Ambient air pollution plays a significant role in an increased risk of incidence and mortality of COVID-19 on a global scale. This study aims to understand the multiscale spatial effect of global air pollution on COVID-19 mortality. Based on forty-six cities from six countries worldwide between 1 April 2020 and 31 December 2020, a Bayesian space–time hierarchical model was used based on the lag effects of seven, fourteen, and twenty-one days to quantify the relative risks of NO2 and PM2.5 on the daily death rates of COVID-19, accounting for the effect of meteorological and human mobility variability based on global and city level. Results show that positive correlations between air pollution and COVID-19 mortality are observed, with the relative risks of NO2 and PM2.5 ranging from 1.006 to 1.014 and from 1.002 to 1.004 with the lag effects of seven, fourteen, and twenty-one days. For the individual city analysis, however, both positive and negative associations are found between air pollution and daily mortality, showing that the relative risks of NO2 and PM2.5 are between 0.754 and 1.245 and between 0.888 and 1.032, respectively. The discrepancies in air pollution risks among cities were demonstrated in this study and further allude to the necessity to explore the uncertainty in the multiscale air pollution–mortality relationship.
- air pollution
- Bayesian space–time hierarchical model
- multiscale analysis
ASJC Scopus subject areas
- Geography, Planning and Development
- Earth-Surface Processes