Research Centre for Environmental Chemistry and Toxicology, Kamenice 126/3, 625 00 Brno, Czech Republic.
Environ Sci Technol. 2009 Dec 15;43(24):9230-6. doi: 10.1021/es902076y.
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. The model explained a high portion of variability in relationship between the soil concentrations of selected organic pollutants and available predictors. A tree for hexachlorobenzene was the most successful with 76.2% of explained variability, followed by trees for polyaromatic hydrocarbons (71%), polychlorinated biphenyls (68.6%), and p,p'-DDT and metabolites (65.4%). The validation results confirmed that the model is stable, general and useful for prediction. The stochastic model applied in this study seems to be a promising tool capable of predicting the environmental distribution of organic pollutants.
本研究预测了捷克共和国土壤中选定的持久性有机污染物（多氯联苯、六氯苯、p,p'-滴滴涕及其代谢物）和多环芳烃的背景浓度，并确定了影响其地理分布的主要因素。该模型基于回归树开发，使用包含 534 个土壤样本中持久性有机污染物浓度的数据库和一组特定的环境预测因子。选定的预测因子针对影响特定污染物群体行为的具体条件：主要和次要污染源的存在、人类住区密度、地理特征和气候条件、土地利用、土地覆盖和土壤特性。该模型解释了选定有机污染物土壤浓度与可用预测因子之间关系的很大一部分可变性。六氯苯的树是最成功的，解释了 76.2%的可变性，其次是多环芳烃（71%）、多氯联苯（68.6%）和 p,p'-滴滴涕及其代谢物（65.4%）。验证结果证实该模型稳定、通用且可用于预测。本研究中应用的随机模型似乎是一种很有前途的工具，能够预测有机污染物的环境分布。
