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.

Application of an unsteady state environmental distribution model to a decadal time series of PAH concentrations in Central Europe.

To explain observed decadal trends in concentrations of polycyclic aromatic hydrocarbons (PAHs) in soil at the Kosetice observatory, Czech Republic, an environmental distribution model for persistent organic pollutants (POPs) based on the fugacity approach was developed. Weekly air concentrations were used as input data for the unsteady state model and concentrations in soil were calculated. In general, agreement between measured and predicted soil concentrations of PAHs was observed.