Приказ основних података о документу

dc.creatorCrnković, Dragan
dc.creatorAntanasijević, Davor
dc.creatorPocajt, Viktor
dc.creatorPerić-Grujić, Aleksandra
dc.creatorAntonović, Dušan
dc.creatorRistić, Mirjana
dc.date.accessioned2021-03-10T13:03:27Z
dc.date.available2021-03-10T13:03:27Z
dc.date.issued2016
dc.identifier.issn0341-8162
dc.identifier.urihttp://TechnoRep.tmf.bg.ac.rs/handle/123456789/3274
dc.description.abstractThe aim of this study was to evaluate the quality of freshwater sediments by means of three chemometric techniques for multi-criteria analysis and decision: self-organizing network (SON), self-organizing map (SOM) and PROMETHEE&GAIA (Preference Ranking Organization Method for Enrichment Evaluation with Geometrical Analysis for Interactive Aid). Selected chemometric techniques were applied to the results of Pb, Cd, Zn, Cu, Ni, Cr, Hg and As content in thirty Danube and fourteen Sava river sediment samples from Serbia. The potential toxicity of sediments was estimated using Probable Effect Concentrations quotients (mean PEC-Q). According to the SON analysis the Danube sediment samples were divided into three classes, Class I (mean PEC-Q range 0.27-0.51), Class II (mean PEC-Qrange 0:50-0.70), and Class III (mean PEC-Qrange 0.77-0.97), while the Sava samples were divided into two classes, Class II (two samples, mean PEC-Qvalues 0.65 and 0.69) and Class III (mean PEC-Q range 0.69-1.00). Using the SOM cluster analysis, both Danube and Sava sediment samples were classified into five subclusters, on the basis of the metal concentration level and further ranked into three levels (for remediation, moderately polluted and not polluted) by the use of multi-criteria ranking PROMETHEE method. Graphical presentation of the results obtained by PROMETHEE method using GAIA descriptive tool has provided an insight into the distribution of examined elements in sediments and has shown a significant correlation between some elements. On the basis of the results obtained, it has been concluded that the proposed chemometric approach could provide useful information in the sediment quality assessment.en
dc.publisherElsevier, Amsterdam
dc.relationinfo:eu-repo/grantAgreement/MESTD/Basic Research (BR or ON)/172007/RS//
dc.rightsrestrictedAccess
dc.sourceCatena
dc.subjectHeavy metalsen
dc.subjectClassificationen
dc.subjectKohonen neural networksen
dc.subjectPROMETHEE&GAIAen
dc.titleUnsupervised classification and multi-criteria decision analysis as chemometric tools for the assessment of sediment quality: A case study of the Danube and Sava Riveren
dc.typearticle
dc.rights.licenseARR
dc.citation.epage22
dc.citation.other144: 11-22
dc.citation.rankaM21
dc.citation.spage11
dc.citation.volume144
dc.identifier.doi10.1016/j.catena.2016.04.025
dc.identifier.scopus2-s2.0-84979288115
dc.identifier.wos000379375900002
dc.type.versionpublishedVersion


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Приказ основних података о документу