Repository logo
 
Publication

Developing a new bayesian risk index for risk evaluation of soil contamination

2017Journal article Restricted access
Simple item page
dc.contributor.authorAlbuquerque, M.T.D.
dc.contributor.authorGerassis, Saki
dc.contributor.authorSierra, C.
dc.contributor.authorTaboada, Javier
dc.contributor.authorMartín, José
dc.contributor.authorAntunes, I.M.H.R.
dc.contributor.authorGallego, J.R.
dc.date.accessioned2017-06-19T10:42:25Z
dc.date.available2017-06-19T10:42:25Z
dc.date.issued2017
dc.description.abstractIndustrial 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.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationALBUQUERQUE, M.T.D. (2017) - Developing a new bayesian risk index for risk evaluation of soil contamination. Science of The Total Environment. ISSN 0048-9697. Vol. 603-604, p. 167–177pt_PT
dc.identifier.doi10.1016/j.scitotenv.2017.06.068pt_PT
dc.identifier.issn0048-9697
dc.identifier.urihttp://hdl.handle.net/10400.11/5577
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S0048969717314729pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectPotentially toxic elementspt_PT
dc.subjectBayesian networkspt_PT
dc.subjectSequential Gaussian simulationpt_PT
dc.subjectLocal G clusteringpt_PT
dc.titleDeveloping a new bayesian risk index for risk evaluation of soil contaminationpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage177pt_PT
oaire.citation.startPage167pt_PT
oaire.citation.titleScience of the Total Environmentpt_PT
oaire.citation.volume603-604pt_PT
person.familyNameAlbuquerque
person.familyNameantunes
person.givenNameMaria Teresa
person.givenNameIsabel Margarida
person.identifier.ciencia-id5A1C-8956-4C0A
person.identifier.ciencia-idCB1E-FAD2-37D6
person.identifier.orcid0000-0002-8782-6133
person.identifier.orcid0000-0003-3456-5926
person.identifier.ridB-1536-2013
person.identifier.ridM-1043-2013
person.identifier.scopus-author-id55507421600
person.identifier.scopus-author-id6701817085
rcaap.rightsrestrictedAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicatione2c2d171-e148-4c23-9cf8-0eb6d810c15e
relation.isAuthorOfPublication1db83c95-f80c-41bb-b5c6-437ab32d9683
relation.isAuthorOfPublication.latestForDiscovery1db83c95-f80c-41bb-b5c6-437ab32d9683

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Developing a new Bayesian Risk Index for risk evaluation of soil contamination.pdf
Size:
1.74 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.02 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

ESTCB - Artigos em revistas com arbitragem científica