tetano
Editor, Senior Moderator
BMC Public Health
. 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[SUP] 1 [/SUP], Jiangtao Liu[SUP] 1 [/SUP], Yanlin Li[SUP] 1 [/SUP], Shihua Fu[SUP] 1 [/SUP], Xiaocheng Xu[SUP] 1 [/SUP], Lanyu Li[SUP] 1 [/SUP], Ji Zhou[SUP] 2 [/SUP], Xingrong Liu[SUP] 1 [/SUP], Xiaotao He[SUP] 1 [/SUP], Jun Yan[SUP] 3 [/SUP], Yanjun Shi[SUP] 4 [/SUP], Jingping Niu[SUP] 1 [/SUP], Yong Yang[SUP] 5 [/SUP], Yiyao Li[SUP] 6 [/SUP], Bin Luo[SUP] 7 8 9 [/SUP], Kai Zhang[SUP] 10 11 [/SUP]
Affiliations
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 (PM[SUB]2.5[/SUB], PM[SUB]10[/SUB]), 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 PM[SUB]10[/SUB], PM[SUB]2.5[/SUB] 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/m[SUP]3[/SUP] increase in the concentration of PM[SUB]10[/SUB] and PM[SUB]2.5[/SUB] 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 PM[SUB]10[/SUB] and PM[SUB]2.5[/SUB] were at lag 014, and the RRs of each 10 μg/m[SUP]3[/SUP] 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.
. 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[SUP] 1 [/SUP], Jiangtao Liu[SUP] 1 [/SUP], Yanlin Li[SUP] 1 [/SUP], Shihua Fu[SUP] 1 [/SUP], Xiaocheng Xu[SUP] 1 [/SUP], Lanyu Li[SUP] 1 [/SUP], Ji Zhou[SUP] 2 [/SUP], Xingrong Liu[SUP] 1 [/SUP], Xiaotao He[SUP] 1 [/SUP], Jun Yan[SUP] 3 [/SUP], Yanjun Shi[SUP] 4 [/SUP], Jingping Niu[SUP] 1 [/SUP], Yong Yang[SUP] 5 [/SUP], Yiyao Li[SUP] 6 [/SUP], Bin Luo[SUP] 7 8 9 [/SUP], Kai Zhang[SUP] 10 11 [/SUP]
Affiliations
- PMID: 33087097
- DOI: 10.1186/s12889-020-09669-3
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 (PM[SUB]2.5[/SUB], PM[SUB]10[/SUB]), 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 PM[SUB]10[/SUB], PM[SUB]2.5[/SUB] 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/m[SUP]3[/SUP] increase in the concentration of PM[SUB]10[/SUB] and PM[SUB]2.5[/SUB] 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 PM[SUB]10[/SUB] and PM[SUB]2.5[/SUB] were at lag 014, and the RRs of each 10 μg/m[SUP]3[/SUP] 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.