Abstract
The aim of the present study is to compare the application of unsupervised and supervised pattern recognition techniques for the quality assessment and classification of the reservoirs used as the source for the domestic and industrial water supply of the city of Athens, Greece. A new optimization strategy for sampling, monitoring, and water management is proposed. During the period of October 2006 to April 2007, 89 samples were collected from the three water reservoirs (Iliki, Mornos, and Marathon), and 13 parameters (metals and metalloids) were analytically determined. Generally, all the elements were found to fluctuate at very low levels, especially for Mornos that comprises the main water reservoir of Athens. Iliki and Marathon showed relatively elevated values, compared to Mornos, but below the legislative limits. Multivariate unsupervised statistical techniques, such as factor analysis/principal components analysis, and cluster analysis and supervised ones, like discriminant analysis and classification trees, were applied to the data set, and their classification abilities were compared. All the chemometric techniques successfully revealed the critical variables and described the similarities and dissimilarities among the sampling points, emphasizing the individual characteristics in every sample and revealing the sources of elements in the region. New data from posterior samplings (November and December 2007) were used for the validation of the supervised techniques. Finally, water management strategies were proposed concerning the sampling points and representative parameters.
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Mr. P.V. Nisianakis (MSc) of the Chemistry Laboratory of the Military Biological Research Department is gratefully acknowledged for providing the ICP-MS instrumentation.
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Farmaki, E.G., Thomaidis, N.S., Simeonov, V. et al. A comparative chemometric study for water quality expertise of the Athenian water reservoirs. Environ Monit Assess 184, 7635–7652 (2012). https://doi.org/10.1007/s10661-012-2524-1
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DOI: https://doi.org/10.1007/s10661-012-2524-1