MULTIVARIATE GEOSTATISTICAL ANALYSIS OF SOIL CONTAMINATIONS

Soil is one of the most endangered compartments of our environment. Th e input of pollutants into the soil caused by industrial production, a griculture, and other human activities is a problem of high relevance. A contour analysis of soil contamination is the first step to charact erize the size and magnitude of pollution and to detect emission sourc es of heavy metals. The evaluation and assessment of the large number of measured samples and pollutants require the use of efficient statis tical methods which are able to discover both spatial and multivariate relationships. The evaluation of the presented case study - soil cont amination by heavy metals - is carried out by means of multivariate ge ostatistical methods, also described as theory of Linear coregionaliza tion. Multivariate geostatistics connects the advantages of common geo statistical methods (spatial correlation) and multivariate data analys is (multivariate relationships). In the given case study of soil pollu tion by heavy metal emissions it is excellently possible to detect mul tivariate spatial correlations between different heavy metals in the s oil and to determine their common emission sources. These emission sou rces are located in the area under investigation.