Application of self-organizing maps for PCDD/F pattern recognition of environmental and biological samples to evaluate the impact of a hazardous waste incinerator.

Kohonen's self-organizing maps (SOM) is one of the most popular artificial neural network models. In this study, SOM were used to assess the potential relationships between polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) congener profiles in environmental (soil, herbage, and ambient air) and biological (plasma, adipose tissue, and breast milk) samples, and the emissions of a hazardous waste incinerator (HWI) in Spain. The visual examination of PCDD/F congener profiles of most environmental and biological samples did not allow finding out any differences between monitors. However, the global SOM analysis of environmental and biological samples showed that the weight of the PCDD/F stack emissions of the HWI on the environmental burden and on the exposure of the individuals living in the surroundings was not significant in relation to the background levels. The results confirmed the small influence of the HWI emissions of PCDD/Fs on the environment and the population living in the neighborhood.