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. 2020 Jul 23;13(10):1167–1178. doi: 10.1007/s11869-020-00881-z

Comparative study on air quality status in Indian and Chinese cities before and during the COVID-19 lockdown period

Aviral Agarwal 1, Aman Kaushik 1, Sankalp Kumar 1, Rajeev Kumar Mishra 1,✉
PMCID: PMC7375877  PMID: 32837619

Abstract

Amidst COVID-19 pandemic, extreme steps have been taken by countries globally. Lockdown enforcement has emerged as one of the mitigating measures to reduce the community spread of the virus. With a reduction in major anthropogenic activities, a visible improvement in air quality has been recorded in urban centres. Hazardous air quality in countries like India and China leads to high mortality rates from cardiovascular diseases. The present article deals with 6 megacities in India and 6 cities in Hubei province, China, where strict lockdown measures were imposed. The real-time concentration of PM2.5 and NO2 were recorded at different monitoring stations in the cities for 3 months, i.e. January, February, and March for China and February, March, and April for India. The concentration data is converted into AQI according to US EPA parameters and the monthly and weekly averages are calculated for all the cities. Cities in China and India after 1 week of lockdown recorded an average drop in AQIPM2.5 and AQINO2 of 11.32% and 48.61% and 20.21% and 59.26%, respectively. The results indicate that the drop in AQINO2 was instantaneous as compared with the gradual drop in AQIPM2.5. The lockdown in China and India led to a final drop in AQIPM2.5 of 45.25% and 64.65% and in AQINO2 of 37.42% and 65.80%, respectively. This study will assist the policymakers in devising a pathway to curb down air pollutant concentration in various urban cities by utilising the benchmark levels of air pollution.

Keywords: Air quality index, China, COVID 19, India, NO2, PM2.5

Introduction

In the present time, with the emergence of rapid globalisation and urbanisation, megacities in developing nations are facing severe health issues due to ambient air pollution. According to WHO (World Health Organization), seven million people die each year because of exposure to polluted air (UN Environment Programme 2018). Numerous epidemiological studies in the past two decades have highlighted outdoor air pollution as a cause of various respiratory diseases such as asthma, premature deaths and cardiovascular diseases. These have been identified as primary causes of mortality. In such cases, the population living in the vicinity of major roadways in metropolitan cities suffers the most (Park et al. 2020). In urban areas, 80% of people live in concentrations exceeding the WHO limits (Błaszczyk et al. 2017). Motor-vehicle emitted compounds in urban areas which include carbon monoxide (CO); nitrogen oxides (NOx); coarse (PM10), fine (PM2.5), and ultrafine (PM0.1) particle mass, black carbon, polycyclic aromatic hydrocarbons and benzene which are found in elevated concentrations as reported by Venkatram and Schulte (2018). Moreover, studies suggest that particulate matter and NO2 levels are higher in cities with greater transportation activity and urban backgrounds (Rodríguez et al. 2016). PM10 and PM2.5 are the two primary particulate matters monitored all over the world. However, PM2.5 possesses a higher health risk as compared with PM10 because of its high retention time and ability to penetrate deep into the lungs and enter the bloodstream (US EPA 2018). The WHO ambient air quality guidelines suggest an annual mean PM2.5 concentration limit of 10 μg/m3 and 25 μg/m3 for the 24-hourly mean. The NO2 limit is 40 μg/m3, and 200 μg/m3 for the annual and 1-h mean, respectively (World Health Organization 2005).

At the dawn of twenty-first century, developing economies like India and China are undergoing rapid industrialisation and modernisation, which are leading to hazardous levels of air pollution similar to the Industrial Revolution in Europe. It is well-understood that megacities in both countries like Beijing, Shenyang, Taiyuan, New Delhi, Mumbai, and Chennai are the world’s most polluted cities (Zhu 2005). The primary sources of air pollution in India have been identified as vehicular emissions, industrial emissions, coal combustion, biomass burning, road dust, and refuse burning (Pant and Harrison 2012). Likewise, the poor air quality in China is a matter of global concern. The air pollution caused by transportation and industries is a serious environmental issue in urban settlements, and 50% of the PM in the urban air comes from traffic emissions (Li et al. 2017). Kumar and Joseph (2006) analysed ambient and kerb site air pollution correlation of PM10, PM2.5, and NO2 in Mumbai, India. The results indicated a strong correlation between PM2.5 and NO2 at the ambient site due to vehicular emission as a result of high traffic density. A 2016 report estimated that only 3% of the Chinese population and less than 1% of the Indian population have exposure to PM2.5 concentrations complying with WHO guidelines (IEA OECD 2016), although, an overall monotonic decrease in air pollutants was recorded in China from 2015 to 2018 (Fan et al. 2020). The existing levels of ambient PM2.5 and NO2 are above the safe limit. A 2017 report on global air pollution stated that China and India contribute to 52% of global PM2.5—attributable deaths (1.525 million deaths) (Health Effects Institute 2019). In the past decades, both countries have been pro-active towards the efforts reducing air pollution. However, no long-term solution has been identified yet. Numerous academic studies have been conducted in both nations regarding the growing air pollution and its health effects. Kumar and Mishra (2018) conducted an assessment of major air pollutants at 36 transport corridors in Delhi, India, and the results of the study concluded that 31 corridors had “severe” and “very poor” AQI, and high traffic volume in most corridors is characterised by traffic-induced human health risks. It has been found that high levels of ambient PM2.5 and NO2 increase the risk of cardiovascular diseases and lung cancer in humans (Liu et al. 2018; Siddique et al. 2010).

In late December 2019, there was an outbreak of a highly contagious disease caused by the novel coronavirus, SARS-CoV-2. The first case emerged from Wuhan City, Hubei Province, China. The disease has been identified as Coronavirus disease (COVID-19). It’s an outbreak, recognised as a “Pandemic” by WHO, has been extensively worldwide and exponential with more than 200 countries and territories reporting 3,267,936 cases and 234,703 deaths (7.18%) as of April 30, 2020 (WHO 2020). Individuals with underlying health problems, weak immunity, and the elderly are most likely to become extreme cases (Chen et al. 2020a). The critical sources of infection are patients infected with the novel coronavirus and those with asymptomatic infection (Wang et al. 2020a). Studies indicate correlation between the long-term exposure to air pollutant and COVID 19 death rate. Cities with hazardous air quality face a serious threat from the pandemic (Wu et al. 2020b; Conticini et al. 2020). Therefore, in the absence of a vaccine or treatment available for COVID-19, there has been a coordinated global response of imposing “lockdown” measures on citizens. As of now, more than a third of the worldwide population is under restriction. India recorded 34,867 COVID-19 cases (as of 30-04-2020) and a nationwide lockdown was imposed in India on March 25, 2020 for 21 days (MoHFW 2020). The lockdown constrained people from stepping out of their homes. Transport services, road, air, and rail, were suspended along with institutions and industrial establishments except for essential goods and services (Jain and Sharma 2020) and has been extended up to May 3, 2020. In parallel, 82,862 cases (as of 30–04-20) were recorded in China and 82% of these cases were recorded in Hubei Province (National Health Commission of the People’s Republic of China 2020).

In Hubei province, Wuhan was regarded as the epicentre (Zhang et al. 2020) of the virus. On January 23, 2020, Hubei province, China, was kept under community quarantine with the shutdown of public transport, educational institutes, business centres, parks, and other social contacts to slow down the spread of COVID-19 (Wilder-Smith and Freedman 2020).

Further restrictions in Hubei province were lifted on March 23, 2020.With the implementation of lockdown and other federal restrictions in various countries around the globe, a visible reduction in air pollution is found in megacities. This study is aimed at quantifying and analysing the reduction in air pollution due to the lockdown imposed in two overpopulated and highly polluted countries of the world, viz. China and India, to determine the effect of lockdown on the air quality in an urban environment. The results of this study will help in gauging the ability of a full lockdown on reducing air pollution. Further, it will help in devising a response plan for unforeseen episodes of the high level of air pollution in urban environments.

Methodology of the study

Site selection

For the present study, 6 cities have been selected, each from India and Hubei Province, China. These locations are selected based on the availability of historical air pollution data, population density, monitoring station network, and the number of positive COVID-19 cases per million people. Selected cites with their population, the number of monitoring stations taken into account, COVID-19 cases per million people, their geographical coordinates, and start and end date of lockdown are given in Table 1.

Table 1.

General Information of the Selected Cities (Office of the Registrar General and Census Commissioner 2011; National Bureau of Statistics of China 2010; Central Intelligence Agency 2018; MoHFW 2020; National Health Commission of the People’s Republic of China 2020)

Country City Population (in million) No. of stations monitored COVID-19 cases per Million Geographical coordinates
China Xiangyang 5.89 5 199.22 30° 48′ 01″ N 110°23′11″ E
Jingzhou 0.97 3 1624.47 30°13′35” N 111° 47′ 18″ E
Huanggang 6.628 2 438.55 30° 24′ 16″ N 114° 42′ 49″ E
Xiaogan 5.17 3 679.26 31° 03′ 41″ N 113° 25′ 37″ E
Wuhan 8.11 5 6204.90 30° 42′ 07″ N 113° 46′ 52″ E
Yichang 4.37 5 213.18 30° 41′ 49″ N 110° 48′ 01″ E
India Delhi 18.62 5 501.03 29° 03′ 55″ N 76° 06′ 09″ E
Lucknow 3.12 5 88.30 26° 52′ 53″ N 80° 41′ 49″ E
Kolkata 4.98 5 240.51 23° 04′ 44″ N 87° 17′ 22″ E
Mumbai 13.80 5 1279.98 19° 10′ 38″ N 72° 23′ 50″ E
Chennai 5.16 5 1153.44 13° 07′ 10″ N 79° 44′ 05″ E
Jaipur 6.42 4 234.51 27° 03′ 33″ N 75° 18′ 19″ E
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Parameters for analysis

For the analysis of the effect of the lockdown imposed by the governing authorities on the air quality, PM2.5 and NO2 are selected as parameters of the study. Both of these pollutants have a direct relationship with various anthropogenic activities that were restricted due to the lockdown (US EPA 2018; Ministry for the Environment New Zealand 2020). Hence, analysing these parameters assist in espying the effect of lockdown on the air quality of the selected locations.

Data collection and interpretation

During the data collection, 24-h average concentration (μg/m3) data is taken for PM2.5, and hourly average concentration data is taken for NO2 (ppb) from respective EPAs of the locations selected. The data is collected in China for 13 weeks starting from January 1, 2020 except for Wuhan for which data was collected for 15 weeks since the lockdown was imposed till April 8, 2020. In India, data is collected for 13 weeks starting from February 1, 2020 to April 30, 2020.  For the years 2016–2019, the data for Hubei Province, China has been collected from January 1 to March 31, and similarly, for India, the data has been collected from February 1 to April 30.

Weekly average data of PM2.5 and NO2 for the mentioned months has been calculated for the selected monitoring stations. The average value of the PM2.5 and NO2 concentration in a city is calculated by taking an average of all the monitoring stations selected, located at various distant locations throughout the city. The average value of the concentration of PM2.5 and NO2 is converted to individual AQI (AQIPM 2.5 and AQINO2) by using the protocol suggested by US EPA for reporting the air quality data using the Air Quality Index (AQI) (Mintz 2012).

To analyse the changes in the AQIPM2.5 and AQINO2 levels, for each city, various drop percentages are calculated. The immediate drop percentage is calculated by the difference in average AQIPM2.5 and AQINO2 of the weeks before and after the lockdown was enforced. The final AQIPM2.5 and AQINO2 drop percentages are calculated by the difference in average AQIPM2.5 and AQINO2 of the week before lockdown and the last week when lockdown restrictions were lifted. Five-year and 1-year AQIPM2.5 drop percentages in the year 2020 are calculated for January, February, and March in China and February, March, and April in India. It has been calculated by the difference in average AQIPM2.5 of months of years 2016 and 2019 to the same months of 2020 for 5-year and 1-year drop percentages, respectively.

Results and discussion

With the parameters of immediate and final AQIPM2.5 and AQINO2 drop percentages, and 5 year and I year drop percentages of AQIPM2.5, analysis for the cities in China and India has been done followed by a comparative assessment between the two countries.

Air quality analysis for the selected cities of China

Due to a large number of reported cases, Wuhan and its neighbouring cities (Huanggang and Ezhou) implemented a lockdown on January 23, 2020 followed by several cities on January 24, 2020 (Wu et al. 2020a). Wuhan, having the highest number of cases than any other city in China enforced lockdown with strict federal orders restraining anthropogenic activity to minimal level (Lu 2020), which entailed the highest immediate AQINO2 (69.35%) and AQIPM2.5 drop (15.95%) among the selected cities. In contrast, Xiangyang recorded the lowest immediate and final AQINO2 drop of − 3.22% and − 16.40%, respectively. This trend is observed since Xiangyang is one of the most industrialised cities in central China, and due to the high demand for PPE kits and testing equipment, industries were working at double shifts to meet the demand (Hubei Provincial People’s Government 2020). The average immediate and final AQINO2 drop recorded is 48.61% and 26.64%, respectively. It can be concluded that the final AQINO2 drop percentage is lower than the immediate AQINO2 drop percentage; as the lockdown progressed, citizens had to get out of their homes for necessary essential commodities. Every city experienced a drop in AQIPM2.5 (Fig. 1a, b, c, d, e, and f); on an average, the immediate AQIPM2.5 drop is 11.28%; subsequently, the average final AQIPM2.5 drop is 26.37%. Before lockdown was implemented, every city had AQIPM2.5 within unhealthy for sensitive groups (101–150) and unhealthy (151–200) range according to US EPA standards. Nevertheless, the AQIPM2.5 level reduced and reached the moderate category during the lockdown period. Furthermore, for January, February, and March 2020, the cities recorded the lowest AQIPM2.5 levels in 5 years (Fig. 2a, b, c, d, e, and f). As the virus started to spread in Hubei province in January, the citizens avoided leaving their homes as a self-precautionary measure. As the lockdown was implemented from January 23, 2020 in various cities, a drastic drop has been recorded in AQIPM2.5 levels from week 3 to week 4 (Fig. 1a, b, c, d, e, and f). The AQIPM2.5 level remained constant through 2016–2019, to a certain degree for January and February. The highest 5-year and 1-year AQIPM2.5 drop were recorded in Wuhan: 39.96% and 34.21%, respectively, while the lowest was found in Xiangyang: 8.53% and 26.55%, respectively. The average 5-year AQIPM2.5 drop for January, February, and March, are 18.14%, 25.74%, and 29.18%, respectively. Subsequently, the average 1-year AQIPM2.5 drop for January, February, and March are 20.17%, 26.31%, and 9.97%, respectively. It can be recorded that the 5-year and 1-year AQIPM2.5 drop percentages are comparable in all months except March.

Fig. 1.

Fig. 1

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Weekly averages of AQIPM2.5 and AQINO2 for cities of China

Fig. 2.

Fig. 2

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Past 5 years’ averages of AQIPM2.5 for cities of China

Additionally, meteorological conditions have an essential influence on the variations of PM2.5 and NO2 concentrations in the ambient environment (Agarwal et al. 2006). The cities Wuhan, Huanggang, and Xiaogan recorded a spike of AQIPM2.5 and AQINO2 levels (Fig. 1a, b, and c) in week 5 which led to a gradual decline in the forthcoming weeks of AQIPM2.5 and AQINO2. The increase in the AQI levels was due to low precipitation recorded in week 5 in the three cities; total average precipitation for the three cities for week 4 and 5 were 42.6 mm and 0.34mm, respectively. In the subsequent weeks, rainfall intensity increased, which led to the drop in AQIPM2.5 and AQINO2 levels; the total average rainfall in weeks 6 and 7 for the three cities were 19.94 mm and 22.07 mm. In week 8, it has been observed from the Fig. 1a, b, c, d, e, and f that there is an increase in AQIPM2.5 and AQINO2 levels. Wuhan experienced 4.2mm precipitation in Week 8, as compared with 28.6mm and 39.4mm precipitation in Week 7 and Week 9, respectively. Hence, the abrupt increase in PM2.5 and NO2 in the cities is due to the low precipitation received in central China in Week 8 (19th–25th, February 2020). In the 13th week, a sharp decrease in AQIPM2.5 and AQINO2 levels is recorded. The mean rainfall in six cities in the 12th week is 14.23 mm. In contrast, the 13th week recorded heavy precipitation in all cities with mean average rainfall as 66.36 mm.

Air quality analysis for the selected cities of India

India enforced a nationwide lockdown from March 24, 2020, to May 3, 2020, after successive extensions as a preventive measure against COVID-19 pandemic. As the lockdown was implemented from March 24, 2020, a drastic drop has been recorded in AQIPM2.5 levels of all selected cities from week 7 to week 8 (Fig. 3a, b, c, d, e, and f). Maharashtra, western peninsular state of India, has recorded the most cases of COVID-19 and deaths, 12,296 and 521, respectively (MoHFW 2020), and among the six megacities of India. Its capital, Mumbai, has shown the highest immediate drop of both AQINO2 and AQIPM2.5, i.e. 76.28% and 34.02%, respectively. Kolkata recorded the highest final AQIPM2.5 drop (76.67%), and Lucknow recorded the least immediate drop in AQIPM2.5 (6.47%) partly due to negligible precipitation in week 8. Chennai experienced the least immediate and final drops of AQINO2, which are 32.14% and 20.95%, respectively. It can be understood because of Chennai having an already low value of AQINO2 (Fig. 3a) in week 7 and the weeks before the lockdown (Table 2).

Fig. 3.

Fig. 3

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Weekly averages of AQIPM2.5 and AQINO2 for cities of India

Table 2.

Chennai weekly AQINO2 averages (CPCB-CCR 2020; Mintz 2012)

Week 1 Week 2 Week 3 Week 4 Week 5 Week
6		 Week
7		 Week 8 Week
9		 Week 10 Week 11 Week 12 Week 13
AQINO2 4 5 4 4 4 3 3 2 3 4 3 3 2
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The 6 cities experienced an average immediate AQIPM2.5 drop of 20.21%, and an average final AQIPM2.5 drop of 37.42%. Each one of the six Indian cities in the study recorded an immediate and final AQINO2 drop with the average immediate AQINO2 drop of 59.26% and an average final AQINO2 drop of 65.80%. It has shown an overall drop in both AQIPM2.5 and AQINO2. The average 5-year AQIPM2.5 drop for the months of February, March, and April is recorded as 16.05%, 26.68%, and 37.51%, respectively; subsequently, average 1-year AQIPM2.5 drop for the months of February, March, and April is 3.48%, 17.98%, and 27.06%, respectively. Chennai recorded the highest 5-year drop and 1-year drop in AQIPM2.5 in April 2020 as 59.79% and 42.90%, respectively. All cities, except Mumbai in April 2020 and Chennai in March 2020, recorded the lowest AQIPM2.5 levels in March and April 2020 as compared with the past 5 years. Mumbai is the only Indian city in the study to have shown a 1-year rise (1.09%) in an average AQIPM2.5 in April 2020 (Fig. 4a, b, c, d, e, and f). A spike in AQIPM2.5 was recorded (Table 3) between weeks 9 and 10 in New Delhi. It is as a result of reported fireworks incidents recorded on April 5, 2020, the day-wise AQIPM2.5 levels of week 9 and 10 are given in Fig. 3b (The Indian Express 2020).

Fig. 4.

Fig. 4

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Past 5 years’ averages of AQIPM2.5 for cities of India

Table 3.

Delhi daily AQINO2 and AQIPM2.5 averages (CPCB-CCR 2020; Mintz 2012)

April 1, 2020 April 2, 2020 April 3, 2020 April 4, 2020 April 5, 2020 April 6, 2020 April 7, 2020 April 8, 2020 April 9, 2020 April 10, 2020 April 11, 2020 April 12, 2020 April 13, 2020
AQINO2 7 9 10.75 12.33 11 8 9.5 11 13 11.67 8.33 12 12.33
AQIPM2.5 92.75 77.2 88.2 97.75 116 137 94.67 90.67 93.5 124.25 122.25 113 128.75
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Furthermore, meteorological factors have an essential factor in the reduction and increase of PM2.5 and NO2 concentrations in the ambient environment. It can be recorded from Fig. 3c, in Kolkata, that AQIPM2.5 and AQINO2 have decreased augmented by heavy precipitation in the weeks 11, 12, and 13 with 29.20 mm, 80.90 mm, and 60.20 mm, respectively. However, in the preceding weeks 9 and 10, there was no precipitation. On the other hand, New Delhi, Lucknow, and Jaipur recorded an increase in AQIPM2.5 in week 11 due to high surface winds in northern India due to dust storms from western India according to the Ministry of Environment and System of Air Quality and Weather Forecasting and Research (SAFAR) (Fig. 3b, e, and f) (ANI News 2020).

Comparative analysis and discussion between China vs India

An entire month lockdown was implemented in February 2020 in Hubei province, China; likewise in India, the month of lockdown was April 2020. The 1-year drop for February in China comes out to be 26.31%, whereas, for April in India, it is 26.06%. Hence, it can be deduced from the results that an entire month lockdown in urban centres results in a drop of around 26% in AQIPM2.5 if compared with previous year value. Several academic studies have been conducted to study the relationship between the local meteorological factors and concentration of various pollutants (viz. PM2.5 and NO2) (Guo et al. 2017). The prime factors which influence the concentration of PM2.5 and NO2 have been identified as precipitation, ambient temperature, wind speed, and relative humidity. However, the 12 selected cities tend to show a tremendous reduction in the concentration of PM2.5 and NO2 due to lockdown enforcement. It reflects that cutting down on anthropogenic sources of various pollutants can be useful in reducing the AQI.

Huanggang, China, has a population of 6.62 million, and the AQIPM2.5 and AQINO2 of Huanggang before the lockdown were 147.1 and 12, respectively. After the first week of implementation of lockdown, there was found a decrease of 13.40% and 58.54% in AQIPM2.5 and AQINO2. Likewise, Jaipur, India, has a population of 6.42 million and the AQIPM2.5 and AQINO2 of Jaipur before the lockdown was implemented were 115.7 and 15, respectively. After the first week of the implementation of lockdown, a decrease of 27.50% and 60.37% in AQIPM2.5 and AQINO2 was recorded. Both cities have a comparable population and immediate AQINO2 drop, but the immediate AQIPM2.5 drop differs by 14.1%. The lowest week average AQIPM2.5 recorded in Huanggang and Jaipur was recorded 68.7 and 73.4, respectively. Both the cities’ AQIPM2.5 has dropped down from unhealthy for sensitive groups to moderate air quality. Within 4 weeks of implementation of lockdown, Huanggang experienced a drop of 53.30% and 65.85% in week average AQIPM2.5 and AQINO2. Furthermore, Jaipur saw a drop of 36.56% and 67.49% in week average AQIPM2.5 and AQINO2 in just 2 weeks.

For a holistic view, the cities selected in India recorded an average immediate AQIPM2.5 and AQINO2 drop of 20.21% and 59.26%, respectively. In contrast, on the other hand, the cities in China recorded an average drop of 11.32% and 48.61%, respectively. After 6 weeks of implementing the lockdown, cities in India recorded an average drop in AQIPM2.5 and AQINO2 of 37.42% and 65.80%, respectively, while cities in China recorded a drop of 42.54% and 56.67% respectively. From these results, it can be inferred that the drop in PM2.5 is rather gradual as compared with the sudden drop in NO2 concentrations throughout the cities.

As shown in Fig. 5, the drop in AQIPM2.5 of coastal cities (viz. Chennai, Mumbai, and Kolkata) is relatively more significant than inland cities. The exceptional drop in AQIPM2.5 in the coastal cities is vastly due to the coastal winds which are very prominent in these cities. Previous studies conducted in these coastal cities show that coastal regions show a significant drop in PM2.5 in the morning as compared with inland regions (Chen et al. 2020b; Gupta et al. 2004). The three coastal cities incorporated in the present study record an average immediate AQIPM2.5 drop of 24.96%, and the final average AQIPM2.5 drop was found as 54.90%. On the other hand, the inland region cities recorded an average immediate AQIPM2.5 drop of 12.70% and the final average AQIPM2.5 drop of 24.23%. The drop in average immediate and final AQIPM2.5 of cities in inland regions is moderate as compared with the drop in coastal regions.

Fig. 5.

Fig. 5

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Immediate and final Drop percentages of AQIPM2.5 for all cities

Drop-in AQINO2 also shows similar trends as AQIPM2.5 (Fig. 6). The coastal cities recorded a much higher percentage of drop in AQINO2 as compared with cities in inland regions. The coastal cities, except for Chennai, show an exceptionally high drop in AQINO2. Mumbai and Kolkata recorded an immediate AQINO2 drop of 76.28% and 55.70%, respectively, whereas the final AQINO2 drop is 92.58% and 76.67%, respectively. However, Chennai is a coastal city that recorded a much lower percentage drop in AQINO2 as compared with the other two coastal cities, AQINO2 of Chennai was already at a record low values between 3 to 5 before even lockdown was implemented. Xiangyang recorded an increase in AQINO2 levels after the implementation of lockdown. Xiangyang, being a heavily industrialised city, had industries that were operating during the lockdown to produce essential medical equipment.

Fig. 6.

Fig. 6

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Immediate and final Drop percentages of AQINO2 for all cities

Conclusion

Both the nations followed different protocols for implementing lockdown in each country, although the lockdown in both of the nations was found effective in declining the rate of spread of COVID-19 cases (Wang et al. 2020b; Barkur et al. 2020), and it played a significant role in reducing the air pollution to record low values. The significant findings of the study are as follows:

  • In China, the week before the lockdown was enforced, 4 out of 6 cities had an AQIPM2.5 in the unhealthy category. Wuhan and Huanggang were found in unhealthy for sensitive group category. In the last week of lockdown, 5 out of 6 cities were found to be in unhealthy for sensitive group category except for Wuhan, which was found in the moderate category.

  • In India, the week before the lockdown was enforced, 5 out of 6 cities had an AQIPM2.5 that is unhealthy for sensitive group category except Chennai that was found under the moderate category. In the sixth week of lockdown, all cities were found in the good and moderate category except Delhi and Lucknow, which were found to be in unhealthy for sensitive group category.

  • Meteorological factors are an essential factor in order to address pollutant concentration in ambient environment. Henceforth, meteorological should be taken into account before the execution of a response plan to mitigate pollution in urban cities around the world.

  • For all 12 cities, a gradual decline has been recorded in AQIPM2.5 levels in subsequent lockdown weeks. The mean immediate and final AQIPM2.5 drops are 15.76% and 31.89%, respectively. However, in the case, AQINO2 levels, a sharp decline has been recorded in the first week of lockdown. The mean immediate and final AQINO2 drops are 53.93% and 46.22%, respectively.

  • The coastal cities (viz. Chennai, Mumbai, and Kolkata) recorded a more significant decline in AQIPM2.5 and AQINO2 as compared with the other inland region cities.

The lockdown implemented in various regions around the world provided us with a unique opportunity to identify the benchmark levels of pollutants in various urban cities around the world. The findings of the study will assist the governing authorities and policymakers to calibrate a proper response plan to bring down the ever-increasing pollution levels in various developing urban regions across the globe, especially China and India.

Acknowledgements

Authors are incredibly grateful to Advance Air & Acoustics Research Laboratory, Delhi Technological University, Delhi (India), for encouraging to conduct this research and providing all the facility to compile this work.

Funding information

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Availability of data and material

The data used in the current study have been taken from respective EPA and is available in public domain.

Compliance with ethical standards

Competing interests

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

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Aviral Agarwal, Email: agarwalaviral1999@gmail.com.

Aman Kaushik, Email: kakukaushik@gmail.com.

Sankalp Kumar, Email: sankalp.vibhu@gmail.com.

Rajeev Kumar Mishra, Email: rajeevkumarmishra@dtu.ac.in.

References

  1. Agarwal R, Jayaraman G, Anand S, Marimuthu P. Assessing respiratory morbidity through pollution status and meteorological conditions for Delhi. Environ Monit Assess. 2006;114:489–504. doi: 10.1007/s10661-006-4935-3. [DOI] [PubMed] [Google Scholar]
  2. ANI News (2020) Delhi's air quality may deteriorate due to western dust storms: SAFAR. https://www.aninews.in/news/environment/delhis-air-quality-may-deteriorate-due-to-western-dust-storms-safar20200415085141/. Accessed 1 Apr 2020
  3. Barkur G, Vibha, Kamath GB (2020) Sentiment analysis of nationwide lockdown due to COVID 19 outbreak: evidence from India. Asian J Psychiatr 51:102089. 10.1016/j.ajp.2020.102089 [DOI] [PMC free article] [PubMed]
  4. Błaszczyk E, Rogula-Kozłowska W, Klejnowski K, Kubiesa P, Fulara I, Mielżyńska-Švach D. Indoor air quality in urban and rural kindergartens: short-term studies in Silesia, Poland. Air Qual Atmos Health. 2017;10:1207–1220. doi: 10.1007/s11869-017-0505-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Central Intelligence Agency (2018) The World Factbook. https://www.cia.gov/library/publications/the-world-factbook/fields/344rank.html. Accessed 18 Apr 2020
  6. Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, Qiu Y, Wang J, Liu Y, Wei Y, Xia J, Yu T, Zhang X, Zhang L. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet. 2020;395:507–513. doi: 10.1016/s0140-6736(20)30211-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Chen Y, Wild O, Conibear L, Ran L, He J, Wang L, Wang Y. Local characteristics of and exposure to fine particulate matter (PM2.5) in four Indian megacities. Atmos Environ. 2020;X 5:100052. doi: 10.1016/j.aeaoa.2019.100052. [DOI] [Google Scholar]
  8. Conticini E, Frediani B, Caro D. Can atmospheric pollution be considered a co-factor in extremely high level of SARS-CoV-2 lethality in northern Italy? Environ Pollut. 2020;261:114465. doi: 10.1016/j.envpol.2020.114465. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. CPCB-CCR (2020) Central control room for air quality management – all India. https://app.cpcbccr.com/ccr/#/caaqm-dashboard-all/caaqm-landing/data. Accessed 16 Apr 2020
  10. Fan H, Zhao C, Yang Y. A comprehensive analysis of the spatio-temporal variation of urban air pollution in China during 2014–2018. Atmos Environ. 2020;220:117066. doi: 10.1016/j.atmosenv.2019.117066. [DOI] [Google Scholar]
  11. Guo J, Xia F, Zhang Y, Liu H, Li J, Lou M, He J, Yan Y, Wang F, Min M, Zhai P. Impact of diurnal variability and meteorological factors on the PM2.5 - AOD relationship: implications for PM2.5 remote sensing. Environ Pollut. 2017;221:94–104. doi: 10.1016/j.envpol.2016.11.043. [DOI] [PubMed] [Google Scholar]
  12. Gupta A, Patil RS, Gupta S. Influence of meteorological factors on air pollution concentration for a coastal region in India. Int J Environ Pollut. 2004;21:253. doi: 10.1504/ijep.2004.004193. [DOI] [Google Scholar]
  13. Health Effects Institute . State of Global Air 2019. Special Report. Boston: Health Effects Institute; 2019. [Google Scholar]
  14. Hubei Provincial People’s Government (2020) Hubei Enterprises. http://en.hubei.gov.cn/business/enterprises/. Accessed 3 Apr 2020
  15. IEA OECD (2016) Energy and Air Pollution: World Energy Outlook Special Report 2016
  16. Jain S, Sharma T. Social and travel lockdown impact considering coronavirus disease (COVID-19) on air quality in megacities of India: present benefits, future challenges and way forward. Aerosol Air Qual Res. 2020;20:1222–1236. doi: 10.4209/aaqr.2020.04.0171. [DOI] [Google Scholar]
  17. Kumar R, Joseph AE. Air pollution concentrations of PM2.5, PM10 and NO2 at ambient and Kerbsite and their correlation in Metro City – Mumbai. Environ Monit Assess. 2006;119:191–199. doi: 10.1007/s10661-005-9022-7. [DOI] [PubMed] [Google Scholar]
  18. Kumar A, Mishra RK. Human health risk assessment of major air pollutants at transport corridors of Delhi, India. J Transp Health. 2018;10:132–143. doi: 10.1016/j.jth.2018.05.013. [DOI] [Google Scholar]
  19. Li S, Feng K, Li M. Identifying the main contributors of air pollution in Beijing. J Clean Prod. 2017;163:S359–S365. doi: 10.1016/j.jclepro.2015.10.127. [DOI] [Google Scholar]
  20. Liu H, Tian Y, Song J, Cao Y, Xiang X, Huang C, Li M, Hu Y. Effect of ambient air pollution on hospitalization for heart failure in 26 of Chinas largest cities. Am J Cardiol. 2018;121:628–633. doi: 10.1016/j.amjcard.2017.11.039. [DOI] [PubMed] [Google Scholar]
  21. Lu D. Inside Wuhan’s lockdown. New Sci. 2020;245:7. doi: 10.1016/s0262-4079(20)30234-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Ministry for the Environment New Zealand (2020) Nitrogen dioxide. https://www.mfe.govt.nz/air/specific-air-pollutants/nitrogen-dioxide. Accessed 5 Apr 2020
  23. Mintz D (2012) Technical assistance document for the reporting of daily air quality – the air quality index. EPA-454/B-12-001
  24. MoHFW, Ministry of Health and Family Welfare (2020) COVID-19 INDIA. https://www.mohfw.gov.in/. Accessed 2 Apr 2020
  25. National Bureau of Statistics of China (2010) Census Data http://www.stats.gov.cn/english/Statisticaldata/CensusData/. Accessed 5 May 2020
  26. National Health Commission of the People's Republic of China (2020) April 30: Daily briefing on novel coronavirus cases in China. http://en.nhc.gov.cn/2020-05/16/c_80207.htm. Accessed 30 Apr 2020
  27. Office of the Registrar General & Census Commissioner, India (2011) 2011 Census Data. http://censusindia.gov.in/2011-Common/CensusData2011.html. Accessed 22 Apr 2020
  28. Pant P, Harrison RM. Critical review of receptor modelling for particulate matter: a case study of India. Atmos Environ. 2012;49:1–12. doi: 10.1016/j.atmosenv.2011.11.060. [DOI] [Google Scholar]
  29. Park S, Allen RJ, Lim CH. A likely increase in fine particulate matter and premature mortality under future climate change. Air Qual Atmos Health. 2020;13:143–151. doi: 10.1007/s11869-019-00785-7. [DOI] [Google Scholar]
  30. Rodríguez MC, Dupont-Courtade L, Oueslati W. Air pollution and urban structure linkages: evidence from European cities. Renew Sust Energ Rev. 2016;53:1–9. doi: 10.1016/j.rser.2015.07.190. [DOI] [Google Scholar]
  31. Siddique S, Ray MR, Lahiri T. Effects of air pollution on the respiratory health of children: a study in the capital city of India. Air Qual Atmos Health. 2010;4:95–102. doi: 10.1007/s11869-010-0079-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. The Indian Express (2020) Indian outrage on social media after people light fireworks, lead processions after lights off. https://indianexpress.com/article/trending/trending-in-india/primisniter-narendra-modi-9pm9minutes-coronavirus-6349647/. Accessed 5 May 2020
  33. UN Environment Programme (2018) Air pollution: know your enemy. https://www.unenvironment.org/news-and-stories/story/air-pollution-know-your-enemy. Accessed 2 Apr 2020
  34. US EPA (2018) Particulate Matter (PM) Basics. https://www.epa.gov/pm-pollution/particulate-matter-pm-basics. Accessed 12 Mar 2020
  35. Venkatram A, Schulte N (2018) Urban transportation and air pollution. Elsevier Science Publishing Co Inc, s.l.
  36. Wang P, Chen K, Zhu S, Wang P, Zhang H. Severe air pollution events not avoided by reduced anthropogenic activities during COVID-19 outbreak. Resour Conserv Recycl. 2020;158:104814. doi: 10.1016/j.resconrec.2020.104814. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Wang S-X, Wang Y, Lu Y-B, Li J-Y, Song Y-J, Nyamgerelt M, Wang X-X. Diagnosis and treatment of novel coronavirus pneumonia based on the theory of traditional Chinese medicine. Journal of Integrative Medicine. 2020;18:275–283. doi: 10.1016/j.joim.2020.04.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. WHO (2020) Coronavirus Disease (COVID-19) Situation Reports. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports. Accessed 30 Apr 2020
  39. Wilder-Smith A, Freedman DO (2020) Isolation, quarantine, social distancing and community containment: pivotal role for old-style public health measures in the novel coronavirus (2019-nCoV) outbreak. J Travel Med 27. 10.1093/jtm/taaa020 [DOI] [PMC free article] [PubMed]
  40. World Health Organization (2005) WHO Air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulphur dioxide: global update 2005: summary of risk assessment (No. WHO/SDE/PHE/OEH/06.02)
  41. Wu JT, Leung K, Leung GM. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. Lancet. 2020;395:689–697. doi: 10.1016/s0140-6736(20)30260-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Wu X, Nethery RC, Sabath BM, Braun D, Dominici F (2020b) Exposure to air pollution and COVID-19 mortality in the United States: a nationwide cross-sectional study. 10.1101/2020.04.05.20054502
  43. Zhang L, Wang B, Zhou J, Kirkpatrick J, Xie M, Johri AM. Bedside focused cardiac ultrasound in COVID-19 infection from the Wuhan epicenter: the role of cardiac point of care ultrasound (POCUS), Limited Transthoracic Echocardiography and Critical Care Echocardiography. J Am Soc Echocardiogr. 2020;33:676–682. doi: 10.1016/j.echo.2020.04.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Zhu Z. Political economy of China and India: dealing with air pollution in the two booming economies. Historia Actual Online. 2005;7:123–132. [Google Scholar]

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Data Availability Statement

The data used in the current study have been taken from respective EPA and is available in public domain.

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