Development of a US national-scale, mixed-source, pesticide, rural well database for use in drinking water risk assessment: an atrazine case study.

For pesticide registrations in the USA under the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA), as implemented by the United States Environmental Protection Agency, drinking water risk assessments for groundwater sources are based on standard scenario modeling concentration estimates. The conceptual model for the drinking water protection goals is defined in terms of (1) a rural well in or near a relatively high pesticide use area, a shallow well (4-10 m); (2) long-term, single-station weather data; (3) soils characterized as highly leachable; (4) upper-end or surrogate, worst-case environmental fate parameters; and (5) maximum, annual use rates repeated every year. To date, monitoring data have not been quantitatively incorporated into FIFRA drinking water risk assessment; even though considerable, US national-scale temporal and spatial data for some chemistries exists. Investigations into drinking water monitoring data development have historically focused on single-source efforts that may not represent wide geographies and/or time periods, whereas Safe Drinking Water Act groundwater monitoring data are focused on a community-level scale rather than an individual, shallow, rural well. In the current case study, US national-scale, rural well data for the herbicide atrazine was collected, quality controlled, and combined into a single database from mixed sources (termed the atrazine rural well database) to (1) characterize differences between exposure estimates from standard EPA modeling approaches for specific characterization, (2) evaluate monitoring data toward direct use in US drinking water risk assessments to compliment or supersede standard modeling approaches to define risk, and (3) evaluate monitoring trends a function of time relative to label changes implemented as part of the registration review process. Of the 75,665 drinking water samples collected from groundwater, atrazine was only detected in 3185, a 4% detection rate.

Development of a mixed-source, single pesticide database for use in ecological risk assessment: quality control and data standardization practices.

Inclusion of pesticide monitoring data in pesticide risk assessment is important yet challenging for several reasons, including infrequent or irregular data collection, disparate sources procedures and associated monitoring periods, and interpretation of the data itself in a policy context. These challenges alone, left unaddressed, will likely introduce unintentional and unforeseen risk assessment conclusions. While individual water quality monitoring programs report standard operating procedures and quality control practices for their own data, cross-checking data for duplicated data from one database to another does not routinely occur. Consequently, we developed a novel quality control and assurance methodology to identify errors and duplicated records toward creating an aggregated, single pesticide database toward use in ecological risk assessment. This methodology includes (1) standardization and reformatting practices, (2) data error and duplicate record identification protocols, (3) missing or inconsistent limit of detection and quantification reporting, and (4) site metadata scoring and ranking procedures to flag likely duplicate records. We applied this methodology to develop an aggregated (multiple-source), national-scale database for atrazine from a diverse set of surface water monitoring programs. The resultant database resolved and/or removed approximately 31% of the total ~ 385,000 records that were due to duplicated records. Identification of sample replicates was also developed. While the quality control and assurances methodologies developed in this work were applied to atrazine, they generally demonstrate how a properly constructed and aggregated single pesticide database would benefit from the methods described herein before use in subsequent statistical and data analysis or risk assessment.

Characterization of field-scale spray drift deposition and non-target plant biological sensitivity: a corn herbicide (mesotrione/s-metolochlor) case study.

BACKGROUND: This work reports a combined, field-scale spray drift deposition and plant bioassay study for a pre-mixture of the herbicides mesotrione and s-metolachlor. Wind direction data and field dimensions were used to evaluate the potential for spray drift to bypass downwind sampling devices. Variability in resulting spray drift across downwind distances was assessed alongside wind speed measured at on-site weather stations. Measured wind angles were used to geometrically adjust traveled drift particle distances and enabling isolation of wind direction impact from wind speed. Further, the use of single and multiple in-field monitoring locations was compared to quantify the benefit of higher-resolution meteorological sampling. RESULTS: Generally, increased wind speed resulted in significantly greater herbicide deposition at distances proximal to the edge of the spray zone. According to the drift deposition curves that included wind speed data from single and multiple onsite weather stations, trials with relatively higher wind speeds were associated with greater spray drift deposition at relatively close sampling distances downwind from the application area. Only marginal improvement of linear mixed-effects model fit was observed when including data from three weather stations, compared to the fit from a single weather station or absence of weather data in the model. Using tomato and lettuce plant bioassay species, the overall no-effect distance was 3.0 m (10 ft). CONCLUSION: Results from this study are informative to refine pesticide risk assessment for non-target plants and indicate that a single weather station is sufficient to capture potential influential effects from wind speed and direction on spray drift. © 2022 Society of Chemical Industry.

Coupling field-scale and watershed models for regulatory modeling of pesticide aquatic exposures in streams.

Estimating exposure in receiving waterbodies is a key step in the regulatory process to evaluate potential ecological risks posed by the use of agricultural pesticides. The United States Environmental Protection Agency (USEPA) currently uses the Variable Volume Water Model (VVWM) to predict environmental concentrations of pesticides in static waterbodies (ponds) that receive edge-of-field runoff inputs from the Pesticide Root Zone Model (PRZM). This regulatory model, however, does not adequately characterize potential pesticide concentrations in flowing water systems (streams and rivers) drained from watershed areas. This study aims at addressing this gap by coupling the regulatory PRZM model with a watershed-level hydrological model, the Soil and Water Assessment Tool (SWAT), to predict pesticide concentrations in flowing water habitats for aquatic organisms. This coupled PRZM-SWAT model was applied in a test watershed (~HUC12), a headwater watershed of Goodwater Creek in Missouri, and simulation results at the outlet of this watershed were compared to daily and near-daily measured streamflow and atrazine concentration data from a decade-long sampling campaign. Overall, the PRZM-SWAT model captured (1) the general magnitude and temporal trend of daily atrazine concentrations, (2) the observed high-end of exposure levels (>3 ppb) of atrazine concentrations, and (3) the 90th centile annual maximum for various exposure durations (1-, 4-, 7-, 21-, and 60-day rolling average), which are important exposure metrics used in assessing the potential ecological risks posed by the application of pesticides. The PRZM-SWAT model is expected to expand the utility of the field-scale regulatory model to include pesticide exposure prediction capability in flowing waterbodies from agricultural watersheds. Integr Environ Assess Manag 2022;18:1678-1693. © 2022 SETAC.