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Position: Home > Articles > Incorporation of source contributions to improve the accuracy of soil heavy metals mapping using small sample sizes at a county scale Pedosphere 2023,34 (1)

Incorporation of source contributions to improve the accuracy of soil heavy metals mapping using small sample sizes at a county scale

作  者：
Jinxi Song;Xin Wang;Dongsheng YU;Jiangang LI;Yu-Guo Zhao;Siwei Wang;Liang M
单  位：
State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008 (China);Agricultural and Rural Bureau of Luanping County, Luanping 068250 (China);State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008 (China;Chinese Academy of Sciences University, Beijing 100049 (China)
关键词：
hms;soil;source contributions;to improve;zn;simultaneousl
摘  要：
Estimating heavy metals (HMs) distribution with high precision is the key to effectively preventing Chinese medicinal plants from being polluted by the native soil. A total of 44 surface soil samples were gathered to detect the concentrations of eight HMs (As, Hg, Cu, Cr, Ni, Zn, Pb and Cd) in the herbal growing area of Luanping County, northeastern Hebei Province, China. An absolute principal component score/multiple linear regression (APCS/MLR) model was used to quantify pollution source contributions to soil HMs. Furthermore, the source contribution of each sampling point and environmental data were simultaneously incorporated into a stepwise linear regression model to identify the crucial indicators for predicting soil HMs spatial distributions. The results showed that 88% of Cu, 72% of Cr and 72% of Ni came from natural sources; 50% of Zn, 49% of Pb and 59% of Cd were mainly caused by agricultural activities; and 44% of As and 56% of Hg originated from industrial inputs. When three source contribution rates and environmental data were simultaneously incorporated in a stepwise linear regression model, the fitting accuracy was significantly improved, and the model could explain 31–86% variance of soil HMs concentration. This study introduced three source contributions of each sampling point based on APCS/MLR analysis as new covariates to improve soil HMs estimation precision, thus providing a new approach for predicting the spatial distribution of HMs using small sample sizes at the county scale.

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