Does air pollution upsurge in megacities after Covid-19 lockdown? A spatial approach

Abstract

The current coronavirus (COVID-19) pandemic has a high spreading and fatality rate. To control the rapid spreading of the COVID-19 virus, the government of India imposed lockdown policies, which creates a unique opportunity to analyze the impact of lockdown on air quality in the two most populous cities of India, i.e., Delhi and Mumbai. To do this, the study employed a spatial approach to examine the concentration of seven criteria pollutants, i.e., PM_2.5, PM₁₀, NH₃, CO, NO₂, O₃, and SO_2, before, during, and after a lockdown in Delhi and Mumbai. Overall, around 42%, 50%, 21%, 37%, 53%, and 41% declines in PM_2.5, PM₁₀, NH₃, CO, NO₂, and SO₂ were observed during the lockdown period as compared to previous years. On the other hand, a 2% increase in O₃ concentration was observed. However, the study analyzed the National Air Quality Index (NAQI) for Delhi and Mumbai and found that lockdown does not improve the air quality in the long term period. Our key findings provide essential information to the cities' administration to develop rules and regulations to enhance air quality.

1. Introduction

Air pollution is one of the biggest challenges humankind is facing in the 21st century, and it has had various direct or indirect effects on human health. The air pollution levels in megacities of the world exceed the air quality standards recommended by the World Health Organization (WHO), and even the pollution concentration in some cities exceeds ten times more than the standard level (Marlier et al., 2016). The air contains acidic substances such as CO, NH₃, PM_2.5, PM₁₀, NO₂, etc., which can cause widespread effects on human health. Long-term inhalation of polluted air can cause lung cancer, stroke, and heart disease, as well as heart failure. These pollutants are mainly emitted from human actions like the burning of fossil fuel, transportation, manufacturing process, and deforestation (Blondet et al., 2019; Kinnon et al., 2019; Li et al., 2019). Air pollution damages the health of about 1 billion people and kills around 7 million people every year (WHO, 2014).

Since the outbreak of coronavirus disease, 2019 (COVID-19) in Wuhan, China, numerous health, economic, and social issues have been arising (Khoo and Lantos, 2020; C. Wang et al., 2020; Zhu et al., 2020). As of January 30, the World Health Organization (WHO) declared the outbreak of COVID-19 as a global health emergency. Besides China, it has also severely affected the whole world. Covid-19 not only cause pneumonia but also damages the liver, heart, and kidney, leading to multiple organ disorders and ultimately leading to death. Globally, as of September 15, 2020, there have been 29.16 million confirmed cases, including 0.93 million deaths (WHO, 2020), out of which India has reported 4.93 million confirmed cases and 80776 death. Thus, India is accounted for 16.91 percent, and 8.72 percent of worldwide COVID-19 confirmed cases and deaths, respectively.

Megacities of the world are facing severe environmental challenges due to industrial waste and vehicle emissions (Yang et al., 2020). However, after the COVID-19 pandemic, most countries put restrictions on all business activities, including transportations (land, sea, air), manufacturing industries, construction, and mining activities. Globally, a significant decline in air pollution has been observed, such as in Japan (Ma and Kang, 2020), Brazil (Dantas et al., 2020), Spain (Baldasano, 2020), China (Bao and Zhang, 2020), India (Mahato et al., 2020; Shehzad et al., 2020), and the United States (Pata, 2020; Sarfraz et al., 2020). Consequently, low environmental pollution has a positive impact on human health, and it helps human immunity to resist the virus (Yang and Liu, 2018). Moreover, Chakraborty and Maity (2020) argue that lockdown amidst COVID-19 is offering short-term benefits to air quality and human health. Still, it owns the double edge threat from the perspective of the long-run aspect. First, ongoing efforts towards climate mitigation are adversely affected due to economic slowdown; besides, rising health issues along with financial fear (recession) could overshadow the problem of climate change. Second, the sudden resumption of businesses after the lockdown will put massive pressure on the environment.

The Indian economy is undergoing the phase of industrialization and modernization, due to which its hazardous air pollution is somewhat similar to the industrial revolution in Europe (Mahato et al., 2020). Moreover, Delhi and Mumbai are among the most polluted megacities (WHO, 2018). After the emergence of COVID-19, nation wise lockdown was imposed in India during March 2020 (Table 1 ), which created a unique opportunity to gauge the impact of lockdown on air quality in Delhi and Mumbai. Overall the effect and significance of lockdown are still not well understood and likely to have a significant role in the restoration of air quality. Limited studies examine the impact of lockdown on air quality in mega cites around the world, but the evidence of Delhi and Mumbai is still missing. The prevailing literature on the impact of lockdown on air quality has only been observed on one or two pollutants. Although discussing the air quality, concentrating solely on one or two pollutants cannot provide a holistic picture. Moreover, none of the studies examined the concentration of pollutants after the lockdown period. Therefore, this study analyzes the impact of lockdown in Delhi and Mumbai by using seven criteria pollutants, PM_2.5 PM₁₀ CO NH₃ NO₂ SO₂ and O₃ parameters from March 20, 2020, to June 30, 2020. In doing so, we analyzed each pollutant's concentration before the announcement of a complete lockdown in Delhi and Mumbai and compared the aftermaths with before, during the lockdown period. The study also evaluates whether Delhi and Mumbai's air quality increases after the end of the lockdown. Our key findings will provide important information for Delhi and Mumbai's city administration to develop new rules and regulations to improve air quality.

Table 1.

Schedule of Covid-19 pandemic history in India.

Phase	Start of Lockdown	End of Lockdown	Total Days
Phase-1	25 March 2020	April 15, 2020	21
Phase-2	15 April 2020	03 May 2020	19
Phase-3	04 May 2020	17 May 2020	14
Phase-4	18 May 2020	31 May 2020	14

Data source: https://www.india.gov.in/

2. Area of study

This study has focused on Delhi and Mumbai, as these are the most populous cities in India. According to the United Nations, as of 2018, Delhi is the second-largest megacity of the world, with a population growth rate of 21 percent and having a population of over 16.78 million (Census, 2011; http://census2011.co.in). The National Capital Territory (NCT) Delhi located in Northern India and lies between N-latitude 28° 36′ 36″ and E-longitude 77° 13′ 48″ with an area of about 1484 km² (700 km² Urban and 783 km² rural). Delhi has dual status as a state and city incorporating Shahdra Block, Najafgarh Block, Mehrauli Block, and Kanjhawla Block. Delhi features a dry winter, semi-arid climate, and weather can be categorized into five major seasons. Summer (Mar–May), Monsoon (Jun–Sept), short Post-monsoon (Oct–Nov), Winter (Dec–Feb), and Pre-monsoon (March–May). Temperature varies between 42 °C and 48 °C in summer and 4 °C to 10 °C in winter.

Mumbai is the capital city of the Indian state of Maharashtra. Mumbai is one of India's most populous cities and ranked the 17th most populous city globally (UN, 2018) with a population of over 20 million (Census, 2011; http://census2011.co.in). Mumbai lies between N-latitude 18° 58′ 30″ and E-longitude 72° 49′ 33″ with an area of about 603 km². Geographically, Mumbai is located on a narrow peninsula on the southwest of Salsette Island, which lies between Vasai creek to the north, Thane Creek to the east, and the Arabian sea to the west. The weather of Mumbai is a tropical, dry, and wet climate that can be categorized into three seasons. Winter (Oct–Feb), Summer (Mar–May), and Monsoon (Jun–Sep). Temperature varies between 25 °C and 35 °C in summer and 18 °C–25 °C in winter.

3. Material and methods

3.1. Data specification

The study examines Delhi and Mumbai's air quality by using the data of numerous air quality observing units mounted there. The study retrieved the air quality information through thirty-six stations working in Delhi and nine stations mounted in Mumbai (Table 2 ). However, the study excluded the stations which have incomplete data. The observing organizations for these air quality measuring units are comprised of continuous ambient air quality monitoring (CAAQM) – CBCB, manual ambient air quality monitoring (MAAQM) – CPCB, Indian Institute of Tropical Meteorology (IITM) Pune, Delhi pollution control committee (DPCC), and System of Air Quality, Weather Forecasting And Research (SAFAR). The information regarding the 24-h concentration of major air pollutants, i.e., Carbon Monoxide (CO), Nitrogen Dioxide (NO₂), Particulate Matters (PM₁₀ and PM_2.5), Ozone (O₃), Ammonia (NH₃), and Sulphur Dioxide (SO₂), have been acquired from the database of Central Pollution Control Board (CPCB).

Table 2.

Air Quality monitoring stations in Delhi in Mumbai.

Delhi stations			Mumbai stations
Alipur	IHBAS	Okhla Phase-2	Bandra
Dr. Karni Singh Shooting Range	Major Dhyan Chand National Stadium	Sri Aurobindo Marg	Chhatrapati Shivaji Intl. Airport (T2)
Ashok Vihar	Jahangirpuri	Punjabi Bagh	Colaba
Aya Nagar	Lodhi Road	Pusa	Kurla
Bawana	ITO	R K Puram	Powai
Burari Crossing	Mandir Marg	Rohini	Sion
CRRI Mathura Road	Mundka	Shadipur	Vasai West
DTU	NSIT Dwarka	Sirifort	Vile Parle West
Anand Vihar	Najafgarh	Sonia Vihar	Worli
Dwarka-Sector 8	Narela	Patparganj
East Arjun Nagar	Nehru Nagar	Vivek Vihar
IGI Airport (T3)	North Campus	Wazirpur

Source. Central pollution control board (http://www.cpcb.nic.in/)

3.2. Estimation strategy

The Air Quality Index (AQI) is estimated based on a benchmark fixed for each pollutant. The impact of a specific pollutant is captured in a single index by utilizing the correct accretion method (Ott, 1978). Previously, the maximum sub-index technique was used to build the air quality index through five pollutants, i.e., PM₁₀, CO, NO₂, SO₂, and PM_2.5. Moreover, IITM introduced a new air quality index (IITM-AQI) that includes O₃ (Beig et al., 2010). It measured the air quality based on a six-point scale, such as Severe, Very poor, Poor, Moderately polluted, Satisfactory, and good. Further, Indian National Air Quality Standards (INAQS) introduced twelve air pollutants, i.e., SO₂, NO₂, O₃, NH₃, CO, PM_2.5, PM₁₀, Arsenic (As), Benzo a Pyrene (BaP), Lead (Pb), Nickel (Ni), and Benzene (C₆H₆) to gauge the air quality in India. Out of these twelve air pollutants, four factors, such as C₆H₆, Ni, As, and BaP, are measured on an annual basis, while others are measured on a one, eight, and 24-h basis. In order to examine the impact of lockdown befallen due to COVID-19 on the environmental quality of Delhi and Mumbai, this study analyzes the seven pollutant factors, i.e., NO₂, SO₂, CO, O₃, PM_2.5, PM₁₀, and NH_3, individually and also as an integrated index. This investigation compares the fallouts during the lockdown era, before the lockdown period, and after the lockdown period. This investigation computed the National Air Quality Index (NAQI) for Delhi and Mumbai in two steps. First, this study computes each pollutant factor's sub-index by utilizing the information of various stations installed in Delhi and Mumbai. Second, this investigation combines each sub-index's values through mathematical function to form the NAQI for Delhi and Mumbai Additionally, to gauge the health impact of lockdown in Delhi and Mumbai, this investigation applied six category scale established by (CPCB, 2015), which is given in Table 3 .

Table 3.

NAQI categories and Health Effects.

AQI categories	Health Effect
Good (0–50)	Minimal impact
Satisfactory (51–100)	Minor breathing discomfort to sensitive people.
Moderately polluted (101–200)	May cause breathing discomfort to people with lung disease
Poor (201–300)	May cause breathing discomfort to people on prolonged exposure, and discomfort to people with heart disease.
Very poor (301–400)	May cause respiratory illness to the people on prolonged exposure. Effect may be more pronounced in people with lung and heart diseases.
Severe (401–500)	May cause respiratory impact even on healthy people, and serious health impacts on people with lung/heart disease.

Source. Central pollution control board (http://www.cpcb.nic.in/)

The sub-index for each pollutant is computed by employing 24 h mean data (8 h for Ozone and Carbon Monoxide) and pollutant concentration breakpoints displayed in Table 4 (CPCB, 2015). We develop the sub-index by following the model of (CPCB, 2015). The equation for the sub-index is given below,

$$I_i=\frac{{\lambda }_{high}-{\lambda }_{low}}{{\psi }_{high}-{\psi }_{low}}\left(\psi -{\psi }_{low}\right)+{\lambda }_{low}$$ (1)

Here I _i indicates sub-index for pollutant parameter i, while $\psi$ defines the value of pollutant concentration at time t. Furthermore, ${\psi }_{low}$ and ${\psi }_{high}$ specified the pollutant factor breakpoint, which is $\le \psi$ and$\ge \psi$, respectively. As well, ${\lambda }_{low}$ and ${\lambda }_{high}$ nominate the pollutant factor breakpoint corresponding to ${\psi }_{low}$ and${\psi }_{high}$, respectively. The accumulation of these indices to achieve the NAQI is performed through mathematical summation, weighted additive form, root mean square from, weighted additive form, maximum or minimum operator, or multiplication as follows (Mahato et al., 2020);

Table 4.

AQI and pollutant concentration breakpoints.

AQI	NO2 (24hr) μg/m3	NH3 (24hr) μg/m3	CO (8hr) mg/m3	SO2 (24hr) μg/m3	PM10 (24hr) μg/m3	PM2.5 (24hr) μg/m3	O3 (8hr) μg/m3
0–50	0–40	0–200	0–1.0	0–40	0–50	0–30	0–50
51–100	41–80	201–400	1.1–2.0	41–80	51–100	31–60	51–100
101–200	81–180	401–800	2.1–10	81–380	101–250	61–90	101–168
201–300	181–280	801–1200	10–17	381–800	251–350	91–120	169–208
301–400	281–400	1200–1800	17–34	801–1600	351–430	121–250	209–748
401–500	400+	1800+	34+	1600+	430+	250+	748+

Source. Central pollution control board (http://www.cpcb.nic.in/)

Weighted additive form;

$$\text{NAQI }=\text{ aggregated sub}-\text{index }\left(\text{I}\right)=\sum WiIi(i=1,2,\dots .,n)$$ (2)

where w indicates the weight of the pollutant factor, and I denote the sub-index for pollutant factor i. while n represents the number of variables utilized in this investigation. The investigation assigns equal weight to these indices, inferring that$\sum W_i=1$.

Non-linear aggregated method;

$$\text{NAQI }=\text{ aggregated sub}-\text{index }\left(\text{I}\right)={[\sum I_i^h]}^{\left(\frac{1}{h}\right)}$$ (3)

Here h represents the positive number > 1.

Root-mean-square method;

$$\text{NAQI }=\text{ aggregated sub}-\text{index }\left(\text{I}\right)=\left[1/\text{A}{\left({\text{I}}_1^2+{\text{I}}_2^2+\mathrm{...}{\text{I}}_{\text{n}}^2\right)}^{0.5}\right]$$ (4)

Minimum or maximum operator method (Ott, 1978);

$$\text{NAQI }=\text{ Minimum or Maximum value of sub}-\text{index }\left(\text{I}\right)=\left[1/\text{A}\left({\text{I}}_1+{\text{I}}_2+\mathrm{...}{\text{I}}_{\text{n}}\right)\right]$$ (5)

4. Results and discussion

4.1. Spatial analysis of each pollutant before, during, and after the lockdown period

To curb the COVID-19 pandemic, the Government of India imposed a strict nation-wise lockdown in the last week of March, which limited the citizens' movement and social interaction. As a result of the lockdown, traffic movement reduced significantly, and all the commercial activities were suspended, which affected the people's daily life. On the other hand, dramatic improvements in air quality have been observed, especially in primary dominated air quality, such as PM_2.5 level in Delhi (Fig. 1 a). Before the lockdown on 20 and 23 March, the PM_2.5 level ranges from 200 μg/m³ to 500 μg/m³ in different Delhi areas. The Indian government announced a phase one lockdown from March 24, 2020, to April 15, 2020, for 21 days. Just after the one day of lockdown, the PM_2.5 level reduced 20 μgm/m³ in most areas of Delhi. The government lifts the lockdown on May 31, and the PM_2.5 level remained between the range of 20 μg/m³ to 120 μg/m³ on June 01, 2020, and June 06, 2020, in Delhi (Fig. 1a). Similarly, the PM_2.5 level in Mumbai before lockdown range between 60 μg/m³ to 120 μg/m³, which is lower as compare to Delhi PM₁₀ (Fig. 1b). Just after the one day of the lockdown, the PM_2.5 level declines between 10 μg/m³ to 40 μg/m³ in various wards of Mumbai. Mumbai shows a consistent improvement in the PM_2.5 level even after the government lifts the lockdown on May 31. The PM_2.5 values range from 10 μg/m³ to 24 μg/m³ in June, inferring that lockdown has a substantial long-term impact on reducing air pollution in Mumbai.

Fig. 1.

Spatial investigation of PM_2.5, PM₁₀, CO, and NO₂ in NCT Delhi and Mumbai.

A similar pattern is shown by PM₁₀ in Delhi up to 600 μg/m³ before lockdown (Fig. 1c). Just after two days of lockdown, it drops down to 200 μg/m³. Similarly, in Mumbai, the PM₁₀ value before lockdown is 225 μg/m^3, but it drops down to 60 μg/m³ after four days of lockdown (Fig. 1d). A slight increase is seen in Delhi up to 400 μg/m³ and 150 μg/m³ in Mumbai in June after lockdown (Fig. 1c and d). The most important dissimilarity is noticeable in PM_2.5 and PM₁₀. It can be seen clearly from the spatial concentration pattern of these two contaminants during the lockout time on different days. Road traffic is the principal cause of PM_2.5 and PM₁₀ in the megacities Delhi and Mumbai. Other causes include industrial production, building works, and the re-suspension of dust. Notably, the concentrations of these two pollutants decreased below the permitted limit after one day of lockdown (March 23 to March 25). These corresponding numbers of PM_2.5 and PM₁₀ are based on geographic locations. Hence, a minor cessation of lockdown measures for the required vehicles and localized industries outside the hot spots, i.e., COVID-19 contaminated area, has increased PM₁₀ and PM_2.5 concentration on June 01 and June 08 (post lockdown). Mumbai shows more improvement in PM10 and PM2.5 concentrations after lockdown compared to NCT Delhi.

Other essential contaminants except PM₁₀ and PM_2.5 are CO and NO₂, which also shows a substantial decline during the lockdown era in Delhi (Fig. 1e and g). The CO level in Delhi varies from 2 μgm/m³ to 6 μgm/m³ in the pre-lockdown era and drops down to 0.5 μg/m³ to 2.5 μg/m³ during the lockdown era and remains consistent even after the expiry of lockdown in June (Fig. 1e). A similar pattern was seen in the NO₂ emissions in Delhi as its concentration lies between 50 μgm/m³ to 150 μgm/m³ before lockdown. Tough, during, and post-lockdown, it remains between 20 μg/m³ to 60 μg/m³ (Fig. 1g). The CO emissions in Mumbai are lower than Delhi before lockdown, ranges from 1 mg/m³ to 3 mg/m³. After lockdown, it reduced up to 0.25 mg/m³ to 0.80 mg/m³ and remains persistent in the post-lockdown period (Fig. 1f). NO₂ level in Mumbai lies between 30 μg/m³ to 100 μg/m³ in the pre-lockdown period, but after two days of lockdown on March 25, it drops up to 10 μg/m³. In the post-lockdown period in June 2020, it slightly goes up to 30 μg/m³ (Fig. 1h), which is lower than NCT Delhi in the same period. In urban areas, CO and NO₂ pollutants are generated primarily from the activity of road traffic combustion, particularly petrol and, to a lesser degree, gasoline, cars, industrial production, and coal plants. All these industries closed their activities during this lockout period. Consequently, it resulted in a decrease in contaminants, such as CO and NO₂.

Subsequently, concentration levels of O₃ in the megacity of Delhi increases significantly from 100 μg/m³ to 120 μg/m³ after three days of lockdown due to three possible collective causes (Fig. 2 a). First, lowering NO in the lockdown will cause urban O₃ to bump up, as opposed to actions in rural areas where the concentration of NO is largely restricted (Monks et al., 2015). Second, the depletion of nitrogen oxide (NO) decreases the usage of O₃ (titration, NO + O₃ = NO₂ + O₂), contributing to a rise in O₃ concentrations. Third, The inevitable increase of temperatures and insolation in the northern hemisphere from March to August as a result of sun movement to the north leads to an increase in O_3, when the maximum O₃ is usually recorded (Gorai et al., 2017). Further, O₃ slightly increases in Mumbai and again drops down to 40 μg/m³ level after in the post-lockdown period (Fig. 2b). Mumbai is the fourth most populous city in the world and one of the world's most populous metropolitan areas. Because of Delhi's offshore position outside the sea, it is a low SO₂ city as most of this pollutant originates from maritime traffic (large cargo ships, cruise ships, and ferries). SO₂ level in Delhi ranges from 25 μg/m³ to 75 μg/m³ in the pre-lockdown period; while it considerably decreases up to 20 μg/m³ after two days of lockdown and remains at 30 μg/m³ levels in the post-lockdown age (Fig. 2c).

Fig. 2.

Spatial investigation of SO₂, NH₃, and O₃, in Delhi and Mumbai.

The concentration of SO₂ in Delhi usually remains below the permissible level, and the concentration of SO₂ during this lockdown time has experienced a decrease compared with the pre-lockdown epoch. Due to the lockdown situation similar pattern was seen in Mumbai, as shown in (Fig. 2d). On the other hand, NH₃ is marginally decreased in Delhi from 75 μg/m³ in pre-lockdown to 60 μg/m³ during the lockdown period and again goes up to 100 μg/m³ in the post-lockdown period (Fig. 2e). Though in Mumbai, it shows a significant decrease during the lockdown period 6 μg/m³ and again goes up to 75 μg/m³ in post lockdown (Fig. 2f). The fact that NH₃ is derived from fields other than agriculture is marginal and widely accepted (Sutton et al., 1995). Consequently, the NH₃ concentration often falls below the appropriate level in Mumbai. Because petrol engine vehicles are a leading cause of urban NH₃, substantial decreases in NH₃ concentrations during the lockdown period were documented (Kean et al., 2000).

4.2. Changes in pollutants from the pre-lockdown period to after lockdown period

During the lockdown, a significant reduction in air pollutants has been observed in the megacities of India, Delhi, and Mumbai (see Fig. 3, Fig. 4 ). These figures show the variations before, during, and after the lockdown period in each pollutant. In particular, concentrations of CO, NO₂, PM_2.5, and PM₁₀ showed substantial declines during the lockdown era in both cities.

Fig. 3.

Variations in PM_2.5, PM₁₀, CO, O₃, NO₂, SO₂, and NH₃ in Delhi.

Fig. 4.

Variations in PM_2.5, PM₁₀, CO, O₃, NO₂, SO₂, and NH₃ in Mumbai.

PM_2.5 concentrations have decreased from 200 μg/m³ to 50 μg/m³ in Delhi (Fig. 3e); it remains lower till June due to lockdown in effect. Similarly, the value of PM_2.5 in Mumbai remains under 100 μg/m³ from January to March, but during the lockdown period, it fell to permissible levels of 20 μg/m³ until June 30, 2020 (Fig. 4e). In the same way, the average PM₁₀ level in Delhi is 300 μg/m³ from January 2020 to March 2020; as the Indian government announced lockdown in late March, its value fells down to 100 μg/m³ and goes up at the end of June (post lockdown period) (Fig. 3f). The average PM₁₀ concentration in Mumbai remains up to 200 μg/m³ from January to March. However, during the lockdown, it reduced up to 40 μg/m³ level at the end of June 2020 (Fig. 4f). The rate of change between these pollutants is higher in Delhi compared to Mumbai.

Other contaminants that display substantial differences between pre and during lockdown are CO and NO₂ in Delhi and Mumbai. The CO emissions in Delhi in the pre-lockdown period from January 2020 to March 2020 shows 1.5 μg/m³, but during the lockdown, its value fell up to 0.5 μg/m³ and again increases up to 1 μg/m³ in post lockdown period (Fig. 3a). Whereas in (Fig. 4a) the CO level in Mumbai is 1.4 mg/m³ in the pre-lockdown period and falls up to 0.4 μg/m³ during the lockdown period. The NO₂ level in Delhi and Mumbai remains 50 μg/m³ in the pre-lockdown age. Nevertheless, during the lockdown in Delhi, NO₂ remains up to 20 μg/m³ and again increases in the post-lockdown era (Fig. 3c). NO₂ level drops to 10 μg/m³ during the lockdown and slightly upsurge after lockdown (Fig. 4c). However, the decrease in SO₂ in Delhi and Mumbai were lower compared to the other pollutants (Fig. 3, Fig. 4g). Furthermore, NH₃ was decreased during the lockdown phase in Delhi and Mumbai compared to the pre-lockdown period. However, its level goes up in the post-lockdown phase as expected because economic activities are resuming (Fig. 3, Fig. 4b). Besides, an 8 h average daily O₃ concentration indicates a rise with an upward trend n Delhi (Fig. 3d), while in Mumbai, it showed a downward trend (Fig. 4d). In this case, it is quite feasible to consider that from April to August is the high O₃ duration in the Indian Subcontinent due to an escalation in insolation (Gorai et al., 2017). The cause of the rise in the concentration of O₃ is a reduction in nitrogen oxide (NO).

4.3. Dissimilarities in NAQI of Delhi and Mumbai through January 01, 2020, to December 31, 2020

Fig. 5a and b exhibited the National Air Quality Index (NAQI) in Delhi and Mumbai, respectively. The absolute improvement in air quality can be seen in both cities of India during the lockdown era. In Delhi, during the pre-lockdown period (January 2020–March 22, 2020), the NAQI remains between the “Poor” to the “Severe” health category. However, as the lockdown transpired in Delhi due to COVID-19 on March 23, 2020, NAQI suddenly fells in the range of the “GOOD” health category. During phase one of lockdown, NAQI lies amid 50 to 150, which represents the good to the moderate category of health index. Hence, the air quality has improved in Delhi during the lockdown era. On the other hand, Mumbai shows more improvement concerning NAQI as its pre lockdown air quality lies among “Moderately polluted” to “Poor” health index (101–250). However, during and after the lockdown period, it remains consistent below 100 points at the “Satisfactory” level (Fig. 5b). It implies that megacities of the world can improve the air quality by strict management practices. However, the smart lockdown to limit human activities may be the other choice to reduce air pollution, significantly reducing the air pollutants and improving environmental quality (Bherwani et al., 2020). Moreover, after the lockdown period (August 31, 2020, to December 31, 2020), NAQI progressively increases in both cities and imperatively achieved its position before the lockdown era, inferring that lockdown does not have a long term effect on air quality.

Fig. 5.

National air quality index (NAQI) for Delhi and Mumbai.

4.4. 70 days examination of each pollutant during and after lockdown era

Table 5, Table 6 show the 70 days analysis of change in each pollutant before and during the lockdown in Delhi and Mumbai, respectively. In particular, concentrations of CO, NO₂, PM_2.5, and PM₁₀ showed substantial declines during the lockdown in both cities. Before lockdown, the PM_2.5 in Delhi was 107.75 μg/m^3, but during the lockdown, it drops to 49.37 μg/m³. Similarly, the PM_2.5 has 58.30 μg/m³ value in the pre-lockdown era, and after lockdown, it declines to 18.80 μg/m³. Thus, 70 days of average PM_2.5 concentrations have decreased by 54.17% in Delhi and 67.75% in Mumbai. PM₁₀ concentrations have a net change of −79.45 in Delhi and −88.67 in Mumbai. Other contaminants that display substantial differences before and during lockdown are CO -31.61% in Delhi and −61.58% in Mumbai. The percentage change in CO emission in Mumbai is more convincing than in Delhi. NO₂ emission has a net change of −22.25 μg/m³ and -31.19 μg/m³ in Delhi and Mumbai, respectively, inferring that there was an imperative decrease in these parameters due to lockdown. However, the reduction in SO₂ in Delhi was 11.87%, and in Mumbai, it revealed a decline of 14.46%. However, NH₃ documented a net change of −7.85 and −14.86 in Delhi and Mumbai during the lockdown era, respectively. The 8hrs average daily O₃ concentration disclosed substantial growth of 66.46% in Delhi during the lockdown era than the pre-lockdown period of 70 days. Nevertheless, O₃ concentration decreased by 47.27 during 70 days of lockdown compared to 70 days of pre-lockdown in Mumbai. These outcomes indicated that lockdown has brought a drastic decrease in emissions levels and dramatically improve air quality.

Table 5.

70 days of analysis before and during the lockdown in Delhi.

Variable	Before Lockdown	During Lockdown	Net change	% change
CO	1.221	0.835	−0.386	−31.611
NH3	37.933	30.076	−7.857	−20.712
NO2	45.287	23.033	−22.254	−49.139
Ozone	29.049	48.356	19.307	66.463
PM2.5	107.758	49.376	−58.382	−54.179
PM10	197.066	117.608	−79.459	−40.321
SO2	13.495	15.098	1.603	11.879

Source: Author's calculation

Table 6.

70 days of analysis before and during the lockdown in Mumbai.

Variable	Before Lockdown	During Lockdown	Net change	% change
CO	0.997	0.383	−0.614	−61.583
NH3	21.511	6.645	−14.866	−69.110
NO2	39.309	8.111	−31.199	−79.367
Ozone	43.048	22.703	−20.346	−47.262
PM2.5	58.310	18.802	−39.508	−67.755
PM10	149.163	60.489	−88.674	−59.447
SO2	10.665	12.207	1.542	14.463

Source: Author's calculation

4.5. Understanding the pattern of major pollutants concentration over the last two years

To complement the major findings as illustrated in the previous sub-sections, we have investigated the entire lockdown era (i.e., March 25, 2020, to May 31, 2020) and compared the findings of this period with the same previous period in 2018 and 2019. Table 7 highlights the fallouts of year-wise mean variations in each pollutant from March 25, 2020, to May 31, 2020, in contrast with the previous two years, i.e., 2018 and 2019, for the same period. The findings stated that PM_2.5 concentration has dramatically reduced by 37.5% and 20.6% during the lockdown period in comparison with the same period in 2018 and 2019, respectively. Moreover, PM₁₀ concentration disclosed a 50.31% decline equated to 2019 over the same period. Additionally, NH₃, NO₂, SO₂, and CO encountered 20.60%, 53.35%, 41.02%, and 36.54% deterioration in their concentrations compared with 2019 over the same period, respectively. Air pollution has significantly reduced due to limited transportation and industrial activities during lockdown days (P. Wang et al., 2020). A few days after the lockout, some of the air pollutants reached the World Health Organization (WHO) standards (Jordan, 2020). Nevertheless, again, the findings indicated that lockdown implementation could lead to a significant increase in air quality, which should be introduced as an additional pollution reduction tool.

Table 7.

Year-wise analysis of each pollutant from 25 March to 31 May.

Variable	2018	2019	2020	% change (2018)	% change (2019)
NH3	42.893	33.759	26.802	−37.514	−20.608
NO2	49.542	49.711	23.187	−53.197	−53.357
PM2.5	89.688	85.383	49.205	−45.137	−42.371
PM10	251.103	237.452	117.986	−53.013	−50.312
Ozone	51.577	47.510	48.638	−5.699	2.376
SO2	13.744	20.479	12.077	−12.134	−41.029
CO	1.4368	1.3493	0.856	−40.409	−36.543

Source: Author's calculation

5. Conclusion

Air pollution has become the principal problem people are encountering on the earth. However, a recent lockdown that has befallen due to the COVID-19 disease imperatively improves global air quality. This behavior allows us to analyze the air quality in the two most populated cities globally, i.e., Delhi and Mumbai. The study examined the influence of lockdown on PM_2.5, PM₁₀, NH₃, CO, O₃, and SO₂ before, during, and after the lockdown era. First, the study analyzes these pollutants through a spatial approach; second, the study builds the National Air Quality Index (NAQI) in NCT Delhi and Mumbai. The findings revealed that two days before the lockdown, PM_2.5 concentration remains between the 200 μgm/m³ to 500 μgm/m³ in Delhi. However, as the lockdown was announced in Delhi, it decreased to 20 μgm/m³ in most NCT Delhi areas. Similarly, the PM_2.5 level in Mumbai before lockdown range between 60 μgm/m³ to 120 μgm/m^3, and on the second day of lockdown, it diminishes between 10 μgm/m³ to 40 μgm/m³ in various wards of Mumbai. Moreover, PM₁₀ concentration before lockdown remains 225 μg/m^3, but it fell to 60 μg/m³ as a lockdown announced in Mumbai. Further, NO₂ emissions in Delhi stay between 50 μgm/m³ to 150 μgm/m³ before lockdown. Although, during and post-lockdown remains amongst 20 μg/m³ to 60 μg/m³. Conversely, NH₃ emission was reduced in Delhi from 75 μg/m³ to 60 μg/m³ after two days of lockdown. The findings revealed that CO emissions from January 2020 to March 23, 2020, showed the level of 1.5 mg/m³, but during the lockdown in Delhi, its value fell to 0.5 mg/m³ and again rise to 1 mg/m³ in post lockdown period. However, Mumbai's carbon monoxide emission was 1.4 mg/m³ in the pre-lockdown period and compacted up to 0.4 mg/m³ during the lockdown period. The Ozone concentration in Delhi has increased by 2.37% during the lockdown period in comparison with 2019 over the same period, i.e., March 23, 2019, to May 31, 2019. The inspection documented that NAQI in Delhi varies from 201 to 480, representing the poor to severe health index. However, during the lockdown period, NAQI in Delhi decreased to a good category of health index. However, after the lockdown, the AQI index in Delhi gradually increased from satisfactory to severe health index. Likewise, NAQI in Mumbai remains between a good and satisfactory level of health index during the lockdown, but after the lockdown, it changes from satisfactory to poor health index. Hence, the study confirmed that lockdown does not pose a long-term effect on the air quality in Delhi and Mumbai.

Based on the results, this study offers the following policy recommendations. First, the government should design a proactive strategy to reduce air pollution levels. Second, the government should implement a regional long-term joint-emissions control plan to improve environmental quality rather than sporadic and abrupt interventions. Third, we emphasize the Indian government to minimize fossil fuel consumption in vehicles and manufacturing industries to mitigate air pollution, which helps improves the air quality in megacities.

Credit author statement

Khurram Shehzad: Conceptualization, Methodology, Software, Data curation, Formal analysis, Writing – original draft, Revision; Liu Xiaoxing: Writing – review & editing, Supervision, Project administration, Funding acquisition; Mahmood Ahmad: Conceptualization, Writing – original draft, Revision; Abdul Majeed: Writing – original draft, Writing – review & editing; Farheen Tariq: Conceptualization, Writing – review & editing; Salman Wahab: Writing – review & editing

Funding

The study is supported by National Natural Science Foundation of China (No.71673043).

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.