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A Bayesian Index for Soil Contamination Risk Assessment

A Bayesian Index for Soil Contamination Risk Assessment

Presented at the 2023 BayesiaLab Conference.

Abstract

Heavy economic activities such are industry and agriculture strongly limit soil quality. Potentially Toxic Elements (PTEs) are a threat to public health and the environment. In this respect, it is critical to identify areas that require remediation. In the herein research, a geochemical dataset (230 samples) comprising 14 elements (Cu, Pb, Zn, Ag, Ni, Mn, Fe, As, Cd, V, Cr, Ti, Al, and S) was gathered throughout eight different zones distinguished by their main activity, namely, recreational, agriculture/livestock and heavy industry in the Avilés Estuary (North of Spain). Next, a stratified systematic sampling method was used at short, medium, and long distances from each area to obtain representative visualization of the total variability of the chosen attributes. The information was then combined into four risk classes (low, average, high, and in need of remediation) based on multiple sediment quality guideline (SRM) baseline values. Bayesian analysis, inferred for each area, was used to characterize PET correlations, with the unsupervised learning network technique being the best solution. According to the Bayesian network structure obtained, Pb, As, and Mn were chosen as key contamination parameters. For these three elements, the conditional probability obtained was allocated to each observed point, and a simple, direct index (Bayesian Risk Index-BRI) was constructed as a linear rating of the pre-defined risk classes weighted by the previously obtained probability. Lastly, BRI underwent geostatistical modeling. One hundred Sequential Gaussian Simulations (SGS) were computed. The mean image and standard deviation maps were obtained, resulting in high/low-risk clusters (local G clusters) and spatial uncertainty calculation. High-risk clusters are mostly located in the area with the highest elevation (agriculture/livestock) associated with low spatial uncertainty, which indicates the need for remediation. Air emissions, primarily from the metal industry, contribute to soil contamination by ETPs.

Presentation Video

Presentation Slides

About the Presenter

María Pazo Rodríguez is a doctoral student at the School of Mining and Energy Engineering affiliated with the University of Vigo.

My research is focused on developing Bayesian models that can guide the implementation of digital transformation in the mining and energy sector. To this end, the main objective is to equip the mining industry with risk assessment tools and decentralized decision models that enable its rapid decarbonization, thus contributing to meeting the growing demand for strategic minerals and addressing the strict environmental policies imposed by national and international organizations.


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