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).
The schematic procedure for surface water pollution estimation supported by neural network-based classification and multivariate factor analysis
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Acknowledgement
One of the authors (V. Simeonov) would like to express his sincere gratitude to the Bulgarian National Fund for Scientific Research (Project VHU 02/05 – 2437) for financial support. Thanks are also due for financial support of A. Astel by a project (Optimization of chemometric techniques of exploration and modeling results originating from environmental constituents pollution monitoring No.1439/T02/2007/32) sponsored by the Polish Ministry of Science and Higher Education. S. Tsakovski acknowledges also financial support by a CNR-NATO Senior Fellowship (Ann. No 217.36 S).
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Astel, A., Tsakovski, S., Simeonov, V. et al. Multivariate classification and modeling in surface water pollution estimation. Anal Bioanal Chem 390, 1283–1292 (2008). https://doi.org/10.1007/s00216-007-1700-6
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DOI: https://doi.org/10.1007/s00216-007-1700-6