MEMORANDUM 

SUBJECT: The Impact of Emissions Averaging Time on the Stringency of an Emission 

Standard

FROM: Stephen Boone, Roy Neulicht, and Jeff Cole, RTI

TO: Bill Maxwell, EPA, SPPD, Energy Strategies Group

DATE: December 9, 2011

Purpose

These analyses were conducted on the Part II CEMS data to evaluate the impact of averaging 

time on variability and to “predict” the UPL value for different averaging times for the MACT 

floor facilities. This predictive tool has not been previously used and is intended to “ground 

truth” the variability estimated by the UPL calculation methodology. These analyses are not the 

result of additional statistical analyses of the stack testing data collected under Part II or any data 

received under Part III, directly.

Note: These results have not been through an extensive QA.

General Approach

1. Part II data from 87 units were evaluated for data completeness and hours flagged as invalid 

were removed prior to conducting any calculations.

2. For each unit, each hour of data was evaluated to determine the operating load (gross MW) 

and to determine if zero emissions were reported for the hour. Hours of zero reported 

emissions were excluded from the calculation of daily averages. 

3. Hours were classified as startup or shutdown periods if the gross megawatts recorded for the 

hour were less than 5 percent of the maximum hourly generating rate recorded in the data set. 

These hours were not included in the calculation of daily averages. Daily averages were 

calculated in accordance with Equation 19-19 from Method 19.

4. Contiguous six-hour periods of valid CEMS data at operating conditions equivalent to typical 

stack testing conditions (i.e., steady-state flow rate and steady load at greater than 90 percent 

of the maximum recorded operating load) were used to calculate all the short-term emission 

rate averages occurring in the data set that were analogous to short-term stack test averages; 

these periods are referred to as “surrogate stack tests” in this memo.
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5. All averaging periods calculated (30-day, 60-day, 90-day, 180-day, and 360-day) excluded 

startup and shutdown periods. 

6. For each averaging period analyzed, the variability ratio of [the 99th percentile long-term 

average] to [the 3

rd percentile surrogate stack test value] was calculated for each operating 

unit with a sufficient number of hourly records to calculate at least one long-term period.

These percentiles were used to eliminate outliers in the data sets.

7. The variability ratios for all units within a given averaging period data set were used to 

develop a general equation defining the relationship between the 99th percentile long-term 

average and the near-minimum achieved surrogate stack test. The general form of the 

equation is presented as Equation 1:

[Equation 1] y = Cx-z

Where y = the estimated long term emissions average 

x = the near-minimum short-term emissions average

z = was a calculated exponent derived from all available CEMS hourly data.

C = was a calculated coefficient derived from all available CEMS hourly data.

8. For each averaging period analyzed, the general equation, developed in step 6, was used to 

transform short-term emissions data for each unit floor into an estimate of the 99th percentile 

long-term performance for the floor unit. The transformed estimates were calculated by

inserting the minimum actual stack test data from each of the 47 units into the general 

equation from step 6. Appendix A of this memorandum only includes the two plots utilized 

to determine the equation for transforming MACT floor stack test averages to 30-day

averages. The attached spreadsheets contain the plots of all other averaging periods.

9. The upper predictive limit for each long-term average Hg emissions rate was calculated for 

the floor units using the equation from step 6. Only the minimum test value (i.e., row 5 in the 

UPL spreadsheet) was used; all other stack test rows previously used to determine unitspecific variability were excluded.

Conclusion

Table 1 presents the results of the analysis of what the Hg MACT floor for existing coal units 

would look like if EPA decides to promulgate a compliance period longer than 30 days. As 

shown in Table 1, the amount of data available for each averaging period analysis varied. For 

example, only 23 units had sufficient data to calculate at least one 360-day average. Since the 

objective of this analysis was to compare achievable emissions rates based on the duration of the 
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averaging period, the analysis used only the 23 units with sufficient data to calculate all 

averaging periods as a “control” to assess the impact of comparing the different data sets.

Table 1 – UPL’s Calculated with All Available Data

Average Period Available CEMS Calculated UPL

(days) Data Sets With All Available CEMS Data

(lb Hg/MMBtu)

30187 1.11E-06

60 83 1.01E-06

90 77 9.13E-07

180 66 8.04E-07

360 23 7.60E-07

To quantify the effect of the reduction in the count of available datasets with sufficient data as 

the averaging periods increase, the analysis also includes a series of UPL calculations using only

the “control” data set. Table 2 presents a comparison of the calculated UPLs for each averaging 

period without the analytical artifacts caused by the limited availability of very long-term (i.e., 

semi-annual and annual) CEMS data.

Table 2 – UPL’s Calculated with Control Data (23 Units Only)

Average Period Available CEMS Calculated UPL

(days) Data Sets With Control CEMS Data

(lb Hg/MMBtu)

30223 1.32E-06

60 23 1.13E-06

90 23 1.03E-06

180 23 9.17E-07

360 23 7.60E-07

The data in Table 2 confirms that an appropriate ratio between a 30-day compliance period and a 

360-day compliance period is approximately 60 percent. This is consistent with the ratio 

 

1 Note that the data set of 87 units utilized to derive the UPL of 1.11E-06 is presented in Appendix A, Figure A-1.

2 Note that the data set of 23 units utilized to derive the UPL of 1.32E-06 is presented in Appendix A, Figure A-2.
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achieved by San Juan Unit 4 based on the maximum averages recorded by CEMS with no 

statistical variability added.
Figure 1.-San Juan Unit 4 Actual CEMS Data Averages

y = 1E-06x-0.226

R2 = 0.9806

0.0E+00

2.0E-07

4.0E-07

6.0E-07

8.0E-07

1.0E-06

1.2E-06

1.4E-06

1.6E-06

0 50 100 150 200 250 300 350 400

x= Days in AveragngPeriod

y= Avg lbs Hg/MMBtu by CEMS

San Juan Unit 4 -ORIS Code 2451

Maximum Averages for 1 day to 360 day averaging periods.
Appendix A

Typical Data Sets Used to Derive Average Data Sets
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y = 0.0032x-0.521

R2 = 0.7323

0.0 

20.0 

40.0 

60.0 

80.0 

100.0 

120.0 

140.0 

0.0E+00 2.0E-06 4.0E-06 6.0E-06 8.0E-06 1.0E-05 1.2E-05 1.4E-05 1.6E-05

y= (30 Day Rolling Avg)/(6hrCEMS Surrogate for Stack Test)

x= Surrogate Stack 

Test (lbs Hg/MMBtu)

Figure A-1: Relationship Between Minimum Single Point Stack Tests and 30 Day Rolling Averages Based on Hg CEMS Data for 87 Units

6hr Surrogate: 3rd Percentile (0.03)

30 Day Rolling Avg: 99th Percentile (0.99)

UPL = 1.1 E-06 lbs Hg/MMBtu
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y = 0.0034x-0.528

R2 = 0.8326

0.0 

20.0 

40.0 

60.0 

80.0 

100.0 

120.0 

140.0 

0.0E+00 2.0E-06 4.0E-06 6.0E-06 8.0E-06 1.0E-05 1.2E-05 1.4E-05 1.6E-05

y= (30 Day Rolling Avg)/(6hrCEMS Surrogate for Stack Test)

x= Surrogate Stack 

Test (lbs Hg/MMBtu)

Figure A-2: RelationshipBetween Minimum Single Point Stack Tests and 30 Day Rolling Averages Based on Hg CEMS Data for 23 

6hr Surrogate: 3rd Percentile (0.03)

30 Day Rolling Avg: 99th Percentile (0.99)

UPL = 1.32 E-06 lbs Hg/MMBtu

Control Data Set - Same 23 Units Used in 360 Day Average Analysis