Articles | Volume 15, issue 2
Atmos. Meas. Tech., 15, 321–334, 2022
https://doi.org/10.5194/amt-15-321-2022
Atmos. Meas. Tech., 15, 321–334, 2022
https://doi.org/10.5194/amt-15-321-2022

Research article 21 Jan 2022

Research article | 21 Jan 2022

Evaluating uncertainty in sensor networks for urban air pollution insights

Daniel R. Peters 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-2021-210', Laurent Spinelle, 24 Aug 2021
    • EC1: 'Reply on RC1', Dominik Brunner, 25 Sep 2021
    • AC1: 'Response to Reviewer 1', Daniel Peters, 29 Oct 2021
  • RC2: 'Comment on amt-2021-210', Anonymous Referee #2, 11 Sep 2021
    • AC2: 'Response to Reviewer 2', Daniel Peters, 29 Oct 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Daniel Peters on behalf of the Authors (29 Oct 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish subject to minor revisions (review by editor) (17 Nov 2021) by Dominik Brunner
AR by Daniel Peters on behalf of the Authors (19 Nov 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (21 Nov 2021) by Dominik Brunner
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Short summary
We present more than 2 years of NO2 pollution measurements from a sensor network in Greater London and compare results to an extensive network of expensive reference-grade monitors. We show the ability of our lower-cost network to generate robust insights about local air pollution. We also show how irregularities in sensor performance lead to some uncertainty in results and demonstrate ways that future users can characterize and mitigate uncertainties to get the most value from sensor data.