Statistical Method Certification, Coal Combustion Residuals Landfill, Reid Gardner Generating Station

Transcription

1 TECHNICAL MEMORANDUM Statistical Method Certification, Coal Combustion Residuals Landfill, Reid Gardner Generating Station PREPARED FOR: PREPARED BY: REVIEWED BY: NV Energy Nathan Betts, PE/CH2M Charles Holbert, PhD/CH2M Aditya Tyagi, PhD, PE (TX)/CH2M DATE: October 13, 2017 CH2M PROJECT NO.: This technical memorandum is the professional engineer certification for the statistical method for evaluating groundwater at the Reid Gardner Generating Station s coal combustion residuals (CCR) landfill per the requirements of (f)(6) of the U.S. Environmental Protection Agency s CCR Rule. This memorandum should be placed in the Station s operating record by October 17, 2017 to comply with (b), (b)(ii), and (h)(4) of the CCR Rule. Within 30 days of placing this memorandum in the operating record, the relevant State Director should be notified to comply with (d) and (h)(3) of the CCR Rule. Similarly, within 30 days of placing this memorandum in the operating record, the memorandum should be posted on the publicly accessible CCR Web site to comply with (d) and (h)(3) of the CCR Rule. 1.0 Background The Reid Gardner Generating Station is a coal-fired electric power generation facility that historically produced approximately 600 megawatts of power from four generating units. The Station is located approximately 45 miles northeast of Las Vegas, within the Moapa Valley. CCR produced as a byproduct of power generation operations was disposed of in an on-site CCR landfill located southwest of the Station. The landfill is classified as a CCR landfill per the definition in of the CCR Rule. The landfill is also regulated by the Nevada Division of Environmental Protection Bureau of Waste Management as a Class III (Industrial Waste) landfill under Southern Nevada Health District Permit No. LF-006-CMF Available Statistical Methods Per (f) of the CCR Rule, one of the statistical methods specified in (f)(1) through (f)(5) of CCR Rule must be selected for evaluating groundwater monitoring data for each constituent specified in the CCR Rule. The five methods specified in the CCR Rule are listed below. (f)(1): A parametric analysis of variance followed by multiple comparison procedures to identify statistically significant evidence of contamination. The method must include estimation and testing of the contrasts between each compliance well s mean and the background mean levels for each constituent. (f)(2): An analysis of variance based on ranks followed by multiple comparison procedures to identify statistically significant evidence of contamination. The method must include estimation and testing of the contrasts between each compliance well s median and the background median levels for each constituent. 1

2 (f)(3): A tolerance or prediction interval procedure in which an interval for each constituent is established from the distribution of the background data and the level of each constituent in each compliance well is compared to the upper tolerance or prediction limit. (f)(4): A control chart approach that gives control limits for each constituent. (f)(5): Another statistical method that meets the performance standards of [ (g)]. 3.0 Statistical Method Selection Per (f) of the CCR Rule, one of the statistical methods specified in (f)(1) through (f)(5) of CCR Rule must be selected for evaluating groundwater monitoring data for each constituent specified in the CCR Rule. Furthermore, the selected statistical test(s) shall be conducted separately for each constituent in each monitoring well. An individual well comparisons approach (also known as an intrawell comparisons approach) will be used for evaluating the groundwater monitoring data for the Reid Gardner Generating Station s CCR landfill. An intrawell comparisons approach was selected because evidence of significant spatial variation in the background monitoring data was identified during the statistical data exploratory analysis. Namely, a significant variation between upgradient and downgradient wells, and a significant spatial variation between different upgradient wells would make the multiple comparison procedure (also known as the interwell approach) infeasible. 3.1 Statistical Methods for Appendix III Constituents The statistical method selected for Appendix III constituents is a prediction interval procedure as specified under (f)(3) of the CCR Rule. Details of the procedure for each Appendix III constituent and monitoring well are show below in Table 1. Table 1. Selected Statistical Methods a for Appendix III Constituents Statistical Method Certification, Coal Combustion Residuals Landfill, Reid Gardner Generating Station Appendix III Constituent Monitoring Well Name Statistical Method CCR Rule Reference Boron All b Prediction Interval (parametric) (f)(3) Calcium CCR-4, KMW-12, LMW-3, LMW-5R, LMW- 6R, LMW-8R Prediction Interval (parametric) (f)(3) Calcium LMW-4R Prediction Interval (non-parametric) (f)(3) Chloride All b Prediction Interval (parametric) (f)(3) Fluoride KMW-12, LMW-3, LMW-4R, LMW-8R Prediction Interval (parametric) (f)(3) Fluoride CCR-4, LMW-5R, LMW-6R Prediction Interval (non-parametric) (f)(3) ph LMW-3, LMW-6R, LMW-8R Prediction Interval (parametric) (f)(3) ph CCR-4, KMW-12, LMW-4R, LMW-5R Prediction Interval (non-parametric) (f)(3) Sulfate KMW-12, LMW-3, LMW-4R, LMW-5R, LMW- 6R, LMW-8R Prediction Interval (parametric) (f)(3) Sulfate CCR-4 Prediction Interval (non-parametric) (f)(3) TDS KMW-12, LMW-4R, LMW-5R, LMW-6R, LMW-8R Prediction Interval (parametric) (f)(3) TDS CCR-4, LMW-3 Prediction Interval (non-parametric) (f)(3) TDS = total dissolved solids a Methods were selected only for the downgradient wells because those are the compliance wells. b All refers to all of the downgradient wells in the CCR groundwater monitoring network as listed in the Groundwater 2

3 Monitoring System Certification technical memorandum created by CH2M and dated October Statistical Methods for Appendix IV Constituents The statistical method selected for Appendix IV constituents is a tolerance interval procedure as specified under (f)(3) of the CCR Rule. Details of the procedure for each Appendix IV constituent and monitoring well are show in Table 2 (see Attachment 1). 3.3 Rationale for Statistical Method Selection The prediction interval method specified in (f)(3) was selected based on a statistical analysis of background monitoring data. This method was selected because it is exceptionally versatile, can be used with parametric data (i.e. normally distributed data) and non-parametric data, and can be designed to accommodate a wide variety of potential site monitoring conditions. Furthermore, this method has been extensively researched, and provides a straightforward interpretation of the test results. The prediction interval approach offers the most effective means of accounting for the cumulative site-wide false positive rate and the effective power to identify real exceedances. The EPA Unified Guidance (EPA, 2009) strongly encourages the use of a comprehensive design strategy to account for these two criteria. Prediction intervals for the Reid Gardner CCR landfill have been constructed to formally include a retesting strategy as part of the overall statistical analysis. The analysis of variance method (ANOVA) methods specified in (f)(1) and (f)(2) were not selected for several reasons. First, these methods assume stringent assumptions that both background and detection monitoring data sets have similar distributions and equal variances, a condition that almost never occurs in practice. Second, because (g)(2) of the CCR Rule mandates a minimum Type I error of 0.05 when using ANOVA, it would be difficult to maintain the annual site-wide false positive rate of 10 percent recommended in the EPA Unified Guidance (EPA, 2009). Finally, the EPA does not recommend the use of ANOVA for detection monitoring because it is more sensitive to spatial variability than prediction interval or tolerance interval methods (EPA, 2009). The control chart method specified in (f)(4) was not selected for several reasons. First, this method cannot be used for data sets requiring nonparametric procedures. For the Reid Gardner CCR landfill, approximately 11 well-constituent pairs for Appendix III constituents and 14 well-constituent pairs for Appendix IV constituents require nonparametric methods. Second, this method cannot be used when nondetects exceed 50 percent of the data set, a condition that applies to approximately 55 wellconstituent pairs for Appendix IV constituents. Third, control charts usually provide less flexibility (than prediction intervals) when designing a statistical monitoring program. And lastly, the statistical performance of control charts are not well understood (i.e., false positive rates and statistical power). The other statistical method specified in (f)(5) was not chosen because prediction intervals and tolerance intervals were determined to be the most appropriate methods for monitoring groundwater at the site. 4.0 Performance Standards ( (g)) As required by (g)(1), parametric methods were selected only for constituents that were normally distributed or could be transformed into a normal distribution. The selection of parametric compared with nonparametric methods in Tables 1 and 2 show which data sets were normally distributed (or could be transformed) and which were not. Two of the data sets shown in Table 1 were transformed to a normal distribution (sulfide and chloride in LMW-6R), and two of the datasets shown in Table 2 were transformed to a normal distribution (barium in LMW-3 and in LMW-4R). The performance standards (g)(2) and (g)(3) do not apply to this site. Although an individual well comparison procedure (i.e. intrawell) will be used, the statistical tests do not need to be 3

4 done at a Type I error level because tolerance interval and prediction interval methods are being used. The performance requirement in (g)(3) does not apply to this site because a control chart approach was not selected. Because tolerance intervals and prediction intervals were selected, and as required by (g)(4), the levels of confidence and, for tolerance intervals, the percentage of the population that the interval must contain, shall be such that this approach is at least as effective as any other approach in (f) of the CCR Rule. These parameters were determined by considering the factors listed in (g)(4) of the CCR Rule. Nondetect data were accounted for using appropriate statistical procedures as required by (g)(5). The Double Quantification Rule was used for data sets with 100 percent non-detects in Appendix III constituents (EPA, 2009). The highest reporting limit was used for data sets with 100 percent non-detects in Appendix IV constituents. For data sets exhibiting a nondetect frequency greater than 50 percent but less than 100 percent, a nonparametric upper prediction limit was computed. For constituents exhibiting a non-detect frequency less than or equal to 50 percent, the Kaplan-Meier censored estimation technique was used to estimate the background mean and standard deviation to determine the parametric upper prediction limit (g)(6) requires that if necessary, the statistical method must include procedures to control or correct for seasonal and spatial variability as well as temporal correlation in the data. Although a few apparent temporal and seasonal trends were identified in the background monitoring data, it was decided that these apparent trends should not be addressed by statistical methods at this time because of the relatively short sampling timeframe and relatively low number of observations. Intrawell analysis will be used to account for spatial variability. 5.0 Narrative Description of Statistical Method This section of the memorandum contains the narrative description required by (f)(6) of the CCR Rule. 5.1 Detection Monitoring The detection monitoring results will be compared to upper prediction limits for all detection monitoring constituents as required by (f)(3). The only exception is ph, for which both lower and upper limits will be used. The upper prediction limits, also referred to as background values in this document, were determined as part of the statistical analysis used to select the appropriate statistical method. The methods used to determine the upper prediction limits are consistent with the methods recommended in the Unified Guidance (EPA, 2009). Determination of background values, or in this case upper prediction limits, is required by (d) of the CCR Rule. Detection monitoring will be conducted on a semiannual basis as required by (b) of the CCR Rule. During each semiannual sampling event, at least one sample from each well in the groundwater monitoring system will be collected, analyzed for Appendix III constituents and (per (h)(2)) statistically evaluated within 90 days of receiving the analytical results from the laboratory. Samples will be collected only from the wells in the CCR groundwater monitoring system as defined in the Groundwater Monitoring System Certification technical memorandum created by CH2M and dated October However, with the selected statistical method a statistically-significant increase over background values cannot be confirmed or denied until the results of retesting have been obtained. Retesting during detection monitoring is an integral part of the statistical methodology for control of the site wide false positive rate when multiple monitoring locations and parameters are being evaluated. EPA (2009) recommends that prediction intervals be combined with retesting to maintain a low site wide false positive rate while providing high statistical power. Therefore, a one of two retesting strategy will be 4

5 used during detection monitoring to verify an apparent detection that exceeds the background values for Appendix III constituents. If the detection monitoring results do not exceed the upper prediction limit, then no retesting is needed. However, if a detection monitoring result in a downgradient well does exceed the upper prediction limit, then another sample will be collected and tested prior to the next regularly scheduled sampling event. The sampling will occur only at the monitoring well(s) and for the constituent(s) that appear to exceed the upper prediction limit. The sample will be evaluated within 90 days of receiving the analytical results from the laboratory. If the sample does not show a statistically-significant increase over background values then no further action will be taken until the next regularly scheduled detection monitoring sampling event. However, if the sample results do show a statistically-significant increase over background values then action must be taken as required by (e) of the CCR Rule. Due to the complex behavior of groundwater and the need for sufficiently large sample sizes, the EPA Unified Guidance recommends that background may be updated every four to eight observations (EPA, 2009). Using this principle with semiannual sampling, thereid Gardner CCR landfill background values may be updated using statistical analysis every two to four years (assuming no confirmed statistically significant increase is identified). In addition, if hydrogeologic conditions change, then background may be updated to match the latest conditions. 5.2 Assessment Monitoring If required based on the results of detection monitoring, assessment monitoring will be conducted in accordance with of the CCR Rule. During each assessment monitoring sampling event, at least one sample from each well in the groundwater monitoring system will be collected, analyzed for Appendix III and Appendix IV constituents and statistically evaluated within 90 days of receiving the analytical results from the laboratory. The results from the assessment monitoring will be compared to the groundwater protection standard established per (h) of the CCR Rule. Samples will be collected only from the wells in the CCR groundwater monitoring system per the Groundwater Monitoring System Certification technical memorandum created by CH2M and dated October Reference U.S. Environmental Protection Agency (EPA) Statistical Analysis of Groundwater Monitoring Data at RCRA Facilities: Unified Guidance. EPA-530-R Office of Resource Conservation and Recovery, U.S. Environmental Protection Agency. March. 7.0 Certification The selected statistical method is appropriate for evaluating the groundwater monitoring data for the CCR landfill at the Reid Gardner Generating Station. CH2M HILL Engineers, Inc Village View Drive, Suite 350 Henderson, NV

6 Attachment 1 Table 2 6

7 Table 2. Selected Statistical Methods for Appendix IV Constituents Statistical Method Certification, Coal Combustion Residuals Landfill, Reid Gardner Generating Station Well Constituent Method CCR-4 antimony Highest reporting limit (nonparametric) CCR-4 arsenic Nonparametric upper tolerance limit CCR-4 barium Parametric upper tolerance limit CCR-4 beryllium Highest reporting limit (nonparametric) CCR-4 cadmium Highest reporting limit (nonparametric) CCR-4 chromium Nonparametric upper tolerance limit CCR-4 cobalt Highest reporting limit (nonparametric) CCR-4 fluoride Nonparametric upper tolerance limit CCR-4 lead Highest reporting limit (nonparametric) CCR-4 lithium Parametric upper tolerance limit CCR-4 mercury Highest reporting limit (nonparametric) CCR-4 molybdenum Parametric upper tolerance limit CCR-4 CCR-4 radium-226 CCR-4 selenium Parametric upper tolerance limit CCR-4 thallium Highest reporting limit (nonparametric) KMW-12 antimony Highest reporting limit (nonparametric) KMW-12 arsenic Parametric upper tolerance limit KMW-12 barium Parametric upper tolerance limit KMW-12 beryllium Highest reporting limit (nonparametric) KMW-12 cadmium Highest reporting limit (nonparametric) KMW-12 chromium Parametric upper tolerance limit KMW-12 cobalt Highest reporting limit (nonparametric) KMW-12 fluoride Parametric upper tolerance limit KMW-12 lead Highest reporting limit (nonparametric) KMW-12 lithium Parametric upper tolerance limit KMW-12 mercury Highest reporting limit (nonparametric) KMW-12 molybdenum Parametric upper tolerance limit KMW-12 radium-226 Nonparametric upper tolerance limit KMW-12 Nonparametric upper tolerance limit KMW-12 selenium Parametric upper tolerance limit KMW-12 thallium Highest reporting limit (nonparametric) LMW-3 antimony Highest reporting limit (nonparametric) LMW-3 arsenic Parametric upper tolerance limit 7

8 Table 2. Selected Statistical Methods for Appendix IV Constituents Statistical Method Certification, Coal Combustion Residuals Landfill, Reid Gardner Generating Station Well Constituent Method LMW-3 barium LMW-3 beryllium Highest reporting limit (nonparametric) LMW-3 cadmium Highest reporting limit (nonparametric) LMW-3 chromium Parametric upper tolerance limit LMW-3 cobalt Highest reporting limit (nonparametric) LMW-3 fluoride Parametric upper tolerance limit LMW-3 lead Highest reporting limit (nonparametric) LMW-3 lithium Parametric upper tolerance limit LMW-3 mercury Highest reporting limit (nonparametric) LMW-3 molybdenum Parametric upper tolerance limit LMW-3 radium-226 Nonparametric upper tolerance limit LMW-3 LMW-3 selenium Nonparametric upper tolerance limit LMW-3 thallium Highest reporting limit (nonparametric) LMW-4R antimony Highest reporting limit (nonparametric) LMW-4R arsenic Parametric upper tolerance limit LMW-4R barium LMW-4R beryllium Highest reporting limit (nonparametric) LMW-4R cadmium Highest reporting limit (nonparametric) LMW-4R chromium Nonparametric upper tolerance limit LMW-4R cobalt Highest reporting limit (nonparametric) LMW-4R fluoride Parametric upper tolerance limit LMW-4R lead Highest reporting limit (nonparametric) LMW-4R lithium Parametric upper tolerance limit LMW-4R mercury Highest reporting limit (nonparametric) LMW-4R molybdenum Nonparametric upper tolerance limit LMW-4R radium-226 Nonparametric upper tolerance limit LMW-4R LMW-4R selenium Parametric upper tolerance limit LMW-4R thallium Highest reporting limit (nonparametric) LMW-5R antimony Highest reporting limit (nonparametric) LMW-5R arsenic Parametric upper tolerance limit LMW-5R barium Nonparametric upper tolerance limit LMW-5R beryllium Highest reporting limit (nonparametric) 8

9 Table 2. Selected Statistical Methods for Appendix IV Constituents Statistical Method Certification, Coal Combustion Residuals Landfill, Reid Gardner Generating Station Well Constituent Method LMW-5R cadmium Highest reporting limit (nonparametric) LMW-5R chromium Parametric upper tolerance limit LMW-5R cobalt Highest reporting limit (nonparametric) LMW-5R fluoride Nonparametric upper tolerance limit LMW-5R lead Highest reporting limit (nonparametric) LMW-5R lithium Parametric upper tolerance limit LMW-5R mercury Highest reporting limit (nonparametric) LMW-5R molybdenum Parametric upper tolerance limit LMW-5R radium-226 Nonparametric upper tolerance limit LMW-5R LMW-5R selenium Parametric upper tolerance limit LMW-5R thallium Highest reporting limit (nonparametric) LMW-6R antimony Highest reporting limit (nonparametric) LMW-6R arsenic Parametric upper tolerance limit LMW-6R barium Parametric upper tolerance limit LMW-6R beryllium Highest reporting limit (nonparametric) LMW-6R cadmium Highest reporting limit (nonparametric) LMW-6R chromium Parametric upper tolerance limit LMW-6R cobalt Highest reporting limit (nonparametric) LMW-6R fluoride Nonparametric upper tolerance limit LMW-6R lead Highest reporting limit (nonparametric) LMW-6R lithium Parametric upper tolerance limit LMW-6R mercury Highest reporting limit (nonparametric) LMW-6R molybdenum Parametric upper tolerance limit LMW-6R LMW-6R radium-226 LMW-6R selenium Parametric upper tolerance limit LMW-6R thallium Highest reporting limit (nonparametric) LMW-8R antimony Highest reporting limit (nonparametric) LMW-8R arsenic Parametric upper tolerance limit LMW-8R barium LMW-8R beryllium Highest reporting limit (nonparametric) LMW-8R cadmium Highest reporting limit (nonparametric) LMW-8R chromium Highest reporting limit (nonparametric) 9

10 Table 2. Selected Statistical Methods for Appendix IV Constituents Statistical Method Certification, Coal Combustion Residuals Landfill, Reid Gardner Generating Station Well Constituent Method LMW-8R cobalt Highest reporting limit (nonparametric) LMW-8R fluoride Parametric upper tolerance limit LMW-8R lead Highest reporting limit (nonparametric) LMW-8R lithium Parametric upper tolerance limit LMW-8R mercury Highest reporting limit (nonparametric) LMW-8R molybdenum Parametric upper tolerance limit LMW-8R LMW-8R radium-226 LMW-8R selenium Highest reporting limit (nonparametric) LMW-8R thallium Highest reporting limit (nonparametric) 10