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Microplasticsin Aquatic Ecosystems: A MultitieredFramework for Ecological Risk Assessment and Mitigation
Summary
This review proposed a multitiered ecological risk assessment (ERA) framework for microplastics in aquatic ecosystems, integrating statistical, mechanistic, and machine learning models. The framework accounts for polymer type, particle size, surface chemistry, and environmental conditions to improve predictive accuracy for long-term ecosystem impacts.
Microplastics (MPs) are pervasive pollutants in aquatic ecosystems, posing significant ecological risks through bioaccumulation, trophic transfer, and toxicity to aquatic organisms. This study presents a multitiered framework for ecological risk assessment (ERA) of MPs, integrating exposure pathways, toxicity mechanisms, and ecosystem-level impacts. The framework employs a combination of statistical, mechanistic, and machine learning (ML)-based modeling approaches to quantify MP distribution, predict their interactions with biotic and abiotic components, and assess long-term ecological consequences. Key factors such as polymer type, particle size, surface chemistry, and environmental conditions are considered to enhance the predictive accuracy of risk assessment models. The study also explores mitigation strategies, including policy interventions, advanced filtration technologies, and bioremediation approaches, to reduce MP contamination and associated risks. By incorporating interdisciplinary methodologies, this framework aims to improve regulatory decision-making and conservation efforts, ensuring sustainable aquatic ecosystem management. The proposed approach offers a comprehensive tool for policymakers, researchers, and environmental managers to evaluate and mitigate MP-induced ecological risks effectively.
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