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Computational Approaches for Identification of Micro/Nano-Plastic Pollution
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This chapter reviews computational methods for identifying micro- and nanoplastic pollution, including machine learning and computer vision analysis of microscopy images, molecular simulation approaches for studying environmental interactions, and remote sensing for locating suspected pollution hotspots. The authors highlight that high-throughput computing has substantially eased identification of miniaturized plastics whose small size makes traditional methods challenging.
The dissemination of miniaturized plastics, both micro- and nano-plastics, athwart diverse ecosystems is an argument of global apprehension. The accretion of these plastics is due to their chemical steadiness. In arrears to their trivial size, frequently identification of miniaturized plastics is very problematic. The foremost approaches for identification of micro- and-nano plastics rely upon their visual inspection through microscopy and chemical analysis. The advent of high-throughput computing has eased the detection of miniaturized plastic pollution. Machine learning and computer vision methods are being readily applied for analyzing microscopy images to identify and classify microplastics. Molecular simulation methods are also being applied for studying the interaction between environment and microplastics. Additionally, remote sensing methods have also been used to collect and analyze suspected locations of microplastic pollution.
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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.