# MEMORANDUM

SUBJECT: The Impact of Emissions Averag ing 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 CE MS data to evaluate the impact of averaging

time on variability and to “predi ct” 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 met hodology. These analyses are not the

result of additional statistical analyses of the stac k 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 fo r data completeness and hours flagged as invalid

were removed prior to conducting any calculations.

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

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

emissions were excluded from the calculation of daily averages.

3. Hours were classified as start up or shutdown periods if the gr oss 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 aver ages. Daily averages were

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

4. Contiguous six-hour periods of va lid CEMS data at operating condi tions equivalent to typical

stack testing conditions (i.e., stea dy-state flow rate and steady lo ad at greater than 90 percent

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

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

these periods are referred to as “sur rogate stack tests” in this memo.

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# 2 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 99 th 

# percentile long-term

average] to [the 3 rd 

# percentile surrogate stack test valu e] 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 gi ven averaging period data set were used to

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

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

equation is presented as Equation 1:

[Equation 1] y = Cx -z



# Where y = the estimated lo ng 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 genera l equation, developed in step 6, was used to

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

# percentile

long-term performance for the floor unit. Th e 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 me morandum only includes the two plots utilized

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

averages. The attached spreadsheets contai n the plots of all ot her averaging periods.

9. The upper predictive limit for each long-term aver age 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 MA CT 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 cal culate at least one 360- day average. Since the

objective of this analysis was to compare achieva ble emissions rates based on the duration of the

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# 3 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 th e 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)

30 1 

# 87 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 redu ction in the count of available datasets with sufficient data as

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

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

period without the analytical arti facts 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)

30 2 

# 23 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 th at an appropriate ratio betwee n a 30-day compliance period and a

360-day compliance period is approximately 60 per cent. This is consis tent with the ratio

1 

# Note that the data set of 87 units utilized to derive th e 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 th e UPL of 1.32E-06 is presented in Appendix A, Figure A-2.

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# 4 achieved by San Juan Unit 4 based on the ma ximum averages recorded by CEMS with no

statistical variability added.Figure 1.-San Juan Unit 4 Actual CEMS Data Averagesy=1E‐06x‐0.226R2=0.98060.0E+002.0E‐074.0E‐076.0E‐078.0E‐071.0E‐061.2E‐061.4E‐061.6E‐060 50 100 150 200 250 300 350 400x=DaysinAveragng Periody=Avg lbsHg/MMBtubyCEMSSanJuanUnit4‐ORISCode2451MaximumAveragesfor1dayto360dayaveragingperiods.

# Appendix A

Typical Data Sets Used to Derive Average Data SetsDRAFT–DELIBERATIVE–NOTFORDISTRIBUTIONPRELIMINARYRESULTS,QACHECKSNOTCOMPLETED7y=0.0032x‐0.521R2=0.73230.020.040.060.080.0100.0120.0140.00.0E+00 2.0E‐06 4.0E‐06 6.0E‐06 8.0E‐06 1.0E‐05 1.2E‐05 1.4E‐05 1.6E‐05y=(30DayRollingAvg)/(6hr CEMSSurrogateforStackTest)x=Surrogate StackTest(lbsHg/MMBtu)Figure A‐1:Relationship BetweenMinimumSinglePointStackTestsand30DayRollingAveragesBasedonHgCEMSDatafor87Units6hrSurrogate:3rdPercentile(0.03)30DayRollingAvg:99thPercentile(0.99)UPL=1.1E‐06lbsHg/MMBtuDRAFT–DELIBERATIVE–NOTFORDISTRIBUTIONPRELIMINARYRESULTS,QACHECKSNOTCOMPLETED8y=0.0034x‐0.528R2=0.83260.020.040.060.080.0100.0120.0140.00.0E+00 2.0E‐06 4.0E‐06 6.0E‐06 8.0E‐06 1.0E‐05 1.2E‐05 1.4E‐05 1.6E‐05y=(30DayRollingAvg)/(6hr CEMSSurrogateforStackTest)x=Surrogate StackTest(lbsHg/MMBtu)FigureA‐2:Relationship BetweenMinimumSinglePointStackTestsand30DayRollingAveragesBasedonHgCEMSDatafor236hrSurrogate:3rdPercentile(0.03)30DayRollingAvg:99thPercentile(0.99)UPL=1.32E‐06lbsHg/MMBtuControlDataSet‐Same 23UnitsUsedin360DayAverageAnalysis