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

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.

Characterizing environmental geographic inequalities using an integrated exposure assessment.

BACKGROUND: At a regional or continental scale, the characterization of environmental health inequities (EHI) expresses the idea that populations are not equal in the face of pollution. It implies an analysis be conducted in order to identify and manage the areas at risk of overexposure where an increasing risk to human health is suspected. The development of methods is a prerequisite for implementing public health activities aimed at protecting populations. METHODS: This paper presents the methodological framework developed by INERIS (French National Institute for Industrial Environment and Risks) to identify a common framework for a structured and operationalized assessment of human exposure. An integrated exposure assessment approach has been developed to integrate the multiplicity of exposure pathways from various sources, through a series of models enabling the final exposure of a population to be defined. RESULTS: Measured data from environmental networks reflecting the actual contamination of the environment are used to gauge the population's exposure. Sophisticated methods of spatial analysis are applied to include additional information and take benefit of spatial and inter-variable correlation to improve data representativeness and characterize the associated uncertainty. Integrated approaches bring together all the information available for assessing the source-to-human-dose continuum using a Geographic Information System, multimedia exposure and toxicokinetic model. DISCUSSION: One of the objectives of the integrated approach was to demonstrate the feasibility of building complex realistic exposure scenarios satisfying the needs of stakeholders and the accuracy of the modelling predictions at a fine spatial-temporal resolution. A case study is presented to provide a specific application of the proposed framework and how the results could be used to identify an overexposed population. CONCLUSION: This framework could be used for many purposes, such as mapping EHI, identifying vulnerable populations and providing determinants of exposure to manage and plan remedial actions and to assess the spatial relationships between health and the environment to identify factors that influence the variability of disease patterns.