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– Do Not Cite or Quote Technical Support Document (TSD) for the final Transport Rule Docket ID No. EPA- HQ - OAR- 2009 - 0491



# Pow er Sector Variability Final Rule TSD

U.S. Environmental Protection Agency Office of Air and Radiation June 201 1

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– Do Not Cite or Quote Power Sector Variability This Technical Support Document (TSD) provides information in support of section VI .E, “Approach to Power Sector Emissions Variability, ” of the preamble to the final Transport Rule. This TSD is organized as follows: 1. Introduction 2. Estimating variability

in power s ystem operations and emissions 3. Numerical simulation of 1 and 3 - ye ar variability 4 . Results of an analys is using the air quality assessment tool 1. Introduction. Section VI of the preamble to the final Transport Rule discusses EPA’s approach to define “significant contribution” and “interference with maintenance” with respect to the 1997 8 -hour ozone and annual fine particle (PM2.5 

### ) National Ambient Air Quality Standards (NAAQS) and the 2006 24 -hour PM 2.5 

### NAAQS

. 

### As discussed in preamble section VI , EPA has identified the emissions that must be reduced by each state to address the state’s significant contribution and interference with maintenance . To facilitate implementation of the requirement that these emissions be eliminated, EPA also developed SO 2 

### , annual NO X 

### , and ozone - season NO X 

### state budgets based on its projections of state by- state power sector emissions in an average ye ar reflecting these emission reductions . 1 

### However, because of inherent variability in year-to -ye ar baseline emissions

– resulting from the inherent variability in power system operations

– state - level emissions may vary somewhat from year to year even after all the significant contribution and interference with maintenance that EPA has identified in the Transp ort Rule has been eliminated. Therefore, and for the reasons discussed in preamble section s VI .E and VI.F , EPA has determined that it is appropriate to develop variability limits for each state budget. These limits are used to identify the range of emiss ions that EPA believes may

1 EPA developed annual SO 2 and NO X budgets for each state covered for the annual and/or 24 -hour PM 2.5 NAAQS and ozone - season NO X budgets for each state covered for the ozone NAAQS , as discussed in section VI.D of the final Transport Rule’s preamble . Table III - 1 in preamble section III lis ts the 27 states that are c overed by the Transport Rule. As discussed in preamble section III, EPA will issue a supplemental proposal to require ozone - season NO X reductions in 6 additional states.

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– Do Not Cite or Quote occur in each state following the elimination of all emissions identified by EPA as significantly contributing to nonattainment or interfering with maintenance in one or more other states. Preamble sections VI.A through VI.D discuss EPA’s approach to quantify for each upwind state the emissions that significantly contribute to nonattainment or interfere with maintenance downwind for the existing ozone and PM 2.5 

### NAAQS and to determine state emissions budgets for SO 2 

### , annual NO X 

### , and ozone season NO X 

### . Preamble sections VI .E and VI.F discuss the inherent variability in electric power s ystem operations and EPA’s approach to determine appropriate variability limits on emissions for each state covered by the final Transport Rule to account for this variability. As explained in preamble section VI . E , EPA calculate d variability limits for each state emissions budget . The final Transport Rule applies those variability limits in addition to the state budgets (which are based on expected average conditions) to determine the “assurance level” for each state in each compliance period . The Agency believes that because baseline power system operations (and therefore emissions ) are variable at the state level , emissions after the elimination of all significant contribution and interference with maintenance are also variable and thus it is appropriate to take this variability into account while assuring that each state makes necessary reductions . In the final Transport Rule, EPA derived “1 - ye ar” variability limits from an assessment of historical ye ar-to - ye ar variability in heat input , which was originally described in the “Power Sector Variability” TSD from the proposed Transport Rule and is re - analyz ed in this TSD for all states include d in the final Transport Rule , as well as for the additional states EPA is proposing, through a supplemental proposal , to include in the Transport Rule ozone program . For the reasons discussed in section VI.E of the preamble, although EPA proposed to impl ement both 1 -ye ar variability limits and 3 - year average variability

limits, the final Transport Rule decided to finalize only the 1 - ye ar limits . Section 3 of this TSD presents an analys is of 1 and 3 -ye ar variability that supports EPA’s decision to finaliz e only

the 1 - year variability

limits in the final rule. Section 4 of this TSD describe s an analys is using the air quality assessment tool (AQAT) to estimate the resulting air quality effects in a given ye ar assuming several potential degrees of variability in ye ar-to - ye ar emissions from the power sector .

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– Do Not Cite or Quote 2. Estimating variability in power system operations and emissions. This section describes the method that the Agency used to estimate the ye ar-to - ye ar (“1 - ye ar” ) variability

in power s ystem operations and thereb y annual SO 2 

### , annual NO X 

### and ozone season NO X 

### emissions. As discussed above, the goal of this assessment is to determine the amount of variability in state - level emissions that is due to inherent variability

in power s ystem operations . To quantify this expected variability, EPA used variation in historic heat input . EPA considered the use of variation in historic emissions but determined this to be inappropriate because it reflected factors such as the installation of new emissions controls and changes in the operation of existing controls . These factors are not directly

related to variability

in power s ystem operations. In fact, these factors are activities that EPA is attempting to regulate with the implementation of the state budgets themselves . EPA believes that all controls (existing or newly installed) should be operated with consistency under the rule’s programs . A s such , EPA is assuming a constant relationship between heat input variability and emissions varia bility for the purposes of this historic variability

analysis (i.e. , variation in heat input is a proxy for variation in emissions holding the effectiveness of emissions control in that state constant) . This section in the TSD provides information on :  The historical data set EPA established on a state -by- state basis of ye arly heat input values applicable to each of the pollutants regulated in the final Transport Rule (SO 2 

### , annual NO x 

### , and ozone season NO x

### ). EPA used these historical heat input values to e stimate the inherent variability in emissions due to power s ystem operation .  The method EPA utilized to estimate the year-to - year variability in the heat input values. The ye ar-to - ye ar variability in heat input was estimated on a state -by- state basis .  The approach EPA used to link inherent heat input variability with projected pollutant emission levels to estimate the resulting variation expected in future pollutant emissions on a year-to - ye ar basis .

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– Do Not Cite or Quote (a) Establishing a historical data set for use in estimating inherent variability in emissions. The objective of this section is to describe the inherent ye ar-to - ye ar (1 - ye ar) variability in emissions by characterizing the year- to -ye ar variance in total annual heat input for each state in the Transport Rule . EPA is concerned with variation in total emissions from year to year (or the variation in total emissions from one ozone season to the next) , as compliance with the Transport Rule programs must be demonstrated for total tonnage emitted during a single year or ozone season . For this assessment , EPA used total ye arly heat input values equaling the sum of heat input from all units operating in each state during a particular year. EPA estimated the expected variation in power sector emissions for a ye arly time period based on the “ standard deviation” of yearly power sector heat input (HI) assessed over a 7 - ye ar time frame (2002 through 2008 ) . EPA believes such data would capture inherent variation in ye ar-to -ye ar emissions due to factors such as variation in power demand, timing of maintenance activities, and unexpected shutdown of units. These factors are strongly correlated with heat input and variation in electric generation. EPA chose the time period 2002 through 2008 for the analys is of variation in heat input because it features nearly comprehensive reporting of heat input across many states and EGU s while also minimizing unusual (i.e., statistically misleading for long-run averaging) upswings or downswings in state - level heat input . EPA selected 2002 as the first baseline ye ar because prior ye ars (2000 and 2001) experienced large, uneven changes in annual heat input from fossil units for some states due, in part, to structural changes in the composition of the power sector fleet and a significant increase in newly reported heat input due to the commencement of the second phase of the Acid Rain Program (ARP) . Consequently, incorporating data from ye ars prior to 2002 in the analysis would lead to the inclusion of a ye ar where there is large change in heat input that is not representative of ty pical ye ar-to - year variability in power s ystem operation , thereby leading to overestimates of variability . EPA determined that including years marked b y significant changes in the number of units reporting heat input data or ye ars experiencing significant shifts in state level EGU fleet composition (i.e., sharp regional swings from coal to gas generation) would skew the variability estimates . EPA maintained 2 008 as the final ye ar for the

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– Do Not Cite or Quote assessment for a similar reason: The economic downturn in 2009 resulted in an atypical dropoff in electric demand and, thus, in heat input , that would overstate long-run year-to ye ar variability

in heat input . For each ye ar of the 7 - ye ar time period, EPA estimated total power sector heat input on a state -by- state basis using the sum of reported heat input for all units within each of three general classes of EGUs : (1) coal - fired units ; (2) combined cycle turbines ; and (3) a combination of oil - and gas - fired boilers and simple - cycle combustion turbines . Total annual heat input values (in million MMBtu) for the coal - fired EGU s and for all EGU s can be found in Table 1 and Table 2 , respectively . Ozone - season heat input values for all EGU s can be found in Table 3 . EPA assessed the inherent year-to - ye ar variability in annual and ozone season NO x 

### emissions using the total ye arly heat inputs summed across all EGU s (Tables 2 and 3 , respectively) , because heat input from each of these sources is related to significant NO x 

### emissions. The Agency assessed ye ar-to - ye ar variability in annual SO 2 

### emissions using the ye arly heat inputs from the coal - fired EGU s (Table 1) which account for a substantial majority of the SO 2 

### emissions. SO 2 

### emission rates are considerably higher at most coal - fired units compared to other typ es of generators; as a result, heat input variation at coal - fired units has a disproportionate impact on a state’s SO 2 

### emissions . F or this reason , in this assessment , we analyz ed historic variability in heat input at coal - fired units as a better proxy for SO 2 

### emissions variability. EGUs built prior to 2002 that reported heat input and emissions data between 2002 and 2008 and new EGUs built in 2002 or later were included for this analysis. EGUs that were built prior to 2002 but that did not start reporting until after 2002 were excluded from the assessment , in order to prevent their newly reported heat input from falsely registering as “additional” heat input gained in that state in that year .

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– Do Not Cite or Quote Table 1: Annual Heat Input* (million MMBtu) f rom Coal -Fired Units f

or Each Year. State 2002 2003 2004 2005 2006 2007 2008 Average HI AL 779.0 822.6 784.5 828.3 813.0 818.8 768.9 802.1 GA 781.2 787.4 808.8 871.6 872.5 910.6 861.1 841.9 IA 393.1 389.6 386.7 384.5 378.9 415.9 431.2 397.1 IL 930.9 961.9 1,027.1 1,002.1 1,002.1 1,030.5 1,032.9 998.2 IN 1,231.4 1,236.3 1,266.0 1,281.2 1,269.9 1,256.9 1,243.6 1,255.0 KS 427.5 427.5 411.9 405.2 377.6 405.4 373.5 404.1 KY 946.5 931.1 938.1 970.3 987.0 977.9 978.5 961.3 MD 276.9 278.0 269.9 273.7 274.7 277.1 256.0 272.3 MI 700.1 706.9 709.7 721.1 699.7 736.0 714.4 712.5 MN 389.2 411.0 386.7 380.1 370.3 362.9 349.8 378.6 MO 724.5 769.6 770.8 783.6 783.5 762.0 732.8 761.0 NC 693.3 689.1 696.5 719.7 699.1 726.4 713.2 705.3 NE 227.0 238.3 231.2 238.3 234.0 222.4 233.8 232.1 NJ 80.0 75.3 79.6 92.2 81.9 77.8 62.8 78.5 NY 234.1 235.9 226.7 213.4 211.2 211.0 181.0 216.2 OH 1,290.2 1,316.3 1,247.3 1,315.1 1,294.0 1,319.7 1,290.0 1,296.1 PA 1,026.9 1,050.1 1,078.6 1,090.2 1,090.5 1,105.2 1,065.1 1,072.4 SC 381.5 384.7 401.4 407.7 410.4 422.6 417.1 403.7 TN 624.7 574.9 589.4 586.3 606.9 617.2 578.0 596.8 TX 1,529.9 1,572.9 1,580.3 1,581.3 1,546.5 1,590.7 1,565.7 1,566.7 VA 340.5 312.5 296.7 298.4 288.0 298.9 264.2 299.9 WI 457.1 475.9 482.9 473.2 451.2 447.5 450.8 462.6 WV 898.8 898.5 851.0 861.2 852.4 883.3 851.6 871.0 

### * Source: EPA, March 2010 . All relevant ARP units in the Transport Rule region . These data are available at http://www.epa.gov/airmarkets/ through Data and Maps.

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– Do Not Cite or Quote Table 2: Total Annual H eat Input * (million MMBtu) from All Three ** EGU Categories for Units Greater Than or Equal to 25 MW for E ach Year. State 2002 2003 2004 2005 2006 2007 2008 Average HI AL 901.0 916.0 904.6 936.6 964.1 1,000.0 938.9 937.3 GA 841.9 822.4 853.4 948.0 968.5 1,033.0 958.2 917.9 IA 393.2 390.3 391.2 401.3 396.3 438.0 446.5 408.1 IL 976.1 982.7 1,046.2 1,053.8 1,038.2 1,086.2 1,059.9 1,034.7 IN 1,256.8 1,257.5 1,283.6 1,315.1 1,300.4 1,301.2 1,282.2 1,285.3 KS 429.2 429.4 412.5 406.8 381.2 410.0 378.6 406.8 KY 962.4 935.1 942.4 988.3 999.0 997.2 987.8 973.2 MD 288.2 290.6 280.8 287.8 282.6 286.0 263.7 282.8 MI 726.9 725.1 727.3 760.5 730.7 776.5 743.8 741.5 MN 399.0 423.1 396.4 401.8 391.7 394.6 371.6 396.9 MO 752.1 788.7 792.2 813.6 812.5 800.6 770.3 790.0 NC 718.8 706.3 718.4 747.2 727.1 767.7 749.7 733.6 NE 228.4 240.5 233.5 245.3 240.7 231.9 240.3 237.2 NJ 139.3 132.9 151.5 154.9 158.8 172.7 172.8 154.7 NY 288.3 297.3 309.9 322.9 380.6 393.1 366.6 337.0 OH 1,309.6 1,330.3 1,258.3 1,340.0 1,313.6 1,354.5 1,310.4 1,316.7 PA 1,060.2 1,090.0 1,155.1 1,172.4 1,185.4 1,240.9 1,200.0 1,157.7 SC 418.2 402.7 442.1 454.9 461.5 474.0 464.4 445.4 TN 635.7 580.5 592.1 592.6 613.2 623.8 582.3 602.9 TX 2,247.3 2,378.4 2,540.8 2,650.2 2,658.4 2,752.9 2,698.6 2,561.0 VA 356.6 333.6 329.5 339.5 324.0 354.8 312.3 335.7 WI 475.3 494.1 498.4 528.5 492.3 497.4 490.0 496.6 WV 900.5 899.9 852.0 863.3 855.8 887.1 853.3 873.1 

### * Source: EPA, March 2010 . All relevant ARP units in the Transport Rule region. These data are available at http://www.epa.gov/airmarkets/ through Data and Maps. 

### * * C oal - fired units; combined cycle turbines; and a combination of oil - and gas - fired boilers and simple cycle combustion turbines .

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– Do Not Cite or Quote Table 3: Total Annual H eat Input * (million MMBtu) from All Three ** EGU Categories f or Units Greater Than or Equal to 25 MW f or Ozone Season. State 2002 2003 2004 2005 2006 2007 2008 Average HI AL 427.4 428.8 423.6 427.2 456.9 469.9 432.5 438.1 AR 124.0 152.5 149.2 142.0 173.6 170.4 150.5 151.7 FL 548.6 554.9 563.3 615.1 639.0 656.3 646.0 603.3 GA 403.8 382.9 407.1 461.8 469.5 489.6 449.9 437.8 IA 170.8 161.8 161.8 183.5 168.9 189.8 182.0 174.1 IL 454.9 430.2 449.6 478.0 460.8 474.6 452.6 457.2 IN 557.2 545.4 540.7 578.4 566.4 559.8 550.5 556.9 KS 183.2 180.9 176.8 180.6 172.2 177.1 160.7 175.9 KY 433.3 400.1 401.0 434.4 435.3 436.1 417.8 422.6 LA 94.7 121.7 144.9 149.9 161.8 150.7 152.2 139.4 MD 134.8 118.8 121.8 131.5 123.3 126.8 117.1 124.9 MI 331.0 312.1 304.6 339.9 328.5 344.5 321.1 326.0 MO 337.7 347.6 348.0 354.2 362.8 357.7 341.7 349.9 MS 117.8 116.2 129.5 137.3 147.7 155.9 143.2 135.4 NC 332.9 308.5 314.0 341.5 340.4 351.5 344.8 333.4 NJ 68.7 59.9 73.9 76.1 79.9 86.1 86.6 75.9 NY 126.1 121.4 136.3 152.5 174.7 170.5 158.9 148.6 OH 583.7 562.8 538.4 574.4 562.0 596.7 553.3 567.3 OK 204.5 223.4 220.0 248.4 244.6 241.1 247.9 232.8 PA 465.4 468.7 494.6 529.3 530.6 550.8 515.5 507.8 SC 203.1 178.4 204.8 214.1 215.8 222.7 222.0 208.7 TN 291.2 244.3 256.2 270.1 273.2 278.9 261.2 267.9 TX 1,031.9 1,089.9 1,170.0 1,229.1 1,255.4 1,262.8 1,252.7 1,184.6 VA 160.0 146.9 150.9 154.3 151.5 167.9 144.5 153.7 WI 218.5 211.1 207.4 240.0 223.0 219.8 208.5 218.3 WV 379.5 384.5 361.0 375.4 373.1 390.3 366.9 375.8 

### * Source: EPA, March 2010; All relevant ARP units in the Transport Rule region. These data are available at http://www.epa.gov/airmarkets/ through Data and Maps. 

### * * C oal - fired units; combined cycle turbines; and a combination of oil - and gas - fired boilers and simple cycle combustion turbines .

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– Do Not Cite or Quote (b) Estimating the state -by- state variability in heat input using historical heat input data sets. This subsection describ es the method EPA used to estimate the variability in heat input for each state covered by the final Transport Rule . Itrepeats some of the relevant data and calculati ons presented in the proposed Transport Rule TSD “Power Sector Variability” with updates reflecting the states covered b y the final Transport Rule. In the final Transport Rule, EPA used the method presented in the proposed Transport Rule and assessed the ye ar-to - ye ar variability over the 7 - ye ar time period of the ye arly total heat input values or ozone season heat input values by deriving a standard deviation for each state while “screening out” overall growth or decline in heat input over that time period . This method is described in detail in this and the following subsection s and was selected for two reasons: First, it held constant any longer trend in growth or decline in a state’s heat input over the baseline period analyz ed . Second, a statistical approach (i.e., using the standard deviation) is less sensitive to data anomalies present in finite data sets ; in other words, it effectively screens out extraneous information to characterize long-run variability. F or each state, it was important to first adjust the data set to screen out trends (growth or decline) in heat input over the 7 - ye ar time period, so that the data would then reflect year-to - ye ar variability independent of trends in electricity demand . After controlling for growth in heat input over time, the year-to - ye ar variation in heat input was assessed, as the differences between the actual ye arly heat input values and the “yearly” average heat input values estimated according to the trend. T o account for trends , a simple least- squares linear regression equation was fit to the heat input data as a function of time. In other words, this step of the analys is attempts to define the amount of variability in the data set for each state that can be attributed to a trend over time, which EPA is attempting to hold constant in this analysis to determine ye ar-to -ye ar variability independent of longer-run trends in heat input (e.g., rising electricity demand). This process fits a straight line to the data points using an equation of the form (y = mx+b). In this equation, “y” is the estimated heat input (million MMBtu) for a particular ye ar “x” , m is the slope of the line (with units of million MMBtu/year) , and b is the “yintercept” (the heat input value when the line is extrapolated to x = 0). The value of r2 

### , the

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– Do Not Cite or Quote square of the Pearson product moment correlation coefficient, describes the proportion of variance in “y” that is accounted for by knowing “x ” . Itindicates how significant a trend (growth or decline) is present in the data. Large r2 

### values (r2 

### values are maximized at 1) indicate that the least square s fit picks up a strong trend (upward or downward) in the data which should be removed by the pro cedure described below. This is important for states where there was substantial growth or decline in heat input over the time period (i.e., for states that have slopes for the regression line that are substantially different than zero) . Small r2 

### values, on the other hand, indicate that the least squares fit did not detect a strong trend (upward or downward) in the heat input data. Consequently, the procedure described below has less of a corrective role to play, since there is no strong trend that needs to be controlled for (held constant) when characterizing the data set’s ye ar-to - year variability . For each state, the slopes of the regression equations , y- intercepts, and r2 

### values can be found in Tables 4 , 5 , and 6 . Using the slope and y- intercept from the regression equation for each state, yearly heat input values were estimated for each state for each ye ar between 2002 and 2008 ( Tables 4, 5, and 6 ) . Year-to -ye ar v ariation was assessed for each ye ar or for each ozone season as the difference between the actual heat input (from Tables 1 - 3) and the heat input estimated using the regression equation (Tables 4 - 6) .

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– Do Not Cite or Quote Table 4: Heat Input (million MMBtu) from Coal - Fired Units f or Each Year Estimated Using the Regression Equation.

State Slope of

Linear Regression (million MMBtu /year ) Intercept r2 Value 2002 2003 2004 2005 2006 2007 2008 AL - 0.34 1 , 492 0.00 803.2 802.8 802.5 802.1 801.8 801.5 801.1 GA 19.64 - 38 , 527 0.74 783.0 802.6 822.3 841.9 861.5 881.2 900.8 IA 5.69 - 11 , 002 0.41 380.1 385.8 391.4 397.1 402.8 408.5 414.2 IL 14.94 - 28 , 960 0.70 953.4 968.3 983.3 998.2 1 , 013.2 1 , 028.1 1 , 043.0 IN 2.92 -4 , 592 0.12 1 , 246.3 1 , 249.2 1 , 252.1 1 , 255.0 1 , 257.9 1 , 260.9 1 , 263.8 KS - 8.59 17 , 633 0.74 429.9 421.3 412.7 404.1 395.5 386.9 378.3 KY 8.52 - 16 , 113 0.68 935.8 944.3 952.8 961.3 969.9 978.4 986.9 MD -2.13 4 , 545 0.36 278.7 276.6 274.5 272.3 270.2 268.1 265.9 MI 3.26 - 5 , 825 0.30 702.8 706.0 709.3 712.5 715.8 719.1 722.3 MN - 8.25 16 , 915 0.80 403.3 395.1 386.8 378.6 370.3 362.1 353.8 MO 0.80 - 836 0.01 758.6 759.4 760.2 761.0 761.8 762.6 763.4 NC 4.90 - 9 , 111 0.54 690.6 695.5 700.4 705.3 710.2 715.1 720.0 NE - 0.32 869 0.01 233.1 232.8 232.5 232.1 231.8 231.5 231.2 NJ - 1.58 3 , 241 0.15 83.2 81.7 80.1 78.5 76.9 75.4 73.8 NY - 8.01 16 , 282 0.85 240.2 232.2 224.2 216.2 208.2 200.2 192.2 OH 1.88 - 2 , 480 0.03 1 , 290.4 1 , 292.3 1 , 294.2 1 , 296.1 1 , 298.0 1 , 299.8 1 , 301.7 PA 8.46 - 15 , 880 0.46 1 , 047.0 1 , 055.5 1 , 063.9 1 , 072.4 1 , 080.8 1 , 089.3 1 , 097.7 SC 6.84 - 13 , 312 0.90 383.1 390.0 396.8 403.7 410.5 417.3 424.2 TN - 1.36 3 , 314 0.02 600.8 599.5 598.1 596.8 595.4 594.1 592.7 TX 3.89 - 6,231 0.15 1,555.1 1,559.0 1,562.9 1,566.7 1,570.6 1,574.5 1,578.4 VA - 9.45 19 , 252 0.77 328.2 318.8 309.3 299.9 290.4 281.0 271.5 WI - 3.84 8 , 161 0.34 474.2 470.3 466.5 462.6 458.8 455.0 451.1 WV - 6.09 13 , 082 0.36 889.2 883.2 877.1 871.0 864.9 858.8 852.7

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– Do Not Cite or Quote Table 5: Total Heat Input (million MMBtu) from All Three * EGU Categories f or Each Year Estimated Using the Regression Equation.

State Slope of

Linear Regression (million MMBtu /year ) Intercept r2 Value 2002 2003 2004 2005 2006 2007 2008 AL 12.18 - 23,490 0.55 900.8 913.0 925.1 937.3 949.5 961.7 973.9 GA 31.61 - 62,466 0.75 823.1 854.7 886.3 917.9 949.5 981.1 1,012.8 IA 9.30 - 18,233 0.72 380.2 389.5 398.8 408.1 417.4 426.7 436.0 IL 16.09 - 31,231 0.73 986.5 1,002.5 1,018.6 1,034.7 1,050.8 1,066.9 1,083.0 IN 6.44 - 11,635 0.39 1,265.9 1,272.4 1,278.8 1,285.3 1,291.7 1,298.2 1,304.6 KS - 7.92 16,295 0.70 430.6 422.7 414.8 406.8 398.9 391.0 383.1 KY 9.17 - 17,418 0.56 945.7 954.8 964.0 973.2 982.3 991.5 1,000.7 MD -2.89 6,080 0.47 291.5 288.6 285.7 282.8 279.9 277.0 274.1 MI 5.60 - 10,480 0.37 724.7 730.3 735.9 741.5 747.1 752.7 758.3 MN - 5.14 10,702 0.53 412.3 407.2 402.0 396.9 391.7 386.6 381.5 MO 3.53 - 6,280 0.12 779.4 782.9 786.5 790.0 793.5 797.0 800.6 NC 8.00 - 15,302 0.63 709.6 717.6 725.6 733.6 741.6 749.6 757.6 NE 0.92 - 1,612 0.11 234.4 235.4 236.3 237.2 238.1 239.1 240.0 NJ 6.69 - 13,250 0.90 134.6 141.3 148.0 154.7 161.4 168.1 174.8 NY 17.76 - 35,264 0.82 283.7 301.4 319.2 337.0 354.7 372.5 390.2 OH 3.79 - 6,281 0.07 1,305.3 1,309.1 1,312.9 1,316.7 1,320.5 1,324.3 1,328.1 PA 26.84 - 52,648 0.85 1,077.2 1,104.0 1,130.9 1,157.7 1,184.5 1,211.4 1,238.2 SC 10.75 - 21,104 0.79 413.2 423.9 434.6 445.4 456.1 466.9 477.6 TN - 1.88 4,376 0.04 608.5 606.6 604.8 602.9 601.0 599.1 597.2 TX 79.30 - 156,429 0.86 2,323.1 2,402.4 2,481.7 2,561.0 2,640.3 2,719.6 2,798.8 VA - 3.42 7,203 0.21 346.0 342.6 339.2 335.7 332.3 328.9 325.5 WI 1.60 - 2,711 0.05 491.8 493.4 495.0 496.6 498.2 499.8 501.4 WV - 5.83 12,568 0.33 890.6 884.8 879.0 873.1 867.3 861.5 855.6 

### * C oal - fired units; combined cycle turbines; and a combination of oil - and gas - fired boilers and simple - cycle combustion turbines .

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– Do Not Cite or Quote Table 6: Heat Input (million MMBtu) from All Three * EGU Categories f or Ozone Season Estimated Using the Regression Equation.

State Slope of

Linear Regression (million MMBtu /year ) Intercept r2 Value 2002 2003 2004 2005 2006 2007 2008 AL 4.68 - 8,936 0.32 424.0 428.7 433.4 438.1 442.7 447.4 452.1 AR 4.99 - 9,850 0.41 136.8 141.8 146.8 151.7 156.7 161.7 166.7 FL 20.38 -40,256 0.90 542.2 562.5 582.9 603.3 623.7 644.1 664.4 GA 14.80 - 29,231 0.64 393.4 408.2 423.0 437.8 452.6 467.4 482.2 IA 3.44 - 6,732 0.45 163.7 167.2 170.6 174.1 177.5 181.0 184.4 IL 3.33 - 6,214 0.20 447.3 450.6 453.9 457.2 460.6 463.9 467.2 IN 1.23 - 1,911 0.04 553.2 554.4 555.7 556.9 558.1 559.4 560.6 KS -2.84 5,880 0.65 184.5 181.6 178.8 175.9 173.1 170.3 167.4 KY 2.15 - 3,883 0.08 416.1 418.3 420.4 422.6 424.7 426.9 429.0 LA 8.84 - 17,590 0.67 112.9 121.7 130.6 139.4 148.3 157.1 166.0 MD - 1.27 2,671 0.18 128.7 127.4 126.1 124.9 123.6 122.3 121.1 MI 2.10 - 3,891 0.10 319.7 321.8 323.9 326.0 328.1 330.2 332.3 MO 1.68 - 3,026 0.17 344.9 346.6 348.3 349.9 351.6 353.3 355.0 MS 6.21 - 12,325 0.80 116.7 122.9 129.1 135.4 141.6 147.8 154.0 NC 5.29 - 10,265 0.50 317.5 322.8 328.1 333.4 338.7 343.9 349.2 NJ 4.01 - 7,956 0.82 63.9 67.9 71.9 75.9 79.9 83.9 87.9 NY 8.39 - 16,679 0.74 123.4 131.8 140.2 148.6 157.0 165.4 173.8 OH 0.01 552 0.00 567.3 567.3 567.3 567.3 567.3 567.3 567.3 OK 6.80 - 13,402 0.75 212.4 219.2 226.0 232.8 239.6 246.4 253.2 PA 12.52 - 24,588 0.69 470.3 482.8 495.3 507.8 520.4 532.9 545.4 SC 5.58 - 10,974 0.62 192.0 197.5 203.1 208.7 214.3 219.8 225.4 TN - 0.12 517 0.00 268.2 268.1 268.0 267.9 267.7 267.6 267.5 TX 39.05 - 77,113 0.85 1,067.4 1,106.5 1,145.5 1,184.6 1,223.6 1,262.7 1,301.7 VA - 0.14 434 0.00 154.1 154.0 153.9 153.7 153.6 153.4 153.3 WI 0.10 24 0.00 218.0 218.1 218.2 218.3 218.4 218.5 218.6 WV - 0.50 1,387 0.01 377.3 376.8 376.3 375.8 375.3 374.8 374.3 

### * C oal - fired units; combined cycle turbines; and a combination of oil - and gas - fired boilers and simple - cycle combustion turbines . 

### On a state -by- state basis, EPA calculated the difference between the actual heat input and the estimated heat input using the regression equation for each ye ar (2002 through 2008) . The differences for SO 2 

### , annual NO x 

### , and ozone season NO x 

### can be found in Tables 7 , 8, and 9 , respectively. Some of these ye arly differences are positive, while others are negative ; the size of the variation on either side of the regression line is meaningful for this analysis . EPA assess ed the differences between actual and estimated heat input across all ye ars for each state and pollutant by calculating the standard deviation .

### 15 Deliberative

– Do Not Cite or Quote The standard deviation is defined as the square root of the variance (the sum of the square of the differences 2 

### divided by the number of samples minus one). The state - and pollutant- specific representative differences (defined using the standard deviation) were used for the remaining steps in the variability analyses. In using the standard deviation as a representative difference, we assume that: (1) differences between the actual and modeled heat inputs are “normally” distributed; and that (2) yearly mean values are independent. For each state, the standard deviation of the differences is a conventional statisti cal measure of the year-to - year (1 - year) variation in heat input . The statistical definition of a standard deviation in this context conveys that, on average, 68% of all the year- specific heat input values over the long run would be no farther from the average annual heat input (either higher or lower) than by the amount measured as the standard deviation . The standard deviation can also be used to estimate, on average, the probability of larger variations in heat input (for example a difference that we would expect less than 1% of the time). Using the standard deviation, “confidence levels ” representing the variability difference in heat input that could be expected at different probabilities were found for each state for each pollutant. Following the analysis from the proposed Transport Rule and after reviewing all relevant public comment, the two -tailed 9 5 th 

### percent confidence level was selected as the appropriate level of variation. The two -tailed 95 th 

### percent confidence level indicates that we c ould expect that , on average, the total heat input for a particular ye ar for a particular state will be within 1.960 standard deviations of the variation from its mean value 9 5 percent of the time. This statistical confidence interval is very commonly use d to make future projections from data sets similar to EPA’s purpose in this analysis. As described in the proposal, EPA believes that using the 95 th 

### percent confidence level provides a high degree of confidence that sources subject to the rule will be able to operate within the constraints of the variability limits while still meeting their obligations to provide a reliable electricity supply across the states affected .

2 On a state -by- state basis, the standard deviation was calculated from the set of yearly difference values. As described, the yearly values were the difference between the actual heat input and the estimated heat input (using the regression equation) for each year.

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– Do Not Cite or Quote The 95 % upper confidence level heat inputs and fractions (the 95 % heat input difference divided by

the average heat input over the 2002 - 2008 time period) can be found in Tables 7 , 8, and 9 . The standard deviation in the heat input in million MMBtu rounded to three significant digits can be found in Tables 7 , 8, and 9 . The standard deviation of the heat input was divided by the average heat input value , resulting in a fraction (when the fraction is multiplied by

one hundred, the result is the standard deviation percent variation).

### 17 Deliberative

– Do Not Cite or Quote Table 7: Difference b etween H eat Input (million MMBtu) M easured and E stimated for C oal -F ired Units for E ach Year U sing the Regression E quation .

State 2002 2003 2004 2005 2006 2007 2008 Standard Deviation of Heat Input (million MMBtu) Average

Heat I nput (2002 - 2008 ( f rom Table 1) Standard

Deviation a s a Fraction o f Average HI 95%

Confidence Level Variability in HI (million MMBtu) 95%

Confidence Level Variability in HI (as a Fraction of Avg. HI) AL -24.1 19.7 - 18.0 26.1 11.2 17.3 - 32.2 24.0 802.1 0.03 47.0 0.059 GA - 1.8 - 15.2 - 13.5 29.7 11.0 29.4 - 39.7 25.4 841.9 0.03 49.7 0.059 IA 13.0 3.9 -4.8 - 12.7 -23.9 7.4 17.0 14.6 397.1 0.037 28.7 0.073 IL -22.5 - 6.4 43.8 3.8 - 11.0 2.4 - 10.1 21.3 998.2 0.021 41.7 0.041 IN - 14.9 - 12.9 13.9 26.1 11.9 -4.0 -20.2 17.5 1 , 255.0 0.014 34.3 0.027 KS -2.4 6.3 - 0.8 1.1 - 17.9 18.4 -4.8 11.0 404.1 0.027 21.6 0.053 KY 10.7 - 13.2 - 14.8 9.0 17.2 - 0.5 - 8.4 12.6 961.3 0.013 24.7 0.025 MD - 1.8 1.4 -4.6 1.4 4.5 9.0 - 10.0 6.2 272.3 0.023 12.1 0.045 MI -2.7 0.8 0.5 8.5 - 16.1 16.9 - 7.9 10.7 712.5 0.015 21.0 0.029 MN - 14.1 16.0 - 0.2 1.6 0.0 0.8 -4.0 8.9 378.6 0.023 17.4 0.045 MO - 34.0 10.2 10.7 22.6 21.7 - 0.6 - 30.6 23.4 761.0 0.031 45.9 0.061 NC 2.7 - 6.5 -4.0 14.3 - 11.1 11.3 - 6.8 9.7 705.3 0.014 19.1 0.027 NE - 6.1 5.6 - 1.3 6.2 2.2 - 9.1 2.6 5.8 232.1 0.025 11.4 0.049 NJ - 3.3 - 6.4 - 0.5 13.7 5.0 2.4 - 11.0 8.1 78.5 0.103 15.8 0.202 NY - 6.2 3.7 2.5 -2.8 3.1 10.8 - 11.1 7.3 216.2 0.034 14.2 0.067 OH - 0.2 24.0 -46.9 19.0 -4.0 19.8 - 11.8 24.8 1 , 296.1 0.019 48.5 0.037 PA -20.1 - 5.4 14.7 17.8 9.7 15.9 - 32.7 19.9 1 , 072.4 0.019 39.1 0.037 SC - 1.6 - 5.3 4.6 4.1 - 0.1 5.3 - 7.1 4.9 403.7 0.012 9.7 0.024 TN 23.8 -24.6 - 8.7 - 10.4 11.5 23.1 - 14.7 19.3 596.8 0.032 37.8 0.063 TX -25.2 14.0 17.5 14.5 -24.2 16.1 - 12.8 19.8 1,566.7 0.013 38.8 0.025 VA 12.3 - 6.3 - 12.7 - 1.5 -2.4 17.9 - 7.3 11.1 299.9 0.037 21.7 0.073 WI - 17.1 5.6 16.4 10.5 - 7.7 - 7.5 - 0.3 11.7 462.6 0.025 22.9 0.049 WV 9.5 15.4 -26.1 - 9.7 - 12.5 24.5 - 1.1 17.6 871.0 0.02 34.5 0.039

### 18 Deliberative

– Do Not Cite or Quote Table 8: Difference b etween T otal H eat Input (million MMBtu) M easured and E stimated from All Three * EGU Categories f or Each Year Using the Regression Equation .

State 2002 2003 2004 2005 2006 2007 2008 Standard Deviation of Heat Input (million MMBtu) Average

Heat I nput (2002 - 2008 ( f rom Table 2 ) Standard

Deviation a s a Fraction o f Average HI 95%

Confidence Level Variability in HI (million MMBtu) 95%

Confidence Level Variability in HI (as a Fraction of Avg. HI) AL 0.2 3.0 -20.5 - 0.7 14.6 38.3 - 35.0 23.6 937.3 0.025 46.2 0.049 GA 18.8 - 32.3 - 32.9 30.1 18.9 51.8 - 54.5 39.6 917.9 0.043 77.6 0.084 IA 13.0 0.8 - 7.7 - 6.8 -21.2 11.3 10.5 12.6 408.1 0.031 24.8 0.061 IL - 10.4 - 19.8 27.5 19.1 - 12.6 19.3 -23.1 21.2 1 , 034.7 0.020 41.5 0.039 IN - 9.2 - 14.8 4.8 29.8 8.7 3.0 -22.3 17.3 1 , 285.3 0.013 33.9 0.025 KS - 1.4 6.7 -2.3 - 0.1 - 17.7 19.1 -4.4 11.2 406.8 0.027 21.9 0.053 KY 16.8 - 19.7 -21.6 15.1 16.6 5.7 - 12.9 17.5 973.2 0.018 34.3 0.035 MD - 3.3 2.0 -4.9 5.0 2.7 8.9 - 10.4 6.6 282.8 0.023 12.9 0.045 MI 2.2 - 5.3 - 8.7 19.0 - 16.5 23.7 - 14.5 15.9 741.5 0.021 31.1 0.041 MN - 13.3 15.9 - 5.6 4.9 - 0.1 8.0 - 9.8 10.4 396.9 0.026 20.4 0.051 MO -27.3 5.7 5.8 23.6 19.0 3.6 - 30.3 21.1 790.0 0.027 41.3 0.053 NC 9.2 - 11.3 - 7.2 13.6 - 14.5 18.1 - 7.9 13.2 733.6 0.018 25.9 0.035 NE - 6.1 5.1 -2.7 8.1 2.5 - 7.2 0.3 5.7 237.2 0.024 11.2 0.047 NJ 4.7 - 8.4 3.5 0.2 -2.6 4.6 - 1.9 4.8 154.7 0.031 9.4 0.061 NY 4.6 -4.1 - 9.3 - 14.1 25.9 20.6 -23.6 18.1 337.0 0.054 35.5 0.106 OH 4.3 21.2 - 54.6 23.3 - 6.9 30.3 - 17.6 29.6 1 , 316.7 0.023 58.1 0.045 PA - 17.0 - 14.0 24.2 14.7 0.8 29.5 - 38.3 24.6 1 , 157.7 0.021 48.2 0.041 SC 5.0 -21.2 7.4 9.5 5.3 7.2 - 13.2 12.1 445.4 0.027 23.7 0.053 TN 27.2 -26.1 - 12.7 - 10.3 12.2 24.7 - 15.0 21.1 602.9 0.035 41.3 0.069 TX - 75.7 -23.9 59.2 89.2 18.2 33.3 - 100.2 69.8 2,561.0 0.027 136.9 0.053 VA 10.5 - 9.0 - 9.7 3.8 - 8.3 25.9 - 13.2 14.2 335.7 0.042 27.9 0.082 WI - 16.5 0.7 3.4 31.9 - 5.9 -2.3 - 11.3 15.7 496.6 0.032 30.7 0.063 WV 9.8 15.1 -26.9 - 9.8 - 11.5 25.7 -2.4 18.0 873.1 0.021 35.2 0.041 

### * C oal - fired units; combined cycle turbines; and a combination of oil - and gas - fired boilers and simple - cycle combustion turbines .

### 19 Deliberative

– Do Not Cite or Quote Table 9: Difference b etween Total Heat Input Measured and Estimated (million MMBtu) f rom All Three * EGU Categories f or Ozone Season Using the Regression Equation.

State 2002 2003 2004 2005 2006 2007 2008 Standard Deviation of Heat Input (million MMBtu) Average

Heat Input (2002 - 2008 ( f rom Table 3 ) Standard

Deviation a s a Fraction o f Average HI 95%

Confidence Level Variability in HI (million MMBtu) 95%

Confidence Level Variability in HI (as a Fraction of Avg. HI) AL 3.4 0.1 - 9.8 - 10.8 14.1 22.5 - 19.5 14.8 438.1 0.034 29.0 0.067 AR - 12.7 10.7 2.5 - 9.7 16.8 8.6 - 16.2 12.9 151.7 0.085 25.3 0.167 FL 6.4 - 7.6 - 19.6 11.8 15.3 12.2 - 18.5 15.0 603.3 0.025 29.4 0.049 GA 10.4 -25.3 - 15.9 24.0 16.9 22.2 - 32.3 23.8 437.8 0.054 46.7 0.106 IA 7.1 - 5.4 - 8.8 9.4 - 8.6 8.8 -2.5 8.2 174.1 0.047 16.1 0.092 IL 7.6 -20.4 -4.3 20.8 0.3 10.7 - 14.6 14.5 457.2 0.032 28.3 0.063 IN 4.0 - 9.1 - 15.0 21.5 8.3 0.5 - 10.1 12.6 556.9 0.023 24.7 0.045 KS - 1.3 - 0.7 -2.0 4.7 - 0.9 6.9 - 6.7 4.5 175.9 0.026 8.8 0.051 KY 17.1 - 18.2 - 19.4 11.8 10.5 9.3 - 11.2 15.6 422.6 0.037 30.6 0.073 LA - 18.2 - 0.1 14.4 10.5 13.5 - 6.4 - 13.7 13.3 139.4 0.095 26.1 0.186 MD 6.1 - 8.6 -4.4 6.6 - 0.3 4.5 -4.0 5.9 124.9 0.047 11.6 0.092 MI 11.4 - 9.7 - 19.2 13.9 0.4 14.4 - 11.2 13.7 326.0 0.042 26.8 0.082 MO - 7.2 1.0 - 0.3 4.3 11.2 4.4 - 13.3 8.1 349.9 0.023 15.8 0.045 MS 1.1 - 6.8 0.3 1.9 6.1 8.1 - 10.8 6.7 135.4 0.050 13.2 0.098 NC 15.3 - 14.3 - 14.0 8.2 1.7 7.5 -4.4 11.4 333.4 0.034 22.4 0.067 NJ 4.8 - 7.9 2.1 0.2 0.0 2.2 - 1.3 4.0 75.9 0.053 7.9 0.104 NY 2.6 - 10.5 - 3.9 3.9 17.7 5.1 - 14.9 10.8 148.6 0.073 21.3 0.143 OH 16.4 -4.5 - 28.9 7.1 - 5.4 29.3 - 14.0 19.4 567.3 0.034 38.0 0.067 OK - 8.0 4.1 - 6.0 15.6 4.9 - 5.3 - 5.3 8.6 232.8 0.037 16.8 0.073 PA -4.9 - 14.1 - 0.8 21.5 10.2 17.9 -29.9 18.3 507.8 0.036 35.8 0.071 SC 11.1 - 19.1 1.7 5.5 1.5 2.8 - 3.4 9.5 208.7 0.046 18.6 0.090 TN 22.9 -23.9 - 11.8 2.2 5.5 11.3 - 6.3 15.5 267.9 0.058 30.3 0.114 TX - 35.5 - 16.5 24.5 44.5 31.8 0.1 -49.0 35.4 1 , 184.6 0.030 69.4 0.059 VA 5.8 - 7.1 - 3.0 0.6 - 2.1 14.5 - 8.8 8.0 153.7 0.052 15.7 0.102 WI 0.5 - 7.0 - 10.8 21.6 4.5 1.3 - 10.2 11.3 218.3 0.052 22.1 0.102 WV 2.2 7.6 - 15.3 - 0.4 - 2.2 15.5 - 7.4 10.0 375.8 0.027 19.5 0.053



### * C oal - fired units; combined cycle turbines; and a combination of oil - and gas - fired boilers and simple - cycle combustion turbines

### 20 Deliberative

– Do Not Cite or Quote (c) Estimation of variability in emissions using the year-to -year variability in historical heat input. The analys is described in the sections above results in estimated 95% confidence level historic variabilities in heat input for each state and pollutant (Tables 7, 8, and 9) . As described in the preamble in section VI.E.2 , from these variability values, EPA identified a specific variability

percentage level for each state (Table 10) . This was ty pically 10%, unless the state’s historic heat input variability was higher than 10% . In those cases, EPA rounded those budgets’ variability limit percentages up to the next five -percent increment (e.g., 15 percent, 20 percent) that successfully accommodated the historically observed variability, up to a maximum of 20% . The relationship between the historic variability values and the variability percentage levels can be found in T able 10 . As described in section VI.E.2 of the preamble, the final step in estimating state by- state year-to - ye ar (1 - year) variability in emissions is to convert the variability percentage level in each state into variability in pollutant emissions (i.e., a “variability limit”) . EPA did this by multiplying the variability percentage level in each state for each pollutant by the state emissions budgets for each pollutant (section VI .D of the preamble discusses EPA’s approach to determine the final Transport Rule budgets , and lists the budgets in Tables VI.D - 3 and VI.D -4 ) . For each state, Tables VI.F - 1, VI.F - 2, and VI.F - 3 list the resulting variability limits for each state for SO 2 

### , annual NO X 

### and ozone - season NO X 

### , respectively. Table 10. Relationship between 95% Confidence Level Historic Variability in Heat Input and Variability Limit Used in the Transport Rule * . SO 2 Annual NO X Ozone - Season NO X State Historic

Variability Transport

Rule Variability Limit Historic

Variability Transport

Rule Variability Limit Historic

Variability Transport

Rule Variability Limit AL 5.9% 10% 4.9% 10% 6.7% 10% AR 16.7% 20% FL 4.9% 10% GA 5.9% 10% 8.4% 10% 10.6% 15%

### 21 Deliberative

– Do Not Cite or Quote IL 4.1% 10% 3.9% 10% 6.3% 10% IN 2.7% 10% 2.5% 10% 4.5% 10% IA* 7.3% 10% 6.1% 10% 9.2% 10% KS* 5.3% 10% 5.3% 10% 5.1% 10% KY 2.5% 10% 3.5% 10% 7.3% 10% LA 18.6% 20% MD 4.5% 10% 4.5% 10% 9.2% 10% MI* 2.9% 10% 4.1% 10% 8.2% 10% MN 4.5% 10% 5.1% 10% MS 9.8% 10% MO* 6.1% 10% 5.3% 10% 4.5% 10% NE 4.9% 10% 4.7% 10% NJ 20% 20% 6.1% 10% 10.4% 15% NY 6.7% 10% 10.6% 15% 14.3% 15% NC 2.7% 10% 3.5% 10% 6.7% 10% OH 3.7% 10% 4.5% 10% 6.7% 10% OK* 7.3% 10% PA 3.7% 10% 4.1% 10% 7.1% 10% SC 2.4% 10% 5.3% 10% 9.0% 10% TN 6.3% 10% 6.9% 10% 11.4% 15% TX 2.5% 10% 5.3% 10% 5.9% 10% VA 7.3% 10% 8.2% 10% 10.2% 15% WV 3.9% 10% 4.1% 10% 5.3% 10% WI* 4.9% 10% 6.3% 10% 10.2% 15% 

### * Indicates a state (IA, KS, MI , OK, WI, and MO) that is included in the supplemental notice of proposed rulemaking for ozone - season NO x 

### emission reductions. See the preamble for details. 3. Numerical simulation of 1 - and 3 -year variability For the reasons discussed in section VI .E of the preamble, although EPA proposed to implement both 1 -year variability limits and 3 - year average variability limits, the final Transport Rule includes only the 1 - ye ar limits . Inthe proposal, EPA first established 1 - ye ar variability

limits (following the procedure outlined above) and then established corresponding 3 - ye ar variability limits to be statistically

indistinguishable from the 1 -ye ar variability

limits. In other words, the proposal’s 3 -ye ar variability limits were c alculated on a statistical basis to offer states the same degree of future variability (based on historic variability) as the 1 - ye ar variability limits. For the final rule, EPA analyz ed the relationship between the 1 -ye ar and 3 - ye ar variability limits , performing a numerical

### 22 Deliberative

– Do Not Cite or Quote verification of equal likelihood of potential violations of a state’s assurance level when evaluating random variation (10% at the 95% two -tailed confidence level) around a constant budget. As discussed in section VI.E of the final Transport Rule preamble, the state -bystate emissions budgets are based on the availability of emission reductions at an equal cost threshold (within each of the programs ) . As such, EPA exp ects the sources in each covered state to make these cost- effective reductions and to meet the emission budgets each ye ar . On average EPA does not expect any systematic bias in emissions (against the established state budgets) to occur . Thus, a non -biased assessment of variability is appropriate. EPA simulated 1 - and 3 - ye ar variation around a constant budget for a hy pothetical state, and found, as described below, comparable results affirming that on a statistical basis, the 3 -ye ar variability limits are no more likely to be exceeded than the 1 - ye ar variability

limits . Using a random-number generator within Microsoft Excel, EPA numeric ally simulated random variation using the 95% two -tailed confidence level 1 - ye ar variability of 10%. The 1 - ye ar variability standard deviation is 10% divided by 1.960, or 5.1%. Using the formula, =NORMINV(RAND(),100, (10/1.960) ) , EPA simulated yearly emissions for a “state” with a budget of 100, with random variation of 5.1%. EPA simulated five consecutive “years” of emissions , then calculated three 3 - ye ar consecutive averages (“years” 1, 2 , and 3; 2, 3, and 4; and 3, 4, and 5) . EPA, then, assessed the average number of “exceedances” of the 1 -ye ar and 3 - ye ar “budgets plus variability limits”. The 1 - ye ar budget plus variability

limit is 110, while the 3 -ye ar budget plus variability limit (as defined in the proposal) is 105. 77. EPA simulated 10,000 5 -ye ar time -periods , and averaged the number of exceedances of the 1 - ye ar (50,000 data points) and 3 -ye ar limits (30,000 data points) . For both the 1 -ye ar and 3 - ye ar cases, the average number of exceedances was 2.5%, conforming to the expected 95% two -tailed confidence exceedance value of 2.5%. This finding confirms that the proposed 3 - ye ar variability limits are statistically indistinguishable from the 1 - year variability limits for each state; therefore, EPA has determined that the 3 - ye ar variability limits are redundant with the 1 -ye ar variability limits and are thus unnecessary .

### 23 Deliberative

– Do Not Cite or Quote 4 . Results of an analysis using the air quality assessment tool The objective of this section is to estimate the possible effects on air quality in 2014 of the variability limits . This analysis us ed the air quality assessment tool, or AQAT , and state -by- state emissions to estimate downwind state -by- state air quality contributions at receptors with nonattainment and maintenance problems in the CAMx air quality modeling of 2012 base case (section VI.C of the final Transport Rule preamble discusses AQAT ; section V of the final Transport Rule preamble discusses air quality modeling) . This analysis is similar to the analys es performed for the proposed Transport Rule (and reported in the proposed Transport Rule “Power Sector Variability” TSD). It concludes that, as estimated using AQAT, while variability in power s ystem operation (and thereb y SO 2 

### emissions from EGUs under the Transport Rule) do es have perceptible downwind air quality effects on PM 2.5 

### concentrations , these effects are minimal . EPA believes that variation in other factors such as SO 2 

### and NO x 

### emission patterns from non- EGU sources, contributions from other pollutants (i.e., ammonia, elemental and organic carbon for PM2.5 

### and volatile organic carbon for ozone), and meteorological factors play important roles in downwind air quality . For this analys is, EPA varied the EGU SO 2 

### emissions of upwind state s included in the final Transport R

ule that are “linked” to downwind receptors (as described in section V of the preamble) . The variation in emissions was designed to reflect the allowance of inherent year-to - ye ar (1 -ye ar) variability in power system operations above average state level emissions equal to the $2 , 300/ton and $500/ton SO 2 

### cost thresholds for group 1 and group 2 states respectively (“$2,300/ton” for the purposes o f this discussion). Details on this scenario (TR_SO2_2300_Final) can be found in appendix A of the Significant Contribution and State Emissions Budgets Final Rule TSD . EPA estimated the air quality impacts using the change in the maximum daily PM 2.5 

### desi gn value . This analys is focused on variability limits related to annual SO 2 

### emissions and the 24 -hour PM 2.5 

### concentrations for the same reasons listed in the proposed Transport Rule “Power Sector Variability” TSD , with the addition that the improved “calibrated” AQAT used in the final Transport Rule only allows SO 2 

### emissions to be varied for estimating downwind ammonium sulfate concentrations (which is discussed further in section VI.C in the final rule’s preamble) .

### 24 Deliberative

– Do Not Cite or Quote In contrast to the analyses in the proposed Transport Rule, this analysis focuses on the “worst- case ” emissions variability in 2014 . This is intended to estimate upper bounds for the effects of year-to -ye ar variability in power system operations on air quality . For this analysis, the “worst case” situation is defined as the hy pothetical scenario in which all states that are linked to a particular receptor increase their SO 2 

### emissions to a “variability maximum” equal to the 2014 emissions equal to the final Transport Rule cost thresholds plus a fixed percentage (variability level) of the state budget. States that were not linked to the receptor were held at base case SO 2 

### emission levels. Four variability levels were examined (5, 10, 15, and 20%) . These levels were chosen to bracket the variability level selected for most states (i.e., 10%). To be consistent with the analysis of significant contribution and interference with maintenance, the SO 2 

### emissions from the state containing the receptor w ere also increased to the f inal Transport Rule cost thresholds plus variability level . A second assessment of this analysis focused on the effects of variation in emissions due only

to the upwind states. The emissions from the state containing the receptor (home state) were maintained at a constant level, equal to its final Transport Rule cost threshold . By

comparing the two assessments it is possible to get a sense of the relative importance of variation of upwind power system operations compared with variation in the home state’s power system operations . This variability analysis does not balance emission increases in some states with emission decreases in other states (as was done in the analys is for the proposed Transport Rule) . Specifically, this analys is does not account for corresponding decreases from other states included in the final Transport Rule which are not contributing above the 1% threshold to a particular receptor . EPA specifically notes that this analysis is intended to demonstrate the potential air quality impacts from ye ar-to - year variability under “extreme” worst- case scenarios, where states linked to a downwind receptor simultaneously need to utilize the maximum power sector variability ; it is distinct from the “typical” operating conditions represente d in an “average” year under which significant contribution and interference with maintenance are examined to inform EPA’s determination of emission reductions available at various cost thresholds . Inother words, the Transport Rule state budgets are estab lished on the basis of conditions in an average year, while the variability provisions address the inherent year-to -

### 25 Deliberative

– Do Not Cite or Quote ye ar fluctuations of power sector operations around those average conditions . This analys is examines the fluctuations in downwind air quality that may be attributable to various potential degrees of year-to - ye ar power sector emission variability around upwind state budgets. Such an analysis is fundamentally different from the Transport Rule’s analys is to determine significant contribution and interference with maintenance, which includes an examination of downwind air quality impacts not on a fluctuating ye ar-to - ye ar basis, but on the basis of the degree of stringency of upwind emissions control under expected (i.e., average) operating conditions as represented b y EPA’s power sector modeling projections. Therefore, the results of this TSD’s analysis are not pertinent to the Agency ’s consideration of downwind air quality impacts as one factor in its multifactor analys is of significant contribution and interference with maintenance. The results of the analys is can be found in Table 1 1 and in Figure 1 , below . The estimated increases in maximum design value PM 2.5 

### concentrations across the range of variability

levels examined are relatively small, with a maximum of 0.57 g/m3 

### at 20% variability for Jefferson County, AL. Averaged across the receptors with nonattainment and maintenance problems in the CAMx air quality modeling of the 2012 , the increase was 0.29 g/m3 

### at 20% variability. For the second variation of the analys is, when the home state emissions are held constant at the final Transport Rule cost threshold, the increase averaged 0. 19 g/m3



### . (Table 11 and Figure 2 ). The maximal increase was 0. 41 g/m3 

### for receptor number 420031008 in Allegheny, Pennsylvania . For most locations, the largest contributor to increase d PM 2.5 

### concentration from a single upwind state will be from sources within the state containing the receptor itself (this conclusion is drawn from comparing the Worst Case (All States) and Worst Case (Upwind States) columns in Table 22 from the “Power Sector Variability” TSD from the proposed Transport Rule as well as by comparing the difference in air quality estimates between 0 and 20% variation for the two cases (compare the differences from Tables 1 1 and 1 2 ) .

### Deliberative *The six sites are Allegheny, PA (64); Lancaster, PA (07); Wayne, MI (16 and 19); Cook, IL (16); and Lake, IN (22). **The eight sites include the six sites listed above as well as Cuyahoga, 

### Figure 1. Increase in maximum PM threshold level as the variability level is increased from 0 to 20 percent, individually for the eight sites (left panel) as well as for v combinations of sites can be found in Table 10.

26 Deliberative

– Do Not Cite or Quote *The six sites are Allegheny, PA (64); Lancaster, PA (07); Wayne, MI (16 and 19); Cook, IL (16); and Lake, IN (22). **The eight sites include the six sites listed above as well as Cuyahoga, OH (38) and Wayne, MI (33). 

### Figure 1. Increase in maximum PM 2.5 

### design value ( g/m 3 

### ) in 2014 at the $2300/ton cost level as the variability

level is increased from 0 to 20 percent, individually for the eight sites (left panel) as well as for v arious combinations of sites (right panel). The combinations of sites can be found in Table 10. *The six sites are Allegheny, PA (64); Lancaster, PA (07); Wayne, MI (16 and 19); Cook, IL (16); and Lake, IN (22).



### ) in 2014 at the $2300/ton cost level as the variability

level is increased from 0 to 20 percent, individually for arious combinations of sites (right panel). The

### 27 Deliberative

– Do Not Cite or Quote Table 1 1 . Maximum PM2.5 

### DVs ( g/m3 

### ) in 2014 With Linked States at the Final Transport Rule Cost Threshold Level with Various Additional Amounts of Emissions Due to Variability . Site ID State County Variability (%) Difference in air quality between the 0% variability level and another level. 0 5 10 15 20 0 5 10 15 20 avg. of 6 sites* 38.39 38.44 38.50 38.55 38.60 0.00 0.05 0.11 0.16 0.21 avg. of 8 sites** 37.62 37.68 37.74 37.80 37.85 0.00 0.06 0.12 0.17 0.23 max of all 2012 b ase c ase 48.63 48.72 48.85 48.97 49.09 0.00 0.14 0.29 0.44 0.57 420030064 Pennsylvania Allegheny 48.63 48.72 48.85 48.97 49.09 0.00 0.09 0.21 0.34 0.46 420030093 Pennsylvania Allegheny 34.80 34.90 35.01 35.11 35.21 0.00 0.10 0.21 0.31 0.41 390350038 Ohio Cuyahoga 35.41 35.44 35.47 35.50 35.53 0.00 0.03 0.06 0.09 0.12 261630016 Michigan Wayne 35.65 35.69 35.73 35.77 35.81 0.00 0.04 0.08 0.12 0.16 390350060 Ohio Cuyahoga 33.04 33.10 33.14 33.16 33.18 0.00 0.06 0.10 0.12 0.15 170311016 Illinois Cook 36.54 36.62 36.70 36.78 36.85 0.00 0.08 0.16 0.24 0.31 261630033 Michigan Wayne 35.23 35.35 35.46 35.58 35.69 0.00 0.12 0.23 0.35 0.46 180890022 Indiana Lake 36.51 36.57 36.61 36.64 36.67 0.00 0.07 0.10 0.14 0.17 540090011 West

Virginia Brooke 30.02 30.14 30.26 30.38 30.49 0.00 0.12 0.24 0.36 0.47 420710007 Pennsylvania Lancaster 37.18 37.19 37.20 37.21 37.22 0.00 0.01 0.02 0.03 0.04 390350045 Ohio Cuyahoga 27.60 27.67 27.75 27.83 27.90 0.00 0.08 0.15 0.23 0.31 390811001 Ohio Jefferson 28.03 28.14 28.26 28.37 28.49 0.00 0.12 0.23 0.35 0.46 261630019 Michigan Wayne 35.83 35.86 35.89 35.92 35.95 0.00 0.03 0.06 0.09 0.12 390350065 Ohio Cuyahoga 27.00 27.10 27.20 27.30 27.41 0.00 0.10 0.20 0.30 0.41 170313301 Illinois Cook 32.84 32.90 32.95 33.01 33.07 0.00 0.06 0.11 0.17 0.22 420070014 Pennsylvania Beaver 28.70 28.77 28.86 28.96 29.06 0.00 0.06 0.16 0.26 0.36 420033007 Pennsylvania Allegheny 28.81 28.90 28.99 29.08 29.17 0.00 0.09 0.18 0.27 0.37 010730023 Alabama Jefferson 32.12 32.26 32.41 32.55 32.69 0.00 0.14 0.29 0.44 0.57 550790026 Wisconsin Milwaukee 33.21 33.26 33.31 33.36 33.42 0.00 0.05 0.11 0.16 0.21 180970043 Indiana Marion 27.82 27.91 28.00 28.09 28.18 0.00 0.09 0.18 0.27 0.35 261470005 Michigan St Clair 33.38 33.41 33.44 33.47 33.51 0.00 0.03 0.06 0.09 0.13 550790043 Wisconsin Milwaukee 33.92 33.96 34.00 34.04 34.07 0.00 0.04 0.08 0.12 0.15 180890026 Indiana Lake 33.37 33.40 33.44 33.47 33.50 0.00 0.03 0.07 0.10 0.14 180970081 Indiana Marion 27.59 27.72 27.83 27.94 28.05 0.00 0.13 0.24 0.35 0.46 180970066 Indiana Marion 29.13 29.27 29.40 29.51 29.62 0.00 0.14 0.28 0.38 0.49 171191007 Illinois Madison 30.66 30.76 30.82 30.88 30.95 0.00 0.10 0.16 0.22 0.28 550790010 Wisconsin Milwaukee 33.13 33.18 33.23 33.29 33.34 0.00 0.05 0.11 0.16 0.21 390170003 Ohio Butler 27.33 27.42 27.53 27.65 27.76 0.00 0.09 0.20 0.32 0.43 170316005 Illinois Cook 34.82 34.86 34.90 34.95 34.99 0.00 0.04 0.09 0.13 0.18 420031008 Pennsylvania Allegheny 25.62 25.76 25.89 26.03 26.17 0.00 0.14 0.27 0.41 0.54 261610008 Michigan Washtenaw 29.33 29.36 29.40 29.43 29.47 0.00 0.03 0.07 0.10 0.14 170312001 Illinois Cook 32.33 32.38 32.44 32.49 32.54 0.00 0.05 0.10 0.15 0.20 170310052 Illinois Cook 30.31 30.36 30.40 30.45 30.49 0.00 0.04 0.09 0.13 0.18 421330008 Pennsylvania York 33.91 33.94 33.97 34.00 34.03 0.00 0.03 0.06 0.08 0.11 261630015 Michigan Wayne 31.99 32.04 32.08 32.14 32.25 0.00 0.04 0.09 0.15 0.26 010732003 Alabama Jefferson 31.91 31.99 32.06 32.13 32.20 0.00 0.07 0.15 0.22 0.29 390618001 Ohio Hamilton 26.73 26.81 26.89 26.98 27.06 0.00 0.08 0.17 0.25 0.33 171190023 Illinois Madison 29.50 29.57 29.65 29.72 29.79 0.00 0.07 0.15 0.22 0.29 420031301 Pennsylvania Allegheny 26.15 26.23 26.30 26.38 26.45 0.00 0.08 0.15 0.23 0.30 391130032 Ohio Montgomery 24.62 24.70 24.79 24.87 24.96 0.00 0.09 0.17 0.26 0.34 420030116 Pennsylvania Allegheny 26.34 26.42 26.50 26.59 26.67 0.00 0.08 0.17 0.25 0.33 *The six sites are Allegheny, PA (64); Lancaster, PA (07); Wayne, MI (16 and 19); Cook, IL (16); and Lake, IN (22). **The eight sites include the six sites listed above as well as Cuyahoga, OH (38) and Wayne, MI (33).

### 28 Deliberative

– Do Not Cite or Quote Table 1 2 . Maximum PM2.5 

### DVs ( g/m3 

### ) in 2014 with Linked States at the Final Transport Rule Cost Threshold Level with Various Additional Amounts of Emissions Due to Variability

with the Home State Held Constant with No Variability at the 2014 Final Transport Rule Cost Threshold . Site ID State County Variability (%) Difference in air quality between the 0% variability level and another level. 0 5 10 15 20 0 5 10 15 20 avg. of 6 sites* 38.39 38.42 38.46 38.49 38.52 0.00 0.03 0.07 0.10 0.13 avg. of 8 sites** 37.62 37.66 37.69 37.73 37.76 0.00 0.03 0.07 0.10 0.14 max of all 2012 b ase c ase 48.63 48.69 48.78 48.87 48.95 0.00 0.10 0.21 0.31 0.41 420030064 Pennsylvania Allegheny 48.63 48.69 48.78 48.87 48.95 0.00 0.06 0.14 0.23 0.32 420030093 Pennsylvania Allegheny 34.80 34.88 34.95 35.03 35.10 0.00 0.08 0.15 0.23 0.30 390350038 Ohio Cuyahoga 35.41 35.42 35.44 35.46 35.47 0.00 0.02 0.03 0.05 0.06 261630016 Michigan Wayne 35.65 35.67 35.68 35.69 35.70 0.00 0.01 0.02 0.04 0.05 390350060 Ohio Cuyahoga 33.04 33.07 33.11 33.13 33.14 0.00 0.04 0.07 0.09 0.10 170311016 Illinois Cook 36.54 36.60 36.67 36.73 36.79 0.00 0.06 0.13 0.19 0.25 261630033 Michigan Wayne 35.23 35.29 35.35 35.41 35.47 0.00 0.06 0.12 0.18 0.24 180890022 Indiana Lake 36.51 36.55 36.58 36.60 36.62 0.00 0.05 0.07 0.09 0.11 540090011 West

Virginia Brooke 30.02 30.12 30.21 30.31 30.41 0.00 0.10 0.19 0.29 0.39 420710007 Pennsylvania Lancaster 37.18 37.18 37.18 37.19 37.19 0.00 0.00 0.01 0.01 0.02 390350045 Ohio Cuyahoga 27.60 27.64 27.69 27.74 27.79 0.00 0.05 0.09 0.14 0.19 390811001 Ohio Jefferson 28.03 28.11 28.19 28.27 28.35 0.00 0.08 0.16 0.25 0.33 261630019 Michigan Wayne 35.83 35.84 35.86 35.87 35.88 0.00 0.01 0.03 0.04 0.06 390350065 Ohio Cuyahoga 27.00 27.06 27.13 27.19 27.26 0.00 0.06 0.13 0.19 0.26 170313301 Illinois Cook 32.84 32.88 32.93 32.97 33.01 0.00 0.04 0.08 0.13 0.17 420070014 Pennsylvania Beaver 28.70 28.75 28.82 28.90 28.99 0.00 0.05 0.12 0.20 0.29 420033007 Pennsylvania Allegheny 28.81 28.87 28.93 29.00 29.06 0.00 0.06 0.13 0.19 0.26 010730023 Alabama Jefferson 32.12 32.15 32.19 32.22 32.26 0.00 0.03 0.07 0.10 0.14 550790026 Wisconsin Milwaukee 33.21 33.25 33.30 33.34 33.38 0.00 0.04 0.09 0.13 0.18 180970043 Indiana Marion 27.82 27.87 27.93 27.98 28.03 0.00 0.05 0.11 0.16 0.21 261470005 Michigan St Clair 33.38 33.40 33.42 33.44 33.45 0.00 0.02 0.04 0.05 0.07 550790043 Wisconsin Milwaukee 33.92 33.95 33.98 34.01 34.04 0.00 0.03 0.06 0.09 0.12 180890026 Indiana Lake 33.37 33.39 33.42 33.44 33.46 0.00 0.02 0.05 0.07 0.10 180970081 Indiana Marion 27.59 27.67 27.74 27.80 27.85 0.00 0.08 0.15 0.21 0.27 180970066 Indiana Marion 29.13 29.22 29.30 29.37 29.43 0.00 0.09 0.17 0.24 0.30 171191007 Illinois Madison 30.66 30.75 30.80 30.85 30.90 0.00 0.09 0.14 0.19 0.24 550790010 Wisconsin Milwaukee 33.13 33.17 33.22 33.26 33.31 0.00 0.04 0.09 0.13 0.18 390170003 Ohio Butler 27.33 27.39 27.48 27.56 27.65 0.00 0.06 0.15 0.24 0.32 170316005 Illinois Cook 34.82 34.85 34.88 34.91 34.93 0.00 0.03 0.06 0.09 0.12 420031008 Pennsylvania Allegheny 25.62 25.72 25.83 25.93 26.03 0.00 0.10 0.21 0.31 0.41 261610008 Michigan Washtenaw 29.33 29.35 29.38 29.40 29.42 0.00 0.02 0.05 0.07 0.09 170312001 Illinois Cook 32.33 32.38 32.42 32.46 32.50 0.00 0.04 0.08 0.12 0.16 170310052 Illinois Cook 30.31 30.34 30.37 30.40 30.43 0.00 0.03 0.06 0.09 0.12 421330008 Pennsylvania York 33.91 33.93 33.94 33.95 33.97 0.00 0.01 0.03 0.04 0.05 261630015 Michigan Wayne 31.99 32.01 32.02 32.04 32.06 0.00 0.02 0.03 0.05 0.06 010732003 Alabama Jefferson 31.91 31.94 31.97 32.00 32.03 0.00 0.03 0.06 0.09 0.12 390618001 Ohio Hamilton 26.73 26.79 26.85 26.91 26.97 0.00 0.06 0.12 0.18 0.24 171190023 Illinois Madison 29.50 29.56 29.62 29.68 29.73 0.00 0.06 0.12 0.17 0.23 420031301 Pennsylvania Allegheny 26.15 26.21 26.27 26.32 26.38 0.00 0.06 0.11 0.17 0.23 391130032 Ohio Montgomery 24.62 24.69 24.75 24.82 24.89 0.00 0.07 0.13 0.20 0.27 420030116 Pennsylvania Allegheny 26.34 26.40 26.46 26.53 26.59 0.00 0.06 0.12 0.19 0.25 *The six sites are Allegheny, PA (64); Lancaster, PA (07); Wayne, MI (16 and 19); Cook, IL (16); and Lake, IN (22). **The eight sites include the six sites listed above as well as Cuyahoga, OH (38) and Wayne, MI (33).

### Deliberative *The six sites are Allegheny, PA (64); Lancaster, PA (07); Wayne, MI (16 and 19); Cook, IL (16); and Lake, IN (22). **The eight sites include the six sites listed above as well as Cuyahoga, OH (38) and Wayne, MI (33). 

### Figure 2. Increase in maximum PM threshold level as the variability level is increased from 0 to 20 percent, individually for the eight sites (left panel) as well as for various combinations of sites (right panel). The combinations of sites can be found in Table 10. The state containing the receptor is held constant with no variability at the 2014 $2300/ton cost emission level. In conclusion, EPA found that, while allowing accommodate inherent fluctuations 10%), the downwind air quality impacts are small (i.e., 20%), the air quality impacts remain small. allowing substantial year -to - ye ar variability around a budget in any given state’s power sector emissions (i.e., 20%) , the unlikely to be compromised .  The variability due to bounded by

the examined, the resulting variation in air quality is small.  Higher levels of emission variability (beyond as year-to - ye ar such as could emission program downwind air quality

improvements emission reduction 29

Deliberative

– Do Not Cite or Quote *The six sites are Allegheny, PA (64); Lancaster, PA (07); Wayne, MI (16 and 19); Cook, IL (16); and Lake, IN (22). **The eight sites include the six sites listed above as well as Cuyahoga, OH (38) and Wayne, MI (33). 

### Figure 2. Increase in maximum PM 2.5 

### design value ( g/m 3 

### ) in 2014 at the $2300/ton cost threshold level as the variability level is increased from 0 to 20 percent, individually for the eight sites (left panel) as well as for various combinations of sites (right panel). The

sites can be found in Table 10. The state containing the receptor is held constant with no variability at the 2014 $2300/ton cost emission level. found that, while allowing for the limited variability fluctuations in reliable operation of the power sector (ty pically 10%), the downwind air quality impacts are small . At even higher levels of variability (i.e., 20%), the air quality impacts remain small. This suggests that even under a ye ar variability around a budget in any given state’s power the downwind air quality goals of the program are very The variability due to baseline power sector operation (ty picall bounded by

the variability

levels examined in this analys is . At all levels he resulting variation in air quality is small. Higher levels of emission variability (beyond what EPA has calculated here ye ar variability

due to b aseline operation of the power sector), theoretically occur under an unlimited interstate cap emission program , would not undermine the program’s achievement of downwind air quality

improvements . This is especially true if coupled emission reduction s in the state containing the receptor . *The six sites are Allegheny, PA (64); Lancaster, PA (07); Wayne, MI (16 and 19); Cook, IL (16); and Lake, IN (22).



### ) in 2014 at the $2300/ton cost threshold level as the variability level is increased from 0 to 20 percent, individually for the eight sites (left panel) as well as for various combinations of sites (right panel). The

sites can be found in Table 10. The state containing the receptor is held limited variability to operation of the power sector (ty pically of variability This suggests that even under a program ye ar variability around a budget in any given state’s power goals of the program are very

(ty picall y 10%) is At all levels what EPA has calculated here

power sector), interstate cap - and-trade not undermine the program’s achievement of

. This is especially

true if coupled with

### 30 Deliberative

– Do Not Cite or Quote  The heterogeneity of where, when, and how emission reductions occur (processes not explicitly

considered under the Transport Rule , such as intrastate shifting of emissions ) will possibly have larger downwind air quality impacts at a given receptor than the minor variations in air quality attributable to the small shifts in state - level emissions due to inherent ye arto -ye ar variability . As was described in the proposed Transp ort Rule “Power Sector Variability” TSD, the variations in downwind air quality found in this analysis of potential year-to - ye ar variability

in upwind EGU emissions are much smaller than documented year-to - ye ar variability

in air quality

(as measured at the receptors and expressed as the difference s between the average and maximum design values ) , indicating that other factors such as meteorology

play a much larger role in the variability of downwind air quality

outcomes (once emissions are broadly controlle d at a given level of stringency) . Consequently, EPA finds that allowing variation in emissions under the final variability limits in the Transport Rule allow s the power sector to address inherent fluctuations in electric generation without negatively aff

ecting downwind air quality .