Abstract

We have examined the air quality over China, India and demonstrated marked differences in levels of air pollution resulted from the COVID-19 restrictions during December–April, 2019–20 to that of 11 years mean of 2009–19. The criteria air quality indicators i.e., nitrogen dioxide (NO2), sulphur dioxide (SO2), Aerosol Index (AI) and aerosol optical depth (AOD) data are retrieved from the Ozone Monitoring Instrument (OMI), TROPOspheric Monitoring Instrument (TROPOMI), and MODerate Resolution Imaging Spectroradiometer (MODIS) sensor on the Terra and Aqua satellites, respectively. Over China, during COVID-19 lockdown a significant drop in columnar abundances of tropospheric NO2 (–37%), SO2 (–64%) and AOD (–8%) for 2020 in comparison to 11 years mean (2009–19) has been observed. A noticeable difference in NO2 column burden is seen over SE (–35%), NE (–33%), NW (–13%) and SW (–5%) China. Over the SE and NE China, both NO2 and SO2 levels decreased dramatically in 2020 from that of 2009–19, by more than 40% and 65%, respectively, because of both stricter regulations of emissions and less traffic activity due to reduced social and industrial activities during COVID-19 restrictions. In contrast, the curve of monthly mean tropospheric columnar burden of NO2 and SO2 over India has shown moderate reduction of 16% and 20%, respectively because lockdown came into effect much later in March 2020. The mean NO2 and SO2 over IGP region is found to be 25% higher than whole India’s mean concentration due to large scale urban settlement and crop burning events. The statistical t-test analysis results confirm significant (p < 0.05) improvements in AQ during lockdown. The COVID-19 pandemic provided an unprecedented opportunity to investigate such large-scale reduction in emissions of trace gases and aerosols. Therefore, it is important to further strengthen environmental policies to tackle air quality, human health, and climate change in this part of the world.

Highlights

  • On 31st December 2019, pneumonia of unknown origins first detected in Wuhan city of Hubei province of China reported to the World Health Organization (WHO, 2020)

  • Aura Ozone Monitoring Instrument (OMI) NO2 column densities have been utilized as an indicator for NOx, relationships between air quality and imposed restriction during COVID-19 over region of study

  • NO2 columnar concentration are investigated over China and India with differences observed between January–April, 2009–19 and January–April 2020

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Summary

INTRODUCTION

On 31st December 2019, pneumonia of unknown origins first detected in Wuhan city of Hubei province of China reported to the World Health Organization (WHO, 2020). In the early stage of the outbreak, patients in Wuhan, China had demonstrated association to a huge live animal and seafood market, pointing towards animal-to-person transmission (Andersen et al, 2020; CDC Situation Summary, 2020; Lai et al, 2020; Wu et al, 2020; Zhou et al, 2020). After H1N1 (2009), polio (2014), Ebola in West Africa (2014), Zika (2016) and Ebola in the Democratic Republic of Congo (2019), on 30th January 2020, COVID-19 outbreak declared as the sixth public health emergency of international concern by WHO on 20 January, 2020 This global concern needs the cooperation of health workers, public, and governments to prevent its spread at global level (Yoo, 2020). As of 30th April 2020, a total of 30,90445 confirmed cases were reported from 209 regions of the world which includes countries, areas and territories (WHO, 2020d). Staring from only 7 cases reported on 22 January 2020, the disease spread underwent a skyrocketing increase with a total of 3 million confirmed cases (WHO, 2020d)

Rationale
DATA AND METHODS
SATELLITE DATA DESCRIPTION
RESULTS AND DISCUSSIONS
Variation in NO2 Concentration over China
Variation in NO2 Concentration over India
Variation in SO2 Concentration over China
Variation in SO2 Concentration over India
Variation in AI over China and India
Variation in AOD over China and India
Comparison with Ground Observations
Statistical Significance Using Paired T-test
CONCLUSIONS
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