Développement de méthodes de caractérisation d’incertitudes de l’exposition spatialisée

Abstract : Exposure estimates can be calculated using crisp estimates of the exposure explanatory variables (i.e., contaminant concentration, contact rate, exposure frequency and duration, body weight, and averaging time). However, aggregate and cumulative exposure studies require a better understanding of exposure explanatory variables as well as uncertainty and variability associated with them. Probabilistic risk assessment studies use probability distributions for one or more variables of the risk equation to quantitatively characterize variability and uncertainty. Monte Carlo Analysis is one of the useful approaches that may be used to conduct probabilistic risk assessment studies. In this analysis, the most sensitive variables of the exposure equation along with the parameters of these variables are described in terms of probability density functions (PDFs). Statistical methods are employed to process input databases (populational behavior, environmental concentrations in water, air and soil) in the objective of characterizing the exposure. A multimedia model interfaced with a GIS, allows the integration of environmental variables in order to yield exposure doses related to ingestion of food, water and soil and inhalation. The methodology was applied to the lead pollutant and permits to illustrate how to propagate uncertainty among the whole modeling chain
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Julien Caudeville. Développement de méthodes de caractérisation d’incertitudes de l’exposition spatialisée. Rapport Scientifique INERIS, 2018, 2017-2018, pp.34-35. ⟨ineris-02044865⟩

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