Approaches for Quantitative Use of Surface Water Monitoring Data in Pesticide Drinking Water Assessments
      
      DRAFT CHARGE QUESTIONS:  
      EPA is interested in better utilizing surface water pesticide residue data in drinking water assessments. A major challenge is that most surface water monitoring programs do not sample daily and are not targeted to correspond to the timing and locations of pesticide usage. These temporal and spatial data limitations result in uncertainty that impacts the utility of the monitoring data for risk assessment purposes. Previous SAPs emphasized that if daily pesticide concentrations are desired, based only on measured pesticide occurrence data, then sampling needs to occur at least daily. For example, according to the 2010 FIFRA SAP, "if inference is to be at the daily level, then sampling needs to be at least daily, for four-day rolling averages at least two samples are required within that period" (FIFRA SAP, 2010b). There are few monitoring datasets that meet these requirements. As such, EPA investigated various methods for estimating missing or censored (i.e., less than the level of quantification; <LOQ) pesticide concentrations between sampling events and presented these approaches to past SAPs. In addition, EPA presented various methods on developing sampling bias factors to adjust measured pesticide occurrence data to ensure reasonable upper-bound concentrations are estimated. The feedback received through past SAPs (referenced below) provide the foundation of the work leading to the Charge Topics listed below. 
      
      FIFRA Scientific Advisory Panels

             FIFRA SAP, 2007. Preliminary Interpretation of the Ecological Significance of Atrazine Stream-Water Concentrations Using a Statistically-Designed Monitoring Program. FIFRA Scientific Advisory Panel. December 4 - 7, 2007. Document available at: EPA-HQ-OPP-2007-0934.
         
             FIFRA SAP, 2009. The Ecological Significance of Atrazine Effects on Primary Producers in Surface Water Streams in the Corn and Sorghum Growing Region of the United States (Part II). FIFRA Scientific Advisory Panel. May 12 - 15, 2009. Document available at: EPA-HQ-OPP-2009-0104.
             FIFRA SAP, 2010a. Re-Evaluation of Human Health Effects of Atrazine: Review of Experimental Animal and In vitro Studies and Drinking Water Monitoring Frequency. FIFRA Scientific Advisory Panel. April 26-30, 2010. Document available at: EPA-HQ-OPP-2010-0125.
         
             FIFRA SAP, 2010b. Re- Evaluation of Human Health Effects of Atrazine: Review of Non-cancer Effects and Drinking Water Monitoring Frequency. FIFRA Scientific Advisory Panel. September 14-17, 2010. Document available at EPA-HQ-OPP-2010-0481.
         
             FIFRA SAP, 2011. A Set of Scientific Issues Being Considered by the Environmental Protection Agency Regarding: Re- Evaluation of Human Health Effects of Atrazine: Review of Cancer Epidemiology, Non-cancer Experimental Animal and In vitro Studies and Drinking Water Monitoring Frequency. FIFRA Scientific Advisory Panel. July 26-29, 2011. Document available at: EPA-HQ-OPP-2011-0399.
         
             FIFRA SAP, 2012. Problem Formation of the Reassessment of Ecological Effects from the Use of Atrazine. FIFRA Scientific Advisory Panel. June 12-14, 2012. Document available at: EPA-HQ-OPP-2012-0230

 SEAWAVE-QEX: Because of the sporadic nature of pesticide concentrations in surface water, monitoring programs with limited sampling frequency often do not provide a reliable estimate of the range of pesticide concentrations relevant to cancer and non-cancer durations of toxicological concern typically considered in pesticide human health risk assessments (e.g., 1-, 4-, 21-, and 365-days). The SAPs in 2010, 2011, and 2012 suggested that EPA look into the use of a seasonal wave regression model (SEAWAVE-Q) developed by the USGS to help interpret surface water monitoring data by generating daily pesticide concentration chemographs. SEAWAVE-QEX, a modified version of SEAWAVE-Q, is designed to estimate extreme (i.e., peak or daily average) pesticide concentrations, using streamflow as a covariate. EPA evaluated SEAWAVE-QEX using high-frequency surface water monitoring data and streamflow data and concluded that SEAWAVE-QEX is a suitable tool for estimating pesticide concentrations for non-sampled days so that upper-end pesticide concentrations may be estimated. 
         EPA also evaluated alternatives to using streamflow (i.e., precipitation and stream stage) as a covariate. This was done because daily streamflow data are not always complete or readily available and may be seasonal (i.e., periods of each year will not have flow record). This would also allow for sampling sites located in low-flow or no-flow systems where flow would not be a suitable covariate.
      
         Reference White Paper Chapter 3.0, Vecchia, 2018 and SEAWAVE-QEX SOP for background information. 

         1.a. Please discuss the strengths and weaknesses of using SEAWAVE-QEX to estimate short-term (i.e., 1-, 4-, and 21-day) average pesticide concentrations, as well as longer-term (365-day) pesticide concentrations (Section 9.3).

         1.b. EPA subsampled daily or near-daily measured concentration data to generate SEAWAVE-QEX input data that are more reflective of available non-targeted surface water monitoring data (Section 9.1). These subsampled data were used as inputs in SEAWAVE-QEX and the results were compared to the original source data. Discuss the soundness of the approach for evaluating the use of SEAWAVE-QEX to develop reliable daily chemographs.
            
         1.c. Please comment on the use of precipitation and stream stage data as inputs into SEAWAVE-QEX to provide reasonable estimates of pesticide concentrations (SEAWAVE-QEX SOP and Section 9.3 and Section 9.4). Also, please discuss the suitability and limits of using methods for infilling missing streamflow data (e.g., waterData R package, Ryberg and Vecchia, 2017).
         
         1.d. Considering that SEAWAVE-QEX was developed from data from flowing systems and that community drinking water systems use drinking water from a variety of surface water sources, including low- or non-flowing systems (Section 9.5), please comment on the utility of using SEAWAVE-QEX for low- or non-flowing systems. Is the Panel aware of a better tool for infilling monitoring data for low- or non-flowing systems?
         
         1.e. Section 6.6 describes the utility of SEAWAVE-QEX in the context of designing a surface water monitoring program. Please comment on EPA's conclusions regarding how SEAWAVE-QEX can be used to optimize a surface water monitoring program design in order to use monitoring data in pesticide drinking water assessments.

         1.f. During EPA's evaluation of SEAWAVE-QEX, we used the following criteria in running the model; 3 years of data, 12 samples per year, and greater than 30% of the samples were detections. However, Vecchia (2018) notes that there is flexibility around the data requirements for input into SEAWAVE-QEX, provided the diagnostic plots indicated that the model assumptions are fulfilled. Please comment on any data characteristics, such as sampling frequency and timing within and across years, that should be considered when exercising flexibility in data requirements. 

 SAMPLING BIAS FACTORS: While SEAWAVE-QEX provides a way to estimate daily pesticide concentrations from non-daily surface water monitoring data, for many pesticides there are not sufficient monitoring data to use SEAWAVE-QEX. This is because the data are too highly censored or there are not enough samples per year or across years. Therefore, EPA would like to have an alternative approach to estimate drinking water concentrations from non-daily pesticide surface water monitoring data. Previous SAPs supported the development of sampling bias factors to derive bounds on pesticide concentrations in surface water.
       
         EPA used SEAWAVE-QEX to develop daily pesticide chemographs from infrequent surface water data. These chemographs were used to generate short-term sampling bias factors for acute exposure durations of concern, utilizing the methods supported by past SAPs for sites with varying attributes across the contiguous United States. Building upon that previous work, EPA evaluated different sampling strategies and imputation techniques to develop short-term sampling bias factors and concluded that using a random sampling strategy with a log-linear imputation is suitable for deriving short-term sampling bias factors. 
      
         In addition, EPA developed a new method for developing long-term sampling bias factors for chronic or cancer exposure durations of concern. EPA concluded that for pesticides with chronic and/or cancer endpoints, that as few as four samples per year can be used to estimate 365-day average concentrations for use in drinking water assessments.
      
        Reference White Paper Chapter 4.0 for background information.

         2.a. Please comment on the use of SEAWAVE-QEX estimated daily pesticide concentrations, including the underlying data requirements, to expand the data available to derive pesticide-specific sampling bias factors. Please comment on EPA's optimization of short-term sampling bias factors, derived using a random sampling strategy with log-linear imputation, to estimate the range of potential concentrations not measured between sampling events (Section 4.4). Please also comment on the long-term sampling bias factor approach (Section 4.5). 
         
         2.b. Please discuss the strengths and weaknesses of developing site-specific sampling bias factors using a percentile (for example, the median [across realizations] and median [across years]) from SEAWAVE-QEX estimated daily pesticide concentrations (Section 10.1.1 and Section 10.2.2). Describe the utility of this approach for use in pesticide drinking water assessments as highlighted in the attached drinking water assessment case studies.
         
         2.c. Please comment on the utility of using the maximum short-term and median long-term sampling bias factors for the four pesticides (i.e., atrazine, carbaryl, chlorpyrifos, and fipronil) to estimate upper-bound concentrations of other pesticides in surface water for which either sampling bias factors cannot be or have not been derived (Section 6.3). In addition to using these values as a screen, EPA proposes an option to select sampling bias factors for an individual pesticide (i.e., atrazine, carbaryl, chlorpyrifos, and fipronil) based on other defining attributes, such as environmental fate properties, use profile, flow rate, basin size, waterbody type, and/or land use (Section 10.1.3). Please comment on what factors EPA should consider when selecting an alternative sampling bias factor for estimating upper-bound pesticide concentrations on a national or regional scale.
         
         2.d. In Chapter 4.6, EPA concludes that sampling bias factors are a reasonable tool for increasing the amount of available monitoring data that can be used as a quantitative measure of exposure in pesticide drinking water assessments beyond those meeting SEAWAVE-QEX criteria. Considering the answers to the questions 2a-2c above, please comment on this conclusion. 
         
         2.e. Section 6.6 describes the utility of sampling bias factors in the context of designing a surface water monitoring program. Please comment on EPA's conclusions regarding how sampling bias factors can be used to further optimize a surface water monitoring program design for the greatest utility of monitoring data in pesticide drinking water assessments.

 SPATIAL RELEVANCE OF MONITORING DATA AND SAMPLING BIAS FACTORS WITH WATERSHED AND PESTICIDE CHARACTERISTICS: To meet EPA's drinking water protection goals, monitoring data used in drinking water assessments should be relevant to drinking water intakes. EPA developed two methods to investigate the spatial relevancy of monitoring data and sampling bias factors. EPA developed sampling bias factors for four pesticides with a range of use profiles, physical-chemical properties, and environmental fate properties, using occurrence data from sampling sites across the contiguous United States. SEAWAVE-QEX was used in the sampling bias factor development for these sites, which have different watershed or catchment attributes. The sampling bias factors were related to watershed and pesticide attributes to develop regression equations to estimate sampling bias factors for watersheds where there are not enough data available to develop these factors. Additionally, a weight-of-evidence approach was developed to assess how relevant sampling bias factors or monitoring data concentrations are to drinking water intakes.
      
        Reference White Paper Chapter 5.0 for background information.

         3.a. Please discuss the suitability of the underlying data, methods, and parameters (Section 11.2 and Section 11.3) used to develop watershed regression equations for estimating sampling bias factors for systems with limited data. Please comment on EPA's conclusion that the short-term sampling bias factor regression equations for the four pesticides evaluated, as well as the environmental fate properties regression analysis (Section 11.4), provide minimal predictive ability for estimation of sampling bias factors for other sites (Section 5.2). Discuss approaches and the value in continuing to investigate quantitative relationships of sampling bias factors with watershed and pesticide characteristics.
         
         3.b. Please comment on the weight-of-evidence approach to determining spatial relevancy of monitoring sites to source drinking water. Are there additional factors EPA should consider in this approach? Please discuss the relative importance of the factors, considering data availability and quality.  

 DRINKING WATER ASSESSMENT CASE STUDIES: In general, EPA relies primarily on model-estimated pesticide concentrations for drinking water assessments, with limited use of surface water monitoring data for most pesticides. To improve transparency and support communication with stakeholders, OPP developed a DW framework that describes OPP's longstanding peer-reviewed tiered approach to drinking water assessments. The DW framework describes the continuum of approaches from highly-conservative and simple to highly-refined, complex temporal and spatial assessments. EPA applied the DW framework to two cases that represent pesticides with available surface water monitoring data, toxicity endpoints typically evaluated by OPP [i.e., 1-day (acute), 365-day (chronic), and 30 year (cancer)], different use patterns, and different environmental fate and transport properties. 
      
         Reference White Paper Chapter 6.0 and the DW Framework along with the case studies for background.
      
   4.a. Please comment on the clarity and organization of the DW Framework.
         
         4.b. In case study 1, EPA demonstrates the implementation of the DW Framework and specifically the use of SEAWAVE-QEX and bias factors to analyze monitoring data for a pesticide with acute risk concerns. Please comment on the SEAWAVE-QEX analysis used to assess sites that had potential drinking water level of comparison exceedances. Please comment on EPA's use of chem1 short-term sampling bias factors to adjust chem1 concentrations for comparison to the potential drinking water level of comparison.

         4.c.  In case study 2, EPA demonstrates the implementation of the DW Framework and specifically the use of SEAWAVE-QEX and bias factors to analyze monitoring data for a pesticide with chronic and cancer risk concerns. Please comment on the SEAWAVE-QEX analysis used to assess sites that had potential drinking water level of comparison exceedances. Please comment on EPA's use of chem2 long-term sampling bias factors to adjust chem2 concentrations for comparison to the potential drinking water level of comparison.
