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. 2020 Oct 21;20(1):1585.
doi: 10.1186/s12889-020-09669-3.

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

Bo Wang  1 , Jiangtao Liu  1 , Yanlin Li  1 , Shihua Fu  1 , Xiaocheng Xu  1 , Lanyu Li  1 , Ji Zhou  2 , Xingrong Liu  1 , Xiaotao He  1 , Jun Yan  3 , Yanjun Shi  4 , Jingping Niu  1 , Yong Yang  5 , Yiyao Li  6 , Bin Luo  7   8   9 , Kai Zhang  10   11
Affiliations

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

Bo Wang et al. BMC Public Health. .

Abstract

Background: 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.

Methods: We obtained daily confirmed cases of COVID-19, air particulate matter (PM2.5, 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, PM2.5 and MSI on daily confirmed COVID-19 cases.

Results: 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 PM2.5 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 PM2.5 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.

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

Keywords: COVID-19; Generalized additive models; Particulate matter; Population mobility.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Trends of daily PM levels, MSI, and confirmed COVID-19 cases in 63 cities of China from January 01 to March 02, 2020
Fig. 2
Fig. 2
Associations between MSI and the COVID-19 confirmed cases in 63 cities of China from January 01 to March 02, 2020. Note: The results were expressed as the relative risk (RR) and 95% confidence intervals (CIs) for each 1 unit increase in MSI
Fig. 3
Fig. 3
The exposure-response curves of MSI, PM10, PM2.5 and the daily COVID-19 confirmed cases in 63 cities of China from January 01 to March 02, 2020. Note: (a) MSI; (b) PM10; (c) PM2.5. The X-axis is the values of MSI, PM10, PM2.5 in lag 07 or lag 014 days, Y-axis is the predicted log relative risk (RR), is shown by th color solid line, and the color dotted lines represent the 95% confidence interval (CI). The R2 represents the fitting effect, and the closer R2 is to 1, the better the fitting effect of the model
Fig. 4
Fig. 4
Associations between PM10 and the COVID-19 confirmed cases in 63 cities of China from January 01 to March 02, 2020. Note: The results were expressed as the relative risk (RR) and 95% confidence intervals (CIs) for each 10 μg/m3 increase in PM10 concentrations
Fig. 5
Fig. 5
Associations between PM2.5 and the COVID-19 confirmed cases in 63 cities of China from January 01to March 02, 2020. Note: The results were expressed as the relative risk (RR) and 95% confidence intervals (CIs) for each 10 μg/m3 increase in PM2.5 concentrations
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