Articles | Volume 14, issue 8
Research article
18 Aug 2021
Research article |  | 18 Aug 2021

Machine learning calibration of low-cost NO2 and PM10 sensors: non-linear algorithms and their impact on site transferability

Peer Nowack, Lev Konstantinovskiy, Hannah Gardiner, and John Cant

Model code and software

Code in support of Nowack et al. (2021) in Atmospheric Measurement Techniques (Version 2) P. Nowack and L. Konstantinovskiy

Short summary
Machine learning (ML) calibration techniques could be an effective way to improve the performance of low-cost air pollution sensors. Here we provide novel insights from case studies within the urban area of London, UK, where we compared the performance of three ML techniques to calibrate low-cost measurements of NO2 and PM10. In particular, we highlight the key issue of the method-dependent robustness in maintaining calibration skill after transferring sensors to different measurement sites.