The application of fuzzy c-means cluster analysis and non-linear mapping to a soil data set for the detection of polluted sites

It is important to map the distribution of pollutants and to trace their sources to assess potential environmental hazard. The present work concerns the application of multivariate statistical methods to a soil data base from the province of Styria (Austria) to delineate polluted areas and to distinguish between different types of pollution. The soil data base comprised pedological, geochemical and geological data and was extended by magnetic susceptibility measurements to further test the suitability of magnetic susceptibility as a tracer for pollution. Topsoil data from 521 locations were analysed by fuzzy c-means cluster analysis and non-linear mapping. Robust cluster solutions grouped the database according to the geological background and the land use at the sampling sites. The extraction of information on heavy metal pollution appeared to be possible by analysing the geological units separately and reducing the variables to those indicative for the pollution. The link between magnetic susceptibility and the heavy metal content, which was too complex to be described by bivariate statistics, was revealed by the multivariate methods. (C) 2001 Elsevier Science Ltd. All rights reserved.