Multivariate classification and modeling in surface water pollution estimation |
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| Authors: | A Astel S Tsakovski V Simeonov E Reisenhofer S Piselli P Barbieri |
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| Institution: | (1) Biology and Environmental Protection Institute, Environmental Chemistry Research Unit, Pomeranian Academy, 22a Arciszewskego Str., 76 200 Slupsk, Poland;(2) Physical Chemistry, Faculty of Chemistry, University of Sofia “St. Kl. Okhridski, J. Bourchier Blvd. 1, 1164 Sofia, Bulgaria;(3) Analytical Chemistry, Faculty of Chemistry, University of Sofia “St. Kl. Okhridski, J. Bourchier Blvd. 1, 1164 Sofia, Bulgaria;(4) Department of Chemical Sciences, University of Trieste, Via Giorgieri 1, 34127 Trieste, Italy;(5) ACEGAS-APS, Via Maestri del Lavoro 8, 34123 Trieste, Italy |
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| Abstract: | The present study deals with the application of self-organizing maps (SOM) and multiway principal-components analysis to classify,
model, and interpret a large monitoring data set for surface water quality. The chemometric methods applied made it possible
to reveal specific quality patterns of the chemical and biological parameters used to monitor the water quality (relation
between water temperature, turbidity, hardness, colibacteria), seasonal impacts during the long period of observation and
the relative independence on the spatial location of the sampling sites (water supply sources for the City of Trieste).
Figure The schematic procedure for surface water pollution estimation supported by neural network-based classification and multivariate factor
analysis |
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| Keywords: | Chemometrics Surface water N-way PCA SOM City of Trieste |
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