Geostatistical Data Integration Model for Contamination Assessment

Abstract

Soil contamination assessments can be improved with new methods aimed at the accurate estimation of the volume and extension of contaminated soil to be remediated. Geostatistical models that use secondary information to characterize soil contamination are incorporated into a new integration model to provide accurate three-dimensional maps. The proposed integration model is based on a stochastic inversion approach and uses sequential indicator simulation. A two-dimensional reference image representing the areal extension of the contamination is combined with local measurements of contamination in the vertical direction, to render a three-dimensional contamination map. To demonstrate how well the integration model performs, the case study presented focuses on geophysical data and how it can be integrated with soil contamination measurements to improve the characterization of a contaminated site. The results show that the model reproduces successfully the reference image thus providing an accurate three-dimensional contamination map.