Using ensemble models to identify and apportion heavy metal pollution sources in agricultural soils on a local scale
Using ensemble models to identify and apportion heavy metal pollution sources in agricultural soils on a local scale
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利用集成模型识别并解析局部范围农业土壤重金属污染源
DOI：
10.1016/j.envpol.2015.06.040
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发表时间：
2015
影响因子：
8.9
通讯作者：
Li Fangbai
中科院分区：
文献类型：
--
作者：
Wang Qi;Xie Zhiyi;Li Fangbai
本研究旨在利用包括随机梯度增强(SGB)和随机森林(RF)在内的集成模型，从自然和人为输入中识别和分配农业土壤中的多源和多相重金属污染。对重金属污染源进行了定量评价，结果表明集成模型适用于农业土壤多源多相重金属污染的局部尺度评价。SGB和RF的结果一致表明，人为来源对研究区农业土壤中铅和Cd的浓度贡献最大，且SGB的贡献要好于RF。
This study aims to identify and apportion multi-source and multi-phase heavy metal pollution from natural and anthropogenic inputs using ensemble models that include stochastic gradient boosting (SGB) and random forest (RF) in agricultural soils on the local scale. The heavy metal pollution sources were quantitatively assessed, and the results illustrated the suitability of the ensemble models for the assessment of multi-source and multi-phase heavy metal pollution in agricultural soils on the local scale. The results of SGB and RF consistently demonstrated that anthropogenic sources contributed the most to the concentrations of Pb and Cd in agricultural soils in the study region and that SGB performed better than RF.