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Industrial and agricultural activities heavily constrain soil quality. Potentially Toxic Elements (PTEs) are a threat to public health and the environment alike. In this regard, the identification of areas that require remediation is crucial. 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). Then a stratified systematic sampling method was used at short, medium, and long distances from each zone to obtain a representative picture of the total variability of the selected attributes. The information was then combined in four risk classes (Low, Moderate, High, Remediation) following reference values from several sediment quality guidelines (SQGs). A Bayesian analysis, inferred for each zone, allowed the characterization of PTEs correlations, the unsupervised learning network technique proving to be the best fit. Based on the Bayesian network structure obtained, Pb, As and Mn were selected as key contamination parameters. For these 3 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. Finally, the BRI underwent geostatistical modeling. One hundred Sequential Gaussian Simulations (SGS) were computed. The Mean Image and the Standard Deviation maps were obtained, allowing the definition of High/Low risk clusters (Local G clustering) and the computation of spatial uncertainty. High-risk clusters are mainly distributed within the area with the highest altitude (agriculture/livestock) showing an associated low spatial uncertainty, clearly indicating the need for remediation. Atmospheric emissions, mainly derived from the metallurgical industry, contribute to soil contamination by PTEs.

When considering complex scenarios involving several attributes, such as in environmental characterization, a clearer picture of reality can be achieved through the dimensional reduction of data. In this context, maps facilitate the visualization of spatial patterns of contaminant distribution and the identification of enriched areas. A set, of 15 Potentially Toxic Elements (PTEs) – (As, Ba, Cd, Co, Cr, Cu, Hg,Mo, Ni, Pb, Sb, Se, Tl, V, and Zn), was measured in soil, collected in Langreo's municipality (80 km2), Spain. Relative enrichment (RE) is introduced here to refer to the proportion of elements present in a given context. Indeed, a novel approach is provided for research into PTE fate. This method involves studying the variability of PTE proportions throughout the study area, thereby allowing the identification of dissemination trends. Traditional geostatistical approaches commonly use raw data (concentrations) accepting that the elements analyzedmake up the entirety of the soil. However, in geochemical studies the analyzed elements are just a fraction of the total soil composition. Therefore, considering compositional data is pivotal. The spatial characterization of PTEs considering raw and compositional data together allowed a broad discussion about, not only the PTEs concentration's distribution but also to reckon possible trends of relative enrichment (RE). Transformations to open closed data are widely used for this purpose. Spatial patterns have an indubitable interest. In this study, the Centered Log-ratio transformation (clr) was used, followed by its back-transformation, to build a set of compositional data that, combined with raw data, allowed to establish the sources of the PTEs and trends of spatial dissemination.