Corentin Regrain1,2,3, Florence Anna Zeman4, Mohammed Guedda2, Karen Chardon5, Véronique Bach5, Céline Brochot4, Roseline Bonnard1, Frédéric Tognet6, Laure Malherbe7, Laurent Létinois7, Emmanuelle Boulvert1, Fabrice Marlière8, François Lestremau9, Julien Caudeville10,11. 1. Institut National de l'Environnement Industriel et des Risques (INERIS), Unité Impact Sanitaire et Exposition (ISAE), Parc ALATA BP2, 60550, Verneuil-en-Halatte, France. 2. LAMFA, UMR CNRS 7352, Université de Picardie Jules Verne, 33 rue Saint-Leu, 80039, Amiens, France. 3. PériTox (UMR_I 01), INERIS/UPJV, Institut National de l'Environnement Industriel et des Risques (INERIS), Verneuil-en-Halatte, France. 4. Institut National de l'Environnement Industriel et des Risques (INERIS), Unité Toxicologie Expérimentale et Modélisation (TEAM), Parc ALATA BP2, 60550, Verneuil-en-Halatte, France. 5. PériTox (UMR_I 01), UPJV/INERIS, UPJV, Amiens, France. 6. Institut National de l'Environnement Industriel et des Risques (INERIS), Unité Modélisation Atmosphérique et Cartographie Environnementale (MOCA), Parc ALATA BP2, 60550, Verneuil-en-Halatte, France. 7. Institut National de l'Environnement Industriel et des Risques (INERIS), Unité Instrumentation et Exploitation de la Donnée (INDO), Parc ALATA BP2, 60550, Verneuil-en-Halatte, France. 8. Institut National de l'Environnement Industriel et des Risques (INERIS), Unité Accompagnement à la surveillance de la qualité de l'air et des eaux de surfaces (ASUR), Parc ALATA BP2, 60550, Verneuil-en-Halatte, France. 9. Institut National de l'Environnement Industriel et des Risques (INERIS), Unité Méthodes & Développements en Analyses pour l'Environnement (ANAE), Parc ALATA BP2, 60550, Verneuil-en-Halatte, France. 10. Institut National de l'Environnement Industriel et des Risques (INERIS), Unité Impact Sanitaire et Exposition (ISAE), Parc ALATA BP2, 60550, Verneuil-en-Halatte, France. julien.caudeville@ineris.fr. 11. PériTox (UMR_I 01), INERIS/UPJV, Institut National de l'Environnement Industriel et des Risques (INERIS), Verneuil-en-Halatte, France. julien.caudeville@ineris.fr.
Abstract
BACKGROUND: The aim of this study was to use an integrated exposure assessment approach, combining spatiotemporal modeling of environmental exposure and fate of the chemical to assess the exposure of vulnerable populations. In this study, chlorpyrifos exposure of pregnant women in Picardy was evaluated at a regional scale during 1 year. This approach provided a mapping of exposure indicators of pregnant women to chlorpyrifos over fine spatial and temporal resolutions using a GIS environment. METHODS: Fate and transport models (emission, atmospheric dispersion, multimedia exposure, PBPK) were combined with environmental databases in a GIS environment. Quantities spread over agricultural fields were simulated and integrated into a modeling chain coupling models. The fate and transport of chlorpyrifos was characterized by an atmospheric dispersion statistical metamodel and the dynamiCROP model. Then, the multimedia model Modul'ERS was used to predict chlorpyrifos daily exposure doses which were integrated in a PBPK model to compute biomarker of exposure (TCPy urinary concentrations). For the concentration predictions, two scenarios (lower bound and upper bound) were built. RESULTS: At fine spatio-temporal resolutions, the cartography of biomarkers in the lower bound scenario clearly highlights agricultural areas. In these maps, some specific areas and hotspots appear as potentially more exposed specifically during application period. Overall, predictions were close to biomonitoring data and ingestion route was the main contributor to chlorpyrifos exposure. CONCLUSIONS: This study demonstrated the feasibility of an integrated approach for the evaluation of chlorpyrifos exposure which allows the comparison between modeled predictions and biomonitoring data.
BACKGROUND: The aim of this study was to use an integrated exposure assessment approach, combining spatiotemporal modeling of environmental exposure and fate of the chemical to assess the exposure of vulnerable populations. In this study, chlorpyrifos exposure of pregnant women in Picardy was evaluated at a regional scale during 1 year. This approach provided a mapping of exposure indicators of pregnant women to chlorpyrifos over fine spatial and temporal resolutions using a GIS environment. METHODS: Fate and transport models (emission, atmospheric dispersion, multimedia exposure, PBPK) were combined with environmental databases in a GIS environment. Quantities spread over agricultural fields were simulated and integrated into a modeling chain coupling models. The fate and transport of chlorpyrifos was characterized by an atmospheric dispersion statistical metamodel and the dynamiCROP model. Then, the multimedia model Modul'ERS was used to predict chlorpyrifos daily exposure doses which were integrated in a PBPK model to compute biomarker of exposure (TCPy urinary concentrations). For the concentration predictions, two scenarios (lower bound and upper bound) were built. RESULTS: At fine spatio-temporal resolutions, the cartography of biomarkers in the lower bound scenario clearly highlights agricultural areas. In these maps, some specific areas and hotspots appear as potentially more exposed specifically during application period. Overall, predictions were close to biomonitoring data and ingestion route was the main contributor to chlorpyrifos exposure. CONCLUSIONS: This study demonstrated the feasibility of an integrated approach for the evaluation of chlorpyrifos exposure which allows the comparison between modeled predictions and biomonitoring data.
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