 27.6 25.0 26.5
Jefferson/James River Face W ildernessVirginia 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 RefugeSouth 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 Iareas) 26.7 25.2 23.2 24.2

23See 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 27Class 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 MonumentColorado 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 27the 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). Basedon 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 1.0dv. 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 27Appendix
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 UNITIDOnline 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 27STATE UNITIDOnline 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 StationU1B 1979 411 AZ Coronado Generating StationU2B 1980 411 IA Council Bluffs 3 1978 726 CO Craig C1 1980 446 CO Craig C2 1979 446 FL Crist Electric Generating Plant6 1970 370 FL Crist Electric Generating Plant7 1973 578

Page 21 of 27STATE UNITIDOnline 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 Station3 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 27STATE UNITIDOnline 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 27STATE UNITIDOnline 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 27STATE UNITIDOnline 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 Station3 1973 522 WV Mount Storm Power Station1 1965 570 WV Mount Storm Power Station2 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 27STATE UNITIDOnline 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 Station4 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 WAP51977 734 TX W A Parish WAP61978 734

Page 26 of 27STATE UNITIDOnline 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 WAT11970 386 SC Wateree WAT21971 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