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

Ertan Karahanlı

doi:10.3153/AR26027

EN

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.

Keywords

Ethical Statement

This study did not involve human participants or animals. The research adhered to the principles of scientific research and publication ethics.

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Details

Primary Language

English

Subjects

Hydrobiology

Journal Section

Review Article

Authors

Ertan Karahanlı ^*
0000-0002-3202-271X
Türkiye

Early Pub Date

September 14, 2026

Publication Date

-

Submission Date

April 3, 2026

Acceptance Date

May 18, 2026

Published in Issue

Year 2026 Number: Advanced Online Publication

DOI

https://doi.org/10.3153/AR26027

IZ

https://izlik.org/JA85AZ78MB

Cite

RIS / Bibtex

APA

Karahanlı, E. (2026). Heavy metal risk assessment in coastal and inland waters and sediments: A critical review of analytical evolution, ecosystem-based monitoring, and machine learning approaches. Aquatic Research, Advanced Online Publication, 335-355. https://doi.org/10.3153/AR26027

AMA

1.Karahanlı E. Heavy metal risk assessment in coastal and inland waters and sediments: A critical review of analytical evolution, ecosystem-based monitoring, and machine learning approaches. Aquat Res. 2026;(Advanced Online Publication):335-355. doi:10.3153/AR26027

Chicago

Karahanlı, Ertan. 2026. “Heavy Metal Risk Assessment in Coastal and Inland Waters and Sediments: A Critical Review of Analytical Evolution, Ecosystem-Based Monitoring, and Machine Learning Approaches”. Aquatic Research, no. Advanced Online Publication: 335-55. https://doi.org/10.3153/AR26027.

EndNote

Karahanlı E (September 1, 2026) Heavy metal risk assessment in coastal and inland waters and sediments: A critical review of analytical evolution, ecosystem-based monitoring, and machine learning approaches. Aquatic Research Advanced Online Publication 335–355.

IEEE

[1]E. Karahanlı, “Heavy metal risk assessment in coastal and inland waters and sediments: A critical review of analytical evolution, ecosystem-based monitoring, and machine learning approaches”, Aquat Res, no. Advanced Online Publication, pp. 335–355, Sept. 2026, doi: 10.3153/AR26027.

ISNAD

Karahanlı, Ertan. “Heavy Metal Risk Assessment in Coastal and Inland Waters and Sediments: A Critical Review of Analytical Evolution, Ecosystem-Based Monitoring, and Machine Learning Approaches”. Aquatic Research. Advanced Online Publication (September 1, 2026): 335-355. https://doi.org/10.3153/AR26027.

JAMA

1.Karahanlı E. Heavy metal risk assessment in coastal and inland waters and sediments: A critical review of analytical evolution, ecosystem-based monitoring, and machine learning approaches. Aquat Res. 2026;:335–355.

MLA

Karahanlı, Ertan. “Heavy Metal Risk Assessment in Coastal and Inland Waters and Sediments: A Critical Review of Analytical Evolution, Ecosystem-Based Monitoring, and Machine Learning Approaches”. Aquatic Research, no. Advanced Online Publication, Sept. 2026, pp. 335-5, doi:10.3153/AR26027.

Vancouver

1.Ertan Karahanlı. Heavy metal risk assessment in coastal and inland waters and sediments: A critical review of analytical evolution, ecosystem-based monitoring, and machine learning approaches. Aquat Res. 2026 Sep. 1;(Advanced Online Publication):335-5. doi:10.3153/AR26027