Articles | Volume 15, issue 1
https://doi.org/10.5194/essd-15-189-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-189-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
10 Jan 2023
Data description paper |
| 10 Jan 2023
| 10 Jan 2023
A machine learning approach to address air quality changes during the COVID-19 lockdown in Buenos Aires, Argentina
Melisa Diaz Resquin
CORRESPONDING AUTHOR
Comisión Nacional de Energía Atómica, Gerencia Química, Buenos Aires, Argentina
Facultad de Ingeniería, Universidad de Buenos Aires, Buenos Aires, Argentina
Modeling and Observing Systems, Center for Climate and Resilience Research (CR), Santiago, Chile
Pablo Lichtig
Comisión Nacional de Energía Atómica, Gerencia Química, Buenos Aires, Argentina
Comisión de Ambiente, Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina
Diego Alessandrello
Comisión Nacional de Energía Atómica, Gerencia Química, Buenos Aires, Argentina
Marcelo De Oto
Comisión Nacional de Energía Atómica, Gerencia Química, Buenos Aires, Argentina
Darío Gómez
Comisión Nacional de Energía Atómica, Gerencia Química, Buenos Aires, Argentina
Facultad de Ingeniería, Universidad de Buenos Aires, Buenos Aires, Argentina
Cristina Rössler
Comisión Nacional de Energía Atómica, Gerencia Química, Buenos Aires, Argentina
Instituto de Investigación e Ingeniería Ambiental, Universidad Nacional de San Martín, Buenos Aires, Argentina
Paula Castesana
Comisión Nacional de Energía Atómica, Gerencia Química, Buenos Aires, Argentina
Comisión de Ambiente, Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina
Misión Ambiente, YPF Tecnología S. A. (Y-TEC), Buenos Aires, Argentina
Laura Dawidowski
CORRESPONDING AUTHOR
Comisión Nacional de Energía Atómica, Gerencia Química, Buenos Aires, Argentina
Instituto de Investigación e Ingeniería Ambiental, Universidad Nacional de San Martín, Buenos Aires, Argentina
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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.
We explored the performance of the random forest algorithm to predict CO, NOx, PM10, SO2, and O3...
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