Author(s)

Mario E. Rossi

Abstract

Quantifying uncertainty when dealing with predictions about water contamination, both for volume and for contained pollutants, has been recognized as critical in a thorough contamination assessment study. The reason is that errors in the water sampling process, characterizations of the geometry of the contaminant plume or body, interpolation of the sampled values across a contaminated field, and characterizations of the physical processes that predict fate and transport are always present. It is not possible to obtain a realistic assessment of contamination without accepting first, and modelling later, the intrinsic uncertainty carried over the whole process. This paper proposes a rational stochastic methodology, based on geostatistical conditional simulations, that allows assessment and modelling of the e

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