Short-term exposure to ambient air pollution in association with COVID-19 of two clusters in South Korea

Abstract

Objectives: This study aimed to examine the association between six air pollutants and COVID-19 infection in two main clusters, which accounted for 83% of total confirmed cases in Korea.

Methods: We collected the data on daily confirmed cases between February 24, 2020 and September 12, 2020. Data on six air pollutants (PM_2.5 , PM₁₀ , O₃ , NO₂ , CO and SO₂ ) and four meteorological factors (temperature, wind speed, humidity and air pressure) were obtained on seven days prior to the research period. The generalised additive model and the distributed lag nonlinear model were applied to generate the relative risks (RRs) and 95% confidence intervals (CIs) for the associations. Pooled estimates for clusters were obtained by applying a random-effects model.

Results: We found that NO₂ concentration was positively associated with daily confirmed cases in both Seoul-Gyeonggi and Daegu-Gyeongbuk clusters, with RRs (95% CIs) of 1.22 (1.03-1.44) and 1.66 (1.25-2.19), respectively. However, SO₂ concentration was observed to be associated with daily confirmed cases in the Seoul-Gyeonggi cluster only (RR = 1.30, 95% CI = 1.10-1.54), whereas PM_2.5 and CO concentrations were observed to be associated with daily confirmed cases in the Daegu-Gyeongbuk cluster only (RR = 1.14, 95% CI = 1.02-1.27 and RR = 1.30, 95% CI = 1.15-1.48, respectively).

Conclusions: Our data found that NO₂ concentration was positively associated with daily confirmed cases in both clusters, whereas the effect of PM_2.5 , CO and SO₂ on COVID-19 infection in two clusters was different.

Keywords: COVID-19; air pollution; cluster; generalised additive model.