Impact of COVID-19 Control Measures on Trace Gases (NO2, HCHO and SO2) and Aerosols over India during Pre-monsoon of 2020

Received: June 11, 2020
Revised: September 6, 2020
Accepted: October 9, 2020

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Cite this article:

Biswas, M.S., Ayantika, D.C. (2021). Impact of COVID-19 Control Measures on Trace Gases (NO₂, HCHO and SO₂) and Aerosols over India during Pre-monsoon of 2020. Aerosol Air Qual. Res. 21, 200306. https://doi.org/10.4209/aaqr.2020.06.0306

HIGHLIGHTS

• Impact of nationwide COVID19 lockdown on pollutants and aerosols over India.
• 5.6% reduction in NO₂ column density over southern India during pre-monsoon 2020.
• Negative changes in HCHO over south India, with higher magnitude in May.
• Decrease in AOD from reduced emissions, marked decline across India in May 2020.
• Reduced aerosols increase insolation and possibly intensifies pre-monsoon rains.

Keywords: COVID-19, Nitrogen dioxide (NO2), Formaldehyde (HCHO), Aerosol Optical Depth (AOD), Surface solar radiation

1 INTRODUCTION

Since the first case of COVID-19 was registered in Wuhan, China, in December, 2019 (WHO, 2020a) it has rapidly transmitted across the world by ‘SARS-CoV-2’. Till the first week of June, a total of 182 countries and many other territories with confirmed cases of 6,799,713 (WWO, 2020b) have been affected by this novel infectious disease. With WHO declaring COVID-19 as pandemic on 12^th March 2020, countries around the world have implemented strict control measures on social and economic activities to prevent the spread of the virus ‘SARS-CoV-2’ causing COVID-19. India, the second-most populous country in the world, has implemented a nationwide lockdown on 25^th March 2020 (https://www.pmindia.gov.in/en/news_updates/pm-calls-for-complete-lockdown-of-entire-nation-for-21-days/; Last access: 08/06/2020). Since then, India has extended the restrictive measures in four stages till 31^st May 2020. During this period, except the essential and emergency services; all industrial activities, transport services (road, rail and air), educational institutions, hospitality services, social gatherings, etc., were suspended thereby limiting the movement of 1.3 billion people. The extended control measures on social and economic activities enforced from last week of March to end of May has a direct bearing on the anthropogenically emitted trace gases and aerosols over the Indian subcontinent.

Nitrogen dioxide (NO₂), formaldehyde (HCHO) and sulfur dioxide (SO₂) are among the important trace gases and pollutants having an adverse effect on human health (Burnett et al., 2004; WHO, 2013; Ranzi et al., 2014; Rodriguez-Villamizar et al., 2015; U.S. EPA, 2015; Zhu et al., 2017). While fossil fuel combustion in automobiles and heavy industries, thermal power plants and biomass burning (Stavrakou et al., 2008; Lamsal et al., 2011; Beirle et al., 2003) are the primary sources of anthropogenic NO₂, coal-based thermal power plants and industries are chief emitters of anthropogenic SO₂ (Garg et al., 2001, 2002; Pandey et al., 2014). The formation of HCHO occurs through oxidative degradation of higher volatile organic compounds (VOCs) emitted from biogenic (Fu et al., 2007; Smedt et al., 2010) as well as anthropogenic (Herndon et al., 2005; Zhu et al., 2017) sources. Along with impacting the air quality, these trace gases also play a key role in atmospheric chemistry. NO₂ and HCHO contribute to tropospheric O₃ formation (Crutzen, 1970, 1979; Carter and Atkinson, 1987) and participates in atmospheric redox reaction controlling the oxidative capacity of the atmosphere. NO₂ and SO₂, both are responsible for acid rains (Irwin and Williams, 1988) and acts as aerosol precursors (Seinfeld and Pandis, 2006; Lin and Cheng, 2007).

Over India, along with trace gases, atmospheric aerosols impact local pollution (Mitra and Sharma, 2002; Mönkkönen et al., 2005) while having a profound influence on the regional climate through changes in solar radiation, atmospheric temperature, precipitation processes and the hydrological cycle (Krishnan and Ramanathan, 2002; Ramanathan et al., 2005; Bollasina et al., 2011; Ganguly et al., 2012; Krishnan et al., 2016; Soni et al., 2016). The aerosol concentrations over India are dominated by wind-blown dust, sea salt (natural) and black carbon (BC), organic carbon (OC), sulphate, nitrate produced from urbanization, industrialization, biomass burning, agricultural activities, etc. Substantial contribution of aerosol constituents occurs from industries and thermal power plants, while on-road diesel vehicles are the largest contributor of BC and OC in the transport sector (Reddy and Venkataraman, 2002; Sadavarte and Venkataraman, 2014). The primary source of anthropogenic aerosols over major Indian cities are diesel engine exhaust, gasoline engine exhaust, road dust, coal combustion and biomass combustion (Chowdhury et al., 2007). Interestingly, during the pre-monsoon season (March–April–May), BC, soot and dust accumulate over Indo-Gangetic Plains (IGP), Tibetan Plateau and aids in invigoration on monsoon precipitation (Lau and Kim, 2006; Lau et al., 2006). Additionally, uplifted BC from northern India gets deposited over Himalayan snow and leads to accelerated snowmelt (Lau and Kim, 2010; Kopacz et al., 2011).

In recent decades, emerging India has witnessed rapid growth in population, industrialization, urbanization, which resulted in rising emissions over the region (Ramanathan et al., 2001; Pandey et al., 2014; Sadavarte and Venkataraman, 2014). Using long-term observations an associated increase in NO₂ (Mahajan et al., 2015; Georgoulias et al., 2019), tropospheric HCHO column density (Smedt et al., 2010; Mahajan et al., 2015) and SO₂ column amount (Lu et al., 2013; Qu et al., 2019) has been reported over India. An increase in anthropogenic aerosol loading along with a significant positive trend in aerosol optical depth (AOD) has also been observed over the subcontinent (Satheesh et al., 2017). The recent rising concentrations of atmospheric pollutants over urbanized India is a cause for concern (Guttikunda and Calori, 2013; Gurjar et al., 2016). Along with having consequences on human health (Cohen et al., 2017; Balakrishnan et al., 2019), pollutants impact the radiation balance as well as cloud processes (Ramanathan et al., 2005; Rosenfeld et al., 2008; Vasilkov et al., 2009). In the backdrop of the sustained increase in aerosols and trace gases, the disruption of economic and social activities due to the nationwide lockdown is a unique opportunity to investigate and better recognize the effect of human activity on atmospheric pollutants and aerosols. Over other regions around the world, studies (Devi et al., 2020; Fan et al., 2020; Siciliano et al., 2020; Xu et al., 2020) have noted a reduction in pollution and improvement of air quality during the lockdown period. Recent studies focusing on the early stages of the lockdown studies have reported a decrease in atmospheric pollutants over different regions in the Indian subcontinent (Devara et al., 2020; Jain and Sharma, 2020; Mahato et al., 2020; Sharma et al., 2020). Devara et al. (2020) reported an improvement in air quality index in Delhi and nearby region during initial days of COVID-19 induced lockdown (during 25–30 March, 2020). Jain and Sharma (2020) (Jain and Sharma, 2020) reported a decrease in NO₂ concentration by ~51% during 25^th March–6^th April 2020 compared to 10^th–20^th March 2020 over Delhi. Mahato et al. (2020) reported ~53% and ~18% reduction in NO₂ and SO₂ concentration respectively during 25^th March–14^th April, 2020 compared to 3^rd March–24^th March, 2020 in Delhi. Sharma et al. (2020) reported overall 18% decrease in NO₂ over India during 16^th March–14^th, April 2020. Ranjan et al. (2020) reported ~45% decrease in AOD over India compared to long term average (2010-2019) during 25^th March–15^th May, 2020. Metya et al. (2020) reported overall ~17% decrease in NO₂ over India in March 2020 compared to 2010-2020 mean. They also reported ~17% decrease in SO₂ over eastern India during lockdown period.

This present study aims to investigate the variations in NO₂, HCHO, SO₂ and AOD distribution over India during the pre-monsoon (March–April–May) 2020, which includes the entire country-wide extended lockdown period of more than two months compared same period during 2017–2019. Unlike the other recent studies over India investigating the effect of COVID-19 induced lockdown which used station data, this study uses satellite observation which helps understanding the large scale spatial changes in trace gases and AOD. We comprehensively analyze and quantify the changes in atmospheric pollutants and aerosols over different zones along with six most populous cities affected by the restricted anthropogenic activities for the complete pre-monsoon season over India.

2 DATA AND METHODOLOGY

2.1 Data

For analysis we have used gridded satellite datasets from two different satellites (i) OMI (Ozone Monitoring Instrument, on board Aura) and (ii) MODIS (Moderate Resolution Imaging Spectroradiometer, on board Terra and Aqua). OMI observations were used for NO₂, HCHO and SO₂. Daily gridded level 3 datasets of NO₂ (OMI-Aura_L3-OMNO2d), HCHO (OMI-Aura_L3-OMHCHOd (González Abad et al., 2015; Sun et al., 2018)) and SO₂ (OMI-Aura_L3-OMSO2e) were downloaded from EARTHDATA portal by NASA (https://acdisc.gesdisc.eosdis.nasa.gov/data/Aura_OMI_Level3/; last access: 03/06/2020). The spatial resolution of NO₂ and SO₂ datasets is 0.25° × 0.25° whereas the spatial resolution of HCHO datasets is 0.1° × 0.1° (Kelly Chance (2019), OMI/Aura Formaldehyde (HCHO) Total Column Daily L3 Weighted Mean Global 0.1 deg Lat/Lon Grid V003, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: 03/06/2020, https://doi.org/10.5067/Aura/OMI/DATA3010). Daily datasets were temporally averaged to create average monthly datasets. For all NO₂, HCHO and SO₂ datasets, all grid points with negative slant column densities (SCDs) and cloud fraction values lower than 0 and higher than 0.2 were excluded. Grid points representing higher Tibetan plateau and upper Himalayan regions (3000 m above sea level) were not included either. For HCHO, an additional quality control was carried out using ‘data flag’ parameters available with the data. The grid points with ‘data_flag’ values higher than 0 were removed.

The AOD analysis is based on observations from MODIS satellite. Monthly averaged level 3 AOD products (MYD08) were downloaded from EARTHDATA portal from NASA (https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/61/MYD08_M3/; last access: 05/06/2020). For the present study we use the merged 550 nm AOD product ‘AOD_550_Dark_Target_Deep_Blue_Combined’ (Levy et al., 2013). In a recent study Mangla et al. (2020) validated this product using ground-based observation over India. They have compared AOD products from MODIS, OMI and MISR satellites with ground-based Aeronet AOD observations over India. The study concluded that the MODIS AOD product showed higher correlation with ground-based observations (R2 = 0.7 at Gandhi college site in IGP region) followed by MISR and OMI. Negative bias for high aerosol loading is less in MODIS products compared MISR and OMI. The data has a 1° × 1° spatial resolution.

Additionally, the Modern-Era Retrospective Analysis for Research and Applications, version-2 (MERRA2) by Gelaro et al. (2017) is used to analyze the wind at 850 hPa level, the incident shortwave radiation at surface under clear-sky conditions along with the 2m air temperature. It may be noted that MERRA-2 implements an aerosol assimilation system incorporating ground- and satellite-based aerosol observations (AVHRR, MODIS, MISR, and AERONET) and represents their interactions with other processes in the weather and climate system (Randles et al., 2017). Integrated Multi-satellitE Retrievals for GPM (IMERG) data was used to study precipitation (https://gpm.nasa.gov/data/imerg; last access: 10/08/2020). IMERG data is derived from observations of GPM satellite constellation (Global Precipitation Measurement). Also, we use the estimated population density data on a 0.25° × 0.25° grid box for the year 2020 from NASA Socioeconomic Data and Applications Center (SEDAC) (CIESIN, 2018).

2.2 Study Region

To understand the impact of country-wide disruption of anthropogenic activity during the pre-monsoon season of 2020, apart from analyzing the spatial-temporal variation of NO₂, HCHO, SO₂ and AOD over the entire Indian landmass, we have divided the Indian region into six sub-regions. (i) North-Eastern India (NEI): comprising seven states of north-eastern India (Arunachal Pradesh, Manipur, Mizoram, Tripura, Meghalaya, Assam, Nagaland), Sikkim and northern West Bengal. This region has higher vegetation density and lower population density. (ii) Central India (CI): covering the states of Madhya Pradesh, Chhattisgarh, Odisha and part of Maharashtra. It is a sparsely populated area with many thermal power plants, steel plants and coal mines within the states of Chhattisgarh and Odisha. Eastern CI has dense forested area. (iii) Southern India (SI): extending over the peninsular part of India consisting of six states (Andhra Pradesh, Telangana, Kerala, Tamil Nadu, Karnataka and Goa). Three out of the six most populous Indian cities are located in SI. Western SI contains forested region in Western Ghats mountain range. (iv) North-Western India (NWI): This is an arid region consisting of Thar Desert and rest of the Rajasthan state. (v) Western India (WI): covering the state of Gujrat and western Maharashtra containing Mumbai and Pune city. This is a highly industrialized region and is the economic hub of India. (vi) Indo Gangetic Plain (IGP): straddled between CI and Himalaya, encompassing states of Punjab, Haryana, Delhi, Uttar Pradesh, Bihar, Jharkhand and southern West Bengal, this region has the highest population density (> 1000 per sq. km.) in India. IGP is also a highly urbanized, industrialized region.

Furthermore, we analyzed the changes in NO₂, HCHO, SO₂ over six most populous cites in India, i.e., Mumbai, Delhi, Kolkata, Chennai, Bangalore and Hyderabad. Spatial data for 1° × 1° grid boxes around the center points of these cities were extracted and averaged to get the representative values from the cities. Fig. 1(a) shows all the six zones and the six cities discussed in this paper. The cities are denoted by 1° × 1° box around them. Fig. 1(b) show the population density over India. The maximum population density can be seen over IGP region. The urban conglomerate regions including the six cities discussed also show high population densities. 

Fig. 1. (a) Map of India with six zones (NEI, CI, SI, NWI, WI and IGP) and six cities. Study areas around the city are marked by the square boxes. Numbering of the cities are as follows: (1) Mumbai, (2) Delhi, (3) Kolkata, (4) Chennai, (5) Bangalore and (6) Hyderabad. (b) Map of population density in India.

2.3 Methodology

The period of study extends over March, April and May of 2020, which is the pre-monsoon or summer season in India and is characterized by the highest seasonal temperature over the subcontinent. Average monthly gridded data of NO₂, HCHO and SO₂ were created from daily gridded data by temporal averaging for March, April and May (MAM) of 2017–2020. For AOD we used the 550 nm monthly MAM AOD for the period 2017–2020. Since the study focuses on relative changes, three-year seasonal average was created using the data for 2017, 2018, 2019 for every month (March, April and May) as well as for the entire pre-monsoon season (March–April–May). Changes in trace gas and AOD are calculated by subtracting the average monthly and seasonal gridded data for the period 2017-2019 from the corresponding data for the year 2020. The percentage changes are computed after spatially averaging the datasets over all the six zones and the six most populous cities. If not otherwise mentioned, the changes (increase/decrease) in trace gas and AOD will indicate the relative changes (increase/decrease) of the corresponding parameter in 2020 compared to its 2017–2019 average value. Recent studies have reported increasing trend of atmospheric NO₂, HCHO and SO₂ over India (discussed in details in the result section). Time averaging (monthly) over longer period (more years) would have reduced the average values for the pollutants due to underlying increasing trend as in the past year the pollutants concentrations were low. Hence the relative changes between time average pollutant concentration and during COVID19 induced lockdown would have led to misleading results. This is the reason for not using monthly average concentration for longer time period (not more than three years). Student’s t-test was performed to determine statistical significance of the observed changes of mean trace gas concentration and AOD over all the six different geographical region and six different cities in India during 2017–2019 and 2020. In this study we have presented the changes which are statistically significant at 95% confidence interval (p = 0.05) as ‘significant’. The changes which are not statistically significant at 95% confidence interval (p = 0.05) are described as ‘not significant’.

3 RESULTS AND DISCUSSION

3.1 NO₂

The spatial distribution NO₂ column density over India is plotted in Fig. 2. The mean (2017–2019) NO₂ column density for the pre-monsoon season (Fig. 2(a)) shows high values over IGP, eastern parts of CI and over urban conglomerates (e.g., Mumbai, Bangalore, Chennai). NO₂ hotspot over the Indian capital city of Delhi is prominent in Fig. 2(a). Emission from industries, power plants and automobiles are primary contributors to the high NO₂ concentrations observed over urban conglomerates and industrial regions (Ghude et al., 2008). Another hotspot is observed over the eastern boundary between IGP and CI. Emission from the dual thermal power plants of Rihand (Uttar Pradesh) and Vindhyachal (Madhya Pradesh) generate this hotspot while affecting surface NO₂ concentration in nearby regions (Biswas et al., 2019). The steel plants in the eastern CI and eastern IGP (Bhilai, Raurkela, Durgapur) are also responsible for high tropospheric NO₂ concentrations. The highest average NO₂ column density during MAM of 2017–2019 was found over IGP ((8.6 ± 3.0) × 10¹⁴ molec. cm^–2) followed by CI ((7.6 ± 2.8) × 10¹⁴ molec. cm^–2). Among the cities, Delhi shows the highest average NO₂ column density ((11.8 ± 3.7) × 10¹⁴ molec. cm^–2) during MAM of 2017–2019 followed by Kolkata ((7.9 ± 1.2) × 10¹⁴ molec. cm^–2). In the recent decades, satellite observations (OMI, GOME, SCIAMACHY, GOME-2a, 2b) have revealed an increasing trend in tropospheric NO₂ columns over India (Mahajan et al., 2015; Georgoulias et al., 2019). A positive trend in NO₂ is also observed over majority of the Indian cities (Delhi, Kolkata, Chennai, Bengaluru and Hyderabad), while signal over the city of Mumbai is ambiguous (Duncan et al., 2015; Schneider et al., 2015). Fig. 2(b) shows the changes in NO₂ column density for MAM 2020 with respect to the mean NO₂ column density for MAM 2017–2019. An overall decrease in NO₂ is seen over SI, parts of NEI, eastern IGP and CI. Whereas, over north-western IGP, NWI and parts of WI increase can be seen. 

Fig. 2. (a) Spatial distribution of average NO₂ column density during pre-monsoon season of 2017–2019 as observed by OMI satellite. Anomalies of NO₂ column density during (b) pre-monsoon, 2020 (c) March, 2020 (d) April, 2020 (e) May, 2020.

Decrease in NO₂ can be noted over hotspot regions such as Delhi, Rihand – Vindhyachal dual power plants and other urban conglomerates. Fig. 3 shows the zone-wise (Figs. 3(a), 3(b), 3(c) and 3(d)) and city-wise (Figs. 3(e), 3(f), 3(g) and 3(f)) seasonal and monthly percentage changes for pre-monsoon 2020. The ‘*’ over the bar plots in Fig. 3 indicate the statistically significant changes. The MAM, 2020 seasonal percentage change showed decrease over SI and CI. The most substantial decline happened in SI (–5.6%) followed by CI (–2.3%). NWI (+5.8%) showed highest increase during MAM followed by IGP (+1.4%). Fig. 3(e) shows the average percentage changes during the MAM period for all the six cities discussed above. All the six cities have shown negative changes with Mumbai showing the highest decrease (–33.7%) followed by Delhi (–18.5%). The cumulative percentage changes in NO₂ column density in MAM 2020 compared to MAM 2017–2019 over all the six regions and all the six megacities in India are statistically significant. 

Fig. 3. Percentage changes of zonal average NO₂ column densities over the six Indian zones during (a) pre-monsoon season of 2020 (b) March, 2020, (c) April 2020 and (d) May 2020. Percentage changes of average NO₂ column densities over the six Indian cities during (e) pre-monsoon season of 2020 (f) March, 2020, (g) April 2020 and (h) May 2020. The ‘*’ symbol over the bar plots indicate statistically significant results.

Since, the nationwide lockdown in India was enforced on 25^th March, 2020, the anthropogenic activities were mostly normal during that month, hence increase in NO₂ column density are observed over all the six zones (Fig. 2(c)) due to underlying increasing trend mentioned earlier. Fig. 3(b) shows the percentage change in NO₂ during March 2020. All the six zones showed increased NO₂ with NWI (+22.5%) showing highest increase followed by IGP (+14.5%) and both the results are statistically significant. During March, Mumbai showed maximum significant decrease (–40%). Delhi recorded highest increase (+10.58%, Fig. 3(f)) but the result is statistically insignificant. Positive changes were also observed over Kolkata and Bangalore. For April 2020, apart from few regions in IGP, NEI and eastern CI the reduction in NO₂ were seen over India (Fig. 1(d)). All the zones except NEI (+7.3% increase) showed an average decrease in NO₂ column density (Fig. 3(c)). However, NEI showed an increase in NO₂, it was found statistically not significant. SI (–15.6%) showed highest significant decrease followed by CI (–8%) in April. During April, only Chennai (+28.07%) showed increase in NO₂ but the result is not statistically significant. All the other cities showed decrease in NO₂ with Mumbai showing maximum significant decrease (–38%) followed by Delhi (–30%, Fig. 3(g)). Fig. 2(e) shows the spatial distribution of NO₂ anomaly over India during May, 2020. Compared to April, the extent of reduction in NO₂ increased during May. The reduction was more pronounced over NEI, eastern IGP and CI with negative changes spreading over more regions (Fig. 2(e)). During May, NEI showed highest significant decrease in NO₂ with –19.3% followed by IGP (–7.2%) region. NWI (+2.7%) and WI (+2.1%) showed positive changes during May, 2020 (Fig. 3(d)) among which only results for WI is statistically significant whereas results for NWI is not statistically significant. During May 2020, all the six major cities showed decrease in NO₂ column density with Chennai showing highest significant decrease (–29.18%) followed by Delhi (–25.37%).

The seasonal average increase of NO₂ column density over WI and IGP was driven by the increase during March. During the ‘effective’ lockdown period of April and May overall negative departures were observed indicating large decrease in anthropogenic emission and reduction in tropospheric NO₂. Among the six largest Indian cities, Mumbai showed highest significant decrease during MAM (also highest decrease during March and April). This is possibly driven by the underlying negative trend of NO₂ over Mumbai as reported by Duncan et al. (2015). The increase of NO₂ column density over Chennai during April is not statistically significant. Unanimous decrease of NO₂ column densities over six cities and the higher decrease over cities compared to the six zones indicate that urban atmosphere is affected more by anthropogenic emission compared to rest of the country. Using this information in chemical model will help to understand and reduce the uncertainty of anthropogenic emission over India better.

3.2 HCHO

Fig. 4 shows the spatial distribution of average HCHO column density and anomalies over India for MAM 2020. Fig. 4(a) represents average HCHO column density for the pre-monsoon period over India during 2017–2019. Three regions show high HCHO column densities (i) the state of Kerala and adjacent region in SI, (ii) the states of Chhattisgarh and Odisha in eastern CI and (iii) NEI region. All these three regions are associated with forests, and biogenic VOCs emitted from these regions get oxidized to produce HCHO (Surl et al., 2018; Chaliyakunnel et al., 2019). Biogenic VOC emissions are dependent on surface temperature and studying positive correlation of HCHO columns with surface temperature Surl et al. (2018) inferred that the HCHO variability over India is controlled by biogenic emissions rather than anthropogenic emissions. Apart from these three regions, high HCHO also found over western IGP region of Punjab and Haryana. During pre-monsoon period, crop residue burning happens in Punjab and Haryana leading to pyrogenic emission of VOCs which leads to higher HCHO column density. Pre-monsoon forest fires for converting forest into agricultural lands are also common in NEI (Surl et al., 2018). The highest zonal average during MAM of 2017–2019 was observed over NEI ((1.5 ± 0.6) × 10¹⁶ molec. cm^–2) followed by SI ((1.3 ± 0.2) × 10¹⁶ molec. cm^–2) and CI ((1.2 ± 0.1) × 10¹⁶ molec. cm^–2). Chaliyakunnel et al. (2019) estimated that biogenic, anthropogenic and pyrogenic sources are responsible for 44–65%, 37–50% and < 7% of total VOC emission over India. In two different studies Smedt et al. (2010b) and Mahajan et al. (2015) reported increasing trend of HCHO over India at 1.3–1.8% yr^–1 (for the period 1997–2009) and 1.51 ± 0.44% yr–1 (for the period 1995–2013) respectively. Surl et al. (2018) reported that anthropogenic VOCs (mainly propene used as solvent) are the major HCHO sources over Indian cities.

Fig. 4. (a) Spatial distribution of average HCHO column density during pre-monsoon season of 2017–2019 as observed by OMI satellite. Anomalies of HCHO column density during (b) pre-monsoon, 2020 (c) March, 2020 (d) April, 2020 (e) May, 2020.

Fig. 4(b) shows the average changes of HCHO column density over India during pre-monsoon period. Decrease in HCHO can be seen over the SI, NEI and eastern CI. These are the regions with higher biogenic VOC emission resulting in high HCHO column density. Eastern IGP region also show negative anomaly. Fig. 5 shows the zone-wise and city-wise percentage of HCHO anomalies during pre-monsoon period of 2020. The ‘*’ symbol over the bar plots represent statistically significant results. NEI and SI shows the significant decrease in HCHO column density for overall MAM period with NEI (–2.6% decrease) showing highest decrease followed by SI (–1.3% decrease). NWI (+7.8% increase) recorded the highest significant increase in average HCHO column density followed by WI (+5.0% increase) for pre-monsoon period (Fig. 5(a)). Despite the decrease in HCHO over the forested region of CI, the rest of the CI showed increased HCHO contributing to the net zonal average increase (+3%). Mumbai, Delhi and Hyderabad showed overall increase during MAM 2020 with Hyderabad showing highest increase of +4.8% (Fig. 5(e)). Highest significant reduction was observed over Chennai (–6.8%) for pre-monsoon period of 2020. All the results for changes in HCHO during MAM 2020 over six zone and six cities in India are statistically significant.

Fig. 5. Percentage changes of zonal average HCHO column densities over the six Indian zones during (a) pre-monsoon season of 2020 (b) March, 2020, (c) April 2020 and (d) May 2020. Percentage changes of average HCHO column densities over the six Indian cities during (e) pre-monsoon season of 2020 (f) March, 2020, (g) April 2020 and (h) May 2020. The ‘*’ symbol over the bar plots indicate statistically significant results.

Fig. 4(c) shows the HCHO column density anomaly over India during March. SI and eastern CI showed reduction. During March 2020 (Fig. 5(b)), only SI (–2.1%) displayed significant decrease in HCHO column density. Highest increase during March 2020 was observed over NWI (+8.3% increase) followed by WI (+4.8% increase) both the results being statistically significant. Although reduction occurred over east CI, the increase in HCHO column density over west CI caused the zonal average change to be positive over CI (+2.9% increase) being statistically significant. During March (Fig. 5(f)) Mumbai, Kolkata and Chennai showed decrease in HCHO column density, with Chennai showing highest significant decrease (–9.9% decrease). While Kolkata, Bangalore and Hyderabad showed increased HCHO, with Hyderabad showing the highest significant increase (+14.2%) during March.

Fig. 4(d) shows changes in HCHO during April with negative anomaly over SI, eastern CI, eastern IGP and NEI. Despite of decrease in HCHO column density over eastern IGP and eastern CI the average zonal changes over CI and IGP were more positive in April compared to that in March. Also, the average zonal increase over NWI (+12.8% increase) and WI (+9.5% increase) were more positive in April compared to March with both the results being statistically significant. Average zonal decrease over NEI (–4.6%, statistically significant) and SI (–1.7%, statistically not significant) were observed during April 2020 (Fig. 5(c)). During April, only Bangalore (–11.3%) showed significant decrease (Fig. 5(g)) whereas all the other cities showed increase with Mumbai showing the highest significant increase (+8.5%).

During May, decrease in HCHO were observed over almost all the six zones (Fig. 4(e)) in patches. Western IGP region over Punjab and Haryana specifically showed decrease in HCHO during May. Except NWI and WI all the other four zones showed zonal average decrease during May with NEI (–5.6%) region showing highest decrease (Fig. 5(d)) followed by IGP (–1.3%). Whereas the result for IGP is statistically significant, the result for NEI is statistically not significant. NWI showed highest significant increase of +2.7%. Compared to March and April, all the zones showed less positive anomaly during May. During May all the cities except Mumbai showed decrease with Chennai (–10.3%) showing highest significant decrease (Fig. 5(h)).

During the March of 2020 HCHO anomalies over the forested regions of SI and CI were mostly negative. In March 2020 there were negative anomalies in surface temperature all over India except SI and NEI (Fig. 10(a)). This negative surface temperature anomaly could lead to lower HCHO column density over eastern CI due to decrease in biogenic VOC emission. In April the negative temperature anomaly appeared all over the India except WI (Fig. 10(b)) which contributed towards reduced HCHO over the forested regions of CI and NEI. Also, the positive temperature anomaly over WI explains the positive anomaly of HCHO over WI in April. In May increase in temperature (Fig. 10(c)) was observed over NWI, WI and SI. This explains the increase in HCHO over NWI and WI. This also explains reduction in HCHO decrease in May compared to April over SI. During March an increase in incoming solar short wave radiation over part of WI and over forested region of CI was observed (Fig. 9(d)). In April the increase in solar short wave radiation over IGP and NWI region could have increased photosynthetic activity with higher VOC emission (Fig. 9(b)). This could cause increase in HCHO over part of WI during March. High increase in solar radiation during April compared to March could further increase HCHO over IGP and NWI region in April compared to March. Biogenic VOCs (isoprene) react to hydroxyl radical (OH) and form peroxy radicals (RO₂). In high NO_X (NO₂ + NO) environment peroxy radicals react with NO and form HCHO along with methyl vinyl ketone (MVK) and methacrolein (MACR). Further oxidation of MVK and MACR by produces more HCHO within few hours. In low NO_X environment, the production of HCHO become slower due to unavailability of NO (Palmer et al., 2006) resulting in reduced the HCHO column density. Due to lockdown effect reduction in NO₂ column (hence reduction in overall NO_X concentration, up to ~100% decrease in places) over SI and eastern CI and eastern IGP leads to slower oxidation rate of biogenic VOCs resulting in decrease of HCHO column densities in May. In May, due to lockdown effect, less crop residue burning events happened over Punjab and Haryana leading to negative HCHO anomaly. Less number of pre-monsoon forests burning in NEI also could have added to negative HCHO anomaly. However, the decrease in HCHO over SI in March and the increase over NWI (in March) and western CI (during March and April) are not well understood and further research is required. Less emission of anthropogenic VOCs and slower oxidation VOCs in reduced NO_X environment led to decrease in HCHO over all the five cities (except Mumbai) in May. Due to complex interactions between various controlling factors, quantifying the effects of various parameters on HCHO variability during MAM 2020 requires chemistry model-based study.

3.3 SO₂

Fig. 6 represents the spatial distribution of SO₂ column and anomaly over India during MAM, 2020. The mean (2017–2019) spatial distribution of SO₂ column amount (within planetary boundary layer (PBL)) over India is given in Fig. 6(a). The highest values can be found over eastern CI and eastern IGP regions and over major Indian cities due to emissions from thermal power plants, steel plants and automobile emission from cities. The highest seasonal zonal average SO₂ column amount was found over CI (0.2 ± 0.1 D.U.) and IGP (0.2 ± 0.1 D.U.). In two different studies Lu et al. (2013, 2011) have reported 71% and 70% increase in SO₂ emission from India during 2005–2012 and 1996–2010 respectively. Qu et al. (2019) has used OMI observation products from two different group (NASA and BIRA) and found that SO₂ emission have increased by 35% (NASA product) – 48% (BIRA product) during 2005 – 2016 over India.

Fig. 6(b) represents the average seasonal changes of SO₂ column amount over India. The positive anomalies are found all over the India. Top panels of Fig. 8 (Figs. 8(a), 8(b), 8(c) and 8(d)) shows the percentage changes of SO₂ during pre-monsoon season of 2020 compared to average for the pre-monsoon of 2017–2019. Statistically significant results are indicated by the ‘*’ symbol over the bar plots. Fig. 8(a) shows the percentage of average seasonal changes of SO₂ during pre-monsoon period over different zones in India. The highest percentage of statistically significant change was found over NWI with +24.7% increase followed by NEI (+5.2% increase). The highest significant decrease was found over WI (–7.3%). All the changes in SO₂ column amount for MAM over six regions in India are statistically significant. 

Fig. 6. (a) Spatial distribution of average SO₂ column density during pre-monsoon season of 2017–2019 as observed by OMI satellite. Anomalies of SO₂ column density during (b) pre-monsoon, 2020 (c) March, 2020 (d) April, 2020 (e) May, 2020.

Fig. 6(c) shows the SO₂ anomaly during March, 2020 over India. Increase in SO₂ column amount were found all over the India with large increase in SO₂ were found over parts of CI and NWI. NWI (+32.3%) showed highest significant increase (in percentage) followed by IGP (+13.4%) during March, 2020. WI showed highest significant decrease in average SO₂ column amount of –11.8% during March (Fig. 8(b)). During April (Fig. 6(d)) decrease in SO₂ column amount were found over NEI, eastern CI and eastern IGP region. Decrease over Rihand–Vindhyachal dual power plant region on the border of IGP and CI is very prominent. This is due the reduction of anthropogenic emissions from power plants and heavy industries due to lockdown. NEI (+9.7%) and NWI (+7.2%) are showing zonal average increase (Fig. 8(c)) during April. However, the changes in SO₂ over NEI and NWI in April were statistically not significant. The highest decrease in average zonal anomaly was found over WI region (–11.1%, statistically not significant) followed by IGP region (–8.6%, statistically significant). Decrease in SO₂ column amount were observed over parts of SI, eastern CI and eastern IGP and NEI in May (Fig. 6(e)). The negative anomaly over Kolkata and surrounding places in eastern IGP are visible. NWI showed highest significant increase in SO₂ column amount (Fig. 8(d)) of +28% in May. Only IGP region showed significant decrease in SO₂ by –4.7%.

During the pre-monsoon period of 2020 NWI showed highest significant zonal average positive change of +24.7%. During March and May, NWI consistently showed highest significant increase in zonal average. Whereas IGP showed steady significant decrease in SO₂ during April and May (–8.6% and –4.9%). SI showed decrease in SO₂ March (significant) and April (not significant) whereas in May (not significant) slightly increase. However, NEI showed increase in zonal average SO₂ during April (not significant) and May (significant). Hence lockdown actually reduced the SO₂ concentration over IGP, the most populous region in India. However, the extent of reduction is less compared to that of NO₂ and not unanimous as NO₂. This is due to the fact that major anthropogenic sources of SO₂ emission is from thermal power plants and heavy industries, whereas NO₂ occurs from high temperature combustion processes like automobiles along with power plants and heavy industries.

3.4 AOD

Primarily, aerosol particles absorb and scatter incoming shortwave radiation and AOD is a measure of extinction of solar radiation by these particles. Along with direct emission into the atmosphere (dust, sea salt, soot, organic matter), inorganic and organic aerosols (sulfate, nitrate, ammonium and secondary organic aerosols) are also produced from precursor gases (NO_X, SO₂, ammonia) through atmospheric chemical reactions (Boucher, 2015). Therefore, although AOD represents the columnar content, it can also detect the changes in the lower tropospheric particulate matter under restricted anthropogenic activities (Devi et al., 2020). The pre-monsoon (MAM), seasonal and monthly, 550 nm AOD over India for the year 2020 is analyzed and compared with the mean values of past three years (2017, 2018, 2019) and is shown in Fig. 7. The pre-monsoon mean (2017–2019) AOD (Fig. 7(a).) shows high values (> 0.5) over the IGP region with the maximum burden over the eastern parts. The IGP is characterized by high population density and large aerosol precursor emission sources (David et al., 2018), and is dominated by high concentrations of particulate matter throughout the year (Eck et al., 2010; Ramachandran and Kedia, 2013). During pre-monsoon along with wind-blown dust, fine mode particles comprising of BC and secondary OC from industrial and urban sources contribute to the AOD over the region (Dey et al., 2004; Gautam et al., 2009; Giles et al., 2011; Kedia et al., 2014). High values of AOD are also noticed over western part of NEI, east coast of CI, northeastern region of SI, while a much cleaner environment is noticed over NWI. Intense anthropogenic bio-mass burning and long-range transport from the IGP, Bangladesh regions contribute to the high pre-monsoon aerosol burden over NEI (Kaskaoutis et al., 2014, 2009; Pathak et al., 2016), while the coastal regions are influenced by sea-spray aerosols (Ramachandran and Cherian, 2008). The highest average AOD during MAM of 2017–2019 was found over NEI (0.6 ± 0.2) and IGP region (0.6 ± 0.1).

Fig. 7(b) shows the change in 2020 pre-monsoon 550 nm AOD compared to that of (2017–2019). Interestingly in 2020, a marked reduction of aerosol loading occurs over almost the entire Indian subcontinent, except minor pockets of weak positive anomalies in CI and extreme north NEI. The relative seasonal change in 2020 AOD (in % departures) averaged over different zones (Fig. 8(d)) displays negative change for all zones with IGP, WI, NEI showing –16.5%, –16.0%, –16.0% change respectively with a net decrease of –14.2% (figure not shown) over the Indian landmass. Although considerable year-to-year variability exists in AOD distribution over the subcontinent, such prominent widespread decrease is a distinctive feature of 2020 pre-monsoon season. AOD changes over all six zones in India during MAM 2020 were statistically significant. 

Fig. 7. (a) Spatial distribution of average AOD column density during pre-monsoon season of 2017–2019 as observed by OMI satellite. Anomalies of AOD column density during (b) pre-monsoon, 2020 (c) March, 2020 (d) April, 2020 (e) May, 2020.

Fig. 8. Percentage changes of zonal average AOD over the six Indian zones during (a) pre-monsoon season of 2020 (b) March, 2020, (c) April 2020 and (d) May 2020. Percentage changes of average SO₂ column amounts over the six Indian zones during (e) pre-monsoon season of 2020 (f) March, 2020, (g) April 2020 and (h) May 2020. The ‘*’ symbol over the bar plots indicate statistically significant results.

On analyzing the change in monthly AOD values, for March 2020 (Fig. 7(c)) we observe an increase in AOD values over parts of northern, central India and the southern tip of the peninsula. Strong reduction occurs over NEI, eastern IGP, coastal Bay of Bengal, and southern peninsula. Marginal reduction is also noticed over Gujrat and western Maharashtra. The maximum significant decrease is seen over NEI (–14.3%; Fig. 8(e)), while significant increase (+14.1%) is observed over NWI. It may be noted that along with local emissions, the AOD over India is affected by long-range transport, atmospheric circulation and other meteorological and geographical factors (Ramachandran and Cherian, 2008; Ramachandran et al., 2012). Since the country-wide control measures over India was in effect from 25^th March the changes in AOD in March is mostly due to variations in atmospheric circulation. The 850 mb winds (Fig. 9(a)) shows anomalous south-easterlies over IGP and central India indicating reduced dust transport over northern India. In concurrence with restrictions in various anthropogenic activities the AOD anomalies for April 2020 (Fig. 7(d)) shows a notable decrease over entire northern and eastern India, especially over IGP and NEI. Along with wind-driven dust, local industries, automobile and street dust are major sources of aerosols over the densely populated Indo-Gangetic plains (Dey et al., 2004; Singh et al., 2004). Increase in AOD are seen over CI and northern parts of the southern peninsula. The low level circulation change for April 2020 (Fig. 9(b)) do not reflect large changes in horizontal winds over IGP and eastern India indicating that the decrease in AOD’s over this region was primarily due to reduction in anthropogenic emissions. Whereas over parts of CI and peninsular India anomalous easterlies suggest import of natural aerosols from Bay of Bengal. From Fig. 8(f) we can see that the maximum significant decrease in aerosol loading for the month of April occurs over NWI (–31.0%) followed by IGP (–24.0%). Strikingly, in response to the extended control measures, the AOD anomalies for May 2020 (Fig. 7(e)) shows an extensive homogenous reduction in aerosol loading over the entire country. The maximum decrease coincides with the location of major urban-industrial cities in northern IGP. The average changes for all the six zones (Fig. 8(g)) shows decrease in AOD with –32.7%, –20.4%, –24.0%, –22.5%, –27.2%, –19.2% change over NWI, IGP, NEI, CI, WI and SI respectively with results from all the zones being statistically significant. The 850mb circulation change for the month of May 2020 (Fig. 9(c)) shows easterly anomalies over peninsular India with no significant change over northern India. Therefore, the substantial decrease in AOD is largely caused by the cutback in anthropogenically emitted fine particles, mostly BC, OC, sulphate, and would directly impact the incoming solar radiation reaching surface thus altering the surface radiation balance (Ramanathan et al., 2001).

On comparing MERRA2 clear-sky incoming shortwave radiation for March, April and May 2020 (Figs. 9(d), 9(e) and 9(f) respectively) with the average values for (2017–2019) we notice a striking resemblance with the change in AOD patterns for the respective months. For all the three months we notice that decrease (increase) in AOD are coincident with increased (decreased) solar radiation at the surface. In April 2020, in response to diminished aerosol scattering/absorption a considerable increase in insolation (> 5 W m^–2) is observed across IGP. Elevated levels are also seen over eastern, southern and northwest India. Homogenous increase insolation across the entire Indian subcontinent is observed. Additionally, on analyzing the MERRA2 2 m temperature changes for March, April and May 2020 (Figs. 10(a), 10(b) and 10(c) respectively) with respect to the long-term (1980–2009) monthly climatology, we observe profound cooling north of 15°N in March 2020. In April and May although more radiation was available due to reduced aerosol concentrations, the temperature changes showed cooling across northern, central and eastern India. Different meteorological fields influence the surface temperatures during the pre-monsoon season. Along with aerosol loading, changes in precipitation have a major impact on the MAM surface temperatures. Using IMERG data we have studied the changes in precipitation during March, April and May of 2020 compared to the average precipitation during MAM of 2017–2019 over India (Fig. 10(d), 10(e), and 10(f) respectively). Enhanced rainfall activity due to higher incidences of thunderstorms, western disturbances and a very severe cyclonic storm led to the cool surface temperatures in the 2020 pre-monsoon months. Sinha et al. (2019) reported that increase in aerosol loading from industrial emission have resulted in increased available cloud condensation nuclei (CCN) during pre-monsoon season over India. In presence of high available CCN concentration, cloud droplets get reduced in size resulting in delayed onset of collision and coalescence in cloud eventually leading to reduced precipitation. Hence the increased pre-monsoon rainfall was an effect of reduced aerosol loading in MAM 2020 (as evidenced from reduction in AOD). 

Fig. 9. MERRA2 850 hPa wind anomaly during (a) March, (b) April and (c) May 2020. MERRA2 incoming clear-sky shortwave radiation anomaly during (d) March, (e) April and (f) May 2020. 

Fig. 10. MERRA2 2 meter temperature anomaly during (a) March, (b) April and (c) May 2020. IMERG precipitation anomaly during (d) March, (e) April and (f) May 2020.

4 CONCLUSIONS

In this study, satellite observations of NO₂, HCHO, SO₂ (OMI) and 550 nm AOD (MODIS) for the pre-monsoon period (March, April and May) of 2020 were analyzed and compared with that of (2017–2019) pre-monsoon season to evaluate the changes in trace gases and aerosol loading due to restrictions in human activities. Reduction in anthropogenic emissions resulted in reduced seasonal average NO₂ column densities over four zones of NEI, CI, SI and WI with highest decrease over SI (–5.6%). Compared to March 2020, when all the six zones in India showed positive anomalies, implementation of nationwide lockdown resulted in reduced average NO₂ column densities over all the six zones in April and May. The overall increase in seasonal average NO₂ column density over NWI and IGP was driven by higher increase in March 2020. A unanimous substantial decrease of seasonal and monthly average (for May 2020) NO₂ column densities over all the six most populous cities in India compared to the six zones indicate that anthropogenic activities drive NO₂ (and other pollutant) concentrations over urban conglomerate compared to rest of the India. Only two zones (NEI and SI) showed decrease in average HCHO column densities during pre-monsoon season of 2020 with SI showing highest decrease (–1.3%). However, during May 2020, NEI, CI, SI and IGP showed negative anomalies (highest significant decrease over IGP, –1.3%), whereas HCHO anomaly over NWI and WI also decreased compared to March and April. Decrease in HCHO column densities were result of reduction in biogenic, VOC emission due to decrease in surface temperature. Reduction in anthropogenic and pyrogenic (over NEI and western IGP) VOC emission added to decrease in HCHO column densities. Also, the reduction in VOC oxidation rate due to decrease in NO_X concentration at places with normally high NO₂ concentrations resulted in decreased HCHO column height. Lower emission of anthropogenic VOCs and slower oxidation VOCs in reduced NO_X environment resulted in reduction of HCHO column density over all the five cities (except Mumbai) during may, 2020. IGP regions showed significant decrease in zonal average SO₂ column amount during April and May. Whereas NWI region showed continuous positive anomaly over the pre-monsoon season. This indicates that lockdown period reduced the relative SO₂ concentration over IGP by reducing anthropogenic emissions. 

The change in AOD during the 2020 pre-monsoon season shows large-scale decrease spanning almost the entire Indian landmass. Monthly variations indicate increased aerosol loading over north, central and south India for March 2020, caused by the deviations in meteorological conditions. In April, with the implementation of control measures, a major reduction in aerosols occurs along the highly populated industrial IGP region (–24.0%). Remarkably in May 2020, the AOD change shows a harmonized decrease over all the regions (IGP: –20.4%, NEI: –24.0%, CI: –22.5%, WI: –27.2%, SI: –19.2%, NWI: –32.7%). The direct impact of the AOD change is observed in the incident shortwave radiation at the surface. Decreased anthropogenic aerosol loading leads to lower attenuation of solar radiation, thus increasing the available energy at the surface. In contrast, the surface temperature change for the pre-monsoon months of 2020 shows negative anomalies over the north, central and east India. This anomalous surface cooling is connected to the enhanced pre-monsoon rain events occurring over the country in pre-monsoon 2020. As is well known, the interaction of aerosols and clouds is a highly complex and uncertain scientific problem (IPCC, 2013; Fadnavis et al., 2020). Hence along with evaluating air-quality for pollution mitigation policy, the COVID-19 restrictive environment can be used to study the challenging issue of aerosol-precipitation-meteorology interaction.

ACKNOWLEDGMENTS

The authors thank Director, Indian Institute of Tropical Meteorology (IITM), Pune for his support and encouragement. IITM receives full support from the Ministry of Earth Sciences, Government of India. The author also thanks Ms. Debotri Roy for helping with statistical analysis.