Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 12, issue 2
Articles | Volume 12, issue 2
https://doi.org/10.5194/amt-12-1325-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/amt-12-1325-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 12, issue 2
Research article
 | 
28 Feb 2019
Research article |  | 28 Feb 2019

An improved low-power measurement of ambient NO2 and O3 combining electrochemical sensor clusters and machine learning

Kate R. Smith, Peter M. Edwards, Peter D. Ivatt, James D. Lee, Freya Squires, Chengliang Dai, Richard E. Peltier, Mat J. Evans, Yele Sun, and Alastair C. Lewis

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Short summary
Clusters of low-cost, low-power atmospheric gas sensors were built into a sensor instrument to monitor NO2 and O3 in Beijing, alongside reference instruments, aiming to improve the reliability of sensor measurements. Clustering identical sensors and using the median sensor signal was used to minimize drift over short and medium timescales. Three different machine learning techniques were used for all the sensor data in an attempt to correct for cross-interferences, which worked to some degree.
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