Understanding the true effects of the COVID-19 lockdown on air pollution by means of machine learning()

During March 2020, most European countries implemented lockdowns to restrict the transmission of SARS-CoV-2, the virus which causes COVID-19 through their populations. These restrictions had positive impacts for air quality due to a dramatic reduction of economic activity and atmospheric emissions. In this work, a machine learning approach was designed and implemented to analyze local air quality improvements during the COVID-19 lockdown in Graz, Austria. The machine learning approach was used as a robust alternative to simple, historical measurement comparisons for various individual pollutants. Concentrations of NO(2) (nitrogen dioxide), PM(10) (particulate matter), O(3) (ozone) and O(x) (total oxidant) were selected from five measurement sites in Graz and were set as target variables for random forest regression models to predict their expected values during the city’s lockdown period. The true vs. expected difference is presented here as an indicator of true pollution during the lockdown. The machine learning models showed a high level of generalization for predicting the concentrations. Therefore, the approach was suitable for analyzing reductions in pollution concentrations. The analysis indicated that the city’s average concentration reductions for the lockdown period were: -36.9 to −41.6%, and −6.6 to −14.2% for NO(2) and PM(10,) respectively. However, an increase of 11.6–33.8% for O(3) was estimated. The reduction in pollutant concentration, especially NO(2) can be explained by significant drops in traffic-flows during the lockdown period (−51.6 to −43.9%). The results presented give a real-world example of what pollutant concentration reductions can be achieved by reducing traffic-flows and other economic activities.