Articles | Volume 12, issue 2
Atmos. Meas. Tech., 12, 1325–1336, 2019
https://doi.org/10.5194/amt-12-1325-2019

Special issue: In-depth study of air pollution sources and processes within...

Atmos. Meas. Tech., 12, 1325–1336, 2019
https://doi.org/10.5194/amt-12-1325-2019

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 et al.

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Kate Smith on behalf of the Authors (04 Feb 2019)  Author's response    Manuscript
ED: Publish as is (09 Feb 2019) by Pingqing Fu
AR by Kate Smith on behalf of the Authors (11 Feb 2019)  Author's response    Manuscript
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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.