Articles | Volume 15, issue 10
Atmos. Meas. Tech., 15, 3261–3278, 2022
https://doi.org/10.5194/amt-15-3261-2022
Atmos. Meas. Tech., 15, 3261–3278, 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 et al.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-282', Anonymous Referee #1, 19 Nov 2021
    • AC1: 'Reply on RC1', Tony Bush, 24 Nov 2021
    • AC2: 'Reply on RC1 - response to detailed comments from authors', Tony Bush, 27 Jan 2022
  • RC2: 'Comment on amt-2021-282', Anonymous Referee #2, 14 Dec 2021
    • AC3: 'Reply on RC2 - response to detailed comments from authors', Tony Bush, 31 Jan 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Tony Bush on behalf of the Authors (08 Feb 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (09 Feb 2022) by Pierre Herckes
RR by Anonymous Referee #1 (24 Feb 2022)
RR by Anonymous Referee #2 (04 Mar 2022)
ED: Publish subject to minor revisions (review by editor) (04 Mar 2022) by Pierre Herckes
AR by Tony Bush on behalf of the Authors (21 Mar 2022)  Author's response    Author's tracked changes    Manuscript
ED: Reconsider after major revisions (22 Mar 2022) by Pierre Herckes
AR by Tony Bush on behalf of the Authors (08 Apr 2022)  Author's response    Author's tracked changes
ED: Publish as is (29 Apr 2022) by Pierre Herckes
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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.