Journal
ANALYTICA CHIMICA ACTA
Volume 560, Issue 1-2, Pages 172-183Publisher
ELSEVIER
DOI: 10.1016/j.aca.2005.11.086
Keywords
heavy metal pollution; Kohonen artificial neural networks; principal component analysis; river sediments; multivariate analysis
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An investigation on the sediment composition and grain size was carried out along the Bormida river (Piedmont, Northern Italy). The samples were taken both in the riverbed and on its banks. Multivariate statistical exploratory methods permitted to identify possible sources of primary pollution. In particular, principal component analysis (PCA) showed that there are probably at least three anthropogenic main causes of pollution, one connected with Cr, Ni and Sn, the second with cadmium and the last one with mercury. Some correlations were found between heavy metal ion concentrations and organic matter and/or the sediment grain size. Differences between the samples collected in the riverbed and on the banks were identified and the relationships between the principal components and the distance of the samples from the riverbed and from the hot spot represented by the ACNA industrial site were also analyzed. (c) 2005 Elsevier B.V. All rights reserved.
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