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Compositional baseline assessments to address soil pollution : an application in Langreo, Spain

2022Journal article Open access
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dc.contributor.authorBoente, Carlos
dc.contributor.authorAlbuquerque, M.T.D.
dc.contributor.authorGallego, J.R.
dc.contributor.authorPawlowsky-Glahn, Vera
dc.contributor.authorEgozcue, Juan José
dc.date.accessioned2022-01-03T19:28:07Z
dc.date.available2024-01-03T01:31:14Z
dc.date.issued2022
dc.description.abstractPotentially Toxic Elements (PTEs) are contaminants with high toxicity and complex geochemical behaviour and, therefore, high PTEs contents in soil may affect ecosystems and/or human health. However, before addressing the measurement of soil pollution, it is necessary to understand what is meant by pollution-free soil. Often, this background, or pollution baseline, is undefined or only partially known. Since the concentration of chemical elements is compositional, as the attributes vary together, here we present a novel approach to build compositional indicators based on Compositional Data (CoDa) principles. The steps of this new methodology are: 1) Exploratory data analysis through variation matrix, biplots or CoDa dendrograms; 2) Selection of geological background in terms of a trimmed subsample that can be assumed as non-pollutant; 3) Computing the spread Aitchison distance from each sample point to the trimmed sample; 4) Performing a compositional balance able to predict the Aitchison distance computed in step 3. Identifying a compositional balance, including pollutant and non-pollutant elements, with sparsity and simplicity as properties, is crucial for the construction of a Compositional Pollution Indicator (CI). Here we explored a database of 150 soil samples and 37 chemical elements from the contaminated region of Langreo, Northwestern Spain. There were obtained three Cis: the first two using elements obtained through CoDa analysis, and the third one selecting a list of pollutants and non-pollutants based on expert knowledge and previous studies. The three indicators went through a Stochastic Sequential Gaussian simulation. The results of the 100 computed simulations are summarized through mean image maps and probability maps of exceeding a given threshold, thus allowing characterization of the spatial distribution and variability of the CIs. A better understanding of the trends of relative enrichment and PTEs fate is discussed.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationBOENTE, C. [et al.] (2022) - Compositional baseline assessments to address soil pollution : an application in Langreo, Spain. Science of The Total Environment. DOI 10.1016/j.scitotenv.2021.152383
dc.identifier.doi10.1016/j.scitotenv.2021.152383pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.11/7784
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.subjectPotentially toxic elementspt_PT
dc.subjectSoil pollutionpt_PT
dc.subjectCompositional indicatorspt_PT
dc.subjectSequential Gaussian simulationpt_PT
dc.titleCompositional baseline assessments to address soil pollution : an application in Langreo, Spainpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.startPage152383pt_PT
oaire.citation.titleScience of The Total Environmentpt_PT
oaire.citation.volume812pt_PT
person.familyNameAlbuquerque
person.givenNameMaria Teresa
person.identifier.ciencia-id5A1C-8956-4C0A
person.identifier.orcid0000-0002-8782-6133
person.identifier.ridB-1536-2013
person.identifier.scopus-author-id55507421600
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicatione2c2d171-e148-4c23-9cf8-0eb6d810c15e
relation.isAuthorOfPublication.latestForDiscoverye2c2d171-e148-4c23-9cf8-0eb6d810c15e

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