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Introduction to Microplastic and the Role of AI
Summary
Researchers reviewed how AI methods — including machine learning, deep learning, and IoT-integrated real-time monitoring — are transforming microplastic detection, classification, and pollution mapping, while identifying persistent challenges around data availability, computational cost, and the need for explainable models.
Microplastic pollution has become a global environmental challenge of today that directly threatens human health, biodiversity and sustainability. Microplastics – plastic particles smaller than 5 mm – have spread everywhere; in oceans, rivers, soil and air. They also reach the human body through the food chain, where they create health risks through ingestion, inhalation and toxic leaching. Traditional detection techniques such as microscopy, FTIR and Raman spectroscopy are useful but are time-consuming, costly and have limited scalability due to which their efficiency is low. Artificial Intelligence (AI) fills this gap. AI-based methods such as machine learning and deep learning make automated detection, classification and predictive modeling of microplastics possible. AI with sensors and IoT integration also enables real-time monitoring and pollution hotspot mapping. But challenges such as data availability, high computational cost and ethical concerns need to be addressed. In the future, through explainable AI, interdisciplinary collaborations and policy integration, AI could become a game-changer for microplastic management. Overall, this study establishes a strong bridge between microplastics and AI that can provide sustainable solutions for both environment and human health.