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

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

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Estimating heavy metal(HM)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 herb 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 rates and environmental data of each sampling point were simultaneously incorporated into a stepwise linear regression model to identify the crucial indicators for predicting soil HM spatial distributions.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 activities.When three-type(natural,agricultural,and industrial)source contribution rates and environmental data were simultaneously incorporated into the stepwise linear regression model,the fitting accuracy was significantly improved and the model could explain 31％-86％of the total variance in soil HM concentrations.This study introduced three-type source contributions of each sampling point based on APCS-MLR analysis as new covariates to improve soil HM estimation precision,thus providing a new approach for predicting the spatial distribution of HMs using small sample sizes at the county scale.

absolute principal component score-multiple linear regressionChinese herbal medicineinfluencing factorsspatial distributionstepwise multiple regression

Jie SONG、Xin WANG、Dongsheng YU、Jiangang LI、Yanhe ZHAO、Siwei WANG、Lixia MA

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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)

Agricultural and Rural Bureau of Luanping County,Luanping 068250(China)

special project of the National Key Research and Development Program of Chinaspecial project of the National Key Research and Development Program of China

2021YFC18091042018YFC1800104

2024

10.1016/j.pedsph.2023.06.004

土壤圈(英文版)
中国土壤学会,中科院南京土壤研究所和土壤与农业可持续发展国家重点实验室

土壤圈(英文版)

CSTPCD
影响因子：0.477
ISSN：1002-0160
年,卷(期)：2024.34(1)
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