Volume 14, Issue 2 (Spring- In Press 2026)
Iran J Health Sci 2026, 14(2): 0-0 |
Back to browse issues page
Ethics code: IR.MAZUMS.REC.1402.224
Clinical trials code: 0
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Jabari A, Fendereski A, kouhi K, Hashempour Y. Assessing Microplastic Risks: A Systematic Review of Integrated Statistical Approaches to Source Identification and Evaluation. Iran J Health Sci 2026; 14 (2)
URL: http://jhs.mazums.ac.ir/article-1-1064-en.html
URL: http://jhs.mazums.ac.ir/article-1-1064-en.html
Department of Environmental Health Engineering, Faculty of Health, Health Sciences Research Centre, Mazandaran University of Medical Sciences, Sari. Iran. , Y.Hashempour@mazums.ac.ir
Abstract: (42 Views)
Background and Purpose: Microplastics (MPs) pose growing risks to water quality and to both environmental and public health due to their ability to transport hazardous chemicals. Identifying the domain sources of MP contamination is essential for effective monitoring and risk management in aquatic systems. This study aims to systematically review and integrate multivariate statistical approaches used for source identification and associated ecological and human health risk assessment.
Materials and Methods: Following PRISMA 2020 guidelines, we systematically searched PubMed, Scopus, Web of Science, and ScienceDirect for English-language studies published between 2015 and 2025. Eligible studies focused on MP source identification in aquatic environments and evaluated ecological or human health risks. We synthesized analytical approaches using principal component analysis (PCA), cluster analysis (CA), multidimensional scaling (MDS), and multiple correspondence analysis (MCA). The novelty lies in the explicit integration of these multivariate techniques with quantitative risk indices across diverse global aquatic settings.
Results: The review indicates that industrial discharges, urban wastewater, agricultural runoff, and atmospheric deposition are consistently identified as dominant MP sources worldwide. Among risk indicators, bioaccumulation potential, ingestion-based indices, and polymer-specific toxicity metrics were the most sensitive measures of ecological and human exposure. Integrating statistical source identification with risk indices enabled clearer detection of regional patterns and improved interpretation of heterogeneous datasets.
Conclusion: This systematic synthesis provides a robust foundation for environmental and public health decision‑making, demonstrating that integrated multivariate approaches enhance the accuracy of MP source identification and risk characterization. The findings offer practical insights for policymakers and support the development of targeted mitigation strategies for managing microplastic pollution in aquatic environments.
Materials and Methods: Following PRISMA 2020 guidelines, we systematically searched PubMed, Scopus, Web of Science, and ScienceDirect for English-language studies published between 2015 and 2025. Eligible studies focused on MP source identification in aquatic environments and evaluated ecological or human health risks. We synthesized analytical approaches using principal component analysis (PCA), cluster analysis (CA), multidimensional scaling (MDS), and multiple correspondence analysis (MCA). The novelty lies in the explicit integration of these multivariate techniques with quantitative risk indices across diverse global aquatic settings.
Results: The review indicates that industrial discharges, urban wastewater, agricultural runoff, and atmospheric deposition are consistently identified as dominant MP sources worldwide. Among risk indicators, bioaccumulation potential, ingestion-based indices, and polymer-specific toxicity metrics were the most sensitive measures of ecological and human exposure. Integrating statistical source identification with risk indices enabled clearer detection of regional patterns and improved interpretation of heterogeneous datasets.
Conclusion: This systematic synthesis provides a robust foundation for environmental and public health decision‑making, demonstrating that integrated multivariate approaches enhance the accuracy of MP source identification and risk characterization. The findings offer practical insights for policymakers and support the development of targeted mitigation strategies for managing microplastic pollution in aquatic environments.
Keywords: Environmental Monitoring, Microplastics, Principle Component Analysis, Risk Assessment, Water Pollution
Type of Study: Review Article |
Subject:
Environmental Health
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC-By-NC), which permits use, distribution, and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
Contact Information
