Articles | Volume 15, issue 10
https://doi.org/10.5194/amt-15-3261-2022
https://doi.org/10.5194/amt-15-3261-2022
Research article
 | 
01 Jun 2022
Research article |  | 01 Jun 2022

Machine learning techniques to improve the field performance of low-cost air quality sensors

Tony Bush, Nick Papaioannou, Felix Leach, Francis D. Pope, Ajit Singh, G. Neil Thomas, Brian Stacey, and Suzanne Bartington

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Cited articles

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Short summary
Poor air quality is a human health risk which demands high-spatiotemporal-resolution monitoring data to manage. Low-cost air quality sensors present a convenient pathway to delivering these needs, compared to traditional methods, but bring methodological challenges which can limit operational ability. In this study within Oxford, UK, we develop machine learning methods to improve the quality of low-cost sensors for NO2, PM10 (particulate matter) and PM2.5 and demonstrate their effectiveness.