The Impact of the COVID-19 Lockdown on the Urban Air Quality: A Machine Learning Approach.

Abstract: ‘‘SARS-CoV-2’’ which is responsible for the current pandemic of COVID-19 disease was first reported from Wuhan, China, on 31 December 2019. Since then, to prevent its propagation around the world, a set of rapid and strict countermeasures have been taken. While most of the researchers around the world initiated their studies on the Covid-19 lockdown effect on air quality and concluded pollution reduction, the most reliable methods that can be used to find out the reduction of the pollutants in the air are still in debate. In this study, we performed an analysis on how Covid-19 lockdown procedures impacted the air quality in selected cities i.e. New Delhi, Diepkloof, Wuhan, and London around the world. The results show that the air quality index (AQI) improved by 43% in New Delhi,18% in Wuhan,15% in Diepkloof, and 12% in London during the initial lockdown from the 19th of March 2020 to 31st May 2020 compared to that of four-year pre-lockdown. Furthermore, the concentrations of four main pollutants, i.e., NO2, CO, SO2, and PM2.5 were analyzed before and during the lockdown in India. The quantification of pollution drop is supported by statistical measurements like the AVOVA Test and the Permutation Test. Overall, 58%, 61%,18% and 55% decrease is observed in NO2, CO,SO2, and PM2.5 concentrations, respectively. To check if the change in weather has played any role in pollution level reduction or not we analyzed how weather factors are correlated with pollutants using a correlation matrix. Finally, machine learning regression models are constructed to assess the lockdown impact on air quality in India by incorporating weather data. Gradient Boosting is performed well in the Prediction of drop-in PM2.5 concentration on individual cities in India. By comparing the feature importance ranking by regression models supported by correlation factors with PM2.5.This study concludes that COVID-19 lockdown has a significant effect on the natural environment and air quality improvement.