Abstract
The application of multivariate statistical methods to high mountain lake monitoring data has offered some important conclusions about the importance of environmetric approaches in lake water pollution assessment. Various methods like cluster analysis and principal components analysis were used for classification and projection of the data set from a large number of lakes from Rila 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 were found, which enables better interpretation of the pollution situation. A methodology for application of a combination of different environmetric methods is suggested as a pathway to interpret high mountain lake water monitoring data.
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Simeonova, P., Lovchinov, V., Dimitrov, D. et al. Environmetric approaches for lake pollution assessment. Environ Monit Assess 164, 233–248 (2010). https://doi.org/10.1007/s10661-009-0888-7
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DOI: https://doi.org/10.1007/s10661-009-0888-7