Heavy metal risk assessment in coastal and inland waters and sediments: A critical review of analytical evolution, ecosystem-based monitoring, and machine learning approaches

Abstract

Heavy metal contamination in coastal and inland water ecosystems remains a persistent environmental challenge because sediments act not only as long-term sinks of toxic elements but also as potential secondary sources under changing physicochemical conditions. This review examines the methodological evolution of heavy metal assessment from classical wet-chemical and spectrophotometric approaches to modern spectrometric techniques and, more recently, to data-driven machine learning applications for sediment risk prediction. Particular emphasis is placed on the complementary roles of sediment and water sampling within an ecosystem-based monitoring framework, the analytical strengths and limitations of key techniques including atomic absorption spectrometry (AAS), inductively coupled plasma optical emission spectrometry (ICP-OES), inductively coupled plasma mass spectrometry (ICP-MS), and X-ray fluorescence (XRF), and the growing use of machine learning algorithms such as Random Forest, artificial neural networks, support vector machines, and boosting models for hotspot identification, ecological risk interpretation, and decision support. The review argues that contemporary sediment quality assessment should move beyond concentration reporting and instead integrate sampling design, matrix-specific analytical constraints, environmental context, ecological risk indices and predictive modeling within a coherent framework. It further highlights that the scientific and practical value of machine learning depends on representative sampling, robust analytical measurements, transparent preprocessing, appropriate validation strategies, and interpretable modeling approaches. By bringing together analytical chemistry, ecosystem monitoring and artificial intelligence perspectives, this study synthesizes current knowledge, identifies methodological limitations and outlines future priorities for improving heavy metal risk assessment and management in coastal and inland water sediments.