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AI-Driven UAV Systems for Real-Time Detection and Monitoring of Airborne Microplastics in Dhaka, Bangladesh
Summary
Researchers proposed an AI-integrated UAV system using machine learning, hyperspectral imaging, and remote sensing for real-time detection of airborne microplastics in Dhaka, Bangladesh, with preliminary results suggesting up to 95% detection accuracy as a scalable alternative to laboratory-based methods.
Airborne microplastic (AMP) pollution is a growing environmental issue, particularly in urban centers like Dhaka, Bangladesh, where industrial emissions, plastic waste mismanagement, and severe air pollution contribute to the problem. Traditional microplastic detection methods rely on laboratory-based spectroscopy, which is time-consuming, expensive, and impractical for large-scale assessments. This research proposes an AI-integrated UAV system designed for real-time airborne microplastic detection, utilizing machine learning algorithms, hyperspectral imaging, and remote sensing technologies. UAVs provide a scalable, autonomous, and cost-effective solution for monitoring AMPs in urban environments. Preliminary results suggest that AI-powered UAVs can achieve up to 95% detection accuracy, making them an alternative to conventional lab-based methods. This study aims to bridge the gap between manual microplastic detection techniques and real-time environmental monitoring, contributing to both provincial pollution control in Dhaka and global sustainability efforts.