Airborne particulate matter, population mobility and COVID-19: a multi-city study in China

Coronavirus disease 2019 (COVID-19) is an emerging infectious disease, which has caused numerous deaths and health problems worldwide. This study aims to examine the effects of airborne particulate matter (PM) pollution and population mobility on COVID-19 across China.

We obtained daily confirmed cases of COVID-19, air particulate matter (PM_2.5, PM₁₀), weather parameters such as ambient temperature (AT) and absolute humidity (AH), and population mobility scale index (MSI) in 63 cities of China on a daily basis (excluding Wuhan) from January 01 to March 02, 2020. Then, the Generalized additive models (GAM) with a quasi-Poisson distribution were fitted to estimate the effects of PM₁₀, PM_2.5 and MSI on daily confirmed COVID-19 cases.

We found each 1 unit increase in daily MSI was significantly positively associated with daily confirmed cases of COVID-19 in all lag days and the strongest estimated RR (1.21, 95% CIs:1.14 ~ 1.28) was observed at lag 014. In PM analysis, we found each 10 μg/m³ increase in the concentration of PM₁₀ and PM_2.5 was positively associated with the confirmed cases of COVID-19, and the estimated strongest RRs (both at lag 7) were 1.05 (95% CIs: 1.04, 1.07) and 1.06 (95% CIs: 1.04, 1.07), respectively. A similar trend was also found in all cumulative lag periods (from lag 01 to lag 014). The strongest effects for both PM₁₀ and PM_2.5 were at lag 014, and the RRs of each 10 μg/m³ increase were 1.18 (95% CIs:1.14, 1.22) and 1.23 (95% CIs:1.18, 1.29), respectively.

Population mobility and airborne particulate matter may be associated with an increased risk of COVID-19 transmission.