MULTIVARIATE DATA-ANALYSIS APPLIED TO THE INVESTIGATION OF RIVER POLLUTION

A set of quantitative analytical data for the rivers Bormida and Tanaro in the Piedmont region (north-western Italy) has been processed by multivariate statistical techniques. The aim consists in evaluating the usefulness of these methods, compared with usual univariate techniques, in the investigation of surface water pollution phenomena. The experimental data consist of 19 chemical and bacteriological variables determined at 31 sampling sites. The following methods have been used for the treatment of the data: cluster analysis (unsupervised pattern recognition), principal component analysis, non-linear mapping, multivariate feature selection. The treatment has been proved to be useful in determining the various factors of pollution together with their mutual importance and correlation in evaluating the general degree of pollution of rivers, and in rationalizing the data collection by eliminating variables with low information content.