Ernest Vončina,a Darinka Brodnjak Vončina,bNataša Mirkovič,a Marjana Novičc
Institute of Public Health Maribor, Institute of Environmental Protection,
Prvomajska 1, 2000 Maribor, Slovenia
b Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia.
c National Institute of Chemistry, Hajdrihova 19, 1000 Ljubljana, Slovenia
Paper based on a
presentation at the 12th International Symposium on Separation
Sciences, Lipica, Slovenia,
September 27–29, 2006.
The quality of ground water as a source of drinking water in Slovenia is regularly monitored. One of the monitoring programmes is performed on 5 wells for drinking water supply, 3 industrial wells and 2 ground water monitoring wells. Two hundred and fourteen samples of ground waters were analysed in the time 2003–2004. Samples were gathered from ten different sampling sites and physical chemical measurements were performed. The following 13 physical chemical parameters were regularly controlled: temperature, pH, conductivity, nitrate, AOX (adsorbable organic halogens), metals such as chromium, pesticides (desethyl atrazine, atrazine and 2,6-dichlorobenzamide), highly-volatile halogenated hydrocarbons (trichlorometane, 1,1,2,2-tetrachloroethene and 1,1,2-trichloroethene). For handling the results different chemometrics methods were employed, such as basic statistical methods for the determination of mean and median values, standard deviations, minimal and maximal values of measured parameters and their mutual correlation coefficients, cluster analysis (CA), the principal component analysis (PCA), the clustering method based on Kohonen neural network, and linear discriminant analysis (LDA). The study gives the opportunity to follow the quality of ground waters at different sampling sites within the defined time period. Monitoring of general pollution of ground waters and following measuring can be used to search the pollution source, to plan prevention measures and to protect from pollution, as well.
Keywords: ground waters, water quality, chemometrics, principal component analysis, classification, Kohonen neural networks