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Assessing the COVID-19 Impact on Air Quality: A Machine Learning Approach.

Yves Rybarczyk1,2, Rasa Zalakeviciute2,3

  • 1The Department of Data and Information Sciences Dalarna University Falun Sweden.

Geophysical Research Letters
|March 31, 2021
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Summary

Machine learning models accurately quantified air quality changes during COVID-19 lockdowns in Quito, Ecuador. Pollution levels significantly dropped, especially in busy districts, before returning to pre-pandemic levels after restrictions eased.

Keywords:
COVID‐19air pollutionquarantine measuresurban air quality

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Area of Science:

  • Environmental Science
  • Data Science
  • Atmospheric Chemistry

Background:

  • Global research confirms COVID-19 lockdowns reduced air pollution.
  • Quantifying this reduction accurately remains a challenge.
  • Machine learning offers a promising approach for precise impact assessment.

Purpose of the Study:

  • To assess the impact of the COVID-19 outbreak and subsequent lockdowns on air quality in Quito, Ecuador.
  • To quantify the reduction in specific air pollutants (NO2, SO2, CO, PM2.5) during lockdown periods.
  • To evaluate the effectiveness of machine learning models in predicting and measuring these changes.

Main Methods:

  • Development and application of Gradient Boosting Machine (GBM) learning models.
  • Cross-validation using four years of pre-lockdown air quality data for model accuracy assessment.
  • Quantification of pollutant concentration changes during full and partial lockdown phases.

Main Results:

  • Machine learning models demonstrated high accuracy in estimating pre-lockdown pollution levels.
  • Significant air pollution reductions were observed: Nitrogen Dioxide (NO2) by -53%, Sulfur Dioxide (SO2) by -45%, Carbon Monoxide (CO) by -30%, and Particulate Matter (PM2.5) by -15%.
  • Pollution levels in traffic-heavy areas decreased most significantly and returned to pre-pandemic levels after partial relaxation of measures.

Conclusions:

  • Machine learning, specifically GBM, provides a reliable method for quantifying air quality changes during events like pandemics.
  • Lockdowns led to substantial, measurable improvements in air quality in Quito.
  • The study highlights the direct link between human activity, particularly traffic, and urban air pollution levels.