Abstract
The effect of environmental and socioeconomic conditions on the global pandemic of COVID-19 had been widely studied, yet their influence during the early outbreak remains less explored. Unraveling these relationships represents a key knowledge to prevent potential outbreaks of similar pathogens in the future. This study aims to determine the influence of socioeconomic, infrastructure, air pollution, and weather variables on the relative risk of infection in the initial phase of the COVID-19 pandemic in China. A spatio-temporal Bayesian zero-inflated Poisson model is used to test for the effect of 13 socioeconomic, urban infrastructure, air pollution, and weather variables on the relative risk of COVID-19 disease in 122 cities of China. The results show that socioeconomic and urban infrastructure variables did not have a significant effect on the relative risk of COVID-19. Meanwhile, COVID-19 relative risk was negatively associated with temperature, wind speed, and carbon monoxide, while nitrous dioxide and the human modification index presented a positive effect. Pollution gases presented a marked variability during the study period, showing a decrease of CO. These findings suggest that controlling and monitoring urban emissions of pollutant gases is a key factor for the reduction of risk derived from COVID-19.
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Funding
Alberto J. Alaniz was supported by ANID-PFCHA/Doctorado Nacional 2020-21201496. Pablo M. Vergara and Mario A. Carvajal were supported by Proyecto DICYT, Código 082375VE_Ayudante, Vicerrectoría de Investigación, Desarrollo e Innovación, USACH.
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Alberto J. Alaniz and Pablo M. Vergara analyzed data and wrote the manuscript, while Jorge G. Carvajal and Mario A. Carvajal reviewed the document and contributed to discussion section.
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Alaniz, A.J., Vergara, P.M., Carvajal, J.G. et al. Unraveling the socio-environmental drivers during the early COVID-19 pandemic in China. Environ Sci Pollut Res 30, 76253–76262 (2023). https://doi.org/10.1007/s11356-023-27969-0
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DOI: https://doi.org/10.1007/s11356-023-27969-0