Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.11/6321
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Campo DCValorIdioma
dc.contributor.authorBoente, C.-
dc.contributor.authorAlbuquerque, M.T.D.-
dc.contributor.authorGerassis, S.-
dc.contributor.authorRodríguez-Valdés, E.-
dc.contributor.authorGallego, J.R.-
dc.identifier.citationBOENTE, C. [et al.] (2018) - A coupled multivariate statistics, geostatistical and machine-learning approach to address soil pollution in a prototypical Hg-mining site in a natural reserve. Chemosphere. ISSN 0045-6535. https://doi.org/10.1016/j.chemosphere.2018.11.172pt_PT
dc.description.abstractThe impact of mining activities on the environment is vast. In this regard, many mines were operating well before the introduction of environmental law. This is particularly true of cinnabar mines, whose activity has declined for decades due to growing public concern regarding Hg high toxicity. Here we present the exemplary case study of an abandoned Hg mine located in the Somiedo Natural Reserve (Spain). Until its closure in the 1970s, this mine operated under no environmental regulations, its tailings dumped in two spoil heaps, one of them located uphill and the other in the surroundings of the village of Caunedo. This study attempts to outline the degree to which soil and other environmental compartments have been affected by the two heaps. To this end, we used a novel combination of multivariate statistical, geostatistical and machine-learning The techniques used included principal component and clustering analysis, Bayesian networks, indicator kriging, and sequential Gaussian simulations. Our results revealed high concentrations of Hg and, secondarily, As in soil but not in water or sediments. The innovative methodology abovementioned allowed us to identify natural and anthropogenic associations between 25 elements and to conclude that soil pollution was attributable mainly to natural weathering of the uphill heap. Moreover, the probability of surpassing the threshold limits and the local backgrounds was found to 31 be high in a large extension of the area. The methodology used herein demonstrated to be effective for addressing complex pollution scenarios and therefore they are applicable to similar cases.pt_PT
dc.subjectSoil pollutionpt_PT
dc.subjectMultivariate statisticspt_PT
dc.subjectMachine learning,pt_PT
dc.titleA coupled multivariate statistics, geostatistical and machine-learning approach to address soil pollution in a prototypical Hg-mining site in a natural reservept_PT
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