1 Introduction

India is facing air pollution, climate change problem from decades. Air pollution has emerged as a significant environmental issue due to rise in population, surge in vehicular traffic emission and growing industrialization and urbanization as it harms human health and significant economic losses [1]. Undoubtedly, the atmosphere is certainly more polluted in the last few decades, especially in developing countries. According to World Health Organization [2], global air pollution caused nearly 7 million deaths (nearly 11.6% of the deaths) in 2012 making it the greatest single environmental health risk in the world. Out of 640 districts in India, 27% in 1998 exceeded the yearly standard value of 40 g/m3 and 45% in 2010 [3]. Kumar et al. (2023) [4] have presented scientometric analysis and evaluation by using 26,859 documents retrieved from available database on exposure to air pollution and health. Nine cities out of the top 10 most polluted cities in the world by the World Health Organization are from India [5]. Thus, this situation is a complicated issue, which strongly influences human health and a demanding solution is required in order to maintain the air quality level. The primary pollutants (examples are CO, CO2, NO2 and SO2) are the pollutants that are released directly into the atmosphere, usually from domestic, industrial, or transportation sources, while secondary pollutants are gases and particulates that also form in the atmosphere, largely from the primary pollutants. The chemical composition of air pollutants, such as their oxidising power, solubility, concentration and the susceptibility of the affected person or object, largely determines the level of damage they can do. Due to their water solubility, SO2 gases can harm human skin and bronchi. In contrast, O3 and NO2 gases can enter the lungs more deeply due to their lower solubility [6].

The contribution of aerosols in causing climate change and air pollution cannot be ignored. Aerosols also have a significant contribution to the energy balance of the earth-atmospheric system. Thus, studies focusing on aerosol properties and their distribution are required for generating a better understanding of the phenomena of regional and global climate change [7]. The ambient air quality is highly determined by aerosols. Aerosols, also known as particulate matter, are known to be a major contributor to air pollution, particularly in developing nations like China and India which is also affecting human health [8, 9].

The tropospheric aerosols have a large spatial and temporal variability as they have irregular distribution, widespread origin and short residence time in the atmosphere and anthropogenic events like festivals affect the aerosol patterns [10]. Aerosols are generated by anthropogenic and natural sources directly into the atmosphere and are also produced by various physical and chemical processes in the atmosphere [11]. The distribution of atmospheric aerosols and their impact on the earth’s climate system is not understood completely and remains a concern to both the scientific community and policymakers [12]. Aerosols cause scattering and absorption of solar radiation and thus influence climate directly [13,14,15], while it influences climate indirectly by affecting the processes like cloud condensation droplet concentration, cloud optical properties, precipitation rates and a lifetime of clouds [16].

Aerosol Optical Depth (AOD) and Aerosol Index (AI) are quantitative estimates of the aerosols present in the atmosphere and are generally used as a substitute for particulate matter in the atmosphere. AOD is an optical parameter and an indicator of the extinction effect of atmospheric aerosols which is used to determine the degree of air pollution. AI is a qualitative index indicating the presence of higher-concentration layers of aerosols with significant absorption in the atmosphere. Several studies have been carried out to develop a relationship between empirical satellite-derived AOD and particulate matter [17]. AOD is a measure of the quantum of energy absorbed or reflected by airborne particles as it is transmitted through the atmosphere. AI monitoring has been used worldwide to find out air pollution sources, analyse dispersion and migration patterns of air pollutants across continents, measure air quality and its prediction and also study the energy balance between radiations and factors of climate change [18]. The magnitude of AI is controlled by the optical depth of aerosols, the type of aerosol i.e., the composition and origin and the altitude of the aerosol layer and the view angle [19]. The smoke and desert dust particles which are absorbing aerosols show positive values of AI while scattering aerosols are given negative values [20]. However, the values of AI may also be negative due to other factors like high altitude clouds and variations in surface reflectance [21]. AI values near zero indicates the presence of clouds [22], while AI values greater than 1.0 indicate the presence of absorbing aerosols like soot or dust particles [23].

The world was facing the Coronavirus disease (COVID-19) which is a pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Worldwide millions of people have been affected by COVID-19 and many of them lost their life. The disease was first detected in December 2019. On 30th January 2020, the Government of India (GOI) confirmed India’s first COVID-19 case. As the number of confirmed COVID-19-positive cases was increasing, to break the chain or to reduce the spread of this pandemic, on 24th March 2020 the GOI imposed a nationwide lockdown 1.0 for 21 days from 25th March to 5th April. The lockdown was further extended by 19 days from 16th April to 3rd May which was termed lockdown 2.0. The lockdown virtually restricted 1.3 billion people of India from stepping out of their homes, all transport services such as road, rail and airways were suspended except transportation of essential goods and services. Educational institutions, industrial establishments, construction and agricultural activities were also suspended.

Second aspect of this problem is to validate the fact that the anthropogenic activities are contributing significantly in causing the air pollution. This can only be verified if the anthropogenic activities are either stopped or slowed down and then the air pollution status is monitored. As air quality has deteriorated in most of Indian cities over the past few decades, this lockdown provided an excellent opportunity [24,25,26] to study how the atmosphere responds to a rapid and abrupt reduction in pollution produced by various anthropogenic activities, such as industries, biomass burning, transportation, etc. This was also an opportunity to distinguish between the possible impacts of natural and anthropogenic aerosols and air pollution in the atmosphere. Hence, the present study was conducted using the satellite data products to analyse the anticipated changes in the air pollution status in India during the lockdown period which presented an opportunity of reduced anthropogenic activities and compare this scenario with scenario of increased anthropogenic activities in the previous years. This study aims to analyse the aerosols loading along with the concentration of gaseous air pollutants over pan India during the lockdown in India using Moderate Resolution Imagin Spectroradiometer (MODIS) Terra, Ozone Monitoring Instrument (OMI) Aura and Atmospheric Infrared Sounder (AIRS) Aqua satellites. The previous related studies also analysed the air pollution scenarios for lockdown period [27] in which they compared PM2.5 and NO2 concentration. They utilized data from continuous Ambient Air Quality Monitoring Stations (CAAQMS) of Central and State Pollution Control Boards in India for 2018, 2019 and 2020 over six Indian cities. The study also investigates the spatio-temporal variation and time series analysis of aerosols and air pollutants over India during the lockdown period and its comparison to previous years. The present study determined the CO, NO2, SO2, ozone, AOD and AI using satellite data. However, a detailed location-based analysis can be performed and validated elaborately if more ground observation data is available for number of cities in India.

2 Materials and methods

2.1 Study area

The present study of aerosol loading has been performed over pan India. India has high topographical heterogeneity of aerosol and meteorological properties. The extent of India is bounded by the latitude of 6°44′–35°30′ N and longitude of 68°7′–97°25′ E. The area is marked by large variations in temperature across different seasons. The mean temperatures in the region vary between 10 °C in winter to about 32 °C in summer.

Urbanization, industrial activities, agricultural activities and energy utilization are rapidly expanding, resulting in increased anthropogenic emissions and air quality deterioration. In the present study, to analyse the impact of COVID-19 on aerosol loading and air pollution, India has been divided into six regions namely North India (NI), South India (SI), West India (WI), East India (EI), Central India (CI) and North-East India (NEI) as shown in Fig. 1. The green line in Fig. 1 represents the line of Himalayan Mountain Range which acts as the obstruction to the movement of air in the atmosphere.

Fig. 1
figure 1

Geographical location of India including 6 regions selected for study

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WI, EI and SI are bounded by long stretch coastal region where sea salt aerosols may play an important role while NI part is bounded with the great Himalayan Mountain range, which acts as a barrier, trapping the aerosols and air pollutants over the region. In NI, the Indo-Gangetic plain (IGP) (Fig. 1) is regarded as one of the most polluted areas, where anthropogenic activities, especially agricultural burning, are mostly to blame for greater aerosol loading [28]. The IGP has the highest population density in India and is characterized by large sources of emission from precursors of aerosols and air pollutants. WI comprises the desert area as well as the long-stretch coastal region where desert dust aerosol may play an important role. This desert dust is transported by wind also to the atmosphere of NI primarily during the summer season which contributes to higher aerosol concentration in the region. Due to the increase in population, urbanization has increased which has resulted in an increase in the consumption of fossil fuel while biomass burning in the rural areas has led to high aerosol loading over India.

2.2 Data used

Satellites can provide a unique opportunity to monitor and evaluate air quality in order to fill gaps in ground-based measurements, thereby improving our understanding of aerosol contributions to energy balances, air pollution and climate change. Integration of satellite-based continuous monitoring of aerosols and pollutants and measurements using in-situ surface techniques is probably the best proposition to understand the impacts of aerosols on climate and human health. Three Earth Observation System (EOS) satellites MODIS Terra, Aqua and OMI Aura have made daily global aerosol observations over the past two decades. Because of its wide availability, the present study uses the MODIS Terra satellite products for AOD, OMI Aura for AI, Ozone, NO2, SO2 and AIRS Aqua for CO as given in Table 1.

Table 1 Description of satellite/sensor used in this study
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The instruments assess the AOD measurements from reflected solar radiation, radiative transfer measurements and using aerosol models based on spectral responses. A proper collection of AOD satellite data over the region under consideration is necessary to correctly estimate the amount of aerosols over that region [29]. The AI is the difference between measured and estimated spectral contrast of two near UV wavelengths [30]. This spectral contrast is in fact due to differences in the response of absorbing aerosols and also because of phenomena like reflection, scattering, absorption and dispersion from the surface, gases, clouds and aerosols respectively [20]. This range is useful for aerosol detection over land as in the ultraviolet wavelengths; the land has low surface albedo [31]. The OMI Aura is the first satellite which is working on UV and visible spectrum with the charge-coupled device, enabling daily, global monitoring of Ozone, NO2 and SO2 [32, 33].

To carry out this study, satellite data of AOD, AI, CO, NO2, Ozone and SO2 for the period 25 th March to 3rd May 2016–2020 has been obtained from the Giovanni portal of Goddard Earth Science Data and Information Services Centre, NASA. Satellite instruments that measure NO2 are sensitive to the number of molecules between the instrument and the Earth's surface (i.e., a Vertical Column Density or VCD). For NO2 typically the amount high in the atmosphere (in the stratosphere) is removed in order to provide an estimate of the column in the lower part of the atmosphere (the troposphere), a Tropospheric Vertical Column Density or TVCD. The units of VCD or TVCD are molecules per square centimeter or mol/cm2. The description of data products obtained from different satellites is given in Table 2.

Table 2 Properties of satellite data products of aerosols and air pollutants obtained from Giovanni
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2.3 Methodology

The overall methodology adopted to perform the analysis involves spatial analysis of the satellite data described above to determine AOD, AI, gaseous air pollutant concentrations in the atmosphere for pan India. These parameters were determined for the lockdown period (25th March to 3rd May 2020) and were compared with similar parameters determined for 2016–2019 scenarios. The steps involved in the methodology are as below.

  1. 1.

    The above satellite retrieval daily data of aerosol and gaseous pollutants for the period of 25th March to 3rd May 2016–2020 has been averaged over India and also for the selected six regions using raster calculator tools in the ArcGIS environment.

  2. 2.

    To study the impact of the lockdown during COVID-19 on different regions of India, spatio-temporal maps of AOD, AI and air pollutants over India as well as bar charts have been plotted for each region during the lockdown period of 2020 and 2016–2019.

  3. 3.

    The percentage change analysis of air pollutants between the year 2020 and the average of 2016–2019 from 25th March to 3rd May has been carried out to observe the impact of the lockdown on these pollutants. During lockdown periods, heavy to light precipitation has been observed over a major part of India. Hence, to analyse the impact of precipitation on aerosol concentration in the atmosphere, a time area average map is generated for AOD, AI and precipitation.

  4. 4.

    In the atmosphere, gaseous air pollutants have the ability to react with each other which may affect the concentration of other pollutants. The atmospheric chemistry of air pollutants also leads to the formation of fine aerosol particles. A spatial correlation study of aerosol variables (AOD and AI) and air pollutants (Ozone, CO, NO2 and SO2) has been performed for the lockdown period using Giovanni tools.

3 Results and discussion

3.1 Spatial variation of AOD and AI over India during lockdown period

The average MODIS Terra AOD at 550 nm from 25th March to 3rd May for the 2016–2019 average and 2020 is represented in Fig. 2. This spatial map illustrates that, during the lockdown period, MODIS Terra recorded lower AOD values (0.15–0.40) over NI. It may clearly be observed in Fig. 2, that AOD values are very high over NI relative to other regions during 2016–2019. The Indo-Gangetic plain in NI is a highly polluted region, where anthropogenic aerosols are dominant, but due to complete lockdown with no human activities, significantly low AOD values (0.33) have been observed primarily over NI and also over other regions of India. The AOD values have been observed over NI (0.33), CI (0.29), WI (0.26), SI (0.34), EI (0.46) and NEI (0.47). Present study shows that significantly lower AOD values have been observed for the first time in last five years. The percentage reduction in last 5 years in overall AOD values across India is 26.67% while reduction was 25% and 29.41% in CI and EI respectively.

Fig. 2
figure 2

Variation of MODIS AOD @ 550 over India during 25 March to 3 May 2020 (lockdown period) and average of 2016–2019 for same period

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NEI showed relatively higher AOD values (0.40–0.70) during lockdown relative to other regions. These higher AOD values during lockdown may be due to the wind direction and velocities which bring aerosol particles from other places to the atmosphere of NEI. SI and WI also show relatively lower AOD values 0.34 and 0.46 respectively. For the last two to three decades, anthropogenic aerosols are a major concern and IPCC has also mentioned in its assessment report the impact of aerosols on Earth’s climate system and their uncertainties [36]. The present study also gives a clear picture of how anthropogenic activities are majorly responsible for the deterioration of air quality; IGP in NI is a strong example of this.

Similarly, average AI maps have been generated for the 40 days (25th March to 3rd April) lockdown period for 2020 and 2016–2019 as shown in Fig. 3. Since the effect of aerosols and the atmospheric process in UV bands is more intense, the AI is considered as an indication of UV absorbing aerosols like soot and desert sand particles [20]. In all the years, the entire India shows an AI value of greater than zero i.e., no negative value. AI values are observed positive for absorbing aerosols (smoke and dust) and negative for non-absorbing aerosols (sulphate and sea salt). It means that non-absorbing particles are not dominant in India [37].

Fig. 3
figure 3

Variation of OMI Aura UV AI over India during 25 March to 3 May 2020 (lockdown period) and average of 2016–2019 for same period

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In the year 2020, results clearly show that entire India shows low AI value (AI < 1) than the average of 2016–2019 where AI values of more than 1 are also observed over some parts of India mainly over NI (IGP). Over NI and NEI, an AI value of greater than 1.5 (Fig. 3) has also been observed in past years. This may be due to the dust aerosol particles over the region transported by wind from the Thar region, or the dust particles originating through wind erosion and the absorbing aerosols like black carbon or organic carbon generated by anthropogenic activities.

During the lockdown period, all possible sources of black carbon and soot emissions such as industrial activities, transportation and biomass burning were suspended in the country,, hence lower values of AI were recorded compared to previous years. These parameters of aerosol loading clearly show a record low value of aerosol over India during the lockdown period compared to other years which indicates a clear sky without fine or coarse particulate matter primarily over NI and EI.

3.2 Regional variations of AOD and AI during lockdown period

Bar charts histograms have been generated (for AI and AOD) over all six regions of India during the lockdown period of 2020 and the same period during 2016–2019. It is clearly observed that AI is decreased over all the regions except NEI during lockdown period as shown in Fig. 4. It may be due to wind patterns that transport dust, sea salt and other types of aerosols from NI and EI as well as from the neighbouring countries. During the lockdown period, AI values of 1.03(NI), 1.17(CI), 1.26(WI), 1.14(EI)), 0.87(SI) and 1.07(NEI) have been observed compared to 1.42(NI), 1.55(CI), 1.56(WI), 1.58(EI), 1.10(SI) and 0.96(NEI) in 2016–2019 (Fig. 4). Average AI value of 1.09 (2020) has been observed over India relative to 1.36 (2016–2019), which indicates approximately 19.85% reduction. Among all regions, average AI values over NI (1.03) and SI (0.87) are lower than India's average (1.09).

Fig. 4
figure 4

MODIS AOD @ 550 nm and OMI Aura UV AI variation over NI, CI, WI, EI, SI, NEI during lockdown period and horizontal line indicates India’s average for AOD and AI

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Similarly, all regions show lower AOD values during the lockdown period compared to an average of 2016–2019. It is clearly observed from Fig. 4 that average AOD values over EI (0.44) and NEI (0.48) are higher compared to other regions. Nearly a 21.3% reduction in average AOD value has been observed due to the lockdown compared to values in 2016–2019. The average AOD in India is 0.36(2020) compared to 0.44 in previous years. NI (0.33), CI (0.29), WI (0.26) and SI (0.34) show lower values compared to India's average AOD (0.36) whereas EI (0.46) and NEI (0.47) show higher AOD values. Each region gives a clear picture of how human-induced aerosols play a significant role in polluting the environment. The results of the AOD estimated from MODIS data has been validated with AERONET data for Kanpur City. The results show that the average AOD of Kanpur city for the years 2016–2019 is 0.575 while the average AOD of Kanpur for April 2020 which was lockdown month was 0.440. This shows that AERONET data is also indicating reduction in AOD value for North India [38].

Histogram displaying frequency and OMI @UV-AI has been generated for the period 25th March to 3rd May for 2020 and 2019 over India (Fig. 5). During the lockdown period, more frequency has been observed for AI range of 0.5–1.5 compared to 0.5–2 in 2019. Nearly zero frequency is observed for AI value beyond 2.5 in 2020 compared to 3.5 in 2019 (Fig. 5). In spite of the maximum value (4.05) of AI observed during the lockdown period, it shows a low mean (1.15) and standard deviation (1.10) compared to 2019 (3.83, 1.34 and 1.28).

Fig. 5
figure 5

Histogram map of OMTO3d.003: UV Aerosol Index illustrating frequency of AI value during lockdown period

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3.3 Temporal variation of AOD, AI and precipitation during lockdown period

Average time series plots of AOD, AI and precipitation (precipitation data has been obtained from GPM satellite during the lockdown period at a spatial resolution of 0.1 degree) over pan India has been generated during the lockdown period. Results clearly show that during the first week of lockdown, a sharp drop in AOD values is observed while AI shows relatively low constant values (Fig. 6). This sudden drop in AOD value is due to both lockdown and heavy rains (around 25th March, 18th April and 26th April an average rainfall of 7 mm, 6 mm and 6.5 mm respectively is observed) (Fig. 7) which cleaned the aerosols from the atmosphere. But after rainfall events, it is observed that the AOD value gradually increases (till 18th April) but it never goes beyond 0.5 except on 15th April as shown in Fig. 6.

Fig. 6
figure 6

Time area average map over India of AOD, AI and Precipitation which describe the variation during lockdown period and impact of precipitation on aerosols

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Fig. 7
figure 7

Time averaged map of daily accumulated precipitation over 25th March to 3rd April 2020

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Gradual increase in AI is also observed over India from 30th March to 18th April. After the 18th of April, both AOD and AI show a drastic decrease in their values till the 21st of April and after that, both parameters gradually increase. The fitted line for AOD during lockdown also illustrates a lower and nearly constant value of 0.35 over India but AI show's slightly increasing pattern.

During the 40 days lockdown period of 2020, AOD was almost zero two times precisely on 29th March and 21st April. In the time period during which AOD and AI values are lower, high precipitation was also observed at some places over India where aerosols were washed out or rain out with rain droplets. After the rainfall events in such areas, there was less increase in aerosol loadings over India which leads to better atmospheric conditions. This lower value of AI over India is mainly due to the lockdown and partially due to rainfall events during the lockdown over some parts of India primarily over NI, EI and SI as shown in Fig. 7.

3.4 Spatio-temporal variation and change analysis of Ozone, CO, NO2 and SO2

Spatio-temporal variation maps of CO, Ozone, NO2 and SO2 has been generated for 25th March to 3rd May 2020 and an average of 2016–2019 to analyse the variation of air pollutants. During the lockdown period, NO2 (Fig. 8a) has significantly decreased over all regions of India such as NI (− 20%), CI (− 16%), WI (− 13%), SI (− 19%), EI (− 18%) and NEI (− 3%) (Table 3). Among all six regions, EI shows a higher concentration of NO2 (5.085 × 1015 mol/cm2) whereas, over NEI, a lower concentration of 4.26 × 1015 mol/cm2 has been observed (Table 3).

Fig. 8
figure 8

a Spatio temporal variation of OMI SO2 and OMI NO2 over India during lockdown period. b Spatio temporal variation of OMI ozone and AIRS CO over India during lockdown period

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Table 3 Percentage change in the value of CO, Ozone, NO2 and SO2 during lockdown period over different regions of India compare to average of 2016–2019
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Similarly, SO2 also shows a significant decrease in values over all regions as mentioned in Table 3. Volcanic SO2 is frequently pumped into the atmosphere above the planetary boundary layers at high altitudes, while anthropogenic SO2 emissions predominate in or just above the planetary boundary layers. From the spatial map of SO2 which is recorded by OMI in the planetary boundary layer, it may be clearly observed that EI is mostly influenced by SO2 pollutants and it may be due to high coal-related activities over the region [39]. The highest reduction in SO2 concentration has been observed during the lockdown period over WI (− 47.38%) followed by CI (− 31%) and EI (− 27.85%) (Table 3).

Compared to previous years, in lockdown period of 2020, Ozone concentration has increased over all the regions primarily over NI (5.07%) and NEI (4.62%) (Table 3). NO2 plays an important role in the formation of Ozone; NO2 shows decreasing values over all the regions, but ozone shows increasing values (Fig. 8b and Table 3). This may be another factor that affects the formation of Ozone in the atmosphere [40]. Burning of agricultural residue and biomass generates CO, but this 40-day lockdown has not allowed such activities. Despite lower values, EI and CI show more concentration compared to previous years (Fig. 8b and Table 3). Of all the regions, more reduction in CO concentration has been observed over NI (5.14%). This lower concentration may be due to fewer biomass-burning activities during lockdown as NI is strongly known for such types of activities [41].

3.5 Spatial correlation of aerosols and gaseous air pollutants during lockdown period

3.5.1 Relation between AOD, AI and SO2

AI and AOD are critical variables in the estimation of aerosols in the atmosphere, estimating the intensity of atmospheric pollution and examining their impacts on climate. Both parameters measure aerosol concentration at different wavelengths. In the spatial correlation map between AOD and AI (Fig. 9) over India during the lockdown period, WI and EI show a positive correlation with an average correlation coefficient of 0.60 while NEI and CI show a negative correlation, while average correlation coefficient of 0.63 has been observed over India. This spatial correlation variation of AOD and AI over different regions may be due to atmospheric dynamics, atmospheric chemistry, types of aerosols and wavelength at which satellite sensors recorded these variables.

Fig. 9
figure 9

Spatial correlation of aerosol variable and gaseous pollutant during lockdown period (23 March to 3 May 2020)

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In India, major sources of SO2 in the atmosphere are coal power plants where black carbon and soot particles are also released along with SO2 which is measured in the form of AOD and AI and particulate matter. In both the spatial correlation maps (AOD vs. SO2 and AI vs. SO2), it is clearly observed that entire India does not show a positive correlation (Fig. 9). A positive correlation is observed at a particular location where mostly coal power plant is situated or coal activity is observed. SO2 is also responsible for the generation of fine secondary aerosols through atmospheric chemistry in the atmosphere [42].

3.5.2 Relation between Ozone, NO2 and CO

NO2 is crucial for the formation of ozone through photochemical oxidation. The spatial correlation map clearly illustrates that the IGP region in NI and SI shows a positive correlation (Fig. 9; NO2 vs. Ozone). IGP is a highly polluted region in India that is mostly polluted by anthropogenic activities where a higher concentration of NO2 is due to vehicular emission and biomass burning. Since, NO2 in the stratosphere play a critical role in ozone layer depletion by itself, it has been observed in the present study that there is an increase in ozone concentration from 2016 to 2019 average concentration when compared to 2020 lockdown period concentration (Fig. 8b). It is also observed that there is a decrease in the NO2 concentration from 2016 to 2019 average concentration when compared to 2020 lockdown period concentration (Fig. 8a).

The average correlation coefficient of 0.62 has been observed between NO2 and CO over the IGP region during the lockdown period (Fig. 9; NO2 vs. CO). CO is mostly released from agricultural residue or biomass burning and vehicular pollution. Strong biomass burning, high industrial activities and heavy vehicular movements lead to higher concentrations of NO2 and CO in the IGP region [43]. Due to this, a strong positive correlation has been observed in the region. A positive average correlation coefficient of 0.54 has also been observed over the NEI. It may be due to a higher concentration of CO which is transported by wind from neighboring regions. NI shows a positive correlation between ozone and CO during the lockdown period. Ozone in the Tropospheric mostly depends on the concentration of NO2.

4 Conclusions

The aerosol and air Pollution analysis conducted in the present study over pan India indicates that air quality was improved during the lockdown imposed due the COVID-19 pandemic. Overall, the COVID-19 lockdown had reduced human activity due to which, lower air pollutants concentration has been observed over pan India, especially in NI. At the early stage of the lockdown period, the Central Pollution Control Board (CPCB) of India has also reported that, after a long time, major cities in the country have shown good air quality index. From the study, it can be concluded that;

  1. 1.

    Aerosol loading in the atmosphere as those represented by AOD and AI retrieval from MODIS Terra and OMI show relatively lower values during the lockdown period compared to its value in the same period in previous years. Thus, lower values of AOD and AI during the lockdown period indicate that anthropogenic activity is mainly responsible for aerosol loading over pan India. Hence, this study corroborates this hypothesis through spatial analysis of satellite derived data.

  2. 2.

    Even North India's most polluted region IGP also displays remarkably lower AI during the lockdown period. The lower value of AI in India is mainly due to the lockdown and partially due to rainfall events during the lockdown over some parts of India primarily over NI, EI and SI. It can also be concluded that due to lockdown during COVID-19, AOD has decreased by approximately 26.65% over NI followed by 18% (WI) and 17% (SI) and less reduction is observed over CI (7 %).

  3. 3.

    The present study also shows that aerosol parameters such as AOD, AI and air pollutants CO, SO2 and NO2 recorded lower values during the lockdown except ozone which shows increasing value primarily over NI and NEI. Higher reduction in NO2 has been observed over NI (20.17%), CI (16.26%) and EI (18.91%) whereas reduction in SO2 over CI (31%), WI (47%) and EI (27%). From the spatial correlation map, it can be concluded that there is a considerable variation in the values of all air pollutants during the lockdown period when compared to these values in period 2016–2019 mainly over the NI where more human activities are observed. The present study related to aerosol concentration estimation during lockdown period of COVID-19 concludes that anthropogenic activities are primarily responsible for the deterioration of air quality over different parts of India. Therefore, control measures can lead to a better climate and environment.