Institute of Occupational Health and Environmental Health, School of Public Health, Lanzhou University, Lanzhou, Gansu, 730000, People's Republic of China.
Shanghai Key Laboratory of Meteorology and Health, Shanghai Meteorological Bureau, Shanghai, 200030, People's Republic of China.
BMC Public Health. 2020 Oct 21;20(1):1585. doi: 10.1186/s12889-020-09669-3.
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, PM), 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, PM 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/m increase in the concentration of PM and PM 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 and PM were at lag 014, and the RRs of each 10 μg/m 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.
新型冠状病毒病（COVID-19）是一种新兴传染病，已在全球范围内造成众多死亡和健康问题。本研究旨在探讨中国空气中颗粒物（PM）污染和人口流动对 COVID-19 的影响。
我们从 2020 年 1 月 1 日至 3 月 2 日，每天在中国 63 个城市（不包括武汉）获得 COVID-19 确诊病例、空气颗粒物（PM、PM）、环境温度（AT）和绝对湿度（AH）等气象参数以及人口流动规模指数（MSI）。然后，使用广义加性模型（GAM）拟合了具有拟泊松分布的模型，以估计 PM、PM 和 MSI 对每日确诊 COVID-19 病例的影响。
我们发现，每日 MSI 每增加 1 个单位，与所有滞后天数的每日确诊 COVID-19 病例显著正相关，最强的估计 RR（1.21，95%CI：1.14-1.28）在滞后 014 天观察到。在 PM 分析中，我们发现 PM 和 PM 浓度每增加 10μg/m，与 COVID-19 确诊病例呈正相关，估计最强的 RR（均在滞后 7 天）分别为 1.05（95%CI：1.04-1.07）和 1.06（95%CI：1.04-1.07）。在所有累积滞后期（从滞后 01 到滞后 014）也观察到类似的趋势。对于 PM 和 PM，最强的影响都在滞后 014 天，RR 为每增加 10μg/m，分别为 1.18（95%CI：1.14-1.22）和 1.23（95%CI：1.18-1.29）。
人口流动和空气中的颗粒物可能与 COVID-19 传播的风险增加有关。
