Next-Generation Approaches for Predicting Soil Heavy Metal Contamination: Insights from Geospatial and AI-Based Methods

Abstract

Contamination of soil with heavy metals poses a serious risk to ecosystems, agriculture, and human health. The spatial and temporal constraints limit the use of traditional techniques of predicting contamination, such as soil sampling and geochemical mapping. New developments in geospatial methods and artificial intelligence (AI) provide promising options for large-scale, real-time monitoring and prediction. Geospatial tools, such as remote sensing and Geographic Information Systems (GIS), can provide important spatial information for mapping contamination patterns. In contrast, AI-based applications, such as machine learning and deep learning, can process multifaceted datasets to forecast contamination trends. A combination of these two methods increases the predictability of measuring soil contamination dynamically and at high resolution. Nevertheless, there are still difficulties with data quality, model interpretability, and computational complexity. Notwithstanding these issues, the interrelation between geospatial and AI approaches represents a major breakthrough in environmental monitoring, offering a more in-depth and efficient tool for regulating heavy-metal pollution in soil. The review highlights the potential of these next-generation approaches and offers insights into their use, limitations, and future directions for enhancing soil health and environmental sustainability.