Application of machine learning in the study of heavy metal remediation in soil using biochar-based nanocomposites

Abstract

Heavy metal contamination in soil has become a critical environmental and public health issue due to industrialization, agricultural activities, and waste disposal. These pollutants, including lead, cadmium, arsenic, and mercury, persist in the environment, disrupt microbial communities, and enter the food chain, leading to chronic diseases, neurological disorders, and organ damage. Conventional soil remediation techniques, such as physical removal and chemical stabilization, are costly, inefficient, and often introduce secondary pollution. The integration of biochar-based nanocomposites (BC-NPs) has emerged as a promising solution due to their high surface area, functional versatility, and ability to immobilize heavy metals. However, optimizing their synthesis and application remains a major scientific challenge, requiring advanced predictive models and deeper mechanistic understanding. This review focuses on synthesis techniques (chemical reduction, thermal conversion, coprecipitation, ball milling, microwave treatment, and green synthesis), remediation mechanisms (adsorption, ion exchange, complexation, and chemical precipitation), and machine learning applications in predicting metal immobilization efficiency, optimizing BC-NP design, and mapping soil contamination. Literature analysis shows that BC-NPs exhibit up to 86 % Pb and 100 % Cd immobilization efficiency, outperforming unmodified biochar. Advances in big data analytics and artificial intelligence have enabled predictive modeling with accuracy exceeding 90 % (R² = 0.994 for Cr(VI) and 0.998 for Co(II) removal), reducing experimental costs by 15.6 % and improving process optimization. This review highlights the need for multi-disciplinary approaches integrating nanotechnology, soil science, and machine learning to develop next-generation biochar-based nanocomposites for sustainable environmental remediation.