1

Technical Support Document (TSD)

for the Transport Rule

Docket ID No. EPA-HQ-OAR-2009-0491

Power Sector Variability

U.S. Environmental Protection Agency

Office of Air and Radiation

July 2010
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Power Sector Variability

This Technical Support 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-year variability in emissions

3. Estimating variability over a multi-year time period

4. Results of an analysis 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 SO2, annual NOX, and ozone season NOX state emissions budgets

based on its projections of state-by-state power sector emissions in an average year after 

the elimination of the prohibited emissions.1

However, because of the unavoidable variability in baseline emissions – resulting 

from the inherent variability in power system operations – state-level emissions may vary

somewhat from year 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 SO2 and NOX budgets for each state covered for the annual and/or 24-hour PM2.5

NAAQS. Additionally, the EPA developed ozone season NOX budgets for each state covered for the ozone 

NAAQS. Table III.A-1 in preamble section III lists the states that would be covered for the PM2.5 and/or 

ozone NAAQS.
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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 describes 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 SO2, annual NOX, and ozone 

season NOX. 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 proposes 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-year” limit, based on the year-to-year variability in emissions relative to 

the proposed budgets. Second, a “3-year” 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-year variability limits that would apply to a state’s annual (or ozone 

season) emissions on a 3-year rolling average basis.2 Preamble section IV.F discusses 

EPA’s rationale for implementing 1-year and 3-year variability limits. Section IV.F also 

describes EPA’s proposed approach to calculating the proposed 1- and 3-year 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.
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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 discussed in the preamble, is also taking comment on whether to apply them 

starting in 2012.

Preamble section IV.F presents proposed 1- and 3-year variability limits for each 

state and alternative 1- and 3-year limits calculated using the alternative approach. Table 

IV.F-1 in the preamble presents proposed and alternative 1- and 3-year variability limits on 

SO2 emissions for each state. Table IV.F-2 presents proposed and alternative 1- and 3-year 

variability limits on annual NOX emissions. Table IV.F-3 presents proposed and 

alternative 1- and 3-year variability limits on ozone season emissions.

For 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 variability

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

year average and 2-year average variability limits and determined the 2-year limits are 

preferable.

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

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

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-year limits based on expected variation in power sector 

emissions. Section 2 in this TSD, titled “Estimating year-to-year variability in annual 

emissions”, describes in greater detail the method EPA used to estimate expected variation 

in year-to-year power sector emissions and the proposed and alternative approaches to 

determine 1-year limits based on that expected variation.

As discussed in preamble section IV.F, after determining 1-year variability limits, 

EPA used statistical methods to estimate multi-year (3-year and 2-year) average variability

limits for covered states based on each state’s 1-year variability. Section 3 in this TSD, 

titled “Estimating variability over a multi-year time period”, describes the approach in 

greater detail than that provided in the preamble. As discussed in the preamble and in 
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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 multiyear average. Thus, the variability of a 3-year average is equal to the annual variability

divided by the square root of three.

As discussed above and in preamble section IV.F, for 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-year variability limits instead 

of 2-year limits. Section 3 in this TSD compares the 3- and 2-year limits and discusses 

why EPA determined that the 2-year limits are preferable. Like the 3-year average 

variability discussed above, the variability of a 2-year average is equal to the annual 

variability divided 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 year-to-year 

(“1-year”) variability in annual SO2, annual NOX and ozone season NOX 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 types, 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 established on a state-by-state basis of yearly heat input 

values applicable to each of the pollutants regulated in the Transport Rule (annual 

SO2, annual NOx, and ozone season NOx). These historical heat input values are 

used to estimate the inherent variability in emissions due to power system 

operation.

 The method EPA developed (as well as an alternative method) to estimate the yearto-year variability in the heat input values. The year-to-year variability in heat 

input is estimated on a state-by-state basis.

 The approach EPA used to link heat input variability with projected pollutant 

emission rates to estimate the inherent variation in pollutant emissions.
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 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 year-to-year (1-year) 

variability in emissions by characterizing the year-to-year 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 yearly time 

period based on the “standard deviation” of yearly power sector heat input (HI) assessed 

over a 7-year 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 analysis of variation in heat 

input because it represents 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 analysis began in 2009, the last complete year of data available

was for 2008. A starting year of 2002 was selected for this analysis 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 increased 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 years prior to 2002 in the analysis would lead to the inclusion of a 
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single year where there is large change in heat input that is not representative of typical 

year-to-year variability, thereby 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 analysis would skew the variability estimates. Consequently, EPA 

chose 2002 for the start of the analysis 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 year of the 7-year 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 year-to-year variability

in annual and ozone season NOx emissions using the total yearly heat inputs summed 

across all three general EGU source types (Tables 2 and 3, respectively), while the Agency

assessed year-to-year variability in annual SO2 emissions using the yearly heat inputs from 

just the coal-fired EGUs (Table 1). 

EGUs built prior to 2002 that reported heat input and emissions data between 2002

and 2008 and new EGUs 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. 
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Table 1: Heat Input* (million mmbtu) from Coal-Fired Units for Each Year.

State 2002 2003 2004 2005 2006 2007 2008 Average HI

Max. 

Difference 

from 

Average

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 276.9 278.0 269.9 273.7 274.7 277.1 256.0 272.3 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 693.3 689.1 696.5 719.7 699.1 726.4 713.2 705.3 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 457.1 475.9 482.9 473.2 451.2 447.5 450.8 462.6 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.
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Table 2: Total Heat Input* (million mmbtu) from All Three EGU Categories for Units 

Greater Than or Equal to 25 MW for Each 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 393.2 390.3 391.2 401.3 396.3 438.0 446.5 408.1 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 962.4 935.1 942.4 988.3 999.0 997.2 987.8 973.2 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 726.9 725.1 727.3 760.5 730.7 776.5 743.8 741.5 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.
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Table 3: Total Heat Input* (million mmbtu) from All Three EGU Categories for Units 

Greater Than or Equal to 25 MW for 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 454.9 430.2 449.6 478.0 460.8 474.6 452.6 457.2 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.
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(b) Estimating the state-by-state variability in heat input using historical heat 

input data sets.

This subsection describes the method used by EPA to estimate the variability in 

heat input for each state in the Transport Rule region. EPA assessed the year-to-year 

variability over the 7-year time period of the yearly 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 selected 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 yearly value and the average heat input values 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 analysis 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-year time period. After accounting for growth 

in heat input over time, the year-to-year variation in heat input was assessed, as the 

differences between the actual yearly heat input values and the “yearly” average heat input

values estimated according to the trend. For 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 r2describes how well 

the data fit that line. Large r

2

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 
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regression line that are substantially different than zero). For states with relatively constant 

heat input values, low r

2

values are strongly correlated with small values for the slope of 

the regression line. For these states, low r

2

values are indicators of large year-to-year 

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 r

2

value is also very small. 

The conclusion is that for Delaware and other states with similar slopes and r

2

values, the 

year-to-year variation could likely have been adequately 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 r2values, as 

well as the heat input estimated using the regression equations, can be found in Tables 4, 5, 

and 6. Using the regression equation for each state, yearly heat input values were 

estimated for each state for each year (between 2002 and 2008) in Tables 4, 5, and 6. 

Year-to-year variation was assessed for each year or for each ozone season as the 

difference between the actual heat input (from Tables 1-3) and the heat input estimated 

using the regression equation (Tables 4-6).
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Table 4: Heat Input (million mmbtu) from Coal-Fired Units for Each Year Estimated 

Using the Regression Equation.

State

Slope of 

Linear 

Regression

(million 

mmbtu/year)

Intercept r

2

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 0.34 474.2 470.3 466.5 462.6 458.8 455.0 451.1

WV -6.09 13,082 0.36 889.2 883.2 877.1 871.0 864.9 858.8 852.7
14

Table 5: Total Heat Input (million mmbtu) from All Three EGU Categories for Each Year 

Estimated Using the Regression Equation.

State

Slope of 

Linear 

Regression

(million 

mmbtu/year)

Intercept r

2

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 44.30 -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 0.63 241.4 260.5 279.5 298.6 317.6 336.6 355.7

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 0.53 412.3 407.2 402.0 396.9 391.7 386.6 381.5

MO 3.53 -6,280 0.12 779.4 782.9 786.5 790.0 793.5 797.0 800.6

NC 8.00 -15,302 0.63 709.6 717.6 725.6 733.6 741.6 749.6 757.6

NE 0.92 -1,612 0.11 234.4 235.4 236.3 237.2 238.1 239.1 240.0

NJ 6.69 -13,250 0.90 134.6 141.3 148.0 154.7 161.4 168.1 174.8

NY 17.76 -35,264 0.82 283.7 301.4 319.2 337.0 354.7 372.5 390.2

OH 3.79 -6,281 0.07 1,305.3 1,309.1 1,312.9 1,316.7 1,320.5 1,324.3 1,328.1

PA 26.84 -52,648 0.85 1,077.2 1,104.0 1,130.9 1,157.7 1,184.5 1,211.4 1,238.2

SC 10.75 -21,104 0.79 413.2 423.9 434.6 445.4 456.1 466.9 477.6

TN -1.88 4,376 0.04 608.5 606.6 604.8 602.9 601.0 599.1 597.2

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 for Ozone Season 

Estimated Using the Regression Equation.

State

Slope of 

Linear 

Regression

(million 

mmbtu/year)

Intercept r

2 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 26.4 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 SO2, annual NOx, and ozone season NOx 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 differences3divided by the number of samples minus one). The state- and 

pollutant-specific representative differences defined using the standard deviation were used 

for the remaining steps in the variability analyses 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-year” 

variance is the same for each year).

For each state, the standard deviation of the differences is a measure of the year-toyear (1-year) variance in heat input. In essence, it suggests that, on average, 68% of all the 

year-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 difference between the actual heat input and the estimated heat input 

(using the regression equation) for each year.
17

Table 7: Difference Between Heat Input (million mmbtu) Measured and Estimated for 

Coal-Fired Units for Each Year Using the Regression Equation.

State 2002 2003 2004 2005 2006 2007 2008

Standard 

Deviation

of Heat 

Input 

(million 

mmbtu)

Average 

Heat Input 

(2002-

2008 

(from 

Table 1)

Standard 

Deviation

as a

Fraction

of 

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 -11.1 11.3 -6.8 9.7 705.3 0.014 19.1 0.027

NE -6.1 5.6 -1.3 6.2 2.2 -9.1 2.6 5.8 232.1 0.025 11.4 0.049

NJ -3.3 -6.4 -0.5 13.7 5.0 2.4 -11.0 8.1 78.5 0.103 15.8 0.202

NY -6.2 3.7 2.5 -2.8 3.1 10.8 -11.1 7.3 216.2 0.034 14.2 0.067

OH -0.2 24.0 -46.9 19.0 -4.0 19.8 -11.8 24.8 1,296.1 0.019 48.5 0.037

PA -20.1 -5.4 14.7 17.8 9.7 15.9 -32.7 19.9 1,072.4 0.019 39.1 0.037

SC -1.6 -5.3 4.6 4.1 -0.1 5.3 -7.1 4.9 403.7 0.012 9.7 0.024

TN 23.8 -24.6 -8.7 -10.4 11.5 23.1 -14.7 19.3 596.8 0.032 37.8 0.063

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 Between Total Heat Input (million mmbtu) Measured and Estimated 

from All Three EGU Categories for Each Year Using the Regression Equation.

State 2002 2003 2004 2005 2006 2007 2008

Standard 

Deviation

of Heat 

Input 

(million 

mmbtu)

Average 

Heat Input 

(2002-

2008 

(from 

Table 2)

Standard 

Deviation

as a

Fraction

of 

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) 

from All Three EGU Categories for 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 

(from 

Table 3)

Standard 

Deviation

as a

Fraction of 

Average

HI

95%

Confidence 

Level 

Variability in 

HI (million 

mmbtu)

95%

Confidence 

Level 

Variability in 

HI (as a 

Fraction of 

Avg. HI)

AL 3.4 0.1 -9.8 -10.8 14.1 22.5 -19.5 14.8 438.1 0.034 29.0 0.067

AR -12.7 10.7 2.5 -9.7 16.8 8.6 -16.2 12.9 151.7 0.085 25.3 0.167

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 6.7 135.4 0.050 13.2 0.098

NC 15.3 -14.3 -14.0 8.2 1.7 7.5 -4.4 11.4 333.4 0.034 22.4 0.067

NJ 4.8 -7.9 2.1 0.2 0.0 2.2 -1.3 4.0 75.9 0.053 7.9 0.104

NY 2.6 -10.5 -3.9 3.9 17.7 5.1 -14.9 10.8 148.6 0.073 21.3 0.143

OH 16.4 -4.5 -28.9 7.1 -5.4 29.3 -14.0 19.4 567.3 0.034 38.0 0.067

OK -8.0 4.1 -6.0 15.6 4.9 -5.3 -5.3 8.6 232.8 0.037 16.8 0.073

PA -4.9 -14.1 -0.8 21.5 10.2 17.9 -29.9 18.3 507.8 0.036 35.8 0.071

SC 11.1 -19.1 1.7 5.5 1.5 2.8 -3.4 9.5 208.7 0.046 18.6 0.090

TN 22.9 -23.9 -11.8 2.2 5.5 11.3 -6.3 15.5 267.9 0.058 30.3 0.114

TX -35.5 -16.5 24.5 44.5 31.8 0.1 -49.0 35.4 1,184.6 0.030 69.4 0.059

VA 5.8 -7.1 -3.0 0.6 -2.1 14.5 -8.8 8.0 153.7 0.052 15.7 0.102

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 deviation can also be used to estimate, on average, the probability of

larger variations in heat input (for example a difference that we would expect less than 1% 

of the time). Using the standard deviation, “confidence levels” representing the variability

difference in heat input that could be expected at different probabilities were found for 

each state (for each pollutant). Several different levels were examined (notably the 95th

and 99th percent confidence levels). As an illustrative example, the two-tailed 95

th percent 

confidence level indicates that we could expect, on average, that the total heat input for a 

particular year for a particular state will be within 1.960 standard deviations of the 

variation from its mean value 95 percent of the time. For this analysis, we focus on results

for the two-tailed 95th percent confidence level for each state. EPA made the policy

decision that, assessed across the large number of states included in the program, the 95th

percent two-tailed confidence level4 was the appropriate confidence level. EPA believes 

that using the 95th 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 reliability 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 95th confidence level, showing that the proposed 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 difference 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-

year 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 95th percent confidence level is the equivalent of the 97.5th upper (single-tailed) confidence 

level. Hereafter in this TSD, the “95th“ percent confidence level refers to the two-tailed 95th 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) Estimation 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 year-to-year (1-year) variability in 

emissions is to convert the year-to-year 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 95th percent 

confidence 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 95th

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

State-specific modeled emission rates were used in the calculation. The rates 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 rates 

were used, rather than historical emission rates, because EPA wanted to estimate year-toyear variability in emissions within the same time and conditions as those of the proposed 

transport rule when new emissions controls could potentially be in operation.

Using IPM estimates for 2014, the emission rates for annual SO2 were calculated 

using modeled estimates of total heat input and total emissions from coal-fired units, while 

annual and ozone season NOx were calculated using modeled estimates of total heat 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 SO2, annual NOx and ozone season NOx are from IPM runs that required controls at 

$1,600/ton for SO2, $500/ton for annual NOX, and $500/ton for ozone season NOX, 

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 95th 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 SO2, annual NOx, and ozone season NOx, 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, 95

percent of the time. Consequently, the 95% emissions variability value represents a yearto-year 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 if variability is the only reason for emissions increases above 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_SO2_1600”, 

“TR_NOX_500”, and “TR_NOX_OS_500” for SO2, annual NOX, and ozone season NOX rates, respectively. 

The IPM runs and parsed files can be found in the docket; Docket ID No. EPA-HQ-OAR-2009-0491.

6

 Tables 10, 12, and 14 also show resulting estimated emission variability values based on the alternative 

approach (see columns labeled “Alternative approach value”).
23

Table 10. State-by-State SO2 Emissions (thousand short tons), Fuel Use (million mmbtu), 

and Emission Rate Estimated in IPM for Units Greater Than or Equal to 25 MW. Also 

Shown are the Estimated 95th Percent Confidence SO2 Tonnage and Percentage Values as 

well as the Alternative Approach SO2 Tonnage and Percentage Values.

State

Annual SO2

Emissions 

From CoalFired 

Boilers 

(thousand 

tons)

Annual fuel 

use from 

coal-fired 

boilers 

(million 

mmbtu)

SO2 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 158.93 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 0.279 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 11. Annual NOx Emissions (thousand short tons) and Fuel Use (million mmbtu) 

Estimated in IPM, for Units Greater Than or Equal to 25 MW.

State

 Annual NOx Emissions Annual Fuel Usage

CoalFired 

Boilers

(thousand 

tons) 

Combined 

Cycle 

Turbines

(thousand 

tons)

Simple

Cycle CT

(thousand 

tons)

Oil & Gas 

Boilers

(thousand 

tons) 

All Units

(thousand 

tons)

CoalFired 

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 28.204 399.917 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 12. State-by-State Annual NOx Emission Rate Calculated Using the IPM Estimates 

Presented in Table 11. Also Shown Are the Estimated 95th Percent Confidence NOx

Tonnage and Percentage Values as well as the Alternative Approach NOx Tonnage and 

Percentage Values.

State

Annual 

NOx

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 13. Ozone Season NOx Emissions (thousand short tons) and Fuel Use (million 

mmbtu) Estimated in IPM for Units Greater Than or Equal to 25 MW.

State

 Ozone Season NOx Emissions Ozone Season Fuel Usage

CoalFired 

Boilers

(thousand 

tons)

Combined 

Cycle 

Turbines

(thousand 

tons)

Simple

Cycle CT

(thousand 

tons)

Oil & Gas 

Boilers

(thousand 

tons)

All Units

(thousand 

tons)

CoalFired 

Boilers

(million 

mmbtu) 

Combined 

Cycle 

Turbines

(million 

mmbtu) 

Simple 

Cycle CT

(million 

mmbtu) 

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 27.539 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 14. State-by-State Ozone Season NOx Emission Rates Calculated Using the IPM 

Estimates Presented in Table 13. Also Shown Are the Estimated 95th Percent Confidence 

NOx Tonnage and Percentage Values as well as the Alternative Approach NOx Tonnage 

and Percentage Values for Ozone Season.

State

Ozone 

Season NOx

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 0.117 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-year 95th 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., SO2, annual 

NOX, and ozone season NOX) to apply to all covered states. For this analysis, EPA 

assumes that, on average, each state is meeting its proposed budget, but is also subject to 

inherent year-to-year 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-year 95% confidence level emissions 

variability tonnage value . Thus, EPA has identified 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. As 

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 SO2 and ozone season NOx, the tonnage limit

criteria were examined in 300 ton increments, while the percentage criteria were applied in 
29

2% increments. For annual NOX, 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 SO2, annual NOx and ozone season NOx, 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 thereby 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, for 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 can be seen in white cells in Tables 15, 16, and 17 for annual SO2, 

annual NOx and ozone season NOx, respectively.

7

In these tables, white cells represent 

possible combinations of percentage and tonnage limits that could successfully be met by 

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 of Various Combinations of the Proposed Upper 95% Confidence 

Level Tonnage and Percentage Variability Limits on Annual EGU SO2 Emissions (See 

Notes Below).

Percentage Limit

Tonnage Limit 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, 108,304 144,895 184,233 225,113 267,066 309,260 351,925

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, 167,980 195,963 226,429 260,762 297,207 335,349 374,903

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

10,900 194,127 194,127 195,825 210,896 233,118 260,970 290,807 323,630 358,753 395,452

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

11,800 218,427 218,427 219,164 230,701 250,404 276,452 305,577 336,230 370,200 406,011

12,100 226,527 226,527 226,964 237,601 256,638 281,852 310,677 340,947 374,330 409,611

12,400 234,627 234,627 234,764 244,501 262,938 287,252 315,777 345,747 378,530 413,219

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

13,300 258,927 258,927 258,927 265,201 281,838 303,956 331,077 360,384 391,130 425,031

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 is 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 is 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 of Various Combinations of the Proposed Upper 95% Confidence 

Level Tonnage and Percentage Variability Limits on Annual EGU NOx Emissions (See 

Notes Below).

Percentage Limit

Tonnage Limit 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, 240,079 294,420 348,806 403,796 459,331 514,881

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

7,500 143,877 150,471 177,153 216,576 263,666 314,267 366,872 421,101 475,441 529,781

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

9,000 184,377 186,036 205,836 238,391 280,798 328,123 378,415 430,657 484,441 538,781

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

12,000 265,377 265,377 272,267 293,989 325,337 363,969 409,367 457,039 506,983 558,860

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

13,500 305,877 305,877 308,365 326,045 353,017 386,898 427,851 473,651 521,496 571,328

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

15,000 346,377 346,377 347,263 359,564 382,142 412,929 449,152 491,776 537,936 585,954

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

16,500 386,877 386,877 386,877 394,120 413,961 441,045 473,618 511,964 555,936 602,307

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

18,000 427,377 427,377 427,377 430,694 446,961 470,296 500,574 535,405 575,265 620,220

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

19,500 467,877 467,877 467,877 469,558 481,361 501,878 529,074 561,113 597,659 639,125

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

21,000 508,377 508,377 508,377 508,558 516,305 534,878 558,449 588,602 622,044 660,235

21,500 521,877 521,877 521,877 521,877 528,305 545,878 568,949 598,102 630,706 667,912

22,000 535,377 535,377 535,377 535,377 540,523 557,158 579,449 607,602 639,706 675,912

*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 is 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 is 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).
32

Table 17. The Effects of Various Combinations of the Proposed Upper 95% Confidence 

Level Tonnage and Percentage Variability Limits on Ozone Season EGU NOx Emissions

(See Notes Below).

Percentage Limit

Tonnage 

Limit 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

5,600 104,115 104,115 104,115 104,115 104,115 104,115 104,849 106,588 110,536 114,839

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

8,300 171,615 171,615 171,615 171,615 171,615 171,615 171,615 171,615 171,760 172,816

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 is 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 is 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 each 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 established8. The procedure 

used to identify these 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-year 

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

 SO2 – 1,700 tons or 10 percent of state’s budget

 Annual NOX – 5,000 tons or 10 percent of state’s budget

 Ozone season NOX – 2,100 tons or 10 percent of state’s budget

As described in the preamble, after determining for each pollutant a 1-year 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. In other words, 

for SO2, every state has a 1-year variability limit of 1,700 tons or 10 percent of the state’s 

SO2 budget, whichever is greater. For annual NOX, every state has 1-year variability limit 

of 5,000 tons or 10 percent of the state’s annual NOX budget, whichever is greater. And, 

for ozone season NOX, every state has a 1-year variability limit of 2,100 tons or 10 percent 

of the state’s ozone season NOX budget, whichever is greater.

For example, Connecticut’s annual SO2 budget is 3,059 tons, and 10 percent (the 

percentage limit for SO2) of 3,059 tons is 306 tons. Because 1,700 tons (the tonnage limit 

for SO2) is greater than 306 tons, Connecticut’s 1-year SO2 variability limit is 1,700 tons. 

The 1-year variability limits for SO2, annual NOX, and ozone season NOX 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-year 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 SO2, annual NOx and 

ozone season NOx, respectively. The alternative 1-year 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:

 SO2 – 28 percent of state’s budget (equal to the value for Connecticut)

 Annual NOX – 21 percent of state’s budget (equal to the value for 

Louisiana)

 Ozone season NOX – 27 percent of state’s budget (equal to the value for 

Connecticut)

Under this alternative calculation method, for SO2 emissions, a state’s 1-year 

variability limit would be 1,700 tons as long as 1,700 tons is between 10 and 28 percent of 

the state’s SO2 emissions budget. If 1,700 tons is greater than 28 percent of the state’s SO2

budget, then the state’s 1-year variability limit is set at 28 percent of the state’s SO2

budget. If 1,700 tons is less than 10 percent of the state’s SO2 budget, then the state’s 1-

year variability limit is set at 10 percent of the state’s SO2 budget. The alternative 

calculation method would be applied to determine annual and ozone season NOX 1-year 

variability limits in the same manner as for the SO2 limits.

Tables 18, 19, and 20 demonstrate application of this alternative method to 

determining 1-year variability limits for SO2 emissions, annual NOX emissions, and ozone 

season NOX emissions, respectively. In Table 18, the first column lists the state and the 

second column lists the state’s 2014 SO2 emissions budget. The third column lists, for 

each state, 10 percent (the percentage limit for SO2) of the state’s SO2 budget. The fourth 

column lists the tonnage limit for SO2 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 SO2) of the state’s SO2 budget. And finally, the sixth column lists the state’s 
35

alternative 1-year variability limit. The columns in Tables 19 and 20 follow the same 

pattern but for annual NOX and ozone season NOX emissions, respectively.

Again, using Connecticut’s 1-year SO2 variability limit as an example,

Connecticut’s annual SO2 budget is 3,059 tons. Ten percent (the percentage limit for SO2) 

of 3,059 tons is 306 tons. Twenty-eight percent (the percentage ceiling for SO2) of 3,059 

tons is 857 tons. Because 1,700 tons (the tonnage limit for SO2) is greater than 857 tons, 

Connecticut’s 1-year SO2 variability limit is set at 857 tons, as shown in Table 18. In 

contrast, using the proposed approach would result in a 1-year SO2 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 SO2 Emissions for 2014 and Later.

State

2014

SO2 Annual 

Emissions 

Budget

(tons)

10 Percent 

of SO2

Budget

(tons)

SO2

Tonnage 

Limit

(tons)

28 Percent 

of SO2

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 15,876

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 NOX Annual Emissions for 2014 and Later.

State

2014

NOX Annual 

Emissions 

Budget

(tons)

10 Percent 

of NOX

Annual 

Budget

(tons)

NOX

Annual 

Tonnage 

Limit

(tons)

21 Percent 

of NOX

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 20. Application of Alternative Calculation Method for Determining 1-Year 

Variability Limits on NOX Ozone Season Emissions for 2014 and Later.

State

2014

NOX Ozone 

Season

Emissions 

Budget

(tons)

10 Percent 

of NOX

Ozone 

Season

Budget

(tons)

NOX

Ozone 

Season

Tonnage 

Limit

(tons)

27 Percent 

of NOX

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 year-to-year 

(1-year) variability in emissions at the 95% confidence level using the historical year-toyear 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-year” 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 each state’s emissions budget and a 3-

year rolling average of its emissions, comparing it against a “3-year” 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 year are independent of the following year. For this analysis, we 

assume that, for each state, the year-to-year variability in emissions is described using the 

1-year values in section 2 of this TSD. 

What is our expectation for how a 3-year average of emissions would compare to 

the emissions for a single year? We would expect that the emissions for a single year

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-year average to be relative to the variability 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-year 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 yearly emission values). 

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-year average.9 Thus, 

the variability of a 3-year 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-

year 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. 2nd ed. New York: W.H. 

Freeman and Company, 1993. p.395.
39

(where each year 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 yearly variables are called Y1, Y2, and Y3. Each of 

these yearly variables could assume a particular emissions value for a particular year. 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 by a mean value (i.e., the budget value) and a standard deviation (i.e., the yearto-year standard deviation). For explanatory purposes, it will first be assumed without loss 

of generality that the distributions of hourly emissions values from year-to-year is identical 

(i.e., the “within-year” variation 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 years 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 follows10:

( )

3

1

( )

2 2 2

2 Y1 Y2 Y3

Var X     

where,

Var ( X ) = the variance of the mean of yearly emission values from each of three 

independent years, Yi, where i =1 to 3; 

X = (1/3) * (X1 + X2 + X3)

2

Yi

 = 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-year 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-year variance in the sample mean from the year-to-year 

variance.

 

10 http://en.wikipedia.org/wiki/Variance
40

Applying these equations on a state-by-state basis using the state-specific 1-year 

emission variability limits and the mean values (the state emissions budgets), EPA

calculated the 3-year variability limits that are presented in section IV.F in the preamble. 

These limits are the 1-year limits divided by the square root of three. The 3-year 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 by 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-year 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-year 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-year variability

limits would be applied for the 15 SO2 group 1 states, i.e., the more stringent SO2 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-year average 

limits for the 15 SO2 group 1 states in 2012 and 2013, 3-year average limits for these states 

starting in 2014 (for Phase 1). EPA considered the alternative of 3-year average limits 

starting in 2014 for these states because this is the approach EPA would apply to SO2

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 SO2

budgets in 2012 and 2013 than in 2014 and beyond.

The equations described above can be applied to determine 3-year average limits 

for the case of the group 1 states whose SO2 emission budgets change in 2014, in other 

words the situation where the mean value and the standard deviation changes in the third 

year. As an illustrative example, consider a state with emission budgets of 1000, 1000, and 

700 thousand tons of SO2 in 2012, 2013, and 2014, respectively. The 1-year 95th percent 

upper confidence variability limit for this state is 10% of its yearly budgets (or, 100, 100, 

and 70 for each of the three years). The 1-year standard deviation variability for each year 

is the 95th 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-year limit in 

2014, the situation is a little complicated because the expected mean value for the three 

years is changing (because the base budgets are not constant for the three-year time 

period). First, the calculation of the 3-year variance uses the equation described above (for

Var ( X )). In this case, the year-specific variances,

2

Yi

 , are not identical. Putting the 

values into the equation, the expected variance of the 3-year average is

Var ( X ) = (1/3)2*(2+2+2) = 6481.7/32= 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 SO2. Thus, this is the variability that we would expect from the 

“average” value for the three years (2012, 2013, and 2014). An estimate 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 SO2.

EPA calculated the 3-year average of the 95th percent confidence level limits for 

2014 for the group 1 states using these equations and compared these to the 2-year average 

of the 95th percent confidence level limits that are presented in the preamble. These 3-year 

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

the 2-year 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-year average limits presented in the 

preamble and shown in Table 21 are calculated based on the Phase 1 SO2 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 SO2 emissions in Phase 1, are impacted 

(reduced) by the tighter Phase 2 SO2 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-year limits.

Table 21. Phase 1 Variability Limits for SO2 Group 1 States: Comparison of 2-Year 

Average to Alternative 3-Year Average Limits of the 95th Percent Confidence Level.

SO2 Group 1 State SO2 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 6,651 5,281

Kentucky 219,549 113,844 15,524 11,023

Michigan 251,337 155,675 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 SO2 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 SO2 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 analysis, EPA varied the SO2 emissions of each upwind state included in 

the proposed Transport Rule11 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 SO2 emissions

reductions from EGUs relative to total SO2 emissions for daily PM2.5 standard is 

proportionally larger than NOx emissions reductions from EGUs relative to total NOx

emissions. Consequently, the variability in estimated daily PM2.5 concentrations relative to 

variation in SO2 emissions is larger than variability in estimated ozone concentrations 

relative to variation in NOx 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 SO2 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-year 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 

“typical” random variations in emissions and the resulting typical variations in air quality

that might be seen under the Transport Rule. The second approach examined the “worst” 

case 1-year 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 SO2 emissions 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 analysis 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-year 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-year 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 variations 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 year-toyear variability in emissions, we assume, on a state-by-state basis that: (1) from year-toyear, 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 year to year (i.e., the “within-year” variance is the same each year).
45

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

year 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 SO2. The standard deviation in 

emissions (in tons) for each state can be found by multiplying the proposed 2014 state 

budget by 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 SO2

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-year 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 

year-to-year (1-year) variability in air quality impact (g/m3) that we would expect to see, 

on average, at each monitor location due to the year-to-year 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/m3and 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/m3and 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 across 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/m

3

. Recall that, 

from section 3 of this TSD, that a 3-year variability is less than the 1-year variability. 

Since the design values are found on a 3-year rolling basis, we expect that the variability in 

the 1-year values used to create the 3-year design 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 yearly 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 SO2 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 impacts per ton of emissions were modeled to increase their SO2

emissions up to their proposed 2014 SO2 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 

analysis, 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/m3for all states, and 0.20 g/m3for just the upwind states, while the maximum values 

were 0.66 g/m3and 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 values. These results suggest that even under a 

“worst case” scenario, where nearby 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 year-to-year 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 SO2 (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 

Identification 

Number

Receptor 

State

Receptor 

County

2012 

Base 

Case 

Average 

DV 

(g/m3)

2012 

Base 

Case 

Maximum 

DV 

(g/m3)

Difference 

Between 

Average 

and 

Maximum 

2012 Base 

DVs 

(g/m3)

Standard 

Deviation 

(All 

States)

Standard 

Deviation 

(Upwind 

States)

95% 

Confidence 

Level (All 

States)

95% 

Confidence 

Level 

(Upwind 

States)

Worst 

Case 

(All 

States)

Worst 

Case 

(Upwind 

States)

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

261630016 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 Wisconsin Milwaukee 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 0.033 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 Wisconsin Milwaukee 37.7 39.0 1.3 0.049 0.037 0.095 0.072 0.241 0.178

390617001 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 0.388 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 Wisconsin 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 Wisconsin 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 0.1360.083 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 York 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 0.40.052 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

290990012 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 0.1430.484 0.278

181670023 Indiana Vigo 35.1 36.5 1.4 0.099 0.048 0.195 0.093 0.384 0.212

550250047 Wisconsin 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 Illinois Will 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 Wisconsin Waukesha 34.9 35.6 0.70.048 0.042 0.094 0.081 0.225 0.178

550790059 Wisconsin 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 0.123 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 0.112 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 Warren 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

170314007 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 of Various Combinations of the Alternative Method Tonnage and 

Percentage Variability Limits on Annual EGU SO2 Emissions (See Notes Below).

Percentage Limit

Tonnage Limit 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, 223,799 266,853 309,907 352,962

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, 227,671 270,453 313,507 356,562

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, 236,797 278,435 320,362 362,736

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, 169,134 205,498 244,151 284,387 325,762 367,689

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

9,100 155,719 155,719 163,963 183,763 211,048 240,968 274,599 310,421 347,960 386,964

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 is 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 is 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 NOx Emissions (See Notes Below).

Percentage Limit

Tonnage Limit 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, 115,477 169,642 224,017 279,200 334,570 389,939 445,364 501,073

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, 127,840 178,957 232,807 286,972 341,137 395,939 451,309 506,678

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, 111,184 145,758 192,125 242,872 295,972 350,137 404,303 458,468 513,052

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, 143,875 170,203 209,507 256,411 306,850 359,313 413,303 467,468 521,633

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

9,000 177,982 179,199 199,284 231,426 273,639 320,697 370,828 422,932 476,468 530,633

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

10,500 218,482 218,482 231,571 258,082 293,708 337,770 384,983 434,878 486,784 539,670

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

12,000 258,982 258,982 265,572 287,385 318,018 356,535 401,901 449,269 499,052 550,762

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

13,500 299,482 299,482 301,308 319,433 345,982 379,647 420,198 466,033 513,555 563,227

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

15,000 339,982 339,982 340,308 352,769 375,486 405,425 441,758 484,033 530,164 577,841

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

16,500 380,482 380,482 380,482 387,269 407,296 433,883 465,910 504,444 548,164 594,295

17,000 393,982 393,982 393,982 399,156 418,296 443,383 474,832 511,944 554,188 600,295

17,500 407,482 407,482 407,482 411,156 429,296 453,304 483,832 519,869 560,688 606,295

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

19,000 447,982 447,982 447,982 449,416 462,965 484,586 512,283 544,239 582,189 624,433

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

20,500 488,482 488,482 488,482 488,482 497,465 517,159 541,358 571,683 605,980 644,935

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

22,000 528,982 528,982 528,982 528,982 533,199 550,162 572,687 600,183 631,646 668,091

*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 is 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 is 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 NOx Emissions (See Notes Below).

Percentage Limit

Tonnage Limit 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 83,180

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 90,374 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

5,900 112,874 112,874 112,874 112,874 112,874 112,874 113,330 114,651 117,947 122,269

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

6,800 135,374 135,374 135,374 135,374 135,374 135,374 135,374 135,989 137,291 140,269

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

9,500 202,874 202,874 202,874 202,874 202,874 202,874 202,874 202,874 202,874 202,908

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 is 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 is 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).