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Smart and Sustainable Technological Framework for Microplastic Pollution Mitigation
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Researchers proposed a smart technological framework for microplastic pollution mitigation that integrates IoT-based monitoring, machine learning analytics, and eco-friendly remediation technologies. The system uses low-power sensors for continuous detection of microplastic contamination and sustainable filtration mechanisms with biodegradable adsorbent materials for cleanup. The framework emphasizes modular design and renewable energy integration to support long-term deployment across diverse aquatic environments.
Microplastic pollution has become a critical environmental concern due to its persistence, widespread distribution, and adverse impacts on aquatic ecosystems and human health. Large waterbodies and waterways serve as major accumulation and transport channels for microplastics originating from industrial effluents, urban runoff, and degradation of plastic waste. Existing remediation techniques often lack scalability, sustainability, or real-time adaptability. This paper presents a smart and sustainable technological framework for microplastic pollution mitigation, focusing on environmentally friendly and energy-efficient solutions. The proposed framework integrates intelligent monitoring, data-driven analytics, and eco-friendly remediation technologies to enable effective microplastic management. Low-power sensing and Internet of Things (IoT)–based monitoring systems are utilized for continuous detection and spatial assessment of microplastic contamination. Machine learning–based data analysis enhances detection accuracy, trend analysis, and hotspot identification. For mitigation, sustainable filtration mechanisms, biodegradable adsorbent materials, and nature-inspired separation techniques are incorporated to minimize ecological disruption and secondary pollution. The framework also emphasizes modular design, renewable energy integration, and scalability to support long-term deployment across diverse aquatic environments.
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