Chemical Fractions and Magnetic Simulation Based on Machine Learning for Trace Metals in a Sedimentary Column of Lake Taihu

Abstract

In this study, the chemical fractions (CFs) of trace metal (TMs) and multiple magnetic parameters were analysed in the sedimentary column from the centre of Lake Taihu. The sedimentary column, measuring 53 cm in length, was dated using 210Pb and 137Cs to be 124 years old. Surface layers of the column were found to contain significantly higher concentrations of Cd, Co, Cu, Pb, Sb, Ti, and Zn than the middle and bottom layers. The sedimentary core contained a substantial amount of ferrimagnetic minerals. Most of the TMs were present in the residual state, except for Mn and Pb. The chemical fractions of Cd exhibited the most significant variation with depth. The pollution load index (PLI) indicated moderate TMs pollution levels in the region, whereas the risk assessment code (RAC) classified Mn as being heavily polluted. Multiple linear regression (MLR) and random forest (RF), support vector machine (SVM), and XGBoost (1.7.7.1) machine learning models were used to simulate the RAC and total concentration of TMs, using physical and chemical indicators and magnetic parameters of the sediments as input variables. The MLR model outperformed RF, SVM, and XGBoost in simulating the CFs and total concentrations of most TMs in the sedimentary column, with R2 up to 0.668 and 0.87. The SHapley Additive exPlanations (SHAP) method reveals that χarm/χ is the dominant factor influencing the RAC of As in the XGBoost models. For the RAC of Co and Cu in RF models, C% and N% exhibit greater contributions.