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Advancing environmental sustainability through emerging AI-based monitoring and mitigation strategies for microplastic pollution in aquatic ecosystems
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This review explores how artificial intelligence technologies, including machine learning, computer vision, and remote sensing, can improve the detection, tracking, and removal of microplastic pollution in waterways. Researchers found that AI-based approaches offer significant advantages over traditional monitoring methods for identifying microplastic distribution patterns. The study highlights the potential of AI-driven robotic systems to support more efficient and scalable environmental cleanup efforts.
Microplastics have become a significant pollutant in aquatic ecosystems, with serious implications for biodiversity, food safety, and environmental sustainability. This paper reviews the nature and sources of microplastic pollution, alongside its ecological and human health impacts. Recognizing the limitations of traditional monitoring and removal methods, the study explores emerging artificial intelligence (AI)-based strategies as innovative tools for improving environmental monitoring and pollution mitigation. The manuscript discusses how AI techniques such as machine learning, computer vision, and remote sensing can enhance the detection, classification, and prediction of microplastic distribution in water bodies. It also highlights the potential of AI-driven robotic systems in supporting targeted mitigation efforts. While these technologies show promise, further interdisciplinary research and development are necessary to fully realize their application in real-world environmental management. The integration of AI offers a proactive path toward achieving cleaner aquatic ecosystems and supporting global sustainability goals.
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Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.