Uncertainty is a major aspect of the estimation, using models, of the risk of human exposure to pollutants. The Monte Carlo method, which applies probability theory to address model parameter uncertainty, relies on a statistical representation of available information. In recent years, the theory of possibilities has been proposed as an alternative approach to address model parameter uncertainty in situations where available information are insufficient to identify statistically representative probability distributions, due in particular to data scarcity. In practice, it may occur that certain model parameters can be reasonably represented by probability distributions, because there is sufficient data available to substantiate such distributions by statistical analysis, while others are better represented by fuzzy numbers (due to data scarcity). The question then arises as to how these two modes of representation of model parameter uncertainty can be combined for the purpose of estimating the risk of exposure. In this paper an approach (termed a hybrid approach) for achieving such a combination is proposed, and applied to the estimation of human exposure, via vegetable consumption, to cadmium present in the surficial soils of an industrial site located in the north of France. The application illustrates the potential of the proposed approach, which allows the uncertainty affecting model parameters to be represented in a fashion which is consistent with the information at hand.

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