A Data-Driven Approach to Estimating Occupational Inhalation Exposure Using Workplace Compliance Data.

A growing list of chemicals are approved for production and use in the United States and elsewhere, and new approaches are needed to rapidly assess the potential exposure and health hazard posed by these substances. Here, we present a high-throughput, data-driven approach that will aid in estimating occupational exposure using a database of over 1.5 million observations of chemical concentrations in U.S. workplace air samples. We fit a Bayesian hierarchical model that uses industry type and the physicochemical properties of a substance to predict the distribution of workplace air concentrations. This model substantially outperforms a null model when predicting whether a substance will be detected in an air sample, and if so at what concentration, with 75.9% classification accuracy and a root-mean-square error (RMSE) of 1.00 log10 mg m-3 when applied to a held-out test set of substances. This modeling framework can be used to predict air concentration distributions for new substances, which we demonstrate by making predictions for 5587 new substance-by-workplace-type pairs reported in the US EPA's Toxic Substances Control Act (TSCA) Chemical Data Reporting (CDR) industrial use database. It also allows for improved consideration of occupational exposure within the context of high-throughput, risk-based chemical prioritization efforts.

Uncertainty estimation strategies for quantitative non-targeted analysis.

Non-targeted analysis (NTA) methods are widely used for chemical discovery but seldom employed for quantitation due to a lack of robust methods to estimate chemical concentrations with confidence limits. Herein, we present and evaluate new statistical methods for quantitative NTA (qNTA) using high-resolution mass spectrometry (HRMS) data from EPA's Non-Targeted Analysis Collaborative Trial (ENTACT). Experimental intensities of ENTACT analytes were observed at multiple concentrations using a semi-automated NTA workflow. Chemical concentrations and corresponding confidence limits were first estimated using traditional calibration curves. Two qNTA estimation methods were then implemented using experimental response factor (RF) data (where RF = intensity/concentration). The bounded response factor method used a non-parametric bootstrap procedure to estimate select quantiles of training set RF distributions. Quantile estimates then were applied to test set HRMS intensities to inversely estimate concentrations with confidence limits. The ionization efficiency estimation method restricted the distribution of likely RFs for each analyte using ionization efficiency predictions. Given the intended future use for chemical risk characterization, predicted upper confidence limits (protective values) were compared to known chemical concentrations. Using traditional calibration curves, 95% of upper confidence limits were within ~tenfold of the true concentrations. The error increased to ~60-fold (ESI+) and ~120-fold (ESI-) for the ionization efficiency estimation method and to ~150-fold (ESI+) and ~130-fold (ESI-) for the bounded response factor method. This work demonstrates successful implementation of confidence limit estimation strategies to support qNTA studies and marks a crucial step towards translating NTA data in a risk-based context.

Inferring pesticide toxicity to honey bees from a field-based feeding study using a colony model and Bayesian inference.

Honey bees are crucial pollinators for agricultural crops but are threatened by a multitude of stressors including exposure to pesticides. Linking our understanding of how pesticides affect individual bees to colony-level responses is challenging because colonies show emergent properties based on complex internal processes and interactions among individual bees. Agent-based models that simulate honey bee colony dynamics may be a tool for scaling between individual and colony effects of a pesticide. The U.S. Environmental Protection Agency (USEPA) and U.S. Department of Agriculture (USDA) are developing the VarroaPop + Pesticide model, which simulates the dynamics of honey bee colonies and how they respond to multiple stressors, including weather, Varroa mites, and pesticides. To evaluate this model, we used Approximate Bayesian Computation to fit field data from an empirical study where honey bee colonies were fed the insecticide clothianidin. This allowed us to reproduce colony feeding study data by simulating colony demography and mortality from ingestion of contaminated food. We found that VarroaPop + Pesticide was able to fit general trends in colony population size and structure and reproduce colony declines from increasing clothianidin exposure. The model underestimated adverse effects at low exposure (36 µg/kg), however, and overestimated recovery at the highest exposure level (140 µg/kg), for the adult and pupa endpoints, suggesting that mechanisms besides oral toxicity-induced mortality may have played a role in colony declines. The VarroaPop + Pesticide model estimates an adult oral LD50 of 18.9 ng/bee (95% CI 10.1-32.6) based on the simulated feeding study data, which falls just above the 95% confidence intervals of values observed in laboratory toxicology studies on individual bees. Overall, our results demonstrate a novel method for analyzing colony-level data on pesticide effects on bees and making inferences on pesticide toxicity to individual bees.

Comparison and Evaluation of Gridded Precipitation Datasets in a Kansas Agricultural Watershed Using SWAT.

Gridded precipitation datasets are becoming a convenient substitute for gauge measurements in hydrological modeling; however, these data have not been fully evaluated across a range of conditions. We compared four gridded datasets (Daily Surface Weather and Climatological Summaries [DAYMET], North American Land Data Assimilation System [NLDAS], Global Land Data Assimilation System [GLDAS], and Parameter-elevation Regressions on Independent Slopes Model [PRISM]) as precipitation data sources and evaluated how they affected hydrologic model performance when compared with a gauged dataset, Global Historical Climatology Network-Daily (GHCN-D). Analyses were performed for the Delaware Watershed at Perry Lake in eastern Kansas. Precipitation indices for DAYMET and PRISM precipitation closely matched GHCN-D, whereas NLDAS and GLDAS showed weaker correlations. We also used these precipitation data as input to the Soil and Water Assessment Tool (SWAT) model that confirmed similar trends in streamflow simulation. For stations with complete data, GHCN-D based SWAT-simulated streamflow variability better than gridded precipitation data. During low flow periods we found PRISM performed better, whereas both DAYMET and NLDAS performed better in high flow years. Our results demonstrate that combining gridded precipitation sources with gauge-based measurements can improve hydrologic model performance, especially for extreme events.

Endogenous and exogenous biomarker analysis in terrestrial phase amphibians (Lithobates sphenocephala) following dermal exposure to pesticide mixtures.

Pesticide mixtures are frequently co-applied throughout an agricultural growing season to maximize crop yield. Therefore, non-target ecological species (e.g., amphibians) may be exposed to several pesticides at any given time on these agricultural landscapes. The objectives of this study were to quantify body burdens in terrestrial phase amphibians and translate perturbed metabolites to their corresponding biochemical pathways affected by exposure to pesticides as both singlets and in combination. Southern leopard frogs (Lithobates sphenocephala) were exposed either at maximum or 1/10th maximum application rate to single, double, or triple pesticide mixtures of bifenthrin (insecticide), metolachlor (herbicide), and triadimefon (fungicide). Tissue concentrations demonstrate both facilitated and competitive uptake of pesticides when in mixtures. Metabolomic profiling of amphibian livers identified metabolites of interest for both application rates, however; magnitude of changes varied for the two exposure rates. Exposure to lower concentrations demonstrated down regulation in amino acids, potentially due to their being utilized for glutathione metabolism and/or increased energy demands. Amphibians exposed to the maximum application rate resulted in up regulation of amino acids and other key metabolites likely due to depleted energy resources. Coupling endogenous and exogenous biomarkers of pesticide exposure can be utilized to form vital links in an ecological risk assessment by relating internal dose to pathophysiological outcomes in non-target species.

Sensitivity analyses for simulating pesticide impacts on honey bee colonies.

We employ Monte Carlo simulation and sensitivity analysis techniques to describe the population dynamics of pesticide exposure to a honey bee colony using the VarroaPop+Pesticide model. Simulations are performed of hive population trajectories with and without pesticide exposure to determine the effects of weather, queen strength, foraging activity, colony resources, and Varroa populations on colony growth and survival. The daily resolution of the model allows us to conditionally identify sensitivity metrics. Simulations indicate queen strength and forager lifespan are consistent, critical inputs for colony dynamics in both the control and exposed conditions. Adult contact toxicity, application rate and nectar load become critical parameters for colony dynamics within exposed simulations. Daily sensitivity analysis also reveals that the relative importance of these parameters fluctuates throughout the simulation period according to the status of other inputs.

Spatiotemporal sensitivity analysis of vertical transport of pesticides in soil.

Environmental fate and transport processes are influenced by many factors. Simulation models that mimic these processes often have complex implementations, which can lead to over-parameterization. Sensitivity analyses are subsequently used to identify critical parameters whose uncertainties can be further reduced or better described and prediction variability minimized. In this study, a variance decomposition based global sensitivity analysis technique (Sobol' method) is conducted based on estimated concentrations in vertical soil compartments using the Pesticide Root Zone Model (PRZM). Daily simulations are performed that explore the input parameter space. Estimated concentrations are compared to data collected over the course of a growing season from an experimental site in Georgia. Our results suggest that model sensitivity is conditional and should be examined at appropriate spatial and temporal resolution to avoid omitting important parameters. This approach can yield a better understanding about the interplay between sensitivity/uncertainty and model dynamics in non-monotonic, non-linear systems.

Rainfall-induced release of microbes from manure: model development, parameter estimation, and uncertainty evaluation on small plots.

A series of simulated rainfall-runoff experiments with applications of different manure types (cattle solid pats, poultry dry litter, swine slurry) was conducted across four seasons on a field containing 36 plots (0.75 × 2 m each), resulting in 144 rainfall-runoff events. Simulating time-varying release of Escherichia coli, enterococci, and fecal coliforms from manures applied at typical agronomic rates evaluated the efficacy of the Bradford-Schijven model modified by adding terms for release efficiency and transportation loss. Two complementary, parallel approaches were used to calibrate the model and estimate microbial release parameters. The first was a four-step sequential procedure using the inverse model PEST, which provides appropriate initial parameter values. The second utilized a PEST/bootstrap procedure to estimate average parameters across plots, manure age, and microbe, and to provide parameter distributions. The experiment determined that manure age, microbe, and season had no clear relationship to the release curve. Cattle solid pats released microbes at a different, slower rate than did poultry dry litter or swine slurry, which had very similar release patterns. These findings were consistent with other published results for both bench- and field-scale, suggesting the modified Bradford-Schijven model can be applied to microbial release from manure.

Pesticide Uptake Across the Amphibian Dermis Through Soil and Overspray Exposures.

For terrestrial amphibians, accumulation of pesticides through dermal contact is a primary route of exposure in agricultural landscapes and may be contributing to widespread amphibian declines. To show pesticide transfer across the amphibian dermis at permitted label application rates, our study was designed to measure pesticide body burdens after two simulated exposure scenarios. We compared direct exposures, where amphibians were present when spraying occurred, to indirect exposures, where amphibians were exposed to soils after pesticide application. During summer 2012, we reared barking (Hyla gratiosa) and green treefrogs (H. cinerea) through 60-90 days post-metamorphosis at a United States Environmental Protection Agency research laboratory. We tested exposure for 8 h to five pesticide active ingredients (imidacloprid, atrazine, triadimefon, fipronil, or pendimethalin) in glass aquaria lined with soil in the laboratory. We quantified total pesticide body burden and soil concentrations using liquid chromatography-mass spectrometry. All individuals in both treatments had measurable body burdens at the end of the study. A randomized block design analysis of variance (n = 18) showed that body burdens (p = 0.03) and bioconcentration factors (BCFs) (p = 0.01) were significantly greater in the direct overspray treatment relative to the indirect soil spray treatment for both species and tested pesticides. BCFs ranged from 0.1 to 1.16 and from 0.013 to 0.78 in the direct and indirect treatments, respectively. Our study shows dermal uptake for multiple pesticides from both direct spray and indirect soil exposures and provides empirical support for the degree to which terrestrial phase amphibians have higher body burdens after overspray pesticide exposure.

A multi-scale approach for identification of potential pesticide use sites impacting vernal pool critical habitat in California.

Spatially explicit ecological risk assessment (ERA) requires estimating the overlap between chemical and receptor distribution to evaluate the potential impacts of exposure on nontarget organisms. Pesticide use estimation at field level is prone to error due to inconsistencies between ground-reporting and geospatial data coverage; attempts to rectify these inconsistencies have been limited in approach and rarely scaled to multiple crop types. We built upon a previously developed Bayesian approach to combine multiple crop types for a probabilistic determination of field-crop assignments and to examine co-occurrence of critical vernal pool habitats and bifenthrin application within a 5-county area in California (Madera, Merced, Sacramento, San Joaquin, and Stanislaus counties). We incorporated a multi-scale repeated sampling approach with an area constraint to improve the delineation of field boundaries and better capture variability in crop assignments and rotation schemes. After comparing the accuracy of the spatial probabilistic approach to USDA Census of Agriculture crop acreage data, we found our approach allows more specificity in the combination of crop types represented by the potential application area and improves acreage estimates when compared to traditional deterministic approaches. In addition, our multi-scale sampling scheme improved estimates of bifenthrin acreage variability for co-occurrence analysis and allowed for estimates of crop rotations that were previously uncaptured. Our approach could be leveraged for more realistic, spatially resolved exposure and effects models both in and outside of California.

Estimating dermal contact soil exposure for amphibians.

Chemical exposure estimation through the dermal route is an underemphasized area of ecological risk assessment for terrestrial animals. Currently, there are efforts to create exposure models to estimate doses from this pathway for use in ecological risk assessment. One significant limitation has been insufficient published data to characterize exposure and to support the selection and parameterization of appropriate models, particularly for amphibians in terrestrial habitats. Recent publications measuring pesticide doses to terrestrial-phase amphibians have begun to rectify this situation. We collated and summarized available measurements of terrestrial amphibian dermal exposure to pesticides from 11 studies in which researchers measured tissue concentrations associated with known pesticide experimental application rates. This data set included tissue concentrations in 11 amphibian species and 14 different pesticides. We then compared the results of two screening exposure models that differed based on surface area scaling approaches as a function of body weight (one based on birds as surrogates for amphibians and another amphibian-specific) to the measured tissue residue concentrations. We define a false-negative rate for each screening model as the proportion of amphibians for which the predicted concentration is less than the observed concentration (i.e., underestimate), contrary to the intent of screening models, which are intended to have a bias for higher exposure concentrations. The screening model that uses birds as surrogates did not have any instances where estimated expected avian doses were less than measured amphibian body burdens. When using the amphibian-specific exposure model that corrected for differences between avian and amphibian surface area, measured concentrations were greater than model estimates for 11.3% of the 1158 comparisons. The database of measured pesticide concentrations in terrestrial amphibians is provided for use in calculating bioconcentration factors and for future amphibian dermal exposure model development. Integr Environ Assess Manag 2023;19:9-16. © 2022 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Differentiating metabolomic responses of amphibians to multiple stressors.

One of the biggest challenges in ecological risk assessment is determining the impact of multiple stressors on individual organisms and populations in real world scenarios. Frequently, data derived from laboratory studies of single stressors are used to estimate risk parameters and do not adequately address scenarios where other stressors exist. Emerging 'omic technologies, notably metabolomics, provide an opportunity to address the uncertainties surrounding ecological risk assessment of multiple stressors. The objective of this study was to use metabolomic profiling to investigate the effect of multiple stressors on amphibian metamorphs. We exposed post-metamorphosis (180 days) southern leopard frogs (Lithobates sphenocephala) to the insecticide carbaryl (480 µg/L), predation stress, and a combined pesticide and predation stress treatment. Corticosterone analysis revealed mild support for an induction in response to predation stress alone but strongly suggests that carbaryl exposure, alone or in combination with predation cues, can significantly elevate this known biomarker in amphibians. Metabolomics analysis accurately classed, based on relative nearness, carbaryl and predation induced changes in the hepatic metabolome and biochemical fluxes appear to be associated with a similar biological response. Support vector machine analysis with recursive feature elimination of the acquired metabolomic spectra demonstrated 85-96% classification accuracy among control and all treatment groups when using the top 75 ranked retention time bins. Biochemical fluxes observed in the groups exposed to carbaryl, predation, and the combined treatment include amino acids, sugar derivatives, and purine nucleotides. Ultimately, this methodology could be used to interpret short-term toxicity assays and the presence of environmental stressors to overall metabolomic effects in non-target organisms.

Induced Hepatic Glutathione and Metabolomic Alterations Following Mixed Pesticide and Fertilizer Exposures in Juvenile Leopard Frogs (Lithobates sphenocephala).

The increasing use of agrochemicals, alone and in combination, has been implicated as a potential causative factor in the decline of amphibians worldwide. Fertilizers and pesticides are frequently combined into single-use tank mixtures for agricultural applications to decrease costs while meeting the food demands of a growing human population. Limited data are available on the effects of increased nitrogen levels in nontarget species, such as amphibians, and therefore investigating alterations in the nitrogen cycle and its impacts on amphibians needs to be considered in best management practices going forward. The objective of the present study was to elucidate the impact of fertilizer (urea) and herbicide (atrazine and/or alachlor) tank mixtures on the hepatic metabolome of juvenile leopard frogs as well as to investigate alterations in oxidative stress by relating these changes to glutathione (GSH) levels. Herbicide exposure only moderately increased this parameter in amphibians, however, urea alone and in combination with either atrazine or alachlor statistically elevated GSH levels. Interestingly, urea also inhibited pesticide uptake: calculated bioconcentration factors were greatly decreased for atrazine and alachlor when urea was present in the exposure mixture. Metabolomic profiling identified fluxes in hepatic metabolites that are involved in GSH and carbohydrate metabolic processes as well as altered intermediates in the urea cycle. Ultimately, understanding the biological impacts of nitrogenous fertilizers alone and in combination with pesticide exposure will inform best management practices to conserve declining amphibian populations worldwide. Environ Toxicol Chem 2022;41:122-133. © 2021 SETAC.

Satellite-derived cyanobacteria frequency and magnitude in headwaters & near-dam reservoir surface waters of the Southern U.S.

Reservoirs are dominant features of the modern hydrologic landscape and provide vital services. However, the unique morphology of reservoirs can create suitable conditions for excessive algae growth and associated cyanobacteria blooms in shallow in-flow reservoir locations by providing warm water environments with relatively high nutrient inputs, deposition, and nutrient storage. Cyanobacteria harmful algal blooms (cyanoHAB) are costly water management issues and bloom recurrence is associated with economic costs and negative impacts to human, animal, and environmental health. As cyanoHAB occurrence varies substantially within different regions of a water body, understanding in-lake cyanoHAB spatial dynamics is essential to guide reservoir monitoring and mitigate potential public exposure to cyanotoxins. Cloud-based computational processing power and high temporal frequency of satellites enables advanced pixel-based spatial analysis of cyanoHAB frequency and quantitative assessment of reservoir headwater in-flows compared to near-dam surface waters of individual reservoirs. Additionally, extensive spatial coverage of satellite imagery allows for evaluation of spatial trends across many dozens of reservoir sites. Surface water cyanobacteria concentrations for sixty reservoirs in the southern U.S. were estimated using 300 m resolution European Space Agency (ESA) Ocean and Land Colour Instrument (OLCI) satellite sensor for a five year period (May 2016-April 2021). Of the reservoirs studied, spatial analysis of OLCI data revealed 98% had more frequent cyanoHAB occurrence above the concentration of >100,000 cells/mL in headwaters compared to near-dam surface waters (P < 0.001). Headwaters exhibited greater seasonal variability with more frequent and higher magnitude cyanoHABs occurring mid-summer to fall. Examination of reservoirs identified extremely high concentration cyanobacteria events (>1,000,000 cells/mL) occurring in 70% of headwater locations while only 30% of near-dam locations exceeded this threshold. Wilcoxon signed-rank tests of cyanoHAB magnitudes using paired-observations (dates with observations in both a reservoir's headwater and near-dam locations) confirmed significantly higher concentrations in headwater versus near-dam locations (p < 0.001).

Investigating Vernal Pool Fairy Shrimp Exposure to Organophosphate Pesticides: Implications for Population-Level Risk Assessment.

Vernal pool fairy shrimp, Branchinecta lynchi, is a freshwater crustacean endemic to California and Oregon, including California's Central Valley. B. lynchi is listed as a Federally Threatened species under the US Endangered Species Act, and as a vulnerable species on the IUCN Red List. Threats that may negatively impact vernal pool fairy shrimp populations include pesticide applications to agricultural land use (e.g., agrochemicals such as organophosphate pesticides) and climate changes that impact vernal pool hydrology. Pop-GUIDE (Population model Guidance, Use, Interpretation, and Development for Ecological risk assessment) is a comprehensive tool that facilitates development and implementation of population models for ecological risk assessment and can be used to document the model derivation process. We employed Pop-GUIDE to document and facilitate the development of a population model for investigating impacts of organophosphate pesticides on vernal pool fairy shrimp populations in California's Central Valley. The resulting model could be applied in combination with field assessment and laboratory-based chemical analysis to link effects from pesticide exposure to adverse outcomes in populations across their range. B. lynchi has a unique intra-annual life cycle that is largely dependent upon environmental conditions. Future deployment of this population model should include complex scenarios consisting of multiple stressors, whereby the model is used to examine scenarios that combine chemical stress resulting from exposure to pesticides and climate changes.

Using metabolomic profiling to inform use of surrogate species in ecological risk assessment practices.

The U.S. EPA frequently uses avian or fish toxicity data to set protective standards for amphibians in ecological risk assessments. However, this approach does not always adequately represent aquatic-dwelling and terrestrial-phase amphibian exposure data. For instance, it is accepted that early life stage tests for fish are typically sensitive enough to protect larval amphibians, however, metamorphosis from tadpole to a terrestrial-phase adult relies on endocrine cues that are less prevalent in fish but essential for amphibian life stage transitions. These differences suggest that more robust approaches are needed to adequately elucidate the impacts of pesticide exposure in amphibians across critical life stages. Therefore, in the current study, methodology is presented that can be applied to link the perturbations in the metabolomic response of larval zebrafish (Danio rerio), a surrogate species frequently used in ecotoxicological studies, to those of African clawed frog (Xenopus laevis) tadpoles following exposure to three high-use pesticides, bifenthrin, chlorothalonil, or trifluralin. Generally, D. rerio exhibited greater metabolic perturbations in both number and magnitude across the pesticide exposures as opposed to X. laevis. This suggests that screening ecological risk assessment surrogate toxicity data would sufficiently protect amphibians at the single life stage studied but care needs to be taken to understand the suite of metabolic requirements of each developing species. Ultimately, methodology presented, and data gathered herein will help inform the applicability of metabolomic profiling in establishing the risk pesticide exposure poses to amphibians and potentially other non-target species.

Route of exposure influences pesticide body burden and the hepatic metabolome in post-metamorphic leopard frogs.

Pesticides are being applied at a greater extent than in the past. Once pesticides enter the ecosystem, many environmental factors can influence their residence time. These interactions can result in processes such as translocation, environmental degradation, and metabolic activation facilitating exposure to target and non-target species. Most anurans start off their life cycle in aquatic environments and then transition into terrestrial habitats. Their time in the aquatic environment is generally short; however, many important developmental stages occur during this tenure. Post-metamorphosis, most species spend many years on land but migrate back to the aquatic environment for breeding. Due to the importance of both the aquatic and terrestrial environments to the life stages of amphibians, we investigated how the route of exposure (i.e., uptake from contaminated soils vs. uptake from contaminated surface water) influences pesticide bioavailability and body burden for four pesticides (bifenthrin (BIF), chlorpyrifos (CPF), glyphosate (GLY), and trifloxystrobin (TFS)) as well as the impact on the hepatic metabolome of adult leopard frogs (Gosner stage 46 with 60-90 days post-metamorphosis). Body burden concentrations for amphibians exposed in water were significantly higher (ANOVA p < 0.0001) compared to amphibians exposed to contaminated soil across all pesticides studied. Out of 80 metabolites that were putatively identified, the majority expressed a higher abundance in amphibians that were exposed in pesticide contaminated water compared to soil. Ultimately, this research will help fill regulatory data gaps, aid in the creation of more accurate amphibian dermal uptake models and inform continued ecological risk assessment efforts.

Evaluation of Quantitative Structure Property Relationship Algorithms for Predicting Plasma Protein Binding in Humans.

The extent of plasma protein binding is an important compound-specific property that influences a compound's pharmacokinetic behavior and is a critical input parameter for predicting exposure in physiologically based pharmacokinetic (PBPK) modeling. When experimentally determined fraction unbound in plasma (fup) data are not available, quantitative structure-property relationship (QSPR) models can be used for prediction. Because available QSPR models were developed based on training sets containing pharmaceutical-like compounds, we compared their prediction accuracy for environmentally relevant and pharmaceutical compounds. Fup values were calculated using Ingle et al., Watanabe et al. and ADMET Predictor (Simulation Plus). The test set included 818 pharmaceutical and environmentally relevant compounds with fup values ranging from 0.01 to 1. Overall, the three QSPR models resulted in over-prediction of fup for highly binding compounds and under-prediction for low or moderately binding compounds. For highly binding compounds (0.01≤ fup ≤ 0.25), Watanabe et al. performed better with a lower mean absolute error (MAE) of 6.7% and a lower mean absolute relative prediction error (RPE) of 171.7 % than other methods. For low to moderately binding compounds, both Ingle et al. and ADMET Predictor performed better than Watanabe et al. with superior MAE and RPE values. The positive polar surface area, the number of basic functional groups and lipophilicity were the most important chemical descriptors for predicting fup. This study demonstrated that the prediction of fup was the most uncertain for highly binding compounds. This suggested that QSPR-predicted fup values should be used with caution in PBPK modeling.

Honey Bees and Neonicotinoid-Treated Corn Seed: Contamination, Exposure, and Effects.

Most corn (Zea mays) seeds planted in the United States in recent years are coated with a seed treatment containing neonicotinoid insecticides. Abrasion of the seed coating generates insecticide-laden planter dust that disperses through the landscape during corn planting and has resulted in many "bee-kill" incidents in North America and Europe. We investigated the linkage between corn planting and honey bee colony success in a region dominated by corn agriculture. Over 3 yr we consistently observed an increased presence of corn seed treatment insecticides in bee-collected pollen and elevated worker bee mortality during corn planting. Residues of seed treatment neonicotinoids, clothianidin and thiamethoxam, detected in pollen positively correlated with cornfield area surrounding the apiaries. Elevated worker mortality was also observed in experimental colonies fed field-collected pollen containing known concentrations of corn seed treatment insecticides. We monitored colony growth throughout the subsequent year in 2015 and found that colonies exposed to higher insecticide concentrations exhibited slower population growth during the month of corn planting but demonstrated more rapid growth in the month following, though this difference may be related to forage availability. Exposure to seed treatment neonicotinoids during corn planting has clear short-term detrimental effects on honey bee colonies and may affect the viability of beekeeping operations that are dependent on maximizing colony size in the springtime. Environ Toxicol Chem 2021;40:1212-1221. © 2020 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

A sensitivity analysis of pesticide concentrations in California Central Valley vernal pools.

Vernal pools are ephemeral wetlands that provide critical habitat to many listed species. Pesticide fate in vernal pools is poorly understood because of uncertainties in the amount of pesticide entering these ecosystems and their bioavailability throughout cycles of wet and dry periods. The Pesticide Water Calculator (PWC), a model used for the regulation of pesticides in the US, was used to predict surface water and sediment pore water pesticide concentrations in vernal pool habitats. The PWC model (version 1.59) was implemented with deterministic and probabilistic approaches and parameterized for three agricultural vernal pool watersheds located in the San Joaquin River basin in the Central Valley of California. Exposure concentrations for chlorpyrifos, diazinon and malathion were simulated. The deterministic approach used default values and professional judgment to calculate point values of estimated concentrations. In the probabilistic approach, Monte Carlo (MC) simulations were conducted across the full input parameter space with a sensitivity analysis that quantified the parameter contribution to model prediction uncertainty. Partial correlation coefficients were used as the primary sensitivity metric for analyzing model outputs. Conditioned daily sensitivity analysis indicates curve number (CN) and the universal soil loss equation (USLE) parameters as the most important environmental parameters. Therefore, exposure estimation can be improved efficiently by focusing parameterization efforts on these driving processes, and agricultural pesticide inputs in these critical habitats can be reduced by best management practices focused on runoff and sediment reductions.