A Geostatistically Based Probabilistic Risk Assessment Approach

Introduction

The risk assessment is an efficient decision support method in the evaluation on the necessity to remediate a contaminated area; it serves as a tool for the definition of the concentration value to be reached by means of remediation interventions for human health protection. Nevertheless, in the execution of a risk assessment, the required data are characterized for the most part by a high degree of variability and in most cases, especially when a in-depth site assessment study is not carried out, also by a substantial level of uncertainty. Moreover, in many environmental applications, a few random ‘hot spots’ of large concentrations coexist with a background of data below the detection limit (censored observations) (Goovaerts, 1999). An alternative approach to the probabilistic analysis based on the methods of the classical statistics can be that of analyzing and quantifying the spatial uncertainty using geostatistical techniques. Geostatistics offers a series of extremely useful instruments to operate any decisional processes in that it allows to recognize the structures that normally characterize the spatial distributions of the studied properties.

This paper is aimed at providing a geostatistical method for modeling the uncertainty connected to the input parameters in a probabilistic procedure of risk assessment. It is structured in such a way: the first section will provide some legislative notions on risk assessment’s role within the remediation procedure of a contaminated area, the intermediate sections will deal with how to treat uncertainties in risk assessment, whether with classical (stochastic), or geostatistical approaches. In this part the application of geostatistics is described for the delimitation of the potentially contaminated area, the determination of the Source Representative Concentration, and also of the optimal sampling designs for site characterization, an important phase of the remediation procedure. In the final part three case studies are proposed, in which the concepts explained in the theory are applied to contaminated areas.