Carbon and nitrogen stable isotope fractionation during abiotic hydrolysis of pesticides

Compound-specific Stable Isotope Analysis (CSIA) has been recently established as a tool to study pesticide degradation in the environment. Among degradative processes, hydrolysis is environmentally relevant as it can be chemically or enzymatically mediated. Here, CSIA was used to examine stable carbon and nitrogen isotope fractionation during abiotic hydrolysis of legacy or currently used pesticides (chloroacetanilide herbicides: Acetochlor, Alachlor, S-Metolachlor and Butachlor, acylalanine fungicide: Metalaxyl, and triazine herbicide: Atrazine). Degradation products analysis and Csingle bondN dual-CSIA allowed to infer hydrolytic degradation pathways from carbon and nitrogen isotopic fractionation. Carbon isotopic fractionation for alkaline hydrolysis revealed similar apparent kinetic isotope effects (AKIEC = 1.03–1.07) for the 6 pesticides, which were consistent with SN2 type nucleophilic substitutions. Neither enantio-selectivity (EF ≈ 0.5) nor enantio-specific isotope fractionation occurred during hydrolysis of R (AKIEC = 1.04 ± 0.01) and S (AKIEC = 1.04 ± 0.02) enantiomers of a racemic mixture of Metalaxyl. Dual element isotope plots enabled to tease apart Csingle bondCl bond breaking of alkane (Λ ≈ εN/εC ≈ 0, Acetochlor, Butachlor) and aromatic π-system (Λ ≈ 0.2, Atrazine) from Csingle bondO bond breaking by dealkylation (Λ ≈ 0.9, Metalaxyl). Reference values for abiotic versus biotic SN2 reactions derived from carbon and nitrogen CSIA may be used to untangle pesticide degradation pathways and evaluate in situ degradation during natural and engineered remediation

High frequency monitoring of pesticides in runoff water from a vineyard: ecotoxicological and hysteresis pattern analysis

Rainfall-induced peaks in pesticide concentrations can occur rapidly; therefore, low frequency sampling may largely underestimate maximum pesticide concentrations and fluxes. Detailed storm-based sampling of pesticide concentrations in runoff water to better predict pesticide sources, transport pathways and toxicity within the headwater catchments is actually lacking. High frequency monitoring (2 min) of dissolved concentrations and loads for seven pesticides (Dimetomorph, Fluopicolide, Glyphosate, Iprovalicarb, Tebuconazole, Tetraconazole and Triadimenol) and one degradation product (AMPA) were assessed for 20 runoff events from 2009 to 2012 at the outlet of a vineyard catchment in the Layon catchment in France. The pesticide concentrations reached 387 g/L. All of the runoff events exceeded the mandated acceptable concentrations of 0.1 g/L for each pesticide (European directive 2013/39/EC). High resolution sampling used to detect the peak pesticide levels revealed that Toxic Units (TU) for algae, invertebrates and fish often exceeded the European Uniform principles (25%). The instantaneous and average (time or discharge-weighted) concentrations indicated an up to 30- or 4-fold underestimation of the TU obtained when measuring the maximum concentrations, respectively, highlighting the important role of the sampling methods for assessing peak exposure. High resolution sampling combined with concentration-discharge hysteresis analyses revealed that clockwise responses were predominant (52%), indicating that Hortonian runoff is the prevailing surface runoff trigger mechanism in the study catchment. The hysteresis patterns for suspended solids and pesticides were highly dynamic and storm- and chemical-dependent. Intense rainfall events induced stronger C-Q hysteresis (magnitude). This study provides new insights into the complexity of pesticide dynamics in runoff water and highlights the ability of hysteresis analysis to improve the understanding of pesticide supply and transport

Use and fate of 17 pesticides applied on a vineyard catchment.

International audienc

Incertitudes associées aux données géographiques pour la quantification des vitesses de migration des méandres - Application à la vallée de la Bruche

International audienceLa restauration de l'espace de mobilité d'un cours d'eau suppose d'analyser l'évolution historique de son tracé et de caractériser des vitesses de migration de ses méandres. La méthode proposée vise à intégrer l'incertitude associée à chaque tracé sous forme de zone d'incertitude de part et d'autre des tracés des berges. Les résultats obtenus sur la Bruche (BasRhin, France) mettent en évidence une variabilité spatiale des distances de migration des 55 méandres analysés avec des vitesses moyennes de migration. La limite de la méthode réside dans l'étude des migrations à l'échelle annuelle pour lesquelles la migration peut être inférieure ou égale aux incertitudes notamment en zones boisées. Une analyse des paramètres de contrôle de la migration est réalisée en intégrant les incertitudes. La donnée LiDAR (Light Detection And Ranging) permet d'enrichir les données classiquement utilisées dans les études diachroniques

Modelling nitrogen loads at the catchment scale under the influence of land use.

Land use data are essential for water quality models. Pollutant inputs to streams are indeed a direct function of human activities that can be represented, at least approximately, in terms of land use. Remote sensing is a valuable data source to determine the land use on a catchment. However the land use data obtained by this kind of information are subject to significant uncertainties, including misclassification or categorical uncertainty. This paper presents a method to analyse the impact of the land use categorical uncertainty on the responses of a nitrogen load model at the outlet of a catchment. We use the POL model, a semi-distributed event-based model on a French Mediterranean rural catchment and we focus on agricultural land use. First, the sensitivity analysis realised by simulations considering a uniform land use on the catchment, shows a great sensitivity of the estimated load to the land use change. Second, the categorical land use uncertainty is analysed on a total nitrogen load prediction set calculated with randomly generated land use maps consistent with the confusion matrix that characterizes misclassification of land use. Thus, from 1% to 10% of misclassified agricultural area results in a variation of almost 40% on nitrogen loads for the three studied events. Misclassified areas explain from 46% to 75% of the variance of the estimated nitrogen load. These first results illustrate the importance of sensitivity and uncertainty analyses to improve the confidence of a water quality model and need to be extended to other input data sets