Relationship between air pollutants, meteorological variables and cases of COVID-19 in European cities

Abstract

The concern of knowing the relationship between air pollution levels and deaths from viral diseases is not new. A 2003 study found that patients with SARS were 84% more likely to die if they lived in areas with high contamination levels. According to the latest rapid assessment report by the European Center for Disease Prevention and Control, Italy and Spain are the worst affected countries among the European Union nations by the COVID-19 pandemic. As of 31 May 2020, the total number of infected cases per million population of Spain is 5120.95 and 3848.11 for Italy. This study focuses on the cities of Madrid, Barcelona, Milan and Rome during the period before, during and after the lockdown (from March to May 2020). Separate hierarchical Bayesian temporal models are fitted for each city and pollutant, incorporating meteorological covariates and a smooth temporal random effect to capture unobserved time-dependent variation. The goal of this work is to identify the relationship between air pollution levels with the expected value of COVID-19-infected cases and meteorological covariates. Results show that meteorological variables and the COVID-19 case indicator are significantly associated with air pollutant concentrations across cities, although the magnitude of the COVID-19 effect is small. The temporal component explains a substantial part of the variability in pollution levels, highlighting the importance of accounting for time dependence. This Bayesian analysis advances existing statistical approaches and provides new insights into the effects of air quality in urban environments.

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Identifiers

ORCID for Somnath Chaudhuri: orcid.org/0000-0003-4899-1870

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