Modelling pesticide degradation and leaching in conservation agriculture: Effect of no-till and mulching.

No-till and mulching are typical management operations in conservation agriculture (CA). To model pesticide degradation and leaching under a CA scenario, as compared to a conventional-tillage scenario (CT), the mulch module of the agro-hydrological model Daisy was extended. A Daisy soil column was parameterized with measurements of topsoil, mulch, and a realistic subsoil, and tested against published experimental data of pesticide fate in laboratory soil columns covered by mulch. Uncertainty and sensitivity analyses of the new Daisy version were conducted for a series of weather, soil, pesticide, and mulch parameters, using 4939 Monte Carlo simulations under each scenario. Results showed that there was no systematic difference in pesticide leaching from the topsoil (to the subsoil and directly to drains via drain-connected biopores) between CA and CT, but pesticide degradation and sorption were significantly different; degradation in the mulch and uppermost soil surface layer (0-3.5 cm) was larger in CA while degradation was larger in CT when considering the whole topsoil (0-30 cm). This difference for the whole topsoil could be explained by pesticide interception in CA in the part of the mulch not in direct contact with the soil where degradation is assumed not to occur. The sensitivity analysis highlighted non-influential parameters and seven parameters out of twenty-five to be better estimated to improve the accuracy of the predictions.

Evaluation of the ecological risk of pesticide residues from the European LUCAS Soil monitoring 2018 survey.

The 2018 LUCAS (Land Use and Coverage Area frame Survey) Soil Pesticides survey provides a European Union (EU)-scale assessment of 118 pesticide residues in more than 3473 soil sites. This study responds to the policy need to develop risk-based indicators for pesticides in the environment. Two mixture risk indicators are presented for soil based, respectively, on the lowest and the median of available No Observed Effect Concentration (NOECsoil,min and NOECsoil,50) from publicly available toxicity datasets. Two further indicators were developed based on the corresponding equilibrium concentration in the aqueous phase and aquatic toxicity data, which are available as species sensitivity distributions. Pesticides were quantified in 74.5% of the sites. The mixture risk indicator based on the NOECsoil,min exceeds 1 in 14% of the sites and 0.1 in 23%. The insecticides imidacloprid and chlorpyrifos and the fungicide epoxiconazole are the largest contributors to the overall risk. At each site, one or a few substances drive mixture risk. Modes of actions most likely associated with mixture effects include modulation of acetylcholine metabolism (neonicotinoids and organophosphate substances) and sterol biosynthesis inhibition (triazole fungicides). Several pesticides driving the risk have been phased out since 2018. Following LUCAS surveys will determine the effectiveness of substance-specific risk management and the overall progress toward risk reduction targets established by EU and UN policies. Newly generated data and knowledge will stimulate needed future research on pesticides, soil health, and biodiversity protection. Integr Environ Assess Manag 2024;00:1-15. © 2024 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Assessing the relevance of environmental exposure data sets.

Environmental exposure data are used by decision-makers to assess environmental risks and implement actions to mitigate risks from contaminants. The first article in this series summarized the available evaluation schemes for environmental exposure data, of which there are few compared to those available for environmental hazard data. The second article covered the assessment of the reliability of environmental exposure data sets under the Criteria for the Reporting and Evaluation of Exposure Data (CREED). The aim of this article is to provide an overview and practical guidance on the relevance assessment in the context of the CREED approach for evaluating exposure monitoring data sets. Systematically considering relevance is critical for both evaluating existing data sets and for optimizing the design of new monitoring studies. Relevance is defined here as the degree of suitability or appropriateness of a data set to address a specific purpose or to answer the questions that have been defined by the assessor or for those generating exposure data. The purpose definition will be the foundation for the relevance assessment, to clarify how the assessor should rate the assessment criteria (fully met, partly met, not met/inappropriate, not reported, not applicable). This will provide transparency for anyone reviewing the outcomes. An explicit gap analysis (i.e., an articulation of the data set limitations for the stated purpose) is an important outcome of the relevance assessment. The relevance evaluation approach is demonstrated with three case studies, all relating to the freshwater aquatic environment, where the data sets are scored as relevant with or without restrictions, not relevant, or not assignable. The case studies represent both organic and inorganic constituents, and have different data characteristics (e.g., percentage of censored data, sampling frequencies, relation to supporting parameters). Integr Environ Assess Manag 2024;00:1-15. © 2023 SETAC.

Non-target analysis of organic waste amended agricultural soils: Characterization of added organic pollution.

Amendment of soil with organic urban and animal wastes can keep arable soil fertile without the need for synthetic fertilizers. However, pollutants present in these types of waste might be carried into the soil with unintended consequences for the environment. We studied an experimental agricultural plot, which had been amended with either synthetic inorganic fertilizers, human urine, manure, or wastewater treatment sludge at very high rates. We applied chemical non-target analysis to characterize present organic micropollutants, intending to compare treatments and highlight suspects of environmental concern. Soil samples were prepared by pressurized liquid and purified with solid-phase extraction before analysis with nanoflow ultra-high performance liquid chromatography coupled to high-resolution Orbitrap tandem mass spectrometry. Automated elucidation with two mass spectral libraries, multiple large chemical databases and environmental NORMAN suspect lists was able to annotate (level 3 and level 2) âˆ¼ 20% of the 2306 detected features. A following principal component- and differential-analysis could separate the soil treatment groups' pollution profiles and highlight high relative abundance features. From cattle manure, natural compounds such as bile acids and steroids were found. Human urine led to pollution with common pharmaceuticals such as metoprolol and propranolol. The highest number was added by wastewater treatment sludge, with 25 significant contaminants, spanning blood pressure regulators, antidepressants, synthetic steroids and sleep medication. Furthermore, using Kendrick mass defect plots, a series of polypropylene glycols could be revealed in the soil. Non-target analysis appears to be a promising method to characterize organic pollutants in soils.