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

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.