In this study, analysis of variance (ANOVA), cluster analysis (CA) and principal component analysis (PCA) were employed in order to evaluate the concentration profile of organic contaminants found in three main river from central Transylvania, Romania. Samples were collected from nine sampling stations, in two different sampling campaigns (wet season and dry season). Water samples were extracted using solid-phase extraction and analyzed using gas chromatography coupled with mass spectrometry (GC/MS). Twelve organic pollutants belonging to different classes were used for further interpretations. ANOVA highlighted compounds which distinguished Olt River from Mures River, and compounds that are influenced by increased river flow from the wet season. CA was applied to group the sampling stations. Three clusters were obtained, according to their organic load. PCA extracted five principal components explaining 87.330% from data set variability. Based on these results, a future monitoring study may be optimized by reducing the sampling points and compounds to those that are representative for each river, thereby reducing costs, without any information loss.
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Research Article|
July 26 2016
Spatial and seasonal variation of organic pollutants in surface water using multivariate statistical techniques
Ioana-Coralia Feher;
1National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103, Donat Street, Cluj-Napoca 400293, Romania
E-mail: [email protected]
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Zaharie Moldovan;
Zaharie Moldovan
1National Institute for Research and Development of Isotopic and Molecular Technologies, 67-103, Donat Street, Cluj-Napoca 400293, Romania
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Ioan Oprean
Ioan Oprean
2Faculty of Chemistry and Chemical Engineering, Babes-Bolyai University, 11, Arany Janos Street, Cluj-Napoca 400028, Romania
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Water Sci Technol (2016) 74 (7): 1726–1735.
Article history
Received:
November 05 2015
Accepted:
July 15 2016
Citation
Ioana-Coralia Feher, Zaharie Moldovan, Ioan Oprean; Spatial and seasonal variation of organic pollutants in surface water using multivariate statistical techniques. Water Sci Technol 20 October 2016; 74 (7): 1726–1735. doi: https://doi.org/10.2166/wst.2016.351
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