## DRAFT – DELIBERATIVE – NOT FOR DISTRIBUTION PRELIMINARY RESULTS, QA CHECKS NOT COMPLETED **MEMORANDUM** SUBJECT: Assessment of Using Single Point Stack Test Data to Derive 30 -Day Rolling Average

Emissions Limits FROM: Stephen Boone, Roy Neulicht , and Je ff Cole , RT I TO: Bill Maxwell , EPA , SPPD, Energy Strategies Group DATE: August 18 , 2011 **Summary of Technical Directive**  The WAM requested that RT I further assess using single -point data as the bas i s for establishing MACT limits with 30 -day rolling average compliance periods. Specifically ESG asked, “... have we adequately and accurately develop ed an emission limit from manual, single -point-in -time testing that will truly reflect what would occur through the use of CEMS and 30 -day- day rolling averages (e.g., should “m” in the statistical variability analysis be made equal to the “number of future runs” i.e., 1 ... or 30 ...)?” **Problem Statement for Evaluation of Hg Standard for >8,300 Btu/lb coal units**  Based on available CEMS data, what is the relationship between a unit’s 30 day average emission rate (including startups, shutdowns, and malfunctions ) and the emission rates occurring during short-term periods at operating conditions equivalent to typical stack testing conditions (i.e., steady stack flow rate and steady load at greater than or equal to 90 percent of full load conditions). **Background** Eight of the sources included in the initial MACT floor pool of 328 sources (in the > 8,300 Btu/lb coal - fired subcategory) , submitted Hg CEMS data in lieu of manual stack test data. However, only seven of these CEMS data sets reflected the minimum mercury average of the source when compared to available Part II manual stack test data and thus were used in the initial floor ranking. The seven CEMS sources submitted 30 days of daily Hg emissions averages, however none of the seven CEMS sources fell into the top 12 percent of performers in the initial ranking of sources. With the exception of these seven CEMS data sets, all other data used for the initial floor ranking were comprised of Part II or Part III manual stack test data submitted for the ICR . Prior to proposal, the 30 -day CEMs data from the seven units were analyzed to determine the variability . U sing the UPL methodology, the daily values for all the CEMS data from the seven units were averaged to find the mean value and the variability of the daily values from the mean was determined. The variability of the CEMS data was about 2 times the mean value. The CEMS variability was then compared to the manual stack test data variability, and it was found to be about one quarter the variability of the manual stack tests. Since the CEMS data variability was less than the variability of the data set used to establish the MACT floor, EPA determined that the MACT floor emission limit (corrected for variability) was achievable on a 30 -day rolling average basis, as EPA proposed in the rule .

## DRAFT – DELIBERATIVE – NOT FOR DISTRIBUTION PRELIMINARY RESULTS, QA CHECKS NOT COMPLETED Some facilities also provided historical CEMS data with their Part II ICR submittals. These data were received from approximately 100 sources . Review of these data, indicated that information sufficient to conduct an analysis (i.e., unit load, lb/MMBtu emission rate, and data quality flags present to exclude calibrations and other suspect data) was provided from about 60 of the 100 s ources . This analysis is described in the following paragraphs. Note that, to date, only historical CEMS data from 39 sources have been analyzed. **Technical Approach** In this analysis the historical CEMS data were used to derive a mathematical relationship between the 30 -day average emission limit that is achievable (i.e., the maximum 30 -day average emission rate in the CEMS data set) and the minimum six-hour average emission rate that is achievable at stack test conditions (i.e., the minimum emission rate recorded by CEMS during periods of steady operations at greater than or equal to 90 percent of capacity). The Hg CEMS historical data emissions data covers Hg CEMS operated between 1/1/2005 through 12/31/09 (with some data sets continuing a month or two into 2010). Only one of the sources included in the Hg MACT floor pool of 40 units (determined exc lusively based on each unit’s minimum manual stack test result) , also provided Hg CEMS data under Part II of the ICR . This source (Public Service Company of New Mexico ’s San Juan Station , Unit 4 , ORIS 2451 ) had the 33 rd 

## lowest Hg emissions rate in the MACT floor pool. Additionally, a second unit (not included within the top 40 because the source did not provide manual stack test data) provided CEMS data demonstrating 30 -day averages at or below the median MACT floor rate without variability 1 

## at a higher f requency than San Juan Unit 4 . Performance versus the MACT floor (without variability) was based upon analysis of “surrogate emissions tests” as explained in the following paragraphs. First, the Part II CEMS data were reviewed for completeness. The hourly CEMS data selected for analysis were then reduced to 30 -day rolling averages . Further analysis was done to identify 6 -hour periods of CEMS data at operating conditions typical of manual stack tests and to determine the average emission rate during such periods (i.e., periods of steady load and stack flow for at least six hours while the source operated at 90 percent capacity or greater) . These periods are referred to in this memorandum as of “surrogate emissions tests . ” As previously noted, San Juan Unit 4 was the only source that provided CEMS data in the top 40 best performing units based on the manual stack tests . Figure 1 compares the manual stack test data used in the floor analysis for San Juan Unit 4 versus the 30 -day rolling averages measured by CEMS over approximately 8 , 000 hours (including the hours of the Part III San Juan Unit 4 manual stack test used in the floor calculation) . Figure 1a adds to the figure the calculated 6 -hr CEMS average “surrogate stack test” data . The CEMS data averages included periods of startup, shutdown, and malfunction and were calculated based on the lbs Hg/MMBtu compliance option using a diluent cap in accordance with §63.10005 (l) of the proposed rule (if necessary) during startup and shutdown periods . In other words , the data were screened to identify start-up and shut-down periods (based on an estimated startup and shutdown threshold equal to 28 percent of full load) , and for these periods , if necessary based on the actual diluent concentration, the emissions rate s 1 

## 1.97 x 10 - 8 

## lbs Hg/MMBtu , which is the average of Ch ambers Co generation LP, Unit 1 (ORIS 10566 ) and Reid Gardner, Unit 1 (ORIS 2324 )

## DRAFT – DELIBERATIVE – NOT FOR DISTRIBUTION PRELIMINARY RESULTS, QA CHECKS NOT COMPLETED

were recalculated using the appropriate cap value in accordance with the proposed provisions of §63.10005(l). Note that based on these 30 -day average CEMS data, San Juan Unit 4 complied with the proposed MACT limit (1.2 x10 - 6 

## lb/MMBtu) during all 30 day averages occurring in the submitted CEMS data . The overall objective of this analysis was to determine the **averaging period ratio** (APR) or the ratio of the maximum average emission limitation achieved on a 30 -day rolling average basis divided by the minimum surrogate stack test as measured by Hg CEMS . For the purposes of this analysis, the APR was defined as follows: Averaging Period Ratio (APR) **=** (maximum 30 - day rolling average recorded by Hg CEMS) (minimum short-term Hg emission rate at stack test conditions* ) *Surrogate stack test conditions are steady flow operations at a generating rate that is > 90% of capacity. 

## This technical approach provided for a very large data set for each unit with hundreds of surrogate stack tests and hundreds of 30 -day averages during all operating conditions. Each APR is unit specific and provides an estimate of differences between the short-term and the long-term emission averages that are related to factors including

 A particular source’s fuel contracts (i.e., variability of fuel -Hg content) ,

 Hours since the last major maintenance event (for the larger data sets) , and  Se asonal factors (for the larger data sets)  Routine cycling to match system demand Other analyses such as the UPL method, applied across multiple best p erforming units , may be necessary to account for variability associated with unit age, design, measurement technique , and cycling profiles not represented in the CEMS database . Subsequently, Part II CEMS data files for 3 9 units (a total of approximately 18 0,000 hours of Hg CEMS data) submitted in response to the ICR were reviewed to evaluate periods of operation consistent with the normal procedures for manual stack tests (i.e., surrogate stack tests) . Table 1 summarizes these CEMS data. The companies operating units identified in bold text within Table 1 all provided 900 or more hours of Hg CEMS data . These data were utilized to derive an equation for predicting the APR for a unit based strictly on short-term stack test data. A threshold of 900 hours of Hg CEMS data was chosen because a data set of this size or greater provides at least 180 thirty-day averages for comparison against the minimum short-term emission rate. Thirty- six units had 900 or more hours of Hg CEMS data. The APR, as defined above, was calculated for 35 of the units (all the units other than San Juan Unit 4) identified in the bold text, and the ratios were plotted (see Figure 2 ) versus the minimum “surrogate stack test ” in the same data set to obtain a relationship between the lowest, achi evable , short-term (i.e., 6 -hour) , emission rate at stack test conditions and the maximum 30 -day rolling average that was achieved. The relationship was evaluated by calculating the predicted value for San Juan Unit 4 and comparing the predicted value wit

h the actual value recorded by CEMS.

## DRAFT – DELIBERATIVE – NOT FOR DISTRIBUTION PRELIMINARY RESULTS, QA CHECKS NOT COMPLETED **Summary of Findings** The relationship between the maximum 30 -day average emission rate was found to have a good correlation to the value of the minimum “surrogate stack test” as defined by Equation 1 : (Equation 1) y = 0 .00 1 8 x - 0. 569



## r2 = 0. 75 The amount of error in this prediction was determined by applying Equation 1 to the San Juan Unit 4 actual minimum surrogate stack test (1 x 10 - 8 

## lbs Hg/MMBtu on a 6 -hr average) to predict the APR and subsequently the to predict the maximum 30 -day average for comparison against the actual maximum 30 -day average in the CEMS data: APR predicted for San Juan U4 = 0.00 1 8 (1 x 10 - 8 

## ) - 0. 569 

## = 64.2 Therefore, the 30 - day average emission rate predicted for San Juan Unit 4 is estimated as follows:

 Predicted maximum 30 - day average for San Juan Unit 4 = APR x 1 x 10 - 8 

## = 6 . 42 x 10 - 7 

## lbs Hg/MMBtu

 The actual maximum 30 - day average for San Juan Unit 4 was 4 .9 4 x 10 - 7 

## lbs Hg/MMBtu.

 The percent error of the predicted value versus the actual value for San Juan U4 was 30 percent. 

##  Therefore, for minimum emission rates about 1 x 10 - 8 

## lbs Hg/MMBtu, Equation 1 is expected to predict the APR for a large CEMS data set with an error margin of approximately 30 percent. 

## As mentioned earlier, the emission rate at the median position in the data set of minimum manual stack test averages for the forty sources that composed the MACT floor data set was 1.97 x 10 - 8 

## lbs Hg/MMBtu. Applying Equation 1 to this emission rate yields the following estimate of the 30 -day average that could be achieved by a unit with a minimum six -hour stack test averaging 1.97 x 10 - 8 

## lbs Hg/MMBtu:

 APR predicted at median of MACT floor (without variability) = 0.0018(1.97 x 10 - 8 

## ) - 0. 5 6 9 

## = 43

 Predicted maximum 30 - day average at median of MACT f loor (without variability) = AP R x 1.97 x 10 - 8



##  Predicted max . 30 - day avg at median of MACT floor (without variability) = 8.52 x 10 - 7 

## lbs Hg/MMBtu + 30% 

## Additional work to be conducted Additional work planned includes: 1. Additional QA of the data set and calculations, and 2. Variability analysis of the equation 3. Calculate UPL using predicted maximum 30 -day averages

## Table 1 – Summary of Hg CEMS Data

Figure 1

## DRAFT – DELIBERATIVE – NOT FOR DISTRIBUTION PRELIMINARY RESULTS, QA CHECKS NOT COMPLETED

0. 00E +00 2. 00E- 07 4. 00E- 07 6. 00E- 07 8. 00E- 07 1. 00E- 06 1. 20E- 06 1. 40E- 06

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 

### lbs Hg/MMBtu

Max StackTesti n Fl oor San Juan Unit 4 - Fa cilit y ID 2451 - HgE mi ssi ons MACT Fl oor Stack Te sts ve rs us HgC EM S Data lbs Hg/MMBtu

Hg CE MS Hrs

Mi ni mum Stack Te st in F lo or Data Me di an S tack Te st in F loor Data Ma xi mum 30- DayAvgi n CEMS Data

Medi an CEMS Surrogate Stack Te st Ma xi mum CE MS 6- hr

S urrogate Stack Te st



## Figure 1a

## DRAFT – DELIBERATIVE – NOT FOR DISTRIBUTION PRELIMINARY RESULTS, QA CHECKS NOT COMPLETED

Figure 2

## DRAFT – DELIBERATIVE – NOT FOR DISTRIBUTION PRELIMINARY RESULTS, QA CHECKS NOT COMPLETED

Fa ci lity_ID Surrogate Hg CEM S Ma xim um Ma xim um Me dia n Me dia n Mi nimum Mi nimum & "_" & Stack_I D Capaci ty 6- hour Stack Te sts Hours Surr. Stack Te st 30 DayRol ling Avg Surr. Stack Te st 30 DayRol ling Avg Surr. Stack Te st 30 Da y Rol ling Avg Factor (Count) (C ount) lb/M MBtu lb/M MBtu lb/M MBtu lb/M MBtu lb/M MBtu lb/M MBtu **8069_Stk2** 94% 462 3775 1. 14E- 06 1. 01E- 06 3. 48E- 07 3. 66E- 07 1. 00E- 08 2. 42E- 07 **6165_STK3_MDL_ap plied.xlsx**  84% 335 6772 1. 07E- 06 4. 74E- 07 1. 10E- 07 1. 88E- 07 1. 00E- 08 3. 53E- 08 **San Juan Unit 4** 90% 791 8215 1. 21E- 06 4. 94E- 07 2. 58E- 07 2. 58E- 07 1. 00E- 08 1. 22E- 07 **3396_SK001** 89% 283 2168 6. 57E- 07 2. 81E- 07 2. 03E- 07 2. 22E- 07 1. 17E- 08 1. 50E- 07 **8069_Stk1** 95% 482 3610 2. 75E- 06 1. 36E- 06 6. 51E- 07 7. 21E- 07 1. 50E- 08 1. 89E- 07 **703_Scrubbe r Stack (**  **2)** 90% 632 4522 3. 36E- 06 1. 68E- 06 7. 08E- 07 5. 80E- 07 4. 00E- 08 3. 10E- 07 **1378_SK003** 90% 86 900 5. 25E- 06 5. 33E- 07 4. 72E- 07 5. 11E- 07 4. 00E- 08 4. 72E- 07 **6165_STK1** 91% 769 8128 7. 96E- 06 1. 84E- 06 1. 15E- 06 1. 32E- 06 4. 00E- 08 6. 93E- 07 **6257_Stack 3** 89% 750 7469 1. 16E- 05 4. 78E- 06 8. 29E- 07 1. 29E- 06 6. 00E- 08 3. 67E- 07 **4162_STK 1** 89% 363 3700 1. 66E- 05 1. 35E- 06 8. 28E- 07 9. 91E- 07 8. 00E- 08 4. 34E- 07 **3644_Stck 1** 90% 475 4119 5. 91E- 06 6. 17E- 07 3. 82E- 07 5. 24E- 07 9. 00E- 08 3. 29E- 07 **6165_STK2** 90% 550 6153 2. 88E- 06 2. 03E- 06 1. 70E- 06 1. 60E- 06 9. 67E- 08 8. 04E- 07 **4162_STK 3** 87% 173 1756 3. 85E- 06 2. 24E- 06 2. 23E- 06 1. 99E- 06 1. 00E- 07 1. 39E- 06 **3644_Stck 2** 84% 183 4274 1. 87E- 06 1. 12E- 06 4. 82E- 07 5. 50E- 07 1. 05E- 07 3. 93E- 07 **703_Scrubbe r Stack (**  **3)** 87% 632 6341 7. 72E- 06 1. 48E- 06 9. 34E- 07 9. 96E- 07 1. 27E- 07 3. 52E- 07 **1378_SK001** 89% 219 2020 1. 68E- 05 1. 92E- 06 6. 30E- 07 6. 22E- 07 1. 28E- 07 4. 00E- 07 **3403_SK001** 79% 531 8023 8. 33E- 06 6. 13E- 06 4. 92E- 06 4. 97E- 06 1. 28E- 07 3. 92E- 06 **3406_SK001** 60% 20 2492 3. 48E- 06 2. 61E- 06 1. 26E- 06 1. 13E- 06 1. 70E- 07 3. 16E- 07 **4162_STK 2** 87% 252 3231 6. 47E- 06 2. 25E- 06 1. 40E- 06 1. 43E- 06 1. 72E- 07 1. 28E- 06 **47_SK001** 51% 32 2001 1. 22E- 06 1. 01E- 06 6. 12E- 07 8. 31E- 07 2. 02E- 07 5. 93E- 07 **4158_STK41** 89% 470 5673 7. 12E- 06 4. 07E- 06 2. 72E- 06 2. 76E- 06 3. 62E- 07 1. 88E- 06 **3403_SK002** 78% 280 5427 4. 77E- 06 3. 53E- 06 2. 61E- 06 2. 88E- 06 4. 55E- 07 1. 80E- 06 **3399_SK001** 90% 128 1426 4. 12E- 06 1. 23E- 06 8. 91E- 07 8. 60E- 07 5. 08E- 07 7. 66E- 07 **6170_P PPPS2**  63% 408 9365 7. 36E- 06 3. 57E- 06 3. 73E- 06 2. 41E- 06 5. 39E- 07 0. 00E+00 50_S K001 26% 1 757 6. 22E- 07 2. 31E- 07 6. 22E- 07 1. 61E- 07 6. 22E- 07 1. 43E- 07 **4041_OC PP-S1** **3** 48% 17 8513 2. 72E- 06 4. 47E- 06 1. 01E- 06 3. 06E- 06 6. 43E- 07 1. 23E- 06 3399_S K002 89% 40 543 1. 64E- 06 1. 03E- 06 9. 37E- 07 9. 68E- 07 6. 92E- 07 9. 15E- 07 **6170_P PPPS1**  79% 512 8495 7. 35E- 06 5. 26E- 06 5. 26E- 06 3. 67E- 06 7. 08E- 07 1. 03E- 06 50_S K003 80% 29 769 3. 53E- 06 1. 16E- 06 1. 12E- 06 1. 14E- 06 7. 65E- 07 1. 04E- 06 **4158_STK43** 88% 409 5560 9. 70E- 06 6. 93E- 06 5. 29E- 06 5. 31E- 06 9. 07E- 07 3. 67E- 06 **4158_STK42** 91% 638 5723 6. 12E- 06 3. 31E- 06 2. 50E- 06 2. 79E- 06 9. 58E- 07 2. 08E- 06 **6101_STK1** 94% 1135 8647 5. 13E- 05 1. 23E- 05 3. 94E- 06 5. 45E- 06 1. 00E- 06 2. 49E- 06 **6761_S101** 95% 1130 8111 7. 45E- 06 4. 02E- 06 2. 78E- 06 2. 99E- 06 1. 09E- 06 1. 93E- 06 **8066_STK4** 89% 759 6868 1. 45E- 05 5. 49E- 06 3. 69E- 06 4. 03E- 06 1. 25E- 06 2. 28E- 06 **4158_STK44** 84% 103 1637 8. 20E- 06 5. 57E- 06 6. 06E- 06 5. 10E- 06 1. 29E- 06 4. 74E- 06 **8066_STK2** 91% 770 6486 1. 05E- 05 4. 95E- 06 4. 12E- 06 4. 04E- 06 1. 49E- 06 2. 86E- 06 **8066_STK3** 89% 750 6732 4. 21E- 05 4. 90E- 06 3. 99E- 06 4. 18E- 06 1. 81E- 06 2. 97E- 06 **8066_STK1** 90% 688 6365 1. 11E- 05 6. 10E- 06 4. 72E- 06 4. 90E- 06 2. 26E- 06 3. 63E- 06 **709_Sh are**  **d Stack 3 4** 50% 171 7794 1. 41E- 05 7. 68E- 06 7. 21E- 06 6. 78E- 06 3. 21E- 06 5. 28E- 06 

## Table 1 – Summary of Hg CEMS Data