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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. [ABSTRACT FROM AUTHOR]

The practice of stochastic simulation for different environmental and earth sciences applications creates new theoretical problems that motivate the improvement of existing algorithms. In this context, we present the implementation of a new version of the direct sequential co-simulation (Co-DSS) algorithm. This new approach, titled Co-DSS with joint probability distributions, intends to solve the problem of mismatch between co-simulation results and experimental data, i.e. when the final biplot of simulated values does not respect the experimental relation known for the original data values. This situation occurs mostly in the beginning of the simulation process. To solve this issue, the new co-simulation algorithm, applied to a pair of covariates Z1( x) and Z2( x), proposes to resample Z2( x) from the joint distribution F( z1, z2) or, more precisely, from the conditional distribution of Z2( x0), at a location x0, given the previously simulated value $z_{1}^{(l)}(x_{0})$ ( $F(Z_{2}|Z_{1}=z_{1}^{(l)}(x_{0})$ ). The work developed demonstrates that Co-DSS with joint probability distributions reproduces the experimental bivariate cdf and, consequently, the conditional distributions, even when the correlation coefficient between the covariates is low. [ABSTRACT FROM AUTHOR]

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