Articles | Volume 15, issue 1
https://doi.org/10.5194/essd-15-189-2023
https://doi.org/10.5194/essd-15-189-2023
Data description paper
 | 
10 Jan 2023
Data description paper |  | 10 Jan 2023

A machine learning approach to address air quality changes during the COVID-19 lockdown in Buenos Aires, Argentina

Melisa Diaz Resquin, Pablo Lichtig, Diego Alessandrello, Marcelo De Oto, Darío Gómez, Cristina Rössler, Paula Castesana, and Laura Dawidowski

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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 essd-2021-318', Anonymous Referee #1, 13 Dec 2021
    • AC1: 'Reply on RC1', Melisa Diaz Resquin, 29 Aug 2022
  • RC2: 'Comment on essd-2021-318', Anonymous Referee #2, 20 Jun 2022
    • AC2: 'Reply on RC2', Melisa Diaz Resquin, 29 Aug 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Melisa Diaz Resquin on behalf of the Authors (30 Nov 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (05 Dec 2022) by Nellie Elguindi
RR by Anonymous Referee #1 (12 Dec 2022)
RR by Anonymous Referee #2 (12 Dec 2022)
ED: Publish subject to technical corrections (13 Dec 2022) by Nellie Elguindi
AR by Melisa Diaz Resquin on behalf of the Authors (16 Dec 2022)  Author's response   Manuscript 
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Short summary
We explored the performance of the random forest algorithm to predict CO, NOx, PM10, SO2, and O3 air quality concentrations and comparatively assessed the monitored and modeled concentrations during the COVID-19 lockdown phases. We provide the first long-term O3 and SO2 observational dataset for an urban–residential area of Buenos Aires in more than a decade and study the responses of O3 to the reduction in the emissions of its precursors because of its relevance regarding emission control.
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