### 1 Technical Support Document (TSD) for the Transport Rule Docket ID No. EPA

-HQ

- OAR

- 2009

- 0491



# Pow er Sector Variability

U.S. Environmental Protection Agency Office of Air and Radiation July 2010

### 2 Power Sector Variability This Technical Suppor

t Document (TSD) provides information in support of section IV.F, “Emission Reduction Requirements Including Variability”, in the preamble to the proposed Transport Rule. This TSD is organized as follows:
1. Introduction 2. Estimating year-to
- ye ar variab ility in emissions 3. Estimating variability

over a multi

- year time period 4. Results of an analys is done using the air quality assessment tool
1. Introduction. Section IV in the preamble of the proposed 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 PM2.5 

### NAAQS

. 

### As discussed in preamble section IV , the EPA has identified the emissions that must be prohibited by each state to eliminate the state’s significant contribution and interference with maintenance

. To facilitate implementation of the requirement that these emissions be eliminated, the EPA also developed SO 2 

### , annual NO X 

### , and ozone season NO X 

### state emissions budgets based on its projections of state
-by

- state power sector emissions in an average ye ar after the elimination of the prohibited emissions. 1 

### However, because of the unavoidable variability in baseline emissions

– resulting from the inherent variability in power s ystem operations

– state
- level emissions may vary somewhat from ye ar to year after all significant contribution and interference with maintenance that EPA has identified in the Transport Rule proposal are eliminated. This variability

in emissions occurs even when the emission rates of the units within the state do not change. For this reason, as discussed in preamble section IV.F, the EPA has determined that it is appropriate to develop variability limits for each state budget. These

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. Additionally, the EPA develope d ozone season NO X budgets for each state covered for the ozone NAAQS. Table III.A

- 1 in preamble section III lists the states that would be c overed for the PM 2.5 and/or ozone NAAQS

.

### 3

limits are used to identify the range of emissions that EPA believes may occur in each state following the elimination of all significant contribution and interference with maintenance. This TSD describe s the analyses that EPA performed to estimate the inherent variability in emissions from the power sector and to determine variability limits based on that inherent variability

. Preamble section IV.D discusses EPA’s proposed approach to quantify for each upwind state the emissions that significantly contribute to nonattainment or interfere with maintenance downwind for the existing ozone and PM2.5 

### NAAQS. Preamble section IV.E discusses the development of state emissions budgets for SO 2 

### , annual NO X 

### , and ozone season NO X 

### . Preamble section IV.F discusses the inherent variability in electric power system operations and proposes variability limits on emissions for each state covered by the proposed Transport Rule. As explained in section IV.F, the EPA propo ses to calculate variability

limits for each state and to use those variability limits in conjunction with the state budgets (which are based on expected average conditions) to provide limited emissions flexibility

. The Agency believes that because baseline emissions are variable, 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. As discussed in preamble section IV.F , the EPA proposes to use two variability limits: First, a “1

-ye ar” limit, based on the year-to
-ye ar variability in emissions relative to the proposed budgets. Second, a “3

- ye ar” limit, based on the variability in a three (consecutive) year average relative to the proposed budgets. The EPA determined 1

- year variability

limits that would apply

to a state’s emissions annually (or seasonally, for the ozone season) and 3

- ye ar variability limits that would apply

to a state’s annual (or ozone season) emissions on a 3

-ye ar rolling average basis. 2 

### Preamble section IV.F discusses EPA’ s rationale for implementing 1

- ye ar and 3

- year variability limits. Section IV.F also describes EPA’s proposed approach to calculating the proposed 1

- and 3

- ye ar limits and an alternative calculation approach. This TSD describes these approaches in more detail. Preamble section V.D describes the proposed remedy and two alternative remedies. In that section , EPA describes how the remedies would use variability limits in the

2 As discussed in the preamble, for purposes of emissions reductions requirements in the Transport Rule the EPA proposes to define the ozone season as May through September

.

### 4

implementation of assurance provisions designed to ensure that the necessary emissions reductions occur within each covered state . As discussed in preamble section V.D , the EPA proposes to apply

assurance provisions and variability limits starting in 2014 and, as further di scussed in the preamble, is also taking comment on whether to apply them starting in 2012. Preamble section IV.F presents proposed 1

- and 3

-ye ar variability limits for each state and alternative 1

- and 3

-ye ar limits calculated using the alternative approach. Table IV .F

- 1 in the preamble presents proposed and alternative 1

- and 3

- year variability limits on SO 2 

### emissions for each state. Table IV

. F

- 2 presents proposed and alternative 1

- and 3

- ye ar variability

limits on annual NO X 

### emissions. Table IV.F

- 3 pre sents proposed and alternative 1

- and 3

- ye ar variability limits on ozone season emissions. F or the alternative where the limits would apply starting in 2012 instead of 2014, Table IV.F

-4 in the preamble presents proposed and alternative 1

- and 2

- year varia bility limits. Preamble section IV.F explains that, for this alternative, EPA considered both 3

- ye ar average and 2

- year average variability limits and determined the 2

- ye ar limits are preferable

. Preamble section IV.F describes EPA’s proposed approach to determine 1

-ye ar variability

limits. As discussed in the preamble, the approach would determine 1

-ye ar limits based on the expected annual (or ozone season) variation in power sector emissions derived from historical variation in annual (or ozone season) power sector heat input in combination with projected controlled emissions rates. The preamble discusses two approaches to determine 1

-ye ar limits based on expected variation in power sector emissions. Section 2 in this TSD, titled “Estimating ye ar-to
- ye ar variability in annual emissions”, describes in greater detail the method EPA used to estimate expected variation in year-to
- ye ar power sector emissions and the proposed and alternative approaches to determine 1

- ye ar limits based on that expected variati on . As discussed in preamble section IV.F, after determining 1

- year variability limits, EPA used statistical methods to estimate multi

-ye ar ( 3

- ye ar and 2

- ye ar) average variability limits for covered state s based on each state’s 1

- year variability. Section 3 in this TSD, titled “Estimating variability over a multi

- ye ar time period”, describes the approach in greater detail than that provided in the preamble. As discussed in the preamble and in

### 5

section 3 in this TSD, the average variability of a multi

-year- average is the average variability

of a single year divided by the square root of the number of years in the multi

ye ar average. Thus, the variability of a 3

- ye ar average is equal to the annual variability divide d by

the square root of three. As discussed above and in preamble section IV.F , f or the alternative where the limits would apply

starting in 2012 instead of 2014, the EPA determined 1

- and 2

- year variability

limits. For this alternative EPA also considered 3

- ye ar variability limits instead of 2

- ye ar limits . Section 3 in this TSD compares the 3

- and 2

- ye ar limits and discusses why EPA determined that the 2

-ye ar limits are preferable

. Like the 3

- ye ar average variability

discussed above, the variability of a 2

-ye ar average is equal to the annual variability

divide d by

the square root of two (see section 3, below). Section 4 in this TSD presents the results of an analysis using the air quality assessment tool (AQAT)

.
2. Estimating year-to -year variability in emissions. This section describes the method that the Agency used to estimate the ye ar-to
- ye ar (“1

- ye ar”) variability

in annual SO 2 

### , annual NO X 

### and ozone season NO X 

### emissions. The method uses variation in heat input as a proxy for emissions . For an electric generating unit (EGU) fleet equipped with a constant set of control technologies and consistently using specific fuel ty

pes, the variability

in heat input would be directly related to variability

in emissions. This section in the TSD provides information on :
 The historical data set EPA e stablished on a state
-by

- state basis of ye arly heat input values applicable to each of the pollutants regulated in the Transport Rule (annual SO 2 

### , annual NO x 

### , and ozone season NO x

### ). These historical heat input values are used to estimate the inherent varia bility in emissions due to power s ystem operation

.  The method EPA developed (as well as an alternative method) to estimate the yearto
-ye ar variability

in the heat input values. The year-to
- ye ar variability in heat input is estimated on a state

-by

- state b asis

.  The approach EPA used to link heat input variability with projected pollutant emission rates to estimate the inherent variation in pollutant emissions.

### 6

 The method EPA developed to identify a single set of variability parameters that could be applied to all states in the program. (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) , not variation from day to day or month to month within a given year (or ozone season). Thus, EPA used total yearly 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 )

. As described in section IV.F.1.a of the preamble, EPA chose to examine historical variability in heat input rather than emissions because emissions have changed over time due to controllable factors such as fuel switching and installing new emissions controls. EPA is interested in describing inherent variation in 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 represent s a time period where there was substantial reporting of heat input and emissions data across many states and EGU source types for units in the Acid Rain Program (ARP ). When the analys is began in 2009, the last c omplete year of data available was for 2008

. A starting year of 2002 was selected for this analys is because in prior years (2000 and 2001) there had been large, uneven changes in annual heat input from fossil units for some states due, in part, to increas ed electricity demand and changes in composition of the power sector fleet . (Note, for instance, that all EGUs in the US affected by the ARP came under Phase 2 of the program starting in 2000)

. Consequently, incorporating data from ye ars prior to 2002 in the analys is would lead to the inclusion of a

### 7

single ye ar where there is large change in heat input that is not representative of ty pical ye ar-to
- ye ar variability, thereb y leading to overestimates of variability

. Since the objective is to estimate the inherent variability in heat input during time

-periods of relative constancy in the fleet composition , including time

-periods where there is uneven growth in electrical demand in the analys is would skew the variability estimates . Consequently, EPA chose 2002 for the start of the analys is to minimize that effect while still balancing the need to estimate the variance in heat input over as long a time period as possible

. 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 historical heat input for all units within each of three general categories 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 source category and for the total of all EGU source categories can be found in Table 1 and Table 2 , respectively . Ozone

- season heat input values for the total of all three EGU categories can be found in Table 3

. For each state, EPA assessed the inherent ye ar-to
-ye ar variability in annual and ozone season NO x 

### emissions using the total ye arly heat inputs summed across all three general EGU source typ es (Tables 2 and 3 , respe ctively) , while the Agency assessed ye ar-to
- ye ar variability in annual SO 2 

### emissions using the yearly heat inputs from just the coal

- fired EGU s (Table 1)

. EGUs built prior to 2002 that reported heat input and emissions data between 2002 and 2008 and new E GUs 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.

### 8

Table 1: Heat Input* (million mmbtu) f rom Coal

-Fired Units f

or Each Year.

State 2002 2003 2004 2 005 2006 2007 2008 Average HI Max. Difference from A verage AL 779.0 822.6 784.5 828.3 813.0 818.8 768.9 802.1 26.1 CT 18.1 28.3 30.9 30.2 31.5 26.1 30.5 27.9 3.5 DE 42.6 45.4 50.8 52.1 50.1 58.3 53.7 50.4 7.8 FL 700.6 735.9 659.3 697.0 711.4 707.3 660. 8 696.0 39.9 GA 781.2 787.4 808.8 871.6 872.5 910.6 861.1 841.9 68.7 IA 393.1 389.6 386.7 384.5 378.9 415.9 431.2 397.1 34.1 IL 930.9 961.9 1 , 027.1 1 , 002.1 1 , 002.1 1 , 030.5 1 , 032.9 998.2 34.7 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 26.1 KS 427.5 427.5 411.9 405.2 377.6 405.4 373.5 404.1 23.4 KY 946.5 931.1 938.1 970.3 987.0 977.9 978.5 961.3 25.7 LA 120.9 122.1 128.1 134.6 128.7 116.1 118.5 124.1 10.4 MA 108.9 103.9 101.7 115.2 107.2 114.8 101.7 107.6 7.5 MD 276.9 278.0 26 9.9 273.7 274.7 277.1 256.0 272.3 5.7 MI 700.1 706.9 709.7 721.1 699.7 736.0 714.4 712.5 23.4 MN 389.2 411.0 386.7 380.1 370.3 362.9 349.8 378.6 32.5 MO 724.5 769.6 770.8 783.6 783.5 762.0 732.8 761.0 22.6 NC 693.3 689.1 696.5 719.7 699.1 726.4 713.2 7 05.3 21.1 NE 227.0 238.3 231.2 238.3 234.0 222.4 233.8 232.1 6.2 NJ 80.0 75.3 79.6 92.2 81.9 77.8 62.8 78.5 13.7 NY 234.1 235.9 226.7 213.4 211.2 211.0 181.0 216.2 19.8 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 23.6 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 32.8 SC 381.5 384.7 401.4 407.7 410.4 422.6 417.1 403.7 19.0 TN 624.7 574.9 589.4 586.3 606.9 617.2 578.0 596.8 27.9 VA 340.5 312.5 296.7 298.4 288.0 298.9 264.2 299.9 40.6 WI 457.1 475.9 482.9 4 73.2 451.2 447.5 450.8 462.6 20.3 WV 898.8 898.5 851.0 861.2 852.4 883.3 851.6 871.0 27.8 

### * 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.

### 9

T able 2: Total Heat 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 Max. Difference from Average AL 901.0 916.0 904.6 936.6 964.1 1 , 000.0 938.9 937.3 62.7 CT 69.9 67.5 87.5 91.2 101.1 93.9 84.9 85.2 16.0 DE 52.6 53.3 59.1 60.9 55.6 66.4 60.5 58.3 8.0 FL 1 , 134.5 1 , 197.8 1 , 198.8 1 , 283.1 1 , 361.0 1 , 398.3 1 , 360.2 1 , 276.2 122.0 GA 841.9 822.4 853.4 948.0 968.5 1 , 033.0 958.2 917.9 115.1 IA 393.2 39 0.3 391.2 401.3 396.3 438.0 446.5 408.1 38.4 IL 976.1 982.7 1 , 046.2 1 , 053.8 1 , 038.2 1 , 086.2 1 , 059.9 1 , 034.7 51.5 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 29.8 KS 429.2 429.4 412.5 406.8 381.2 410.0 378.6 406.8 22.6 KY 962.4 93 5.1 942.4 988.3 999.0 997.2 987.8 973.2 25.8 LA 193.6 266.6 312.7 332.2 336.2 322.5 326.2 298.6 37.6 MA 159.8 221.4 221.5 244.4 262.4 276.4 242.9 232.7 43.7 MD 288.2 290.6 280.8 287.8 282.6 286.0 263.7 282.8 7.8 MI 726.9 725.1 727.3 760.5 730.7 776.5 7 43.8 741.5 34.9 MN 399.0 423.1 396.4 401.8 391.7 394.6 371.6 396.9 26.2 MO 752.1 788.7 792.2 813.6 812.5 800.6 770.3 790.0 23.6 NC 718.8 706.3 718.4 747.2 727.1 767.7 749.7 733.6 34.1 NE 228.4 240.5 233.5 245.3 240.7 231.9 240.3 237.2 8.1 NJ 139.3 132 .9 151.5 154.9 158.8 172.7 172.8 154.7 18.1 NY 288.3 297.3 309.9 322.9 380.6 393.1 366.6 337.0 56.1 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 37.9 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 83.2 SC 418.2 402.7 442.1 454.9 461.5 474.0 464.4 445.4 28.7 TN 635.7 580.5 592.1 592.6 613.2 623.8 582.3 602.9 32.8 VA 356.6 333.6 329.5 339.5 324.0 354.8 312.3 335.7 20.8 WI 475.3 494.1 498.4 528.5 492.3 497.4 490.0 496.6 31.9 WV 900.5 899.9 852.0 863.3 855.8 887 .1 853.3 873.1 27.3 

### * 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.

### 10

Table 3: Total Heat 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 Max. Difference from Average AL 427.4 428.8 423.6 427.2 456.9 469.9 432.5 438.1 31.9 AR 124.0 152.5 149.2 142.0 173.6 170.4 150.5 151.7 21.8 CT 33.3 28.7 43.5 42.8 46.3 44.4 38.4 39.6 6.7 DE 27.9 24.2 25.4 29.0 23.6 30.3 24.9 26.5 3.8 FL 548.6 554.9 563.3 615.1 639.0 656.3 646.0 603.3 53.0 GA 403.8 382.9 407.1 461.8 469.5 489.6 449.9 437.8 51.8 IL 454.9 430.2 449.6 478.0 460.8 474.6 4 52.6 457.2 20.8 IN 557.2 545.4 540.7 578.4 566.4 559.8 550.5 556.9 21.5 KS 183.2 180.9 176.8 180.6 172.2 177.1 160.7 175.9 7.3 KY 433.3 400.1 401.0 434.4 435.3 436.1 417.8 422.6 13.6 LA 94.7 121.7 144.9 149.9 161.8 150.7 152.2 139.4 22.3 MD 134.8 118. 8 121.8 131.5 123.3 126.8 117.1 124.9 9.9 MI 331.0 312.1 304.6 339.9 328.5 344.5 321.1 326.0 18.6 MS 117.8 116.2 129.5 137.3 147.7 155.9 143.2 135.4 20.6 NC 332.9 308.5 314.0 341.5 340.4 351.5 344.8 333.4 18.1 NJ 68.7 59.9 73.9 76.1 79.9 86.1 86.6 75.9 10.8 NY 126.1 121.4 136.3 152.5 174.7 170.5 158.9 148.6 26.1 OH 583.7 562.8 538.4 574.4 562.0 596.7 553.3 567.3 29.3 OK 204.5 223.4 220.0 248.4 244.6 241.1 247.9 232.8 15.6 PA 465.4 468.7 494.6 529.3 530.6 550.8 515.5 507.8 42.9 SC 203.1 178.4 204.8 214.1 215.8 222.7 222.0 208.7 14.0 TN 291.2 244.3 256.2 270.1 273.2 278.9 261.2 267.9 23.3 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 78.2 VA 160.0 146.9 150.9 154.3 151.5 167.9 144.5 153.7 14.2 WV 379.5 384.5 361.0 375.4 373.1 390.3 366.9 375.8 14.5 

### * 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.

### 11 (b) Estimating the state-by- state variability in heat input using historical heat input data sets. This subsection describ es the method used by EPA to estimate the variability in heat input for each state in the Transport Rule region

. EPA 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) using the “ standard deviation” while accounting for overall growth or decline in heat input over that time period

. This method is described in detail in this and the following subsections and was s elected for three reasons: First, it accounted for growth or decline in heat input over time. Second, a statistical approach (i.e., using the standard deviation) is less sensitive to data anomalies present in finite data sets. Third, a statistical approach also provides options for different levels of variability (i.e., different confidence levels). An alternative method was also examined, where the variability was defined as the difference between the maximum ye arly value and the average heat input value s over the time period. Both the preferred and alternative methods were applied on a state

-by

- state basis. The majority of this section focuses on the analys is for the preferred method using the standard deviation, while accounting for growth. Where appropriate, differences between the preferred and alternative method are described. In the preferred method, for each state, it was important to account for trends (growth or decline) in heat input over the 7

- ye ar time period. After accounting 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. F or each state, to account for trends , a simple least

- squares linear regression equation was fit to the heat input data as a function of time. 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 year “x” , m is the slope of the line (with units of million mmbtu/year) , and b is the “y- intercept” (the heat input value when the line is extrapolated to x = 0). The value of r2 

### describes how well the data fit that line. Large r2 

### values are 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

### 12

regression line that are substantially different than zero) . For states with relatively constant heat input values, low r2 

### values are strongly correlated with small values for the slope of the regression line. For these states, low r2 

### values are indicators of large year-to
- ye ar variability

(relative to small amounts of growth over time). For example, for Delaware (Table 6), the slope of the linear regression is small, and the r2 

### value is also very small. The conclusion is that for Delaware and other states with similar slopes and r2 

### values , the ye ar-to
- ye ar variation could likely have been ade quately

characterized without using the regression equation (which would account for growth in heat input over time)

. For each state, the slopes of the regression equations , y- intercepts, and r2 

### values, as well as the heat input estimated using the regre ssion equations , can be found in Tables 4 , 5 , and 6

. Using the regression equation for each state, ye arly heat input values were estimated for each state for each ye ar (between 2002 and 2008) in Tables 4, 5, and 6

. Year

-to
- year variation 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)

.

### 13

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 CT 1.19

-2 , 365 0.30 24.4 25.6 26.7 27.9 29.1 30. 3 31.5 DE 2.08

-4 , 126 0.75 44.2 46.3 48.3 50.4 52.5 54.6 56.7 FL -4.44 9 , 600 0.12 709.4 704.9 700.5 696.0 691.6 687.2 682.7 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 LA - 0.67 1 , 468 0.05 126.2 125.5 124.8 124.1 123.5 122.8 122.1 MA 0.20

-290 0.01 107.0 107.2 107.4 107.6 107.8 108.0 108.2 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 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 4 62.6 458.8 455.0 451.1 WV - 6.09 13 , 082 0.36 889.2 883.2 877.1 871.0 864.9 858.8 852.7

### 14

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 CT 3.98

- 7,895 0.48 73.2 77.2 81.2 85.2 89.1 93.1 97.1 DE 1.65

- 3,256 0.54 53.4 55.0 56.7 58.3 60.0 61.6 63.3 FL 44.3 0

- 87,543 0.89 1,143.3 1,187.6 1,231.9 1,276.2 1,320.5 1,364.8 1,409.1 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 LA 19.04

- 37,877 0.63 24 1.4 260.5 279.5 298.6 317.6 336.6 355.7 MA 14.29

-28,415 0.67 189.8 204.1 218.4 232.7 247.0 261.3 275.6 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 41 2.3 407.2 402.0 396.9 391.7 386.6 381.5 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 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

### 15

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 CT 1.77

- 3,507 0.35 34.3 36.1 37.8 39.6 41.4 43.2 44.9 DE 0.04

- 54 0.00 26.4 26.4 2 6.4 26.5 26.5 26.6 26.6 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 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 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 WV - 0.50 1,387 0.01 377.3 376.8 376.3 375.8 375.3 374.8 374.3



### On a state

-by

- state basis, the difference between the actual heat input and the estimated heat input using the regression equation was calculated for each year. 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 yearly differences are positive, while others are negative. Assessing the differences between actual and estimated heat input across all years for each state (and pollutant), a representative difference was estimated using the “standard deviation”

.

### 16 The standard deviation is defined as the square root of the variance (the sum of the square of the differences 3 

### 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 analys es for the preferred method

. In using the standard deviation as a representative difference, we assume that: (1) differences between the actual and modeled heat inputs are “normally”distributed; (2) yearly mean values are independent; and, (3) distribution of hourly values is the same (i.e. , the “within

-ye ar” variance is the same for each ye ar). For each state, the standard deviation of the differences is a measure of the year-to
ye ar (1

- year) variance in heat input . In essence, it suggests that, on average, 68% of all the ye ar- specific heat input values could be expected to be within one standard deviation from the expected value (either higher or lower). The standard deviation in the heat input in million mmbtu rounded to three significant digits (as well as a percentage of the average heat input value) can be found in Tables 7 , 8, and 9

.

3 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 dif ference between the actual heat input and the estimated heat input (using the regression equation) for each year.

### 17

Table 7: Difference B etween Heat Input (million mmbtu) M easured and E stimated for C oal

-F ired Units for E ach Year U sing 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 1) Standard

Deviation as 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 CT - 6.2 2.7 4.1 2.3 2.3

-4.2

- 1.0 3.9 27.9 0.141 7.7 0.276 DE - 1.6

- 0.8 2.5 1.6

-2.4 3.7

-2.9 2.6 50.4 0.051 5.1 0.100 FL - 8.8 31.0

-41.2 1.0 19.8 20.1

-21.9 25.9 696.0 0.037 50.7 0.073 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 LA - 5.2

- 3.4 3.3 10.4 5.2

- 6.7

- 3.6 6.4 124.1 0.051 12.5 0.100 MA 1.9

- 3.3

- 5.7 7.5

- 0.6 6.7

- 6.5 5.7 107.6 0.053 11.1 0.104 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

- 1 1.1 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 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

Table 8: Difference B etween T otal Heat 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 as 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 CT - 3.3

- 9.7 6.3 6.1 12.0 0.8

- 12.2 8.9 85.2 0.105 17.5 0.206 DE - 0.7

- 1.8 2.4 2.6

-4.4 4.7

-2.8 3.3 58.3 0.056 6.5 0.110 FL - 8.8 10.1

- 33.1 6.9 40.5 33.4

-48.9 32.8 1 , 276.2 0.026 64.4 0.051 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 LA -47.8 6.1 33.2 33.6 18.6

- 14.1

-29.5 31.5 298.6 0.106 61.8 0.208 MA - 30.0 17.3 3.1 11.7 15.5 15.1

- 32.7 21.9 232.7 0.094 42.9 0.184 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 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

### 19

Table 9: Difference Between 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 as 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 0.1 67 CT - 1.0

- 7.4 5.6 3.2 4.9 1.2

- 6.6 5.3 39.6 0.133 10.3 0.261 DE 1.6

-2.2

- 1.0 2.5

- 3.0 3.7

- 1.7 2.6 26.5 0.097 5.0 0.190 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 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 MS 1.1

- 6.8 0.3 1.9 6.1 8.1

- 10.8 6.7 1 35.4 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 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

### 20 The standard deviati on 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) . Several different levels were examined (notably the 95 th 

### and 99 th 

### percent confidence levels) . As an illustrative example, the two
-tailed 9 5 th 

### percent confidence level indicate s that we could expect, on average, that 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. For this analys is, we focus on results for the two
-tailed 95 th 

### percent confidence level for each state

. EPA made the policy decision that, assessed across the large number of states included in the program, the 95 th 

### percent two
-tailed confidence level 4 

### was the appropriate confidence level

. 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 and without electric relia bility problems arising. When the alternative method for calculating variability (the maximum difference over the time period) was applied, the results for many states were comparable to the results using the 95 th 

### confidence level, showing that the propo sed variation was similar to the maximum measured historical values. The major differences were for states that had growth (or decline) in heat input over the time period. The 95 % upper confidence level heat inputs and percentages (the 95 % heat input diff erence divided by

the average heat input over the 2002

- 2008 time period) can be found in Tables 7 , 8, and 9

. The results from the alternative method using the maximum difference in heat input from the 7

- year average heat input can be found in Tables 1, 2 , and 3

. For states where there were differences between the proposed and alternative approaches, the difference was often a result of substantial growth in heat input over the 7

- ye ar time period. For example, as seen in Table 1, for Virginia, the heat input in 2002 is 340

. 5 million mmbtu. By 2008, the heat input had decreased to 264

. 2 million mmbtu. The average heat input over this time period was 299 .9 million mmbtu. Consequently, the

4 The two -tailed 95 th percent confidence level is the equivalent of the 97.5 th upper (single

-tailed) confidence level. Hereafter in this TSD, the “95 th“ percent confidence level refers to the two -tailed 95 th percent confidence level.

### 21

maximum difference from the average was 40.6 million mmbtu (Table 1)

. Using the proposed approach (the 95% confidence level) and accounting for decline in heat input over time using the regression equation , the estimated variability in heat input is substantially

reduced (21.7 million mmbtu, as seen in Table 7 ). (c) Esti mation of the emission rates projected in 2014 used to estimate variability in emissions from the year-to -year variability in historical heat input. The final step in estimating state
-by

- state ye ar-to
-ye ar (1

- year) variability in emissions is to convert the ye ar-to
- ye ar variability in heat input into variability in pollutant emissions. This was done by multiplying the estimated variability in heat input by a representative pollutant emission rate

. For each state , the state

- specific 95 th 

### percent confidenc e level heat input variability value (converted to mmbtu) was multiplied by a state
- specific emission rate (tons of pollutant per mmbtu) . The resulting value is the 95 th 

### percent confidence level variability emission value (in tons of pollutant). State

- specific modeled emission rate s were used in the calculation. The rate s were based on IPM emissions and heat inputs projected to occur in 2014 when levels of controls similar to that for the proposed remedy are applied to each state

. Modeled emission rate s were used, rather than historical emission rates , because EPA wanted to estimate ye ar-to
ye ar variability

in emissions within the same time and conditions as those of the proposed transport rule when new emissions controls could potentially be in operati on. Using IPM estimates for 2014, the emission rates for annual SO 2 

### were calculated using modeled estimates of total heat input and total emissions from coal

- fired units, while annual and ozone season NO x 

### were calculated using modeled estimates of total he at input and total emissions from all three EGU categories

. EPA derived the state
- specific emission rates from IPM projections parsed for 2014; the state
- specific emission rates for annual SO 2 

### , annual NO x 

### and ozone season NO x 

### are from IPM runs that required controls at $1,600/ton for SO 2 

### , $500/ton for annual NO X 

### , and $500/ton for ozone season NO X 

### , respectively . 5 

### Tables 10 through 14 list, for each state and the three EGU sectors defined

5 EPA developed the proposed variability limits in parallel with developing the overall control requirements. As such, while these IPM runs used to develop the variability limits assumed reasonable levels of emissions

### 22

above , the modeled emissions, the modeled fuel usage (the sum of the heat input) , and the emission rates

. For each state, the state

- specific 95 th 

### percent confidence level heat input variability

value was multiplied by the state
- specific emission rate . The resulting 95% confidence level emission variability value , in units of tons of emissions , can be found in Tables 10 , 12 , and 14 for annual SO 2 

### , annual NO x 

### , and ozone season NO x 

### , respectively (see the columns labeled “ 95% confidence level variability value ”)

. 6 

### The 95% confidence level emission variability value was normalized by dividing by each state’s emissions, resulting in a “coefficient of variation” value. In this TSD, the value is referred to as the “percentage” variability

value. The 95% confidence level emission variability value can be compared to the difference between the emissions for a particular year and the emissions budget . We expect the difference to be less than the 95% emissions variability value, on average, 9 5 percent of the time. Consequently, the 95% emissions variability value represents a ye arto
-ye ar variability

that is sufficient, most of the time, to encompass the inherent variability in power sector generation

. That is, sources within a state should not exceed the state’s budget i f variability

is the only

reason for emissions increases ab ove the budget. Subsequent sections in this TSD discuss how the 95% confidence level variability values were used to construct the variability limits applied in the proposed Transport Rule assurance provisions.

control, they are not identical to the final control strategy chosen for the proposed rule. Whereas IPM output files report aggregated results for "model" plants (i.e., aggregates of generating units with similar operating characteristics), parsed files show IPM results at the generating unit level. The IPM runs that are the bases for the 2014 parsed files used to derive the state

- specific rates are designated “TR_SO 2_1600”, “T R_NO X_500”, and “TR_NO X_OS_500” for SO 2 , annual NO X , and ozone season NO X rates, respectively. The IPM runs and parsed files can be found in the docket; Docket ID No. EPA

-HQ

- OAR

- 2009

- 0491

. 6 Ta bles 10, 12, and 14 also show resulting estimated emission variability values base d on the alternative approach (see columns labeled “Alternative approach value”).

### 23

Table 1 0 . State

-b y- State SO 2 

### Emissions (thousand short tons), Fuel Use (million mmbtu), and Emission Rate Estimated in IPM f or Units Greater Than or Equal to 25 MW

. Also Shown are the Estimated 95 th 

### Percent Confidence SO 2 

### Tonnage and Percentage Values as well as the Alternative Approach SO 2 

### Tonnage and Percentage Values

.

State Annual SO 2

Emissions From Coal

Fired Boilers (thousand tons) Annual f

uel use from coal

- fired boilers (million mmbtu) SO 2 Emission

Rate (lbs/mmbtu) 95%

Confidence Level Variability Value (tons) 95%

Confidence Level Variability Value (percentage) Alternative

Approach Value (tons) Alternative

Approach Value (percentage)
AL 103.38 852.69 0.242 5 , 693 5.9% 3 , 170 3.3% CT 2.72 42.05 0.129 498 27.6% 228 12.6% DE 7.38 62.66 0.236 595 10.0% 923 15.6% FL 80.73 1 , 084.86 0.149 3 , 775 7.3% 2 , 968 5.7% GA 94.39 977.67 0.193 4 , 801 5.9% 6 , 631 8.2% IL 159.15 1 , 140.91 0.279 5 , 816 4.1% 4 , 840 3.5% IN 231.11 1 , 380.68 0.335 5 , 733 2.7% 4 , 375 2.1% IA 87.27 466.65 0.374 5 , 363 7.3% 6 , 378 8.6% KS 44.99 380.57 0.236 2 , 555 5.3% 2 , 771 5.8% KY 158.9 3 1 , 083.19 0.293 3 , 628 2.5% 3 , 767 2.7% LA 92.78 387.40 0.479 2 , 992 10.0% 2 , 500 8.4% MD 42.67 436.37 0.196 1 , 186 4.5% 557 2.1% MA 8.44 111.12 0.152 844 10.4% 572 7.0% MI 196.34 770.31 0.510 5 , 359 2.9% 5 , 975 3.3% MN 40.10 386.00 0.208 1 , 807 4.5% 3 , 371 8 .6% MO 185.27 823.10 0.450 10 , 337 6.1% 5 , 087 3.0% NE 68.91 287.50 0.479 2 , 730 4.9% 1 ,489 2.7% NJ 14.23 155.23 0.183 1 ,449 20.2% 1 , 255 17.4% NY 45.11 261.98 0.344 2 ,450 6.7% 3 ,401 9.1% NC 88.43 931.48 0.190 1 , 809 2.7% 2 , 001 3.0% OH 200.28 1 ,433.48 0.2 79 6 , 781 3.7% 3 , 299 1.8% PA 150.88 1 , 362.73 0.221 4 , 325 3.7% 3 , 636 3.1% SC 73.44 487.02 0.302 1 ,457 2.4% 2 , 858 4.7% TN 109.09 617.16 0.354 6 , 688 6.3% 4 , 927 4.7% VA 60.60 407.67 0.297 3 , 222 7.3% 6 , 039 13.5% WV 138.89 1 , 029.88 0.270 4 , 652 3.9% 3 , 748 3.2 % WI 78.48 495.03 0.317 3 , 630 4.9% 3 , 215 4.4%

### 24

Table 1 1 . Annual NO x 

### Emissions (thousand short tons) and Fuel U se (million mmbtu) E stimated in IPM , for Units Greater Than or Equal to 25 MW

.

State Annual NO x Emissions Annual Fuel Usage Coal

Fired Boilers (thousand tons) Combined

Cycle Turbines (thousand tons) Simple

Cycle CT (thousand tons) Oil & Gas

Boilers (thousand tons) All Units

(thousand tons) Coal

Fired Boilers (million mmbtu) Combined

Cycle Turbines (million mmbtu) Simple

Cycle CT (million mmbtu) Oil & Gas

Boilers (million mmbtu) All Units

(million mmbtu) AL 60.805 0.888 0.030 61.724 856.091 122.084 1.449 979.624 CT 2.051 0.716 0.043 0.000 2.809 42.050 103.449 1.986 0.000 147. 485 DE 3.725 0.584 0.017 0.000 4.327 60.215 26.258 0.685 0.000 87.158 FL 98.493 7.814 2.839 12.350 121.496 1 , 057.478 645.095 56.530 107.598 1 , 866.701 GA 44.781 0.437 0.128 0.000 45.346 984.344 57.183 6.240 0.000 1 , 047.766 IL 55.470 0.123 0.203 0.000 55 .796 1 , 134.540 9.555 7.355 0.000 1 , 151.449 IN 111.365 0.058 0.047 0.000 111.470 1 , 392.974 7.880 1.393 0.000 1 ,402.248 IA 50.717 0.034 0.140 50.891 480.860 3.991 1.687 486.539 KS 37.399 0.000 0.000 37.399 377.821 0.000 0.000 377.821 KY 72.244 0.011 72.256 1 , 085.560 0.289 1 , 085.849 LA 34.268 0.754 0.090 1.197 36.310 384.835 75.389 3.188 11.737 475.149 MD 19.643 0.026 0.105 0.000 19.774 428.182 2.058 5.361 0.000 435.601 MA 5.584 1.171 0.019 0.000 6.774 111.117 204.806 0.742 0.000 316.666 MI 63. 816 0.575 0.171 0.000 64.562 812.765 23.096 5.095 0.000 840.956 MN 32.037 0.069 0.075 0.000 32.181 373.950 10.424 3.089 0.000 387.463 MO 78.419 0.018 0.000 78.437 843.016 2.056 0.000 845.071 NE 32.967 0.010 0.151 0.000 33.128 298.044 1.494 1.887 0.000 301.425 NJ 10.622 0.952 0.517 0.000 12.091 155.481 112.882 7.173 0.000 275.536 NY 13.240 2.356 0.651 7.561 23.808 261.921 239.245 9.342 202.341 712.849 NC 59.417 0.112 0.145 59.674 914.612 3.404 6.215 924.231 OH 99.323 0.159 0.145 99.627 1 ,450.382 18.752 4.609 1 ,473.743 PA 112.489 0.657 0.074 0.000 113.220 1 , 353.586 105.537 2.365 0.000 1 ,461.488 SC 34.301 0.201 0.030 0.000 34.532 491.149 18.015 1.338 0.000 510.501 TN 28.272 0.403 28.675 616.230 6.243 622.473 VA 27.524 0.491 0.189 0.000 28.2 04 399.917 46.858 5.747 0.000 452.522 WV 54.084 0.000 54.084 1 , 010.968 0.000 1 , 010.968 WI 39.771 0.031 0.152 0.000 39.953 521.020 4.279 0.608 0.000 525.908

### 25

Table 1 2 . State

-by

- S tate Annual NO x 

### Emission Rate C alculated U sing the IPM E stimates Presented in Table 11. Also S hown Are the E stimated 95 th 

### P ercent C onfidence NO x 

### T onnage and P ercentage Values as well as the Alternative Approach NO x 

### T onnage and P ercentage Values

.

State Annual

NO x Emission Rate (lb/mmbtu) 95%

Confidence Level Variability Value (tons) 95%

Confidence Level Variability Value (percentage) Alternative

Approach Value (tons) Alternative

Approach Value (percentage) AL 0.126 2 , 912 4.9% 3 , 951 6.7% CT 0.038 333 20.6% 305 18.8% DE 0.099 321 11.0% 398 13.7% FL 0.130 4 , 190 5.1% 7 , 941 9.6% GA 0.087 3 , 359 8.4% 4 , 980 12.5% IL 0.097 2 , 010 3.9% 2 , 496 5.0% IN 0.159 2 , 699 2.5% 2 , 369 2.3% IA 0.209 2 , 591 6.1% 4 , 015 9.4% KS 0.198 2 , 166 5.3% 2 , 237 5.6% KY 0.133 2 , 283 3.5% 1 , 716 2.6% LA 0.153 4 , 723 20.8% 2 , 873 12.6% MD 0.091 586 4.5% 353 2 .8% MA 0.043 918 18.4% 934 18.8% MI 0.154 2 , 390 4.1% 2 , 682 4.7% MN 0.166 1 , 691 5.1% 2 , 178 6.6% MO 0.186 3 , 832 5.3% 2 , 191 3.0% NE 0.220 1 , 226 4.7% 889 3.4% NJ 0.088 412 6.1% 795 11.7% NY 0.067 1 , 187 10.6% 1 , 874 16.7% NC 0.129 1 , 673 3.5% 2 , 199 4.6% OH 0.135 3 , 928 4.5% 2 , 559 2.9% PA 0.155 3 , 732 4.1% 6 , 447 7.2% SC 0.135 1 , 603 5.3% 1 , 938 6.4% TN 0.092 1 , 904 6.9% 1 , 512 5.4% VA 0.125 1 , 738 8.2% 1 , 297 6.2% WV 0.107 1 , 886 4.1% 1 , 463 3.1% WI 0.152 2 , 333 6.3% 2 , 426 6.4%

### 26

Table 1 3

. Ozone S eason NO x 

### Emi ssions (thousand short tons) and Fuel U se (million mmbtu) E stimated in IPM for Units Greater Than or Equal to 25 MW

.

State Ozone Season NO x Emissions Ozone Season Fuel Usage Coal

Fired Boilers (thousand tons) Combined

Cycle Turbines (thousand tons) Simple

Cycle CT (thousand tons) Oil & Gas

Boilers (thousand tons) All Units

(thousand tons) Coal

Fired Boilers (million mmbtu) Combined

Cycle Turbines (million mmbtu) Simple

Cycle CT (million mmbt u) Oil & Gas Boilers (million mmbtu) All Units

(million mmbtu) AL 26.193 0.615 0.030 26.838 370.7 85.0 1.4 457.1 AR 10.730 1.021 0.036 0.000 11.787 169.1 60.5 1.0 0.0 230.6 CT 0.894 0.292 0.017 0.000 1.203 18.3 42.4 0.6 0.0 61.3 DE 1.564 0.255 0 .016 0.000 1.835 25.3 11.4 0.6 0.0 37.3 FL 46.385 3.962 1.367 12.580 64.294 465.6 321.2 30.4 110.1 927.3 GA 19.583 0.344 0.128 0.000 20.055 431.9 43.0 6.2 0.0 481.2 IL 23.649 0.106 0.203 0.000 23.958 485.2 8.1 7.3 0.0 500.6 IN 47.093 0.049 0.047 0.000 47.188 589.4 6.2 1.4 0.0 597.1 KS 16.200 0.000 0.000 16.200 163.5 0.0 0.0 163.5 KY 29.843 0.011 29.855 450.2 0.3 450.5 LA 14.992 0.638 0.090 1.197 16.918 168.3 55.2 3.2 11.7 238.5 MD 8.217 0.016 0.095 0.000 8.328 178.3 1.1 4.0 0.0 183.4 MI 27.53 9 0.498 0.175 0.000 28.212 352.6 16.6 5.2 0.0 374.4 MS 7.651 0.167 0.049 0.000 7.866 67.5 26.4 2.5 0.0 96.4 NJ 4.522 0.537 0.446 0.000 5.506 65.2 56.3 5.4 0.0 126.9 NY 5.759 1.168 0.641 3.946 11.514 114.1 113.2 7.4 95.6 330.3 NC 25.468 0.084 0.132 25. 684 390.7 2.4 5.3 398.4 OH 42.052 0.110 0.145 42.308 608.8 13.0 4.6 626.4 OK 19.566 1.895 0.165 2.378 24.003 257.2 75.4 2.0 16.0 350.7 PA 48.089 0.310 0.074 0.000 48.474 579.1 50.5 2.4 0.0 631.9 SC 14.729 0.164 0.030 0.000 14.922 211.6 14.8 1.3 0.0 227.8 TN 11.542 0.423 11.965 251.4 6.4 257.8 TX 56.728 6.237 0.645 4.824 68.434 1 , 022.3 375.3 14.2 118.7 1 , 530.5 VA 11.927 0.308 0.125 0.000 12.360 172.2 26.3 3.6 0.0 202.0 WV 24.235 0.000 24.235 429.9 0.0 429.9 WI 17.104 0.027 0.152 0.000 17. 283 223.8 3.7 0.6 0.0 228.1

### 27

Table 1 4

. State

-by

- S tate Ozone S eason NO x 

### Emission Rate s C alculated U sing the IPM E stimates Presented in Table 13. Also S hown Are the E stimated 95 th 

### P ercent C onfidence NO x 

### T onnage and P ercentage Values as well as the Alternative Approach NO x 

### T onnage and P ercentage Values for Ozone S eason .

State Ozone

S eason NO x Emission Rate (lb/mmbtu) 95%

C onfidence Level Variability Value (tons) 95%

C onfidence Level Variability Value (percentage) Alternative

Approach Value (tons) Alternative

Approach Value (percentage) AL 0.117 1 , 702 6.7% 1 , 871 7.3% AR 0.102 1 , 292 16.7% 1 , 116 14.4% CT 0.039 203 26.1% 131 16.9% DE 0.098 248 19.0% 187 14.3% FL 0.139 2 , 037 4.9% 3 , 674 8.8% GA 0.083 1 , 945 10.6% 2 , 159 11.8% IL 0.096 1 , 355 6.3% 994 4.5% IN 0.158 1 , 952 4.5% 1 , 697 3.9% KS 0.198 873 5.1% 721 4.1% KY 0.133 2 , 028 7.3% 900 3.2% LA 0.142 1 , 849 18.6% 1 , 585 16.0% MD 0.091 527 9.2% 451 8.0% MI 0.151 2 , 016 8.2% 1 ,400 5.7% MS 0.163 1 , 073 9.8% 1 , 679 15.2% NJ 0.087 341 10.4% 466 14.2% NY 0.070 741 14.3% 909 17.5% NC 0.129 1 ,443 6.7% 1 , 166 5.4% OH 0.135 2 , 569 6.7% 1 , 982 5.2% OK 0.137 1 , 150 7.3% 1 , 068 6.7% PA 0.153 2 , 747 7.1% 3 , 293 8.5% SC 0.131 1 , 221 9.0% 915 6.7% TN 0.093 1 ,408 11.4% 1 , 082 8.7% TX 0.089 3 , 104 5.9% 3 ,498 6.6% VA 0.122 960 10 .2% 868 9.2% WV 0.113 1 , 102 5.3% 816 3.8%

### 28 (d) Procedure for identifying a single set of variability parameters (a tonnage and a percentage limit) to uniformly apply to all states in the program. From the state

-by

- state 1

-ye ar 95 th 

### confidence level tonnage and percentage emission variability

values in Tables 10 , 12 , and 14 , EPA identified a single set of variability limits (i.e., a tonnage and a percentage) for each pollutant (i.e., SO 2 

### , annual NO X 

### , and ozone season NO X

### ) to apply to all covered states. F or this analysis, EPA assumes that, on average, each state is meeting its proposed budget, but is also subject to inherent year-to
- ye ar variability in electric power system operations that could lead to short

-term increases (or decreases) in emissions up to the variability limits. In identifying a single set of percentage and tonnage variability limits to apply

across all states, EPA assumes that for some future year, each state would experience conditions such that it would need to utilize (but not exceed) its 1

-ye ar 95% confidence level emissions variability tonnage value

. Thus, EPA has identif ied a set of minimum variability limits (a tonnage and a percentage) that when compared against the 95% confidence level tonnage and percentage values for each state, all states could (and would be required to) meet at least one of the two limits (the tonnage or the percentage)

. Preamble section IV.F provides EPA’s rationale for identifying a single tonnage and percentage combination to apply to all covered states. Preamble section IV.F also provides EPA’s rationale for identifying both a tonnage limit and a percentage limit . A s explained in section IV.F , the effect of identifying both a tonnage and percentage is to ensure that each state is allowed adequate inherent variability while minimizing the total amount of emissions allowed ; this approach addresses the difficulty that smaller states with fewer units could face if only percentages were used to set the limits (or that larger states with many

units could face if only tonnages were used)

. Most of the details of EPA’s approach to determine the 1

- year variability limits are provided in section IV.F in the preamble . This TSD presents some additional information. To identify the 1

- year tonnage and percentage limits , EPA looked at a wide range of percentage and tonnage combinations, and chose for further investigation combinations that provided states sufficient variability (based on historic variability) while minimizing the total allowed emissions. For annual SO 2 

### and ozone season NO x 

### , the tonnage limit criteria were examined in 300 ton increments, while the percentage criteria were applied in

### 29

2% increments. For annual NO X 

### , the tonnage limit criteria were applied in 500 ton increments, while the percentage criteria were applied in 5% increments. All combinations of these criteria were considered. For each pairing of percentage and tonnage limits, the first step is to determine, based on the estimates of each state’s historical variability values , whether any of the states would exceed both of the limits (i.e., both the tonnage and the percentage)

. If more than four states were not able to meet one of the limits, this number of states was recorded (this can be seen in grayed squares in Tables 15, 16, and 17 for annual SO 2 

### , annual NO x 

### and ozone season NO x 

### , respectively) . If four or fewer states were not able to meet one of the limits, the state abbreviations are listed in Tables 15, 16, and 17 (the cells are also grayed). If one of the percentage and tonnage limits could be met by each state (and thereb y the combination of limits would be applicable to all states) , both limits were applied to the state, and the larger of the two limits was chosen. This would then be the emission variability limit used for that state
. The combinations of limits that were applicable (i.e., where the 95% confidence level variability values for all states was below at least one of the two limits) are shown in Tables 15, 16, and 17 by the white shading of cells, while combinations of limits where the 95% confidence level variability value for at least one state exceeds both limits are shown by the gray shading of cells. The difference between the emission variability limit and the state
- specific 95% confidence level emissions variability value (from Tables 10, 12, and 14) for each state was calculated, and, f

or each percentage and tonnage pairing , the total difference for all states was summed. The total differences for all states for each combination of tonnage and percentage limits c an be seen in white cells in Tables 15 , 16 , and 17 for annual SO 2 

### , annual NO x 

### and ozone season NO x 

### , respectively. 7 

### In these tables, white cells represent possible combinations of percentage and tonnage limits that could successfully be met b y all states included in the proposed rule. The optimal solution (marked in yellow in the table) was one where: (a) all states included in the proposed rule are able to meet at least one of the criteria, and (b) the sum of the total emissions was minimized.

7 Similar tables based on the alternative approach (where the max

. heat input over the time period was found relative to the average value over the period) are in Appendix A.

### 30

Table 15. The Effects o f Various Combinations o f the Proposed Upper 95% Confidence Level Tonnage and Percentage Variability Limits on Annual EGU SO 2 

### Emissions (See Notes Below)

. P ercentage Limit Tonnage L imit 2 4 6 8 10 12 14 16 18 20 1 , 000 24 16 8 LA, NJ, NJ, NJ, NJ, NJ, NJ, NJ, 1 ,300 23 15 8 LA, NJ, NJ, NJ, NJ, NJ, NJ, NJ, 1 , 600 21 14 7 LA, 121,876 165,006 208,135 251,265 294,395 337,549 1 ,900 19 13 7 LA, 123,076 166,206 209,335 252,465 295,595 338,725 2 ,200 19 13 7 LA, 124,276 167,406 210, 535 253,665 296,795 339,925 2 ,500 18 12 6 LA, 125,476 168,606 211,735 254,865 297,995 341,125 2 , 800 16 10 6 LA, 126,845 169,806 212,935 256,065 299,195 342,325 3 , 100 15 9 5 86, 605 128,452 171,006 214,135 257,265 300,395 343,525 3 ,400 14 8 FL, IA, MO, TN, 88,897 130,252 172,449 215,335 258,465 301,595 344,725 3 ,700 12 7 FL, IA, MO, TN, 91,444 132,074 174,056 216,552 259,665 302,795 345,925 4 , 000 11 6 IA, MO, TN, 94,281 134,174 175,856 218,052 260,865 303,995 347,125 4 ,300 11 6 IA, MO, TN, 97,417 136,565 177,656 219,661 262,156 305,195 348,325 4 , 600 10 6 IA, MO, TN, 100,861 139,123 179,644 221,461 263,656 306,395 349,525 4 ,900 8 5 IA, MO, TN, 104,461 141,895 181,833 223,261 265,266 307,760 350,725 5 ,200 8 5 IA, MO, TN, 108,304 144,895 184,23 3 225,113 267,066 309,260 351,925 5 ,500 6 AL, IL, MO, TN, MO, TN, 112,429 148,190 186,802 227,213 268,866 310,871 353,363 5 , 800 IL, MO, OH, TN, IL, MO, TN, MO, TN, 116,629 151,719 189,509 229,500 270,666 312,671 354,863 6 , 100 MO, OH, TN, MO, TN, MO, TN, 121,187 155,319 192,509 231,900 272,683 314,471 356,476 6 ,400 MO, OH, TN, MO, TN, MO, TN, 125,987 159,123 195,662 234,481 274,783 316,271 358,276 6 ,700 MO, OH, MO, MO, 130,956 163,129 198,977 237,181 277,168 318,152 360,076 7 , 000 MO, MO, MO, 136,056 167,329 202,577 240,122 279,568 320,252 361,876 7 ,300 MO, MO, MO, 141,156 171,529 206,177 243,135 282,160 322,435 363,676 7 , 600 MO, MO, MO, 146,256 176,127 209,942 246,435 284,860 324,835 365,722 7 ,900 MO, MO, MO, 151,511 180,927 213,842 249,836 287,736 327,235 367,822 8 ,200 MO, MO, MO, 156,911 185,763 218,029 253,436 290,736 329,839 370,103 8 ,500 MO, MO, MO, 162,311 190,863 222,229 257,036 293,907 332,539 372,503 8 , 800 MO, MO, MO, 167,980 195,963 226,429 260,762 297,2 07 335,349 374,903 9 , 100 MO, MO, MO, 173,680 201,063 231,067 264,662 300,694 338,349 377,518 9 ,400 MO, MO, MO, 179,489 206,163 235,867 268,730 304,294 341,380 380,218 9 ,700 MO, MO, MO, 185,696 211,282 240,667 272,930 307,894 344,680 382,963 10 , 000 MO, MO, MO, 191,996 216,682 245,670 277,130 311,581 347,980 385,963 10 ,300 MO, MO, MO, 198,296 222,082 250,770 281,330 315,481 351,553 388,963 10 , 600 186,027 186,027 188,625 204,596 227,482 255,870 286,007 319,430 355,153 392,152 10 , 900 194,1 27 194,127 195,825 210,896 233,118 260,970 290,807 323,630 358,753 395,452 11 , 200 202,227 202,227 203,564 217,196 238,818 266,070 295,607 327,830 362,400 398,811 11 , 500 210,327 210,327 211,364 223,801 244,518 271,170 300,477 332,030 366,300 402,411 11 , 8 00 218,427 218,427 219,164 230,701 250,404 276,452 305,577 336,230 370,200 406,011 12 , 100 226,527 226,527 226,964 237,601 256,638 281,852 310,677 340,947 374,330 409,611 12 , 400 234,627 234,627 234,764 244,501 262,938 287,252 315,777 345,747 378,530 413,2 19 12 , 700 242,727 242,727 242,727 251,401 269,238 292,652 320,877 350,547 382,730 417,119 13 , 000 250,827 250,827 250,827 258,301 275,538 298,256 325,977 355,347 386,930 421,019 13 ,300 258,927 258,927 258,927 265,201 281,838 303,956 331,077 360,384 391,1 30 425,031 13 , 600 267,027 267,027 267,027 272,101 288,138 309,656 336,223 365,484 395,887 429,231

*Numbers in white cells represent the sum (across all states) of the differences in emissions between the state

- specific 95% confidence level variability values and the state

- specific variability limit (in tons); the variability limit selected for each state is the larger of either the percentage limit or the tonnage limit. ** If the cell i s grey, it means that there is a state or several states whose state

- specific 95% confidence level variability values exceed both the tonnage and percentage limits . The cell either lists the states that could exceed the limits, or lists the number of states. ** * If the cell i s yellow, this is the combination tonnage and percentage limits that minimize the sum of the differences between the state
- specific 95% confidence level variability values and the state

- specific variability limit (in tons)

.

### 31

Table 16. The Effects o f Various Combinations o f the Proposed Upper 95% Confidence Level Tonnage and Percentage Variability Limits on Annual EGU NO x 

### Emissions (See Notes Below)

. P ercentage Limit Tonnage L imit 5 10 15 20 25 30 35 40 45 50 1 , 000 12 LA, NY, LA, LA, 224,069 280,207 336,368 392,653 448,937 505,222 1 ,500 11 LA, LA, LA, 225,323 281,212 337,346 393,484 449,622 505,761 2 , 000 7 LA, LA, LA, 227,129 282,712 338,601 394,490 450,622 506,761 2 ,500 5 LA, LA, LA, 229,129 284,680 340,230 395,990 451,879 507,768 3 , 000 FL, GA,

LA, MO, LA, LA, LA, 231,326 286,680 342,230 397,781 453,379 509,268 3 ,500 FL, LA,

MO, LA, LA, LA, 234,079 288,816 344,230 399,781 455,331 510,881 4 , 000 FL, LA, LA, LA, LA, 237,079 291,420 346,306 401,781 457,331 512,881 4 ,500 LA, LA, LA, LA, 240,079 294,420 348,806 403,796 459,3 31 514,881 5 , 000 76,672 98,561 140,230 189,970 243,079 297,420 351,760 406,296 461,331 516,881 5 ,500 89,877 107,933 146,230 194,258 246,302 300,420 354,760 409,101 463,786 518,881 6 , 000 103,377 117,683 152,673 199,208 250,118 303,420 357,760 412,101 466 ,441 521,276 6 ,500 116,877 128,211 160,138 204,649 254,118 306,587 360,760 415,101 469,441 523,781 7 , 000 130,377 139,211 168,266 210,514 258,666 310,267 363,760 418,101 472,441 526,781 7 ,500 143,877 150,471 177,153 216,576 263,666 314,267 366,872 421,10 1 475,441 529,781 8 , 000 157,377 161,971 186,461 223,105 269,092 318,448 370,415 424,101 478,441 532,781 8 ,500 170,877 173,748 195,961 230,420 274,798 323,123 374,415 427,157 481,441 535,781 9 , 000 184,377 186,036 205,836 238,391 280,798 328,123 378,415 4 30,657 484,441 538,781 9 ,500 197,877 198,968 216,336 246,745 287,001 333,535 382,793 434,563 487,442 541,781 10 , 000 211,377 211,968 227,128 255,745 293,537 339,083 387,581 438,563 490,942 544,781 10 , 500 224,877 224,968 238,128 264,989 300,806 345,083 392,581 442,638 494,712 547,781 11 , 000 238,377 238,377 249,267 274,489 308,645 351,083 397,978 447,138 498,712 551,228 11 , 500 251,877 251,877 260,767 283,989 316,692 357,426 403,478 452,039 502,712 554,860 12 , 000 265,377 265,377 272,267 293,989 325,337 36 3,969 409,367 457,039 506,983 558,860 12 , 500 278,877 278,877 283,934 304,489 334,337 371,192 415,367 462,421 511,496 562,860 13 , 000 292,377 292,377 295,934 315,045 343,517 378,898 421,367 467,921 516,496 566,860 13 , 500 305,877 305,877 308,365 326,045 35 3,017 386,898 427,851 473,651 521,496 571,328 14 , 000 319,377 319,377 321,263 337,045 362,517 395,155 434,401 479,651 526,864 575,954 14 , 500 332,877 332,877 334,263 348,064 372,017 403,929 441,578 485,651 532,364 580,954 15 , 000 346,377 346,377 347,263 35 9,564 382,142 412,929 449,152 491,776 537,936 585,954 15 , 500 359,877 359,877 360,263 371,064 392,642 422,045 457,152 498,276 543,936 591,307 16, 000 373,377 373,377 373,377 382,564 403,142 431,545 465,152 504,833 549,936 596,807 16, 500 386,877 386,877 38 6,877 394,120 413,961 441,045 473,618 511,964 555,936 602,307 17, 000 400,377 400,377 400,377 406,120 424,961 450,545 482,521 519,464 562,200 608,220 17, 500 413,877 413,877 413,877 418,194 435,961 460,127 491,521 527,405 568,700 614,220 18 , 000 427,377 42 7,377 427,377 430,694 446,961 470,296 500,574 535,405 575,265 620,220 18 , 500 440,877 440,877 440,877 443,558 458,361 480,796 510,074 543,581 582,349 626,220 19 , 000 454,377 454,377 454,377 456,558 469,861 491,296 519,574 552,113 589,849 632,625 19 , 500 46 7,877 467,877 467,877 469,558 481,361 501,878 529,074 561,113 597,659 639,125 20 , 000 481,377 481,377 481,377 482,558 492,861 512,878 538,574 570,113 605,659 645,697 20 , 500 494,877 494,877 494,877 495,558 504,361 523,878 548,252 579,113 613,659 652,735 2 1 , 000 508,377 508,377 508,377 508,558 516,305 534,878 558,449 588,602 622,044 660,235 21 , 500 521,877 521,877 521,877 521,877 528,305 545,878 568,949 598,102 630,706 667,912 22 , 000 535,377 535,377 535,377 535,377 540,523 557,158 579,449 607,602 639,706 67 5,912

*Numbers in white cells represent the sum (across all states) of the differences in emissions between the state

- specific 95% confidence level variability values and the state

- specific variability limit (in tons); the variability limit selected for each state is the larger of either the percentage limit or the tonnage limit. ** If the cell i s grey, it means that there is a state or several states whose state

- specific 95% confidence level variability values exceed both the tonnage and percentage limits . The cell either lists the states that could exceed the limits, or lists the number of states. ** * If the cell i s yellow, this is the combination tonnage and percentage limits that minimize the sum of the differences between the state
- specific 95% confi dence level variability values and the state

- specific variability limit (in tons)

.

### 32

Table 17. The Effects o f Various Combinations o f the Proposed Upper 95% Confidence Level Tonnage and Percentage Variability Limits on Ozone Season EGU N O x 

### Emissions (See Notes Below)

. Percentage Limit Tonnage L imit 0 2 4 6 8 10 12 14 16 18
800 20 20 20 15 8 5 AR, LA, AR,

LA, AR,

LA, LA, 1 , 100 17 17 17 13 6 AR, GA, LA, TN, AR,

LA, AR,

LA, AR,

LA, LA, 1 ,400 12 12 12 9 GA, LA, MI, TN, GA, LA,

TN, LA, LA, LA, LA, 1 ,700 10 10 10 7 GA, LA, MI, GA, LA, LA, LA, LA, LA, 2 , 000 6 6 6 KY, MI, OH, PA, MI, 27,836 35,240 43,315 51,653 60,326 2 ,300 OH, PA,

TX, OH, PA,

TX, OH, PA,

TX, OH, PA, 27,326 32,529 39,180 46,694 54,787 63,096 2 , 600 PA, TX, PA, TX, PA, TX, PA, 33,326 37,824 43,491 50,524 58,149 66,258 2 ,900 TX, TX, TX, 36,882 39,326 43,628 48,591 54,724 61,909 69,603 3 ,200 44,115 44,115 44,115 44,115 45,528 49,628 54,086 59,554 66,069 73,320 3 ,500 51,615 51,615 51,615 51,615 52,338 55,628 59 ,931 64,725 70,517 77,413 3 , 800 59,115 59,115 59,115 59,115 59,538 61,628 65,931 70,348 75,617 81,666 4 , 100 66,615 66,615 66,615 66,615 66,738 68,081 71,931 76,234 80,910 86,580 4 ,400 74,115 74,115 74,115 74,115 74,115 74,993 77,931 82,234 86,610 91,680 4 ,700 81,615 81,615 81,615 81,615 81,615 82,193 84,034 88,234 92,536 97,172 5 , 000 89,115 89,115 89,115 89,115 89,115 89,393 90,645 94,234 98,536 102,872 5 ,300 96,615 96,615 96,615 96,615 96,615 96,615 97,649 100,234 104,536 108,839 5 , 600 104,115 104,1 15 104,115 104,115 104,115 104,115 104,849 106,588 110,536 114,839 5 ,900 111,615 111,615 111,615 111,615 111,615 111,615 112,049 113,267 116,536 120,839 6 ,200 119,115 119,115 119,115 119,115 119,115 119,115 119,249 120,305 122,569 126,839 6 ,500 126,615 126,615 126,615 126,615 126,615 126,615 126,615 127,505 129,141 132,839 6 , 800 134,115 134,115 134,115 134,115 134,115 134,115 134,115 134,705 135,889 138,839 7 , 100 141,615 141,615 141,615 141,615 141,615 141,615 141,615 141,905 142,960 145,094 7 ,400 149 ,115 149,115 149,115 149,115 149,115 149,115 149,115 149,115 150,160 151,694 7 ,700 156,615 156,615 156,615 156,615 156,615 156,615 156,615 156,615 157,360 158,510 8 , 000 164,115 164,115 164,115 164,115 164,115 164,115 164,115 164,115 164,560 165,616 8 ,30 0 171,615 171,615 171,615 171,615 171,615 171,615 171,615 171,615 171,760 172,816 8 , 600 179,115 179,115 179,115 179,115 179,115 179,115 179,115 179,115 179,115 180,016 8 ,900 186,615 186,615 186,615 186,615 186,615 186,615 186,615 186,615 186,615 187,216 9 ,200 194,115 194,115 194,115 194,115 194,115 194,115 194,115 194,115 194,115 194,416 9 ,500 201,615 201,615 201,615 201,615 201,615 201,615 201,615 201,615 201,615 201,616 9 , 800 209,115 209,115 209,115 209,115 209,115 209,115 209,115 209,115 209,115 209 ,115 10 , 100 216,615 216,615 216,615 216,615 216,615 216,615 216,615 216,615 216,615 216,615 10 , 400 224,115 224,115 224,115 224,115 224,115 224,115 224,115 224,115 224,115 224,115 10 , 700 231,615 231,615 231,615 231,615 231,615 231,615 231,615 231,615 231 ,615 231,615 11 , 000 239,115 239,115 239,115 239,115 239,115 239,115 239,115 239,115 239,115 239,115 11 ,300 246,615 246,615 246,615 246,615 246,615 246,615 246,615 246,615 246,615 246,615 11 , 600 254,115 254,115 254,115 254,115 254,115 254,115 254,115 254 ,115 254,115 254,115 11 , 900 261,615 261,615 261,615 261,615 261,615 261,615 261,615 261,615 261,615 261,615 12 , 200 269,115 269,115 269,115 269,115 269,115 269,115 269,115 269,115 269,115 269,115 12 , 500 276,615 276,615 276,615 276,615 276,615 276,615 276 ,615 276,615 276,615 276,615 12 , 800 284,115 284,115 284,115 284,115 284,115 284,115 284,115 284,115 284,115 284,115 13 , 100 291,615 291,615 291,615 291,615 291,615 291,615 291,615 291,615 291,615 291,615 13 , 400 299,115 299,115 299,115 299,115 299,115 299 ,115 299,115 299,115 299,115 299,115

*Numbers in white cells represent the sum (across all states) of the differences in emissions between the state

- specific 95% confidence level variability values and the state

- specific variability limit (in tons); the variability limit selected for each state is the larger of either the percentage limit or the tonnage limit. ** If the cell i s grey, it means that there is a state or several states whose state

- specific 95% confidence level variability values exceed both t he tonnage and percentage limits . The cell either lists the states that could exceed the limits, or lists the number of states. ** * If the cell i s yellow, this is the combination tonnage and percentage limits that minimize the sum of the differences between the state
- specific 95% confidence level variability values and the state

- specific variability limit (in tons)

.

### 33 From the tables above, EPA determined that a number of tonnage and percentage combinations resulted in fairly similar total emissions. For e ach pollutant, a percentage limit of 10 percent coupled with a tonnage limit specific to the pollutant was a combination amongst those that would result in the lowest total emissions, so EPA chose 10 percent. The tonnage limits for each pollutant, as shown below, were established 8



### . The procedure used to identify

the se tonnage and percentage limits ensures that every state is able to meet at least one of the limits, while minimizing total EGU emissions

. The resulting 1

- ye ar tonnage and percentage limits (which are also presented in the preamble) are as follows:

 SO 2 

### – 1,700 tons or 10 percent of state’s budget

 Annual NO X 

### – 5,000 tons or 10 percent of state’s budget

 Ozone season NO X 

### – 2,100 tons or 10 percent of state’s budget As described in the preamble, af ter determining for each pollutant a 1

- ye ar tonnage and 1

- year percentage limit, EPA assigned each state one of these values

– either the tonnage limit or the percentage limit, whichever was greater for that state. Inother words, for SO 2 

### , every

state has a 1

-ye ar variability limit of 1,700 tons or 10 percent of the state’s SO 2 

### budget, whichever is greater. For annual NO X 

### , every state has 1

- ye ar variability limit of 5,000 tons or 10 percent of the state’s annual NO X 

### budget, whichever is greater. And, for ozone season NO X 

### , every state has a 1

- ye ar variability limit of 2,100 tons or 10 percent of the state’s ozone season NO X 

### budget, whichever is greater

. For example , Connecticut’s annual SO 2 

### budget is 3,059 tons, and 10 percent (the percentage limit for SO 2

### ) of 3,059 tons is 306 tons . Because 1,700 tons (the tonnage limit for SO 2

### ) is greater than 306 tons , Connecticut ’s 1

-ye ar SO 2 

### variability limit is 1,700 tons. The 1

- ye ar variability

limits for SO 2 

### , annual NO X 

### , and ozone season NO X 

### emissions for each covered state that result from this approach are presented in section IV.F in the preamble. As discussed in preamble section IV.F, the EPA also requests comment on an alternative calculation method for determining 1

-ye ar variability limits . The alternative

8 EPA developed the proposed variability limits in parallel with developing the overall control requirements. As such, while the particular “optimal” 1

-year tonnage and percentage limits presented here are close to the optimal values (presented in Tables 15, 16, and 17), they are not exact because during the development of the limits, changes were made in the states covered by the proposed rule. The IPM modeling and CAMx air quality modeling for the proposed rule used these values. As discussed in the preamble, EPA requests comment on the variability limits.

### 34

method would use the results of the proposed method but add a “ ceiling percentage ” equal to the maximum 95% confidence level percentage of variability among all covered states as observed in the historic heat input data described previously . The percentage variability limits for all states are shown in Tables 10, 12, and 14 for annual SO 2 

### , annual NO x 

### and ozone season NO x 

### , respectively

. The alternative 1

-ye ar variability limits resulting from this calculation method are presented in preamble section IV.F. EPA explained in the preamble its rationale for considering this alternative calculation method. The ceiling percentages for each pollutant, based on the historic data, are as follows:

 SO 2 

### – 28 percent of state’s budget (equal to the value for Connecticut)

 Annual NO X 

### – 21 percent of state’s budget (equal to the value for Louisiana)

 Ozone season NO X 

### – 27 percent of state’s budget (equal to the value for Connecticut) Under this alternative calculation method, for SO 2 

### emissions, a state’s 1

- ye ar variability limit would be 1,700 tons as long as 1,700 tons is between 10 and 28 percent of the state’s SO 2 

### emissions budget. If 1,700 tons is greater than 28 percent of the state’s SO 2 

### budget, then the state’s 1

-ye ar variability limit is set at 28 percent of the state’s SO 2 

### budget. If 1,700 tons is less than 10 percent of the state’s SO 2 

### budget, then the state’s 1

- ye ar variability

limit is set at 10 percent of the state’s SO 2 

### budget. The alternative calculation method would be applied to determine annual and ozone season NO X 

### 1

- ye ar variability

limits in the same manner as for the SO 2 

### limits. Tables 18 , 19 , and 20 demonstrate application of this alternative method to determining 1

- year variability limits for SO 2 

### emissions, annual NO X 

### emissions, and ozone season NO X 

### emissions, respectively. In Table 18 , the first column lists the state and the second column lists the state’s 2014 SO 2 

### emissions budget. The third column lists, for each state, 10 percent (the percentage limit for SO 2

### ) of the state’s SO 2 

### budget. The fourth column lists the tonnage limit for SO 2 

### emissions, which is 1,700 tons for all states , as discussed above. The fifth column lists, for each state, 28 percent (the percentage ceiling for SO 2

### ) of the state’s SO 2 

### budget. And finally, the sixth column lists the state’s

### 35

alternative 1

- ye ar variability limit. The columns in Tables 19 and 20 follow the same pattern but for annual NO X 

### and ozone season NO X 

### emissions , respectively . Again , using Connecticut’s 1

- year SO 2 

### variability limit as an example , Connecticut’s annual SO 2 

### budget is 3,059 tons. Ten percent (the percentage limit for SO 2

### ) of 3,059 tons is 306 tons. Twenty

- eight percent (the percentage ceiling for SO 2

### ) of 3,059 tons is 857 tons. Because 1,700 tons (the tonnage limit for SO 2

### ) is greater than 85 7 tons , Connecticut ’s 1

- ye ar SO 2 

### variability limit is set at 857 tons , as shown in Table 18

. In contrast, using the proposed approach would result in a 1

-ye ar SO 2 

### variability limit of 1,700 tons for Connecticut, as discussed above and in preamble section V.F.

Table 18

. Application of Alternative Calculation Method for Determining 1

-Year Variability

Limits on SO 2 

### Emissions for 2014 and Later .
State 2014

SO 2 Annual Emissions Budget (tons) 10 Percent

of SO 2 Budget (tons) SO 2

Tonnage Limit (tons) 28 Percent

of SO 2 Budget (tons) Alternative

1

-Year Limit (tons) Alabama 161,871 16,187 1,700 45,324 16,187 Connecticut 3,059 306 1,700 857 857 Delaware 7,784 778 1,700 2,180 1,700 District of Columbia 337 34 1,700 94 94 Florida 161,739 16,174 1,700 45,287 16,174 Georgia 85,717 8,572 1,700 24,001 8,572 Illinois 151,530 15,153 1,700 42,428 15,153 Indiana 201,412 20,141 1,700 56,395 20,141 Iowa 86,088 8,609 1,700 24,105 8,609 Kansas 57,275 5,728 1,700 16,037 5,728 Kentucky 113,844 11,384 1,700 31,876 11,384 Louisiana 90,477 9,048 1,700 25,334 9,048 Maryland 39,665 3,967 1,700 11,106 3,967 Massachusetts 7,902 790 1,700 2,213 1,700 Michigan 155,675 15,568 1,700 43,589 15,568 Minnesota 47,101 4,710 1,700 13,188 4,710 Missouri 158,764 15,876 1,700 44,454 15,8 76 Nebraska 71,598 7,160 1,700 20,047 7,160 New Jersey 11,291 1,129 1,700 3,161 1,700 New York 42,041 4,204 1,700 11,771 4,204 North Carolina 81,859 8,186 1,700 22,921 8,186 Ohio 178,307 17,831 1,700 49,926 17,831 Pennsylvania 141,693 14,169 1,700 39 ,674 14,169 South Carolina 116,483 11,648 1,700 32,615 11,648 Tennessee 100,007 10,001 1,700 28,002 10,001 Virginia 40,785 4,079 1,700 11,420 4,079 West Virginia 119,016 11,902 1,700 33,324 11,902 Wisconsin 66,683 6,668 1,700 18,671 6,668

### 36

Table 19

. Application of Alternative Calculation Method for Determining 1

-Year Variability

Limits on NO X 

### Annual Emissions for 2014 and Later

.

State 2014

NO X Annual Emissions Budget (tons) 10 Percent

of NO X Annual Budget (tons) NO X

Annual Tonnage Limit (tons) 21 Percent

of NO X Annual Budget (tons) Alternative

1

-Year Limit (tons) Alabama 69,169 6,917 5,000 14,525 6,917 Connecticut 2,775 278 5,000 583 583 Delaware 6,206 621 5,000 1,303 1,303 District of Columbia 170 17 5,000 36 36 Florida 120,001 12,000 5,000 25, 200 12,000 Georgia 73,801 7,380 5,000 15,498 7,380 Illinois 56,040 5,604 5,000 11,768 5,604 Indiana 115,687 11,569 5,000 24,294 11,569 Iowa 46,068 4,607 5,000 9,674 5,000 Kansas 51,321 5,132 5,000 10,777 5,132 Kentucky 74,117 7,412 5,000 15,565 7,412 Louisiana 43,946 4,395 5,000 9,229 5,000 Maryland 17,044 1,704 5,000 3,579 3,579 Massachusetts 5,960 596 5,000 1,252 1,252 Michigan 64,932 6,493 5,000 13,636 6,493 Minnesota 41,322 4,132 5,000 8,678 5,000 Missouri 57,681 5,768 5,000 12,113 5,768 Nebraska 43,228 4,323 5,000 9,078 5,000 New Jersey 11,826 1,183 5,000 2,483 2,483 New York 23,341 2,334 5,000 4,902 4,902 North Carolina 51,800 5,180 5,000 10,878 5,180 Ohio 97,313 9,731 5,000 20,436 9,731 Pennsylvania 113,903 11,390 5,000 23,920 11,390 South Carolina 33,882 3,388 5,000 7,115 5,000 Tennessee 28,362 2,836 5,000 5,956 5,000 Virginia 29,581 2,958 5,000 6,212 5,000 West Virginia 51,990 5,199 5,000 10,918 5,199 Wisconsin 44,846 4,485 5,000 9,418 5,000

### 37

Table 2 0 . Application of Alternative Calculation Method for Determining 1

-Year Variability

Limits on NO X 

### Ozone Season Emissions for 2014 and Later

.

State 2014

NO X Ozone Season Emissions Budget (tons) 10 Percent

of NO X Ozone Season Budget (tons) NO X

Ozone Season Tonnage Limit (tons) 27 P ercent

of NO X Ozone Season Budget (tons) Alternative

1

-Year Limit (tons) Alabama 29,738 2,974 2,100 8,029 2,974 Arkansas 16,660 1,666 2,100 4,498 2,100 Connecticut 1,315 132 2,100 355 355 Delaware 2,450 245 2,100 662 662 District of Columbia 105 11 2, 100 28 28 Florida 56,939 5,694 2,100 15,374 5,694 Georgia 32,144 3,214 2,100 8,679 3,214 Illinois 23,570 2,357 2,100 6,364 2,357 Indiana 49,987 4,999 2,100 13,496 4,999 Kansas 21,433 2,143 2,100 5,787 2,143 Kentucky 30,908 3,091 2,100 8,345 3,091 Louisiana 21,220 2,122 2,100 5,729 2,122 Maryland 7,232 723 2,100 1,953 1,953 Michigan 28,253 2,825 2,100 7,628 2,825 Mississippi 16,530 1,653 2,100 4,463 2,100 New Jersey 5,269 527 2,100 1,423 1,423 New York 11,090 1,109 2,100 2,994 2,100 North Carolina 23,539 2,354 2,100 6,356 2,354 Ohio 40,661 4,066 2,100 10,978 4,066 Oklahoma 37,087 3,709 2,100 10,013 3,709 Pennsylvania 48,271 4,827 2,100 13,033 4,827 South Carolina 15,222 1,522 2,100 4,110 2,100 Tennessee 11,575 1,158 2,100 3,125 2,100 Texas 75,574 7,557 2,100 20,405 7,557 Virginia 12,608 1,261 2,100 3,404 2,100 West Virginia 22,234 2,223 2,100 6,003 2,223


### 3. Estimating variability over a multi-year time period. As described in section 2, for each state, EPA estimated the inherent ye ar-to
-ye ar (1

- year) variability in emissions at the 95% confidence level using the historical year-to
ye ar standard deviation in heat input along with a modeled emission rate. From these values EPA identified a set of variability limits (a percentage and tonnage) and assigned a particular proposed “1

- ye ar” variability tonnage limit to each state for each year under the proposed Transport Rule. As described in section IV

. F.1 of the preamble, for each state, EPA proposes to also assess the difference between e ach state’s emissions budget and a 3

- ye ar rolling average of its emissions , comparing it against a “3

- ye ar” variability limit

. This

### 38

section of the TSD describes the method EPA used to determine each state ’ s 3

- year variability

limit. EPA used a standard statistical method to estimate the variability that could be expected (on average) in a 3

- year average of a state’s yearly emissions, assuming that the emissions for each ye ar are independent of the following year. For this analysis, we assume that, for each state, the year-to
- ye ar variability in emissions is described using the 1

-ye ar values in section 2 of this TSD

. What is our expectation for how a 3

- ye ar average of emissions would compare to the emissions for a single year? We would expect that the emissions for a single ye ar could be higher or lower than the budget . What happens if we begin to average the emissions from multiple years? We would expect that we would average some years that are higher than the budget with some years that are lower than budget . The resulting average would be relatively close to the budget. Some variation from the budget value is expected, though , when multiple 3

-year time periods are examined

. How variable could we expect the 3

- ye ar average to be relative to the vari ability for a single year? The answer to these questions can be estimated with the standard deviation of the annual emissions (i.e., the year-to
- ye ar variability) and the number of years that are being averaged together (using some standard statistics equations along with some basic assumptions about normality and independence of the ye arly emission values ). The average variability

of a multi

-ye ar- average is the average variability of a single ye ar divided by

the square root of the number of years in the multi

-ye ar average. 9 

### Thus, the variability

of a 3

- ye ar average is equal to the annual variability divided by

the square root of three. EPA used this approach to determine 3

- year variability limits based on the 1

- ye ar limits. Most general introductory statistics textbooks state the general equation, and some describe a basic derivation. Derivation of the equation is found in those textbooks and the statistics literature. However, a rudimentary outline of some of the principles is described here. Usually, the problem is cast as combining the variances for either three different random samples of a variable, or alternatively, averaging three independent “variables”

9 Moore, David S. and George P. McCabe. Introduction to the Practice of Statistics . 2 nd ed. New York: W.H. Freeman and Company, 1993. p.395.

### 39

(where each ye ar in the three sequential years that are going to be averaged together would be a different “variable”). For this example, we will describe three independent yearly “variables” of the emissions. These ye arly variables are called Y 1 

### , Y2 

### , and Y 3 

### . Each of these yearly

variables could assume a particular emissions value for a particular ye ar. It is assumed that the individual realized yearly values are uncorrelated and independent from each other, that their average and variability can be described with a normal distribution classified b y a mean value (i.e., the budget value) and a standard deviation (i.e., the yearto
-ye ar standard deviation). For explanatory purposes, it will first be assumed without loss of generality that the distributions of hourly emissions values from year-to
-ye ar is identical (i.e. , the “within

- ye ar” variati on is the same) and that all three years have the same mean value and expected variation. The expected variance of the average of the three years of emissions can be estimated. If the ye ars are assumed to be uncorrelated, and random samples are drawn independently from each of the year

- specific distributions, the variance in the resulting estimate of the sample mean can be written as follows 10 

### :
) ( 3 1 ) ( 2 2 2 2 3

2 1 Y Y Y 

### X Var 

     

### where, Var ( X 

### ) = the variance of the mean of ye arly emission values from each of three independent ye ars, Yi 

### , where i =1 to 3; X 

### = (1/3) * (X 1 

### + X2 

### + X3 

### )
2 i Y 

###  = the year- specific variance of three independent years, Yi 

### , where i = 1 to 3 If the variance from each of the three years is equal (as would be the case if the state budget is not changing over the three

- ye ar time period) , the variance in the sample mean can be reduced to the following equation, n 2 

###  

### (where n, is the number of years in the average and is equal to 3) . This form of the equation is used by EPA in the proposed approach to calculate the 3

-ye ar variance in the sample mean from the ye ar-to
-ye ar variance.

10 http://en.wikipedia.org/wiki/Variance

### 40 Applying these equations on a state

-by

- state basis using the state

- specific 1

-ye ar emission variability limits and the mean values (the state emissions budgets), EPA calculated the 3

- ye ar variability limits that are presented in section IV.F in the preamble

. These limits are the 1

- ye ar limits divided by the square root of three. The 3

-ye ar limits can be derived from the state

- specific standard deviations and 95 % confidence level variability values

. The method for identifying a set of limits is identical (where all the values have been divided b y the square root of 3) . The results are identical (except all values are divided by

the square root of three). Consequently, EPA elected to simply divide the proposed 1

- ye ar limits by the square root of three. Similarly, EPA applied the equations above to define variability for the 2

- year variability limits presented as an alternative in preamble section IV.F

. These limits are the 1

-ye ar limits divided by

the square root of two (since we are interested in the variance of just two years, rather than three). As discussed in the preamble, the 2

- ye ar variability limits would be applied for the 15 SO 2 

### group 1 states, i.e., the more stringent SO 2 

### tier, if EPA were to finalize an alternative remedy that uses variability

limits for the years 2012 and 2013 (the Transport Rule’s first phase). As discussed in the preamble, EPA also considered, instead of 2

- ye ar average limits for the 15 SO 2 

### group 1 states in 2012 and 2013, 3

-ye ar average limits for these states starting in 2014 (for Phase 1) . EPA considered the alternative of 3

- year average limits starting in 2014 f

or these states because this is the approach EPA would apply to SO 2 

### group 2 states (and all states for NOX 

### emissions) in the event EPA were to finalize a remedy that uses variability limits in the first phase. The group 1 states have different SO 2 

### budgets in 2012 and 2013 than in 2014 and beyond. The equations described above can be applied to determine 3

- ye ar average limits for the case of the group 1 states whose SO 2 

### emission budgets change in 2014, in other words the situation where the mean value and the standard deviation changes in the third ye ar. As an illustrative example, consider a state with emission budgets of 1000, 1000, and 700 thousand tons of SO 2 

### in 2012, 2013, and 2014, respectively. The 1

- ye ar 95 th 

### percent upper confidence variability limit for this state is 10% of its ye arly budgets (or, 100, 100, and 70 for each of the three ye ars). The 1

- ye ar standard deviation variability for each year is the 95 th 

### percent upper confidence variability limit divided by

1.960. The values are 51,

### 41

51, and 35.7 thousand tons for each of the three years, respectively . For a 3

-ye ar limit in 2014, the situation is a little complicated because the expected mean value for the three ye ars is changing (because the base budgets are not constant for the three

-ye ar time period). First, the calculation of the 3

- ye ar variance uses the equation described above (for Var ( X 

### ) )

. In this case , the year- specific variances , 2 i Y 

###  , are not identical. Putting the values into the equation, the expected variance of the 3

-ye ar average is

Var ( X 

### ) = (1/3) 2 

### *(  2

### +  2

### +  2

### ) = 6481.7/3 2 

### = 720.2

. Thus , the standard deviation is the square root of the variance, or 26.8 thousand tons . The 95 % confidence level is 1.960 multiplied by the standard deviation (26.8) which equals 52.6 thousand tons of SO 2 

### . Thus, this is the variability that we would expect from the “average” value for the three ye ars (2012, 2013, and 2014). An e stimate of the mean value for the three years , X 

### , is simply the mean of the budgets for the three years , which is calculated as: X 

### =(1/3)*(1000+1000+700) =2700/3=900 thousand tons of SO 2 

### . EPA calculated the 3

- ye ar average of the 95 th 

### percent confidence level limits for 2014 for the group 1 states using these equations and compared these to the 2

- year average of the 95 th 

### percent confidence level limits that are presented in the preamble. These 3

- ye ar and 2

- year limits are shown in Table 21. As discussed in the preamble, EPA believes that the 2

-ye ar average limit approach is reasonable (and preferable if the alternative approach is chosen where variability limits are applied during the first phase) . EPA’s proposed remedy does not use variability limits during the first phase (2012 and 2013), however, as explained in the preamble, EPA is also taking comment on an alternative approach that would use variability

limits in Phase 1. The 2

-ye ar average limits presented in the preamble and shown in Table 21 are calculated based on the Phase 1 SO 2 

### emissions budgets and are intended for use in 2013. In contrast, the alternative 3

- year average limits shown in Table 21, while intended to limit SO 2 

### emissions in Phase 1, are impacted (reduced) b y the tighter Phase 2 SO 2 

### budgets that apply to these states starting in 2014. If it is ultimately

decided to use variability limits in Phase 1 , EPA believes it is more appropriate to base the multi

-year average limits for Phase 1 on the stringency of the Phase

### 42

1 emissions budgets, not the stringency of the Phase 2 budgets, thus it is preferable to use the 2

-ye ar limits.
Tabl e 21 . Phase 1 Variability Limits for SO 2 

### Group 1 States: Comparison of 2

-Year Average to Alternative 3

-Year Average Limits o f the 95 th 

### Percent Confidence Level. SO 2 Group 1 State SO 2 Annual Emissions Budgets (tons) Phase 1 Multi-Year

Variability Limits (tons) 2012 2014 2 -Year Limits* 3 -Year

Limits**
Georgia 233,260 85,717 16,494 11,361 Illinois 208,957 151,530 14,775 11,070 Indiana 400,378 201,412 28,311 20,033 Iowa 94,052 86,088 6,65 1 5,281 Kentucky 219,549 113,844 15,524 11,023 Michigan 251,33 7 155,675 17,772 12,935 Missouri 203,689 158,764 14,403 10,964 New York 66,542 42,041 4,705 3,436 North Carolina 111,485 81,859 7,883 5,922 Ohio 464,964 178,307 32,878 22,710 Pennsylvania 388,612 141,693 27,479 18,918 Tennessee 100,007 100,007 7,072 5,774 Virginia 72,595 40,785 5,133 3,682 West Virginia 205,422 119,016 14,526 10,465 Wisconsin 96,439 66,683 6,819 5,060 * 2

-year average limits on 2012

-2013 emissions for SO 2 group 1 states for the alternative where variability limits would be used in the first phase (as presented in preamble section IV.F). ** Alternative approach of 3

-year average limits on 2012

-2014 emissions for SO 2 group 1 states for the alternative where variability limits would be used in the first phase.

### 43 4. Results of an analysis done using the air quality assessment tool The objective of this section is to estimate the possible effects on air quality of the variability

in emissions. This analysis was done using the air quality assessment tool, or AQAT , and uses state
-by

- state emissions to estimate downwind state

-by

- state air quality contributions at various nonattainment and maintenance monitors (See the Analysis for Significant Contribution TSD for details on the construction and use of the AQAT as well as the estimated downwind air quality concentrations resulting from the proposed remedy). See preamble section IV.C and the Air Quality Modeling TSD for a list of the nonattainment and maintenance monitors. For this analys is, EPA varied the SO 2 

### emissions of each upwind state included in the proposed Transport R ule 11 

### around the proposed budgets, simulating the effects of variation in emissions resulting from the proposed allowed variability under the variability limits, and estimated the resulting variability in air quality (daily PM2.5 

### concentrations)

. This analysis focused on variability in emissions related to the daily PM2.5 

### concentrations for several reasons

. First, the number of monitors classified as nonattainment and/or maintenance was larger than for the other standards. Second, generally, sites required larger emissions reductions and larger relative air quality improvements (under the daily PM2.5 

### NAAQS, compared with the annual PM2.5 

### NAAQS). Lastly, the SO 2 

### emissions reductions from EGUs relative to total SO 2 

### emissions for daily PM2.5 

### standard is proportionally

larger than NO x 

### emissions reductions from EGUs relative to total NO x 

### emissions. Consequently, the variability in estimated daily

PM2.5 

### concentrations relative to variation in SO 2 

### emissions is larger than variability in estimated ozone concentrations relative to variation in NO x 

### emissions. Two approaches were taken to estimate the variation in downwind air quality at each monitor for daily PM2.5 

### allowed under the Transport Rule in 2014 due to the inherent variability

in SO 2 

### emissions. Each of these approaches will be described, in turn, in the following paragraphs. To summarize the two approaches: The first approach examined the 1

-ye ar variability effects on daily PM2.5 

### concentration when variations in emissions
11 The states included in this analysis were Alabama, Connecticut, Delaware, District of Columbia, Florida, Georgia, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maryland, Massachusetts, Michigan, Minnesota, Missouri, Nebraska, New Jersey, New York, North Carolina, Ohio, Pennsylvania, South Carolina, Tennessee, Virginia, West Virginia, and Wisconsin.

### 44

from different states are independent from each other. This is intended to represent “ty pical” random variations in emissions and the resulting ty pical variations in air quality that might be seen under the Transport Rule. The second approach examined the “worst” case 1

- ye ar scenario for each monitor, when the upwind states with the largest impacts per ton emit at the upper end of the variability limit, while upwind states with the lowest impacts per ton emit below their budgets

. This is intended to estimate an upper bound for the effects of emissions variability on air quality

. For both approaches, the effects of the inherent variation in emissions on daily PM2.5 

### concentrations were estimated to be small. For the first approach, the SO 2 

### emis sions for each state included in the proposed control region were allowed to randomly vary around the level of the budgets

. That is, by chance, one state may increase its emissions, while another state may decrease its emissions. This random variation is intended to represent the inherent “random” variations in heat input (and emissions) that were characterized in section 2. As with the analys is in section 2, variations were assumed to be “normally” distributed and characterized by a standard deviation equal to 5.1% of the state’s budget. This standard deviation is derived from the two

-tailed 95% confidence level 1

- ye ar variability limit (equal to 10% of a state’s budget for many

states )

. (Note that this process did not account for the fact that the proposed 1

- ye ar variability limits for some of the smaller states are larger than 10% of the budgets

. ) This process also did not account for banking due to early reductions. The reader should keep in mind that for this approach, when the state

-by

- state vari ations in emissions are assessed across all states included in the proposed Transport Rule region, the emissions from states that emit over their budgets largely cancel out with emissions from states that emit below their budgets, with the result that total region

-wide emissions are relatively constant
. As described in section 2b of this TSD when determining the state

-by

- state ye ar-to
ye ar variability

in emissions, we assume , on a state
-by

- state basis that : (1) from year-to
ye ar, the differences between the budget and modeled emissions are “normally” distributed; (2) the yearly emissions are independent from each other and are independent from the emissions of any other state ; and, (3) the distribution of hourly emissions values is the same from ye ar to year (i.e. , the “within

- ye ar” variance is the same each ye ar).

### 45 Consequently, for a particular downwind monitor, it is possible to estimate the 1

- ye ar variance in air quality concentration as a function of the variance in emissions for each of the upwind states. Specifically, the variance in air quality concentration at the downwind monitor due to a particular upwind state would equal the square of the product of the air quality impact per ton of emissions multiplied by the standard deviation of the variation in emissions. The total variance in air quality impact at the downwind monitor is the sum of the variances resulting from each individual upwind state. The square root of this value is the estimated variability in air quality impact (in g/m3 

### ). For an individual monitor, the air quality impact per ton of emissions can be estimated using the daily

PM2.5 

### 2012 base case air quality sulfate contributions from the CAMx source - apportionment modeling results and the 2012 base case emissions inventory also used in the modeling (see the Air Quality Modeling TSD and section IV.C of the preamble for details on both of these data sets)

. For each individual upwind state contributing to a particular monitor, the estimated impact per ton of sulfate is the air quality contribution of sulfate from that state to the particular monitor, divided by the state’s total 2012 base case emission inventory of SO 2 

### . The standard deviation in emissions (in tons) for each state can be found b y multiplying the propose d 2014 state budget b y 0.1 (10%) and dividing by 1.960 (to convert the variability limit in emissions at the 95% confidence level back to the standard deviation level). The state
-by

- state SO 2 

### budgets in 2014 (and the budgets multiplied by 0.1) can be found in Table 18. For each combination of upwind state and downwind monitor, the impact per ton is then multiplied by

the standard deviation of emissions (in tons). This product is then squared, becoming the variance. The variances from states that were modeled in IPM as well as in the CAMx source

- apportionment air quality modeling, but were not included in the proposed remedy, were assumed to be equal to zero (i.e., they were assumed to have no variability

in their emissions)

. For a downwind monitor, the total 1

-ye ar variance in air quality contribution was found by adding the variances from all states together. This was done for all states (including the state containing the monitor) as well as, separately, for just upwind states (where the state containing the monitor was excluded). Following this, the square root of the total variance was taken, resulting in the standard deviation of the variability in air

### 46

quality. The standard deviation of the total variance from all states as well as just from upwind states can be found in Table 22. These standard deviation values represent the ye ar-to
- ye ar (1

- ye ar) variability in air quality impact ( g/m3 

### ) that we would expect to see, on average, at each monitor location due to the ye ar-to
- ye ar variability in emissions (Table 22)

. The estimated variations in air quality are quite small (a fraction of a percent) relative to the 2012 base case design values. The locations in Table 22 are in order of decreasing 2012 base case average design value. To assess more unusual variations in air quality air quality (i.e., those that, on average, will occur with a lower probability) , we calculated the two
-tailed 95% confidence level 1

- year variability in air quality ( g/m3 

### ) by multiplying the standard deviations in air quality by 1.960 . The resulting 95% confidence variability values can be found in Table 22. Assessed across all monitoring locations, the average and maximum estimated 95% confidence level variability values are 0.14 g/m3 

### and 0.26 g/m3 

### , respectively. When just variability

due to the upwind states is examined, the average and maximum 95% confidence level variability values are 0.10 g/m3 

### and 0.22 g/m3 

### , respectively

. All of the values (the standard deviation values and the 95% confidence level values in Table 22) are usually substantially

smaller than the differences between the average and maximum design values (with the maximum still less than 1% of the level of the NAAQS standard)

. Assessed acros s all the daily PM2.5 

### monitors shown in Table 22, the average difference between the 2012 base case average and maximum design values is 1.4 g/m3



### . Recall that, from section 3 of this TSD, that a 3

- ye ar variability is less than the 1

- ye ar variability

. Since the design values are found on a 3

- ye ar rolling basis, we expect that the variability in the 1

-ye ar values used to create the 3

- ye ar desi gn values would be substantially larger than is seen based on the difference between the maximum and average design values. Consequently, we expect the year-to
- year differences between the average design value and a ye arly value to be substantially larger than 1.4 g/m3



### . In conclusion, we found that, even while allowing each state’s emissions to randomly vary up to 10% of its budget (the 2

-tailed 95% confidence variability level prescribed for many states in the Transport Rule) , the combined downwind air quality impacts were essentially negligible

.

### 47 In the second approach, EPA examined the “worst” case scenario for each monitor location

. This approach is intended to simulate a situation where all of the states in the region surrounding a monitor increase their SO 2 

### emissions to the maximum amount possible (the 1

- year variability limit for each state) , while states far away make all of the emissions reductions to compensate
. In this scenario , the covered upwind states with the largest air quality impact s per ton of emissions were modeled to increase their SO 2 

### emissions up to their proposed 2014 SO 2 

### budget variability limit (Table 18) . The upwind states with the lowest air quality impacts per ton of emissions were modeled to reduce their emissions by the number of tons equal to the variability limit (Table 18). This was done such that the total emissions (i.e., the sum of the budgets with variability changes) remained exactly at the total emissions level for the proposed region (i.e., the sum of all the state budgets )

. In other words, for this approach, on a monitor

-by

-monitor basis, emissions increases in the upwind states that have the largest air quality impacts per ton are paired with equivalent emissions decreases in states that are having the lowest air quality impacts per ton. A result of this approach is that overall regional emissions do not change at all from the sum of the budgets. The state containing the monitor was included in this analysis (unless the state was not included in the proposed Transport Rule region) . For the “middle” state (i.e., the state whose impact per ton value was such that the total emissions from states with higher impacts per ton equaled the total emissions from states with lower impacts per ton) , the emissions were set at an intermediate level. States that were not included in the proposed Transport Rule region had their variability in emissions set to zero. The results of this analys is , seen in Table 22 in the “worst” case columns, show the cumulative air quality impact from all states and from just the upwind states. The average values were 0. 30 g/m3 

### for all states, and 0. 20 g/m3 

### for just the upwind states , while the maximum values were 0. 66 g/m3 

### and 0. 35 g/m3 

### , respectively . Again, the air quality impacts, while larger than for the random variability approaches, are still substantially less than the difference between the average and maximum design value s. These results suggest that even under a “worst case” scenario, where nearb y states minimize reductions in emissions , while states far away maximize reduction, the resulting increases in air quality are small relative to other factors (i.e., weather).

### 48 Collectively, by

assessing these two approaches, it appears that the magnitude of the variability

in air quality

resulting from the proposed levels of variability in emissions is likely to be smaller than other factors impacting air quality

. These results suggest that the estimated variations in air quality resulting from the small variations in emissions (even under “worst
- case” scenarios) are not substantial. The variations are much smaller than documented ye ar-to
- ye ar variability in air quality

(as measured at the monitors and expressed as the difference between the average and maximum design values). Consequently, allowing variation in emissions under the proposed variability limits in the Transport Rule , while allowing flexibility for the power sector to address inherent fluctuations in electric generation, does not overly affect air quality.

### 49

Table 22. Using AQAT , the Estimates of the Effects of Variability in Emissions of SO 2 

### (Using the Proposed Budgets) on Downwind Sulfate Concentrations ( g/m3 

### ) in 2014 are Shown for Each Downwind Monitor for Daily PM2 .5. The 2012 Base Case Average and Maximum Design Values as well as the Estimated Standard Deviation, the 95% Confidence Level, and the Worst Case Variability Estimates Are Shown.

Monitor I dentification N umber Receptor

S tate Receptor

C ounty 2012

Base Case Average DV ( g/m 3) 2012

Base Case Maximum DV ( g/m 3) Difference

B etween Average and Maximum 2012 Base DVs ( g/m 3) Standard

D eviation (All S tates) Standard

D eviation ( U pwind S tates) 95%

C onfidence L evel (All S tates) 95%

C onfidence L evel ( U pwind S tates) Worst

C ase (All S tates) Worst

C ase ( U pwind S tates)
420030064 Pennsylvania Allegheny 58.8 62.3 3.5 0.047 0.038 0.092 0.074 0.195 0.141 261630033 Michigan Wayne 42.1 42.6 0.5 0.104 0.043 0.205 0.085 0.345 0.159 390350038 Ohio Cuyahoga 41.2 44.0 2.8 0.086 0.048 0.168 0.094 0.342 0.202 420030093 Pennsylvania Allegheny 41.1 46.2 5.1 0.060 0.052 0.117 0.102 0.253 0.196 170311016 Illinois Cook 41.0 44.1 3.1 0.050 0.039 0.098 0.076 0.218 0.156 261 630016 Michigan Wayne 40.6 43.0 2.4 0.093 0.033 0.182 0.065 0.307 0.138 180970043 Indiana Marion 40.5 42.0 1.5 0.126 0.057 0.248 0.112 0.458 0.237 390170003 Ohio Butler 40.3 42.3 2.0 0.109 0.089 0.214 0.174 0.472 0.348 180970066 Indiana Marion 40.3 41.8 1.5 0.132 0.058 0.258 0.113 0.480 0.247 420210011 Pennsylvania Cambria 40.3 40.7 0.4 0.102 0.055 0.200 0.107 0.389 0.220 180970081 Indiana Marion 40.1 41.1 1.0 0.117 0.054 0.228 0.106 0.425 0.223 010730023 Alabama Jefferson 40.0 40.7 0.7 0.132 0.020 0. 259 0.040 0.324 0.068 171191007 Illinois Madison 40.0 40.6 0.6 0.067 0.055 0.132 0.108 0.282 0.208 540090011 West Virginia Brooke 39.9 40.8 0.9 0.050 0.047 0.097 0.091 0.219 0.187 390618001 Ohio Hamilton 39.6 40.3 0.7 0.071 0.051 0.140 0.100 0.286 0.188 390350060 Ohio Cuyahoga 39.4 42.8 3.4 0.072 0.034 0.142 0.066 0.266 0.140 171190023 Illinois Madison 39.4 40.2 0.8 0.105 0.084 0.206 0.164 0.422 0.298 180970083 Indiana Marion 39.0 39.3 0.3 0.132 0.059 0.259 0.115 0.485 0.253 550790043 Wi sconsin Milwa ukee 38.8 39.7 0.9 0.070 0.060 0.137 0.117 0.318 0.248 180970078 Indiana Marion 38.7 39.7 1.0 0.126 0.057 0.246 0.111 0.455 0.236 261630019 Michigan Wayne 38.6 39.1 0.5 0.063 0.032 0.124 0.062 0.235 0.128 170310052 Illinois Cook 38.5 39.7 1.2 0.049 0.03 3 0.096 0.064 0.196 0.125 261630015 Michigan Wayne 38.5 39.1 0.6 0.105 0.050 0.206 0.098 0.365 0.184 390170017 Ohio Butler 38.5 38.5 0.0 0.090 0.073 0.176 0.144 0.390 0.287 261470005 Michigan St. Clair 38.4 39.4 1.0 0.059 0.035 0.115 0.068 0.233 0.141 170313301 Illinois Cook 38.2 41.0 2.8 0.059 0.043 0.115 0.084 0.252 0.173 340172002 New Jersey Hudson 38.2 38.2 0.0 0.035 0.030 0.069 0.059 0.186 0.132 180190006 Indiana Clark 38.1 40.2 2.1 0.081 0.055 0.159 0.107 0.341 0.223 261610008 Michigan Washtenaw 38.1 39.8 1.7 0.067 0.046 0.130 0.090 0.261 0.166 010732003 Alabama Jefferson 38.1 38.9 0.8 0.083 0.017 0.163 0.034 0.226 0.066

### 50 170313103 Illinois Cook 38.1 38.7 0.6 0.046 0.028 0.090 0.055 0.189 0.118 420031008 Pennsylvania Allegheny 38.0 39.3 1.3 0. 070 0.058 0.137 0.114 0.291 0.215 390610006 Ohio Hamilton 38.0 38.0 0.0 0.091 0.074 0.179 0.146 0.385 0.281 261250001 Michigan Oakland 37.9 38.4 0.5 0.082 0.052 0.160 0.102 0.325 0.201 390171004 Ohio Butler 37.8 38.6 0.8 0.089 0.071 0.175 0.139 0.381 0. 274 420710007 Pennsylvania Lancaster 37.7 40.1 2.4 0.033 0.012 0.066 0.024 0.135 0.074 420070014 Pennsylvania Beaver 37.7 39.1 1.4 0.057 0.051 0.111 0.100 0.242 0.195 550790010 Wi sconsin Milwaukee 37.7 39.0 1.3 0.049 0.037 0.095 0.072 0.241 0.178 39061 7001 Ohio Hamilton 37.7 38.1 0.4 0.085 0.064 0.166 0.126 0.367 0.259 390610014 Ohio Hamilton 37.5 38.5 1.0 0.081 0.059 0.159 0.116 0.330 0.221 390170016 Ohio Butler 37.5 37.8 0.3 0.086 0.075 0.169 0.146 0.378 0.293 170316005 Illinois Cook 37.4 39.8 2.4 0.048 0.031 0.094 0.060 0.193 0.121 180890022 Indiana Lake 37.3 42.1 4.8 0.058 0.028 0.114 0.054 0.218 0.117 180970079 Indiana Marion 37.2 38.3 1.1 0.119 0.061 0.234 0.119 0.456 0.255 171192009 Illinois Madison 37.2 38.2 1.0 0.095 0.076 0.186 0.149 0.38 8 0.278 390610042 Ohio Hamilton 37.2 38.0 0.8 0.083 0.058 0.163 0.113 0.342 0.225 360610056 New York New York 37.1 38.0 0.9 0.040 0.016 0.078 0.032 0.167 0.096 420030116 Pennsylvania Allegheny 37.1 37.1 0.0 0.060 0.051 0.117 0.100 0.259 0.198 261150005 Michigan Monroe 37.0 38.0 1.0 0.074 0.063 0.145 0.123 0.321 0.243 210590005 Kentucky Daviess 37.0 37.0 0.0 0.133 0.110 0.260 0.215 0.494 0.347 550790099 Wi sconsin Milwaukee 36.8 37.7 0.9 0.053 0.044 0.104 0.086 0.260 0.201 191630019 Iowa Scott 36.8 36. 8 0.0 0.083 0.073 0.162 0.143 0.407 0.330 340390004 New Jersey Union 36.7 37.2 0.5 0.030 0.026 0.059 0.050 0.173 0.127 420031301 Pennsylvania Allegheny 36.6 38.6 2.0 0.062 0.052 0.121 0.101 0.266 0.199 471251009 Tennessee Montgomery 36.6 37.9 1.3 0.068 0.058 0.133 0.113 0.320 0.249 390490024 Ohio Franklin 36.6 37.6 1.0 0.064 0.040 0.126 0.078 0.267 0.168 390811001 Ohio Jefferson 36.5 39.9 3.4 0.065 0.043 0.126 0.084 0.261 0.166 390350065 Ohio Cuyahoga 36.5 38.9 2.4 0.072 0.035 0.142 0.070 0.274 0.151 180372001 Indiana Dubois 36.5 38.0 1.5 0.102 0.055 0.200 0.109 0.380 0.212 171193007 Illinois Madison 36.5 37.3 0.8 0.103 0.083 0.202 0.163 0.420 0.300 295100087 Missouri St. Louis City 36.4 36.9 0.5 0.097 0.071 0.191 0.139 0.395 0.264 550790026 Wi scon sin Milwaukee 36.3 40.1 3.8 0.046 0.036 0.091 0.071 0.226 0.170 180890026 Indiana Lake 36.3 39.3 3.0 0.051 0.036 0.101 0.070 0.218 0.145 391130032 Ohio Montgomery 36.3 38.5 2.2 0.090 0.068 0.177 0.134 0.393 0.277 245100040 Maryland Baltimore (City) 36.3 38.3 2.0 0.068 0.013 0.133 0.026 0.187 0.057 170310076 Illinois Cook 36.3 37.3 1.0 0.066 0.051 0.130 0.099 0.278 0.195 180970042 Indiana Marion 36.3 37.2 0.9 0.129 0.060 0.252 0.118 0.480 0.257 261630036 Michigan Wayne 36.3 36.9 0.6 0.069 0.042 0.136 0 .083 0.256 0.149 360610128 New York New York 36.2 38.0 1.8 0.042 0.032 0.083 0.062 0.221 0.167 390490025 Ohio Franklin 36.1 36.4 0.3 0.062 0.039 0.122 0.077 0.253 0.157 390350045 Ohio Cuyahoga 36.0 39.0 3.0 0.078 0.040 0.153 0.078 0.302 0.170 211110044 Kentucky Jefferson 36.0 36.5 0.5 0.094 0.076 0.184 0.149 0.359 0.252 390610043 Ohio Hamilton 36.0 36.4 0.4 0.091 0.078 0.179 0.154 0.395 0.304 295100007 Missouri St. Louis City 36.0 36.3 0.3 0.133 0.075 0.261 0.147 0.497 0.282

### 51 421330008 Pennsylvania Yo rk 35.9 38.8 2.9 0.058 0.025 0.113 0.049 0.215 0.113 181570008 Indiana Tippecanoe 35.9 36.9 1.0 0.080 0.050 0.156 0.099 0.345 0.225 180830004 Indiana Knox 35.9 36.5 0.6 0.093 0.049 0.182 0.096 0.355 0.200 420030008 Pennsylvania Allegheny 35.9 36.3 0.4 0 .052 0.042 0.102 0.081 0.217 0.155 360050080 New York Bronx 35.9 36.2 0.3 0.041 0.023 0.081 0.045 0.201 0.133 390610040 Ohio Hamilton 35.8 36.8 1.0 0.084 0.065 0.166 0.128 0.377 0.272 211110043 Kentucky Jefferson 35.8 36.4 0.6 0.101 0.087 0.197 0.170 0. 382 0.282 420430401 Pennsylvania Dauphin 35.7 37.1 1.4 0.038 0.017 0.074 0.034 0.160 0.095 170310057 Illinois Cook 35.7 37.0 1.3 0.050 0.032 0.097 0.063 0.210 0.136 090091123 Connecticut New Haven 35.7 36.6 0.9 0.031 0.030 0.060 0.059 0.223 0.162 29099 0012 Missouri Jefferson 35.7 36.5 0.8 0.122 0.074 0.239 0.144 0.474 0.284 340171003 New Jersey Hudson 35.7 36.1 0.4 0.038 0.029 0.075 0.057 0.217 0.143 170312001 Illinois Cook 35.6 38.2 2.6 0.064 0.059 0.125 0.116 0.258 0.211 391530017 Ohio Summit 35.6 37.2 1.6 0.069 0.040 0.135 0.078 0.274 0.164 211110048 Kentucky Jefferson 35.6 36.4 0.8 0.084 0.068 0.165 0.134 0.331 0.234 291831002 Missouri Saint Charles 35.5 37.1 1.6 0.107 0.072 0.209 0.141 0.435 0.281 245100049 Maryland Baltimore (City) 35.5 35.5 0.0 0.066 0.013 0.130 0.025 0.180 0.052 261630001 Michigan Wayne 35.4 37.8 2.4 0.085 0.056 0.166 0.111 0.345 0.221 360610062 New York New York 35.3 37.0 1.7 0.033 0.025 0.064 0.050 0.180 0.140 420410101 Pennsylvania Cumberland 35.3 37.0 1.7 0.033 0.018 0.065 0.035 0.142 0.087 390810017 Ohio Jefferson 35.3 36.8 1.5 0.062 0.042 0.121 0.082 0.256 0.167 171630010 Illinois Saint Clair 35.3 35.9 0.6 0.099 0.080 0.195 0.157 0.402 0.288 295100085 Missouri St. Louis City 35.3 35.7 0.4 0.128 0.073 0.251 0.143 0 .484 0.278 181670023 Indiana Vigo 35.1 36.5 1.4 0.099 0.048 0.195 0.093 0.384 0.212 550250047 Wi sconsin Dane 35.1 36.1 1.0 0.056 0.040 0.109 0.078 0.254 0.179 471650007 Tennessee Sumner 35.1 36.0 0.9 0.081 0.067 0.159 0.131 0.376 0.285 171971002 Illino is Wi ll 35.1 35.8 0.7 0.052 0.040 0.101 0.079 0.222 0.158 210290006 Kentucky Bullitt 35.0 36.3 1.3 0.090 0.073 0.176 0.143 0.377 0.273 170310022 Illinois Cook 34.9 36.6 1.7 0.070 0.069 0.138 0.134 0.214 0.184 551330027 Wi sconsin Wa ukesha 34.9 35.6 0.7 0 .048 0.042 0.094 0.081 0.225 0.178 550790059 Wi sconsin Milwaukee 34.8 36.3 1.5 0.056 0.047 0.111 0.093 0.261 0.202 245100035 Maryland Baltimore (City) 34.7 35.5 0.8 0.065 0.012 0.127 0.023 0.171 0.046 390350027 Ohio Cuyahoga 34.5 36.6 2.1 0.065 0.027 0. 128 0.053 0.231 0.115 191390015 Iowa Muscatine 34.5 36.0 1.5 0.038 0.026 0.074 0.050 0.158 0.104 211451004 Kentucky McCracken 34.4 36.8 2.4 0.087 0.079 0.171 0.155 0.362 0.289 420030095 Pennsylvania Allegheny 34.3 36.6 2.3 0.052 0.043 0.102 0.083 0.223 0.165 180431004 Indiana Floyd 34.3 35.7 1.4 0.097 0.062 0.191 0.121 0.388 0.240 391130031 Ohio Montgomery 34.3 35.6 1.3 0.065 0.048 0.127 0.094 0.275 0.189 391351001 Ohio Preble 34.3 35.5 1.2 0.087 0.073 0.170 0.143 0.376 0.284 390950024 Ohio Lucas 34. 2 36.5 2.3 0.058 0.047 0.113 0.093 0.240 0.176 360610079 New York New York 34.2 36.4 2.2 0.050 0.027 0.099 0.054 0.244 0.161 390990014 Ohio Mahoning 34.2 35.8 1.6 0.053 0.030 0.103 0.059 0.211 0.127 170310050 Illinois Cook 34.1 35.8 1.7 0.063 0.055 0.12 3 0.108 0.273 0.214

### 52 110010041 District Of

Columbia District Of

Columbia 34.0 35.6 1.6 0.043 0.043 0.085 0.085 0.655 0.193 540090005 West Virginia Brooke 33.9 36.1 2.2 0.055 0.049 0.107 0.096 0.226 0.178 391550007 Ohio Trumbull 33.9 35.6 1.7 0.051 0.033 0.100 0.064 0.217 0.141 421255001 Pennsylvania Washington 33.9 35.5 1.6 0.071 0.068 0.139 0.132 0.288 0.245 420033007 Pennsylvania Allegheny 33.8 38.5 4.7 0.066 0.056 0.130 0.109 0.281 0.210 240031003 Maryland Anne Arundel 33.8 36.7 2.9 0.057 0.027 0.11 2 0.053 0.225 0.126 180390003 Indiana Elkhart 33.8 35.6 1.8 0.056 0.030 0.110 0.059 0.197 0.104 212270007 Kentucky Wa rren 33.7 36.3 2.6 0.086 0.079 0.169 0.154 0.385 0.315 390350034 Ohio Cuyahoga 33.7 35.7 2.0 0.061 0.036 0.120 0.070 0.246 0.149 170314 007 Illinois Cook 33.6 35.7 2.1 0.065 0.047 0.128 0.093 0.268 0.180 390950026 Ohio Lucas 33.6 35.6 2.0 0.068 0.050 0.134 0.098 0.299 0.208 110010042 District Of

Columbia District Of

Columbia 33.0 35.6 2.6 0.034 0.034 0.067 0.067 0.606 0.159

# Appendix A:

Alternative Method for Identifying 1 -Year Tonnage and Percentage Limits

### Table A

- 1. The Effects o f Various Combinations o f the Alternative Method Tonnage and Percentage Variability

Limits on Annual EGU SO 2 

### Emissions (See Notes Below).

Percentage Limit Tonnage L imit 2 4 6 8 10 12 14 16 18 20 1 , 000 22 12 7 7 NJ, VA, NJ,

VA, NJ, NJ, 302,343 345,824 1 ,300 21 11 6 6 VA, VA, 216,599 259,662 302,879 346,236 1 , 600 20 11 6 6 VA, VA, 217,799 260,853 303,907 346,997 1 ,900 20 11 6 6 VA, VA, 218,999 262,053 305,107 348,162 2 ,200 19 11 6 6 VA, VA, 220,199 263,253 306,307 349,362 2 ,500 18 10 5 5 VA, VA, 221,399 264,453 307,507 350,562 2 , 800 17 9 5 5 VA, VA, 222,599 265,653 308,707 351,762 3 , 100 15 7 5 5 VA, VA, 223,799 266,853 309,90 7 352,962 3 ,400 12 5 GA, IA, NY, VA, GA, IA, NY,

VA, VA, VA, 224,999 268,053 311,107 354,162 3 ,700 10 GA, IA, TN, VA, GA, IA, VA, GA, IA, VA, VA, VA, 226,199 269,253 312,307 355,362 4 , 000 8 GA, IA, TN, VA, GA, IA, VA, GA, IA, VA, VA, VA, 2 27,671 270,453 313,507 356,562 4 ,300 8 GA, IA, TN, VA, GA, IA, VA, GA, IA, VA, VA, VA, 229,308 271,692 314,707 357,762 4 , 600 7 GA, IA, TN, VA, GA, IA, VA, GA, IA, VA, VA, VA, 231,108 273,192 315,907 358,962 4 ,900 6 GA, IA, TN, VA, GA, IA, VA, GA, IA, VA, VA, VA, 232,908 274,835 317,214 360,162 5 ,200 GA, IA,

MI, VA, GA, IA, VA, GA, IA, VA, GA, IA, VA, VA, VA, 234,708 276,635 318,714 361,362 5 ,500 GA, IA,

MI, VA, GA, IA, VA, GA, IA, VA, GA, IA, VA, VA, VA, 236,797 278,435 320,3 62 362,736 5 , 800 GA, IA,

MI, VA, GA, IA, VA, GA, IA, VA, GA, IA, VA, VA, VA, 239,191 280,235 322,162 364,236 6 , 100 GA, IA, GA, IA, GA, IA, GA, IA, 165,234 202,313 241,591 282,179 323,962 365,889 6 ,400 GA, GA, GA, GA, 169,134 205,498 244,15 1 284,387 325,762 367,689 6 ,700 90,919 93,882 112,952 140,982 173,038 208,821 246,862 286,787 327,562 369,489 7 , 000 99,019 100,931 118,652 146,082 177,238 212,421 249,862 289,187 329,662 371,289 7 ,300 107,119 108,222 124,781 151,182 181,438 216,021 252, 911 291,755 331,983 373,089 7 , 600 115,219 116,022 131,081 156,282 186,077 219,916 256,211 294,455 334,383 375,044 7 ,900 123,319 123,822 137,381 161,502 190,877 223,816 259,621 297,411 336,783 377,179 8 ,200 131,419 131,622 143,681 166,902 195,748 227,881 263,221 300,411 339,360 379,579 8 ,500 139,519 139,519 150,163 172,363 200,848 232,081 266,821 303,624 342,060 381,979 8 , 800 147,619 147,619 157,063 178,063 205,948 236,281 270,699 306,924 344,960 384,379 9 , 100 155,719 155,719 163,963 183,763 211,048 24 0,968 274,599 310,421 347,960 386,964 9 ,400 163,819 163,819 170,863 189,466 216,148 245,768 278,525 314,021 351,037 389,664 9 ,700 171,919 171,919 177,763 195,668 221,248 250,568 282,725 317,621 354,337 392,509 10 , 000 180,019 180,019 184,663 201,968 226, 623 255,614 286,925 321,482 357,637 395,509 10 ,300 188,119 188,119 191,586 208,268 232,023 260,714 291,157 325,382 361,221 398,509 10 , 600 196,219 196,219 198,786 214,568 237,474 265,814 295,859 329,282 364,821 401,750 10 , 900 204,319 204,319 206,024 220, 868 243,174 270,914 300,659 333,369 368,421 405,050 11 , 200 212,419 212,419 213,824 227,228 248,874 276,014 305,459 337,569 372,265 408,422 11 , 500 220,519 220,519 221,624 234,045 254,574 281,114 310,380 341,769 376,165 412,022 11 , 800 228,619 228,619 229,424 240,945 260,328 286,343 315,480 346,034 380,065 415,622 12 , 100 236,719 236,719 237,224 247,845 266,555 291,743 320,580 350,751 384,012 419,222 12 , 400 244,819 244,819 245,024 254,745 272,855 297,143 325,680 355,551 388,212 423,048 12 , 700 252,919 252, 919 252,919 261,645 279,155 302,585 330,780 360,351 392,412 426,948 13 , 000 261,019 261,019 261,019 268,545 285,455 308,285 335,880 365,151 396,612 430,848 13 ,300 269,119 269,119 269,119 275,445 291,755 313,985 340,980 370,245 400,911 434,748 13 , 600 277, 219 277,219 277,219 282,345 298,055 319,685 346,080 375,345 405,642 438,856

*Numbers in white cells represent the sum (across all states) of the differences in emissions between the state

- specific “alternative approach” variability values and the state

- specific variability limit (in tons); the variability limit selected for each state is the larger of either the percentage limit or the tonnage limit. ** If the cell i s grey, it means that there is a state or there are several states whose “alternative approach” level variability values exceed both the tonnage and percentage limits . The cell either lists the states that could exceed the limits, or lists the number of states. ** * If the cell i s yellow, this is the combination tonnage and percentage limits that minimizes the sum of the differences between the state

- specific “alternative approach” variability values and the state

- specific variability limit (in tons)

.

### Table A

- 2. The Effects of Various Combinations of the Alternative Method Tonnage and

Percentage Variability

Limits on Annual EGU NO x 

### Emissions (See Notes Below).
P ercentage Limit T onnage L imit 3 5 10 15 20 25 30 35 40 45 50 1 , 000 13 GA, LA, NY, NY, 160,818 216,772 272,730 328,701 384,804 440,907 497,010 1 ,500 12 GA, LA, NY, NY, 162,461 218 ,027 273,737 329,688 385,646 441,604 497,562 2 , 000 9 GA, LA, 109,477 164,461 219,830 275,237 330,946 386,655 442,604 498,562 2 ,500 6 GA, LA, 112,477 166,710 221,830 277,200 332,570 388,155 443,864 499,573 3 , 000 5 GA, 115,477 169,642 224,017 279,200 3 34,570 389,939 445,364 501,073 3 ,500 5 GA, 118,916 172,642 226,807 281,324 336,570 391,939 447,309 502,678 4 , 000 FL, GA, IA,

PA, GA, 123,005 175,642 229,807 283,972 338,631 393,939 449,309 504,678 4 ,500 FL, GA, PA, GA, 127,840 178,957 232,807 286,9 72 341,137 395,939 451,309 506,678 5 , 000 FL, PA, 91,796 133,376 182,894 235,807 289,972 344,137 398,438 453,309 508,678 5 ,500 FL, PA, 101,282 139,376 187,180 239,076 292,972 347,137 401,303 455,745 510,678 6 , 000 FL, PA, 111,184 145,758 192,125 242,87 2 295,972 350,137 404,303 458,468 513,052 6 ,500 FL, 121,708 153,315 197,600 246,872 299,195 353,137 407,303 461,468 515,633 7 , 000 FL, 132,708 161,315 203,507 251,411 302,850 356,137 410,303 464,468 518,633 7 ,500 FL, 143,875 170,203 209,507 256,411 30 6,850 359,313 413,303 467,468 521,633 8 , 000 150,982 155,375 179,682 216,017 261,879 311,029 362,828 416,303 470,468 524,633 8 ,500 164,482 167,069 189,182 223,463 267,639 315,697 366,828 419,432 473,468 527,633 9 , 000 177,982 179,199 199,284 231,426 273,6 39 320,697 370,828 422,932 476,468 530,633 9 ,500 191,482 192,199 209,784 239,610 279,708 326,159 375,203 426,806 479,551 533,633 10 , 000 204,982 205,199 220,571 248,610 286,276 331,770 379,983 430,806 483,051 536,633 10 , 500 218,482 218,482 231,571 258,08 2 293,708 337,770 384,983 434,878 486,784 539,670 11 , 000 231,982 231,982 242,572 267,582 301,536 343,770 390,438 439,378 490,784 543,170 11 , 500 245,482 245,482 254,072 277,197 309,536 349,953 395,938 444,269 494,784 546,762 12 , 000 258,982 258,982 265,57 2 287,385 318,018 356,535 401,901 449,269 499,052 550,762 12 , 500 272,482 272,482 277,112 297,885 327,018 363,954 407,901 454,718 503,555 554,762 13 , 000 285,982 285,982 289,112 308,433 336,482 371,647 413,901 460,218 508,555 558,762 13 , 500 299,482 299,48 2 301,308 319,433 345,982 379,647 420,198 466,033 513,555 563,227 14 , 000 312,982 312,982 314,308 330,433 355,482 387,647 426,794 472,033 518,997 567,841 14 , 500 326,482 326,482 327,308 341,433 365,250 396,425 434,199 478,033 524,497 572,841 15 , 000 339,98 2 339,982 340,308 352,769 375,486 405,425 441,758 484,033 530,164 577,841 15 , 500 353,482 353,482 353,482 364,269 385,986 414,883 449,758 490,443 536,164 583,277 16, 000 366,982 366,982 366,982 375,769 396,486 424,383 457,758 497,052 542,164 588,777 16, 50 0 380,482 380,482 380,482 387,269 407,296 433,883 465,910 504,444 548,164 594,295 17, 000 393,982 393,982 393,982 399,156 418,296 443,383 474,832 511,944 554,188 600,295 17, 500 407,482 407,482 407,482 411,156 429,296 453,304 483,832 519,869 560,688 606,29 5 18 , 000 420,982 420,982 420,982 423,416 440,296 463,586 493,283 527,869 567,311 612,295 18 , 500 434,482 434,482 434,482 436,416 451,465 474,086 502,783 535,869 574,689 618,295 19 , 000 447,982 447,982 447,982 449,416 462,965 484,586 512,283 544,239 582,18 9 624,433 19 , 500 461,482 461,482 461,482 462,416 474,465 495,159 521,783 553,239 589,980 630,933 20 , 000 474,982 474,982 474,982 475,416 485,965 506,159 531,358 562,239 597,980 637,570 20 , 500 488,482 488,482 488,482 488,482 497,465 517,159 541,358 571,68 3 605,980 644,935 21 , 000 501,982 501,982 501,982 501,982 509,199 528,159 551,687 581,183 613,980 652,435 21 , 500 515,482 515,482 515,482 515,482 521,199 539,159 562,187 590,683 622,646 660,091 22 , 000 528,982 528,982 528,982 528,982 533,199 550,162 572,68 7 600,183 631,646 668,091

*Numbers in white cells represent the sum (across all states) of the differences in emissions between the state

- specific “alternative approach” variability values and the state

- specific variability limit (in tons); the variability limit selected for each state is the larger of either the percentage limit or the tonnage limit. ** If the cell i s grey, it means that there is a state or there are several states whose “alternative approach” level variability values exceed both the tonnage and percentage limits . The cell either lists the states that could exceed the limits, or lists the number of states. ** * If the cell i s yellow, this is the combination tonnage and percentage limits that minimizes the sum of the differences between the state
- specific “alternative approach” variability values and the state

- specific variability limit (in tons)

.

### Table A

- 3. The Effects of Various Combinations of the Alternative Method Tonnage and Percentage

Variability

Limits on Ozone Season EGU NO x 

### Emiss ions (See Notes Below). P ercentage Limit T onnage L imit 0 2 4 6 8 10 12 14 16 18 800 20 20 17 13 9 5 AR, LA, MS, NY, AR, LA,

MS, NY, LA,

NY, 55,182 1 , 100 12 12 11 8 6 AR, GA, LA, MS, AR, LA,

MS, AR, LA,

MS, LA, 56,328 1 ,400 9 9 8 7 5 GA, LA, MS , LA, MS, LA, MS, LA, 57,832 1 ,700 6 6 6 5 FL, GA, PA, GA, 33,340 41,805 50,605 59,639 2 , 000 FL, GA,

PA, TX, FL, GA,

PA, TX, FL, GA,

PA, TX, FL, GA,

PA, TX, FL, GA, PA, GA, 36,727 44,851 53,249 61,971 2 ,300 FL, PA, TX, FL, PA, TX, FL, PA, TX, FL, PA, TX, FL, PA, 33,911 40,645 48,219 56,362 64,733 2 , 600 FL, PA, TX, FL, PA, TX, FL, PA, TX, FL, PA, TX, FL, PA, 39,183 44,923 52,023 59,711 67,872 2 ,900 FL, PA, TX, FL, PA, TX, FL, PA, TX, FL, PA, TX, FL, PA, 44,982 49,998 56,223 63 ,421 71,203 3 ,200 FL, PA, TX, FL, PA, TX, FL, PA, TX, FL, PA, TX, FL, PA, 50,982 55,464 60,985 67,602 74,865 3 ,500 FL, FL, FL, FL, FL, 56,982 61,304 66,146 71,973 78,980 3 , 800 60,374 60,374 60,374 60,374 60,812 62,982 67,304 71,746 77,072 83,1 80 4 , 100 67,874 67,874 67,874 67,874 68,012 69,455 73,304 77,625 82,342 88,059 4 ,400 75,374 75,374 75,374 75,374 75,374 76,272 79,304 83,625 88,027 93,159 4 ,700 82,874 82,874 82,874 82,874 82,874 83,471 85,431 89,625 93,947 98,609 5 , 000 90,374 90,374 9 0,374 90,374 90,374 90,671 92,031 95,625 99,947 104,309 5 ,300 97,874 97,874 97,874 97,874 97,874 97,874 98,930 101,625 105,947 110,269 5 , 600 105,374 105,374 105,374 105,374 105,374 105,374 106,130 108,007 111,947 116,269 5 ,900 112,874 112,874 112,874 11 2,874 112,874 112,874 113,330 114,651 117,947 122,269 6 ,200 120,374 120,374 120,374 120,374 120,374 120,374 120,530 121,589 124,016 128,269 6 ,500 127,874 127,874 127,874 127,874 127,874 127,874 127,874 128,789 130,583 134,269 6 , 800 135,374 135,374 135,3 74 135,374 135,374 135,374 135,374 135,989 137,291 140,269 7 , 100 142,874 142,874 142,874 142,874 142,874 142,874 142,874 143,189 144,249 146,560 7 ,400 150,374 150,374 150,374 150,374 150,374 150,374 150,374 150,389 151,449 153,160 7 ,700 157,874 157,874 157,874 157,874 157,874 157,874 157,874 157,874 158,649 159,931 8 , 000 165,374 165,374 165,374 165,374 165,374 165,374 165,374 165,374 165,849 166,908 8 ,300 172,874 172,874 172,874 172,874 172,874 172,874 172,874 172,874 173,049 174,108 8 , 600 180,374 180 ,374 180,374 180,374 180,374 180,374 180,374 180,374 180,374 181,308 8 ,900 187,874 187,874 187,874 187,874 187,874 187,874 187,874 187,874 187,874 188,508 9 ,200 195,374 195,374 195,374 195,374 195,374 195,374 195,374 195,374 195,374 195,708 9 ,500 202,87 4 202,874 202,874 202,874 202,874 202,874 202,874 202,874 202,874 202,908 9 , 800 210,374 210,374 210,374 210,374 210,374 210,374 210,374 210,374 210,374 210,374 10 , 100 217,874 217,874 217,874 217,874 217,874 217,874 217,874 217,874 217,874 217,874 10 , 400 225,374 225,374 225,374 225,374 225,374 225,374 225,374 225,374 225,374 225,374 10 , 700 232,874 232,874 232,874 232,874 232,874 232,874 232,874 232,874 232,874 232,874 11 , 000 240,374 240,374 240,374 240,374 240,374 240,374 240,374 240,374 240,374 240,374 11 ,300 247,874 247,874 247,874 247,874 247,874 247,874 247,874 247,874 247,874 247,874 11 , 600 255,374 255,374 255,374 255,374 255,374 255,374 255,374 255,374 255,374 255,374 11 , 900 262,874 262,874 262,874 262,874 262,874 262,874 262,874 262,874 262,874 262,874 12 , 200 270,374 270,374 270,374 270,374 270,374 270,374 270,374 270,374 270,374 270,374 12 , 500 277,874 277,874 277,874 277,874 277,874 277,874 277,874 277,874 277,874 277,874 12 , 800 285,374 285,374 285,374 285,374 285,374 285,374 285,374 285,374 285,374 285,374 13 , 100 292,874 292,874 292,874 292,874 292,874 292,874 292,874 292,874 292,874 292,874 13 , 400 300,374 300,374 300,374 300,374 300,374 300,374 300,374 300,374 300,374 300,374

*Numbers in white cells represent the sum (across all states) of the differences in emissions between the state

- specific “alternative approach” variability values and the state

- specific variability limit (in tons); the variability limit selected for each state is the larger of either the percentage limit or the tonnage limit. ** If the cell i s grey, it means that there is a state or there are several states whose “alternative approach” level variability values exceed both the tonnage and percentage limits . The cell either lists the states that could exceed the limits, or lists the number of states. ** * If the cell i s yellow, this is the combination tonnage and percentage limits that minimizes the sum of the differences between the state

- specific “alternative approach” variability values and the state

- specific variability limit (in tons)

.