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      COVID-19 mortality and exposure to airborne PM 2.5: A lag time correlation

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          Abstract

          COVID-19 has escalated into one of the most serious crises in the 21st Century. Given the rapid spread of SARS-CoV-2 and its high mortality rate, here we investigate the impact and relationship of airborne PM 2.5 to COVID-19 mortality. Previous studies have indicated that PM 2.5 has a positive relationship with the spread of COVID-19. To gain insights into the delayed effect of PM 2.5 concentration (μgm −3) on mortality, we focused on the role of PM 2.5 in Wuhan City in China and COVID-19 during the period December 27, 2019 to April 7, 2020. We also considered the possible impact of various meteorological factors such as temperature, precipitation, wind speed, atmospheric pressure and precipitation on pollutant levels. The results from the Pearson's correlation coefficient analyses reveal that the population exposed to higher levels of PM 2.5 pollution are susceptible to COVID-19 mortality with a lag time of >18 days. By establishing a generalized additive model, the delayed effect of PM 2.5 on the death toll of COVID-19 was verified. A negative correction was identified between temperature and number of COVID-19 deaths, whereas atmospheric pressure exhibits a positive correlation with deaths, both with a significant lag effect. The results from our study suggest that these epidemiological relationships may contribute to the understanding of the COVID-19 pandemic and provide insights for public health strategies.

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          Author and article information

          Journal
          Sci Total Environ
          Sci Total Environ
          The Science of the Total Environment
          Published by Elsevier B.V.
          0048-9697
          1879-1026
          29 October 2021
          29 October 2021
          : 151286
          Affiliations
          [a ]State Key Laboratory of Coal Resources and Safe Mining, College of Geoscience and Surveying Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
          [b ]School of Earth and Environmental Sciences, Cardiff University, Park Place, Cardiff CF10 3AT, UK
          [c ]School of Earth Sciences and Resources, China University of Geoscience Beijing, Beijing 100083, China
          [d ]Department of Earth Science, University of Adelaide, Adelaide, SA 5005, Australia
          [e ]Department of Civil and Environmental, Universidad de la Costa, Calle 58 #55-66, 080002 Barranquilla, Atlántico, Colombia
          [f ]School of Biosciences, Cardiff University, Museum Avenue, Cardiff CF10 3AX, Wales, UK
          Author notes
          [* ]Corresponding author.
          Article
          S0048-9697(21)06364-6 151286
          10.1016/j.scitotenv.2021.151286
          8553633
          34743816
          a0f5f237-ecfa-49aa-be36-686636c2d6fc
          Crown Copyright © 2021 Published by Elsevier B.V. All rights reserved.

          Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

          History
          : 17 July 2021
          : 7 October 2021
          : 23 October 2021
          Categories
          Article

          General environmental science
          correlation analysis,covid-19,lag time,mortality,pm2.5
          General environmental science
          correlation analysis, covid-19, lag time, mortality, pm2.5

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