The COVID-19 caused by the severe acute respiratory syndrome coronavirus was reported in China in December 2019. The severity and lethality of this disease have been linked to poor air quality indicators such as tropospheric nitrogen dioxide (NO2) and dust surface mass concentration particulate matter (PM2.5) as possible contributors. The Arab League has 22 member countries and is home to almost 420 million people. The primary objective of this study is to assess the relationship between NO2, PM2.5 and vertical pressure velocity (hereafter: OMEGA) (extracted from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) database), socio-economic factors (the population and geographic area of each member country) and COVID-19 deaths using Bayesian model averaging. The total plausible models (25) were estimated. The results show that the posterior inclusion probability (PIP), which indicates the probability that a particular indicator is included in the best model, was 0.69, 0.94, 0.68, 0.47, and 0.61 for OMEGA, PM2.5, NO2, geographical area, and population, respectively, meaning that these variables are important contributors in predicting COVID-19 fatalities in the Arab League states. This study shows that atmospheric satellite measurements from MERRA-2 datasets are capable of being used to quantify trace gases in pandemic studies.
Keywords: Arab League States; Bayesian Model Averaging; COVID-19; Geospatial mapping; Nitrogen Dioxide; PM2.5.
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.