Next Article in Journal
Interferometric SAR Observation of Permafrost Status in the Northern Qinghai-Tibet Plateau by ALOS, ALOS-2 and Sentinel-1 between 2007 and 2021
Next Article in Special Issue
Evaluation and Comparison of MODIS C6 and C6.1 Deep Blue Aerosol Products in Arid and Semi-Arid Areas of Northwestern China
Previous Article in Journal
Multi-Sensor Analysis of Snow Seasonality and a Preliminary Assessment of SAR Backscatter Sensitivity to Arctic Vegetation: Limits and Capabilities
Previous Article in Special Issue
Changes in Long-Term PM2.5 Pollution in the Urban and Suburban Areas of China’s Three Largest Urban Agglomerations from 2000 to 2020
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Remote Sensing
Volume 14
Issue 8
10.3390/rs14081869
remotesensing-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

How Does COVID-19 Lockdown Impact Air Quality in India?

by
Zhiyuan Hu
1,2,
Qinjian Jin
3,*,
Yuanyuan Ma
4,
Zhenming Ji
1,2,
Xian Zhu
1,2 and
Wenjie Dong
1,2
1
School of Atmospheric Sciences, and Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Sun Yat-sen University, Zhuhai 519082, China
2
Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519082, China
3
Department of Geography and Atmospheric Science, The University of Kansas, Lawrence, KS 66045, USA
4
Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(8), 1869; https://doi.org/10.3390/rs14081869
Submission received: 28 February 2022 / Revised: 5 April 2022 / Accepted: 11 April 2022 / Published: 13 April 2022
(This article belongs to the Special Issue Satellite Remote Sensing of Atmospheric Aerosols for Air Quality Applications)
Browse Figures
Review Reports Versions Notes

Abstract

:
Air pollution is a severe environmental problem in the Indian subcontinent. Largely caused by the rapid growth of the population, industrialization, and urbanization, air pollution can adversely affect human health and environment. To mitigate such adverse impacts, the Indian government launched the National Clean Air Programme (NCAP) in January 2019. Meanwhile, the unexpected city-lockdown due to the COVID-19 pandemic in March 2020 in India greatly reduced human activities and thus anthropogenic emissions of gaseous and aerosol pollutants. The NCAP and the lockdown could provide an ideal field experiment for quantifying the extent to which various levels of human activity reduction impact air quality in the Indian subcontinent. Here, we study the improvement in air quality due to COVID-19 and the NCAP in the India subcontinent by employing multiple satellite products and surface observations. Satellite data shows significant reductions in nitrogen dioxide (NO2) by 17% and aerosol optical depth (AOD) by 20% during the 2020 lockdown with reference to the mean levels between 2005–2019. No persistent reduction in NO2 nor AOD is detectable during the NCAP period (2019). Surface observations show consistent reductions in PM2.5 and NO2 during the 2020 lockdown in seven cities across the Indian subcontinent, except Mumbai in Central India. The increase in relative humidity and the decrease in the planetary boundary layer also play an important role in influencing air quality during the 2020 lockdown. With the decrease in aerosols during the lockdown, net radiation fluxes show positive anomalies at the surface and negative anomalies at the top of the atmosphere over most parts of the Indian subcontinent. The results of this study could provide valuable information for policymakers in South Asia to adjust the scientific measures proposed in the NCAP for efficient air pollution mitigation.

1. Introduction

The fast growth in the population has led to rapid industrialization and urbanization in developing countries during the past several decades, which has resulted in considerable increases in anthropogenic emissions of gases and particulates and, consequently, exacerbates air quality [1,2]. As the world’s second most populous country, India has experienced a severe air pollution problem in the past two decades [3], which can have significant harmful impacts on human health [4,5]. The World Health Organization (WHO) reported that nine of the world’s top ten most polluted cities were from India [6]. Moreover, 99.5% of the 640 districts in India exceeded the WHO guideline for annual mean Particulate Matter (PM2.5) concentration (i.e., 10 µg/m3) in 2016 [7]. Deteriorating air quality may have caused about 1.54 million premature mortality per year in India alone [2].
To mitigate and prevent air pollution, the Indian government launched the National Clean Air Programme (NCAP) in January 2019 [8]. The NCAP aims to augment the existing air quality monitoring network across the country and to reduce anthropogenic emissions of nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), ozone (O3), ammonia (NH3), PM2.5, and PM10 [9]. The objectives of the NCAP are (1) to ensure stringent implementation of mitigation measures for prevention, control, and abatement of air pollution; (2) to augment and enhance effective and proficient ambient air quality monitoring across the country to ensure quality data collection; (3) to promote public awareness and capacity-building measures that encompass data dissemination and public outreach programmes, for the purpose of inclusive public participation ensuring proper training and infrastructure building. The NCAP targets a national reduction of 20–30% of PM2.5 and PM10 concentrations by 2024 as compared to 2017 levels.
Concentrations of particulate matter in India often exceed the unhealthy threshold stipulated by the WHO. For example, annual mean PM2.5 concentrations are often more than 100 μg m−3 in Delhi, consistently exceeding the corresponding WHO standard [10]. In the 640 districts in India, PM2.5 concentrations from more than 45% of the districts exceeded 40 μg m−3 (the NCAP standard) in 2010, and the fraction increased to 63% in 2016. 99.5% of the districts also exceeded the WHO guideline of 10 μg m−3 in 2016 [7]. During the burning season (October–November), PM2.5 concentrations could skyrocket to 547 μg m−3, which results in an enhancement of aerosol optical depth (AOD) by 0.1–0.3 over the Indo-Gangetic Plain. Therefore, studies on the sources and impacts of air pollution in India are warranted [2,7,11,12,13,14].
Based on observation and chemical transport models, previous studies have revealed that pollutant emissions are dominated by domestic cooking and heating, followed by industry and agriculture in Northern India [15]. Direct emissions, particularly diesel soot, are the main sources of primary PM [16]. Moreover, secondary inorganic components including nitrate, sulfate, and ammonia are major contributors to PM2.5, followed by dust, vehicle emissions, and biomass burning [17]. It is shown that meteorological factors, such as relative humidity and wind speed, could also exert significant effects on air quality over India [18]. However, questions remain unanswered about the contributions of natural and anthropogenic factors to the air pollution levels in India.
In January 2020, the WHO declared a global health emergency due to the outbreak of the novel coronavirus pneumonia (COVID-19). As of 26 November 2021, there have been about 259,502,031 confirmed cases of COVID-19 in the world, including 5,183,003 deaths (WHO reported at https://covid19.who.int/, accessed on 31 November 2021). To curb the spread of the epidemic, governments in South Asia, including India, launched national emergency responses to reduce population movement and mass gatherings. Transportation, energy consumption, and industrial production were significantly reduced during this period. The consequent dramatic reduction of anthropogenic emissions during the lockdown period provided an ideal and natural experiment for studying the control and prevention of air pollution issues in India. Investigating the air quality changes during COVID-19 (2020) and NCAP (2019) can provide useful information for policy makers in South Asia to inform the design of air pollution mitigation strategies.
During the COVID-19 lockdown in India, the prohibition of unnecessary anthropogenic activities led to significant concentration reductions of atmospheric air pollutants (PM2.5, PM10, NO2 and CO) [19,20,21,22,23]. For example, Muhammad et al. [24] reported that air pollution levels in India were reduced by up to 30% due mainly to the corresponding mobility reductions. Mahato et al. [19] found that surface PM10 and PM2.5 concentrations declined by more than 50% in Delhi. Meanwhile, a significant reduction of PM10 concentrations from 189–278 to 50–60 μg m−3 was observed during lockdown over the Dwarka river basin in Eastern India. Moreover, Agarwal et al. [25] reported that the reduction of PM2.5 was gradual compared with the almost instantaneous reduction in NO2. However, O3 pollution levels over Delhi remained high during the lockdown period, which can be attributed to non-linear ozone chemistry and dynamics under low aerosol loading [26]. Further, Datta et al. [23] showed that the variation in ambient O3 concentrations over Delhi and Kolkata could be explained primarily by spatial variation rather than the lockdown. For carbonaceous aerosols, their relative changes were positive over the high-altitude Himalayan region, which was caused by the enhanced formation of secondary OC through photochemical reactions involving biogenic emissions [27].
At present, most studies are mainly focused on the changes in air pollution levels for specific cities during the lockdown period over India, and an overview of the changes in aerosols and their effects over the entire Indian subcontinent are still lacking (e.g., radiative forcing). Meanwhile, such large-scale changes in aerosol characteristics have the potential to modulate the radiation budget through direct and indirect radiative effects and subsequently impact the regional climate, so detailed investigations are needed [18,28]. Further, meteorological conditions are known to significantly influence regional air quality, and changes in aerosol characteristics also reversely impact meteorology, but such impacts during COVID-19 remain under-investigated. Additionally, most previous studies focused on a shorter period, i.e., starting from 16 March to 14 April 2020, which did not coincide with the actual lockdown period. In this study, we utilize multiple satellite products and surface observations to investigate and compare the changes in air quality over the Indian subcontinent due to NCAP and the 2020 lockdown. The entire lockdown period (March 24–June 30) was considered here. By contrasting aerosol changes during 2020 and 2019, we aim to quantify the following: (1) the contribution of human activities to the total column and surface aerosol abundances, then (2) the potential impacts of meteorological conditions on air quality, and (3) radiation responses to COVID-19 emission reductions.

2. Data and Methods

2.1. OMI

The Ozone Monitoring Instrument (OMI) onboard the Aqua satellite is a nadir-viewing solar backscatter. It measures solar irradiance and Earth radiance from 270 to 500 nm (ultraviolet (UV) to visible (VIS)) at high spectral and spatial resolutions with daily global coverage [29]. The entering light is split into the following two channels using a scrambler: the UV channel with the range 270–380 nm and the VIS channel that covers 350–500 nm. OMI provides the column tropospheric amounts of trace gases (i.e., NO2) and ozone. Generally, NO2 has a short lifetime and is primarily emitted from anthropogenic sources, including industries, powerplants, transportation, and residential combustion [30,31]. It serves as a key precursor for both secondary aerosol formation and ozone production [31,32,33]. NO2 is often used to indicate surface air quality during specific events, such as the 2008 Olympic Games in Beijing [34], and the 2014 Asia-Pacific Economic Cooperation summit in Beijing [35]. Here, the level-3 daily global gridded tropospheric NO2 at 0.25° × 0.25° from OMNO2d is used as a surrogate for human activities. The OMI tropospheric NO2 has been shown to correlate well with ground-based and in-situ NO2 measurements and bottom-up emission inventories [36]. In this study, tropospheric NO2 retrieved from OMI was used to quantify the contribution of human activities.

2.2. MODIS

The MODIS instruments onboard the Aqua and Terra satellites observe the Earth system in 36 spectral bands ranging from 0.4 to 14.4 μm and provide a nearly global coverage within 1 to 2 days owing to their wide swath of 2330 km [37]. MODIS AOD is retrieved from the deep blue (DB) algorithm over bright land (e.g., desert) and dark target (DT) algorithms over both vegetated lands and waters [38,39,40,41,42]. In MODIS collection 6.1, the DB algorithm is updated to produce a dynamic surface reflectance dataset depending on the normalized difference vegetation index (NDVI); the DT algorithm is updated to reduce the biases in urban areas based on a surface reflectance model [43]. In MODIS collection 6.1, a “merged” dataset of AOD at 550 nm is produced by combining the DT and DB retrievals to increase the data spatial coverage [44,45]. In this study, the merged daily AOD data with the resolution of 1° × 1° between 2005–2020 are used to analyze aerosol changes.

2.3. CERES

The Clouds and the Earth’s Radiant Energy System (CERES) instruments measure Earth’s radiation budget and cloud properties in 15 shortwave (SW, 0.2 to 4.0 μm) and 12 longwave (LW, 2.850 μm to 1 cm) spectral bands. The radiation fluxes at the top of atmosphere (TOA) and the Earth’s surface are assessed using delta-two stream radiation transfer model [46] under clear-sky (without clouds and aerosols) and cloudy-sky (with clouds and aerosols). The TOA radiation fluxes are derived using satellite-derived aerosol and cloud properties together with a radiative transfer model and observed radiances together with angular distribution models [47]. The surface fluxes are computed with cloud properties derived from geostationary satellites (GEO) and MODIS [47]. In this study, daily radiation flux data from 2005 to 2020 are used to quantify radiation response to emission reductions. Flux datasets are derived from Level 3 SYN1deg products with a spatial resolution of 1° × 1° [48]. Here, we have considered radiation fluxes at the surface, in the atmosphere (computed as a residual term), and at the TOA.

2.4. Surface Observation

Surface observational data are retrieved from 230 operational stations in Indian National Air Monitoring Network (https://app.cpcbccr.com/AQI_India/, accessed on 31 November 2021). Generally, the air quality is continuously monitored by sophisticated instruments. Therefore, the chemical method is used to measure SO2 and NO2, and the high-volume sampler is being widely used for particulate matter measurement. In this study, hourly surface concentrations (units: µg/m3) of PM2.5, PM10, NO2, NH3, SO2, CO, and O3 were used. Based on the availability of measurements from 2015 to 2020, observations from seven cities were selected for analysis—Ahmedabad, Bengaluru, Hyderabad, Mumbai, Lucknow, Chennai, and Delhi to quantify the contribution of human activities to surface air pollution.

2.5. ERA5

The fifth generation ECMWF reanalysis (ERA5) is the latest generation of atmospheric reanalyses of the global climate. It can provide dozens of commonly used atmospheric and land-surface variables with temporal coverage from 1950 to now [49]. Many studies have proved that ERA5 can provide reasonable temporal and spatial variability of meteorological fields (i.e., wind and precipitation) on a large scale by assimilating remote sensing data, atmospheric sounding data, and ground-based observations. Also, ERA5 with reasonable temporal and spatial variability can be used as the background input of the proposed correction-downscaling model. In this study, variables of the precipitation, wind speed, planetary boundary layer and relative humidity from ERA5 are used to analyze the impact of meteorological conditions on air pollution levels in India. Although the meteorological field from ERA5 is shown reasonable in the spatiotemporal distributions, we also further compare the wind speed, planetary boundary layer, and relative humidity with the corresponding variables from the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), and the precipitation from the Global Precipitation Measurement (GPM).

2.6. Method

India reported the first COVID case on 30 January 2020, and the number of confirmed cases increased to 519 on March 24 (Table 1). To curb the spread of COVID-19 in the Indian subcontinent, a strict lockdown was enforced. The first phase was officially announced on 24 March 2020 and lasted for 21 days from 25 March to 14 April 2020. The lockdown was further extended to 30 June 2020. Here, the following two periods are defined: the pre-lockdown period (1 January–23 March) and the lockdown period (24 March–30 June). As the city locks down, the aerosol mass changes obviously due to the reduction of anthropogenic emissions. To quantify aerosol changes in the Indian subcontinent, daily anomalies of AOD and tropospheric NO2 are calculated from 1 January to 30 June in 2019 and 2020 based on the fifteen-year climatology mean (2005–2019). Corresponding daily anomalies of meteorological fields, and radiation fluxes are also calculated. The anomalies in 2019 and 2020 represent NCAP-induced and city-lockdown-induced changes, respectively. The anomalies during 2019 and 2020 are further compared for two periods—pre-lockdown and lockdown, to investigate the potential impacts of COVID-19 lockdown on air quality and radiation. Note that although pre-lockdown and lockdown periods are defined for the year of 2020, these two periods are also selected for 2019 for comparison purposes. Additionally, the Indian subcontinent is divided into the following three sub-regions: North, Central, and South (Figure 1b), based mainly on population density.

3. Results

3.1. Changes in Satellite Retrieved Tropospheric NO2 and AOD

Dramatic reductions in air pollution levels during the 2020 lockdown can be observed by satellite retrieved concentrations of NO2 in the troposphere. The main sources of tropospheric NO2 are transportation and industrial activities, thus tropospheric NO2 is a good indicator of anthropogenic emissions [30,31]. The spatial distributions of climatological (2005–2019) tropospheric NO2 are also shown in Figure 1a,b for the pre-lockdown and lockdown periods. The spatial distributions of tropospheric NO2 concentrations generally follow population density as follows: the highest concentrations over the Indo-Gangetic Plain (IGP) are found in the North Indian subcontinent, followed by the Central Indian and lower concentrations over the South Indian subcontinent.
An analysis of tropospheric NO2 anomalies strongly suggests significant reductions in anthropogenic emissions in 2020 during the lockdown period (Figure 1c–f). In 2019, tropospheric NO2 anomalies did not show spatially consistent differences in any of the three aforementioned sub-regions during either pre-lockdown or lockdown periods, implying that NCAP did not make substantial differences in air quality during the first half of 2019. However, in 2020, tropospheric NO2 anomalies during the pre-lockdown period showed negative values in large areas of Central and East India, suggesting reductions in anthropogenic emissions likely due to NCAP. Moreover, tropospheric NO2 in 2020 showed larger (~2 times) reductions during the lockdown period than during the pre-lockdown period across most regions of the Indian subcontinent, with the largest reductions in IGP, followed by the Central and South subcontinent. A total reduction of 7.1% (–0.006 DU) in tropospheric NO2 concentrations was detected over the entire three sub-regions in comparison with the 2005–2019 climatology.
The time series of tropospheric NO2 during the pre-lockdown and lockdown periods over the three sub-regions is shown in Figure 2a–c (left). To better show the trend in tropospheric NO2, we calculated the de-trended tropospheric NO2 over the three sub-regions. Tropospheric NO2 levels in 2020 are the smallest due to the reductions in anthropogenic emissions during the lockdown period (light-red dots), in which the regional mean tropospheric NO2 is 0.067, 0.076, and 0.045 DU (Figure S3). In comparison with the averaged tropospheric NO2 of 2015–2019 (Figure S3), tropospheric NO2 over the three sub-regions changed by –0.011 (–13.9%, North), –0.016 (–18.5%, Central), and –0.011 DU (–23.1%, South) during the lockdown, respectively. Also, the interannual variability of regional mean tropospheric NO2 during the pre-lockdown period over the three sub-regions is similar to that of the lockdown period. However, tropospheric NO2 in 2020 over the three sub-regions changed by 0.002 (2.9%, North), –0.012 (–12.5%, Central), and –0.008 (–14.9%, South) DU during the pre-lockdown, which may be caused by the reductions in anthropogenic emissions due to NCAP. Further, the time series of daily mean tropospheric NO2 over the three sub-regions are shown in Figure 3a–c (left). The daily mean tropospheric NO2 in 2019 is close to climatology during most of the days in the three sub-regions. However, the daily mean tropospheric NO2 in 2020 is far below climatological levels during the lockdown period in all three sub-regions, despite that it is close to climatological levels during the pre-lockdown period over the North and South Indian subcontinents, with generally lower levels in the Central Indian subcontinent. Also, the reductions in tropospheric NO2 are more apparent during the first three to four weeks of the lockdown. It is worth noting that daily mean tropospheric NO2 in 2020 during the lockdown period in all three sub-regions is possible within the fifteen-year spread of tropospheric NO2 (gray shadings), which is the minimum and maximum tropospheric NO2 levels between 2005–2019, and they can include larger uncertainties of tropospheric NO2. With the reductions in anthropogenic emissions during COVID-19 lockdown, tropospheric NO2 in the 2020 lockdown period is expected to decrease significantly. Moreover, the tropospheric NO2 and AOD are almost out of phase in all presented regions during the lockdown period, which could be partially attributed to the influences of natural aerosol loadings (i.e., dust aerosol) on AOD [50].
In addition to tropospheric NO2 concentrations, the satellite-retrieved AOD is another useful variable for investigating persistent haze issues, which can well represent the aerosol loadings. Figure 4a,b shows the climatology of the averaged AOD during the pre-lockdown and lockdown periods. It shows that the high AOD occurs over North India along the southern slope of the Himalayas, which is similar to the spatial feature of tropospheric NO2 climatology. In order to better understand the improvement in air quality due to city-lockdown, AOD anomalies are illustrated in Figure 4c–f. Significantly, positive AOD anomalies are generally observed over the Indian subcontinent in 2019 and 2020 during the pre-lockdown period. The regional mean of AOD anomalies over the three Indian sub-regions is about 0.03 (North), 0.07 (Central), and 0.001 (South) in 2019, respectively (Figure S3). However, during the lockdown period, the AOD anomalies in 2019 turn into negative values, especially over the Indus plain. The reduction of aerosol loading over the Indus plain is mainly induced by the decrease in dust aerosol loading, as shown in a previous study [50]. In 2020, however, the decrease in aerosol loading is mainly observed over the Indo Gangetic Plain and the Ganges Delta, which are mainly induced by anthropogenic pollution emissions. Those larger negative AOD anomalies are closely related to the reduction of emissions from the traffic and manufacturing sectors, which are expected to be substantially impacted by the city lockdown [18,19]. The consistent positive anomalies of tropospheric NO2 and AOD during the lockdown period of 2020 implies that the dramatic reductions in tropospheric NO2 and other anthropogenic emissions played more important roles than the variations of meteorological conditions in leading to negative AOD anomalies. Quantitatively, in 2020, AOD was reduced by about 22.6% (0.05, North), 2.9% (0.11, Central), and 20.1% (0.02, South) over the three Indian sub-regions during the lockdown period, which is about 1.7 (North), 1.6 (Central), and 20 (South) times of that in 2019, respectively.
The annual time series of AOD exhibits increasing trends over the Central and South Indian subcontinent during the pre-lockdown and lockdown periods from 2005 to 2019, as shown in Figure 2d–f (right). In 2020, AOD during the lockdown period are the smallest in comparison with 2005–2019 climatology. Moreover, the AOD in 2020 during the lockdown period was 0.38, 0.41, 0.29 versus 0.50, 0.45, 0.39 of the fifteen-year average. This observation may indicate that aerosol loading has dramatically decreased due to the reductions in anthropogenic emissions during the lockdown period. Further, the time series of daily mean AOD exhibits an increasing trend from April to June, despite a decreasing trend in tropospheric NO2 (Figure 3d–f). The reason is attributed to the more contributions of natural sea salt aerosol and dust emitted from the Thar Desert and eastward transported to AOD [50], and enhancement of the photolysis of NO2 due to the warmer and more humid conditions [51,52]. During the pre-lockdown period in 2020, AOD was almost located within the climatological ranges of the three sub-regions. However, during the lockdown period in 2020, a significant decrease in AOD was observed over the North and South Indian subcontinent, especially in the first few weeks of the lockdown period. Over the Central Indian subcontinent, close-to-climatology AOD is found in the first few weeks during the lockdown period. This unexpected high AOD is induced by positive anomalies in mid-tropospheric relative humidity [18]. Overall, the decreases of AOD induced by city-lockdown are the most significant over North Indian subcontinent, where population is the densest and thus anthropogenic emissions are the highest. Also, daily AOD within the fifteen-year spread is naturally due to the larger range of AOD variation in the past fifteen years.

3.2. Changes in Gaseous Emissions near the Surface

Observed surface concentrations of NO2, PM2.5, O3, SO2, and CO from surface stations are also utilized to demonstrate the impact of COVID-19 on air quality. Due to data availability, data from seven stations operated by the Pollution Control Board (CPCB, https://www.cpcb.nic.in/, accessed on 31 November 2021) are selected across India from 2015 to 2020 (Figure 5). Data anomalies during the lockdown period (with reference to 2015–2019) are shown in Figure 5. For NO2, reductions are observed in all cities, with the highest reduction over Bengaluru (city 2, 22.8 µg/m3 and a 48.3% reduction), followed by city 1 (14.3 µg/m3 and 39.8%) and city 7 (15.4 µg/m3 and 50.7%). However, the highest reduction in the tropospheric NO2 is over Ahmedabad (city 1, 0.1 DU and 40%), followed by city 2 (0.037 DU and 30%) and city 5 (0.036 DU and 33%) (Figure S4a). For PM2.5, decreases are detected in all cities except for city 4, with the highest decrease being over Ahmedabad (city 1, 70 µg/m3, and 58%). These observations are generally consistent with the changes in satellite retrieved AOD, which also show a significant decreasing trend (Figure 5c). The highest reduction in column AOD is observed over Delhi (city 7, 0.16 and 25%), followed by city 1 (0.13 and 19%) and city 2 (0.13 and 37%) (Figure S4a). Generally, PM2.5 concentrations decrease by about 39 µg/m3 (52%) over North India, 20 µg/m3 (45%) over Central India, and 27 µg/m3 (41%) over South India, respectively. As for O3, reductions are observed in all cities except for cities 1, 2, and 6. The increases in O3 in these three cities may be related to the variations of VOCs and other factors that can influence the production and/or consumption of tropospheric O3. For SO2 and CO, the largest reductions were seen in Bengaluru, a pattern consistent with the reductions in NO2 and PM2.5. While over other cities, the changes in SO2 and CO are much smaller. Overall, the surface observed changes in NO2 and PM2.5 are consistent with the reductions in satellite retrieved NO2 and AOD. Additionally, the relative changes of meteorological conditions within the same time periods show that precipitation (except Bengaluru and Delhi), wind speed, and planetary boundary layer (except Hyderabad) decrease, while the relative humidity increases (except Hyderabad) (Figure S4e). Moreover, the largest reduction in precipitation is observed over Hyderabad (city 3, 1.46 mm/day and 40%), followed by city 4 (1.31 mm/day and 23%). For the wind speed, the largest reduction is found over Mumbai (city 4, 0.83 m/s and 25%), followed by city 5 (0.58 m/s and 18%) and city 3 (0.56 m/s and 36%). For the planetary boundary layer and relative humidity, the largest reduction and increment are found over Delhi (city 7; 167 m and 21%; 10.3% and 20%), followed by city 1 (130 m and 17%; 9.7% and 19%), respectively.

3.3. Potential Impacts of Meteorological Fields on Air Quality

Meteorological conditions could affect air pollution levels [18,53]. For example, particulate matter levels in northern China during the city-lockdown period increased significantly and even led to several severe haze formations [53]. The unexpected air pollution was induced by the anomalously high humidity and uninterrupted emissions from petrochemical facilities and power plants, whose emissions could promote aerosol heterogeneous chemistry. The unexpected AOD increase in the central part of India is likely due to the simultaneous increase in relative humidity and decrease in wind speed [18]. Here, the climatology of meteorological conditions is analyzed to study the potential impacts of meteorological fields on air quality over the Indian subcontinent, based on ERA5. Firstly, we evaluated ERA5 data against GPM and MERRA-2. The results show that ERA5 can well represent the precipitation and wind filed at 10 m over the India-subcontinent (Figures S5 and S6). Generally, the climatology of precipitation is mainly distributed over the Northeastern and Southwestern Indian subcontinents, with a maximum value of 40 mm/day during the lockdown period (Figure 6). Also, fractional changes ((lockdown minus 2005–2019 climatology)/(climatology average)) in precipitation, circulations, boundary layer height, and relative humidity during the 2020 lockdown period are shown in Figure 7. Compared to climatology for the year 2020, there is a significant increase in precipitation over the Indian subcontinent, with a maximum increase of 100%. The mean fractional changes over North, Central, and South India are 9.6%, 9.7%, and 0.3%, respectively, which indicate the reductions of aerosol particles by wet scavenging. Simultaneously with the precipitation, the fractional changes in wind speed show a decreasing trend of 30–50% over the whole Indian subcontinent, which is consistent with the observed results in Pandey and Vinoj [18]. In general, the prevailing wind at 10 m during the lockdown period over South India is westerly, which turns northwesterly over North India (Figure 6). The mean wind speeds are 1.3, 2.0, and 1.7 m/s over North, Central, and South India. The reductions in wind speed could provide a conducive environment for the stagnation of air-pollutants and increase fire counts over this region [18]. Here, the correlations between AOD, precipitation, and wind speed are also examined, and the day-to-day variation of AOD is associated with the two aforementioned meteorological variables. Clearly, the decreased (increased) AOD is consistent with the increased (decreased) precipitation a few days ago and the increased (decreased) wind during the lockdown period speed (Figure S7).
Humidity is an important atmospheric parameter and can strongly influence AOD through chemical reaction processes [54]. The relative humidity at 2 m is significantly higher over the Indian coastal zones than in the inland areas (Figure 6), and the maximum relative humidity along the coastal regions can reach over 90%. Compared to climatology, the fractional changes show an increasing tendency. Notably, the increase in relative humidity is prominent over North and Central India. Compared to climatology, the fractional changes in relative humidity over North, Central, and South India show increasing variations of 9.6%, 9.7%, and 0.3%, respectively. Pandey and Vinoj [18] have reported that the increase in AOD that occurred over Central India is due to the increase in relative humidity. The larger negative anomalies of tropospheric NO2 and AOD that occurred over North India were also because of the higher relative humidity (Figure 7). Similar to the increase in relative humidity, the planetary boundary layer in the Indian subcontinent generally declined during the lockdown period, which could favor the accumulation of pollutants (Figure 6). Even so, the AOD shows a decreasing tendency. The reason is mainly attributed to the reduction of anthropogenic emissions during the lockdown period. It is worth noting that aerosols can reduce planetary boundary layer height via radiative effects due to the positive feedback to the meteorology. Also, the day-to-day variation of AOD over the three sub-regions is significantly associated with variations in relative humidity and planetary boundary layer height during the lockdown period (Figure S7).
To illustrate the correlations between daily AOD and the four meteorology variables in the three sub-regions during the lockdown period, their scatter plots and correlations are provided in Figure 8. The orthogonal linear regression is used to calculate the correlation coefficients between AOD and meteorological variables, and the statistical significance of the coefficients is evaluated using the two-tailed Student’s t-test. No statistically significant correlation between daily AOD and precipitation and wind speed was detected at the 95% confidence level. Relatively, AOD over North and Central India is highly correlated with relative humidity (positive). However, over South India, the correlation between AOD and relative humidity is not a statistically significant correlation. Over Central India, AOD has a highly negative correlation with the planetary boundary layer, but the correlation is not statistically significant over the other two regions. These results indicate that AOD over North and Central India could be conditioned on the relative humidity and planetary boundary layer, though how to disentangle these multiple effects is challenging and beyond the capability of this analysis.

3.4. Radiation Response to COVID-19 Emission Reductions

The considerable reductions in anthropogenic emissions due to COVID-19 lockdown could be strong enough to cause changes in the regional radiation budget. The changes (lockdown minus 2005–2019 climatology) in radiation fluxes at clear sky conditions for shortwave, longwave, and net at the top of the atmosphere (TOA), in the atmosphere, and at the surface are shown in Figure 9. For SW radiation at the surface, positive anomalies are observed over the northeastern (10–15 W m−2) and western (5–10 W m−2) Indian subcontinents (Table 2), which is consistent with the spatial patterns of decreases in AOD (Figure 4). Less SW radiation is absorbed by the atmosphere, which is likely caused by reductions in absorbing aerosols such as black carbon. Negative radiation anomalies at the TOA indicate a higher planetary albedo (i.e., more scattering), which can also be attributed to reductions in absorbing aerosols in the atmosphere. For longwave radiation at the surface, changes in radiation fluxes have much weaker magnitudes and opposite directions at the surface and in the atmosphere, but the same direction of change at the TOA. For net radiation, it generally follows the changes of shortwave radiation with a slightly smaller magnitude.

4. Conclusions and Discussion

In this study, we investigate the impacts of COVID-19 and the NCAP on the air quality in the Indian subcontinent by using multiple satellite retrievals from OMI and MODIS and surface observation datasets from the Indian National Air Monitoring Network. The results show a significant reduction of 10% and 15% in tropospheric nitrogen dioxide (NO2) and aerosol optical depth (AOD) during the 2020 lockdown period compared to their climatological levels (2005–2019) due to restricted human activities in many regions in India. The surface observations further demonstrate that PM2.5 and NO2 have significant reduced by 33–50% and 41–58% during the 2020 lockdown period in seven cities over the India subcontinent, except for Mumbai in Central India. However, no significant reduction has been detected for tropospheric NO2 and AOD during the same period in 2019. Moreover, the potential impacts of meteorological fields on air quality, such as precipitation, wind speed, relative humidity, and planetary boundary layer height, have also been examined. The increase in relative humidity could, respectively, enhance aerosol hygroscopic growth and induce a more stable boundary layer, which favors the accumulation of pollutants. However, total AOD over the Indian subcontinent decreases by more than 20% compared to climatology. These findings suggest air pollution levels over the Indian subcontinent are significantly influenced by anthropogenic emissions. Further, the analysis of radiation demonstrates an overall increase in surface net radiation over the Indian subcontinent, with decreases in atmosphere-absorbed net radiation and decreases in radiation entering the TOA. The increase in surface net radiation is consistent with a surface temperature increase (0.04–0.07 K) over the South Indian subcontinent in March–May of 2020 [55,56]. These results provide valuable information for policymakers on the effectiveness of the measures proposed in the NCAP in improving air quality compared to the nationwide shutdown due to COVID-19.
However, such city-lockdown induced reductions in air pollutants are conflicted with economic development. Further observation-based analysis and model simulations are needed to investigate more economically efficient measures to mitigate aerosol pollution in the Indian subcontinent and to address the associated climatic and economic impacts.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/rs14081869/s1, Figure S1: The changes of tropospheric NO2 in 2020 (a and d), 2019 (b and e), 2018 (c and f) as compared with that in 2017. (a)–(c): pre-lockdown period and (d)–(f): lockdown period. Pre-lockdown and lockdown are defined as periods of January 1–March 23, 2020 and March 24–June 30, 2020, respectively. Data are from the Ozone Monitoring Instrument (OMI); Figure S2: Annual series of the regional mean tropospheric NO2 and total column AOD from 2005 to 2020 over three subregions in the Indian subcontinent (Figure 1). (a)–(c): NO2 and (d)–(f): AOD. The black and light-red lines respectively represent the period of January 1–March 23 and March 24–June 30. The light-red dotted line represents average value of 2015–2019 during the period of March 24–June 30. The light-red dot shaded represents the value of 2020 during the period of March 24–June 30; Figure S3: Same as Figure S1, but for total column AOD. Data are from MODIS; Figure S4: The changes (lockdown minus climatology) of the tropospheric NO2, AOD, precipitation, wind, relative humidity, and planetary boundary layer at 7 sites in 2020 (light-red bar); Figure S5: Spatial distribution of precipitation for the period of 2015-2020. (a): ERA5 during the per-lockdown period, (b): ERA5 during the lockdown period, (c): GPM during the per-lockdown period, and (d) GPM during the lockdown period; Figure S6: Spatial distribution of wind at 10 meter for the period of 2015-2020. (a): ERA5 during the per-lockdown period, (b): ERA5 during the lockdown period, (c): MERRA2 during the per-lockdown period, and (d) MERRA2 during the lockdown period; Figure S7: Time evolution of AOD, precipitation, wind, relative humidity and planetary boundary layer in 2020 over three subregions in the Indian subcontinent. The black, light-blue, and light-red lines respectively represent North, Central, and South Indian subcontinent.

Author Contributions

Z.H. and Q.J. designed the study. Z.H. and Q.J. performed the analyses and wrote the paper with contributions from all co-authors. Y.M., Z.J., X.Z. and W.D. review and edit the paper. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Natural Science Foundation of China (No. 41905013, No. 42075105 and No. 42175173). Y.M. was also funded by the Chinese Academy of Sciences (CAS) “Light of West China” Program.

Data Availability Statement

OMI data are available at https://acdisc.gesdisc.eosdis.nasa.gov/data/Aura_OMI_Level3/OMTO3e.003/ (accessed on 31 November 2021). The MODIS data are available at https://ladsweb.modaps.eosdis.nasa.gov/archive/allData/61/ accessed on 31 November 2021). CERES data are available at https://asdc.larc.nasa.gov/project/CERES (accessed on 31 November 2021). Surface Observation data can be downloaded at https://app.cpcbccr.com/AQI_India/ (accessed on 31 November 2021). The precipitation, wind speed, planetary boundary layer and relative humidity from ERA5 were acquired from Copernicus Climate Change Service (C3S) portal (https://cds.climate.copernicus.eu, accessed on 31 November 2021).

Acknowledgments

We would like to thank Haofei Yu from University of Central Florida, who makes valuable suggestions for this paper.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Cohen, A.J.; Ross Anderson, H.; Ostro, B.; Pandey, K.D.; Krzyzanowski, M.; Kunzli, N.; Gutschmidt, K.; Pope, A.; Romieu, I.; Samet, J.M.; et al. The global burden of disease due to outdoor air pollution. J. Toxicol. Environ. Health A 2005, 68, 1301–1307. [Google Scholar] [CrossRef] [PubMed]
  2. Chen, K.; Guo, H.; Hu, J.; Kota, S.; Deng, W.; Ying, Q.; Myllyvirta, L.; Dahiya, S.; Zhang, H. Projected air quality and health benefits from future policy interventions in India. Resour. Conserv. Recycl. 2019, 142, 232–244. [Google Scholar] [CrossRef]
  3. Hu, Z.; Jin, Q.; Ma, Y.; Pu, B.; Ji, Z.; Wang, Y.; Dong, W. Temporal evolution of aerosols and their extreme events in polluted Asian regions during Terra’s 20-year observations. Remote Sens. Environ. 2021, 263, 112541. [Google Scholar] [CrossRef]
  4. Lelieveld, J.; Evans, J.S.; Fnais, M.; Giannadaki, D.; Pozzer, A. The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature 2015, 525, 367–371. [Google Scholar] [CrossRef] [PubMed]
  5. Sahu, S.K.; Kota, S.H. Significance of PM2.5 Air Quality at the Indian Capital. Aerosol Air Qual. Res. 2017, 17, 588–597. [Google Scholar] [CrossRef] [Green Version]
  6. Yuda, M. Asian Countries Rush to Fight Toxic Air Pollution. 2019. Available online: https://asia.nikkei.com/Economy/Asian-countries-rush-to-fight-toxic-air-pollution (accessed on 24 January 2019).
  7. Guttikunda, S.K.; Nishadh, K.A.; Jawahar, P. Air pollution knowledge assessments (APnA) for 20 Indian cities. Urban Clim. 2019, 27, 124–141. [Google Scholar] [CrossRef]
  8. NCAP. National Clean Air Programme. Central Pollution Control Board; Ministry of Environmental Forests and Climate Change, The Government of India: New Delhi, India, 2019.
  9. Sundaray, S.N.K.; Bhardwaj, S.R. National Clean Air Programme; Indian Ministry of Environment, Forest & Climate Change: New Delhi, India, 2019; pp. 1–122.
  10. Tiwari, S.; Srivastava, A.K.; Bisht, D.S.; Parmita, P.; Srivastava, M.K.; Attri, S.D. Diurnal and seasonal variations of black carbon and PM2.5 over New Delhi, India: Influence of meteorology. Atmos. Res. 2013, 125, 50–62. [Google Scholar] [CrossRef]
  11. Mittal, S.K.; Singh, N.; Agarwal, R.; Awasthi, A.; Gupta, P.K. Ambient air quality during wheat and rice crop stubble burning episodes in Patiala. Atmos. Environ. 2009, 43, 238–244. [Google Scholar] [CrossRef]
  12. Mishra, A.K.; Shibata, T. Synergistic analyses of optical and microphysical properties of agricultural crop residue burning aerosols over the Indo-Gangetic Basin (IGB). Atmos. Environ. 2012, 57, 205–218. [Google Scholar] [CrossRef]
  13. Bhanarkar, A.D.; Purohit, P.; Rafaj, P.; Amann, M.; Bertok, I.; Cofala, J.; Rao, P.S.; Vardhan, B.H.; Kiesewetter, G.; Sander, R.; et al. Managing future air quality in megacities: Co-benefit assessment for Delhi. Atmos. Environ. 2018, 186, 158–177. [Google Scholar] [CrossRef]
  14. Conibear, L.; Butt, E.W.; Knote, C.; Arnold, S.R.; Spracklen, D.V. Residential energy use emissions dominate health impacts from exposure to ambient particulate matter in India. Nat. Commun. 2018, 9, 617. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Garaga, R.; Sahu, S.K.; Kota, S.H. A Review of Air Quality Modeling Studies in India: Local and Regional Scale. Curr. Pollut. Rep. 2018, 4, 59–73. [Google Scholar] [CrossRef]
  16. Sharma, R.; Kumar, R.; Sharma, D.K.; Son, L.H.; Priyadarshini, I.; Pham, B.T.; Tien Bui, D.; Rai, S. Inferring air pollution from air quality index by different geographical areas: Case study in India. Air Qual. Atmos. Health 2019, 12, 1347–1357. [Google Scholar] [CrossRef]
  17. Guo, H.; Kota, S.H.; Sahu, S.K.; Hu, J.; Ying, Q.; Gao, A.; Zhang, H. Source apportionment of PM2.5 in North India using source-oriented air quality models. Environ. Pollut. 2017, 231, 426–436. [Google Scholar] [CrossRef] [PubMed]
  18. Pandey, S.K.; Vinoj, V. Surprising Changes in Aerosol Loading over India Amid COVID-19 Lockdown. Aerosol Air Qual. Res. 2020, 21, 426–436. [Google Scholar] [CrossRef]
  19. Mahato, S.; Pal, S.; Ghosh, K.G. Effect of lockdown amid COVID-19 pandemic on air quality of the megacity Delhi, India. Sci. Total Environ. 2020, 730, 139086. [Google Scholar] [CrossRef]
  20. Zhang, M.; Katiyar, A.; Zhu, S.; Shen, J.; Xia, M.; Ma, J.; Kota, S.H.; Wang, P.; Zhang, H. Impact of reduced anthropogenic emissions during COVID-19 on air quality in India. Atmos. Chem. Phys. 2020, 21, 4025–4037. [Google Scholar] [CrossRef]
  21. Lal, P.; Kumar, A.; Bharti, S.; Saikia, P.; Adhikari, D.; Khan, M.L. Lockdown to Contain the COVID-19 Pandemic: An Opportunity to Create a Less Polluted Environment in India. Aerosol Air Qual. Res. 2021, 21, 200229. [Google Scholar] [CrossRef]
  22. Dutta, A.; Jinsart, W. Air Quality, Atmospheric Variables and Spread of COVID-19 in Delhi (India): An Analysis. Aerosol Air Qual. Res. 2021, 21, 200417. [Google Scholar] [CrossRef]
  23. Datta, A.; Rahman, M.H.; Suresh, R. Did the COVID-19 lockdown in Delhi and Kolkata improve the ambient air quality of the two cities? J. Environ. Qual. 2021, 50, 485–493. [Google Scholar] [CrossRef]
  24. Muhammad, S.; Long, X.; Salman, M. COVID-19 pandemic and environmental pollution: A blessing in disguise? Sci. Total Environ. 2020, 728, 138820. [Google Scholar] [CrossRef] [PubMed]
  25. Agarwal, A.; Kaushik, A.; Kumar, S.; Mishra, R.K. Comparative study on air quality status in Indian and Chinese cities before and during the COVID-19 lockdown period. Air Qual. Atmos. Health 2020, 13, 1167–1178. [Google Scholar] [CrossRef] [PubMed]
  26. Dhaka, S.K.; Chetna, V.; Kumar, V.; Panwar, A.P.; Dimri, N.; Singh, P.K.; Patra, Y.; Matsumi, M.; Takigawa, T.; Nakayama, K.; et al. PM2.5 diminution and haze events over Delhi during the COVID-19 lockdown period: An interplay between the baseline pollution and meteorology. Sci. Rep. 2020, 10, 13442. [Google Scholar] [CrossRef] [PubMed]
  27. Chatterjee, A.; Mukherjee, S.; Dutta, M.; Ghosh, A.; Ghosh, S.K.; Roy, A. High rise in carbonaceous aerosols under very low anthropogenic emissions over eastern Himalaya, India: Impact of lockdown for COVID-19 outbreak. Atmos. Environ. 2021, 244, 117947. [Google Scholar] [CrossRef] [PubMed]
  28. Aman, M.A.; Salman, M.S.; Yunus, A.P. COVID-19 and its impact on environment: Improved pollution levels during the lockdown period—A case from Ahmedabad, India. Remote Sens. Appl. Soc. Environ. 2020, 20, 100382. [Google Scholar] [CrossRef] [PubMed]
  29. Chand, D.; Wood, R.; Anderson, T.L.; Satheesh, S.K.; Charlson, R.J. Satellite-derived direct radiative effect of aerosols dependent on cloud cover. Nat. Geosci. 2009, 2, 181–184. [Google Scholar] [CrossRef]
  30. Levelt, P.F.; Hilsenrath, E.; Leppelmeier, G.W.; van den Oord, G.H.J.; Bhartia, P.K.; Tamminen, J.; de Haan, J.F.; Veefkind, J.P. Science objectives of the ozone monitoring instrument. IEEE Trans. Geosci. Remote Sens. 2006, 44, 1199–1208. [Google Scholar] [CrossRef]
  31. Li, M.; Zhang, Q.; Kurokawa, J.I.; Woo, J.H.; He, K.; Lu, Z.; Ohara, T.; Song, Y.; Streets, D.G.; Carmichael, G.R.; et al. MIX: A mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP. Atmos. Chem. Phys. 2017, 17, 935–963. [Google Scholar] [CrossRef] [Green Version]
  32. Zhang, R.; Wang, Y.; Smeltzer, C.; Qu, H.; Koshak, W.; Boersma, K.F. Comparing OMI-based and EPA AQS in situ NO2 trends: Towards understanding surface NOx emission changes. Atmos. Meas. Tech. 2018, 11, 3955–3967. [Google Scholar] [CrossRef] [Green Version]
  33. Seinfeld, J.H.; Pandis, S.N. Atmospheric Chemistry and Physics: From Air Pollution to Climate Change; John Wiley & Sons: Hoboken, NJ, USA, 2016. [Google Scholar]
  34. Wang, G.; Zhang, R.; Gomez, M.E.; Yang, L.; Levy Zamora, M.; Hu, M.; Lin, Y.; Peng, J.; Guo, S.; Meng, J.; et al. Persistent sulfate formation from London Fog to Chinese haze. Proc. Natl. Acad. Sci. USA 2016, 113, 13630–13635. [Google Scholar] [CrossRef] [Green Version]
  35. Le, T.; Wang, Y.; Liu, L.; Yang, J.; Yung, Y.L.; Li, G.; Seinfeld, J.H. Unexpected air pollution with marked emission reductions during the COVID-19 outbreak in China. Science 2020, 369, 702–706. [Google Scholar] [CrossRef] [PubMed]
  36. Guo, S.; Hu, M.; Guo, Q.; Zhang, X.; Schauer, J.J.; Zhang, R. Quantitative evaluation of emission controls on primary and secondary organic aerosol sources during Beijing 2008 Olympics. Atmos. Chem. Phys. 2013, 13, 8303–8314. [Google Scholar] [CrossRef] [Green Version]
  37. Liu, H.; Liu, C.; Xie, Z.; Li, Y.; Huang, X.; Wang, S.; Xu, J.; Xie, P. A paradox for air pollution controlling in China revealed by „APEC Blue“ and „Parade Blue“. Sci. Rep. 2016, 6, 34408. [Google Scholar] [CrossRef] [Green Version]
  38. Lamsal, L.N.; Duncan, B.N.; Yoshida, Y.; Krotkov, N.A.; Pickering, K.E.; Streets, D.G.; Lu, Z. U.S. NO2 trends (2005–2013): EPA Air Quality System (AQS) data versus improved observations from the Ozone Monitoring Instrument (OMI). Atmos. Environ. 2015, 110, 130–143. [Google Scholar] [CrossRef]
  39. King, M.D.; Kaufman, Y.J.; Tanre, D.; Nakajima, T. Remote sensing of tropospheric aerosols from space: Past, present, and future. Bull. Am. Meteorol. Soc. 1999, 80, 2229–2259. [Google Scholar] [CrossRef] [Green Version]
  40. Kaufman, Y.J.; Tanré, D.; Remer, L.A.; Vermote, E.; Chu, A.; Holben, B. Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer. J. Geophys. Res. Atmos. 1997, 102, 17051–17067. [Google Scholar] [CrossRef]
  41. Hsu, N.C.; Tsay, S.C.; King, M.D.; Herman, J.R. Aerosol properties over bright-reflecting source regions. IEEE Trans. Geosci. Remote Sens. 2004, 42, 557–569. [Google Scholar] [CrossRef]
  42. Hsu, N.C.; Tsay, S.C.; King, M.D.; Herman, J.R. Deep Blue Retrievals of Asian Aerosol Properties During ACE-Asia. IEEE Trans. Geosci. Remote Sens. 2006, 44, 3180–3195. [Google Scholar] [CrossRef]
  43. Levy, R.C.; Remer, L.A.; Mattoo, S.; Vermote, E.F.; Kaufman, Y.J. Second-generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance. J. Geophys. Res. Atmos. 2007, 112, D13. [Google Scholar] [CrossRef] [Green Version]
  44. Remer, L.A.; Kaufman, Y.; Tanré, D.; Mattoo, S.; Chu, D.; Martins, J.V.; Li, R.-R.; Ichoku, C.; Levy, R.; Kleidman, R. The MODIS aerosol algorithm, products, and validation. J. Atmos. Sci. 2005, 62, 947–973. [Google Scholar] [CrossRef] [Green Version]
  45. Gupta, P.; Levy, R.C.; Mattoo, S.; Remer, L.A.; Munchak, L.A. A surface reflectance scheme for retrieving aerosol optical depth over urban surfaces in MODIS Dark Target retrieval algorithm. Atmos. Meas. Tech. 2016, 9, 3293–3308. [Google Scholar] [CrossRef] [Green Version]
  46. Levy, R.C.; Mattoo, S.; Munchak, L.A.; Remer, L.A.; Sayer, A.M.; Patadia, F.; Hsu, N.C. The Collection 6 MODIS aerosol products over land and ocean. Atmos. Meas. Tech. 2013, 6, 2989–3034. [Google Scholar] [CrossRef] [Green Version]
  47. Sayer, A.M.; Munchak, L.A.; Hsu, N.C.; Levy, R.C.; Bettenhausen, C.; Jeong, M.J. MODIS Collection 6 aerosol products: Comparison between Aqua’s e-Deep Blue, Dark Target, and “merged” data sets, and usage recommendations. J. Geophys. Res. Atmos. 2014, 119, 13,965–913,989. [Google Scholar] [CrossRef]
  48. Fu, Q.; Liou, K.N. Parameterization of the radiative properties of cirrus clouds. J. Atmos. Sci. 1993, 50, 2008–2025. [Google Scholar] [CrossRef] [Green Version]
  49. Doelling, D.R.; Loeb, N.G.; Keyes, D.F.; Nordeen, M.L.; Morstad, D.; Nguyen, C.; Wielicki, B.A.; Young, D.F.; Sun, M. Geostationary enhanced temporal interpolation for CERES flux products. J. Atmos. Ocean. Technol. 2013, 30, 1072–1090. [Google Scholar] [CrossRef]
  50. Doelling, D.R.; Sun, M.; Nguyen, L.T.; Nordeen, M.L.; Haney, C.O.; Keyes, D.F.; Mlynczak, P.E. Advances in Geostationary-Derived Longwave Fluxes for the CERES Synoptic (SYN1deg) Product. J. Atmos. Ocean. Technol. 2016, 33, 503–521. [Google Scholar] [CrossRef]
  51. Hersbach, H.; Bell, B.; Berrisford, P.; Hirahara, S.; Horányi, A.; Muñoz-Sabater, J.; Nicolas, J.; Peubey, C.; Radu, R.; Schepers, D.; et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 2020, 146, 1999–2049. [Google Scholar] [CrossRef]
  52. Jin, Q.; Wei, J.; Pu, B.; Yang, Z.L.; Parajuli, S.P. High summertime aerosol loadings over the Arabian Sea and their transport pathways. J. Geophys. Res.-Atmos. 2018, 123, 10568–10590. [Google Scholar] [CrossRef]
  53. Hu, Z.; Zhao, C.; Huang, J.; Leung, L.R.; Qian, Y.; Yu, H.; Huang, L.; Kalashnikova, O.V. Trans-Pacific transport and evolution of aerosols: Evaluation of quasi-global WRF-Chem simulation with multiple observations. Geosci. Model Dev. 2016, 9, 1725–1746. [Google Scholar] [CrossRef] [Green Version]
  54. David, L.M.; Nair, P.R. Tropospheric column O3 and NO2 over the indian region observed by ozone monitoring instrument (OMI): Seasonal changes and long-term trends. Atmos. Environ. 2013, 65, 25–39. [Google Scholar] [CrossRef]
  55. Jin, Q.; Crippa, P.; Pryor, S. Spatial characteristics and temporal evolution of the relationship between PM2. 5 and aerosol optical depth over the eastern USA during 2003–2017. Atmos. Environ. 2020, 239, 117718. [Google Scholar] [CrossRef]
  56. Yang, Y.; Ren, L.; Li, H.; Wang, H.; Wang, P.; Chen, L.; Yue, X.; Liao, H. Fast Climate Responses to Aerosol Emission Reductions During the COVID-19 Pandemic. Geophys. Res. Lett. 2020, 47, e2020GL089788. [Google Scholar] [CrossRef]
Remotesensing 14 01869 g001 550
Figure 1. Column abundance of tropospheric NO2 climatology (2005–2019) and anomaly (2019 and 2020). (a–c): pre-lockdown period and (d–f): lockdown period. The anomaly is defined as the deviation of tropospheric NO2 over 1 January–23 March and 24 March–30 June from the climatological value, and the anomalies in 2019 and 2020 are calculated based on 2005–2019 climatology. Pre-lockdown and lockdown are defined as periods of 1 January–23 March 2020 and 24 March–30 June 2020, respectively. DU is the Dobson unit, which is equivalent to ~0.45 mmol m−2. Data are retrieved from the Ozone Monitoring Instrument (OMI). The asterisks in (a,d) represent major cities in the Indian subcontinent.
Figure 1. Column abundance of tropospheric NO2 climatology (2005–2019) and anomaly (2019 and 2020). (a–c): pre-lockdown period and (d–f): lockdown period. The anomaly is defined as the deviation of tropospheric NO2 over 1 January–23 March and 24 March–30 June from the climatological value, and the anomalies in 2019 and 2020 are calculated based on 2005–2019 climatology. Pre-lockdown and lockdown are defined as periods of 1 January–23 March 2020 and 24 March–30 June 2020, respectively. DU is the Dobson unit, which is equivalent to ~0.45 mmol m−2. Data are retrieved from the Ozone Monitoring Instrument (OMI). The asterisks in (a,d) represent major cities in the Indian subcontinent.
Remotesensing 14 01869 g001
Remotesensing 14 01869 g002 550
Figure 2. Annual time series of de-trended tropospheric NO2 and AOD from 2005 to 2020 over three subregions in the Indian subcontinent (Figure 1). (a–c): tropospheric NO2 and (d–f): AOD. The black and light-red lines respectively represent the period of 1 January–23 March and 24 March–30 June. The black and light-red dots respectively represent the mean values of tropospheric NO2 and AOD during the period of 1 January–23 March and 24 March–30 June in 2020, respectively.
Figure 2. Annual time series of de-trended tropospheric NO2 and AOD from 2005 to 2020 over three subregions in the Indian subcontinent (Figure 1). (a–c): tropospheric NO2 and (d–f): AOD. The black and light-red lines respectively represent the period of 1 January–23 March and 24 March–30 June. The black and light-red dots respectively represent the mean values of tropospheric NO2 and AOD during the period of 1 January–23 March and 24 March–30 June in 2020, respectively.
Remotesensing 14 01869 g002
Remotesensing 14 01869 g003 550
Figure 3. Time series of the tropospheric NO2 and total column AOD over three subregions in the Indian subcontinent (Figure 1). (a–c): tropospheric NO2 and (d–f): AOD. The black, light-blue, and light-red lines respectively represent the 2005–2019 climatology, 2019, and 2020 time series. The grey shadings indicate the fifteen-year spread. The vertical black dash line indicates the initiation of lockdown on 24 March 2020.
Figure 3. Time series of the tropospheric NO2 and total column AOD over three subregions in the Indian subcontinent (Figure 1). (a–c): tropospheric NO2 and (d–f): AOD. The black, light-blue, and light-red lines respectively represent the 2005–2019 climatology, 2019, and 2020 time series. The grey shadings indicate the fifteen-year spread. The vertical black dash line indicates the initiation of lockdown on 24 March 2020.
Remotesensing 14 01869 g003
Remotesensing 14 01869 g004 550
Figure 4. The same as Figure 1, but for total column AOD. Data are from MODIS.
Figure 4. The same as Figure 1, but for total column AOD. Data are from MODIS.
Remotesensing 14 01869 g004
Remotesensing 14 01869 g005 550
Figure 5. (a) Population density (×103 persons/km2). (b–f) The changes (lockdown minus climatology) of near surface NO2, PM2.5, O3, SO2, and CO at 7 cities in 2020 (light-red bar). Data are from surface station observations.
Figure 5. (a) Population density (×103 persons/km2). (b–f) The changes (lockdown minus climatology) of near surface NO2, PM2.5, O3, SO2, and CO at 7 cities in 2020 (light-red bar). Data are from surface station observations.
Remotesensing 14 01869 g005
Remotesensing 14 01869 g006 550
Figure 6. Climatology of meteorological conditions between 2005 and 2019. (a): precipitation (units: mm/day), (b): wind at 10 m (unites: m/s), (c): relative humidity (unites: %), and (d) planetary boundary layer height (unites: m). Data retrieved from ERA5 reanalysis.
Figure 6. Climatology of meteorological conditions between 2005 and 2019. (a): precipitation (units: mm/day), (b): wind at 10 m (unites: m/s), (c): relative humidity (unites: %), and (d) planetary boundary layer height (unites: m). Data retrieved from ERA5 reanalysis.
Remotesensing 14 01869 g006
Remotesensing 14 01869 g007 550
Figure 7. Fractional changes (%) ((lockdown minus climatology)/(climatology average)) in (a) precipitation, (b) wind speed (m/s), (c) relative humidity, and (d) planetary boundary layer. Data retrieved from ERA5 reanalysis.
Figure 7. Fractional changes (%) ((lockdown minus climatology)/(climatology average)) in (a) precipitation, (b) wind speed (m/s), (c) relative humidity, and (d) planetary boundary layer. Data retrieved from ERA5 reanalysis.
Remotesensing 14 01869 g007
Remotesensing 14 01869 g008 550
Figure 8. Relationships between daily mean AOD with precipitation, wind speed, radiative humidity and planetary boundary layer height over North, Central, and South India during the lockdown period. Correlation coefficients (r) are calculated via the orthogonal linear regression, and the statistical significance (p) is using the two-tailed Student’s t-test. Inhere, the statistical significance level at 95% (p < 0.05) is marked with the asterisk (red color).
Figure 8. Relationships between daily mean AOD with precipitation, wind speed, radiative humidity and planetary boundary layer height over North, Central, and South India during the lockdown period. Correlation coefficients (r) are calculated via the orthogonal linear regression, and the statistical significance (p) is using the two-tailed Student’s t-test. Inhere, the statistical significance level at 95% (p < 0.05) is marked with the asterisk (red color).
Remotesensing 14 01869 g008
Remotesensing 14 01869 g009 550
Figure 9. Changes (lockdown minus climatology) in shortwave (SW), longwave (LW), and NET (SW + LW) radiation flux (unites: W/m2) at the top of the atmosphere (TOA), in the atmosphere (ATM), and at the surface (SFC) under clear-sky condition. Data are retrieved from CERES.
Figure 9. Changes (lockdown minus climatology) in shortwave (SW), longwave (LW), and NET (SW + LW) radiation flux (unites: W/m2) at the top of the atmosphere (TOA), in the atmosphere (ATM), and at the surface (SFC) under clear-sky condition. Data are retrieved from CERES.
Remotesensing 14 01869 g009
Table
Table 1. The dates of key COVID-19 events in India, including confirmed cases, deaths, and implementation of governmental policies.
Table 1. The dates of key COVID-19 events in India, including confirmed cases, deaths, and implementation of governmental policies.
DateConfirmed CasesDeathsThe Government’s Policy
30 January 20201\\
24 March 20205199Nationwide lockdown from 25 March to 14 April 2020
14 April 202010,815353Nationwide lockdown Extended to 3 May 2020
4 May 202042,5331373Nationwide lockdown extended to 31 May 2020
31 May 2020181,8275185Nationwide lockdown extended to 30 June 2020
30 June 2020585,79217,410Nationwide unlock
Table
Table 2. Changes (lockdown minus climatology) of the shortwave (SW), longwave (LW), and NET (SW + LW) radiation flux at the top of atmosphere, in the atmosphere, at the surface over North, Central, and South Indian subcontinent (Figure 1) during the lockdown period in 2020. Data are retrieved from the Clouds and the Earth’s Radiant Energy System (CERES). The lockdown period is defined as 1 January–23 March in 2020, and the climatology is the same date range from 2005 to 2019.
Table 2. Changes (lockdown minus climatology) of the shortwave (SW), longwave (LW), and NET (SW + LW) radiation flux at the top of atmosphere, in the atmosphere, at the surface over North, Central, and South Indian subcontinent (Figure 1) during the lockdown period in 2020. Data are retrieved from the Clouds and the Earth’s Radiant Energy System (CERES). The lockdown period is defined as 1 January–23 March in 2020, and the climatology is the same date range from 2005 to 2019.
Radiation
(W m−2)		Top of AtmosphereAtmosphereSurface
SWLWNETSWLWNETSWLWNET
North–2.3 –0.7–3.0–8.2+1.0–7.2+5.9–1.7+4.2
Central–0.7–0.7–1.4–3.0+0.4–2.6+2.3–1.1+1.2
South–3.2–0.6–3.8–11.8+0.1–11.7+8.6–0.7+7.9
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Hu, Z.; Jin, Q.; Ma, Y.; Ji, Z.; Zhu, X.; Dong, W. How Does COVID-19 Lockdown Impact Air Quality in India? Remote Sens. 2022, 14, 1869. https://doi.org/10.3390/rs14081869

AMA Style

Hu Z, Jin Q, Ma Y, Ji Z, Zhu X, Dong W. How Does COVID-19 Lockdown Impact Air Quality in India? Remote Sensing. 2022; 14(8):1869. https://doi.org/10.3390/rs14081869

Chicago/Turabian Style

Hu, Zhiyuan, Qinjian Jin, Yuanyuan Ma, Zhenming Ji, Xian Zhu, and Wenjie Dong. 2022. "How Does COVID-19 Lockdown Impact Air Quality in India?" Remote Sensing 14, no. 8: 1869. https://doi.org/10.3390/rs14081869

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop