A new method for spatial three-dimensional prediction of soil heavy metals contamination
Fengbei Shen, Chengdong Xu, Jinfeng Wang, Maogui Hu, Guanlin Guo, Tingting Fang, Xingbao Zhu, Hongying Cao, Huan Tao, Yixuan Hou
Abstract
Soil heavy metals contamination are highly correlated to human health. It is crucial to employ three-dimensional heavy metals modeling and mapping for site assessment and remediation. However, current methods are limited due to the poor consideration of both spatial auto-correlation and stratified heterogeneity concurrently. The present study established a novel methodology 3D-MSN to model soil metals, encompassing autocorrelation and heterogeneity. In addition to considering in-strata correlation and between-strata heterogeneity like traditional methods, 3D-MSN accounted for the between-strata correlation to enhance the accuracy of prediction. A former agrochemical plant was used as a case to validate the superiority of the 3D-MSN method over traditional approaches. The accuracy of different methods was evaluated using mean absolute error (MAE) and root-mean-square error (RMSE), through leave-one-out cross-validation. Results demonstrated significant spatial autocorrelation and stratified heterogeneity for the presence of As and Cu in soil. 3D-MSN exhibited the lowest MAEs (2.424 mg/kg for As, 4.863 mg/kg for Cu) and RMSEs (3.439 mg/kg, 7.279 mg/kg) compared to 3D-ordinary kriging (MAEs (2.949 mg/kg, 6.482 mg/kg) and RMSEs (3.890 mg/kg, 8.364 mg/kg)) and 3D-stratified kriging (MAEs (2.571 mg/kg,5.184 mg/kg) and RMSEs (3.570 mg/kg, 7.412 mg/kg)). 3D-MSN also accounted for estimation uncertainties. Considering autocorrelation and stratified heterogeneity, 3D-MSN presented superior performance. This research contributes to advancing the field of three-dimensional heavy metal modeling and provides valuable insights for site assessment and remediation efforts.