Modelling the Effect of COVID-19 Lockdown on Air Pollution in Makkah Saudi Arabia with a Supervised Machine Learning Approach.

To reduce the spread of COVID-19, lockdowns were implemented in almost every single country in the world including Saudi Arabia. In this paper, the effect of COVID-19 lockdown on O, NO, and PM in Makkah was analysed using air quality and meteorology data from five sites. Two approaches were employed: (a) comparing raw measured concentrations for the lockdown period in 2019 and 2020; and (b) comparing weather-corrected concentrations estimated by the machine learning approach with observed concentrations during the lockdown period. According to the first approach, the average levels of PM and NO decreased by 12% and 58.66%, respectively, whereas the levels of O increased by 68.67%. According to the second approach, O levels increased by 21.96%, while the levels of NO and PM decreased by 13.40% and 9.66%, respectively. The machine learning approach after removing the effect of changes in weather conditions demonstrated relatively less reductions in the levels of NO and PM and a smaller increase in the levels of O. This showed the importance of adjusting air pollutant levels for meteorological conditions. O levels increased due to its inverse correlation with NO, which decreased during the lockdown period.