This paper represents an effort to demonstrate the opportunities of some environmetric methods like regression analysis, cluster analysis and principal components analysis. Their role for data modeling is stressed and the basic theoretical principles are given. The application of the multivariate statistical methods is illustrated by two major examples: Assessment of metal pollution based on multivariate statistical modeling of “hot spot” sediments from the Black Sea; and a trend study of Kamchia River water quality. In the first part of the study the environmetric approach makes it possible to separate three zones of the marine environment with different levels of pollution (Bourgas gulf, Varna gulf and lake buffer zone). Further, the extraction of four latent factors offers a specific interpretation of the possible pollution sources and separates the natural factors from the anthropogenic ones, the latter originating from contamination by chemical and steel-works and an oil refinery. In the second part of the study nine sampling sites along Kamchia River were considered as sources for water quality monitoring data. Trends for all parameters are calculated by the use of linear regression analysis and special attention is paid to a specific coastal site. Then five latent factors were extracted from the monitoring data set in order to gain information about some structural characteristics of the set.

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