White Paper: EPRI s Preliminary Evaluation of the Available HAPs ICR Data Paul Chu, Naomi Goodman Amended January 7, 2011

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1 White Paper: EPRI s Preliminary Evaluation of the Available HAPs ICR Data Paul Chu, Naomi Goodman Amended January 7, Background In December 2009, the U.S. Environmental Protection Agency (EPA) issued an Information Collection Request (ICR) to owners of all fossil fuel-fired, electric generating units (EGU). Part III of the ICR required 500 power plant stacks to be tested for emissions of four groups of substances classified as hazardous air pollutants (HAPs) under the Clean Air Act: acid gases and hydrogen cyanide; metals; volatile and semivolatile organics; and polychlorinated dibenzodioxins, dibenzofurans, and polychlorinated biphenyls. The ICR also required testing for parameters such as particulate matter that could potentially serve as surrogates for the hazardous air pollutants. EPA plans to use the results to develop Maximum Achievable Control Technology (MACT) emission limits for HAPs for the power industry. 1.1 EPRI ICR Data Analyses Study EPRI has conducted a two-phased study consisting of (1) a QA/QC review of the individual reports and electronic files made available to EPRI for certain individual ICR units; and (2) an analysis of the available ICR data currently posted on the EPA website for potential trends or patterns to help inform the rulemaking. In the first phase, EPRI reviewed draft Part III stack test data produced for the ICR. The reviews covered sampling and analytical procedures, calculations, and data entry into the ICR reporting tools. EPRI has completed reviews on about 250 of the ICR units. EPRI s initial findings from the data quality evaluation were summarized in an October 27, 2010 memorandum to EPA [1]. This memorandum highlighted key fixable critical errors such as the incorrect filter temperature for filterable particulate matter (PM) measurements, incorrect or missing detection flags, and organic artifacts. The intent of this memorandum was to inform EPA and the various stakeholders of some of the potential data quality issues which could impact the MACT rulemaking process. A more detailed discussion of EPRI s data quality evaluation is summarized in EPRI s ICR Data Quality Report [2]. In the second phase, EPRI analyzed the ICR data for statistically appropriate averages, trends, or patterns that may help inform EPA s current HAPs MACT rulemaking. EPRI s analyses focus on the technical/scientific aspects of the HAPs measurement data. This White Paper presents the results of those analyses, using Version 3 ICR data available from the EPA website posted on November 12, It is comprised of an Access database and a set of Excel spreadsheets that provide average emissions for coal-, oil-, and petroleum-coke-fired EGUs.

2 Note that as a significant amount of ICR data are still to be released, EPRI s data analyses are ongoing. For example, the petroleum coke units have not been examined to date. In addition, the current analysis does not include an evaluation of the best single source for each HAP compound, as this type of analysis is best conducted once data from all test sites have been posted by EPA. Thus, this White Paper is an interim review and will be revised and reissued after all the ICR data are made available. 1.2 Technical Approach Based on our count of the ICR Part I and II data, there are 1082 coal-fired units that may be subject to MACT regulation. This total includes 53 fluidized bed units, of which 22 fire waste coal. We understand from EPA that the performance of the best 12% of these (130 units) will provide the data set that EPA will use to develop MACT limits, provided that there is no subcategorization conducted. EPA indicated that it selected a theoretical top 15% of the units for ICR Part III testing; e.g. those units with the newest 15% of particulate matter (PM) control devices were selected for testing of trace metals. An additional 50 randomly selected units ( Random 50 ) were chosen for testing from all EGUs that were not selected as the theoretical top 15%. About 200+ Part III ICR units are available in the Version 3 database, but EPRI based most of its evaluation on the data pool of the 130 best performing coal-fired units for any given HAP or HAPs family. This methodology was chosen in an attempt to parallel EPA s presumed approach of considering the best performing 12% of the data pool to develop MACT limits. For the purposes of this White Paper, best performing and lowest emitting are used interchangeably. Further, it should be noted that the emission levels reported from these units represent the best performing systems for the typical 3-day test period used to collect stack emission samples for the ICR. In EPRI s analysis, the Random 50 units were included in the set of best performing units if their emissions were low enough to place them in that category. Averages and standard deviations presented here are based on this set of 130 best performing units. Since multiple test runs were conducted for each HAPs substance, all analyses presented in this White Paper are based on the average of test runs for each EGU. In the current analyses, no attempt has been made to exclude potential outlier test run values and/or unit average values. 1.3 ICR Data Analyses Limitations Several significant factors must be considered when using and interpreting the ICR data as these may impact data use and analyses. a. The ICR data, and especially the top 12% best performing units, represent a limited cross-section of the industry. While the review of the MACT ICR data sets shows that all of the current control technologies employed at coal-fired power plants are present in the lowest emitting 12% of units, the percentage distribution by control type differs from the overall industry distribution as a result of the unit selection criteria. EPA selected test units for the various HAPs classes based on the age of the appropriate control device. Hence, fabric filters, 2

3 which have been installed at a greater percentage than ESPs over the last decade, are overrepresented in the metals dataset compared to the total distribution in industry. Similarly, many FBC boilers have been built recently, and while FBCs are a small fraction of the total boiler population, they represent a large fraction of the units selected for testing of organics (boiler age was a selection criteria used by EPA to select units for organic substance measurements). b. There are data quality issues which may impact the usefulness of some of the ICR data in quantifying HAPs emissions. This is described in detail in EPRI s ICR Data Quality Report, and summarized below. It is critical for correct interpretation of the ICR data to know whether the concentration reported in a sample is high enough to be distinguished from a sample that does not contain the measured substance, i.e., whether the measurement is above the detection limit. It is even more important for critical uses of measurement data (i.e., rulemaking or compliance testing) to know whether the concentration is high enough to be measured accurately with the test method, i.e., is the concentration above the quantitation limit. Without these two pieces of information, it is difficult to state with any confidence whether a reported parameter is actually present in a sample and its reported value is accurate. As discussed extensively in the ICR Data Quality Report, EPRI noted several problems with detection limit reporting that will make it exceedingly difficult to determine detection and quantitation limits for the ICR data: Inconsistent detection limit basis or basis not stated Missing and incorrect detection flags Same numerical format used for emissions below detection limit (BDL) and detection level limited (DLL) These problems make interpretation of detection limit information collected for the ICR problematic. Specifically, based on inspection of a subset of ICR data reports reviewed by EPRI, ICR measurements that are likely to have a considerable fraction of data below quantitation limits include the following: All particulate parameters Metals (particularly arsenic, beryllium and selenium) Semivolatile and volatile organic compounds Dioxins/furans/PCBs Formaldehyde by EPA Method 320 Total Hydrocarbons Methane 3

4 EPRI is in the process of reviewing ICR data reports in an attempt to establish the range of detection limits for each ICR parameter. However, the actual quantitation limits of each method should be determined in the field using dual sampling trains operated under conditions identical to those that will be used during compliance testing. 2. Summary of Initial Observations Based on EPRI s on-going analyses of the available ICR Part III data, we have developed the following preliminary observations for each possible HAP category. These are described in more detail in subsequent sections in this White Paper. a. Trace metals: Particulate phase metals have a higher correlation coefficient with filterable PM (R 2 = 0.31) than with PM2.5 (R 2 = 0.27) or inorganic condensable particulate (R 2 = 0.068) when all the available ICR data are included in the analysis; this applies to both coal and oil plants. The truncation of the coal data sets to include just the lowest 130 values, corresponding to 12% of the U.S. coal-fired power plant population, generally retains the same relative ranking, but reduces the strength of the correlation relationships observed. As all trace substance data have significant variability, removing the high end of the data cloud in any given set of data reduces the effect of single independent parameters on emitted values. b. Mercury emissions vary by fuel type, boiler design, and with activated carbon injection (ACI). However the differences in the averages for these various bins among the set of boilers that were selected for testing in the ICR did not appear to be as pronounced as one might expect, especially based on earlier EPRI analyses such as from the 1999 Mercury ICR [3]. c. HCl emissions and penetration (fraction of the coal chlorine emitted) correlate with SO 2 emissions and penetration, respectively, as the correlation coefficients are significant at the 95% confidence level when all the ICR data are included in the analysis. However, when evaluating subsets of data for wet FGD and dry FGD, the correlation coefficient was not statistically significant (R 2 of 0.029) for HCl emission with SO 2 emission for only wet FGD units. For only dry FGD units, the correlation coefficient was statistically significant (R 2 of 0.27) for HCl emission with SO 2 emission. The truncation of the data sets to include just the lowest 130 values impacted the correlations but in opposite directions. The correlation for only wet FGDs was statistically significant (R 2 of 0.21) and the correlation for only dry FGDs was not statistically significant (R 2 of 0.056). Truncating the higher HCl emissions data appears to impact the correlation somewhat randomly. d. Dioxins/Furans/PCBs are found at extremely low levels in the ICR samples, and the accuracy of the results are suspect due to the very common occurrence of the target species in method and field blanks. e. Non-Dioxin/Furan Organics are predominantly below detection limits. The concentrations of detected species evaluated to date do not correlate with any 4

5 surrogate species being considered by EPA. The usefulness of the organics data are constrained by data quality concerns. 2.1 Coal-Fired Units Trace Metals (excluding mercury) EPRI began field measurements of HAPs in the early 1990s. Our previous review of these historical data indicated that emissions of the particulate phase metals, specifically those that are associated with the fly ash, generally can be predicted based on two factors: 1) the level of particulate emissions (i.e. filterable particulate matter) from the unit and 2) the concentration of the trace metal in the fuel. EPRI expects that similar correlations will be found in the ICR data. Earlier versions of the ICR database posted by EPA had very limited coal trace element analyses; thus, EPRI has not yet been able to fully re-evaluate the correlations between trace element emissions and filterable PM plus the coal trace element concentration. Version 3, released in mid-november, appears to contain significantly more data for coal trace element analyses, and EPRI plans to incorporate these data into our analyses in the future. EPRI did analyze the data for possible correlation between trace element emissions and filterable particulate matter (FPM), filterable PM2.5 (PM2.5), and inorganic condensable particulate matter. Our analyses found that the particulate phase metals generally had a higher correlation coefficient with filterable PM than with PM2.5 or inorganic condensable particulate. A possible exception is selenium, which had a slightly higher correlation coefficient with PM2.5 than FPM. The stack test method for PM2.5 is not applicable to wet stacks; thus, these correlations omit units with wet scrubbers. In our analysis, the filterable particulate matter data set was limited to measurements made by Method 5 or Method 29. Filterable particulate data measurements obtained by method OTM-27 were not included in the correlation analysis. Trace elements examined in this analysis included antimony, arsenic, beryllium, cadmium, chromium, lead, nickel, manganese, and selenium. All data in the Version 3 EPA database were used in evaluating possible correlations, including data from the Random 50 units specified in the ICR, so that the widest possible range of FPM and trace metals emissions were represented in the correlations. At this time, all data points were included in the regression analysis, even values that appeared to be outliers. Further examination of these possible outliers, followed by data correction or data exclusion, would likely improve the regression coefficients. Figure 1 shows the arsenic emissions as a function of FPM emissions for all coal-fired units (169) units currently in the EPA ICR database for which paired sets of arsenic and Method 5/Method 29 FPM emissions data are available. Each data point represents the average for all test runs conducted at a unit (typically three runs). The data are plotted on logarithmic axes, due to the large range of emissions values. A power function curve fit for these data has a regression correlation coefficient (R 2 ) of 0.31, which is statistically 5

6 significant at greater than a 95% confidence level. With large numbers of data pairs, low R 2 values can be statistically significant. For example, with 60 data pairs, an R 2 of or greater is significant at a 95% confidence level. The confidence level is the degree of certainty that the correlation examined - e.g. FPM as an indicator of arsenic emissions - is not due to random chance. This does not mean that FPM is the only indicator or variable that impacts arsenic emissions, as there are likely a number of other variables, especially the coal arsenic concentration. Similar observations apply to the other particulate phase metals. Data for the best performing 130 units for arsenic are shown in Figure 2 to illustrate how the correlation between arsenic and FPM emissions changes when only data from the lowest emitting units are considered. Here the ICR data set is truncated to include only arsenic emission values less than approximately 1.4 lb/trillion Btu. Although the R 2 value (0.12) is still considered statistically significant at 95% confidence or greater, the correlation between arsenic and FPM emissions is not as strong as that observed for the entire ICR data set. 1.0E+03 Arsenic Emissions (lb/trillion Btu) 1.0E E E E-01 R 2 = E E E E E E+00 Method 5/Method 29 Filterable Particulate (lb/mmbtu) Figure 1. Arsenic Emissions as a Function of FPM Emissions for all ICR Coal-Fired Units 6

7 1.0E+01 Arsenic Emissions (lb/trillion Btu) 1.0E E-01 R 2 = E E E E E E+00 Method 5/Method 29 Filterable Particulate (lb/mmbtu) Figure 2. Arsenic Emissions as a Function of FPM Emissions for the Best Performing 130 Coal-Fired Units Note that for any given FPM emission level in Figure 1, the data show measured arsenic emissions spanning approximately two orders of magnitude. Likewise, any selected level of arsenic emission has a range of FPM emissions spanning two orders of magnitude. This relatively large scatter in the data is observed in part because the correlation does not take into account the concentration of arsenic in the coal. Coal arsenic concentrations can span as much as two orders of magnitude depending on the rank and geographic origin of the coal. There may also be a larger than expected range of FPM due to incorrect filter temperature used during sampling collection. This issue is described in detail in the October 27, 2010 EPRI memorandum to EPA and ICR Data Quality Report. A third source of data scatter is that many of the lower arsenic measurements may be below the quantitation level of the test method. EPRI reviewed a cross-section of ICR test reports in which arsenic method detection levels (MDLs) ranged from 0.04 to 0.1 lb/tbtu. Quantitation levels have not been determined for Method 29; however, EPA sets Minimum Levels for laboratory methods at 3.18 times the MDL. From the range of MDLs identified by EPRI (which may not reflect the full range of MDLs in all ICR tests), it appears that anywhere from about 10 to 50 percent of the arsenic values in Figure 1 may be below the quantitation limit of the ICP-MS (inductively coupled plasma mass spectrometry) instrumentation specified by the EPA method. Those values are therefore subject to a high degree of uncertainty. 7

8 Similar variability is observed for the other trace metal HAPs. Previous analyses of historical test data by EPRI have shown that improved predictive correlations for trace elements can be obtained by taking into account both the concentration of the particular trace element in the coal and the coal ash content [3]. Measurement accuracy at low emission levels may also be an issue for metals other than arsenic; beryllium and selenium both had many values flagged as nondetected, and therefore are likely to have a high percentage of measurements below quantitation limits. Another observation is that the best performing units for any given metal do not necessarily correspond to the best performing units for FPM. For example, the 130 best performing units for arsenic show average arsenic emissions of 0.47 lb/trillion Btu and corresponding average Method 5/Method 29 FPM of lb/million Btu. By comparison, the 130 best performing units for Method 5/Method 29 filterable particulate emissions show average FPM emissions of lb/million Btu; however, average arsenic emissions for this set of FPM data are 9.3 lb/trillion Btu. Therefore, the selection of the best performing units based on metal emissions (e.g. arsenic) may not produce the same best performing units if the potential FPM surrogate is used. Additional plots showing correlations of arsenic emission with filterable PM2.5 and inorganic condensable particulate using the entire ICR data set are provided in Figures 3 and 4, respectively. Again, no attempt has been made to exclude potential outliers in this current analysis. These two PM parameters indicate a lower correlation coefficient with arsenic emissions than FPM. When only the 130 best performing units are considered, the correlation coefficients for PM2.5 (0.18) and condensable particulates (0.011) are also lower than those observed when all the ICR data are included in the correlations; however, the PM2.5 correlation coefficient for the 130 best performing units (0.18) is slightly higher than the value for observed for FPM (0.12) shown previously in Figure 2. Regardless, all three PM parameters show a relatively weak correlation with arsenic emissions within the data pool of 130 best performing units. Similar analyses, using all of the ICR data, were conducted for the other non-mercury HAPs metals included in the ICR. With the possible exception of selenium, these trace elements generally have a higher correlation coefficient with FPM than either PM2.5 or inorganic condensable, as indicated by the correlation coefficients shown in Table 1. All the correlation coefficients shown in Table 1 are considered statistically significant at the 95% confidence level or higher, including the inorganic condensable particulates. Further analysis is planned to continue evaluating all ten trace metals, including an approach to evaluate the correlation of combination of various metals with FPM as well as incorporating the trace metal concentration in the coal. The FPM correlation coefficients for chromium, manganese, and nickel are relatively low in part due to potential high emission outliers. Gross sample contamination with these metals was identified as an issue for a number of sites examined in EPRI s data quality review process. EPRI s ICR Data Quality Report will provide a discussion of this issue. 8

9 1.0E+03 Arsenic Emissions (lb/trillion Btu) 1.0E E E E-01 R 2 = E E E E E E E E E+00 Filterable PM2.5 (lb/mmbtu) Figure 3. Arsenic Emissions as a Function of Filterable PM2.5 Emissions for all ICR Coal- Fired Units 1.0E+03 Arsenic Emissions (lb/trillion Btu) 1.0E E E E-01 R 2 = E E E E E E+00 Inorganic Condensable Particulate (lb/mmbtu) Figure 4. Arsenic Emissions as a Function of Inorganic Condensable Particulate Emissions for all ICR Coal-Fired Units 9

10 Table 1. Correlation Regression Coefficients (R 2 ) for Particulate-Phase Trace Metals - All ICR Coal-Fired Units Method 5 / Method 29 Filterable PM OTM-27 Filterable PM2.5 OTM-28 Inorganic Condensable PM Number of Data Pairs Antimony Arsenic Beryllium Cadmium Cobalt Chromium Lead Nickel Manganese Selenium Mercury EPRI s review of the 1999 Mercury ICR results identified that the mercury speciation, specifically elemental and oxidized mercury, may significantly impact mercury removal, especially in FGD systems [4]. The fraction of oxidized mercury is significantly impacted by the chloride concentration in the coal, where the chloride likely reacts with elemental mercury, possibly catalyzed by surface reactions on the unburned carbon content of fly ash, to form mercuric chloride [HgCl 2 ]. Significant research work has been conducted by EPRI, DOE, and others to evaluate mercury flue gas chemistry and ultimate removal in existing air pollution control technologies. This work has identified other factors that may impact mercury chemistry and removal including the concentration of other halogens (i.e. bromide), alkali (i.e. calcium), percent unburned carbon in the fly ash, control device type (including use of ACI), and flue gas temperature-time profiles. Impact of Fuel Type Fuels from the eastern United States tend to have a higher chloride concentration than those from western states, most notably sub-bituminous coal from the Powder River Basin. Table 2 shows the average mercury emission rate by fuel type for the 130 best performing units in the Version 3 EPA database. The Random 50 units that appear in the pool of 130 best performing units were included in the analysis, as were all boiler types, including fluidized bed combustion (FBC) units. As shown in Table 2 and Figure 5, emissions vary based on coal type, although the variations are not as pronounced as one might expect for bituminous coal as compared to sub-bituminous coals based on expected differences in coal chloride content. This is likely attributed to the fact that two-thirds of the sub-bituminous units in the pool of 130 best performing units employ activated carbon injection (ACI) systems, based on information in the EPA s Part I/II plant configuration database (21 of 33 units indicate ACI). Sixty six of the 84 bituminous units were equipped with FGD controls. Overall, 92 of the 130 best performing units employed some type of FGD control system (wet or dry). Only one lignite-fired unit appears in the group of 130 best-performing units. 10

11 Five additional lignite units (all FBC) were tested as part of the ICR but did not make the 130 group. The largest difference in average mercury emissions relative to the overall average is observed for waste coal units, all of which are FBC boilers. Table 2. Mercury Emissions by Coal Type for the 130 Best Performing Coal-Fired Units Standard Fuel Type Average (lb/trillion Btu) Deviation (lb/trillion Btu) No. of Units No. with ACI No. with FGD System** All Fuels Bituminous Lignite Sub-bituminous Waste Coal* * All waste coal units are fluidized bed combustion boilers. ** FGD system includes wet FGD, dry FGD, and FBC boilers which use limestone addition to the boiler for SO 2 control Mercury Emissions (lb/trillion Btu) N = Average of 130 Best-Perfoming Units Bituminous Liginte Sub-bituminous Waste Coal N = Number of units used to compute the average value for each fuel type category. Figure 5. Mercury Emissions by Fuel Type for the 130 Best Performing Coal-Fired Units 11

12 FBC boilers operate at lower furnace temperatures, have much greater fly ash/flue gas contact than conventional PC boilers, and typically use limestone addition directly to the boiler for control of SO 2 emissions. All of these factors may favor keeping more mercury in the fly ash stream. The relatively large standard deviation values shown in Table 2 (i.e. standard deviations equal to or greater than the average value) are indicative of the variability in emission values within the pool of 130 best performing units. For example, for all fuel types, reported mercury emission values ranged from to 2.4 lb/trillion Btu for best performing units. A statistical analysis of the data in Table 2 indicates that the average mercury emission values for bituminous and waste coals are statistically different at the 95% confidence level. Values for bituminous and sub-bituminous coals were not found to be statistically different, using a Student s t test performed with the hypotheses that the mean values were equal. The population standard deviation of each data set was assumed to be equal in the calculation (s1 = s2). The data set standard deviation values are as shown in Table 2. Impact of Boiler Type Boiler type may also have an impact on mercury emissions from coal-fired units. As shown in Table 3, FBC boilers exhibit somewhat lower emissions than conventional pulverized coal boilers. Of the 23 FBC units in the pool of 130 best performing units, 12 fired waste coal and the remainder fired bituminous coal. Of the 28 FBC units in the entire Version 3 ICR database (over half of the entire industry population of FBC units), 23 appeared in the data pool of 130 best performing units, indicating that a large percentage of the FBC units tested tended to exhibit low mercury emissions. The higher mercury emissions for cyclone boilers are likely attributed to the fact that all eight of these units fired sub-bituminous coal, and none employed any type of FGD control or ACI mercury control systems. Seven of the eight sub-bituminous cyclone units were equipped with cold-side ESP systems and one was equipped with a FF system. The lowest average mercury emission value occurred for the five stoker units, all of which fired bituminous coal and were equipped with spray dryer/fabric filter systems. This combination of high chloride coal, SO 2 /PM control, and firing system that may produce higher unburned carbon levels than conventional PC plants would be expected to result in higher levels of mercury capture and therefore lower emissions than many other configurations. 12

13 Table 3. Mercury Emissions by Boiler Type for the 130 Best Performing Coal-Fired Units Standard Deviation Boiler Type Average (lb/trillion Btu) (lb/trillion Btu) No. of Units All Boiler Types Cyclone Fluidized Bed Conventional PC Stoker If all FBC units are excluded, the average mercury emission value for all remaining boiler types in the group of best performing non-fbc coal-fired units is 0.94 lb/trillion Btu. In this case, 123 units are used to establish the best performing set of units based on 12% of the total non-fbc boiler population in industry as determined from the ICR Part I information (there are 53 total coal-fired FBC units in the industry, leaving 1029 non- FBC coal-fired units; 12% of 1029 is 123). Evaluation of whether the observed differences in boiler type are statistically significant is confounded by the fact that other factors such as type of control device come into play, making the statistical evaluation more complex. For example, some boiler type groups include only a small number of control device types (e.g. all stokers in the best performing group employ only dry FGD with fabric filters) whereas other boiler type categories such as conventional PC include a wide range of control device combinations. Thus, a more detailed evaluation is needed to ascertain possible difference in boiler type classification for the best performing 130 units. Impact of ACI An evaluation of the impact of ACI on mercury emissions is presented in Table 4; this is for the group of 130 best performing units which includes some Random 50 data sets. Twenty six of the lowest 130 mercury emission values are for sites that employed ACI; 21 of these fire sub-bituminous coal, 4 fire bituminous coal and 1 fires lignite, based on the ICR Part I unit configuration information. A comparison of average values and associated standard deviations for units with and without ACI suggests no obvious bias that the best performing units employ ACI. Table 4. Average Mercury Emissions for the 130 Best Performing Coal-Fired Units With and Without Activated Carbon Injection (ACI) Average (lb/trillion Btu) Standard Deviation (lb/trillion Btu) No. of Units All Units With ACI Without ACI

14 2.1.3 Hydrogen Chloride and Acid Gases EPRI analyzed the available hydrogen chloride (HCl) and SO 2 data to determine whether a correlation exists. Our analysis included an evaluation of HCl/ SO 2 emissions as well as removal, i.e. penetration (fraction of the coal chloride emitted). It is important to note that the data subset for evaluating removal/penetration is very limited as about 30% of the data sets did not have the necessary coal analyses data to estimate penetration. In addition, many data points have at least one value below the detection limit, thus there is additional uncertainty about the data for evaluating correlations. In summary, our analysis indicates that HCl emissions and penetration correlate with SO 2 emissions and penetration, respectively, as the correlation coefficients are significant at the 95% confidence level when all the ICR data are included in the analysis. However, when evaluating limited subsets of data within the group of top 130 best performing plants for only wet FGD, the correlation coefficient was not statistically significant for HCl penetration with SO 2 penetration for only wet FGD units; similarly the correlation coefficient was not statistically significant for only dry FGD units. Correlation with SO 2 Emissions Emission data pairs for hydrogen chloride and SO 2 were also examined to determine if there was a correlation between these compounds. Data from all units, including the Random 50 units, in the Version 3 Excel spreadsheet are presented in Figure 6. The correlation coefficient of 0.39 is statistically significant for this data set, when including all 176 unit average data pairs. However, evaluating the correlation for only wet FGDs and only dry FGDs (specifically spray dryers, but excluding 4 data pairs for dry sodium injection), the correlation coefficient (R 2 of 0.029) between HCl and SO 2 is not statistically significant at the 95% confidence level for only wet FGDs; however the correlation coefficient (R 2 of 0.27) is statistically significant at the 95% confidence level only dry FGDs. EPRI also evaluated the data pairs for the 130 best performing units (Figure 7). Of the 130 units with the lowest HCl emissions, 107 had paired SO 2 data in the current EPA Version 3 Excel spreadsheet. In this case, the correlation coefficient was 0.12 between HCl emissions and SO 2 emissions for all the available data for the 130 best performing units. Interestingly, the correlation coefficients and statistical significance are reversed. The correlation coefficient (R 2 of 0.21) between HCl and SO 2 is statistically significant at the 95% confidence level for only wet FGDs; however the correlation coefficient (R 2 of 0.056) is not statistically significant at the 95% confidence level only dry FGDs. This was surprising, but suggests that the smaller subsets created by truncating the higher HCl emissions data impacts the correlation somewhat randomly, in one instance the high outliers are no longer included and the other the high values help define the correlation. 14

15 1 HCl Emissions (lb/mmbtu) All ICR Sites FGDd FGDw Dry Sodium Injection All Others R 2 = (FGDw) R 2 = 0.39 (All Data) R 2 = 0.27 (FGDd) SO 2 Emissions (lb/mmbtu) Figure 6. Hydrogen Chloride and SO 2 Emission Data Pairs for All ICR Coal-Fired Units 1 HCl Emissions (lb/mmbtu) Best-Performing FGDd FGDw Dry Sodium Injection All Others R 2 = 0.21 (FGDw) R 2 = 0.12 (All Data) R 2 = (FGDd) SO 2 Emissions (lb/mmbtu) Figure 7. Hydrogen Chloride and SO 2 Emission Data Pairs for Top 130 Best Performing ICR Coal-Fired Units 15

16 The fundamental removal mechanism for HCl and SO 2 in FGD systems is not the same. For example, in wet FGD systems, the strong halogen acids (HCl, HF, etc) dissociate into their respective ions very rapidly upon contact with the aqueous scrubbing solution. At the operating ph of FGD systems, no "HCl" is present, only the chloride ion. In contrast, H 2 SO 3 is a weak acid, thus some of this acid as well as bisulfite and sulfite ions forms at typical FGD operating ph levels. As the alkalinity of the scrubbing solution is depleted by absorption of the acid gases in the tower, the SO 2 liquid phase equilibrium vapor pressure can limit the removal. Consequently, the design parameters of a wet FGD system (e.g. liquid to gas ratio, amount of excess limestone, spray droplet size and distribution) can greatly affect SO 2 removal because of these chemistry effects. But, the chemistry of the halogen is so strong, that only gas-liquid contact efficiency affects the removal rate. Historically, "pre-scrubbers" were used in some regenerable FGD designs to remove HCl and/or particulates upstream of the SO 2 absorber. These devices operated at ph<2, effectively removing HCl, but not SO 2. For these reasons, one would expect that HCl removal would always be high for scrubbed units provided there is good liquid/gas contact; however, the SO 2 removal is more dependent on the design and chemistry of the FGD system. Correlation with SO 2 Penetration (Removal) Further data analyses were conducted using the Version 3 database and Excel spreadsheet to determine if there was a correlation between HCl removal and SO 2 removal. Removal was defined as the coal-to-stack reduction calculated based on paired sets of coal chloride and stack HCl values, or paired sets of coal sulfur and stack SO 2 values. The coal-tostack removals are expressed as percent penetration where Penetration(%) = 100 Removal(%) For example, 10% penetration would correspond to 90% removal, and 0.1% penetration would correspond to 99.9% removal. The number of paired sets of removal data gleaned from the current EPA database for coal-fired units is shown below. Total No. of Units with HCl Stack Data No. of Units with HCl/SO 2 Removal Data Pairs All ICR Data Best performing EPRI was able to develop paired sets of data for approximately 30% of all ICR coal-fired units (including Random 50 units) as well as the 130 best performing units for HCl emissions. Figure 8 shows the currently available set of penetration data pairs for all ICR test sites. Many of the paired data points for the dry FGD set are based on HCl stack data that were flagged as BDL, which further adds to the uncertainty in the data and correlation analyses. Of the 63 data pairs, 15 data pairs include one or more of the individual run 16

17 values for HCl stack emissions below detection limit (BDL); these data points are denoted with circles in Figure 8. In these cases, the calculated HCl penetration represents an upper limit estimated based on the reported BDL emission value % BDL HCl Emissions 10.00% HCl Penetration 1.00% 0.10% R 2 = % 0.1% 1.0% 10.0% 100.0% SO 2 Penetration Figure 8 HCl and SO 2 Penetration Data Pairs for All ICR Coal-Fired Units EPRI then reviewed the paired removal data for the 130 best-performing units for HCl emissions; only 44 of the 130 units had sufficient coal data to yield a paired data set. In this case, the regression coefficient dropped to 0.19 for all data. For only wet FGDs, there was no correlation (R 2 = 0.008) between HCl and SO 2 penetration. The regression coefficient (R 2 = 0.25) for the 15 dry FGD data pairs was also not statistically significant at the 95% confidence level. Additional analyses are needed with a more complete set of paired data before final conclusions regarding possible correlations can be established between HCl and SO 2 penetration. Correlation with Coal Cl Concentration One might also expect that HCl emissions would be a function of coal chloride levels. However, data for the best performing 130 units indicate there is no correlation between coal chloride levels and HCl emissions within this data set. As discussed above, many of the low HCl emission values in the data set of best performing units may be at or near the limit of detection, making analysis of possible correlations difficult. 17

18 Impact of Fuel Type and FGD Design HCl emissions were evaluated by fuel type and FGD design. This evaluation of the 130 best performing units is summarized in Table 5, which shows the average, standard deviation, and count of best performing units within each fuel/fgd category. The overall average HCl emissions for this group of best performing units are 260 lb/trillion Btu input. Eighty-five of the 87 bituminous-fired facilities employed either a wet or dry FGD system. Ten low chloride sub-bituminous units without FGD controls appear in the group of 130 best performing units. Table 5. Hydrogen Chloride Emissions by Fuel Type and Control Type for the 130 Best Performing Coal-Fired Units Average (lb/trillion Btu) Standard Deviation (lb/trillion Btu) No. of Units Fuel Type FGD Type All Fuels All Bituminous None East Bituminous* Dry FGD West Bituminous* Dry FGD Bituminous** Wet FGD Lignite Wet FGD 94 NA 1 Subbituminous None Subbituminous Dry FGD Subbituminous Wet FGD Bituminous FBC Lignite FBC Waste Coal FBC * Units located in the state of CO were classified as western bituminous. All other units were located in VA, NJ, NC, FL and OH, and were classified as eastern bituminous. ** Five of 65 bituminous units were located in CO and AZ, all others were located in eastern states. Other Acid Gases The results of data analyses for acid gases are limited to HCl in this current white paper. EPRI's sampling and analytical data quality review for acid gas species measured as part of the ICR indicated several potential data quality issues, particularly for HCN measurements. Specifically, EPRI noted the inability of testers to maintain a low ph in the collection impingers. A low impinger ph reduces HCN capture efficiency and causes a low bias in the results. Due to the high percentage of HCN tests reviewed by EPRI that suffered from this issue, EPRI considers the overall quality of the HCN data to be poor. Therefore, further data/correlation analyses have not been conducted for HCN at this time. EPRI also noted very high detection limits for HCl and HF at some sites due to variability in sampling and laboratory procedures. If EPA treats the high HCl and HF detection limits as actual values, this will result in overestimation of actual emissions. Use of detection limits as actual values may complicate development of correlations and evaluation of potential surrogate relationships. A detailed discussion of these issues can 18

19 be found in the ICR Data Quality Report. HF emissions tend to be much lower than HCl emissions, and are more often nondetected (BDL). Significant effort is required to screen and remove the nondetected values, thus evaluation of HF emissions is planned in future data analyses Dioxin/Furan/PCB Compounds Evaluation of the ICR data for this HAPs group is complicated by the high proportion of non-detected values and the difficulty of interpreting values with missing or incorrect detection flags. As explained below, ignoring values without a standard detection flag (i.e. those with a missing flag or a flag other than ADL, DLL, or BDL), makes apparent that a high percentage of dioxin and furan measurements in the ICR database are below detection limits. For all fuels, the average number of World Health Organization toxic dioxin congeners reported as detected (ADL or DLL) across all of the ICR units was less than one-third (5.2 out of 17). A smaller percentage of ICR measurements are expected to be below quantitation limits; however, the ICR database does not contain information required to estimate quantitation limits for this method. Many of the dioxin/furan tests reviewed by EPRI had detections in the method or field blanks, indicating non-sample related sources of contamination. Method 23 results were not blank-corrected; thus, the overall data are biased high due to the non-sample-related contamination as evidenced by the method and field blanks. For PCBs, the average number of congeners detected (ADL or DLL) was 6.7 out of 12. However, EPRI research indicates that it is very difficult to avoid non-sample related contamination with these compounds due to their ubiquitous presence in laboratory ambient air and in the environment. Most of the PCB tests reviewed by EPRI had detections in the method or field blanks, indicating non-sample related sources of contamination. In a recent EPRI validation study of EPA Method 1668, every one of the 12 target PCBs was detected in method blanks from seven participating laboratories [5]. The number of laboratories reporting method blank contamination ranged from 3 to 7 of the 7 labs, depending on the congener. Emission limits for dioxins/furans are often set in units of toxicity equivalents (TEQ). As shown in Figure 9, dioxin/furans detected in the ICR data are predominantly the less toxic congeners, as indicated by the World Health Organization (WHO) toxicity equivalence factors (TEFs). The TEQ will reflect primarily the detection limits of the highly toxic, infrequently detected congeners such as 2,3,7,8-TCDD (flagged ADL in only 6% of the ICR samples in the EPA s interim database). PCBs generally will not contribute much to the TEQ, due to their much lower toxicity factors (0.1 to ); therefore, they are not included in Figure 9. Conversely, if an emission limit is set based on the total mass of dioxins/furans/pcbs, the sum would be dominated almost entirely by PCBs, due to the much higher levels reported. Laboratory background of PCBs would become a major obstacle to the correct interpretation of the calculated TEQ values. 19

20 60 Percent of All Values Flagged ADL TEF = 1 TEF= 0.1 TEF = 0.3 TEF = 0.01 TEF = 0.03 TEF = ,3,7,8-TCDD 1,2,3,7,8-PeCDD 1,2,3,4,7,8-HxCDD 1,2,3,6,7,8-HxCDD 1,2,3,7,8,9-HxCDD 1,2,3,4,6,7,8-HpCDD OCDD 2,3,7,8-TCDF 1,2,3,7,8-PeCDF 2,3,4,7,8-PeCDF 1,2,3,4,7,8-HxCDF 1,2,3,6,7,8-HxCDF 1,2,3,7,8,9-HxCDF 2,3,4,6,7,8-HxCDF 1,2,3,4,7,8,9-HpCDF 1,2,3,4,6,7,8-HpCDF OCDF Figure 9. Percentages of Dioxin and Furan Congeners Reported as ADL Volatile and Semi-Volatile Organic Compounds An extensive list of volatile and semivolatile organic HAPs was reported in the ICR database. Interpretation of the data is difficult due to inconsistencies in the analyte lists reported among laboratories and extensive failure to correctly indicate the detection status of the measurements. However, the great majority of measurements appear to be below detection, and it is likely that even a greater number are below their respective quantitation limits. Where detections were reported, many of the measurements are suspected to represent contamination of the sample, based on the presence of the same chemicals in field or method blanks, as well as literature reports of sampling artifacts. Potential sources of contamination include cross-contamination from solvents used in other test methods, laboratory contamination, and breakdown products of the sorbents used in sample collection. EPRI evaluated possible correlations between three organic chemicals - benzene, toluene, and naphthalene - and the species EPA requested be measured as potential surrogates - carbon monoxide, total hydrocarbons (THC), and methane. Benzene, toluene, and naphthalene were selected because they were detected at a higher frequency than other VOCs and SVOCs. None of these species had statistically significant correlations with 20

21 the potential surrogates. Figures 10 and 11 provide example results for benzene and naphthalene paired with CO data, respectively. Regression coefficients were for benzene and for naphthalene. Similarly, regression coefficients for benzene paired with both THC and methane were less than 0.03 and not statistically significant. Likewise, naphthalene data paired with other potential surrogates resulted in regression coefficients less than 0.08 and correlations were not statistically significant. 1.0E E+03 Benzene (lb/trillion Btu) BDL values excluded 1.0E E E E-01 R 2 = E E E E E E E E+01 CO (lb/mmbtu) Figure 10. Paired Benzene and CO Emissions Data 21

22 1.0E E+01 Naphthalene (lb/trillion Btu) BDL values excluded 1.0E E-01 R 2 = E E E E E E E+01 Figure 11. Paired Naphthalene and CO Emissions Data CO (lb/mmbtu) It should be noted that EPRI s ICR data quality reviews found that many measurements of THC and methane were within the range of the zero gas measurement of the instrument; i.e., not distinguishable from background levels. Formaldehyde measurements using EPA Method 0011 had issues relating to elevated level in blanks, indicating likely positive bias, and all formaldehyde measurements using EPA Method 320 were probably below the detection limit of the method (calculated as approximately 8 x 10-4 lb/mmbtu assuming typical instrumental noise levels, interferences, and sampling conditions for coal-fired power plants). These findings indicate that these data may not be informative as to the presence or absence of a correlation for speciated organics; rather, they may reflect the limitations of the test methods. 2.2 Oil-Fired Units Due to limited the number of data sets (approximately 44) as well as time constraints, EPRI has not focused on the data analyses from oil plants. EPRI plans to do so when more data are made available. The EPRI analyses conducted to date are described below. As described previously for coal-fired units, the relationships between non-mercury HAPs metals emissions and various particulate matter measurements (filterable 22

23 particulate matter, filterable PM2.5, and inorganic condensable particulate) were evaluated to identify possible correlations. Figure 12 shows the emissions of nickel plotted with Method 5/Method 29 filterable particulate measured emissions for 44 siteaverage pairs currently available in EPA s Version 3 ICR database. The power function fit of the data shows a regression coefficient of 0.60, which is statistically significant at greater than a 95% confidence level. 1.0E+04 Nickel Emissions (lb/trillion Btu) R 2 = E E E E E E E E E E+00 Method 5/Method 29 Filterable Particulate (lb/mmbtu) Figure 12. Nickel Emissions as a Function of Filterable Particulate Matter Emissions for Oil- Fired Units Table 6 shows results of similar correlation analyses for the other non-mercury HAPs trace metals. In general, the particulate phase metals have a higher correlation coefficient with filterable PM than filterable PM2.5 or condensable PM, with the possible exception of antimony, cobalt, and selenium. With approximately 40 data pairs, any regression coefficient below 0.1 is not considered statistically significant at the 95% level, thus none of the correlations for inorganic condensable particulate are statistically significant. The current EPA database contains data from about half of the total oil-fired units selected for ICR testing; thus no additional analyses of the best performing units has been conducted at this time. This analysis will be conducted once additional data become available. 23

24 Table 6. Correlation Regression Coefficients (R 2 ) for Particulate-Phase Trace Elements at Oil-Fired Units Method 5 / Method 29 OTM-27 Filterable Filterable PM PM2.5 OTM-28 Inorganic Condensable PM Number of Data Pairs Antimony Arsenic Beryllium Cadmium Cobalt Chromium Lead Nickel Manganese Selenium Planned Data Analyses This White Paper presents EPRI s initial observations of the available ICR Part III data. We intend to continue our analyses, including additional new data when they are made available by the EPA. Future analyses may also include a comparison of the current ICR data set with historic measurement data used in previous EPRI assessment of HAPs emissions from fossil-fuel fired EGUs. Some variables which were expected to impact emissions did not appear to have an obvious impact on their emissions, e.g., the impact of fuel type on mercury emissions. This may be due to the approach that EPA used to select the ICR sites, i.e., the newest PM control devices, as well as the way the analyses truncated the data by selecting the top 130 best performing units. EPRI's data analyses are ongoing, as we have "only scratched the surface" of the largest EGU HAPs emissions data set. Additional efforts are planned to evaluate a number of topics. Some examples previously noted in this White Paper and in the Data Quality Report include: The impact of "truncating" the data by selecting the top 130 best performing units as well as the approach that EPA used to select the ICR sites, i.e. the newest PM control devices. Additional analyses would evaluate the ICR test sites with the broader industry as well as the entire ICR data set, including the Random 50 units. Comparison of the ICR data, the Random 50 units, and the existing, available HAPs data (pre-2010 ICR) to evaluate potential biases and identify potential issues in general data usability. Evaluation of the remaining 10 particulate phase metals, as the current analyses focused primarily upon arsenic and its correlation with the various measures of PM, including an approach to evaluate the correlation of combination of various metals with FPM as well as incorporating the trace metal concentration in the coal. 24