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Introduction to Microplastic and the Role of AI
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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.
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AI‐Driven Technologies in Mitigation of Microplastic Pollution
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Researchers reviewed how AI-assisted detection methods address the accuracy, efficiency, and cost limitations of traditional microplastic identification, while also surveying how artificial intelligence is being applied to model and mitigate microplastic impacts on marine ecosystems and human health including metabolic and respiratory effects.
Microplastic Monitoring Using Artificial Intelligence
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Researchers outlined how deep learning and explainable AI are replacing slow, labor-intensive manual methods for microplastic identification, enabling real-time, large-scale detection while addressing key concerns about model transparency, regulatory implications, and the scalability of AI-driven environmental monitoring.
The Role of Artificial Intelligence in Microplastic Pollution Studies and Management
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This review explores how artificial intelligence is transforming microplastic research, from automating detection in microscopy images and spectral analysis to predicting how plastics interact with pollutants and living organisms. AI-powered sensors and real-time monitoring systems are also being integrated into wastewater treatment to reduce microplastic release, making the technology a powerful tool for both understanding and managing plastic pollution.
Role of AI in Microplastic Pollution Detection and management studies
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This review assessed how artificial intelligence approaches—including machine learning and deep learning—are being applied to detect, identify, and monitor microplastics in environmental and biological samples. The authors found AI substantially accelerates microplastic characterization workflows but that training data quality and standardization across studies remains a limiting factor.
Advancing microplastic analysis in the era of artificial intelligence: From current applications to the promise of generative AI
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This review summarizes existing research on how artificial intelligence can assist microplastic research, including collection using drones, identification using computer vision, and predicting pollution levels using machine learning. It also discusses current gaps, such as limited data and model transparency issues, noting these need to be addressed for AI to improve microplastic pollution research.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.