Using ensemble models to identify and apportion heavy metal pollution sources in agricultural soils on a local scale

被引：133

作者：

Wang, Qi ^{[1
]}

Xie, Zhiyi ^{[2
]}

Li, Fangbai ^{[1
]}

机构：

[1] Guangdong Inst Ecoenvironm & Soil Sci, Guangdong Key Lab Agr Environm Pollut Integrated, Guangzhou, Guangdong, Peoples R China

[2] Guangdong Environm Monitoring Ctr, Guangzhou, Guangdong, Peoples R China

来源：

ENVIRONMENTAL POLLUTION | 2015年 / 206卷

基金：

中国国家自然科学基金;

关键词：

Heavy metals; Pollution source; Agricultural soil; Local scale; Ensemble model; REGRESSION TREES; SURFACE SOILS; HEALTH-RISK; CONTAMINATION; WATER; AREA; IDENTIFICATION; CONSUMPTION; INDUSTRIAL; SEDIMENTS;

D O I：

10.1016/j.envpol.2015.06.040

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

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 (RE) 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. (C) 2015 Elsevier Ltd. All rights reserved.

引用

页码：227 / 235

页数：9

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