Rapid diagnosis of heavy metal pollution in lake sediments based on environmental magnetism and machine learning

Environmental magnetism in combination with machine learning can be used to monitor heavy metal pollution in sediments. Magnetic parameters and heavy metal concentrations of sediments from Chaohu Lake (China) were analyzed. The magnetic measurements, high- and low-temperature curves, and hysteresis loops showed the primary magnetic minerals were ferrimagnetic minerals in sediments. For most metals, their concentrations were highest during the wet season and lowest during the medium-water period. Cd, Hg, and Zn were moderately enriched and Cd and Hg posed a considerable ecological risk. A redundancy analysis indicated a relationship between physicochemical indexes and magnetic parameters and heavy metal concentrations. An artificial neural network (ANN) and support vector machine (SVM) were used to construct six models to predict the heavy metal concentrations and ecological risk index. The inclusion of both the physicochemical indexes and magnetic parameters as input factors in the models were significantly ameliorated the simulation accuracy for the majority of heavy metals. The training and test R, for Be, Fe, Pb, Zn, As, Cu, and Cr were > 0.8. The SVM showed better performance and hence it has potential for the efficient and economical long-term tracking and monitoring of heavy metal pollution in lake sediments.

Automated summary

The study demonstrates that environmental magnetism combined with machine learning can be effectively used to monitor heavy metal pollution in lake sediments. Analysis of magnetic parameters and heavy metal concentrations in Chaohu Lake sediments revealed ferrimagnetic minerals as the primary magnetic minerals and significant variations in heavy metal concentrations across different seasons, with Cd and Hg posing considerable ecological risks. The utilization of physicochemical indexes and magnetic parameters in predictive models significantly improved accuracy, with SVM showing better performance and potential for efficient long-term monitoring of heavy metal pollution in lake sediments.