essay on air quality has improved in the lockdown period

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The impact of the COVID-19 lockdown on global air quality: A review

Affiliations.

• 1 Department of Environmental Sciences, Central University of Jharkhand, Ranchi, 835205 India.
• 2 Department of Botany, Lucknow University, Lucknow, 226007 India.
• 3 Department of Biology and Global Environmental Sustainability, Oral Roberts University, Tulsa, OK 74171 USA.
• 4 Plant Stress Biology Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, 221005 India.
• PMID: 37519773
• PMCID: PMC8819204
• DOI: 10.1007/s42398-021-00213-6

The coronavirus disease 2019 (COVID-19) was declared a pandemic by the World Health Organization (WHO) on March 11, 2020. As a preventive measure, the majority of countries adopted partial or complete lockdown to fight the novel coronavirus. The lockdown was considered the most effective tool to break the spread of the coronavirus infection worldwide. Although lockdown damaged national economies, it has given a new dimension and opportunity to reduce environmental contamination, especially air pollution. In this study, we reviewed, analyzed and discussed the available recent literature and highlighted the impact of lockdown on the level of prominent air pollutants and consequent effects on air quality. The levels of air contaminants like nitrogen dioxide (NO 2 ), sulphur dioxide (SO 2 ), carbon monoxide (CO), and particulate matter (PM) decreased globally compared to levels in the past few decades. In many megacities of the world, the concentration of PM and NO 2 declined by > 60% during the lockdown period. The air quality index (AQI) also improved substantially throughout the world during the lockdown. Overall, the air quality of many urban areas improved slightly to significantly during the lockdown period. It has been observed that COVID-19 transmission and mortality rate also decreased in correlation to reduced pollution level in many cities.

Keywords: COVID-19; Global air pollution; Greenhouse gases; Lockdown; Particulate matter.

© The Author(s) under exclusive licence to Society for Environmental Sustainability 2022.

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Total confirmed cases throughout the…

Total confirmed cases throughout the world on November 05, 2021 ( Source-WHO 2021)

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The impact of covid-19 lockdowns on air quality—a global review.

1. Introduction

2. materials and methods, keywords for search of academic databases, 3.1. geographical distribution and covid-19 studies, 3.2. impact of covid-19 on air quality over asian countries, 3.3. impact of covid-19 on air quality over european countries, 3.4. impact of covid-19 on air quality over north american countries, 3.5. impact of covid-19 on air quality over south american countries, 3.6. impact of covid-19 on air quality over african countries, 3.7. number of publications and journal distributions, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Addas, A.; Maghrabi, A. The Impact of COVID-19 Lockdowns on Air Quality—A Global Review. Sustainability 2021 , 13 , 10212. https://doi.org/10.3390/su131810212

Addas A, Maghrabi A. The Impact of COVID-19 Lockdowns on Air Quality—A Global Review. Sustainability . 2021; 13(18):10212. https://doi.org/10.3390/su131810212

Addas, Abdullah, and Ahmad Maghrabi. 2021. "The Impact of COVID-19 Lockdowns on Air Quality—A Global Review" Sustainability 13, no. 18: 10212. https://doi.org/10.3390/su131810212

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• Published: 19 February 2021

Positive effects of COVID-19 lockdown on air quality of industrial cities (Ankleshwar and Vapi) of Western India

• Ritwik Nigam 1 ,
• Kanvi Pandya 2 ,
• Alvarinho J. Luis 3 ,
• Raja Sengupta 4 &
• Mahender Kotha 1  

Scientific Reports volume  11 , Article number:  4285 ( 2021 ) Cite this article

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• Atmospheric science
• Climate change
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On January 30, 2020, India recorded its first COVID-19 positive case in Kerala, which was followed by a nationwide lockdown extended in four different phases from 25th March to 31st May, 2020, and an unlock period thereafter. The lockdown has led to colossal economic loss to India; however, it has come as a respite to the environment. Utilizing the air quality index (AQI) data recorded during this adverse time, the present study is undertaken to assess the impact of lockdown on the air quality of Ankleshwar and Vapi, Gujarat, India. The AQI data obtained from the Central Pollution Control Board was assessed for four lockdown phases. We compared air quality data for the unlock phase with a coinciding period in 2019 to determine the changes in pollutant concentrations during the lockdown, analyzing daily AQI data for six pollutants (PM 10 , PM 2.5 , CO, NO 2 , O 3 , and SO 2 ). A meta-analysis of continuous data was performed to determine the mean and standard deviation of each lockdown phase, and their differences were computed in percentage in comparison to 2019; along with the linear correlation analysis and linear regression analysis to determine the relationship among the air pollutants and their trend for the lockdown days. The results revealed different patterns of gradual to a rapid reduction in most of the pollutant concentrations (PM 10 , PM 2.5, CO, SO 2 ), and an increment in ozone concentration was observed due to a drastic reduction in NO 2 by 80.18%. Later, increases in other pollutants were also observed as the restrictions were eased during phase-4 and unlock 1. The comparison between the two cities found that factors like distance from the Arabian coast and different industrial setups played a vital role in different emission trends.

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Introduction.

The novel coronavirus termed as COVID-19 by World Health Organization (WHO), first emerged in late December 2019 in Wuhan, China. In early March 2020, due to its rapid spread, the WHO declared COVID-19 as a pandemic. By July 8, 2020, it spread to more than 210 countries worldwide, infecting over 11 million people and causing 539,026 mortalities 1 . As COVID-19 is highly transmissible, along with a high mortality rate 2 , countries worldwide have taken various precautionary measures, such as large scale COVID-19 screening tests, quarantine, social distancing, wearing of mask, sanitization of hands, etc 3 . This led to 2–4 weeks of regional lockdowns to limit the spread of the virus, all of which have subsequently restricted economic activities around the world leading to different regional repercussions 4 .

In India, a student who had returned from Wuhan, China, was the first COVID-19 positive case recorded on January 30 in Kerala 5 . India took unprecedented measures to contain the infection from across borders and within its territory. International travel and non-essential traveling visas were suspended on March 13, 2020. The Indian railways shut down its operations on March 23, 2020, for the first time in its history spanning over 167 years. A 21-day nationwide lockdown phase-1 was enforced from March 25 to April 14, which was extended further until May 31, 2020. Divided into different phases, the lockdown was marked by increasing relaxations in socio-economic activities in less infected regions. The timeline of the various COVID-19 lockdown phases in India 6 is depicted in Fig.  1 .

COVID-19 timeline of India. (Source: Ministry of Home Affairs, Govt. of India).

While the socio-economic devastation due to COVID-19 has been colossal around the world, which required "a wartime" plan from every corner of the world 7 it has also come as the silver lining for the environment 8 . The United Nations Environment Program chief Inger Andersen believes these environmental changes are temporary 7 , as the global environment had a small respite before industrial activities resumed since February 2020. Recent studies have reported improvement in air quality due to restrictions placed upon industrial activities during the lockdown. Climate scientists have indicated that greenhouse gaseous (GHGs) concentration could drop to levels not seen since World War II. Highly industrialized cities located in cold climate zones observed a higher reduction in air pollution 9 .

Lockdown in various countries viz., France, Germany, Italy, Spain, and China led to shutting down of power plants, transportation, and other industries which resulted in drastic decrease in concentration levels of GHGs, NO 2 , PM 2.5 , PM 10 and CO but spikes in ozone concentration simultaneously, primarily in Europe and large Chinese cities 3 , 10 , 11 , 12 , 13 , 14 . The air quality changes during COVID-19 lockdown over the Yangtze River Delta Region suggest that the reduced human activity and industrial operations lead to significant reduction in PM 2.5 , NO 2 , and SO 2 14 . Significant improvement in air quality, as evidenced from the reduction in Particulate Matter, NOx, SO2 and CO, during the COVID19 lockdown period was observed in the Hangzhou megacity 15 . Reduction of NO 2 (49%), and CO (37%) concentrations in the USA during lockdown were positively correlated with higher population density 3 . The impact of the measures on the air quality is discussed 16 for the city of Rio de Janeiro, Brazil by comparing the particulate matter, carbon monoxide, nitrogen dioxide and ozone concentrations during the partial lockdown with those of the same period of 2019 and also with the weeks prior to the virus outbreak. A positive impact of the social distancing measures is reported 17 on the concentrations of the three main primary air pollutants (PM 10 , NO 2 and CO) of the São Paulo and Rio the Janeiro, the two most populated cities, wherein, the CO levels showed the most significant reductions (up to 100%) which was related to light-duty vehicular emissions. Changes in levels of some air pollutants due to a set of rapid and strict countermeasures limiting population's mobility and prohibiting almost all avoidable activities was evaluated in the in Salé city (North-Western Morocco) 18 . Barcelona city was assessed 19 for air quality using a remote sensing dataset provided by ESA's Tropospheric monitoring instrument (TROPOMI) along with local air quality monitoring data to assess differences in air quality during the lockdown and one month before the lockdown. The observed reductions were 31% and 51% in NO 2 and PM 2.5 , respectively, due to lockdown. The National Aeronautics and Space Agency (NASA), using the TROPOMI sensor, observed a reduction of 10–30% in Nitrogen Dioxide (NO 2 ) in central and eastern China during early 2020 20 . 27% reduction was observed in nitrogen oxides concentration in comparison to the last five years, and non-uniform trends in O 3 concentrations during the lockdown in California basin region 21 . Black carbon reduction due to the lockdown imposed restricted anthropogenic activities is observed in Hangzhou city of China 22 . A reduction of 43% and 31% in PM 10 and PM 2.5 , while a 17% increment in O 3 concentration during the lockdown period and past 4-year values for different regions of India has also been reported 23 .

The common air pollutants in cities and industrial towns are NO 2 , SO 2 , PM 10 , which are responsible for cardiovascular and respiratory diseases 24 , 25 . The primary sources of these pollutants are vehicular exhaust, road dust, and mainly metal processing industries 26 , 27 . The majority of the health benefits were observed with the reduction in NO 2 in 31 provincial capital cities in China 12 . Continuous degradation of air quality in some of the Indian metropolitan cities (New Delhi, Mumbai, Kolkata, Chennai), that often exceed the standards set by WHO and Central Pollution Control Board (CPCB), India, cemented their regular presence in the list of top 20 polluted cities of the world 28 , 29 , 30 . The Ministry of Earth, Forest, and Climate change (MoEFC) under its National Clean Air Programme (NCAP) launched a five-year action plan in 2019 to reduce by 30% the nationwide concentration of particulate matter 31 . Due to the mandatory lockdown imposed across the country, 88 Indian cities have observed a drastic reduction in air pollution 23 .

Gujarat, which is the industrial state in western India, observed a significant reduction in major air pollutants between the lockdown period (March 25 to April 20, 2020) mainly due to restrictions on traffic and slowdown of production at factories 32 . According to the CPCB-AQI database, air pollution reduction occurred merely in four days since the lockdown 33 . In Vapi, PM 10 , PM 2.5 , NO 2 , SO 2 are the major air pollutants significantly emitted by transport vehicles and industrial 34 .

This present study is undertaken to determine the differences in concentration of six pollutants (PM 10 , PM 2.5 , CO, NO 2 , O 3 , and SO 2 ) during the lockdown period (March 25 to June 15, 2020) with the comparable period in 2019, to assess the impact of lockdown on air quality in cities Vapi and Ankleshwar of Gujarat, India.

Ankleshwar is located at 21.62°N, 73.01°E is a municipality under Bharuch district juridiction in Gujarat, India (Fig.  2 ). It is located in the south Gujarat region in between Ahmedabad—Mumbai industrial corridor on the southern banks of lower reaches of the Narmada river. The city has plain topography with an average elevation of 15 m above mean sea level. The climate of South Gujarat region is mainly influenced by the Arabian Sea. Pre-monsoon showers announce the arrival of monsoon only in late june, with hot summer months (March to June), heavy to moderate monsoon rain (July to September), and moderate winter months (November to February). Ankleshwar Gujarat Industrial Development Corporation is spread over an area of 1600 hectares and houses more than 2000 industries with over 1500 chemical plants producing pharmaceuticals, paints, and pesticides.

The locations of industrialized cities Ankleshwar and Vapi in Gujarat, India.

Vapi, located at 20.3893°N 72.9106°E, is a municipality under the Valsad district in Gujarat, India (Fig.  2 ), located at the southernmost tip of Gujarat between Surat in the north and Mumbai (Maharashtra) in the South. Sandwiched between the union territories of Daman & Diu and Dadar and Nagar Haveli, the city is 7 km inland from the Arabian sea; thus, experiences coastal tropical weather with annual rainfall ranging from 100 to 120 in. starting from late June and go on till September 35 . Vapi is also a major industrial hub, 160 km south of Ankleshwar, predominantly housing chemical plants that account for 70% of the total industries in the city. Other industries are packaging, paper, plastics, and rubber. Vapi is also known as a ‘Paper hub’ as it houses the best quality Kraft paper manufacturing units in India. The city has the largest Common Effluent Treatment Plant (or CETP) in Asia 36 . However, due to ever-increasing air and water pollution, Vapi and Ankleshwar are the most industrial clusters of Gujarat (especially Vapi regularly makes it to the list of the most polluted cities in India 37 .

Material and method

The Government of India, in 2016, under its 'Swacch Bharat Mission,' launched the 'National Air Quality Index' (NAQI) 38 . The NAQI bulletin is published daily by the Central Pollution Control Board (CPCB). There are two different techniques to monitor air quality; online monitoring network and manual monitoring network. The online monitoring network is more reliable than its counterpart as it provides pollutant concentration data almost in real-time. The automatic monitoring network AQI consists of monitoring of eight major parameters (Table 1 ) to compute the index value, while the manual monitoring network AQI considers mainly PM 10 , SO 2 , and NO 2 pollutants 39 . Under the NAQI, the averaging time for pollutants such as: PM 2.5 , PM 10 , NO 2 , SO 2 , Pb, and NH 3 is 24-h whereas, O 3 and CO have the averaging time of 1-h. Except for CO which is measured in mg/m 3 , all other seven pollutants are measured in μg/m 3 .

The breakpoint table of daily NAQI provides numeric values and color codes. The color codes are dependent on the numeric values: the AQI value between 0 and 50 suggests it as good with minimal impact on health and shown by dark green color code. Likewise, values in the range of 51–100 are termed as satisfactory (light green) wherein minor breathing discomfort occurs to sensitive people, range of 101–200 is termed as moderately polluted (yellow), range of 201–300 is termed as Poor (orange), values in the range of 301–400 are termed as very poor (light red) and, values in the range of 401–500 as listed as severe (dark red) 40 .

The objective of the NAQI is to assist with monitoring of daily ambient air quality and generate a multi-temporal database. The AQI keeps vigil on air pollutant concentration levels to determine their violation above the permissible limits of ambient air quality in the given area. 200 AQI stations are continuously monitoring air quality across the country 40 . All India AQI monitoring consists of several organizations like the CPCB, the State Pollution Control Boards (SPCB), National Environmental Engineering Research Institute (NEERI), Nagpur, and pollution control committees. The CPCB coordinates with all these agencies to ensure the uniformity, consistency of the air quality data, and provide technical and financial support to them for operating the monitoring stations 38 .

The mathematical equation for calculating sub-indices of AQI is as follows:

where I P is AQI for pollutant “P” (Rounded to the nearest integer), C P the actual ambient concentration of pollutant “P”, B PHI the upper-end breakpoint concentration that is greater than or equal to C P , B PLO the lower end breakpoint concentration that is less than or equal to C P , I LO the sub-index or AQI value corresponding to B PLO , I HI the sub-index or AQI value corresponding to B PHI.

The total lockdown duration of 84 days was divided into five different periods based on different nationwide lockdown phases imposed by the Government of India to compare with 2019. The five phases of lockdown (four lockdowns and one unlock-1.0) had different sets of rules and restrictions for the general public. Phase-1 of the nationwide lockdown lasted from March 25 to April 14, 2020, with complete restrictions on economic activities. During the second lockdown phase which lasted for 19 days starting from April 15 to May 3, various parts of the cities were color-coded into green, orange, and red zones based on the number of COVID-19 positive cases; the red zones indicating rapidly rising cases had total lockdown, orange zones (moderately rising cases) were provided with some relaxation, and the green zone (low positive cases) had least restrictions among them. The third phase lasted for 14 days (May 4–17, 2020), whereas, the fourth phase (labeled as the last period of nationwide lockdown with change from the green, orange and red zones to the containment zone and buffer zone) was extended from May 18–31, 2020. The unlock 1.0 (referred to in the study as ‘phase-5’) commenced on June 1, 2020, with several restrictions uplifted everywhere, except in the containment zones. The differences in the magnitude of restrictions imposed during various lockdown phases had an indirect impact on the fluctuation in the air pollutant level due to the restarting of several economic activities in the cities.

The concentration values of six air pollutants (PM 10 , PM 2.5 , NO 2 , O 3 , CO, and SO 2 ) during the nationwide lockdown period in 2020 and for the similar period of 2019 were downloaded from the daily NAQI data portal available at cpcb.nic.in which is maintained by the Ministry of Environment, Forest and Climate Change, Government of India 40 . The Meta-analysis of continuous data was performed using descriptive and inferential statistical techniques to determine the number of variations (reduction or increase) in the air pollutant levels during the different phases, to calculate the mean differences. Additionally, standard deviation was also computed to determine the fluctuation of air pollutants during different periods. Each air pollutant's mean differences during different phases for both the cities were compared to determine the impact of lockdown restrictions at two different geographical locations with similar economic characteristics. Lastly, a linear regression analysis was performed to determine the relationship of the AQI values with the different lockdown phases.

The mean distribution of air pollutants during lockdown for COVID-19 and a similar period in 2019 for the two cities is shown in (Fig.  3 and Table 2 ). The phase-wise percentage variation of the mean and standard deviation of pollutants for both cities during the lockdown is shown in Table 3 . In Ankleshwar, the maximum decline was observed in NO 2 (80%) during phase-2, while in Vapi the maximum drop was also in NO 2 (91%) during phase-4 of lockdown. O 3 increased by 192% and 310% in Ankleshwar and Vapi, respectively during phase-1. SO 2 declined by − 67% during phase-1 and rose to − 28% during unlock 1.0 in Ankleshwar but it dropped to a maximum of 81% during phase-2, followed by an increase to more than 7% in comparison to the 2019 period during phase-4 in Vapi. PM 2.5 ranged between − 36 and − 5% during phase-2 and phase-5, respectively in Ankleshwar. While in Vapi it ranged between − 48 and − 19% in phase-4 and phase-3, respectively. PM 10 also showed a similar trend but it did not decline below 29% and 52% during phase-2 in Ankleshwar and Vapi, respectively. CO continued to increase in Ankleshwar from 30% in phase-1 to 150% in phase-5 (unlock 1.0), while in Vapi it had varied from 132% in phase-1 to − 38% in phase-3.

Mean concentrations of air pollutants from March 25 to June 15 for the year 2019 and 2020 for Ankleshwar and Vapi.

The linear correlations of metadata of each pollutant and linear regression between pollutant concentrations and lockdown days are shown in the matrix plot (Fig.  4 ) and regression plot (Fig.  5 ) respectively. A declining trend is observed in particulate matter (PM 2.5 and PM 10 ), SO 2, and NO 2 levels during the COVID-19 lockdown period, as compared to the pre-COVID-19 year (Table 1 ). The O 3 concentration increased during the lockdown period compared to the pre-lockdown due to a decrease in NO 2 content. The distribution of CO shows a variable trend. A scatter plot matrix (Fig.  4 ), a grid (or matrix) of scatter plots, a graphical equivalent of the correlation matrix, is used to assess air pollutant variable data and to visualize the bivariate relationships between combinations of variables of pre-COVID19 (2019) to COVID19 (2020) in both cities. Each scatter plot in the matrix visualizes the relationship between a pair of variables, allowing many relationships between several pairs of all air pollutant variables to be explored at once. The Matrix of scatter plot (Fig.  4 ) clearly shows the variable distribution of the pollutant variables from pre-COVID19 (2019) and COVID19 lockdown period (2020) in all combination of scatter plots. It is also clearly observable from Matrix plot, a positive correlation between particulate matter (PM 2.5 and PM 10 ) with NO 2 , SO 2, and CO. O 3 shows a negative correlation with particulate matter (PM 2.5 and PM 10 ), SO 2 and NO 2.

Correlation matrix scatter plot of the air pollutants (The Diagonal Bar Diagrams are the density plots fo various pollutant variables showing the distribution of data).

Linear regression of AQI for Vapi and Ankleshwar for 2019 and 2020.

Further, we also note that the linear regression analysis exhibited a negative correlation between the daily AQI and the growing number of lockdown days (Fig.  5 ). The main reason behind the negative correlation could be the meteorological conditions prevailing in the region. In South Gujarat region during 2020 and 2019 a southerly gentle breeze with a speed of 2–4 m s ‒ 1 prevailed 23 combined with the closure of transport and industries for a longer continuous period in comparison to the similar pre-COVID-19 period 41 . We believe that the prevalence of consistent wind speed and direction in the South Gujarat region during the 2020 lockdown and similar period of 2019 (along with different restrictions imposed during the lockdown in 2020) has helped in reducing pollutant levels.

The results of the present study corroborates with other recent similar studies conducted in various cities across the globe (USA 3 ; China 12 , 13 , 15 ; Brazil 16 ; Italy 42 India 23 ).

Environmental degradation due to anthropogenic pollution has become a chronic problem the world over. In India, haphazard development has led to various problems such as land degradation and air–water quality degradation, mainly in urban areas. Air pollution has become a severe problem in various metropolitan cities and industrialized centers across the country. Incomplete combustion of fossil fuels by vehicles and industrial operations 42 , and improper disposal of anthropogenic waste are the root causes of the rapid increases in air pollution. In March 2020, the COVID-19 pandemic led to a nationwide lockdown to control the spread of infection. The total stretch of various phases of lockdown was 68 days, in which the restrictions were eased subsequently. Although temporarily, the long stretch of restrictions on economic activities provided an opportunity for the environment to heal itself from the continuous exploitation by human activities 8 .

It is evident from the recent studies that reductions in most of the pollutants was observed all over India during the lockdown period. In a study across 12 cities, located in different spatial segments Indo-Gangetic Plain (IGP), showed a substantial decrease (35%) of PM2.5 concentrations across the cities located in IGP after implementation of lockdown 43 . In Saurashtra and South Gujarat regions in Gujarat state, reductions up to 30–84% in NO 2 concentration was observed, while O 3 increased by 16–48% due to reduction in NO 2 . The average decrease in AQI values of 58% was mainly observed in industrial cities such as Ahmedabad, Gandhinagar, Jamnagar, and Rajkot 32 . The atmospheric pollution level (NO2, PM2.5, and PM10) in Ahmedabad city also showed a significant improvement during the study period, implying a positive response of COVID-19 imposed lockdown on the environmental front 44 . In Delhi, the pollutant level came down to its 5-year low during the first week of lockdown phase-1, where PM 2.5 concentration dropped to 42 μg/m 3 (similar to the values observed in March 2016) 45 . AQI reduction in Delhi was 49% compared to the previous year, thus improvement of about 60% was mainly observed in the industrial and transport hub 33 . During the lockdown period, reduction in PM 2.5 among all the pollutants was maximum in Gaya, Kanpur, Nagpur, and Kolkata 23 . The results of the study 46 of variation in ambient air quality during COVID-19 lockdown in Chandigarh showed significant reductions in all air pollutants during the first and second phases of the Lockdown. The concentration of PM10, PM2.5, NO2 and SO2 reduced by 55%, 49%, 60% and 19%, and 44%, 37%, 78% and 39% for Delhi and Mumbai, respectively, during post-lockdown phase leading to a significant improvement in air quality 47 . The reduction in mean concentration from the pre-lockdown phase to during lockdown of the main air pollutants is observed in Kolkatta City 48 . In a similar study 49 , on 16 cities designated as Hotspot region covering almost two thirds of India, also reported a significant reduction in the observed (mean) levels of PM10, PM2.5 and NO2 concentration during the lockdown period from March 25 to April 25.

The present study to assess the effect of COVID-19 lockdown in reducing air pollution in the two industrial cities viz., Ankleshwar and Vapi, Gujarat, India substantiate earlier studies. In comparison to a similar period in 2019, Ankleshwar observed a reduction in PM 10 (primarily emitted by vehicles) and PM 2.5 (caused by dust, ash, etc.) 39 concentrations during phase-1, while in Vapi the concentrations were reduced to almost half of those in the previous year (Fig.  3 ). The most plausible reason being the total restrictions on vehicular movements and industrial activities during the lockdown period. Similar drastically decreasing trends were observed for SO 2 in Ankleshwar (highest reduction among all phases) produced by transportation and oil refineries 50 , 51 . Vapi also recorded a one-third reduction in its mean SO 2 values.

Reduction in NO 2 , (which is emitted by heavy vehicles 52 during all the phases consequently spiked the ozone concentration in both the cities (reduction in NO x can increase ozone due to nonlinear relationships just above the ground level 53 ). CO, which is commonly produced by incomplete combustion of carbon-containing fuels 54 , showed rising trends in Ankleshwar during all the phases but dropped in Vapi.

Although rainfall, a significant determinant which helps to lower the pollutant levels occurred negligibly during the study period in both the cities, which implies that, in the absence of good amount of rainfall, which is a vital factor of pollution reduction, the difference in the trends is a result of continuous operation of the majority of pharmaceutical industries establishments in Ankleshwar during the lockdown. At the same time, Vapi showed a reduction in CO (except in phase-4 and phase-5) due to the non-operation of the majority of industries during the lockdown phase. Phase-2 lockdown continued for 14 days during which the PM 2.5 along with PM 10 , SO 2 , and NO 2 showed decreasing trends, whereas SO 2 significantly dropped in all phases in both the cities (Table 2 ).

During phase-3, with restrictions eased, economic activities were restarted in a gradual manner, which led to the increasing trend in the pollutants. In Ankleshwar, during phase-4 (lockdown) and phase-5 (unlock-1), PM 2.5 and PM 10 mean concentrations exhibited an increasing trend due to increased vehicular movement and industrial operations, but Vapi still showed significant decreasing trends compared to 2019. The continuous operations of pharmaceutical industries during the lockdown phases, primarily the large-scale production ‘Hydroxychloroquine' which is considered effective in COVID-19 treatment at Zydus Cadila, Vital Pharma and Mangalam Drugs and Organics located in Ankleshwar and Vapi, and its transportation to the seaports, is probably one of the main reasons for increases in vehicle-related pollutants (NOx, SO 2, CO) since mid-phase-3 due to its global demand. Which can further be verified from the statement “In Ankleshwar, Vatva and Vapi the major sectors contributing to air pollution were transport, industries and power plants” given by a senior Gujarat Pollution Control Board official (John, April 7, 2020). “Construction, road dust re-suspension, and residential activities also contributed to pollution. In addition landfill fires, operation of DG sets, cooking at restaurants also added to pollution” 55 .

Concurrently, it is evident from the negative correlation between air quality index values of Ankleshwar and Vapi and COVID-19 lockdown days (Fig.  5 ) that the majority of air pollutants decreased since March 25, 2020 as the lockdown period got extended in a phased manner. The most obvious reason was the shutting down of the industrial and transport sector since the lockdown phase-1 started and as the days progressed, pollutants were subsequently flushed out 40 .

However, different trends of some pollutants (minimal increment during later phases) are probably due to two primary causes. First, the diversity in the industrial setup and decline in the number of the on-road vehicle 3 , 56 . Ankleshwar hosts the majority of pharmaceutical manufacturing plants, while Vapi houses more heterogeneous industries, such as pharmaceutical, petrochemical, etc. This difference indirectly influences the emission of different pollutants (as per the raw material used for the processing) and the transportation of the finished products to the market.

Secondly, and more importantly, the proximity of Vapi to the Arabian coast (8 km) is significantly lesser than Ankleshwar (38 km), which also plays a vital role in mixing up and higher fluctuation of pollutant range (due to sea-land breeze) in comparison to Ankleshwar. These causes suggest the differences in the rate of reduction and increment of the concentration of pollutants in the two cities. Figure  6 presents a summary of highlights and the variation in the Air Quality Index (AQI) corresponding to different lockdown phases.

Mean Air Quality Index for Ankleshwar and Vapi for 2019 and 2020 during different lockdown phases.

It is undoubtedly evidenced from this study and the others in several cities 57 , that the lockdown measures imposed to contain the spread of COVID-19 infection was found to be very effective resulting in a positive impact during the Pandemic as a blessing in disguise. It not only restricted the spread of infection rate, but also has given a scope to realize the restoration ability of environment and health with reduced ambient air pollutants levels leading to improved air quality.

The bold decision to impose strict lockdown measures by the Government of India despite economic losses, on the positive front, these measures brought significant improvement in air quality. The present study takes into consideration of the air pollutant observation of all the 5 phases of lockdown period, in contrast to the earlier studies from different parts of the country, that are restricted to only the earlier phases of lockdown period, supports the improved air quality due to lockdown measures. This study highlights the air quality index data of two industrialized cities Ankleshwar and Vapi to determine the trends of different pollutants during all the lockdown phases of COVID-19 in India. Both cities have been classified as critically polluted in Gujarat during the last decade 58 . However, the present study showed a drastic overall reduction of pollutants in both the cities. The results revealed different patterns of gradual to a rapid reduction in most of the pollutant concentrations additionally an increment in O 3 concentration due to drastic reduction in NO 2 by as much as 80.18%. Increases in other pollutants were also observed as the restrictions were eased during phase-4 and unlock 1.

The different lockdown phases were differentiated based on subsequent relaxation in the norms to restart economic activities. The world’s largest lockdown event has provided an actual example instead of modeled scenarios to determine how the pollutants can fluctuate due to different economic restrictions. Due to fear of infection, individuals embraced the restrictions imposed by the lockdown. Thus, it allowed us to show the levels of reduction possible to curb pollution levels.

Although the meteorological conditions (specifically rainfall) could also play a role in bringing down the levels of air pollutions, however, the impact of rainfall is in very minimal in the present study area as no rainfall is reported during the studied duration. The impact of the meteorological conditions cannot be ignored and should be considered in the future for understanding the long term trends.

It is obvious that there is clear reduction in the pollutants levels due to COVID-19 related lockdown improving the air quality in most of parts of the earth as observed from the earlier studies and from the present study. The system imposed was certainly harsh for the economy. However, a modified mode of various reservations for the economy could be used to manage pollution levels on a case-to-case basis. The findings of the present study certainly offers potential scope to plan air pollution reduction strategies. For policymakers, the need for the hour is to acknowledge the role of lockdown in curbing air pollution and not to lose the lead unintentionally achieved during this period against rising air pollution to critical levels. It is hoped that the present situation will open the perspective of humans in understanding deleterious effects of anthropogenic activities.

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Acknowledgements

The first author (Mr. Ritwik Nigam) acknowledges the financial support provided by the University Grant Commission (UGC), Govt. of India, New Delhi, to conduct this research. The authors also thank the University Administration for their support.

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Nigam, R., Pandya, K., Luis, A.J. et al. Positive effects of COVID-19 lockdown on air quality of industrial cities (Ankleshwar and Vapi) of Western India. Sci Rep 11 , 4285 (2021). https://doi.org/10.1038/s41598-021-83393-9

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The Impact of COVID-19 Lockdowns on Air Quality-A Global Review

2021, Sustainability

The outbreak of the COVID-19 pandemic has emerged as a serious public health threat and has had a tremendous impact on all spheres of the environment. The air quality across the world improved because of COVID-19 lockdowns. Since the outbreak of COVID-19, large numbers of studies have been carried out on the impact of lockdowns on air quality around the world, but no studies have been carried out on the systematic review on the impact of lockdowns on air quality. This study aims to systematically assess the bibliographic review on the impact of lockdowns on air quality around the globe. A total of 237 studies were identified after rigorous review, and 144 studies met the criteria for the review. The literature was surveyed from Scopus, Google Scholar, PubMed, Web of Science, and the Google search engine. The results reveal that (i) most of the studies were carried out on Asia (about 65%), followed by Europe (18%), North America (6%), South America (5%), and Africa (3%); (ii) in the case of countries, the highest number of studies was performed on India (29%), followed by China (23%), the U.S. (5%), the UK (4%), and Italy; (iii) more than 60% of the studies included NO2 for study, followed by PM2.5 (about 50%), PM10, SO2, and CO; (iv) most of the studies were published by Science of the Total Environment (29%), followed by Aerosol and Air Quality Research (23%), Air Quality, Atmosphere & Health (9%), and Environmental Pollution (5%); (v) the studies reveal that there were significant improvements in air quality during lockdowns in comparison with previous time periods. Thus, this diversified study conducted on the impact of lockdowns on air quality will surely assist in identifying any gaps, as it outlines the insights of the current scientific research.

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Air quality improvement from COVID-19 lockdown: evidence from China

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• Volume 14 , pages 591–604, ( 2021 )

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As we move through 2020, our world has been transformed by the spread of COVID-19 in many aspects. A large number of cities across the world entered “sleep mode” sequentially due to the stay-at-home or lockdown policies. This study exploits the impact of pandemic-induced human mobility restrictions, as the response to COVID-19 pandemic, on the urban air quality across China. Different from the “traditional” difference-in-differences analysis, a human mobility-based difference-in-differences method is used to quantify the effect of intracity mobility reductions on air quality across 325 cities in China. The model shows that the air quality index (AQI) experiences a 12.2% larger reduction in the cities with lockdown. Moreover, this reduction effect varies with different types of air pollutants (PM 2.5 , PM 10 , SO 2 , NO 2 , and CO decreased by 13.1%, 15.3%, 4%, 3.3%, and 3.3%, respectively). The heterogeneity analysis in terms of different types of cities shows that the effect is greater in northern, higher income, more industrialized cities, and more economically active cities. We also estimate the subsequent health benefits following such improvement, and the expected averted premature deaths due to air pollution declines are around 26,385 to 38,977 during the sample period. These findings illuminate a new light on the role of a policy intervention in the pollution emission, while also providing a roadmap for future research on the pollution effect of COVID-19 pandemic.

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Introduction

Many cases of viral pneumonia were found in Wuhan, the capital city of Hubei Province in central China since early December of 2019, which were subsequently confirmed to have been caused by a new kind of virus. The World Health Organization (WHO) officially named this new virus as coronavirus disease 2019 (COVID-19) on February 11, 2020, which was confirmed to have the capabilities of human-to-human transmission (Chan et al. 2020 ). The large-scale spread of this COVID-19 occurred during China’s Spring Festival, which is a period of the world’s most massive annual migration. The mass migration movements provide COVID-19 with an active environment for its spread; the virus spreads from the epicenter of the pandemic, Wuhan, to the rest of the world. The fast-spreading COVID-19 has been reported to have infected 8,385,440 people, with 450,686 deaths across 216 countries, areas, or territories as of June 19, 2020, Footnote 1 according to the statistics of the WHO, and the growth trend in the number of infected persons is still ongoing, which suggests that one of the worst global pandemics is looming. At the onset of COVID-19 outbreaks in early December of 2019, the academia and citizens have little knowledge about the virus transmission, resulting in the delay of effective measures to counter this deteriorating situation. At least it was not until 10:00 am on January 23, 2020, when the imposed lockdown of Wuhan was put into implementation, in the other remaining cities of Hubei Province after a few days. As of February 29, 2020, different levels of lockdown policies were issued across 107 cities in 22 provinces. Footnote 2 These lockdown policies have created the most extensive quarantine in public health history, with impacts on every aspect of lives, including the environment. Since the lockdown policies strictly restricted human mobility and the logistics suffering from staffing shortages, both have resulted in a remarkable decrease in industrial activities and vehicle use in cities. Consequently, most cities experience a considerably low level of pollution compared with normal conditions (Lewis 2020 ; Freedman and Tierney 2020 ; Monks 2020 ; Singh and Chauhan 2020 ). Concerning China, almost all researchers found a strong positive association between the reduction of air pollution and human mobility restrictions during this pandemic, focusing on different regions in China (Li et al. 2020 ; Bao and Zhang 2020 ; Xu et al. 2020 ). The improved air quality and the possible associated health benefits may be the only light in the “darkness” caused by negative impacts of the COVID-19 pandemic.

It is important to get a comprehensive understanding of how lockdown policies have affected pollution and pollution-related mortality. On the one hand, both the COVID-19 pandemic and corresponding measures to contain it have been unprecedented in modern times, including a better understanding of every aspect of the economic, environmental, and health impacts of this pandemic, contributing to hastening the recovery and drawing some lessons for the future pandemics. A city-level lockdown offers us an opportunity to examine how different pollutants may respond to human mobility restrictions, which provides a reference for policymakers and planners in considering milder forms of restrictions on human activities to reduce pollution. Moreover, through calculating the lockdown-induced pollution, the authors identify the pollution costs accrue from reliance on fossil-based transportation and power generation during normal times. On the other hand, the improved air quality caused by the lockdown is also conducive to lessening the strain on health services by reducing morbidity and mortality, since air pollution has been the most substantial single environmental health risk (WHO 2014 ), particularly leading to an increase in cancer incidences, such as lung cancer and cancer of the urinary tract/bladder. While the continuing increase of various pollutants in China over the past four decades makes it worse; for instance, the concentration of PM 2.5 has increased more than twofold from 1980 to 2015 to 66.90 μg/m 3 (Chen et al. 2017 ), which is more than five times as much of the criteria of PM 2.5 in the National Ambient Air Quality Standard (NAAQS) of the USA. Footnote 3 Given the severity of air pollution in China, hospital attendance and admissions would almost certainly have even been higher in the absence of air quality improvement. Besides, the positive connection between exposure to pollution and COVID-19 mortality has been confirmed by the research of Wu et al. ( 2020 ). Therefore, the lockdown-induced air quality improvement can play a role in relieving the pressure on hospitals in terms of accommodating COVID-19 patients and reducing the associated death rate.

This study, therefore, examines the causal impacts of the city lockdown on air pollution across 325 cities of China. Given the potential problem of endogeneity in simple time-series regressions, this study adopts a human mobility-based difference-in-differences (DID) analysis as our main specification to address this concern. This study may contribute to policy intervention–air quality literature in the following four areas. First, the intracity population migration data from Baidu Migration is used to measure the magnitude of human mobility change caused by lockdown policies instead of using a binary variable to represent the sample period before or after the lockdown date. Second, the meteorological data and daily city-level concentrations of six pollutants (particulate matter (PM 10 and PM 2.5 ), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ), carbon monoxide (CO), and air quality index (AQI) data) at the monitor-level are merged to investigate the causal impact of city lockdown on air quality. Third, this study adopts the human mobility-based DID, according to Correia et al. ( 2020 ), in combination with Sant’Anna and Zhao ( 2018 ) and Callaway and Sant’Anna ( 2018 ) researches to handle DID with multiple time periods. Last, this paper adds to the existing literature exploring policy interventions-related impacts on pollution and offers a reference for the central and local governments in air pollution reduction through milder forms of restrictions on human activities in normal times.

The remainder of this study is structured as follows. The “ Data and descriptive statistics ” section presents a summary of the data sets used in this study, while the “ Empirical strategy ” section presents different empirical strategies. Moreover, the “ Results and discussion ” section presents the main empirical results, which form the basis for the discussion on the heterogeneity and the health implications of our findings. The “ Conclusions and future outlooks ” section is where we conclude.

Data and descriptive statistics

Air pollution and weather data.

We have collected the city-level daily concentrations of five pollutants (SO 2 , NO 2 , CO, PM 10 , and PM 2.5 ) and AQI Footnote 4 for 325 cities of China from China National Environmental Monitoring Centre (CNEMC)’s daily air quality readings of local monitoring stations from January 1, 2020, to May 2, 2020 (day − 24 to 98 around the Lunar New Year in 2020). Footnote 5 Daily weather variables, including daily average temperature, air pressure, daily average wind speed, wind direction, Footnote 6 and precipitation, were also added to our specification as local weather conditions are essential determinants of air quality (Gendron-Carrier et al. 2018 ). On the one hand, sun and high temperatures can function as catalysts for chemical processes of air pollutants. In contrast, on the other hand, air pollutants will be removed from the atmosphere by precipitation. Because of the unit of analysis based on daily data, a set of time fixed effects (year, month, weekend, and holidays) was also added. The meteorological data used in this study were all collected from local meteorological stations across China. The industrial scale and structure, investment in infrastructure, vehicle fleet, and other socio-economic factors are not controlled due to their unavailability at a monthly or daily level.

Population migration data

The population migration data from Baidu Qianxi dataset, which covered 364 Chinese cities per day between January 1 and May 2 in 2020, was obtained. This migration data is captured from Baidu’s mapping app that records the real-time locations through individuals’ smartphones. Considering the monopoly of Baidu in search engines due to China’s ban on using all Google search sites, this dataset can precisely reflect the human mobility at the city level. Footnote 7 Three daily migration intensity indicators, including in-migration (IM) index, out-migration (OM) index, and the within-city migration (WM) index, are available in 364 cities of China, with all of them being consistent across cities and time. A higher value is directly proportional to a higher degree of population mobility, as one index unit in the former two indicators and WM index corresponds to 90,848 and 2,182,264 person movements, respectively, according to the estimation of Fang et al. ( 2020 ). Since the focus of this study is pandemic-induced air quality change in each city of China, the inter-city population mobility cannot reflect the change in the share of fossil-based transportation and power generation in the destination cities. Moreover, the daily WM index is capable of reflecting the change of economic activities of a specific city, which is generally correlated with the air pollution level of the corresponding city. Therefore, the WM index was employed to measure the intensity of pandemic-induced human mobility restrictions. Figure 1 depicts the change in the migration statistics and the number of new cities to come under lockdown during the sample period, which shows a remarkable discontinuity in the average intracity migration around the Wuhan lockdown date. Meanwhile, the largest share of cities to come under lockdown happened followed by the significant decline in the intracity migration intensity during February, 2020.

Daily within-city migration intensity for the national city averages and new locked-down cities in China during the sample period. The green dash line represents the date of official confirmation of the person-to-person transmission of COVID-19 (January 20, 2020), and the red solid line represents the date of Wuhan lockdown (January 23, 2020)

Data matching and descriptive statistics

The abovementioned three datasets were then matched into one panel at the city-day level from January 1, 2020, to May 2, 2020. All the cities with population migration data were first matched with the locations where weather variables data were collected, after which a circle with a radius of 100 km was then drawn from the city’s centroid as the cut-off distance. According to Zhang et al. ( 2017 ) and Qiu et al. ( 2020 ), if a city had no monitoring stations lay in this circle, such a city would be dropped. On the other hand, if a city with multiple monitoring stations lay in this circle, the average daily weather variables across all of the stations will then be used. The final balanced panel covered 325 cities (the spatial distribution of them is shown in Fig. 2 ) with at least one meteorological station within 100 km from each city’s centroid and one air quality monitoring station within the urban area of each city. Figure 2 illustrates the change in the average value of AQI from cities that imposed lockdown policies during the sample period; it can be seen that all cities in the treatment group experienced a varying degree of reduction in air pollution.

The reduction of AQI before and after lockdowns. This figure reflects the geographic distributions of cities involved in this study and the relative change in the average value of AQI after lockdown compared with that before lockdown in treated cities during the sample period. The non-white area indicates cities that enforce lockdown policies

Table 1 reports the descriptive statistics of the key variables involved in this study. It shows that the intracity population flows in Wuhan and other cities in Hubei province with exclusion of Wuhan; both are lower than the average level of non-Hubei cities during the sample period, so does the air pollution level.

The unit of observation is city-day. The arithmetic mean and standard deviation of wind directions do not have statistical meaning; hence, they are not reported here

Empirical strategy

In this section, the study initially employs a simple ordinary least squares (OLS) approach with the daily WM index as the key independent variable of interest to estimate the correlation between the intracity migration index and the air pollution level. After that, a human mobility-based DID approach was used to examine the environmental effect of lockdown policies to achieve a baseline result, followed by a “traditional” DID method as well as a set of alternative strategies to confirm the robustness of our baseline result. Furthermore, the heterogeneous effects of city lockdown for different air pollutants and the health implication out of the air quality improvement were also investigated.

Basic ordinary least squares model

The study specifies a reduced form model to quantify the effects of a marginal increase in WM index on local air quality in the target city in order to quantify the effects of city lockdown on air pollution level. For each pollutant, p ∈{AQI, PM 2.5 , PM 10 , CO, NO 2 , SO 2 } in city i at time t :

where \( \mathit{\ln}\left(\mathrm{Air}\ {\mathrm{pollution}}_{it}^p\right) \) the dependent variable is the daily air pollution level of each city, with each pollutant data being logarithmically transformed to avoid the potential nonnormality and heteroscedasticity. Migration it represents the level of within-city migration intensity city i at time t , while City i indicates the city fixed effects to control unobserved city-related attributes that may affect air quality. Moreover, Date t controls a set of time fixed effects to capture those time-varying unobservables including holiday, day of the week, and season fixed effects, while Weather it is a vector of weather covariates that includes meteorological variables like daily average temperature, wind direction, average wind speed, and precipitation or snowfall amount. Furthermore, Trend it is a city-by-day variable that picks up time-varying city-specific trends, while in similarity, \( {\mathrm{Trend}}_{it}^1 \) is the interaction of the logarithm of WM index of city i and the linear time trend t , of which both alleviate the endogeneity concerns in terms of the city-specific unobservables Footnote 8 and the distribution of cities. δ it is an error term. The study expects θ 1 to capture a positive relationship between within-city migration intensity and air pollution level, as more population mobility increases vehicle emissions and industrial waste gas emissions; that is, people would travel more frequently in the absence of lockdown policies.

The difference in differences specification

The potential challenge in our attempt to causally estimate the air quality improvement effect caused by city lockdown based on a simple time-series regression is failing to disentangle the effect of city lockdown on population migration from other confounding effects like the Spring Festival effect (the Spring Festival in 2020 is on January 25, 2020, 2 days after Wuhan lockdown) that usually leads to mass migration movements during the Spring Festival; virus effect and the associated panic effect, all of which, can affect the human mobility. The spread of the virus, particularly after the official announcement that human-to-human transmission exists, was released on January 20, 2020, and discouraged people from traveling within a city or across different cities, which is not attributable to the lockdown policies. To address this concern, we also estimate a human migration-based DID specification based on the research of Correia et al. ( 2020 ) combining with Sant’Anna and Zhao ( 2018 ) and Callaway and Sant’Anna ( 2018 ) research to address the DID with multiple time periods. The specification is:

where Lockdown it takes a value for one if city i imposed lockdown policies at time t , zero for otherwise. The rest of the control variables are the same as Eq. ( 1 ). The set of coefficients β 1 denotes the difference in pollution changes before versus after city lockdown. Different from the “traditional” DID, we add the WM index in our specification, which allows us to compare the difference in the effects of a marginal change in within-city human mobility on equilibrium air quality between cities with lockdown and cities without lockdown. Since most cities experience a decrease in the level of WM index after city lockdown, we expect the key coefficient of interest, β 1 , to be negative.

Given the sensitivity of our specification, we also follow the “traditional” DID to construct Eq. ( 3 ):

This equation can be seen as a reduced form of Eq. ( 2 ). Moreover, it is more similar to the case in “traditional” DID studies, where the WM index is not added in the specification.

Since the key assumption of DID is that treated units’ and the untreated units’ air pollution levels show parallel trends in the absence of city lockdown. One may have concern for the existence of different trends between two groups as the distribution, physical environment, and economic development level vary in different cities. To address this concern, we employ an event study analysis to confirm that the parallel trends assumption holds.

where −43 ≤  n  ≤ 100 indicates leads and lags of the launch of the lockdown policy. We set the 1 day before the lockdown dates (i.e., n = − 1) as the base interval, which is omitted in our regression; the post-lockdown effects were compared with the period immediately before the implementation of lockdown policies. Figure 3 plots the coefficient estimates of ∂ n together with their pointwise 95% confidence intervals for different air pollutants. The results supported the parallel trends assumption in general, which suggests that cities with earlier interventions and increased aggressiveness during the COVID-19 performed poorly in terms of air quality ( n  ≤ − 2) and if anything, experience a lower air pollution level after the implementation of lockdown policies ( n  ≥ 0).

Event study analysis of city lockdown. These figures present the results of the parallel trend tests based on Eq. ( 4 ). The control group is the cities without lockdown. The last group of cities that declared the lockdown policies on February 13, 2020, are all located at Inner Mongolia, as our sample date ranges from January 1 to May 2, 2020. Forty-three days are remaining until they impose the lockdown, while there are 99 days left after Wuhan firstly imposed the lockdown policy on January 23, 2020, during the sample period

Results and discussion

Ols results.

Table 2 summarizes the results of OLS estimates based on Eq. ( 1 ). These results present the conditional association between WM index and air quality, with columns (1) to (6) revealing the results of ln ( AQI ), ln ( SO 2 ), ln ( PM 2.5 ), ln ( PM 10 ), ln ( NO 2 ), and ln ( CO ) as the dependent variable, respectively. The coefficient estimate of ln (Migration it ) shows a consistent positive relationship between WM index and air pollution level in terms of AQI and other five air pollutants, as all the estimations of weather variables are consistent with our intuitive judgments. High temperature contributes to the increase of air pollution level, while high wind speed and rain or snow also assist in the dispersion of air pollutants. According to the definition of wind direction, a higher value indicates a high possibility that winds blow across the ocean toward the coast of the East Asian continent. Therefore, the higher the value of wind direction, the lower the air pollution level.

Standard errors are clustered at the city-day level. t statistics are reported below the coefficients in parentheses. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. All columns have been controlled for city fixed effects and city-specific time trend

Main results: double difference analysis

Table 3 reports the estimated results with ln ( AQI ) as the dependent variable, as specified according to Eq. ( 2 ), with the weather variables in columns (1) and (2) being uncontrolled. The coefficient suggests a negative connection between lockdown and air pollution, and even after controlling the city and date fixed effect, this negative relationship still holds. Weather variables were added to columns (3) to (5), while a city-specific time trend was further included in column (4), and WM index-specific time trend added to column (5). Interestingly, this negative relationship still retains its robustness across columns (2) to (5), indicating that there exists a negative correlation between city lockdown and air pollution levels. Specifically, column (5) suggests that city lockdown-induced human mobility reduction within a city leads to a significant decrease in the AQI level by 12.2% compared with those without lockdown. All the estimations of control variables are consistent with Table 2 .

Also, considering the different characteristics of various air pollutants, we estimate Eq. ( 2 ) for five different pollutants. The results, as shown in Table 4 , also reported a negative correlation between lockdown and air pollution level for all the pollutants. It also reveals that the concentrations of PM 2.5 and PM 10 decrease to a greater extent, which is consistent with Chauhan and Singh’s ( 2020 ) findings in major cities across the world, since the primary source of fine particulate matter is vehicular emissions, and the concentration is mainly localized near major roadways. However, city lockdown restricts human mobility from using vehicles as well as industrial production, which consequently resulted in a more substantial reduction in fine particulate matter.

The estimates of Eq. ( 3 ) are not reported here due to the limit of space, which are available upon request. The results are still significant and very similar to Table 4 , hence suggesting that our results are not sensitive to the specified estimation strategy.

The estimations so far are based on 2020 data only; we also include 2019 data at the same period to investigate the environmental effect of lockdown policies. Due to the data availability, the 2019 data on intracity migration index is available from Jan 12 to Mar 28, 2019. We also depict the change in the migration statistics of 2019 and 2020 in Fig. 4 . We can find a similar trend between 2019 and 2020 before quarantine measures were put into implementation, while a big gap between 2020 and 2019 in terms of the average intracity migration can be observed after the Wuhan lockdown date. The abovementioned empirical results have confirmed that lockdown-induced reduction in human mobility is likely to improve the air quality, which leads us to believe the existence of environmental effect of lockdown policies.

Daily within-city migration intensity for the national city averages in 2020 and the same periods of 2019. The green line represents the date of official confirmation of the person-to-person transmission of COVID-19 (January 20, 2020), and the red solid line represents the date of Wuhan lockdown (January 23, 2020)

After comparing the specifications under a set of DID estimations, the study found that cities with lockdown exhibited a 12.2% reduction in air pollution level compared to those without travel restrictions.

Robustness check

The pandemic-induced city lockdown has slowed down the Chinese economy, and each city has been allocated the incentives to speed up the economic recovery. Therefore, local governments are more likely to encourage enterprises to resume work as soon as possible during back-to-work time, which will increase more pollution emissions during recovery compared with that in the back-to-work time in previous years. Moreover, since the incubation period of COVID-19 is somewhere between 2 and 14 days after exposure (Lauer et al. 2020 ), the restrictions would be stricter within 14 days after the declaration of city lockdown; hence, the magnitude of lockdown effect on cities’ air quality will be larger. In order to rule out these disturbing factors, we use a shorter post period to handle this concern, and the result is reported in column (1) in Table 5 . The estimated coefficient shows a more significant lockdown effect on the local air pollution level compared with our main DID results, which is consistent with our expectation. In addition, Chinese families still follow the tradition to set off fireworks and commemorate their ancestors during the Spring Festival, particularly in suburban and rural areas. Fireworks have been banned in many cities for the sake of air quality, while this regulation was not implemented during the Spring Festival in 2020 due to COVID-19. We exempted the Chinese New Year’s Eve, Lunar January 1, and the Lantern Festival Day to address this concern, and the results are presented in column (2) in Table 5 , which is still consistent with our main results. Also, cities were divided into two subgroups (i.e., non-Hubei cities versus cities in Hubei Province); the estimated results for the two subgroups are reported in columns (3) and (4). It shows that the magnitude of the lockdown effect on air quality for cities in Hubei Province is larger than the results for the full samples or non-Hubei cities, which further confirmed the robustness of our results. Column (5) reports the results with AQI as the dependent variable, while column (6) reports the results based on a dynamic panel data model, and both are consistent with the main results.

• Heterogeneity

As the largest developing country, China is still confronted with severe imbalance across regions in terms of economic development and ecological environment. Some researches on China’s pollution have confirmed a relatively higher concentration of PM 2.5 in the east than the middle or western region (Zhang and Cao 2015 ; Wang et al. 2017 ), so, the study infers that there may exist heterogeneity in the effect of lockdown policies on air pollution. According to the difference in population, GDP level, per capita GDP level, electricity consumption by city, and geographical distribution (i.e., South and North along the Huai River), all cities in this study were divided into different subgroups. For instance, when a city’s population is higher than the mean population, it will fall into a “high” group, otherwise the “low” group. Table 6 presents estimates of the regression specification for the different subgroups.

It shows that northern cities experience a more significant lockdown effect on air quality compared with the southern cities. The main reason may be attributable to northern cities relying more on coal for electricity and heating. City lockdown significantly reduces people’s travel needs and associated consumption in coal for power or heating, which consequently results in the reduction of air pollution. In contrast, the effect becomes less substantial for cities with smaller GDP and population, which is consistent with our expectation that more agglomerated economies consume more energy. Columns (7) and (8) revealed the results of subgroups in terms of different income levels measured by per capita GDP. The study finds a more significant effect in higher-income cities, which is consistent with the fact that a high level of energy consumption generally accompanies a high-income level. Columns (9) and (10) reported the results of two subgroups in terms of electricity consumption. Higher electricity consumption generally suggests more industrial activities, and the results indicated that cities that rely more on industrial activities experience a more significant effect, suggesting that industrial activities are an essential source of air pollution in China.

Health implications of city lockdown

In this section, we conduct a brief analysis of the health benefits from improved air quality during the lockdown. According to the research of He et al. ( 2020a ), this study quantifies the reduced mortality attributable to the air quality improvement:

where Mortality i represents the estimated saved deaths of city i during the sample period. ∆AQL i represents the estimated change in air pollution level in city i during the same period, where we calculate it based on Eq. ( 2 ) for treated cities and based on Eq. ( 1 ) for control cities with ln ( PM 2.5 ) as the dependent variable. Elasticity denotes the sensitivity of mortality to a one-unit change in air pollution level; since its estimate is not the focus of interest in this study, we use the estimates from existing researches that provide the estimated effects of changes in air quality on human health. Since the estimated effects may vary over time, researches on this topic using data from earlier years may be less effective as the reference. In addition, in view of the credit of estimates based on quasi-experimental studies compared with those based on associated regression models (Graff Zivin and Neidell 2013 ), we identify academic articles and book chapters that meet these criteria based on Web of Science, Google Scholar, Scopus, and other databases. Finally, two main references are chosen after the article selection. Specially, Fan et al. ( 2020 ), in their empirical study based on a regression discontinuity (RD) analysis, found that a 10-μg/m 3 increase in PM 2.5 would lead to a 2.2% increase in mortality rate. The other one is He et al. ( 2020b ), who indicated that this rate could reach over 3.25 percent based on an estimation of the effect of straw burning on air pollution and associated health problem in China. Therefore, we set the range of mortality increase as 2.2~3.25% following a 10-μg/m 3 increase in PM 2.5 . Base MR i represents the annual mortality rate in city i at the base year, and Popu i denotes the population in city i at the same time. Considering the data availability, we set 2018 as the base year of mortality rate and population. The results suggest that the total number of averted premature deaths is around 26,385 to 38,977 due to the air quality improvement during the sample period. Meanwhile, China’s total number of deaths attributable to the COVID-19 (4643 as of May 3, 2020 according to the statistics of WHO Footnote 9 ) is much less than these numbers. They also reflect the enormous social costs out of air pollution during the normal time.

Conclusions and future outlooks

With the spread of COVID-19 worldwide, most countries responded by implementing containment measures with varying degrees of restriction, with almost every aspect of lives being affected during this period; among which, air pollution impacts have already been assessed. Through a quantitative analysis on the effect of pandemic-induced lockdown on the air quality across Chinese 325 cities, this study contributes to the fast-growing literature on COVID-19 pandemic-related shock effects, mostly on changes in air pollution concentration associated with specific causes. Through leveraging daily air quality data and the within-city migration index of 325 cities from January 1 to May 2, 2020, in China, this study discovered that the pandemic-induced lockdown improves urban air quality based on a set of empirical specifications. Specifically, the results showed that the air quality index experiences a 12.2% larger reduction in the cities with lockdown, with the result being statistically significant at the 1% level. With respect to other air pollutants, this reduction effect is 4%, 13.1%, 15.3%, 3.3%, and 3.3% for SO 2 , PM 2.5 , PM 10 , NO 2 , and CO, respectively. The paper also examined the various effects of lockdowns across different classifications of cities in terms of spatial distribution, income level, and industrial structure. It shows that these effects are more remarkable in northern, higher income, more industrialized, and more economically active cities. Despite the enormous economic and political turmoil created by the pandemic, we may benefit from the improved air quality due to city lockdown in terms of health benefits. The expected averted premature deaths due to air pollution declines are around 26,385 to 38,977 during the sample period.

Our findings imply that anthropogenic activities are significantly positively related to the air pollution. Although our results show a temporary air quality improvement resulting from the lockdown, other than AQI, PM 10 , and PM 2.5 , the reduction ratios of the other three air pollutants were small. Therefore, the effectiveness of the driving restriction policy or the license-plate lottery policy that directly bans some vehicles from driving so as to curb the air pollution should be given a comprehensive assessment. Moreover, our findings suggested important policy implications for enforcing environmental regulations such as milder forms of restrictions on human activities to decrease pollution and associated health loss in the post-pandemic period (Singh and Chauhan 2020 ). Future research could explore the specific health benefits from the lockdown-induced changes in air pollution levels, particularly from a long-term perspective. A comparative assessment of the economic damage and health benefits out of this pandemic-induced lockdown would help policymakers to make a rational decision for future air pollution abatement policy formulations (Bherwani et al. 2020 ). This pandemic also provides us a tremendous opportunity for natural experiments to understand how Chinese economy respond to the changes in pollution emission, which could play an important role in guiding the industrial restructuring in the future (Balsalobre-Lorente et al. 2020 ). Furthermore, the heterogeneity of the reduction effect on different pollutants results from city lockdown also requires further analysis. As the pandemic has spread to more than 200 countries around the world, travel restrictions are also imposed in many other countries; hence, future study could replicate these results in other contexts and unpack the specific channel through which city lockdown affects air pollution.

See https://www.who.int/emergencies/diseases/novel-coronavirus-2019 for more information.

More countries follow China’s widespread containment measures including business closures and movement restrictions later, which has been confirmed effective in curbing the spread of pandemic; more information about countries’ responses to the pandemic can be found at https://www.ft.com/content/a26fbf7e-48f8-11ea-aeb3-955839e06441 .

More information, see https://www.epa.gov/criteria-air-pollutants/naaqs-table .

AQI ranging from 0 to 500, the higher the value, the more serious air pollution. More information about it can be found at http://www.mee.gov.cn/ywgz/fgbz/bz/bzwb/jcffbz/201203/W020120410332725219541.pdf .

More information about CNEMC and the air pollution data availability can be found at http://www.cnemc.cn/en/ .

Wind direction variable indicates the angle, measured in a clockwise direction, between the true north and the direction from which the wind is blowing, a value of 0 for calm winds.

More information about this dataset, please see http://qianxi.baidu.com/ .

The central and local governments attach increasing importance to the environmental quality, while different cities are in some ways different in economic development level and environmental regulating intensity, which results in the initial difference in pollution level across cities. This difference may consequently induce different air quality trajectories. In order to address this concern, we include city-specific linear day trends.

More information can be found at https://covid19.who.int/region/wpro/country/cn .

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This study was financially supported by the National Natural Science Foundation of China (NO. 71603047). Meichang Wang was supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions and Southeast University graduate innovation project (NO. KYZZ:160100).

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Wang, M., Liu, F. & Zheng, M. Air quality improvement from COVID-19 lockdown: evidence from China. Air Qual Atmos Health 14 , 591–604 (2021). https://doi.org/10.1007/s11869-020-00963-y

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Lockdown Effects on Air Quality in Megacities During the First and Second Waves of COVID-19 Pandemic

J. aswin giri.

1 Indian Institute of Technology Madras, Chennai, India

Benjamin Schäfer

2 School of Mathematical Sciences, Queen Mary University of London, London, UK

Rulan Verma

3 Indian Institute of Technology Delhi, New Delhi, India

S. M. Shiva Nagendra

Mukesh khare, christian beck, associated data.

The code to reproduce the figures, along with the publicly available LondonAir data is also uploaded here: https://osf.io/jfw7n/?view_only=9b1d2320cf2c46a1ad890dff079a2f6b .

Air pollution is among the highest contributors to mortality worldwide, especially in urban areas. During spring 2020, many countries enacted social distancing measures in order to slow down the ongoing COVID-19 pandemic. A particularly drastic measure, the “lockdown”, urged people to stay at home and thereby prevent new COVID-19 infections during the first (2020) and second wave (2021) of the pandemic. In turn, it also reduced traffic and industrial activities. But how much did these lockdown measures improve air quality in large cities, and are there differences in how air quality was affected? Here, we analyse data from two megacities: London as an example for Europe and Delhi as an example for Asia. We consider data during first and second-wave lockdowns and compare them to 2019 values. Overall, we find a reduction in almost all air pollutants with intriguing differences between the two cities except Delhi in 2021. In London, despite smaller average concentrations, we still observe high-pollutant states and an increased tendency towards extreme events (a higher kurtosis of the probability density during lockdown) during 2020 and low pollution levels during 2021. For Delhi, we observe a much stronger decrease in pollution concentrations, including high pollution states during 2020 and higher pollution levels in 2021. These results could help to design policies to improve long-term air quality in megacities.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40030-022-00702-9.

Introduction

Numerous environmental challenges are faced by cities around the world, and air pollution is among the most pressing topics [ 1 ]. Citizens are forced to breathe air of persistent low quality and it has a detrimental impact on their health and well-being [ 2 , 3 ]. Overall, air pollution is linked to various diseases, like lower respiratory infections, strokes, cancers, asthma attacks, coughs, and chronic obstructive pulmonary diseases [ 4 – 8 ]. As per the State of Global Air 2019 [ 9 ], long-term exposure to outdoor and indoor air pollution contributed to nearly 5 million deaths in 2017. Out of these, 3 million deaths are directly attributed to particulate matter of 2.5 microns or smaller (PM2.5). Furthermore, high pollutant concentrations also damage the environment [ 10 – 12 ].

In March 2020, the World Health Organisation declared the quickly spreading COVID-19 disease (caused by the SARS-COV-2 virus) as a global pandemic due to its rapid transmission and severe health effects. Many nations around the world were observing an alarming increase in the number of infected cases. To combat COVID-19, many countries initiated a “lockdown”, asking or forcing citizens to stay at home, leading to decreased mobility, increased social distancing and more working hours spent at home. During the COVID-19 lockdown, there was a significant reduction in air pollution levels across many countries. The situation is a unique opportunity to understand the baseline emissions in urban environment under lockdown conditions in different areas of the cities (suburban, traffic, and urban). Different governments imposed lockdowns of varying degrees at different time to reduce the spread of SARS-CoV-2 virus (Fig.  1 ).

Lockdown timeline due to first and second waves of COVID-19 pandemic in London and Delhi

In this study, we focus on the analysis of air quality in two megacities, namely London as an example of an established Western megacity and Delhi as an Asian megacity in an emerging region. These two cities have very different properties when it comes to climate and pollution. London has a temperate oceanic climate, whereas Delhi features a dry-winter humid subtropical climate.

Delhi was identified as one of the world’s most polluted regions for PM2.5 in 2019 [ 13 ], and during the winter months, the PM concentrations were observed to be 5 times higher than the annual averages due to stable meteorological conditions[ 14 ]. In contrast, air quality in London has improved in recent years as a result of policies to reduce emissions, primarily from road transport, such as Low and Ultra Low Emission Zones [ 15 , 16 ]. Further information on air quality in Delhi and London is provided in Supplementary Note 1.

Within this paper, we first give an overview of the air quality in both London and Delhi. Next, we compare measurements from the first lockdown period (March–April 2020) and second lockdown (Jan–Feb 2021 in London, and April–May 2021 in Delhi) with measurements from the previous year (2019). In particular, we analyse individual trajectories, probability distributions and also higher statistical moments. Then, we continue with a discussion of which sources cause which type of air pollution and how adequate guidelines could improve air quality in cities. We conclude that air quality is much easily improved in emerging regions by taking regulatory actions, while Western cities can still profit from reduced traffic and should also investigate residential and background pollution.

In contrast to previous studies on air pollution during COVID-19 lockdown, such as [ 17 – 19 ], we investigate the detailed probability distributions of different pollutants, analysing various locations within the cities, while still noting a general decline in pollution levels. Also, we analyse higher statistical moments and compare two very different megacities in detail.

Data Overview

To quantify the impact of the lockdown, we compare the “COVID-19-lockdown” in 2020 with the “business-as-usual” scenario from 2019. The reason to compare the March-April 2020 values with the same period in 2019, instead of January to February 2020 is that there is a clear seasonal dependence in air quality, e.g. due to the efficiency of catalysts depending on ambient temperature [ 20 ]. The first (2020) and second (2021) lockdown in Delhi occurred during similar summer months, whereas for London, the second lockdown (2021) happened during the winter month of January and February. We’ve used Ventilation Coefficient (V.C) to offer insights into the effect of seasonal change.

For London, we utilize open data available from the London air quality network [ 21 ]. From the available data, we select a total of ten locations for our analysis: three urban, suburban and road locations each and one industrial location (in the London data set very few industrial locations are available). The approximate locations are marked in Fig.  2 . As first lockdown period, we chose the dates from March 20 at 0:00 up to May 1 at 0:00. The UK closed off schools [ 22 ] on March 20 and went into a wider lockdown on 23 March. The second lockdown period was considered from 6 January 2021 at 0:00 up to 15 February 2021 at 0:00.

Measurement sites in London ( a ) and Delhi ( b ). We chose three urban, suburban and road locations each, as well as one industrial location. Map by open street maps

For Delhi, we utilize data provided by the Delhi Pollution Control Committee. From the available data, we again select three urban, suburban and road locations each and one industrial location. The exact locations of monitoring stations are marked in Fig.  2 . We analyse the main lockdown period between March 24 and April 21 and second main lockdown during the second wave was considered from April 20, 2021, to May 23, 2021.

We considered nitrogen oxides (NO and NO 2 denoted as NO x ) as well as particulate matter, i.e. particulates of size less than 2.5 and 10  μ m (PM2.5 and PM10). Not only do NO x themselves have harmful impact on health, but they are also commonly used to indicate the presence of other pollutants [ 23 ]. In Supplementary Notes 2 and 3, we present further analysis in which NO and NO 2 are analysed individually for the London data set, instead of being aggregated into NO x . Unfortunately, ozone measurements, while found relevant in other cities [ 24 ], were not available for all measurements sites considered here, and have been omitted to maintain uniformity across the two cities (Fig. ​ (Fig.1 1 ).

We use several road (R), urban (U) and suburban (SU) locations, with the following key:

For London we abbreviate: R1: Blackwall, R2: Thurrock, R3: Euston Road, U1: Sir John Cass School, U2: Stanmore, U3: Streatham Green, SU1: Eltham, SU2: Slade Green, SU3: Keats Way, I1: Beddington Lane.

For Delhi we abbreviate: R1: JLN Stadium, R2: Mandir Marg, R3: Vivek Vihar, U1: Nehru Nagar, U2: Patparganj, U3: Punjabi Bagh, SU1: Dr.Karni Singh shooting range, SU2: Najafgarh, SU3:Sonia Vihar, I1: Anand Vihar.

When plotting individual locations, we use the “1” index, i.e. R1, U1 and SU1 if not specified differently.

Ventilation Coefficient

The ventilation coefficient (VC) indicates the ability of the atmosphere to disperse the pollutants and is computed as a function of the height of Planetary Boundary Layer (PBL) and wind speed [ 25 ], namely

where we typically measure height in meters, wind speed in meters per second and hence the VC in m 2 /s.

To compare Delhi and London and 2019 with 2020, we obtained the approximate PBL height by using radiosonde data from the University of Wyoming [ 26 ] as follows. The PBL is the layer above the ground surface in which the pollutants are mixed and dispersed effectively. Right above the PBL is an inversion layer, which prevents the vertical movement of each air parcel. Here, we identify the PBL height as the lower boundary of this inversion layer, utilizing changes in potential temperature, relative humidity, moisture level, etc. The altitude corresponding to the maximum gradient of the potential temperature, mixing ratio and relative humidity profiles is taken as the PBL height similar to [ 27 ].

Time Series

We plot the temporal evolution of the pollution time series for individual locations and pollutants, to obtain an initial impression of the data. The pollutant concentrations display very large fluctuations but with a considerable drop in overall pollutant concentration after the 1st lockdown was initiated around March 20, see Fig.  3 . The trend of decreasing pollutants is also observable for other pollutants (see Supplementary Notes 2 and 3). For Delhi, we observe a reduction in all pollutant levels after 25th March 2020. During the lockdown, we can see some instances of an increase in pollutant concentrations in early to mid-April. These may be due to the dust storms that occurred in Delhi during those days[ 14 , 28 , 29 ]. This effect can be clearly seen on the PM10 trajectory. Even though PM10 and NOx in Delhi showed similar trend after lockdown, PM2.5 showed large variability (Fig.  3 ). [ 30 ] reported that low temperature and stagnation winds along with absence of solar radiation during early morning and evening hours resulted in formation of haze and mist. Even though there were no anthropogenic sources, there wasn’t any change in the diurnal variability of PM2.5. They suggested that surface level PM1 particles may grow with moisture and mix after the sun rise, causing large variability in PM2.5 concentration.

Pollution levels dropped substantially during the lockdown in mid-March 2020. We plot the trajectories of the concentrations of NO x (left), PM10 (center) and PM2.5 (right) for Delhi (top row) and London (bottom row). We depict the urban location Punjabi Bagh for Delhi and the road location Blackwall for London. Dashed black lines indicate the approximate initiation of the lockdown in each city

For London, we notice a reduction of pollutants for the baseline NO x concentration (Fig.  3 ). Furthermore, pronounced peaks are visible in concentrations, both for NO x and for PM10. These peaks persist after the lockdown is enacted, and we will return to their systematic analysis via kurtosis values later.

Probability Distributions

To investigate how likely certain pollution concentrations are reached, the empirical probability density functions (PDF) for NO x , PM10 and PM2.5 (Fig.  4 ) at an urban, a suburban and a road location were visualised. To this end, both the normalized histograms and a Gaussian-Kernel estimate of the empirical PDF were plotted [ 31 ].

Probability density functions (PDFs) of the NO x , PM10 and PM2.5 concentrations during 2019, 2020 and 2021. Top: Delhi . Bottom: London . We plot both the normalized histogram and a Gaussian-Kernel estimate of the PDF

The PDFs in 2019 tend to be broader than in 2020, i.e. reaching higher pollution states more frequently. Consistently, the 2020 distributions have a much more pronounced peak at low concentration levels. As expected, the pollution levels at the suburban location are generally lower than at the two other sites. The PDFs in 2021 are narrow and lower in London compared to 2019 and 2020, this may be due to the better meteorological conditions during Jan–Feb 2021. But in Delhi, the PDFs in 2021 are broader than 2019 for PM10 and PM2.5, even though they have similar meteorological conditions.

We note that the London distributions are all very similar in their peak near 0 concentration, with a following decay. In contrast, the Delhi data displays a maximum probability density at non-zero values, see e.g. the suburban measurement site. This might be explained by the different distributions observed when comparing NO and NO 2 [ 20 ]. In Supplementary Note 2, we further disentangle the impact of NO and NO 2 for London.

To analyse the data more systematically, the first and (normalized) forth moments of the empirical distributions were used. In particular, we compute the mean concentration μ = 1 N ∑ i = 1 N u i and the kurtosis κ = 1 N ∑ i = 1 N u i - μ σ 4 , where u i is the pollutant concentration at step i , σ is the standard deviation and N is the number of measurements available. The kurtosis quantifies how many extreme events occur in the pollution concentration time series, i.e. how often high-pollution states are assumed. To exclude singular effects specific to one measurement site, we compute the moments for all ten measurement sites in both Delhi and London for 2019, 2020 and 2021.

The mean of NO x concentrations for all locations was lower in 2020 than it was in 2019. The observed drop in NO x concentrations were quite substantial within some locations, such as R3 in London, recording a decrease from more than 70  μ gm - 3 down to merely 20  μ gm - 3 . This may be due to the reduced vehicular and industrial activities, leading to reduced emissions. The same trend of decreasing mean values was also observed for particulate matter (PM10 in this case) in Delhi, but not for London. Notably, PM10 levels in Delhi reached four to ten times the values observed in London in the reference years. This is likely linked to background sources and non-human factors such as pollen or dust contributing substantially to PM10 concentrations. During lockdown, PM10 concentrations in Delhi drop substantially, almost reaching the same levels as in London. During the 2021 lockdown in London, the concentration of PM10, PM2.5 and NOx have substantially decreased compared to 2019. This is due to the meteorological conditions being better suited for dispersion of pollutants. Conversely, in Delhi, during the 2nd lockdown the pollutant levels are almost equal to 2019, and in some cases exceeding the 2019 values (Fig. ​ (Fig.5 5 ).

We compare the mean and kurtosis values from the lockdown period in 2020 and 2021 with 2019. Mean pollution levels dropped during lockdown, except Delhi in 2021 and the Likelihood of extreme events occasionally rose during lockdown. Locations are abbreviated as road (R), urban (U), suburban (SU) or industrial (I). Top: Delhi Bottom: London . Note the different y-axes scales

In contrast to the mean, the kurtosis in 2020 and 2021 occasionally exceeded the values recorded in 2019 substantially. This observation is valid for NO x , PM10 and PM2.5 and might be explained as follows: While on average the pollution levels were reduced, there were high-pollution states. These high pollution levels constitute extreme events under an otherwise reduced pollution level. Furthermore, their frequency occurrence contribute to a much higher kurtosis in 2020 than in 2019, where large pollution concentrations were more likely. Interestingly, the kurtosis and hence the tendency to observe (local) extreme pollution states increased much more in London than in Delhi.

Weather Effects and High Pollution States

In this section, we answer two important questions: How much of the improved air quality could be attributed to weather effects, such as increased ventilation? Secondly, do high-pollution snapshots differ between 2021, 2020 and 2019?

Meteorology and Ventilation

The Planetary Boundary Layer (PBL) was calculated from sounding data observed at 12 UTC. The mean PBL height was found to be 1940 m and 2200 m for London and Delhi, respectively. The typical PBL height varies from 1600 to 2200 m in London [ 32 , 33 ] and 2250 to 2700 m in Delhi [ 34 ].

Even though Delhi lies in a subtropical region and has a higher PBL height, its VC is lower than that for London due to relatively lower wind speeds (Table  1 ) [ 35 ]. The National Meteorological Centre, USA and Atmospheric Environment Services, Canada, defined criteria for ventilation coefficients [ 36 , 37 ]. The criteria for high pollution potential are VC < 6000 m 2 / s and mean wind speed < 4 m / s . The dispersion potential is classified as low [ 38 ] for VC < 2000 m 2 / s , medium for 2000 m 2 / s < VC < 6000 m 2 / s and high for VC > 6000 m 2 / s . Thus, both the cities show a low dispersion and hence high potential for pollution during the 1st lockdown. Furthermore, the increased ventilation in London during 2020 has to be considered as small and cannot account for drastic changes in pollution levels. But during 2021 lockdown in London, the wind speed is high, enabling a better dispersion of pollutants and therefore reducing the pollutant concentration. Further analysis of wind statistics is given in Supplementary Note 4.

Average Ventilation Coefficient (V.C), m 2 /s and Wind Speed (W.S), m/s in Delhi and London

High-Pollution Snapshots

Here, we compare typical high-pollution states in 2019, 2020 and 2021 to better understand the effect of the lockdown. We select data with a typical time window of length T = 7 days. Details of how such windows are selected based on super statistical approaches [ 39 , 40 ] can be found in [ 20 ]. We select a snapshot within the 2019 data so that the variance of this snapshot is maximal, i.e. we select a local high-pollution state and then repeat this selection for 2020 and 2021. Analysing these high-pollution snapshots in Fig.  6 , we note that the data in 2020 can also reach high-pollution levels, almost as high as in 2019 (for London). However, these high pollution levels are less likely in 2020 than they were in 2019. PM2.5 high-pollution snapshots between 2019 and 2020 are almost identical. In 2021, there was very less chance of high pollution event in London, whereas in Delhi the high pollution levels were much more likely than 2019 (for PM10 and PM2.5).

High-pollution states in Delhi became cleaner, but stayed almost constant in London in 2020. Much higher pollution levels were observed in Delhi and cleaner pollution level in London, during 2021 lockdown. We display high-concentration NO x snapshots, by comparing the 7-day period with the highest NO x , PM10, & PM2.5 variance between 2019, 2020 and 2021 in Delhi (Top) and London (bottom). Each data is evaluated at a suburban measurement site. Note again the log-scale of the y-axis

Attribution of Pollutants Emission

This section discusses the attribution of the observed pollutant concentrations to individual emitters, and how they changed during the lockdown.

Past studies estimated that about 75% of PM pollution and 18% of NO 2 in London originated from outside the city, while road transport is considered one of the main contributors of NOx (50%) and PM10 (53%) pollution. Contributing to road transport are the 3.98 million licensed motorized vehicles, which are mainly cars (3.2 million) [ 41 ]. Therefore, to achieve clean air in London, both local and national policies are required [ 42 ] to reduce emission from within and outside the city.

In Delhi, air pollution arises from a variety of local and regional emission sources. The number of licensed vehicles in the city is 10.9 million, with a dominant share of two-wheelers (7.07 million) [ 43 ]. Past studies estimated that about 23% of the PM pollution and 36% of the NO x emission in the city were contributed from the road transport sector. About 52% of NO x emissions are attributed to industrial sources [ 44 ].

For Delhi, the drastic reduction in pollutant concentrations could be attributed to a substantial reduction in vehicular and industrial activities. Not only did cars and industry emit fewer pollutants, but also less dust was re-suspended [ 45 ]. Hence, for the future, road traffic regulations and dust resuspension should be monitored. For London, traffic and industrial activities in the city and its surrounding decreased during the lockdown. Hence, air pollutant concentrations dropped, but the new mean distributions in NO x were higher than the lockdown NO x concentrations observed in Delhi. With road and industrial activities contributing less, it leaves the residential areas, the geographical surroundings and background sources [ 17 ]. All of these should be monitored more thoroughly and regulations should be considered to improve air quality in the long term.

Summary and Conclusions

We compared the impact of COVID-19 induced lockdown measures on the air quality in two major cities. Mean pollution values tend to drop due to lockdown across all pollutants and for almost all investigated measurement sites. This holds for NO x in both London and Delhi, and for PM in Delhi. While pollutant concentrations dropped both in London and Delhi, the reduction was much stronger in Delhi than in London. A specific observation for London is the change of the probability distributions, partially manifesting itself as an increase of kurtosis during lockdown. This is explained by the fact that temporary high-pollution states during and before the lockdown are not qualitatively different in London, but persist. In Contrast, not only did the mean drop, but the extremely polluted states are much rarer in Delhi during 1st lockdown.

Contrary to an earlier analysis for the London data [ 17 ], we did not observe a statistically significant increase in particulate matter (PM) concentrations during lockdown. In Delhi, there was a very substantial drop in PM concentrations. Note that we compared the spring 2020 season with the spring 2019 season, while [ 17 ] compared it to the winter 2020. Hence, the increase in PM concentrations reported in [ 17 ] might be a seasonal effect.

Another study comparing the effect of the lockdown on different cities uses data based on daily air quality indices [ 46 ]. This is different from our study, where we make use of higher-resolved time series and also study higher moments systematically, such as the kurtosis.

The comparison between Delhi and London during the lockdown provides insightful lessons on air quality control: A very strict lockdown in London did improve the air quality significantly, particularly in terms of NOx, highlighting the effectiveness of decreasing the traffic of vehicles. Simultaneously, it also shows that a very drastic improvement by regulating traffic or industry alone will not suffice, but pollution caused by other causes, such as residential or background, has to be taken into account as well.

The picture for Delhi is quite different: Without a lockdown, the pollutant concentrations are regularly 3–5 times as high as in London, indicating a much worse air quality in general. The 1st lockdown (2020) in Delhi improved air quality very drastically. This points to the great potential of clean air in Delhi if traffic and industrial emissions were reduced in the future by suitable control or regulation mechanisms.

There still remain many open questions, such as: Which sources in western and eastern cities can further be reduced to guarantee a long-term improvement in air quality? The lockdown was at an enormous economic cost, but can a small change in behaviour or a well-designed and balanced control mechanism lead to a sustainable and significant improvement in air quality in the future?

Lockdown gave us many challenges and some information on baseline air quality levels. More insights about the basic atmospheric interactions sans anthropogenic intervention could be studied to widen our understanding of the natural atmospheric mechanisms.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Acknowledgements

The authors would like to thank the Delhi Pollution Control Committee (DPCC) New Delhi for their support and cooperation to this study.

Author Contributions

BS, SN, MK and CB conceived and designed the research. BS, AG, RV and HH performed the data analysis and generated the figures. All authors contributed to discussing and interpreting the results and writing the manuscript.

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie Grant Agreement No. 840825.

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J. Aswin Giri, Email: [email protected] .

S. M. Shiva Nagendra, Email: ni.ca.mtii@ardnegans .