The emergence of a novel human corona virus disease (COVID-19) has been declared as a pandemic by the World Health Organization. One of the mechanisms of airborne transmission of the severe acute respiratory syndrome - corona virus ( SARS-CoV-2 ) amid humans is through direct ejection of droplets via sneezing, coughing and vocalizing. Nevertheless, there are ample evidences of the persistence of infectious viruses on inanimate surfaces for several hours to a few days. Through a critical review of the current literature and a preliminary analysis of the link between SARS-CoV-2 transmission and air pollution in the affected regions, we offer a perspective that polluted environment could enhance the transmission rate of such deadly viruses under moderate-to-high humidity conditions. The aqueous atmospheric aerosols offer a conducive surface for adsorption/absorption of organic molecules and viruses onto them, facilitating a pathway for higher rate of transmission under favourable environmental conditions. This mechanism partially explains the role of polluted air besides the exacerbation of chronic respiratory diseases in the rapid transmission of the virus amongst the public. Hence, it is stressed that more ambitious policies towards a cleaner environment are required globally to nip in the bud what could be the seeds of a fatal outbreak such as COVID-19.

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A stratosphere–troposphere (ST) wind profiler radar operating at a high VHF range (205 MHz) installed at Cochin (10.03°N, 76.33°E), India, provides high‐resolution winds in time and altitude extending from 315 m to 20 km. We evaluated the horizontal and vertical winds obtained from atmospheric global reanalyses (ERA5, ERA‐Interim, MERRA‐2 and NCEP) data and the high‐resolution regional model (WRF) output winds with the ST radar (STR) wind observations at Cochin. The data for one whole year, from 1 August 2018 to 31 July 2019, for 0600 UTC and 1200 UTC were used. Wind data appraisal has been carried out at 24 levels from 315 m to 18.5 km altitude for the reanalyses datasets and 29 levels for model‐simulated winds in different seasons. Vertical profiles of horizontal winds from reanalysis and model‐simulated data somewhat catch the major changes observed from STR. The zonal winds match very well with the STR, whereas meridional winds show moderate similarity. Higher correlations in zonal winds are found during the summer monsoon, whereas meridional winds show apparent likeness during winter. The ERA5, ERA‐Interim, and model‐simulated winds have better agreement with the STR winds. Observed vertical winds from STR displays large fluctuations, while the estimated vertical winds in reanalysis are highly smoothed. Vertical profiles of vertical winds from the reanalysis datasets are not uniform. The reliability of vertical winds in reanalysis and WRF model output values are dubious compared to that of the directly observed winds from STR. The study indicates that improved quality of vertical and meridional winds in reanalysis data is needed by assimilating directly observed data from the available advanced facilities.

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