Assessing the COVID‐19 Impact on Air Quality: A Machine Learning Approach. Issue 4 (16th February 2021) - Rybarczyk, Yves; Zalakeviciute, Rasa
Abstract: The worldwide research initiatives on Corona Virus disease 2019 lockdown effect on air quality agree on pollution reduction, but the most reliable method to pollution reduction quantification is still in debate. In this paper, machine learning models based on a Gradient Boosting Machine algorithm are built to assess the outbreak impact on air quality in Quito, Ecuador. First, the precision of the prediction was evaluated by cross‐validation on the four years prelockdown, showing a high accuracy to estimate the real pollution levels. Then, the changes in pollution are quantified. During the full lockdown, air pollution decreased by −53 ± 2%, −45 ± 11%, −30 ± 13%, and −15 ± 9% for NO2, SO2, CO, and PM2.5, respectively. The traffic‐busy districts were the most impacted areas of the city. After the transition to the partial relaxation, the concentrations have nearly returned to the levels as before the pandemic. The quantification of pollution drop is supported by an assessment of the prediction confidence. Key Points: A data driven modeling is applied to quantify the reduction of air pollution during the Corona Virus disease 2019 outbreak in Quito, Ecuador The accuracy of the models is high (mean PCC = 0.78), especially for predicting NO2 (mean PCC = 0.87) and CO (mean PCC = 0.86) The average drop of pollution during the lockdown is: −53% for NO2, −45% for SO2, −30% for CO, and −15% for PM2.5 The industrial areas are less impacted by the quarantine than the traffic and residential districts The concentration of pollution tends to return to usual levels, as soon as the relaxed restriction is implemented