Methods Used in the Detection of Heavy Metal Pollution in Soils

Heavy metal pollution, laboratory analyses, geostatistics, remote sensing, machine learning

Authors

  • Mohammed Oday Al HAMDANI Harran University, Faculty of Agriculture, Soil Science and Plan Nutrition, Şanlıurfa, Türkiye
  • Fatma Kaplan Harran University, Faculty of Agriculture, Soil Science and Plan Nutrition, Şanlıurfa, Türkiye
  • Süreyya Betül RUFAİOĞLU Harran University, Faculty of Agriculture, Soil Science and Plan Nutrition, Şanlıurfa, Türkiye
Vol. 13 No. 03 (2025)
Agriculture and Horticulture
March 27, 2025

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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.