Analysis of sources of dioxin contamination in sediments and soils using multivariate statistical methods and neural networks

Multivariate statistical methods and neuronal networks were used to evaluate the concentration dioxin patterns of a large data set(407 samples) in order to identify the dioxin sources of contaminated waters (sediment and suspended particulate matter samples). The evaluations indicated that a considerable proportion of the dioxin contamination of the river Elbe in the section between the Mulde tributary and the North Sea and their flood plains (soil samples) and the Port of Hamburg was caused by pollution originating from the Bitterfeld region, an industrial area of the former German Democratic Republic. The dioxin patterns of sediment samples from tributaries of the river Elbe in the Bitterfeld area itself are similar to dioxin patterns that can be attributed to metalworking processes. The dioxin patterns of the Hamburg inner city waters could be attributed to incineration dioxin sources, for example waste incineration plants. The results of cluster analysis applying different modes of distance measure and linkage compared well with neuronal networks. The number of clusters was determined based on the stability of the results of different cluster analyses and background information.