Methods Used in the Detection of Heavy Metal Pollution in Soils

Abstract

Heavy metal contamination in soils poses a major environmental risk to ecosystem health and human life. In this study, the methods used for the detection of heavy metals were analyzed. Although traditional laboratory techniques (AAS, ICP-MS, XRF) offer high sensitivity, they are costly and time-consuming. Geostatistical methods model the distribution of pollution by spatial analyses, and remote sensing techniques (satellite and UAV imaging) provide rapid detection over large areas. Machine learning approaches (RF, SVM, ANN) improve prediction accuracy by processing large data sets. Hybrid methods combine these techniques to provide more reliable and comprehensive analyses. In the future, heavy metal monitoring processes are projected to become more effective with real-time sensors, AI-based predictions, and cloud computing systems.