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 PM_2.5 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 PM_2.5 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 PM_2.5 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.

环境空气污染在全球范围内增加 COVID-19 发病率和死亡率的风险中发挥着重要作用。本研究旨在了解全球空气污染对 COVID-19 死亡率的多尺度空间影响。基于 2020 年 4 月 1 日至 2020 年 12 月 31 日期间全球 6 个国家的 46 个城市，使用基于 7、14 和 21 天滞后效应的贝叶斯时空分层模型来量化 NO 的相对风险₂和 PM _2.5对 COVID-19 每日死亡率的影响，说明了基于全球和城市层面的气象和人类流动性变化的影响。结果表明，空气污染与 COVID-19 死亡率呈正相关，NO _{2的相对风险}PM _2.5范围从 1.006 到 1.014 和从 1.002 到 1.004，具有 7 天、14 天和 21 天的滞后效应。然而，对于单个城市的分析，空气污染与每日死亡率之间存在正相关和负相关，表明 NO ₂和 PM _2.5的相对风险分别在 0.754 和 1.245 之间以及 0.888 和 1.032 之间。本研究证明了城市之间空气污染风险的差异，并进一步暗示有必要探索多尺度空气污染与死亡率关系的不确定性。