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Technical Support Document (TSD)

for the final Transport Rule

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

Power Sector Variability

Final Rule TSD

U.S. Environmental Protection Agency

Office of Air and Radiation

June 2011
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Power Sector Variability

This Technical Support Document (TSD) provides information in support of 

section VI.E, “Approach to Power Sector Emissions Variability,” of the preamble to the 

final Transport Rule. This TSD is organized as follows:

1. Introduction

2. Estimating variability in power system operations and emissions

3. Numerical simulation of 1 and 3-year variability

4. Results of an analysis using the air quality assessment tool

1. Introduction.

Section VI of the preamble to the final Transport Rule discusses EPA’s approach to 

define “significant contribution” and “interference with maintenance” with respect to the 

1997 8-hour ozone and annual fine particle (PM2.5) National Ambient Air Quality

Standards (NAAQS) and the 2006 24-hour PM2.5 NAAQS. As discussed in preamble 

section VI, EPA has identified the emissions that must be reduced by each state to address

the state’s significant contribution and interference with maintenance. To facilitate 

implementation of the requirement that these emissions be eliminated, EPA also developed 

SO2, annual NOX, and ozone-season NOX state budgets based on its projections of stateby-state power sector emissions in an average year reflecting these emission reductions.

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However, because of inherent variability in year-to-year baseline emissions –

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

may vary somewhat from year to year even after all the significant contribution and 

interference with maintenance that EPA has identified in the Transport Rule has been 

eliminated. Therefore, and for the reasons discussed in preamble sections VI.E and VI.F, 

EPA has determined that it is appropriate to develop variability limits for each state 

budget. These limits are used to identify the range of emissions that EPA believes may

 

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 EPA developed annual SO2 and NOX budgets for each state covered for the annual and/or 24-hour PM2.5

NAAQS and ozone-season NOX budgets for each state covered for the ozone NAAQS, as discussed in 

section VI.D of the final Transport Rule’s preamble. Table III-1 in preamble section III lists the 27 states 

that are covered by the Transport Rule. As discussed in preamble section III, EPA will issue a supplemental 

proposal to require ozone-season NOX reductions in 6 additional states. 
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occur in each state following the elimination of all emissions identified by EPA as 

significantly contributing to nonattainment or interfering with maintenance in one or more 

other states. 

Preamble sections VI.A through VI.D discuss EPA’s approach to quantify for each 

upwind state the emissions that significantly contribute to nonattainment or interfere with 

maintenance downwind for the existing ozone and PM2.5 NAAQS and to determine state 

emissions budgets for SO2, annual NOX, and ozone season NOX. Preamble sections VI.E 

and VI.F discuss the inherent variability in electric power system operations and EPA’s 

approach to determine appropriate variability limits on emissions for each state covered by

the final Transport Rule to account for this variability. As explained in preamble section 

VI.E, EPA calculated variability limits for each state emissions budget. The final 

Transport Rule applies those variability limits in addition to the state budgets (which are 

based on expected average conditions) to determine the “assurance level” for each state in 

each compliance period. The Agency believes that because baseline power system 

operations (and therefore emissions) are variable at the state level, emissions after the 

elimination of all significant contribution and interference with maintenance are also 

variable and thus it is appropriate to take this variability into account while assuring that 

each state makes necessary reductions. 

In the final Transport Rule, EPA derived “1-year” variability limits from an 

assessment of historical year-to-year variability in heat input, which was originally 

described in the “Power Sector Variability” TSD from the proposed Transport Rule and is 

re-analyzed in this TSD for all states included in the final Transport Rule, as well as for the 

additional states EPA is proposing, through a supplemental proposal, to include in the 

Transport Rule ozone program. For the reasons discussed in section VI.E of the preamble, 

although EPA proposed to implement both 1-year variability limits and 3-year average 

variability limits, the final Transport Rule decided to finalize only the 1-year limits. 

Section 3 of this TSD presents an analysis of 1 and 3-year variability that supports EPA’s 

decision to finalize only the 1-year variability limits in the final rule.

Section 4 of this TSD describes an analysis using the air quality assessment tool 

(AQAT) to estimate the resulting air quality effects in a given year assuming several 

potential degrees of variability in year-to-year emissions from the power sector. 
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2. Estimating variability in power system operations and emissions. 

This section describes the method that the Agency used to estimate the year-to-year 

(“1-year”) variability in power system operations and thereby annual SO2, annual NOX and 

ozone season NOX emissions. As discussed above, the goal of this assessment is to 

determine the amount of variability in state-level emissions that is due to inherent 

variability in power system operations. To quantify this expected variability, EPA used 

variation in historic heat input. EPA considered the use of variation in historic emissions

but determined this to be inappropriate because it reflected factors such as the installation 

of new emissions controls and changes in the operation of existing controls. These factors 

are not directly related to variability in power system operations. In fact, these factors are

activities that EPA is attempting to regulate with the implementation of the state budgets 

themselves. EPA believes that all controls (existing or newly installed) should be 

operated with consistency under the rule’s programs. As such, EPA is assuming a constant 

relationship between heat input variability and emissions variability for the purposes of 

this historic variability analysis (i.e., variation in heat input is a proxy for variation in 

emissions holding the effectiveness of emissions control in that state constant). This 

section in the TSD provides information on:

 The historical data set EPA established on a state-by-state basis of yearly heat input 

values applicable to each of the pollutants regulated in the final Transport Rule 

(SO2, annual NOx, and ozone season NOx). EPA used these historical heat input 

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

operation.

 The method EPA utilized to estimate the year-to-year variability in the heat input 

values. The year-to-year variability in heat input was estimated on a state-by-state 

basis.

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

pollutant emission levels to estimate the resulting variation expected in future 

pollutant emissions on a year-to-year basis.
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(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), as compliance with the Transport Rule programs must be demonstrated for total 

tonnage emitted during a single year or ozone season. For this assessment, EPA used total 

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). EPA believes such data would capture

inherent variation in year-to-year 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 features nearly comprehensive reporting of heat input across many states 

and EGUs while also minimizing unusual (i.e., statistically misleading for long-run 

averaging) upswings or downswings in state-level heat input. EPA selected 2002 as the 

first baseline year because prior years (2000 and 2001) experienced large, uneven changes

in annual heat input from fossil units for some states due, in part, to structural changes in 

the composition of the power sector fleet and a significant increase in newly reported heat 

input due to the commencement of the second phase of the Acid Rain Program (ARP). 

Consequently, incorporating data from years prior to 2002 in the analysis would lead to the 

inclusion of a year where there is large change in heat input that is not representative of 

typical year-to-year variability in power system operation, thereby leading to overestimates 

of variability. EPA determined that including years marked by significant changes in the 

number of units reporting heat input data or years experiencing significant shifts in statelevel EGU fleet composition (i.e., sharp regional swings from coal to gas generation) 

would skew the variability estimates. EPA maintained 2008 as the final year for the 
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assessment for a similar reason: The economic downturn in 2009 resulted in an atypical

dropoff in electric demand and, thus, in heat input, that would overstate long-run year-toyear variability in heat input. 

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 reported heat input for all units within each 

of three general classes of EGUs: (1) coal-fired units; (2) combined cycle turbines; and (3) 

a combination of oil- and gas-fired boilers and simple-cycle combustion turbines. Total 

annual heat input values (in million MMBtu) for the coal-fired EGUs and for all EGUs can 

be found in Table 1 and Table 2, respectively. Ozone-season heat input values for all

EGUs can be found in Table 3. EPA assessed the inherent year-to-year variability in 

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

all EGUs (Tables 2 and 3, respectively), because heat input from each of these sources is 

related to significant NOx emissions. The Agency assessed year-to-year variability in

annual SO2 emissions using the yearly heat inputs from the coal-fired EGUs (Table 1)

which account for a substantial majority of the SO2 emissions. SO2 emission rates are 

considerably higher at most coal-fired units compared to other types of generators; as a 

result, heat input variation at coal-fired units has a disproportionate impact on a state’s SO2

emissions. For this reason, in this assessment, we analyzed historic variability in heat 

input at coal-fired units as a better proxy for SO2 emissions variability.

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

and 2008 and new EGUs built in 2002 or later were included for this analysis. EGUs that 

were built prior to 2002 but that did not start reporting until after 2002 were excluded from 

the assessment, in order to prevent their newly reported heat input from falsely registering 

as “additional” heat input gained in that state in that year. 
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Table 1: Annual Heat Input* (million MMBtu) from Coal-Fired Units for Each Year.

State 2002 2003 2004 2005 2006 2007 2008 Average HI

AL 779.0 822.6 784.5 828.3 813.0 818.8 768.9 802.1

GA 781.2 787.4 808.8 871.6 872.5 910.6 861.1 841.9

IA 393.1 389.6 386.7 384.5 378.9 415.9 431.2 397.1

IL 930.9 961.9 1,027.1 1,002.1 1,002.1 1,030.5 1,032.9 998.2

IN 1,231.4 1,236.3 1,266.0 1,281.2 1,269.9 1,256.9 1,243.6 1,255.0

KS 427.5 427.5 411.9 405.2 377.6 405.4 373.5 404.1

KY 946.5 931.1 938.1 970.3 987.0 977.9 978.5 961.3

MD 276.9 278.0 269.9 273.7 274.7 277.1 256.0 272.3

MI 700.1 706.9 709.7 721.1 699.7 736.0 714.4 712.5

MN 389.2 411.0 386.7 380.1 370.3 362.9 349.8 378.6

MO 724.5 769.6 770.8 783.6 783.5 762.0 732.8 761.0

NC 693.3 689.1 696.5 719.7 699.1 726.4 713.2 705.3

NE 227.0 238.3 231.2 238.3 234.0 222.4 233.8 232.1

NJ 80.0 75.3 79.6 92.2 81.9 77.8 62.8 78.5

NY 234.1 235.9 226.7 213.4 211.2 211.0 181.0 216.2

OH 1,290.2 1,316.3 1,247.3 1,315.1 1,294.0 1,319.7 1,290.0 1,296.1

PA 1,026.9 1,050.1 1,078.6 1,090.2 1,090.5 1,105.2 1,065.1 1,072.4

SC 381.5 384.7 401.4 407.7 410.4 422.6 417.1 403.7

TN 624.7 574.9 589.4 586.3 606.9 617.2 578.0 596.8

TX 1,529.9 1,572.9 1,580.3 1,581.3 1,546.5 1,590.7 1,565.7 1,566.7

VA 340.5 312.5 296.7 298.4 288.0 298.9 264.2 299.9

WI 457.1 475.9 482.9 473.2 451.2 447.5 450.8 462.6

WV 898.8 898.5 851.0 861.2 852.4 883.3 851.6 871.0

*Source: EPA, March 2010. All relevant ARP units in the Transport Rule region. These data are available 

at http://www.epa.gov/airmarkets/ through Data and Maps.
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Table 2: Total Annual 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

AL 901.0 916.0 904.6 936.6 964.1 1,000.0 938.9 937.3

GA 841.9 822.4 853.4 948.0 968.5 1,033.0 958.2 917.9

IA 393.2 390.3 391.2 401.3 396.3 438.0 446.5 408.1

IL 976.1 982.7 1,046.2 1,053.8 1,038.2 1,086.2 1,059.9 1,034.7

IN 1,256.8 1,257.5 1,283.6 1,315.1 1,300.4 1,301.2 1,282.2 1,285.3

KS 429.2 429.4 412.5 406.8 381.2 410.0 378.6 406.8

KY 962.4 935.1 942.4 988.3 999.0 997.2 987.8 973.2

MD 288.2 290.6 280.8 287.8 282.6 286.0 263.7 282.8

MI 726.9 725.1 727.3 760.5 730.7 776.5 743.8 741.5

MN 399.0 423.1 396.4 401.8 391.7 394.6 371.6 396.9

MO 752.1 788.7 792.2 813.6 812.5 800.6 770.3 790.0

NC 718.8 706.3 718.4 747.2 727.1 767.7 749.7 733.6

NE 228.4 240.5 233.5 245.3 240.7 231.9 240.3 237.2

NJ 139.3 132.9 151.5 154.9 158.8 172.7 172.8 154.7

NY 288.3 297.3 309.9 322.9 380.6 393.1 366.6 337.0

OH 1,309.6 1,330.3 1,258.3 1,340.0 1,313.6 1,354.5 1,310.4 1,316.7

PA 1,060.2 1,090.0 1,155.1 1,172.4 1,185.4 1,240.9 1,200.0 1,157.7

SC 418.2 402.7 442.1 454.9 461.5 474.0 464.4 445.4

TN 635.7 580.5 592.1 592.6 613.2 623.8 582.3 602.9

TX 2,247.3 2,378.4 2,540.8 2,650.2 2,658.4 2,752.9 2,698.6 2,561.0

VA 356.6 333.6 329.5 339.5 324.0 354.8 312.3 335.7

WI 475.3 494.1 498.4 528.5 492.3 497.4 490.0 496.6

WV 900.5 899.9 852.0 863.3 855.8 887.1 853.3 873.1

*Source: EPA, March 2010. All relevant ARP units in the Transport Rule region. These data are available 

at http://www.epa.gov/airmarkets/ through Data and Maps.

** Coal-fired units; combined cycle turbines; and a combination of oil- and gas-fired boilers and simplecycle combustion turbines.
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Table 3: Total Annual 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

AL 427.4 428.8 423.6 427.2 456.9 469.9 432.5 438.1

AR 124.0 152.5 149.2 142.0 173.6 170.4 150.5 151.7

FL 548.6 554.9 563.3 615.1 639.0 656.3 646.0 603.3

GA 403.8 382.9 407.1 461.8 469.5 489.6 449.9 437.8

IA 170.8 161.8 161.8 183.5 168.9 189.8 182.0 174.1

IL 454.9 430.2 449.6 478.0 460.8 474.6 452.6 457.2

IN 557.2 545.4 540.7 578.4 566.4 559.8 550.5 556.9

KS 183.2 180.9 176.8 180.6 172.2 177.1 160.7 175.9

KY 433.3 400.1 401.0 434.4 435.3 436.1 417.8 422.6

LA 94.7 121.7 144.9 149.9 161.8 150.7 152.2 139.4

MD 134.8 118.8 121.8 131.5 123.3 126.8 117.1 124.9

MI 331.0 312.1 304.6 339.9 328.5 344.5 321.1 326.0

MO 337.7 347.6 348.0 354.2 362.8 357.7 341.7 349.9

MS 117.8 116.2 129.5 137.3 147.7 155.9 143.2 135.4

NC 332.9 308.5 314.0 341.5 340.4 351.5 344.8 333.4

NJ 68.7 59.9 73.9 76.1 79.9 86.1 86.6 75.9

NY 126.1 121.4 136.3 152.5 174.7 170.5 158.9 148.6

OH 583.7 562.8 538.4 574.4 562.0 596.7 553.3 567.3

OK 204.5 223.4 220.0 248.4 244.6 241.1 247.9 232.8

PA 465.4 468.7 494.6 529.3 530.6 550.8 515.5 507.8

SC 203.1 178.4 204.8 214.1 215.8 222.7 222.0 208.7

TN 291.2 244.3 256.2 270.1 273.2 278.9 261.2 267.9

TX 1,031.9 1,089.9 1,170.0 1,229.1 1,255.4 1,262.8 1,252.7 1,184.6

VA 160.0 146.9 150.9 154.3 151.5 167.9 144.5 153.7

WI 218.5 211.1 207.4 240.0 223.0 219.8 208.5 218.3

WV 379.5 384.5 361.0 375.4 373.1 390.3 366.9 375.8

*Source: EPA, March 2010; All relevant ARP units in the Transport Rule region. These data are available 

at http://www.epa.gov/airmarkets/ through Data and Maps.

** Coal-fired units; combined cycle turbines; and a combination of oil- and gas-fired boilers and simplecycle combustion turbines.
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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 EPA used to estimate the variability in heat 

input for each state covered by the final Transport Rule. It repeats some of the relevant 

data and calculations presented in the proposed Transport Rule TSD “Power Sector 

Variability” with updates reflecting the states covered by the final Transport Rule. 

In the final Transport Rule, EPA used the method presented in the proposed 

Transport Rule and 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 by deriving a standard 

deviation for each state while “screening out” overall growth or decline in heat input over 

that time period. This method is described in detail in this and the following subsections

and was selected for two reasons: First, it held constant any longer trend in growth or 

decline in a state’s heat input over the baseline period analyzed. Second, a statistical 

approach (i.e., using the standard deviation) is less sensitive to data anomalies present in 

finite data sets; in other words, it effectively screens out extraneous information to 

characterize long-run variability. 

For each state, it was important to first adjust the data set to screen out trends 

(growth or decline) in heat input over the 7-year time period, so that the data would then 

reflect year-to-year variability independent of trends in electricity demand. After 

controlling 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. To account for trends, a simple 

least-squares linear regression equation was fit to the heat input data as a function of time. 

In other words, this step of the analysis attempts to define the amount of variability in the 

data set for each state that can be attributed to a trend over time, which EPA is attempting 

to hold constant in this analysis to determine year-to-year variability independent of 

longer-run trends in heat input (e.g., rising electricity demand).

This process fits a straight line to the data points using an equation of the form (y = 

mx+b). In this equation, “y” is the estimated heat input (million MMBtu) for a particular 

year “x”, m is the slope of the line (with units of million MMBtu/year), and b is the “yintercept” (the heat input value when the line is extrapolated to x = 0). The value of r2

, the 
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square of the Pearson product moment correlation coefficient, describes the proportion of 

variance in “y” that is accounted for by knowing “x”. It indicates how significant a trend 

(growth or decline) is present in the data. Large r

2

values (r2values are maximized at 1) 

indicate that the least squares fit picks up a strong trend (upward or downward) in the data 

which should be removed by the procedure described below. This is important for states 

where there was substantial growth or decline in heat input over the time period (i.e., for 

states that have slopes for the regression line that are substantially different than zero). 

Small r

2

values, on the other hand, indicate that the least squares fit did not detect a strong 

trend (upward or downward) in the heat input data. Consequently, the procedure described 

below has less of a corrective role to play, since there is no strong trend that needs to be 

controlled for (held constant) when characterizing the data set’s year-to-year variability. 

For each state, the slopes of the regression equations, y-intercepts, and r2values can 

be found in Tables 4, 5, and 6. Using the slope and y-intercept from the regression 

equation for each state, yearly heat input values were estimated for each state for each year 

between 2002 and 2008 (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

GA 19.64 -38,527 0.74 783.0 802.6 822.3 841.9 861.5 881.2 900.8

IA 5.69 -11,002 0.41 380.1 385.8 391.4 397.1 402.8 408.5 414.2

IL 14.94 -28,960 0.70 953.4 968.3 983.3 998.2 1,013.2 1,028.1 1,043.0

IN 2.92 -4,592 0.12 1,246.3 1,249.2 1,252.1 1,255.0 1,257.9 1,260.9 1,263.8

KS -8.59 17,633 0.74 429.9 421.3 412.7 404.1 395.5 386.9 378.3

KY 8.52 -16,113 0.68 935.8 944.3 952.8 961.3 969.9 978.4 986.9

MD -2.13 4,545 0.36 278.7 276.6 274.5 272.3 270.2 268.1 265.9

MI 3.26 -5,825 0.30 702.8 706.0 709.3 712.5 715.8 719.1 722.3

MN -8.25 16,915 0.80 403.3 395.1 386.8 378.6 370.3 362.1 353.8

MO 0.80 -836 0.01 758.6 759.4 760.2 761.0 761.8 762.6 763.4

NC 4.90 -9,111 0.54 690.6 695.5 700.4 705.3 710.2 715.1 720.0

NE -0.32 869 0.01 233.1 232.8 232.5 232.1 231.8 231.5 231.2

NJ -1.58 3,241 0.15 83.2 81.7 80.1 78.5 76.9 75.4 73.8

NY -8.01 16,282 0.85 240.2 232.2 224.2 216.2 208.2 200.2 192.2

OH 1.88 -2,480 0.03 1,290.4 1,292.3 1,294.2 1,296.1 1,298.0 1,299.8 1,301.7

PA 8.46 -15,880 0.46 1,047.0 1,055.5 1,063.9 1,072.4 1,080.8 1,089.3 1,097.7

SC 6.84 -13,312 0.90 383.1 390.0 396.8 403.7 410.5 417.3 424.2

TN -1.36 3,314 0.02 600.8 599.5 598.1 596.8 595.4 594.1 592.7

TX 3.89 -6,231 0.15 1,555.1 1,559.0 1,562.9 1,566.7 1,570.6 1,574.5 1,578.4

VA -9.45 19,252 0.77 328.2 318.8 309.3 299.9 290.4 281.0 271.5

WI -3.84 8,161 0.34 474.2 470.3 466.5 462.6 458.8 455.0 451.1

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

GA 31.61 -62,466 0.75 823.1 854.7 886.3 917.9 949.5 981.1 1,012.8

IA 9.30 -18,233 0.72 380.2 389.5 398.8 408.1 417.4 426.7 436.0

IL 16.09 -31,231 0.73 986.5 1,002.5 1,018.6 1,034.7 1,050.8 1,066.9 1,083.0

IN 6.44 -11,635 0.39 1,265.9 1,272.4 1,278.8 1,285.3 1,291.7 1,298.2 1,304.6

KS -7.92 16,295 0.70 430.6 422.7 414.8 406.8 398.9 391.0 383.1

KY 9.17 -17,418 0.56 945.7 954.8 964.0 973.2 982.3 991.5 1,000.7

MD -2.89 6,080 0.47 291.5 288.6 285.7 282.8 279.9 277.0 274.1

MI 5.60 -10,480 0.37 724.7 730.3 735.9 741.5 747.1 752.7 758.3

MN -5.14 10,702 0.53 412.3 407.2 402.0 396.9 391.7 386.6 381.5

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

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

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

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

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

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

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

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

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

TX 79.30 -156,429 0.86 2,323.1 2,402.4 2,481.7 2,561.0 2,640.3 2,719.6 2,798.8

VA -3.42 7,203 0.21 346.0 342.6 339.2 335.7 332.3 328.9 325.5

WI 1.60 -2,711 0.05 491.8 493.4 495.0 496.6 498.2 499.8 501.4

WV -5.83 12,568 0.33 890.6 884.8 879.0 873.1 867.3 861.5 855.6

* Coal-fired units; combined cycle turbines; and a combination of oil- and gas-fired boilers and simple-cycle 

combustion turbines.
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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

FL 20.38 -40,256 0.90 542.2 562.5 582.9 603.3 623.7 644.1 664.4

GA 14.80 -29,231 0.64 393.4 408.2 423.0 437.8 452.6 467.4 482.2

IA 3.44 -6,732 0.45 163.7 167.2 170.6 174.1 177.5 181.0 184.4

IL 3.33 -6,214 0.20 447.3 450.6 453.9 457.2 460.6 463.9 467.2

IN 1.23 -1,911 0.04 553.2 554.4 555.7 556.9 558.1 559.4 560.6

KS -2.84 5,880 0.65 184.5 181.6 178.8 175.9 173.1 170.3 167.4

KY 2.15 -3,883 0.08 416.1 418.3 420.4 422.6 424.7 426.9 429.0

LA 8.84 -17,590 0.67 112.9 121.7 130.6 139.4 148.3 157.1 166.0

MD -1.27 2,671 0.18 128.7 127.4 126.1 124.9 123.6 122.3 121.1

MI 2.10 -3,891 0.10 319.7 321.8 323.9 326.0 328.1 330.2 332.3

MO 1.68 -3,026 0.17 344.9 346.6 348.3 349.9 351.6 353.3 355.0

MS 6.21 -12,325 0.80 116.7 122.9 129.1 135.4 141.6 147.8 154.0

NC 5.29 -10,265 0.50 317.5 322.8 328.1 333.4 338.7 343.9 349.2

NJ 4.01 -7,956 0.82 63.9 67.9 71.9 75.9 79.9 83.9 87.9

NY 8.39 -16,679 0.74 123.4 131.8 140.2 148.6 157.0 165.4 173.8

OH 0.01 552 0.00 567.3 567.3 567.3 567.3 567.3 567.3 567.3

OK 6.80 -13,402 0.75 212.4 219.2 226.0 232.8 239.6 246.4 253.2

PA 12.52 -24,588 0.69 470.3 482.8 495.3 507.8 520.4 532.9 545.4

SC 5.58 -10,974 0.62 192.0 197.5 203.1 208.7 214.3 219.8 225.4

TN -0.12 517 0.00 268.2 268.1 268.0 267.9 267.7 267.6 267.5

TX 39.05 -77,113 0.85 1,067.4 1,106.5 1,145.5 1,184.6 1,223.6 1,262.7 1,301.7

VA -0.14 434 0.00 154.1 154.0 153.9 153.7 153.6 153.4 153.3

WI 0.10 24 0.00 218.0 218.1 218.2 218.3 218.4 218.5 218.6

WV -0.50 1,387 0.01 377.3 376.8 376.3 375.8 375.3 374.8 374.3

* Coal-fired units; combined cycle turbines; and a combination of oil- and gas-fired boilers and simple-cycle 

combustion turbines.

On a state-by-state basis, EPA calculated the difference between the actual heat 

input and the estimated heat input using the regression equation for each year (2002 

through 2008). 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; the size of the variation on either side of the regression line is 

meaningful for this analysis. EPA assessed the differences between actual and estimated 

heat input across all years for each state and pollutant by calculating the standard 

deviation. 
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The standard deviation is defined as the square root of the variance (the sum of the 

square of the differences2divided by the number of samples minus one). The state- and 

pollutant-specific representative differences (defined using the standard deviation) were 

used for the remaining steps in the variability analyses. In using the standard deviation as 

a representative difference, we assume that: (1) differences between the actual and 

modeled heat inputs are “normally” distributed; and that (2) yearly mean values are 

independent.

For each state, the standard deviation of the differences is a conventional statistical 

measure of the year-to-year (1-year) variation in heat input. The statistical definition of a 

standard deviation in this context conveys that, on average, 68% of all the year-specific 

heat input values over the long run would be no farther from the average annual heat input

(either higher or lower) than by the amount measured as the standard deviation. 

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

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

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

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

each state for each pollutant. Following the analysis from the proposed Transport Rule and 

after reviewing all relevant public comment, the two-tailed 95

th percent confidence level 

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

level indicates that we could expect that, on average, 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. This statistical confidence interval is very commonly

used to make future projections from data sets similar to EPA’s purpose in this analysis. 

As described in the proposal, EPA believes that using the 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 while still meeting their obligations to 

provide a reliable electricity supply across the states affected. 

 

2 On a state-by-state basis, the standard deviation was calculated from the set of yearly difference values. As 

described, the yearly values were the difference between the actual heat input and the estimated heat input 

(using the regression equation) for each year.
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The 95% upper confidence level heat inputs and fractions (the 95% heat input 

difference divided by the average heat input over the 2002-2008 time period) can be found 

in Tables 7, 8, and 9. 

The standard deviation in the heat input in million MMBtu rounded to three 

significant digits can be found in Tables 7, 8, and 9. The standard deviation of the heat 

input was divided by the average heat input value, resulting in a fraction (when the fraction 

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

GA -1.8 -15.2 -13.5 29.7 11.0 29.4 -39.7 25.4 841.9 0.03 49.7 0.059

IA 13.0 3.9 -4.8 -12.7 -23.9 7.4 17.0 14.6 397.1 0.037 28.7 0.073

IL -22.5 -6.4 43.8 3.8 -11.0 2.4 -10.1 21.3 998.2 0.021 41.7 0.041

IN -14.9 -12.9 13.9 26.1 11.9 -4.0 -20.2 17.5 1,255.0 0.014 34.3 0.027

KS -2.4 6.3 -0.8 1.1 -17.9 18.4 -4.8 11.0 404.1 0.027 21.6 0.053

KY 10.7 -13.2 -14.8 9.0 17.2 -0.5 -8.4 12.6 961.3 0.013 24.7 0.025

MD -1.8 1.4 -4.6 1.4 4.5 9.0 -10.0 6.2 272.3 0.023 12.1 0.045

MI -2.7 0.8 0.5 8.5 -16.1 16.9 -7.9 10.7 712.5 0.015 21.0 0.029

MN -14.1 16.0 -0.2 1.6 0.0 0.8 -4.0 8.9 378.6 0.023 17.4 0.045

MO -34.0 10.2 10.7 22.6 21.7 -0.6 -30.6 23.4 761.0 0.031 45.9 0.061

NC 2.7 -6.5 -4.0 14.3 -11.1 11.3 -6.8 9.7 705.3 0.014 19.1 0.027

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

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

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

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

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

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

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

TX -25.2 14.0 17.5 14.5 -24.2 16.1 -12.8 19.8 1,566.7 0.013 38.8 0.025

VA 12.3 -6.3 -12.7 -1.5 -2.4 17.9 -7.3 11.1 299.9 0.037 21.7 0.073

WI -17.1 5.6 16.4 10.5 -7.7 -7.5 -0.3 11.7 462.6 0.025 22.9 0.049

WV 9.5 15.4 -26.1 -9.7 -12.5 24.5 -1.1 17.6 871.0 0.02 34.5 0.039
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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

GA 18.8 -32.3 -32.9 30.1 18.9 51.8 -54.5 39.6 917.9 0.043 77.6 0.084

IA 13.0 0.8 -7.7 -6.8 -21.2 11.3 10.5 12.6 408.1 0.031 24.8 0.061

IL -10.4 -19.8 27.5 19.1 -12.6 19.3 -23.1 21.2 1,034.7 0.020 41.5 0.039

IN -9.2 -14.8 4.8 29.8 8.7 3.0 -22.3 17.3 1,285.3 0.013 33.9 0.025

KS -1.4 6.7 -2.3 -0.1 -17.7 19.1 -4.4 11.2 406.8 0.027 21.9 0.053

KY 16.8 -19.7 -21.6 15.1 16.6 5.7 -12.9 17.5 973.2 0.018 34.3 0.035

MD -3.3 2.0 -4.9 5.0 2.7 8.9 -10.4 6.6 282.8 0.023 12.9 0.045

MI 2.2 -5.3 -8.7 19.0 -16.5 23.7 -14.5 15.9 741.5 0.021 31.1 0.041

MN -13.3 15.9 -5.6 4.9 -0.1 8.0 -9.8 10.4 396.9 0.026 20.4 0.051

MO -27.3 5.7 5.8 23.6 19.0 3.6 -30.3 21.1 790.0 0.027 41.3 0.053

NC 9.2 -11.3 -7.2 13.6 -14.5 18.1 -7.9 13.2 733.6 0.018 25.9 0.035

NE -6.1 5.1 -2.7 8.1 2.5 -7.2 0.3 5.7 237.2 0.024 11.2 0.047

NJ 4.7 -8.4 3.5 0.2 -2.6 4.6 -1.9 4.8 154.7 0.031 9.4 0.061

NY 4.6 -4.1 -9.3 -14.1 25.9 20.6 -23.6 18.1 337.0 0.054 35.5 0.106

OH 4.3 21.2 -54.6 23.3 -6.9 30.3 -17.6 29.6 1,316.7 0.023 58.1 0.045

PA -17.0 -14.0 24.2 14.7 0.8 29.5 -38.3 24.6 1,157.7 0.021 48.2 0.041

SC 5.0 -21.2 7.4 9.5 5.3 7.2 -13.2 12.1 445.4 0.027 23.7 0.053

TN 27.2 -26.1 -12.7 -10.3 12.2 24.7 -15.0 21.1 602.9 0.035 41.3 0.069

TX -75.7 -23.9 59.2 89.2 18.2 33.3 -100.2 69.8 2,561.0 0.027 136.9 0.053

VA 10.5 -9.0 -9.7 3.8 -8.3 25.9 -13.2 14.2 335.7 0.042 27.9 0.082

WI -16.5 0.7 3.4 31.9 -5.9 -2.3 -11.3 15.7 496.6 0.032 30.7 0.063

WV 9.8 15.1 -26.9 -9.8 -11.5 25.7 -2.4 18.0 873.1 0.021 35.2 0.041

* Coal-fired units; combined cycle turbines; and a combination of oil- and gas-fired boilers and simple-cycle 

combustion turbines.
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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

FL 6.4 -7.6 -19.6 11.8 15.3 12.2 -18.5 15.0 603.3 0.025 29.4 0.049

GA 10.4 -25.3 -15.9 24.0 16.9 22.2 -32.3 23.8 437.8 0.054 46.7 0.106

IA 7.1 -5.4 -8.8 9.4 -8.6 8.8 -2.5 8.2 174.1 0.047 16.1 0.092

IL 7.6 -20.4 -4.3 20.8 0.3 10.7 -14.6 14.5 457.2 0.032 28.3 0.063

IN 4.0 -9.1 -15.0 21.5 8.3 0.5 -10.1 12.6 556.9 0.023 24.7 0.045

KS -1.3 -0.7 -2.0 4.7 -0.9 6.9 -6.7 4.5 175.9 0.026 8.8 0.051

KY 17.1 -18.2 -19.4 11.8 10.5 9.3 -11.2 15.6 422.6 0.037 30.6 0.073

LA -18.2 -0.1 14.4 10.5 13.5 -6.4 -13.7 13.3 139.4 0.095 26.1 0.186

MD 6.1 -8.6 -4.4 6.6 -0.3 4.5 -4.0 5.9 124.9 0.047 11.6 0.092

MI 11.4 -9.7 -19.2 13.9 0.4 14.4 -11.2 13.7 326.0 0.042 26.8 0.082

MO -7.2 1.0 -0.3 4.3 11.2 4.4 -13.3 8.1 349.9 0.023 15.8 0.045

MS 1.1 -6.8 0.3 1.9 6.1 8.1 -10.8 6.7 135.4 0.050 13.2 0.098

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

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

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

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

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

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

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

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

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

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

WI 0.5 -7.0 -10.8 21.6 4.5 1.3 -10.2 11.3 218.3 0.052 22.1 0.102

WV 2.2 7.6 -15.3 -0.4 -2.2 15.5 -7.4 10.0 375.8 0.027 19.5 0.053

* Coal-fired units; combined cycle turbines; and a combination of oil- and gas-fired boilers and simple-cycle 

combustion turbines
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(c) Estimation of variability in emissions using the year-to-year variability in 

historical heat input. 

The analysis described in the sections above results in estimated 95% confidence 

level historic variabilities in heat input for each state and pollutant (Tables 7, 8, and 9). As

described in the preamble in section VI.E.2, from these variability values, EPA identified a 

specific variability percentage level for each state (Table 10). This was typically 10%, 

unless the state’s historic heat input variability was higher than 10%. In those cases, EPA 

rounded those budgets’ variability limit percentages up to the next five-percent increment 

(e.g., 15 percent, 20 percent) that successfully accommodated the historically observed 

variability, up to a maximum of 20%. The relationship between the historic variability

values and the variability percentage levels can be found in Table 10.

As described in section VI.E.2 of the preamble, the final step in estimating stateby-state year-to-year (1-year) variability in emissions is to convert the variability

percentage level in each state into variability in pollutant emissions (i.e., a “variability 

limit”). EPA did this by multiplying the variability percentage level in each state for each 

pollutant by the state emissions budgets for each pollutant (section VI.D of the preamble 

discusses EPA’s approach to determine the final Transport Rule budgets, and lists the 

budgets in Tables VI.D-3 and VI.D-4). For each state, Tables VI.F-1, VI.F-2, and VI.F-3 

list the resulting variability limits for each state for SO2, annual NOX and ozone-season 

NOX, respectively.

Table 10. Relationship between 95% Confidence Level Historic Variability in Heat Input 

and Variability Limit Used in the Transport Rule*.

SO2 Annual NOX Ozone-Season NOX

State Historic 

Variability

Transport 

Rule 

Variability 

Limit

Historic 

Variability

Transport 

Rule 

Variability 

Limit

Historic 

Variability

Transport 

Rule 

Variability 

Limit

AL 5.9% 10% 4.9% 10% 6.7% 10%

AR 16.7% 20%

FL 4.9% 10%

GA 5.9% 10% 8.4% 10% 10.6% 15%
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IL 4.1% 10% 3.9% 10% 6.3% 10%

IN 2.7% 10% 2.5% 10% 4.5% 10%

IA* 7.3% 10% 6.1% 10% 9.2% 10%

KS* 5.3% 10% 5.3% 10% 5.1% 10%

KY 2.5% 10% 3.5% 10% 7.3% 10%

LA 18.6% 20%

MD 4.5% 10% 4.5% 10% 9.2% 10%

MI* 2.9% 10% 4.1% 10% 8.2% 10%

MN 4.5% 10% 5.1% 10%

MS 9.8% 10%

MO* 6.1% 10% 5.3% 10% 4.5% 10%

NE 4.9% 10% 4.7% 10%

NJ 20% 20% 6.1% 10% 10.4% 15%

NY 6.7% 10% 10.6% 15% 14.3% 15%

NC 2.7% 10% 3.5% 10% 6.7% 10%

OH 3.7% 10% 4.5% 10% 6.7% 10%

OK* 7.3% 10%

PA 3.7% 10% 4.1% 10% 7.1% 10%

SC 2.4% 10% 5.3% 10% 9.0% 10%

TN 6.3% 10% 6.9% 10% 11.4% 15%

TX 2.5% 10% 5.3% 10% 5.9% 10%

VA 7.3% 10% 8.2% 10% 10.2% 15%

WV 3.9% 10% 4.1% 10% 5.3% 10%

WI* 4.9% 10% 6.3% 10% 10.2% 15%

*Indicates a state (IA, KS, MI, OK, WI, and MO) that is included in the supplemental 

notice of proposed rulemaking for ozone-season NOx emission reductions. See the 

preamble for details.

3. Numerical simulation of 1- and 3-year variability

For the reasons discussed in section VI.E of the preamble, although EPA proposed 

to implement both 1-year variability limits and 3-year average variability limits, the final 

Transport Rule includes only the 1-year limits. In the proposal, EPA first established 1-

year variability limits (following the procedure outlined above) and then established 

corresponding 3-year variability limits to be statistically indistinguishable from the 1-year 

variability limits. In other words, the proposal’s 3-year variability limits were calculated 

on a statistical basis to offer states the same degree of future variability (based on historic 

variability) as the 1-year variability limits. For the final rule, EPA analyzed the 

relationship between the 1-year and 3-year variability limits, performing a numerical 
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verification of equal likelihood of potential violations of a state’s assurance level when 

evaluating random variation (10% at the 95% two-tailed confidence level) around a 

constant budget. 

As discussed in section VI.E of the final Transport Rule preamble, the state-bystate emissions budgets are based on the availability of emission reductions at an equal 

cost threshold (within each of the programs). As such, EPA expects the sources in each 

covered state to make these cost-effective reductions and to meet the emission budgets 

each year. On average EPA does not expect any systematic bias in emissions (against the 

established state budgets) to occur. Thus, a non-biased assessment of variability is 

appropriate. EPA simulated 1- and 3-year variation around a constant budget for a

hypothetical state, and found, as described below, comparable results affirming that on a 

statistical basis, the 3-year variability limits are no more likely to be exceeded than the 1-

year variability limits.

Using a random-number generator within Microsoft Excel, EPA numerically

simulated random variation using the 95% two-tailed confidence level 1-year variability of 

10%. The 1-year variability standard deviation is 10% divided by 1.960, or 5.1%. Using 

the formula, =NORMINV(RAND(),100,(10/1.960)), EPA simulated yearly emissions for a 

“state” with a budget of 100, with random variation of 5.1%. EPA simulated five 

consecutive “years” of emissions, then calculated three 3-year consecutive averages 

(“years” 1, 2, and 3; 2, 3, and 4; and 3, 4, and 5) . EPA, then, assessed the average number 

of “exceedances” of the 1-year and 3-year “budgets plus variability limits”. The 1-year 

budget plus variability limit is 110, while the 3-year budget plus variability limit (as 

defined in the proposal) is 105.77. EPA simulated 10,000 5-year time-periods, and 

averaged the number of exceedances of the 1-year (50,000 data points) and 3-year limits 

(30,000 data points). For both the 1-year and 3-year cases, the average number of 

exceedances was 2.5%, conforming to the expected 95% two-tailed confidence exceedance 

value of 2.5%.

This finding confirms that the proposed 3-year variability limits are statistically

indistinguishable from the 1-year variability limits for each state; therefore, EPA has 

determined that the 3-year variability limits are redundant with the 1-year variability limits 

and are thus unnecessary.
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4. Results of an analysis using the air quality assessment tool

The objective of this section is to estimate the possible effects on air quality in 

2014 of the variability limits. This analysis used the air quality assessment tool, or AQAT, 

and state-by-state emissions to estimate downwind state-by-state air quality contributions 

at receptors with nonattainment and maintenance problems in the CAMx air quality 

modeling of 2012 base case (section VI.C of the final Transport Rule preamble discusses 

AQAT; section V of the final Transport Rule preamble discusses air quality modeling). 

This analysis is similar to the analyses performed for the proposed Transport Rule (and 

reported in the proposed Transport Rule “Power Sector Variability” TSD). It concludes 

that, as estimated using AQAT, while variability in power system operation (and thereby 

SO2 emissions from EGUs under the Transport Rule) does have perceptible downwind air 

quality effects on PM2.5 concentrations, these effects are minimal. EPA believes that 

variation in other factors such as SO2 and NOx emission patterns from non-EGU sources, 

contributions from other pollutants (i.e., ammonia, elemental and organic carbon for PM2.5 

and volatile organic carbon for ozone), and meteorological factors play important roles in 

downwind air quality.

For this analysis, EPA varied the EGU SO2 emissions of upwind states included in 

the final Transport Rule that are “linked” to downwind receptors (as described in section V

of the preamble). The variation in emissions was designed to reflect the allowance of 

inherent year-to-year (1-year) variability in power system operations above average statelevel emissions equal to the $2,300/ton and $500/ton SO2 cost thresholds for group 1 and 

group 2 states respectively (“$2,300/ton” for the purposes of this discussion). Details on 

this scenario (TR_SO2_2300_Final) can be found in appendix A of the Significant 

Contribution and State Emissions Budgets Final Rule TSD. EPA estimated the air quality 

impacts using the change in the maximum daily PM2.5 design value. This analysis focused 

on variability limits related to annual SO2 emissions and the 24-hour PM2.5 concentrations 

for the same reasons listed in the proposed Transport Rule “Power Sector Variability” 

TSD, with the addition that the improved “calibrated” AQAT used in the final Transport 

Rule only allows SO2 emissions to be varied for estimating downwind ammonium sulfate 

concentrations (which is discussed further in section VI.C in the final rule’s preamble).
24

Deliberative – Do Not Cite or Quote

In contrast to the analyses in the proposed Transport Rule, this analysis focuses on 

the “worst-case” emissions variability in 2014. This is intended to estimate upper bounds 

for the effects of year-to-year variability in power system operations on air quality. For 

this analysis, the “worst case” situation is defined as the hypothetical scenario in which all 

states that are linked to a particular receptor increase their SO2 emissions to a “variability

maximum” equal to the 2014 emissions equal to the final Transport Rule cost thresholds

plus a fixed percentage (variability level) of the state budget. States that were not linked to 

the receptor were held at base case SO2 emission levels. Four variability levels were 

examined (5, 10, 15, and 20%). These levels were chosen to bracket the variability level 

selected for most states (i.e., 10%). To be consistent with the analysis of significant 

contribution and interference with maintenance, the SO2 emissions from the state 

containing the receptor were also increased to the final Transport Rule cost thresholds plus 

variability level. A second assessment of this analysis focused on the effects of variation 

in emissions due only to the upwind states. The emissions from the state containing the 

receptor (home state) were maintained at a constant level, equal to its final Transport Rule 

cost threshold. By comparing the two assessments it is possible to get a sense of the 

relative importance of variation of upwind power system operations compared with 

variation in the home state’s power system operations.

This variability analysis does not balance emission increases in some states with 

emission decreases in other states (as was done in the analysis for the proposed Transport 

Rule). Specifically, this analysis does not account for corresponding decreases from other 

states included in the final Transport Rule which are not contributing above the 1% 

threshold to a particular receptor. 

EPA specifically notes that this analysis is intended to demonstrate the potential air 

quality impacts from year-to-year variability under “extreme” worst-case scenarios, where 

states linked to a downwind receptor simultaneously need to utilize the maximum power 

sector variability; it is distinct from the “typical” operating conditions represented in an 

“average” year under which significant contribution and interference with maintenance are

examined to inform EPA’s determination of emission reductions available at various cost 

thresholds. In other words, the Transport Rule state budgets are established on the basis of 

conditions in an average year, while the variability provisions address the inherent year-to-
25

Deliberative – Do Not Cite or Quote

year fluctuations of power sector operations around those average conditions. This 

analysis examines the fluctuations in downwind air quality that may be attributable to

various potential degrees of year-to-year power sector emission variability around upwind 

state budgets. Such an analysis is fundamentally different from the Transport Rule’s 

analysis to determine significant contribution and interference with maintenance, which 

includes an examination of downwind air quality impacts not on a fluctuating year-to-year 

basis, but on the basis of the degree of stringency of upwind emissions control under 

expected (i.e., average) operating conditions as represented by EPA’s power sector 

modeling projections. Therefore, the results of this TSD’s analysis are not pertinent to the 

Agency’s consideration of downwind air quality impacts as one factor in its multifactor 

analysis of significant contribution and interference with maintenance.

The results of the analysis can be found in Table 11 and in Figure 1, below. The 

estimated increases in maximum design value PM2.5 concentrations across the range of 

variability levels examined are relatively small, with a maximum of 0.57 g/m3at 20%

variability for Jefferson County, AL. Averaged across the receptors with nonattainment 

and maintenance problems in the CAMx air quality modeling of the 2012, the increase was 

0.29 g/m3at 20% variability. For the second variation of the analysis, when the home 

state emissions are held constant at the final Transport Rule cost threshold, the increase 

averaged 0.19 g/m3. (Table 11 and Figure 2). The maximal increase was 0.41g/m3for 

receptor number 420031008 in Allegheny, Pennsylvania. For most locations, the largest 

contributor to increased PM2.5 concentration from a single upwind state will be from 

sources within the state containing the receptor itself (this conclusion is drawn from 

comparing the Worst Case (All States) and Worst Case (Upwind States) columns in Table 

22 from the “Power Sector Variability” TSD from the proposed Transport Rule as well as 

by comparing the difference in air quality estimates between 0 and 20% variation for the

two cases (compare the differences from Tables 11 and 12).
Deliberative 

*The six sites are Allegheny, PA (64); Lancaster, PA (07); Wayne, MI (16 and 19); Cook, IL (16); and Lake, IN (22). 

**The eight sites include the six sites listed above as well as Cuyahoga, 

Figure 1. Increase in maximum PM

threshold level as the variability level is increased from 0 to 20 percent, individually for 

the eight sites (left panel) as well as for v

combinations of sites can be found in Table 10.

26

Deliberative – Do Not Cite or Quote

*The six sites are Allegheny, PA (64); Lancaster, PA (07); Wayne, MI (16 and 19); Cook, IL (16); and Lake, IN (22). 

**The eight sites include the six sites listed above as well as Cuyahoga, OH (38) and Wayne, MI (33).

Figure 1. Increase in maximum PM2.5 design value (g/m3) in 2014 at the $2300/ton cost 

level as the variability level is increased from 0 to 20 percent, individually for 

the eight sites (left panel) as well as for various combinations of sites (right panel). The 

combinations of sites can be found in Table 10.

*The six sites are Allegheny, PA (64); Lancaster, PA (07); Wayne, MI (16 and 19); Cook, IL (16); and Lake, IN (22). 

) in 2014 at the $2300/ton cost 

level as the variability level is increased from 0 to 20 percent, individually for 

arious combinations of sites (right panel). The 
27

Deliberative – Do Not Cite or Quote

Table 11. Maximum PM2.5 DVs (g/m3) in 2014 With Linked States at the Final Transport 

Rule Cost Threshold Level with Various Additional Amounts of Emissions Due to 

Variability.

Site ID State County Variability (%)

Difference in air quality between 

the 0% variability level and 

another level. 

0 5 10 15 20 0 5 10 15 20

avg. of 6 sites* 38.39 38.44 38.50 38.55 38.60 0.00 0.05 0.11 0.16 0.21

avg. of 8 sites** 37.62 37.68 37.74 37.80 37.85 0.00 0.06 0.12 0.17 0.23

max of all 2012 base case 48.63 48.72 48.85 48.97 49.09 0.00 0.14 0.29 0.44 0.57

420030064 Pennsylvania Allegheny 48.63 48.72 48.85 48.97 49.09 0.00 0.09 0.21 0.34 0.46

420030093 Pennsylvania Allegheny 34.80 34.90 35.01 35.11 35.21 0.00 0.10 0.21 0.31 0.41

390350038 Ohio Cuyahoga 35.41 35.44 35.47 35.50 35.53 0.00 0.03 0.06 0.09 0.12

261630016 Michigan Wayne 35.65 35.69 35.73 35.77 35.81 0.00 0.04 0.08 0.12 0.16

390350060 Ohio Cuyahoga 33.04 33.10 33.14 33.16 33.18 0.00 0.06 0.10 0.12 0.15

170311016 Illinois Cook 36.54 36.62 36.70 36.78 36.85 0.00 0.08 0.16 0.24 0.31

261630033 Michigan Wayne 35.23 35.35 35.46 35.58 35.69 0.00 0.12 0.23 0.35 0.46

180890022 Indiana Lake 36.51 36.57 36.61 36.64 36.67 0.00 0.07 0.10 0.14 0.17

540090011

West 

Virginia Brooke

30.02 30.14 30.26 30.38 30.49 0.00 0.12 0.24 0.36 0.47

420710007 Pennsylvania Lancaster 37.18 37.19 37.20 37.21 37.22 0.00 0.01 0.02 0.03 0.04

390350045 Ohio Cuyahoga 27.60 27.67 27.75 27.83 27.90 0.00 0.08 0.15 0.23 0.31

390811001 Ohio Jefferson 28.03 28.14 28.26 28.37 28.49 0.00 0.12 0.23 0.35 0.46

261630019 Michigan Wayne 35.83 35.86 35.89 35.92 35.95 0.00 0.03 0.06 0.09 0.12

390350065 Ohio Cuyahoga 27.00 27.10 27.20 27.30 27.41 0.00 0.10 0.20 0.30 0.41

170313301 Illinois Cook 32.84 32.90 32.95 33.01 33.07 0.00 0.06 0.11 0.17 0.22

420070014 Pennsylvania Beaver 28.70 28.77 28.86 28.96 29.06 0.00 0.06 0.16 0.26 0.36

420033007 Pennsylvania Allegheny 28.81 28.90 28.99 29.08 29.17 0.00 0.09 0.18 0.27 0.37

010730023 Alabama Jefferson 32.12 32.26 32.41 32.55 32.69 0.00 0.14 0.29 0.44 0.57

550790026 Wisconsin Milwaukee 33.21 33.26 33.31 33.36 33.42 0.00 0.05 0.11 0.16 0.21

180970043 Indiana Marion 27.82 27.91 28.00 28.09 28.18 0.00 0.09 0.18 0.27 0.35

261470005 Michigan St Clair 33.38 33.41 33.44 33.47 33.51 0.00 0.03 0.06 0.09 0.13

550790043 Wisconsin Milwaukee 33.92 33.96 34.00 34.04 34.07 0.00 0.04 0.08 0.12 0.15

180890026 Indiana Lake 33.37 33.40 33.44 33.47 33.50 0.00 0.03 0.07 0.10 0.14

180970081 Indiana Marion 27.59 27.72 27.83 27.94 28.05 0.00 0.13 0.24 0.35 0.46

180970066 Indiana Marion 29.13 29.27 29.40 29.51 29.62 0.00 0.14 0.28 0.38 0.49

171191007 Illinois Madison 30.66 30.76 30.82 30.88 30.95 0.00 0.10 0.16 0.22 0.28

550790010 Wisconsin Milwaukee 33.13 33.18 33.23 33.29 33.34 0.00 0.05 0.11 0.16 0.21

390170003 Ohio Butler 27.33 27.42 27.53 27.65 27.76 0.00 0.09 0.20 0.32 0.43

170316005 Illinois Cook 34.82 34.86 34.90 34.95 34.99 0.00 0.04 0.09 0.13 0.18

420031008 Pennsylvania Allegheny 25.62 25.76 25.89 26.03 26.17 0.00 0.14 0.27 0.41 0.54

261610008 Michigan Washtenaw 29.33 29.36 29.40 29.43 29.47 0.00 0.03 0.07 0.10 0.14

170312001 Illinois Cook 32.33 32.38 32.44 32.49 32.54 0.00 0.05 0.10 0.15 0.20

170310052 Illinois Cook 30.31 30.36 30.40 30.45 30.49 0.00 0.04 0.09 0.13 0.18

421330008 Pennsylvania York 33.91 33.94 33.97 34.00 34.03 0.00 0.03 0.06 0.08 0.11

261630015 Michigan Wayne 31.99 32.04 32.08 32.14 32.25 0.00 0.04 0.09 0.15 0.26

010732003 Alabama Jefferson 31.91 31.99 32.06 32.13 32.20 0.00 0.07 0.15 0.22 0.29

390618001 Ohio Hamilton 26.73 26.81 26.89 26.98 27.06 0.00 0.08 0.17 0.25 0.33

171190023 Illinois Madison 29.50 29.57 29.65 29.72 29.79 0.00 0.07 0.15 0.22 0.29

420031301 Pennsylvania Allegheny 26.15 26.23 26.30 26.38 26.45 0.00 0.08 0.15 0.23 0.30

391130032 Ohio Montgomery 24.62 24.70 24.79 24.87 24.96 0.00 0.09 0.17 0.26 0.34

420030116 Pennsylvania Allegheny 26.34 26.42 26.50 26.59 26.67 0.00 0.08 0.17 0.25 0.33

*The six sites are Allegheny, PA (64); Lancaster, PA (07); Wayne, MI (16 and 19); Cook, IL (16); and Lake, IN (22). 

**The eight sites include the six sites listed above as well as Cuyahoga, OH (38) and Wayne, MI (33).
28

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Table 12. Maximum PM2.5 DVs (g/m3) in 2014 with Linked States at the Final Transport 

Rule Cost Threshold Level with Various Additional Amounts of Emissions Due to 

Variability with the Home State Held Constant with No Variability at the 2014 Final 

Transport Rule Cost Threshold.

Site ID State County Variability (%)

Difference in air quality between 

the 0% variability level and 

another level. 

0 5 10 15 20 0 5 10 15 20

avg. of 6 sites* 38.39 38.42 38.46 38.49 38.52 0.00 0.03 0.07 0.10 0.13

avg. of 8 sites** 37.62 37.66 37.69 37.73 37.76 0.00 0.03 0.07 0.10 0.14

max of all 2012 base case 48.63 48.69 48.78 48.87 48.95 0.00 0.10 0.21 0.31 0.41

420030064 Pennsylvania Allegheny 48.63 48.69 48.78 48.87 48.95 0.00 0.06 0.14 0.23 0.32

420030093 Pennsylvania Allegheny 34.80 34.88 34.95 35.03 35.10 0.00 0.08 0.15 0.23 0.30

390350038 Ohio Cuyahoga 35.41 35.42 35.44 35.46 35.47 0.00 0.02 0.03 0.05 0.06

261630016 Michigan Wayne 35.65 35.67 35.68 35.69 35.70 0.00 0.01 0.02 0.04 0.05

390350060 Ohio Cuyahoga 33.04 33.07 33.11 33.13 33.14 0.00 0.04 0.07 0.09 0.10

170311016 Illinois Cook 36.54 36.60 36.67 36.73 36.79 0.00 0.06 0.13 0.19 0.25

261630033 Michigan Wayne 35.23 35.29 35.35 35.41 35.47 0.00 0.06 0.12 0.18 0.24

180890022 Indiana Lake 36.51 36.55 36.58 36.60 36.62 0.00 0.05 0.07 0.09 0.11

540090011

West 

Virginia Brooke

30.02 30.12 30.21 30.31 30.41 0.00 0.10 0.19 0.29 0.39

420710007 Pennsylvania Lancaster 37.18 37.18 37.18 37.19 37.19 0.00 0.00 0.01 0.01 0.02

390350045 Ohio Cuyahoga 27.60 27.64 27.69 27.74 27.79 0.00 0.05 0.09 0.14 0.19

390811001 Ohio Jefferson 28.03 28.11 28.19 28.27 28.35 0.00 0.08 0.16 0.25 0.33

261630019 Michigan Wayne 35.83 35.84 35.86 35.87 35.88 0.00 0.01 0.03 0.04 0.06

390350065 Ohio Cuyahoga 27.00 27.06 27.13 27.19 27.26 0.00 0.06 0.13 0.19 0.26

170313301 Illinois Cook 32.84 32.88 32.93 32.97 33.01 0.00 0.04 0.08 0.13 0.17

420070014 Pennsylvania Beaver 28.70 28.75 28.82 28.90 28.99 0.00 0.05 0.12 0.20 0.29

420033007 Pennsylvania Allegheny 28.81 28.87 28.93 29.00 29.06 0.00 0.06 0.13 0.19 0.26

010730023 Alabama Jefferson 32.12 32.15 32.19 32.22 32.26 0.00 0.03 0.07 0.10 0.14

550790026 Wisconsin Milwaukee 33.21 33.25 33.30 33.34 33.38 0.00 0.04 0.09 0.13 0.18

180970043 Indiana Marion 27.82 27.87 27.93 27.98 28.03 0.00 0.05 0.11 0.16 0.21

261470005 Michigan St Clair 33.38 33.40 33.42 33.44 33.45 0.00 0.02 0.04 0.05 0.07

550790043 Wisconsin Milwaukee 33.92 33.95 33.98 34.01 34.04 0.00 0.03 0.06 0.09 0.12

180890026 Indiana Lake 33.37 33.39 33.42 33.44 33.46 0.00 0.02 0.05 0.07 0.10

180970081 Indiana Marion 27.59 27.67 27.74 27.80 27.85 0.00 0.08 0.15 0.21 0.27

180970066 Indiana Marion 29.13 29.22 29.30 29.37 29.43 0.00 0.09 0.17 0.24 0.30

171191007 Illinois Madison 30.66 30.75 30.80 30.85 30.90 0.00 0.09 0.14 0.19 0.24

550790010 Wisconsin Milwaukee 33.13 33.17 33.22 33.26 33.31 0.00 0.04 0.09 0.13 0.18

390170003 Ohio Butler 27.33 27.39 27.48 27.56 27.65 0.00 0.06 0.15 0.24 0.32

170316005 Illinois Cook 34.82 34.85 34.88 34.91 34.93 0.00 0.03 0.06 0.09 0.12

420031008 Pennsylvania Allegheny 25.62 25.72 25.83 25.93 26.03 0.00 0.10 0.21 0.31 0.41

261610008 Michigan Washtenaw 29.33 29.35 29.38 29.40 29.42 0.00 0.02 0.05 0.07 0.09

170312001 Illinois Cook 32.33 32.38 32.42 32.46 32.50 0.00 0.04 0.08 0.12 0.16

170310052 Illinois Cook 30.31 30.34 30.37 30.40 30.43 0.00 0.03 0.06 0.09 0.12

421330008 Pennsylvania York 33.91 33.93 33.94 33.95 33.97 0.00 0.01 0.03 0.04 0.05

261630015 Michigan Wayne 31.99 32.01 32.02 32.04 32.06 0.00 0.02 0.03 0.05 0.06

010732003 Alabama Jefferson 31.91 31.94 31.97 32.00 32.03 0.00 0.03 0.06 0.09 0.12

390618001 Ohio Hamilton 26.73 26.79 26.85 26.91 26.97 0.00 0.06 0.12 0.18 0.24

171190023 Illinois Madison 29.50 29.56 29.62 29.68 29.73 0.00 0.06 0.12 0.17 0.23

420031301 Pennsylvania Allegheny 26.15 26.21 26.27 26.32 26.38 0.00 0.06 0.11 0.17 0.23

391130032 Ohio Montgomery 24.62 24.69 24.75 24.82 24.89 0.00 0.07 0.13 0.20 0.27

420030116 Pennsylvania Allegheny 26.34 26.40 26.46 26.53 26.59 0.00 0.06 0.12 0.19 0.25

*The six sites are Allegheny, PA (64); Lancaster, PA (07); Wayne, MI (16 and 19); Cook, IL (16); and Lake, IN (22). 

**The eight sites include the six sites listed above as well as Cuyahoga, OH (38) and Wayne, MI (33).
Deliberative 

*The six sites are Allegheny, PA (64); Lancaster, PA (07); Wayne, MI (16 and 19); Cook, IL (16); and Lake, IN (22). 

**The eight sites include the six sites listed above as well as Cuyahoga, OH (38) and Wayne, MI (33).

Figure 2. Increase in maximum PM

threshold level as the variability level is increased from 0 to 20 percent, individually for 

the eight sites (left panel) as well as for various combinations of sites (right panel). The 

combinations of sites can be found in Table 10. The state containing the receptor is held 

constant with no variability at the 2014 $2300/ton cost emission level.

In conclusion, EPA found that, while allowing 

accommodate inherent fluctuations 

10%), the downwind air quality impacts are small

(i.e., 20%), the air quality impacts remain small.

allowing substantial year-to-year variability around a budget in any given state’s power 

sector emissions (i.e., 20%), the 

unlikely to be compromised.

 The variability due to 

bounded by the 

examined, the resulting variation in air quality is small.

 Higher levels of emission variability (beyond 

as year-to-year 

such as could

emission program

downwind air quality improvements

emission reduction

29

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*The six sites are Allegheny, PA (64); Lancaster, PA (07); Wayne, MI (16 and 19); Cook, IL (16); and Lake, IN (22). 

**The eight sites include the six sites listed above as well as Cuyahoga, OH (38) and Wayne, MI (33).

Figure 2. Increase in maximum PM2.5 design value (g/m3) in 2014 at the $2300/ton cost 

threshold level as the variability level is increased from 0 to 20 percent, individually for 

the eight sites (left panel) as well as for various combinations of sites (right panel). The 

sites can be found in Table 10. The state containing the receptor is held 

constant with no variability at the 2014 $2300/ton cost emission level.

found that, while allowing for the limited variability

fluctuations in reliable operation of the power sector (typically 

10%), the downwind air quality impacts are small. At even higher levels of variability

(i.e., 20%), the air quality impacts remain small. This suggests that even under a 

year variability around a budget in any given state’s power 

the downwind air quality goals of the program are very

The variability due to baseline power sector operation (typicall

bounded by the variability levels examined in this analysis. At all levels 

he resulting variation in air quality is small.

Higher levels of emission variability (beyond what EPA has calculated here 

year variability due to baseline operation of the power sector), 

theoretically occur under an unlimited interstate cap

emission program, would not undermine the program’s achievement of 

downwind air quality improvements. This is especially true if coupled 

emission reductions in the state containing the receptor.

*The six sites are Allegheny, PA (64); Lancaster, PA (07); Wayne, MI (16 and 19); Cook, IL (16); and Lake, IN (22). 

) in 2014 at the $2300/ton cost 

threshold level as the variability level is increased from 0 to 20 percent, individually for 

the eight sites (left panel) as well as for various combinations of sites (right panel). The 

sites can be found in Table 10. The state containing the receptor is held 

limited variability to 

operation of the power sector (typically 

of variability

This suggests that even under a program

year variability around a budget in any given state’s power 

goals of the program are very

(typically 10%) is 

At all levels 

what EPA has calculated here 

power sector), 

interstate cap-and-trade 

not undermine the program’s achievement of 

. This is especially true if coupled with 
30

Deliberative – Do Not Cite or Quote

 The heterogeneity of where, when, and how emission reductions occur 

(processes not explicitly considered under the Transport Rule, such as intrastate shifting of emissions) will possibly have larger downwind air quality

impacts at a given receptor than the minor variations in air quality

attributable to the small shifts in state-level emissions due to inherent yearto-year variability.

As was described in the proposed Transport Rule “Power Sector Variability” TSD, 

the variations in downwind air quality found in this analysis of potential year-to-year 

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

variability in air quality (as measured at the receptors and expressed as the differences

between the average and maximum design values), indicating that other factors such as 

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

(once emissions are broadly controlled at a given level of stringency). Consequently, EPA 

finds that allowing variation in emissions under the final variability limits in the Transport 

Rule allows the power sector to address inherent fluctuations in electric generation without 

negatively affecting downwind air quality.