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@article{874049,
author = {Kubošová, Klára and Komprda, Jiří and Jarkovský, Jiří and Sáňka, Milan and Hájek, Ondřej and Dušek, Ladislav and Holoubek, Ivan and Klánová, Jana},
article_location = {USA},
article_number = {24},
keywords = {POP concentration spatial model soil},
language = {eng},
issn = {0013-936X},
journal = {Environmental Science & Technology},
title = {Spatially Resolved Distribution Models of POP Concentrations in Soil: A Stochastic Approach Using Regression Trees},
volume = {43},
year = {2009}
}
TY - JOUR
ID - 874049
AU - Kubošová, Klára - Komprda, Jiří - Jarkovský, Jiří - Sáňka, Milan - Hájek, Ondřej - Dušek, Ladislav - Holoubek, Ivan - Klánová, Jana
PY - 2009
TI - Spatially Resolved Distribution Models of POP Concentrations in Soil: A Stochastic Approach Using Regression Trees
JF - Environmental Science & Technology
VL - 43
IS - 24
SP - 9230–9236
EP - 9230–9236
SN - 0013936X
KW - POP concentration spatial model soil
N2 - Background concentrations of selected persistent organic pollutants (PCBs, HCB, p,p-DDT including metabolites and PAHs) 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. The model explained a high portion of variability in relationship between the soil concentrations of selected organic pollutants and available predictors. The validation results confirmed that the model is stable, general and useful for prediction.
ER -
KUBOŠOVÁ, Klára, Jiří KOMPRDA, Jiří JARKOVSKÝ, Milan SÁŇKA, Ondřej HÁJEK, Ladislav DUŠEK, Ivan HOLOUBEK a Jana KLÁNOVÁ. Spatially Resolved Distribution Models of POP Concentrations in Soil: A Stochastic Approach Using Regression Trees. \textit{Environmental Science \&{} Technology}. USA, 2009, roč.~43, č.~24, s.~9230–9236. ISSN~0013-936X.
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