Year: 2025
Type: article
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.
Authors: Mohammed Oday Al HAMDANI, Fatma Kaplan, Süreyya Betül RUFAİOĞLU
Institution: Harran University
Language: English
Topic: Heavy metals in environment
Subfield: Pollution
Field: Environmental Science
Domain: Physical Sciences
SDG: Life on land
Open Access status: diamond
OpenAlex ID: W4408892172