A novel spatiotemporal prediction approach to fill air pollution data gaps using mobile sensors, machine learning and citizen science techniques

A novel spatiotemporal prediction approach to fill air pollution data gaps using mobile sensors, machine learning and citizen science techniques

Abstract Particulate Matter (PM) air pollution poses significant threats to public health. We introduce a novel machine learning methodology to predict PM2.5 levels at 30 m long segments along the roads and at a temporal scale of 10 seconds. A hybrid dataset was curated from an intensive PM campaign...

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Bibliographic Details
Main Authors: Arunik Baruah, Dimitrios Bousiotis, Seny Damayanti, Alessandro Bigi, Grazia Ghermandi, O. Ghaffarpasand, Roy M. Harrison, Francis D. Pope
Format: Article
Language:English
Published: Nature Portfolio 2024-12-01
Series:npj Climate and Atmospheric Science
Online Access:https://doi.org/10.1038/s41612-024-00859-z
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https://doi.org/10.1038/s41612-024-00859-z

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