Characterizing environmental geographic inequalities using an integrated exposure assessment

Abstract

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.

Keywords: Exposure; Inequities; Integrated; Modeling; Spatial.

Fig. 1

Example of a modeling framework to characterize an integrated EHI

Fig. 2

Conceptual scheme of the modeling approach used in this study. Environmental data (blue) are integrated into models (green) which characterize the transfers of pesticide from the source to contamination of the target populations. The output data generated by these models (white) are themselves integrated as input data for the following model. The external and internal exposure doses (yellow) are estimated at the end of the modeling chain

Fig. 3

Mapping for urinary concentrations of 3-PBA in the general population. a Mean annual urinary concentrations (lower bound); b) Mean annual urinary concentrations (upper bound)

Fig. 4

Mean contributions of pyrethroids to cumulated 3-PBA urinary concentrations for the lower bound and upper bound scenarios