Institute of Environment and Sustainable Development in Agriculture, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
Academy for Multidisciplinary Studies, Capital Normal University, Beijing 100048, China.
Int J Environ Res Public Health. 2022 Jan 4;19(1):531. doi: 10.3390/ijerph19010531.
The 2019 novel coronavirus disease (COVID-19) has become a severe public health and social problem worldwide. A limitation of the existing literature is that multiple environmental variables have not been frequently elaborated, which is why the overall effect of the environment on COVID-19 has not been conclusive. In this study, we used generalized additive model (GAM) to detect the relationship between meteorological and air pollution variables and COVID-19 in four urban agglomerations in China and made comparisons among the urban agglomerations. The four urban agglomerations are Beijing-Tianjin-Hebei (BTH), middle reaches of the Yangtze River (MYR), Yangtze River Delta (YRD), and the Pearl River Delta (PRD). The daily rates of average precipitation, temperature, relative humidity, sunshine duration, and atmospheric pressure were selected as meteorological variables. The PM, PM, sulfur dioxide (SO), nitrogen dioxide (NO), ozone (O), and carbon monoxide (CO) contents were selected as air pollution variables. The results indicated that meteorological and air pollution variables tended to be significantly correlated. Moreover, the nature of the relationship between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and meteorological and air pollution variables (i.e., linear or nonlinear) varied with urban agglomerations. Among the variance explained by GAMs, BTH had the highest value (75.4%), while MYR had the lowest value (35.2%). The values of the YRD and PRD were between the above two, namely 45.6% and 62.2%, respectively. The findings showed that the association between SARS-CoV-2 and meteorological and air pollution variables varied in regions, making it difficult to obtain a relationship that is applicable to every region. Moreover, this study enriches our understanding of SARS-CoV-2. It is required to create awareness within the government that anti-COVID-19 measures should be adapted to the local meteorological and air pollution conditions.
2019 年新型冠状病毒病(COVID-19)已成为全球严重的公共卫生和社会问题。现有文献的一个局限性是,没有经常详细阐述多个环境变量,这就是为什么环境对 COVID-19 的总体影响没有定论的原因。在这项研究中,我们使用广义加性模型(GAM)来检测中国四个城市群的气象和空气污染变量与 COVID-19 之间的关系,并对城市群之间进行了比较。这四个城市群是北京-天津-河北(BTH)、长江中游(MYR)、长江三角洲(YRD)和珠江三角洲(PRD)。选择日平均降水量、温度、相对湿度、日照时间和大气压力作为气象变量。选择 PM、PM、二氧化硫(SO)、二氧化氮(NO)、臭氧(O)和一氧化碳(CO)含量作为空气污染变量。结果表明,气象和空气污染变量往往呈显著相关。此外,SARS-CoV-2 与气象和空气污染变量之间的关系性质(即线性或非线性)因城市群而异。在 GAMs 解释的方差中,BTH 的值最高(75.4%),而 MYR 的值最低(35.2%)。YRD 和 PRD 的值介于两者之间,分别为 45.6%和 62.2%。研究结果表明,SARS-CoV-2 与气象和空气污染变量之间的关联在不同地区存在差异,很难获得适用于每个地区的关系。此外,本研究丰富了我们对 SARS-CoV-2 的认识。需要让政府意识到,抗 COVID-19 措施应适应当地的气象和空气污染条件。