Global COVID-19 pandemic trends and their relationship with meteorological variables, air pollutants and socioeconomic aspects

Yi Han; Wenwu Zhao; Paulo Pereira

doi:10.1016/j.envres.2021.112249

Global COVID-19 pandemic trends and their relationship with meteorological variables, air pollutants and socioeconomic aspects

Environ Res. 2022 Mar;204(Pt C):112249. doi: 10.1016/j.envres.2021.112249. Epub 2021 Nov 3.

Authors

Yi Han¹, Wenwu Zhao², Paulo Pereira³

Affiliations

¹ State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; Institute of Land Surface System and Sustainable Development, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China. Electronic address: meaning@mail.bnu.edu.cn.
² State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; Institute of Land Surface System and Sustainable Development, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China. Electronic address: zhaoww@bnu.edu.cn.
³ Environmental Management Center, Mykolas Romeris University, Ateities g. 20, LT-08303, Vilnius, Lithuania. Electronic address: pereiraub@gmail.com.

Abstract

Meteorological variables, air pollutants, and socioeconomic factors are associated with COVID-19 transmission. However, it is unclear what impact their interactions have on COVID-19 transmission, whether their impact on COVID-19 transmission is linear or non-linear, and where the inflexion points are. This study examined 1) the spatial and temporal trends in COVID-19 monthly infection rate of new confirmed cases per 100,000 people (R_n) in 188 countries/regions worldwide from March to November 2020; 2) the linear correlation between meteorological variables (temperature (T), rainfall (R), wind speed (WS), relative humidity (RH), air pressure (AP)), air pollutants (nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO), ozone (O₃)) and socioeconomic aspects (population density (PD), gross domestic product per capita (GDP), domestic general government health expenditure per capita (GHE)) and R_n, and 3) the interaction and non-linear effects of the different variables on R_n, based on GeoDetector and Boosted regression tree. The results showed that the global R_n had was spatially clustered, and the average R_n increased From March to November 2020. Global R_n was negatively correlated with meteorological variables (T, R, WS, AP) and positively correlated with air pollutants (NO₂, SO₂, O₃) and socioeconomic aspects (GDP, GHE). The interaction of SO₂ and O₃, SO₂ and RH, and O₃ and T strongly affected R_n. The variables effect on COVID-19 transmission was non-linear, with one or more inflexion points. The findings of this work can provide a basis for developing a global response to COVID-19 for global sustainable development.

Keywords: Air pollutants; Boosted regression tree; COVID-19; GeoDetector; Meteorological variables; Socioeconomic aspects.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Air Pollutants* / analysis
Air Pollution* / analysis
COVID-19*
Humans
Ozone*
Pandemics
Particulate Matter / analysis
SARS-CoV-2
Socioeconomic Factors

Substances

Air Pollutants
Particulate Matter
Ozone