Modelling the Global Air Quality Conditions in Perspective of COVID-19 Stimulated Lockdown Periods Using Remote Sensing Data

Abstract COVID-19 outbreak across the world has invited forced lockdown conditions, which causes a huge economic landslide. But it has brought an opportunity for restoring the environment of its own which may cause ecosystem well-being. Focusing on the second issue, the present work has intended to explore the streams of air quality change based on some quality components and develop a multi-date air quality state (AQS) model for the world in consequence of emergency lockdown. It is very clear from the result that amid lockdown aerosol optical depth (AOD), sulfur dioxide (SO2), ozone, carbon monoxide (CO), particulate matter (PM2.5), and black carbon (BC) concentration level have been significantly reduced in fully lockdown countries. AQS is considerably improved amid lockdown. Hotspots of COVID-19 were under unhealthy, very unhealthy air quality class in pre lockdown condition, but amid lockdown, these countries have been shifted to good and moderate healthy air quality classes