Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 17, issue 13
Articles | Volume 17, issue 13
https://doi.org/10.5194/amt-17-3917-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-3917-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 13
Research article
 | 
03 Jul 2024
Research article |  | 03 Jul 2024

Transferability of machine-learning-based global calibration models for NO2 and NO low-cost sensors

Ayah Abu-Hani, Jia Chen, Vigneshkumar Balamurugan, Adrian Wenzel, and Alessandro Bigi

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Short summary
This study examined the transferability of machine learning calibration models among low-cost sensor units targeting NO2 and NO. The global models were evaluated under similar and different emission conditions. To counter cross-sensitivity, the study proposed integrating O3 measurements from nearby reference stations, in Switzerland. The models show substantial improvement when O3 measurements are incorporated, which is more pronounced when in regions with elevated O3 concentrations.
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