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

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

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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 2.5 , PM 10 ), 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 10 , PM 2.5 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/m 3 increase in the concentration of PM 10 and PM 2.5 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 PM 10 and PM 2.5 were at lag 014, and the RRs of each 10 μg/m 3 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.

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  1. Version published to 10.1186/s12889-020-09669-3
  2. ScreenIT

    SciScore for 10.1101/2020.04.09.20060137: (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
    The meta-analysis was performed based on STATA software (version 11, StataCorp LLC, USA).
    STATA
    suggested: (Stata, RRID:SCR_012763)
    StataCorp
    suggested: (Stata, RRID:SCR_012763)

    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:
    Nevertheless, there are some potential limitations in this study. First, there are some other factors affecting the incidence of COVID-19, such as public health interventions, but we examined the impact of air pollution after controlling the population migration and meteorological factors. Second, there were changing COVID-19 case definitions at different stage of the epidemic, which may affect the confirmed counts. To reduce the bias caused by the changing definition, we included 72 cities with confirmed more than 50 cases in our analysis. In addition, the diagnosis of COVID-19 cases is much influenced by government screening standards, especially in Wuhan, so we didn’t include Wuhan in this study. Finally, the study was only conducted in China, while the COVID-19 is recognized as an emergent world pandemic, so our conclusions need future evaluation with global data. Despite of these limitations, our study provide some evidence from multiple cities across China and increased the knowledge over understanding the effect of PM pollution on COVID-19. In conclusion, our results indicate that airborne PM likely increase the risk of having COVID-19 in China. However, the ecological fallacy and many uncontrolled confounding effects like different public health interventions may have biased our results. Further investigations including global data would be critical to study the association between COVID-19 and air pollution.

    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

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.04.09.20060137v1 on medRxiv