A risk-based methodology for ranking environmental chemical stressors at the regional scale

A “Risk-based Tool for the Regional Ranking of Environmental Chemical Stressors” has been developed, aimed at supporting decision-makers in the identification of priority environmental contaminants, as well as priority areas, to be further assessed. The tool implements a methodology based on a quantitative Weight-of-Evidence approach, integrating three types of information, identified as “Lines-of-Evidence” (LoE), namely: LoE “Environmental Contamination” (including data on chemical contamination in environmental matrices in the region, thus providing information on potential population exposure), LoE “Intake” (including results from human biomonitoring studies, i.e. concentration of chemicals in human biological matrices, thus providing an integrated estimation of exposure) and LoE “Observed Effects” (including information on the incidence of adverse health outcomes associated with environmental exposure to chemicals).A Multi-Criteria Decision Analysis (MCDA) methodology based on fuzzy logic has been developed to support the integration of information related to these three LoEs for each chemical stressor. The tool allows one to rank chemical stressors at different spatial scales, such as at the regional level as well as within each sub-area (e.g., counties). Moreover, it supports the identification of priority sub-areas within the region, where environmental and health data suggest possible adverse health effects and thus more investigation efforts are needed.To evaluate the performance of this newly developed tool, a case-study in the Flemish region (north of Belgium) has been selected. In the case-study, data on soil contamination by metals and organic contaminants were integrated with data on exposure and effect biomarkers measured in adolescents within the framework of the human biomonitoring study performed by the Flemish Centre of Expertise on Environment and Health in the period 2002–2006. The case-study demonstrated the performance of the tool in integrating qualitative and quantitative data with expert judgement for the identification of priority contaminants and areas. The proposed approach proved to be flexible, allowing for the incorporation of individual decision-maker's preferences, and, at the same time, to be transparent since all assumptions and value attributions are traceable.