Association of air pollution and meteorological variables with the two waves of COVID-19 pandemic in Delhi: A critical analysis

Abstract

Various countries across the globe have been affected by different COVID-19 waves at different points in time and with varying levels of virulence. With the backdrop of the two COVID-19 waves that broke out in Delhi, this study examines the variations in the concentrations of criteria pollutants, air quality, and meteorological variables across the waves and their influence on COVID-19 morbidity/mortality. Descriptive statistics, violin plots, and Spearman rank correlation tests were employed to assess the variations in environmental parameters and investigate their associations with COVID-19 incidence under the two waves. The susceptible-infected-recovered model and multiple linear regression were used to assess the wave-wise basic reproduction number (R0) and infection spreading trajectory of the virus. Our results show that the first wave in Delhi had three successive peaks and valleys, and the first peak of the second wave was the tallest, indicating the severity of per-day infection cases. During the analysed period (April 2020 and April 2021), concentrations of criteria pollutants varied across the waves, and air pollution was substantially higher during the second wave. In addition, the results revealed that during the second wave, NO₂ maintained a significant negative relationship with COVID-19 (cases per day), while SO₂ had a negative relationship with COVID-19 (cumulative cases) during the first wave. Our results also show a significant positive association of O₃ with COVID-19 deaths during the first wave and cumulative cases and deaths during the second wave. The study indicates that a higher relative humidity in Delhi had a negative relation with COVID-19 cumulative cases and mortality during the first wave. The study confirms that the estimated R0 was marginally different during the two waves, and the spread of COVID-19 new cases followed a cubic growth trajectory. The findings of this study provide valuable information for policymakers in handling COVID-19 waves in various cities.

Keywords: Air quality; COVID-19 successive waves; Environmental variables; Infection spreading trajectory; Reproduction rate.

Figure 1

COVID-19 per day and cumulative cases during (a) first wave (1 March 2020 to 16 February 2021) (b) second wave (17 February 2021 onwards) in Delhi.

Figure 2

Incidence of COVID-19 cases in Delhi (a) daily cumulative, April 2020 (b) daily cumulative, April 2021 (c) daily cumulative, April 2020 (log scale) (d) daily cumulative, April 2021 (log scale) (e) daily incremental incidence, April 2020 (f) daily incremental incidence, April 2021 (g) recovery cases per day, April 2020 (h) recovery cases per day, April 2021 (i) daily deaths, April 2020 (moving mean), (j) daily deaths April 2021 (moving mean).

Figure 3

Violin plots of (a) PM_2.5 (μg m⁻³)_, (b) PM₁₀ (μg m⁻³), (c) NO₂ (μg m⁻³), (d) SO₂ (μg m⁻³), (e) CO (μg m⁻³), (f) O₃ (μg m⁻³), (g) Relative humidity (%), (h) Temperature (ºC).

Figure 4

The mean (a) PM_2.5 (b) PM₁₀ (c) NO₂ (d) SO₂ (e) O₃ (f) CO concentrations of Delhi during April 2020 and April 2021 of COVID-19 outbreak.

Figure 5

Spearman correlation matrix, (a) April 2020 and (b) April 2021 generated using R program. Colour code, Blue: positive correlations, Red: negative correlations, White: no correlation.

Figure 6

Curve fitting under different models for the observed number of COVID-19 cumulative cases for (a) April 2020 and (b) April 2021.