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To monitor the spread of the novel coronavirus (COVID-19), India, during the last week of March 2020, imposed national restrictions on the movement of its citizens (lockdown). Although India's economy was shut down due to restrictions, the nation observed a sharp decline in particulate matter (PM) concentrations. In recent years, Delhi has experienced rapid economic growth, leading to pollution, especially in urban and industrial areas. In this paper, we explored the linkages between air quality and the nationwide lockdown of the city of Delhi using a geographic information system (GIS)-based approach. Data from 37 stations were monitored from 12 March, 2020 to 2 April, 2020 and it was found that the Air Quality Index for the city was almost reduced by 37% and 46% concerning PM2.5 and PM10, respectively. The study highlights that, in regular conditions, the atmosphere's natural healing rate against anthropogenic activities is lower, as indicated by a higher AQI. However, during the lockdown, this sudden cessation of anthropogenic activities leads to a period in which the natural healing rate is greater than the induced disturbances, resulting in a lower AQI, and thus proving that this pandemic has given a small window for the environment to breathe and helped the districts of Delhi to recover from serious issues related to bad air quality. If such healing windows are incorporated into policy and decision-making, these can prove to be effective measures for controlling air pollution in heavily polluted regions of the World. [ABSTRACT FROM AUTHOR]

We use a state-of-the-art regional chemistry transport model (WRF-Chem v4.2.1) to simulate particulate air pollution over northern India during September–November 2016. This period includes a severe air pollution episode marked by exceedingly high levels of hourly PM 2.5 (particulate matter having an aerodynamic diameter ≤ 2.5 µ m) during 30 October to 7 November, particularly over the wider Indo-Gangetic Plain (IGP). We provide a comprehensive evaluation of simulated seasonal meteorology (nudged by ERA5 reanalysis products) and aerosol chemistry (PM 2.5 and its black carbon (BC) component) using a range of ground-based, satellite and reanalysis products, with a focus on the November 2016 haze episode. We find the daily and diurnal features in simulated surface temperature show the best agreement followed by relative humidity, with the largest discrepancies being an overestimate of night-time wind speeds (up to 1.5 m s -1) confirmed by both ground and radiosonde observations. Upper-air meteorology comparisons with radiosonde observations show excellent model skill in reproducing the vertical temperature gradient (r>0.9). We evaluate modelled PM 2.5 at 20 observation sites across the IGP including eight in Delhi and compare simulated aerosol optical depth (AOD) with data from four AERONET sites. We also compare our model aerosol results with MERRA-2 reanalysis aerosol fields and MODIS satellite AOD. We find that the model captures many features of the observed aerosol distributions but tends to overestimate PM 2.5 during September (by a factor of 2) due to too much dust, and underestimate peak PM 2.5 during the severe episode. Delhi experiences some of the highest daily mean PM 2.5 concentrations within the study region, with dominant components nitrate (∼25 %), dust (∼25 %), secondary organic aerosols (∼20 %) and ammonium (∼10 %). Modelled PM 2.5 and BC spatially correlate well with MERRA-2 products across the whole domain. High AOD at 550nm across the IGP is also well predicted by the model relative to MODIS satellite (r≥0.8) and ground-based AERONET observations (r≥0.7), except during September. Overall, the model realistically captures the seasonal and spatial variations of meteorology and ambient pollution over northern India. However, the observed underestimations in pollutant concentrations likely come from a combination of underestimated emissions, too much night-time dispersion, and some missing or poorly represented aerosol chemistry processes. Nevertheless, we find the model is sufficiently accurate to be a useful tool for exploring the sources and processes that control PM 2.5 levels during severe pollution episodes. [ABSTRACT FROM AUTHOR]

The present study investigates the reduction in nitrogen dioxide (NO2) levels using satellite-based (Sentinel-5P TROPOMI) and ground-based (Central Pollution Control Board) observations of 2020. The lockdown duration, monthly, seasonal and annual changes in NO2 were assessed comparing the similar time period in 2019. The study also examines the role of atmospheric parameters like wind speed, air temperature, relative humidity, solar radiation and atmospheric pressure in altering the monthly and annual values of the pollutant. It was ascertained that there was a mean reduction of ~ 61% (~ 66.5%), ~ 58% (~ 51%) in daily mean NO2 pollution during lockdown phase 1 when compared with similar period of 2019 and pre-lockdown phase in 2020 from ground-based (satellite-based) measurements. April month with ~ 57% (~ 57%), summer season with ~ 48% (~ 32%) decline and an annual reduction of ~ 20% (~ 18%) in tropospheric NO2 values were observed (p < 0.001) compared to similar time periods of 2019. It was assessed that the meteorological parameters remained almost similar during various parts of the year in 2019 and 2020, indicating a negligent role in reducing the values of atmospheric pollution, particularly NO2 in the study area. It was concluded that the halt in anthropogenic activities and associated factors was mainly responsible for the reduced values in the Delhi conglomerate. Similar work can be proposed for other pollutants to holistically describe the pollution scenario as an aftermath of COVID-19-induced lockdown. [ABSTRACT FROM AUTHOR]