Abstract
This work is a study of multivariate simulations of pollutants to assess the sampling uncertainty for the risk analysis of a contaminated site. The study started from data collected for a remediation project of a steel- works in northern Italy. The soil samples were taken from boreholes excavated a few years ago and analyzed by a chemical laboratory. The data set comprises concentrations of several pollutants, from which a subset of ten organic and inorganic compounds were selected. The first part of study is a univariate and bivariate sta- tistical analysis of the data. All data were spatially analyzed and transformed to the Gaussian space so as to reduce the effects of extreme high values due to contaminant hot spots and the requirements of Gaussian simulation procedures. The variography analysis quantified spatial correlation and cross-correlations, which led to a hypothesized linear model of coregionalization for all variables. Geostatistical simulation methods were applied to assess the uncertainty. Two types of simulations were performed: correlation correction of univariate sequential Gaussian simulations (SGS), and sequential Gaussian co-simulations (SGCOS). The outputs from the correlation correction simulations and SGCOS were analyzed and grade-tonnage curves were produced to assess basic environmental risk.
Highlights
The assessment of the risks associated with contamination by elevated levels of pollutants is a major issue in most parts of the world
The main geostatistical tools are used to model the local uncertainty of environmental attributes, which prevail at any unsampled site, in particular by means of stochastic simulation
There are less than 0.001 million tons of contaminated soil with a mean concentration of 80 mg/kg at the terrain acceptable concentration limits (TACL) threshold. Environmental risk in this contaminated site arises from the presence of several pollutants and from the uncertainty in estimating their concentrations, extents and trajectories
Summary
The assessment of the risks associated with contamination by elevated levels of pollutants is a major issue in most parts of the world. If risk is a measure of the probability of pollutant concentrations exceeding specified thresholds variability, or variance, is the key characteristic in risk assessment and risk analysis For this reason, geostatistical simulation provides an appropriate way of quantifying risk by simulateing possible “realities” and determining how many of these realities exceed the contamination thresholds [1]. Geostatistics is a name associated with a class of techniques utilized to analyze and predict values of a variable distributed in space or time. Such values are implicitly assumed to be spatially or/and temporally correlated with each other, and the study of such a correlation is usually called a “structural analysis” or “variogram modeling”. Predictions at unsampled locations are made using any of the various forms of “kriging” or they can be simulated using “conditional simulations”
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