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.

Assessment of risks to listed species from the use of atrazine in the USA: a perspective.

Atrazine is a triazine herbicide used predominantly on corn, sorghum, and sugarcane in the US. Its use potentially overlaps with the ranges of listed (threatened and endangered) species. In response to registration review in the context of the Endangered Species Act, we evaluated potential direct and indirect impacts of atrazine on listed species and designated critical habitats. Atrazine has been widely studied, extensive environmental monitoring and toxicity data sets are available, and the spatial and temporal uses on major crops are well characterized. Ranges of listed species are less well-defined, resulting in overly conservative designations of "May Effect". Preferences for habitat and food sources serve to limit exposure among many listed animal species and animals are relatively insensitive. Atrazine does not bioaccumulate, further diminishing exposures among consumers and predators. Because of incomplete exposure pathways, many species can be eliminated from consideration for direct effects. It is toxic to plants, but even sensitive plants tolerate episodic exposures, such as those occurring in flowing waters. Empirical data from long-term monitoring programs and realistic field data on off-target deposition of drift indicate that many other listed species can be removed from consideration because exposures are below conservative toxicity thresholds for direct and indirect effects. Combined with recent mitigation actions by the registrant, this review serves to refine and focus forthcoming listed species assessment efforts for atrazine.Abbreviations: a.i. = Active ingredient (of a pesticide product). AEMP = Atrazine Ecological Monitoring Program. AIMS = Avian Incident Monitoring SystemArach. = Arachnid (spiders and mites). AUC = Area Under the Curve. BE = Biological Evaluation (of potential effects on listed species). BO = Biological Opinion (conclusion of the consultation between USEPA and the Services with respect to potential effects in listed species). CASM = Comprehensive Aquatic System Model. CDL = Crop Data LayerCN = field Curve Number. CRP = Conservation Reserve Program (lands). CTA = Conditioned Taste Avoidance. DAC = Diaminochlorotriazine (a metabolite of atrazine, also known by the acronym DACT). DER = Data Evaluation Record. EC25 = Concentration causing a specified effect in 25% of the tested organisms. EC50 = Concentration causing a specified effect in 50% of the tested organisms. EC50RGR = Concentration causing a 50% reduction in relative growth rate. ECOS = Environmental Conservation Online System. EDD = Estimated Daily Dose. EEC = Expected Environmental Concentration. EFED = Environmental Fate and Effects Division (of the USEPA). EFSA = European Food Safety Agency. EIIS = Ecological Incident Information System. ERA = Environmental Risk Assessment. ESA = Endangered Species Act. ESU = Evolutionarily Significant UnitsFAR = Field Application RateFIFRA = Federal Insecticide, Fungicide, and Rodenticide Act. FOIA = Freedom of Information Act (request). GSD = Genus Sensitivity Distribution. HC5 = Hazardous Concentration for ≤ 5% of species. HUC = Hydrologic Unit Code. IBM = Individual-Based Model. IDS = Incident Data System. KOC = Partition coefficient between water and organic matter in soil or sediment. KOW = Octanol-Water partition coefficient. LC50 = Concentration lethal to 50% of the tested organisms. LC-MS-MS = Liquid Chromatograph with Tandem Mass Spectrometry. LD50 = Dose lethal to 50% of the tested organisms. LAA = Likely to Adversely Affect. LOAEC = Lowest-Observed-Adverse-Effect Concentration. LOC = Level of Concern. MA = May Affect. MATC = Maximum Acceptable Toxicant Concentration. NAS = National Academy of Sciences. NCWQR = National Center of Water Quality Research. NE = No Effect. NLAA = Not Likely to Adversely Affect. NMFS = National Marine Fisheries Service. NOAA = National Oceanic and Atmospheric Administration. NOAEC = No-Observed-Adverse-Effect Concentration. NOAEL = No-Observed-Adverse-Effect Dose-Level. OECD = Organization of Economic Cooperation and Development. PNSP = Pesticide National Synthesis Project. PQ = Plastoquinone. PRZM = Pesticide Root Zone Model. PWC = Pesticide in Water Calculator. QWoE = Quantitative Weight of Evidence. RGR = Relative growth rate (of plants). RQ = Risk Quotient. RUD = Residue Unit Doses. SAP = Science Advisory Panel (of the USEPA). SGR = Specific Growth Rate. SI = Supplemental Information. SSD = Species Sensitivity Distribution. SURLAG = Surface Runoff Lag Coefficient. SWAT = Soil & Water Assessment Tool. SWCC = Surface Water Concentration Calculator. UDL = Use Data Layer (for pesticides). USDA = United States Department of Agriculture. USEPA = United States Environmental Protection Agency. USFWS = United States Fish and Wildlife Service. USGS = United States Geological Survey. WARP = Watershed Regressions for Pesticides.

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.

Integrated watershed- and farm-scale modeling framework for targeting critical source areas while maintaining farm economic viability.

Quantitative risk assessments of pollution and data related to the effectiveness of mitigating best management practices (BMPs) are important aspects of nonpoint source pollution control efforts, particularly those driven by specific water quality objectives and by measurable improvement goals, such as the total maximum daily load (TMDL) requirements. Targeting critical source areas (CSAs) that generate disproportionately high pollutant loads within a watershed is a crucial step in successfully controlling nonpoint source pollution. The importance of watershed simulation models in assisting with the quantitative assessments of CSAs of pollution (relative to their magnitudes and extents) and of the effectiveness of associated BMPs has been well recognized. However, due to the distinct disconnect between the hydrological scale in which these models conduct their evaluation and the farm scale at which feasible BMPs are actually selected and implemented, and due to the difficulty and uncertainty involved in transferring watershed model data to farm fields, there are limited practical applications of these tools in the current nonpoint source pollution control efforts by conservation specialists for delineating CSAs and planning targeting measures. There are also limited approaches developed that can assess impacts of CSA-targeted BMPs on farm productivity and profitability together with the assessment of water quality improvements expected from applying these measures. This study developed a modeling framework that integrates farm economics and environmental aspects (such as identification and mitigation of CSAs) through joint use of watershed- and farm-scale models in a closed feedback loop. The integration of models in a closed feedback loop provides a way for environmental changes to be evaluated with regard to the impact on the practical aspects of farm management and economics, adjusted or reformulated as necessary, and revaluated with respect to effectiveness of environmental mitigation at the farm- and watershed-levels. This paper also outlines steps needed to extract important CSA-related information from a watershed model to help inform targeting decisions at the farm scale. The modeling framework is demonstrated with two unique case studies in the northeastern United States (New York and Vermont), with supporting data from numerous published, location-specific studies at both the watershed and farm scales. Using the integrated modeling framework, it can be possible to compare the costs (in terms of changes required in farm system components or financial compensations for retiring crop lands) and benefits (in terms of measurable water quality improvement goals) of implementing targeted BMPs. This multi-scale modeling approach can be used in the multi-objective task of mitigating CSAs of pollution to meet water quality goals while maintaining farm-level economic viability.

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.

A data accuracy evaluation strategy to improve the representation of potential pesticide use areas for endangered species assessments.

The use of "best available data" is a fundamental requirement for all scientific forms of analysis. This paper discusses ways to improve the accuracy of data used to evaluate the potential impacts of pesticides on species that are listed as threatened or endangered under the Endangered Species Act (ESA) by ensuring the best available spatial data representing pesticide use sites are applied correctly. A decision matrix is presented that uses accuracy information from metadata contained in the US Department of Agriculture's (USDA's) Cropland Data Layer (CDL) and the Census of Agriculture (CoA) to improve how labeled pesticide use sites are spatially delineated. We suggest recommendations for the current pesticide evaluation process used by the US Environmental Protection Agency (USEPA) and subsequently by the US Fish and Wildlife Services and National Marine Fisheries Service (collectively known as the Services) in Section 7 consultation activities. The decision matrix is applied to each cultivated land layer in the USDA's CDL with recommendations for how best to use each layer in the evaluation process. Application of this decision matrix will lead to improved representation of labeled uses and more accurate overlap calculations used in the assessment of potential impacts of pesticides on endangered species. Integr Environ Assess Manag 2022;18:1655-1666. © 2022 SETAC.

Probabilistic co-occurrence assessment for suites of listed species.

Section 7 of the Endangered Species Act requires the US Environmental Protection Agency (US EPA) to consult with the Services (US Fish and Wildlife Service and National Marine Fisheries Service) over potential pesticide impacts on federally listed species. Consultation is complicated by the large number of pesticide products and listed species, as well as by lack of consensus on best practices for conducting co-occurrence analyses. Previous work demonstrates that probabilistic estimates of species' ranges and pesticide use patterns improve these analyses. Here we demonstrate that such estimates can be made for suites of sympatric listed species. Focusing on two watersheds, one in Iowa and the other in Mississippi, we obtained distribution records for 13 species of terrestrial and aquatic listed plants and animals occurring therein. We used maximum entropy modeling and bioclimatic, topographic, hydrographic, and land cover variables to predict species' ranges at high spatial resolution. We constructed probabilistic spatial models of use areas for two pesticides based on the US Department of Agriculture Cropland Data Layer and reduced classification errors by incorporating information on the relationships between individual pixels and their neighbors using object-based images analysis. We then combined species distribution and crop footprint models to derive overall probability of co-occurrence of listed species and pesticide use. For aquatic species, we also integrated an estimate of downstream residue transport. We report each separate species-by-use-area co-occurrence estimate and also combine these modeled co-occurrence probabilities across species within watersheds to produce an overall metric of potential pesticide exposure risk for these listed species at the watershed level. We propose that the consultation process between US EPA and the Services be based on such batched estimation of probabilistic co-occurrence for multiple listed species at a regional scale. Integr Environ Assess Manag 2022;18:1088-1100. © 2021 SETAC.

Remote-sensing Based Assessment of Long-term Riparian Vegetation Health in Proximity to Agricultural Lands with Herbicide Use History.

Riparian ecosystems provide various ecosystem services including habitat for a variety of plant and animal communities, biofiltering, and stabilizing stream and river systems. Due to their location, riparian zones often share long borders with agricultural fields where herbicides are commonly applied to eliminate unwanted plants. There is a general concern that exposure of riparian vegetation to off-target drifted herbicides may adversely impact their health and diversity. We utilized the Normalized Difference Vegetation Index (NDVI) to investigate the long-term (between 1992 and 2011) trend of riparian vegetation health at 17 locations in the Midwest and Great Plains areas of the United States, where herbicide usage was likely most intense. Assessment of NDVI data demonstrated that long-term vegetation health did not decline for the studied riparian zones located in proximity to croplands during spring months (April and May). During summer (June and July), while the long-term vegetation health did not decline for the majority of the sites, there were a few cases in Kansas and Nebraska with a decline in vegetation health (negative-trending NDVI). Cluster analysis of the negative-trending NDVI pixels showed that the majority of these pixels were randomly distributed throughout these riparian sites, indicating a lack of shared common causing factors. Similarly, proximity analysis suggested that distance from croplands was not associated with the decline of vegetation health found in these sites, suggesting that exposure to herbicide drift may not be a plausible factor because this would have shown higher impact on pixels closer to the cropland. Changes in canopy coverage and vegetation diversity also did not show any dependence on distance from croplands. Finally, the remote-sensing-based NDVI data sets used provide only an indirect way of assessing the impact of herbicide drift, and therefore, further work based on field survey data is recommended to completely isolate the impacts of herbicides. Integr Environ Assess Manag 2019;15:528-543. © 2019 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Relative Abundance Trends of Bird Populations in High Intensity Croplands in the Central United States.

Declining bird populations across the United States have been noted in a number of studies. Although multiple explanations have been proposed as causes of these declines, agricultural intensification has often been suggested as a significant driver of bird population dynamics. Using spatially explicit USDA-NASS Cropland Data Layer, we examined this relationship by comparing bird count data from the Breeding Bird Survey collected between 1995 and 2016 across 13 states in the central United States to corresponding categorical changes in land cover within a 2-km radius of each survey transect. This approach allowed us to compare the slopes of counts for 31 species of birds between grassland- and cropland-dominated landscapes and against increasing levels of cropland (all types combined) and pooled corn and soybean land cover types. Nearly all birds demonstrated significant responses to land cover changes. In all cases, the number of species exhibiting positive or negative responses was comparable, and median differences in percent change per year ranged from -0.5 to 0.7%. Species that responded either positively or negatively did not appear to fall into any particular foraging guild. If changes in agricultural practices are a major cause of declines, we would expect to see it across the spatial scale studied and across the majority of species. While these results do not rule out potential agricultural effects, such as toxicity resulting from pesticide exposure, which may have species-specific or localized effects, a variety of factors related to habitat are likely the most significant contributor overall. Given these results over a large spatial scale basis (multistate) and across numerous bird species, there is not a broad general trend of greater decline in crop-intensive areas. Integr Environ Assess Manag 2018;14:692-702. © 2018 SETAC.