Air pollution is one of the prime public health concerns influencing infectious diseases. From May 13 to July 29, 2020(77 days), Tehran experienced unhealthy conditions caused by high levels of O3
and PM2.5, whereas other pollutants remained at safe levels. This study, for the first time, sought to investigate the linkage between not only PM pollutants, but also O3
and the number of daily confirmed new cases of COVID-19 in Tehran, Iran.
Materials and Methods:
In this experimental study, the data on air pollution were obtained from an average of 23 air quality monitoring stations scattered in 20 districts of Tehran municipality during the 77days. Pearson’s correlation and log-linear generalized additive model (GAM) were used to examine the association of the daily numbers of confirmed cases of COVID-19 and levels of O3
and PM2.5. Also, effective degrees of freedom (edf) used to determine the structural relationship between independent and dependent variables. GAM was performed by R software (version 3.5.3) with the “mgcv” package (version 1.8-27).
The results show a significant relationship betweenO3
, PM2.5, and COVID-19 (P <0.001), while other pollutants such as NO2
, PM10, CO, and SO2
remain at healthy levels during the study period. Besides, O3
and PM2.5 with edfs greater than 1 had significant nonlinear effects on the daily number of COVID-19 cases (P < 0.001).
Considering the results of this study, there is a positive nonlinear association between O3
, PM2.5, and daily confirmed cases of COVID-19. These findings suggest that O3
and PM2.5 levels should be considered as influential factors that can aggravate coronavirus infection.