Articles | Volume 10, issue 2
Geosci. Instrum. Method. Data Syst., 10, 265–285, 2021
https://doi.org/10.5194/gi-10-265-2021
Geosci. Instrum. Method. Data Syst., 10, 265–285, 2021
https://doi.org/10.5194/gi-10-265-2021

Research article 03 Nov 2021

Research article | 03 Nov 2021

Evaluation of multivariate time series clustering for imputation of air pollution data

Wedad Alahamade 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 gi-2021-11', Anonymous Referee #1, 15 Jun 2021
    • CC1: 'Reply on RC1', wedad Alahamade, 15 Jun 2021
      • AC1: 'Comment on gi-2021-11', wedad Alahamade, 22 Sep 2021
  • RC2: 'Comment on gi-2021-11', Anonymous Referee #2, 05 Sep 2021
    • AC1: 'Comment on gi-2021-11', wedad Alahamade, 22 Sep 2021
  • AC1: 'Comment on gi-2021-11', wedad Alahamade, 22 Sep 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by wedad Alahamade on behalf of the Authors (22 Sep 2021)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (22 Sep 2021) by Salvatore Grimaldi
RR by Anonymous Reviewer #2 (03 Oct 2021)
ED: Publish as is (04 Oct 2021) by Salvatore Grimaldi
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Short summary
The goal is to reduce the uncertainty in air quality assessment by imputing all missing pollutants in monitoring stations and identify where more measurements can be beneficial. The proposed approach is based on spatial or temporal similarity between stations. Our proposed approach enables us to impute and estimate plausible concentrations of multiple pollutants at stations across the UK, and the modelled concentrations from the selected models correlated well with the observed concentrations.