Ambient air pollution and COVID-19 in Delhi, India: a time-series evidence

Abstract

Objectives: This study aimed to explore the short-term health effects of ambient air pollutants PM2.5, PM10, SO2, NO2, O3 and, CO on COVID-19 daily new cases and COVID-19 daily new deaths. Study design: A time-series design used in this study. Data were obtained from 1 April 2020 to 31 December 2020 in the National Capital Territory (NCT) of Delhi, India. Methods: The generalized additive models (GAMs) were applied to explore the associations of six air pollutants with COVID-19 daily new cases and COVID-19 daily new deaths. We also conducted sensitivity analysis using the population mobility variable in terms of lockdowns. Results: The GAMs revealed statistically significant associations of ambient air pollutants with COVID-19 daily new cases and COVID-19 daily new deaths. Besides, in sensitivity analysis after controlling for the population mobility, these associations became more prominent. Conclusions: These findings suggest that governments need to give greater considerations to regions with higher concentrations of PM2.5, PM10, SO2, NO2, O3 and, CO, since these areas may experience a more serious COVID-19 pandemic or, in general, any respiratory disease.

Publication
International Journal of Environmental Health Research, advance online publication
Abhishek Singh

My research interests include time series models, statistical computing and parametric inference.

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