The application of multivariate statistical methods to high mountain lakes monitoring data has offered some important conclusions about the importance of environmetric approaches in lake water quality assessment. Various methods like cluster analysis and principal components analysis were used for classification and projection of the data set from a big number of lakes from Pirin Mountain in Bulgaria. Additionally, self-organizing maps of Kohonen were constructed in order to solve some classification tasks. An effort was made to relate the maps with the input data in order to detect classification patterns in the data set. Thus, discrimination chemical parameters for each pattern (cluster) identified was found, which enables better interpretation of the ecological state of the system. A methodology for application of combination of different environmetric methods was suggested as a pathway to interpret high mountain lake waters monitoring data.

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Abstract:The present communication deals with the application of the most important environmetric approaches like cluster analysis, principal components analysis and principal components regression (apportioning models) to environmental systems which are of substantial interest for environmental physics -surface waters, aerosols, and coastal sediments. Using various case studies we identify the latent factors responsible for the data set structure and construct models showing the contribution of each identified source (anthropogenic or natural) to the total measure of the pollution. In this way the information obtained by the monitoring data becomes broader and more intelligent, which help in problem solving in environmental physics.

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