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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.

An original method has been developed to model geology using the location of the geological interfaces and orientation data from structural field. Both types of data are cokriged to interpolate a continuous 3D potential-field scalar function describing the geometry of the geology. Geology contact locations set the position of reference isovalues while orientation data are the gradients of the scalar function. Geometry of geological bodies is achieved by discretising reference isovalues. Faults are modelled using the same method by inserting discontinuities in the potential field. Potential fields can be combined to model realistic, complex geometry: scalar functions representing separate geological series are merged automatically using geological rules to enable fast computation and easy update of interpretation. The methodology has been applied to a wide range of geological contexts including orogenic domains, basins, intrusive and extrusive environments.

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We develop a technique allowing 3D gridding of large sets of 1D resistivity models obtained after inversion of extensive airborne EM surveys. The method is based on the assumption of a layered-earth model. 2D kriging is used for interpolation of geophysical model parameters and their corresponding uncertainties. The 3D grid is created from the interpolated data, its structure accurately follows the geophysical model, providing a lightweight file for a good rendering. Propagation of errors is tracked through the quantification of uncertainties from both inversion and interpolation procedures. The 3D grid is exported to a portable standard, which allows flexible visualization and volumetric computations, and improves interpretation. The method is validated and illustrated by a case-study on Santa Cruz Island, in the Galapagos Archipelago. dx