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dc.creatorMarković, Jelena
dc.creatorJović, Mihajlo D.
dc.creatorSmičiklas, Ivana D.
dc.creatorPezo, Lato
dc.creatorŠljivić-Ivanović, Marija Z.
dc.creatorOnjia, Antonije E.
dc.creatorPopović, Aleksandar R.
dc.date.accessioned2019-09-09T16:18:14Z
dc.date.available2018-02-23
dc.date.issued2016
dc.identifier.issn0375-6742
dc.identifier.urihttp://cherry.chem.bg.ac.rs/handle/123456789/3400
dc.description.abstractThe distribution of elements in soil fractions affects their mobility and availability and thus their potential beneficial or harmful impact on ecosystems, biota and humans. Different mineralogical and chemical characteristics of soil influence elemental distribution. In the present study, chemical speciation of macro and micro elements (Al, Fe, Mn, K, Cd, Cr, Cu, Li, Ba, Ni, Pb and Zn) in unpolluted soils of different types, collected from the territory of the Republic of Serbia, were analysed by sequential extraction procedure. The impact of the physicochemical soil properties on the content, distribution, mobility and availability of elements was investigated. Principal component analysis was employed for the evaluation and characterization of the experimental data, understanding of the relationships between soil properties and the distribution, affiliation and connection of the elements. Finally, an artificial neural network (ANN) model was developed to explore the applicability of this approach for the prediction of the elemental distribution based on soil properties. Good agreement between the model and the experimental results implied that the ANN could be considered as a useful tool for control and prediction purposes. (C) 2016 Elsevier B.V. All rights reserved.en
dc.publisherElsevier Science Bv, Amsterdam
dc.relationinfo:eu-repo/grantAgreement/MESTD/Integrated and Interdisciplinary Research (IIR or III)/43009/RS//
dc.rightsembargoedAccess
dc.sourceJournal of Geochemical Exploration
dc.subjectUncontaminated soilen
dc.subjectSoil propertiesen
dc.subjectSequential extractionen
dc.subjectElement distributionen
dc.subjectPattern recognition techniquesen
dc.subjectArtificial neural networken
dc.titleChemical speciation of metals in unpolluted soils of different types: Correlation with soil characteristics and an ANN modelling approachen
dc.typearticle
dc.rights.licenseBY-NC-ND
dcterms.abstractПезо, Лато; Јовић, Михајло Д.; Смичиклас, Ивана Д.; Шљивић-Ивановић, Марија З.; Оњиа, Aнтоније Е.; Поповић, Aлександар Р.; Марковић, Јелена;
dc.citation.volume165
dc.citation.spage71
dc.citation.epage80
dc.identifier.wos000375515000007
dc.identifier.doi10.1016/j.gexplo.2016.03.004
dc.citation.other165: 71-80
dc.citation.rankM22
dc.description.otherThis is peer-reviewed version of the following article: Marković, J.; Jović, M.; Smičiklas, I.; Pezo, L.; Šljivić-Ivanović, M.; Onjia, A.; Popović, A. Chemical Speciation of Metals in Unpolluted Soils of Different Types: Correlation with Soil Characteristics and an ANN Modelling Approach. Journal of Geochemical Exploration 2016, 165, 71–80. [https://doi.org/10.1016/j.gexplo.2016.03.004]
dc.type.versionacceptedVersionen
dc.identifier.scopus2-s2.0-84960088318
dc.identifier.fulltexthttp://cherry.chem.bg.ac.rs/bitstream/id/14805/Chemical_speciation_of_acc_2016.pdf
dc.identifier.rcubKon_3051


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