Time-Variable Exposure Experiments in Conjunction with Higher Tier Population and Effect Modeling to Assess the Risk of Chlorotoluron to Green Algae

An algae population model was applied to describe measured effects of pulsed exposure to chlorotoluron on populations of Pseudokirchneriella subcapitata in 2 laboratory flow-through chemostat tests with different exposure regimes. Both tests enabled evaluation of adverse effects on algae during the exposure and population recovery afterward. Impacts on population densities after chlorotoluron exposure were directly visible as biomass loss in the chemostats. Recovery was observed after each exposure peak. The test results indicate that P. subcapitata is unlikely to show an increased sensitivity to chlorotoluron after pulsed exposure. No altered response or adaptation of the algae to chlorotoluron was observed, with the exception of the last high peak in flow-through test 2. Therefore, an adaptation to the test substance cannot be excluded after long-term exposure. However, recovery to the steady-state level after this peak indicates that the growth rate (fitness) was not significantly reduced in the population with higher tolerance. No differences in chlorotoluron impact on the populations over time in terms of growth were detected. Model predictions agreed well with the measured data. The tests and modeling results validate the model to simulate population dynamics of P. subcapitata after pulsed exposure to chlorotoluron. Model predictions and extrapolations with different exposure patterns are considered reliable for chlorotoluron. The good reproducibility of the population behavior in the test systems supports this conclusion. An example modeled extrapolation of the experimental results to other (untested) exposure scenarios shows a potential approach to using the validated model as a supportive tool in risk assessment. Environ Toxicol Chem 2019;38:2520-2534. © 2019 SETAC.

Quantifying pesticide deposits and spray patterns at micro-scales on apple (Malus domesticus) leaves with a view to arthropod exposure

BACKGROUND

Pesticides used in commercial crop systems can adversely affect non-target arthropod populations. The spatial distribution of pesticide residues is rarely studied at scales relevant to these populations. Here, we combine two methods for assessing pesticide spray deposits at spatial scales relevant to non-target arthropods found in apple orchards. Pesticide residues were determined on individual apple leaves through conventional residue analysis; water-sensitive paper was used to investigate spatial distributions in deposits at the micro-scale. We also evaluated how accurately a digital image analysis program estimated pesticide residues.

RESULTS

We found that mean pesticide spray coverage on water-sensitive paper varied by up to 6.1% (95% CI 9.4%, 2.7%) within an apple orchard, and leaf residues varied by up to 0.95 (95% CI 0.54, 1.36) mg kg^-1 within a tree. Leaf residues based on analytical chemistry were six times lower than pesticide deposition estimated through image analysis of water-sensitive paper, although these correlated strongly. This correlation allowed estimation of actual residues by application of a correction factor.

CONCLUSION

Our method demonstrates accurate estimation of pesticide deposits at the individual leaf scale through digital analysis of water-sensitive paper and is a low-cost, rapid alternative to conventional residue analysis techniques. © 2018 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Using toxicokinetic-toxicodynamic modeling as an acute risk assessment refinement approach in vertebrate ecological risk assessment

Recent guidance identified toxicokinetic-toxicodynamic (TK-TD) modeling as a relevant approach for risk assessment refinement. Yet, its added value compared to other refinement options is not detailed, and how to conduct the modeling appropriately is not explained. This case study addresses these issues through 2 examples of individual-level risk assessment for 2 hypothetical plant protection products: 1) evaluating the risk for small granivorous birds and small omnivorous mammals of a single application, as a seed treatment in winter cereals, and 2) evaluating the risk for fish after a pulsed treatment in the edge-of-field zone. Using acute test data, we conducted the first tier risk assessment as defined in the European Food Safety Authority (EFSA) guidance. When first tier risk assessment highlighted a concern, refinement options were discussed. Cases where the use of models should be preferred over other existing refinement approaches were highlighted. We then practically conducted the risk assessment refinement by using 2 different models as examples. In example 1, a TK model accounting for toxicokinetics and relevant feeding patterns in the skylark and in the wood mouse was used to predict internal doses of the hypothetical active ingredient in individuals, based on relevant feeding patterns in an in-crop situation, and identify the residue levels leading to mortality. In example 2, a TK-TD model accounting for toxicokinetics, toxicodynamics, and relevant exposure patterns in the fathead minnow was used to predict the time-course of fish survival for relevant FOCUS SW exposure scenarios and identify which scenarios might lead to mortality. Models were calibrated using available standard data and implemented to simulate the time-course of internal dose of active ingredient or survival for different exposure scenarios. Simulation results were discussed and used to derive the risk assessment refinement endpoints used for decision. Finally, we compared the "classical" risk assessment approach with the model-based approach. These comparisons showed that TK and TK-TD models can bring more realism to the risk assessment through the possibility to study realistic exposure scenarios and to simulate relevant mechanisms of effects (including delayed toxicity and recovery). Noticeably, using TK-TD models is currently the most relevant way to directly connect realistic exposure patterns to effects. We conclude with recommendations on how to properly use TK and TK-TD model in acute risk assessment for vertebrates.