Preprints
https://doi.org/10.5194/essd-2021-318
https://doi.org/10.5194/essd-2021-318

  13 Oct 2021

13 Oct 2021

Review status: this preprint is currently under review for the journal ESSD.

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

Melisa Diaz Resquin1,2,3,, Pablo Lichtig1,4, Diego Alessandrello1, Marcelo De Oto1, Darío Gómez1,2, Cristina Rössler1,5, Paula Castesana1,4, and Laura Dawidowski1,5, Melisa Diaz Resquin et al.
  • 1Comisión Nacional de Energía Atómica, Gerencia Química, Buenos Aires, Argentina
  • 2Facultad de Ingeniería, Universidad de Buenos Aires, Buenos Aires, Argentina
  • 3Center for Climate and Resilience Research (CR) 2 , Santiago, Chile
  • 4Consejo Nacional de Investigaciones Científicas y Técnicas, Buenos Aires, Argentina
  • 5Instituto de Investigación e Ingeniería Ambiental, Universidad Nacional de San Martín, Buenos Aires, Argentina
  • These authors contributed equally to this work.

Abstract. The COVID-19 (COronaVIrus Disease 2019) pandemic provided the unique opportunity to evaluate the role of a sudden and deep decline in air pollutant emissions in the ambient air of numerous cities worldwide. Argentina, in general, and the Metropolitan Area of Buenos Aires (MABA), in particular, were under strict control measures from March to May 2020. Private vehicle restrictions were intense, and primary pollutant concentrations decreased substantially. To quantify the changes in CO, NO, NO2, PM10, SO2 and O3 concentrations under the stay-at-home orders imposed against COVID-19, we compared the observations during the different lockdown phases with both observations during the same period in 2019 and concentrations that would have occurred under a business-as-usual (BAU) scenario under no restrictions. We employed a Random Forest (RF) algorithm to estimate the BAU concentration levels. This approach exhibited a high predictive performance based on only a handful of available indicators (meteorological variables, air quality concentrations and emission temporal variations) at a low computational cost. Results during testing showed that the model captured the observed daily variations and the diurnal cycles of these pollutants with a normalized mean bias (NMB) of less than 11 % and Pearson correlation coefficients of the diurnal variations of between 0.65 and 0.89 for all the pollutants considered. Based on the Random Forest results, we estimated that the lockdown implied concentration decreases of up to 47 % (CO), 60 % (NOx) and 36 % (PM10) during the strictest mobility restrictions. Higher O3 concentrations (up to 87 %) were also observed, which is consistent with the response in a VOC-limited chemical regime to the decline in NOx emissions. Relative changes with respect to the 2019 observations were consistent with those estimated with the Random Forest model, but indicated that larger decreases in primary pollutants and lower increases in O3 would have occurred. This points out to the need of accounting not only for the differences in emissions, but also in meteorological variables to evaluate the lockdown effects on air quality. The findings of this study may be valuable for formulating emission control strategies that do not disregard their implication on secondary pollutants. The data set used in this study and an introductory machine learning code are openly available at https://data.mendeley.com/datasets/h9y4hb8sf8/1 (Diaz Resquin et al., 2021).

Melisa Diaz Resquin et al.

Status: open (until 08 Dec 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Melisa Diaz Resquin et al.

Data sets

AQ-CNEA-CAC Air quality dataset (2019-2020): "A machine learning approach to address air quality changes during the COVID-19 lockdown in Buenos Aires, Argentina" Melisa Diaz Resquin, Diego Alessandrello, Marcelo De Oto, Pablo Lichtig, Hector Bajano, Alejandro Ponso, Facundo Bajano, Darío Gomez, Laura Dawidowski https://data.mendeley.com/datasets/h9y4hb8sf8/1

Model code and software

AQ-CNEA-CAC Air quality dataset (2019-2020): "A machine learning approach to address air quality changes during the COVID-19 lockdown in Buenos Aires, Argentina" Melisa Diaz Resquin, Diego Alessandrello, Marcelo De Oto, Pablo Lichtig, Hector Bajano, Alejandro Ponso, Facundo Bajano, Darío Gomez, Laura Dawidowski https://data.mendeley.com/datasets/h9y4hb8sf8/1

Melisa Diaz Resquin et al.

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Short summary
This work 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 COVID-19 lockdown phases. We also provide the first long term O3 and SO2 observational dataset for an urban-residential area of Buenos Aires in more than a decade and studied the responses of O3 to the reduction in emissions of its precursors because of its relevance regarding emission control.