Associations of Ambient Air Pollutants and Meteorological Factors With COVID-19 Transmission in 31 Chinese Provinces: A Time Series Study

  1. Han Cao
  2. Bingxiao Li
  3. Tianlun Gu
  4. Xiaohui Liu
  5. Kai Meng
  6. Ling Zhang

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Abstract

Evidence regarding the effects of environmental factors on COVID-19 transmission is mixed. We aimed to explore the associations of air pollutants and meteorological factors with COVID-19 confirmed cases during the outbreak period throughout China. The number of COVID-19 confirmed cases, air pollutant concentrations, and meteorological factors in China from January 25 to February 29, 2020, (36 days) were extracted from authoritative electronic databases. The associations were estimated for a single-day lag as well as moving averages lag using generalized additive mixed models. Region-specific analyses and meta-analysis were conducted in 5 selected regions from the north to south of China with diverse air pollution levels and weather conditions and sufficient sample size. Nonlinear concentration–response analyses were performed. An increase of each interquartile range in PM 2.5 , PM 10 , SO 2 , NO 2 , O 3 , and CO at lag4 corresponded to 1.40 (1.37–1.43), 1.35 (1.32–1.37), 1.01 (1.00–1.02), 1.08 (1.07–1.10), 1.28 (1.27–1.29), and 1.26 (1.24–1.28) ORs of daily new cases, respectively. For 1°C, 1%, and 1 m/s increase in temperature, relative humidity, and wind velocity, the ORs were 0.97 (0.97–0.98), 0.96 (0.96–0.97), and 0.94 (0.92–0.95), respectively. The estimates of PM 2.5 , PM 10 , NO 2 , and all meteorological factors remained significantly after meta-analysis for the five selected regions. The concentration–response relationships showed that higher concentrations of air pollutants and lower meteorological factors were associated with daily new cases increasing. Higher air pollutant concentrations and lower temperature, relative humidity and wind velocity may favor COVID-19 transmission. Controlling ambient air pollution, especially for PM 2.5 , PM 10 , NO 2 , may be an important component of reducing risk of COVID-19 infection. In addition, as winter months are arriving in China, the meteorological factors may play a negative role in prevention. Therefore, it is significant to implement the public health control measures persistently in case another possible pandemic.

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  1. Version published to 10.1177/00469580211060259
  2. ScreenIT

    SciScore for 10.1101/2020.06.24.20138867: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All of the statistical analyses were performed using R version 3.5.3 (https://www.r-project.org/).
    https://www.r-project.org/
    suggested: (R Project for Statistical Computing, RRID:SCR_001905)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our study also had some limitations. First, this is a province-level ecological study without considering the implementation ability of COVID-19 control policy, urbanization rate and availability of medical resources, which may affect COVID-19 transmission and confound our results. Second, demographic variables (e.g., age, gender and history of cardiopulmonary diseases) were not considered in our study due to a deficiency of detailed information for each infectious case. Third, our study was conducted with a certain range of air pollutant concentrations and meteorological factors. The results may not be applicable to countries with different air pollution levels and weather conditions. Therefore, global studies are needed to verify our results.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  3. Version published to 10.1101/2020.06.24.20138867v1 on medRxiv