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Caractérisation de l’exposition environnementale spatialisée à un pyréthrinoïde en Picardie

Abstract : Pesticides are present in all areas of the environment and absorbed via many pathways (food, water, soil, and air). To characterize environmental exposures in detail, it is first necessary to combine within the same analysis data on the population’s lifestyles and on the local contamination of environmental media at appropriate resolutions and over large areas. As part of this pilot study,the objective of the CartoExpo project was to test the feasibility of an integrated methodology to map exposure indicators for small-area resolutions and short time intervals. To illustrate the approach, contamination of the general population was studied for a pyrethroid (cypermethrin) in the Picardy region (Northern France). For atmospheric dispersion, an innovative statistical meta-model method was developed, based on a machine learning technique that used a large database of simulations on a representative parcel. The meta-model was then applied to cypermethrin application at three-hourly intervals on all agricultural parcels in the Picardy region. Multimedia exposure models are needed to: 1) estimate emissions from soil and plant volatilization phenomena on agricultural land, 2) quantify contamination of local food products (excluding home vegetable gardens) due to proximity to agricultural land,a nd3)combine external exposures. Characterizing exposure involves numerous uncertainties, primarily about the assumptions underlying the different modeling approaches, and the representativeness and accuracy of the integrated data. Further measured and modeled data are therefore needed to validate the different approaches.
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Submitted on : Wednesday, May 19, 2021 - 10:51:00 AM
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Julien Caudeville, Corentin Regrain, Roseline Bonnard, Laurent Letinois, Frédéric Tognet, et al.. Caractérisation de l’exposition environnementale spatialisée à un pyréthrinoïde en Picardie. Environnement, Risques and Santé, John Libbey Eurotext, 2019, 18 (5), pp.392-400. ⟨10.1684/ers.2019.1340⟩. ⟨ineris-03229712⟩



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