Articles | Volume 15, issue 1
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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Latest update: 22 Jul 2024

The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.

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.
Final-revised paper