Spatially resolved distribution models of POP concentrations in soil: a stochastic approach using regression trees.

Background concentrations of selected persistent organic pollutants (polychlorinated biphenyls, hexachlorobenzene, p,p'-DDT including metabolites) and polyaromatic hydrocarbons in soils of the Czech Republic were predicted in this study, and the main factors affecting their geographical distribution were identified. A database containing POP concentrations in 534 soil samples and the set of specific environmental predictors were used for development of a model based on regression trees. Selected predictors addressed specific conditions affecting a behavior of the individual groups of pollutants: a presence of primary and secondary sources, density of human settlement, geographical characteristics and climatic conditions, land use, land cover, and soil properties.