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. 2022 Jan;32(1):156-168.
doi: 10.1038/s41370-021-00315-7. Epub 2021 Apr 6.

Spatio-temporal assessment of pregnant women exposure to chlorpyrifos at a regional scale

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Spatio-temporal assessment of pregnant women exposure to chlorpyrifos at a regional scale

Corentin Regrain et al. J Expo Sci Environ Epidemiol. 2022 Jan.

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.

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