key: cord-0857991-v1r0400r
authors: Saxena, Abhishek; Raj, Shani
title: Impact of lockdown during COVID-19 pandemic on the air quality of North Indian cities
date: 2020-12-07
journal: Urban Clim
DOI: 10.1016/j.uclim.2020.100754
sha: bfaedd1787e2259e5079e9341fc623dddc92fa51
doc_id: 857991
cord_uid: v1r0400r

The World Health Organization, which proclaimed the COVID-19 a pandemic in early March 2020, imposed a partial lockdown by the Government of India on 21 March 2020. The aim of this investigation was to measure the change in air pollutants, including particulate matter (PM(2.5) and PM(10)) and gaseous pollutants (NO(2), CO and O(3)) during COVID-19 lockdown (25th March to 14th April 2020) across four major polluted cities in North India. In all region, PM(2.5), PM(10), NO(2) and CO were significantly reduced while O(3) has been shown mixed variation with increased in Agra and decreased in all other stations during lockdown. PM(2.5) was reduced by ~20–50% and highly decreased in Noida. PM(10) was most significantly decreased by 49% in Delhi. NO(2) was reduced by ~10–70%, and high reduction was observed in Noida. Likewise, ~10–60% reduction was found in CO and most significantly decreased in Gurugram. However, an increased in O3 was observed in Agra by 98% while significantly reduced in other sites. Compared to the same timeframe in 2018–2019, PM(2.5) and PM(10) values for all sites were reduced by more than 40%.

Pollution levels have emerged as an increasing concern globally, particularly in Asian economic countries such as India and China. In India, with increasing economic growth, industrialization and development of infrastructure since modernizing has also increased in Air pollution level to significant health hazards leading to a cause of premature death . In India, almost one million people have died due to atmospheric particulate matter (PM 2.5 ) pollution in 2015 (Guo et al., 2017) . From the past few years, the big Indian cities have been listed in top 20 world's most polluted cities as recommended by WHO and CPCB (Central Pollution Control Board) (Lawrence and Fatima, 2014; Sharma and Mandal, 2017) . PM (PM 2.5 and PM 10 ) is the prominent pollutant and the households, transport and industries are the primary anthropogenic emission source of the PM (Guo et al., 2019) . PM 2.5 affects more, even in a low concentration in the air is linked to adverse impacts on human health according to WHO report comparing to other pollutants (Fann et al., 2012) . NO (nitrogen oxide) and CO (carbon monoxide) and are gaseous pollutants, and their emission is by transport, industries, coal-based thermal power plants and incomplete burning of fossil fuels in any manner emits various forms of combustion or waste into the environment as fine particulates matter (Gurjar et al., 2016; Sharma and Dikshit, 2016) . NO 2 is a precursor element which leads to the production of O 3 (ozone) in the existence of Sunlight and high temperatures via CO, CH 4 , and VOC reactions (Li et al., 2018) . Unfavorably, surface ozone O 3 is very harmful air pollutant affects the crop yield, human health and ecosystem as a third most imperative greenhouse gas (Li et al., 2020) . Ozone is a popular oxidant gas in urban air, and ozone exposure

During the lockdown time, data from four cities covering different areas of India, i.e., Agra, Noida, Gurugram and Delhi were collected to determine air quality improves. The concentration levels of the various pollutants for the period of 24 March to 14 April from 2018 to 2020, were analysed. The CPCB online database for air quality data collected daily average concentrations of four pollutants including PM 2.5 , PM 10 , NO 2 and CO and at 8 h average for surface O 3 (https://app. cpcbccr.com/ ccr/#/caaqm-dashboard-all/caaqm-landing). Meteorology data i.e. air temperature, relative humidity (RH), wind Speed, PBLH (planetary boundary layer height) from NOAA Air Resources Laboratory's (ARL) NASA web (https://ready.arl.noaa.gov/ index.php).

In this report, four major north Indian cities have selected for the study of variation in the pollutant's concentration during lockdown Fig. 1 . Agra is about 206 km south of New Delhi, the national capital. The Agra climate is semi-arid, bordering on a humid subtropical climate. The mean rainy season is 628.6 mm from June to September. The daytime temperature during summer is about 46-50 • C. Nights are slightly colder, and the temperature drops to 30 • C. Noida is a centre of industry. Noida is situated in Uttar Pradesh state district of Gautam Buddh Nagar. Noida is approximately 25 km southeast of New Delhi, about 20 km northwest of the district headquarters-Greater Noida and 457 km northwest of the state capital, Lucknow. Noida city is situated in the Indian State of Uttar Pradesh district of Gautam Buddh Nagar. Gurugram is a city located in Haryana, a northern Indian state. It is located close to the Delhi-Haryana border, approximately 30 km southwest of the national capital New Delhi and 268 km south of the state capital Chandigarh. Delhi is considered among the most polluted megacities of the globe based on environment performance index (WHO, 2016) . According to the environmental monitoring database for the world-leading megacities encompassing 100 countries published in April 2018 by WHO for the period of 2011 and 2016 Delhi ranks high in the list of PM 2.5 pollution (WHO, 2018) . The detail of emission sources, vehicle etc. of the observed station are given in Table 1 . A recently release IQAir 2019 World Air Quality study showed that 14 out of the 20 most polluted cities in the world are from India. In 2019, the concentration of PM 2.5 levels in Delhi, Noida and Gurugram was 98.6 μg/m 3 , 97.7 μg/m 3 and 93.1 μg/m 3 compared to the allowable maximum of 60 μg/m 3 (daily average) (IQAir report, 2019). This suggests a significant degree of air pollution, which even after many attempts could not be regulated by state and central government.

Descriptive data for meteorological factors and air pollution are shown in Table 2 (a-d). During the study period (25th March 2020 to 14th April 2020), the Pearson correlation coefficient and p-value among Ozone, CO, NO 2, PM 2.5 , PM 10 and Meteorological Variables for these four cities, presents the Pearson correlation coefficient and p-value between air pollutants and meteorological variables. Air pollutants had significant correlations with each other and all of them were correlated with mean temperature and relative humidity. NO 2 , CO were negatively correlated with air temperature, while surface zone, PM 10 , PM 2.5 had positive correlations with air temperature. All of these air pollutants were significantly correlated with wind speed. 

The variation in the average concentration of PM 2.5 and PM 10 in Agra, Delhi, Noida and Gurugram before and during the lockdown have shown in Fig. 2a -b. In Agra, the average concentration of PM 2.5 is 56.1 μg/m 3 before the lockdown and the linear reduction (− 24%) was noticeable during the lockdown. Though in Noida, PM 2.5 decreases are − 49.6% and − 40% in PM 10 during lockdown which was observed at 145.2 μg/m 3 before lockdown. Similarly, in Delhi and Gurugram, the concentration of PM 2.5 is about 45.1 μg/ m 3 and 34.6 μg/m 3 were noted during lockdown were significantly declined by − 43.1% and − 38.1% respectively Fig. 2a . Likewise, the PM 10 was also reduced during the lockdown in Delhi and Gurugram by − 43%, and − 40% which were observed 145.2 mg/m 3 and 122.2 mg/m 3 , (Fig. 2b ) respectively before lockdown ( Table 2 ). The most visible declines in PM 2.5 are in Noi-da>Delhi>Gurugram>Agra and PM 10 are in Delhi> Gurugram> Noida. PM 2.5 at Noida (from 60.2 μg/m 3 to 33.6 μg/m 3 ) and PM 10 at Delhi (145.2 μg/m 3 to 81.5 μg/m 3 ) recorded the largest decrease during lockdown (Table 2) . Owing to lockdown, air pollutant levels were significantly higher in 2019 relative to the same period in 2020 from 24 March to 14 April ( .3 μg/m 3 also higher than in comparison to 2020 (Fig. 4a ), which are declined by 42%, 61%, 53% and 51% in Agra, Noida, Delhi and Gurugram respectively shown in Table 3 . Similarly, in Noida, Delhi and Gurugram PM 10 concentration were also reduced by 68%, 63% and 62% respectively during 24 March to 14 April 2020 in comparison to average concentration were 276 μg/ m 3 , 224 μg/m 3 and 192.5 μg/m 3 in 2018-2019 ( Fig. 4b) (Table 3) . Another measure of air quality is particulate matter PM 2.5 and PM 10 which comes directly from various pollution sources such as industrial sectors, thermal power plant, burning waste, road dust through vehicular activity etc., although the lockdown blocked all the countries operational activities, it is the significant reason of reduction in particulate matter (Srivastava et al., 2020; Agarwal et al., 2020) .

Fig. 3a depicted the changes in concentration of NO 2 before and during the lockdown in the four cities. In Agra, Delhi, Noida and Gurugram NO 2 level have been dropped by -9.9%, − 64.6%, − 71.8% and − 38.5% respectively ( Table 2) (2019) while increasing in Agra and Gurugram by 58% and 28% (Table 3) . Similarly, in comparison to the previous two years (2018-2019) during 24 March to 14 April the NO 2 level decline by − 76%, − 66% and − 35.1% in Noida, Delhi and Gurugram while a slight increase in Agra by 23% (Fig. 4d, Table 3 ). NO 2 is emitted from biogenic sources such as soil and rain, pyrogenic sources such as natural explosions, and anthropogenic sources such as vehicle emissions and power plants dependent on fossil fuel (Reddy et al., 2012) . Though transport and industrial operations were limited during the lockout, power generation and biomass burning remained operational, contributing to the NO2 pollution from the atmosphere. It is identified in the Ghude et al. (2008) report that the industrial and thermal power plant region in India are major sources of NO 2 emissions. This drop-in NO 2 may be mainly due to decreased automobile emissions. Sharma et al. (2020) also recorded the approximately 18% decrease in NO 2 in the IGP (Indo-Gangetic Plain) zone during the lockdown.

Changes in the CO concentration shown in Fig. 3c . Before lockdown from 2 March to 24 March the CO average concentration 1.1 mg/m 3 , 0.9 mg/m 3 , 0.7 mg/m 3 and 1.5 mg/m 3 in Agra, Noida, Delhi and Gurugram respectively, were decrease at 1 mg/m 3 , 0.4 mg/ m 3 , 0.4 mg/m 3 and 0.5 mg/m 3 due to the lockdown from 24 March to 14 April which corresponds to variation of − 9%, − 55%, − 42.8% and − 60% ( Table 2 ). The sequence of declines is Gurugram >Noida >Delhi >Agra and the most visible drop was observed in Gurugram (from 1.5 to 0.6 mg/m 3 ). Though the CO concentration decreased at all observing stations during lockdown time, in the previous two years (2018-2019) the average concentration of CO from 24 March to 14 April has declined in Noida 00Delhi and Gurugram by − 45% -55% and − 69% as decreased NO 2 in the same station. While the CO level increased in Agra by (6%) (Fig. 4c , Table 3 ) due to two thermal power plant (coal-based) was continued during the lockdown (Saini et al., 2014) . CO has mostly emitted from incomplete combustion processes vehicular sources and biomass burning as well as oxidation of hydrocarbons. However, other sources include forest fires, agricultural waste burning, biofuel burning, (Holloway et al., 2000) . Road vehicles, airlines, trains and all types of transport activities are mostly banned during the lock-down period which decreases CO concentration overall observation sites. Higher levels can be attributed to the use of biofuels in heavily populated areas, while the levels in central India may be due to forest fires and farm burning, as reported by Sahu et al. (2015) , prevalent during this season.

The variation in the concentration of surface ozone at 8 h average daily maximum is shown in Table 2 . Fig. 3b shows, the significant decrease in the O 3 level is observed in Noida, Delhi and Gurugram during lockdown by − 36%, − 1% and − 25% respectively. In contrast, the surface O 3 level is increased in Agra by 98% compared to prior lockdown. Fig. 3b (Table 2) (Fig. 4e, Table 3) . O 3 is a secondary pollutant and its increase in the atmosphere due to the presence of its precursors such as NOx and VOCs and sunlight. During the lockdown, the concentration of the precursors may vary based on physical and chemical elimination, photochemistry and transportation on local, regional and global scales can increase the O 3 (Filonchyk et al., 2020; Filonchyk and Yan, 2019; . It is also documented that a decrease in the concentration of PM causes more infiltration of solar radiation through the atmosphere. The existence of much more solar radiation helps to increase the photochemical activity, thus increasing the production of surface O 3 in the atmosphere (Dang and Liao, 2019; Li et al., 2019) . The temperature began to rise due to the onset of summers, also causing an increase in surface O 3 concentrations. On the contrary, a decrease in the O 3 in mainly due to reduction in NO 2 and CO concentration. The reduction in concentration of O 3 precursor such as NO 2 by − 71.8%, − 64%, − 38.5% and CO by − 55%, − 42%, − 66% in Noida, Delhi and Gurugram, respectively whereas in Agra very less reduction in NO 2 (− 9%) and CO (− 9.9%) were found. Due to the high reduction in NO 2 and CO at Noida, Delhi and Gurugram, the O 3 concentration was decreased compare to Agra. The level of NO 2 and CO declined sharply (Table 3) due to restrictions in all the sectors mentioned, leading to a reduction in the concentration of NO 2 and CO emissions in the VOC-limited region which could be correlated with the rise in O 3 concentration in the most selected city while in Agra two thermal power plant were continued during the lockdown.

In the Pearson correlation test, data of meteorological variables were used to evaluate the relationship between pollutants PM 2.5, PM 10, and O 3 , CO, NO 2 during the lockdown period are summarized in Table 4 a-d. The coefficient of Pearson correlation is an indicator of the magnitude of the linear association between two variables. In Agra the PM 2.5 concentration was highly correlated with NO 2 (r = 0.40) and CO (r = 0.66). In Noida, the concentration of PM 2.5 was strongly correlated with NO 2 (r = 0.76), CO (r = 0.62) and O 3 (r = 0.50) likewise, PM 10 was correlated with NO 2 (r = 0.73) and CO (r = 0.92). The concentration of PM 2.5 significantly correlated with CO (r = 0.72) and NO 2 (r = 0.79) and similarly PM 10 is correlated with CO (r = 0.76) and NO 2 (r = 0.80) found in Delhi. In Gurugram PM 2.5 concentration was significantly correlated with NO 2 (r = 0.62), CO (r = 0.52) and PM 10 was correlated with NO 2 (r = 0.67), CO (r = 0.49) and least correlated with O 3 (r = 0.17). The average daily temperature for all stations has substantially positive correlations with the average daily PBL height (correlation ≈ 0.83) and daily average wind speed (correlation ≥0.04) for all stations, and negative correlations with the average daily relative humidity were (correlation ≈ − 0.83). The p teste indicates that all variables of air quality are statistically significant (p < 0.05) for almost all cities. Planetary boundary layer height properties play a significant role on the dispersion of pollutants near to the earth's surface. Lower pollutant concentrations are observed close to the ground during lockdown days with higher average PBL height. Increasing PBL levels and increased convective activity allow pollutants to be diluted Note: The correlations are expressed as Pearson's correlation coefficient, where *, ** and *** denote significant correlations at p < 0.1, p < 0.05, p < 0.01, and then concentrated close to the ground, even as air pollutants are stuck close to the ground in the days with lower PBL levels (Levi et al., 2020) . During the winter season, low PBL height may be partly responsible for several pollutants such as Anthropogenic: burning of fossil fuel, wood burning, natural sources (e.g., pollen), conversion of precursors (NOx, SOx, VOCs) and Biogenic: dust storms, forest fires, dirt roads in the air of the urban area associated with fog and can increase human cardiovascular risk (Al-Delaimy et al., 2020) . O 3 has a positive Pearson correlation with air temperature in Agra and Delhi, it is evident that the O 3 variation is directly correlated to air temperature in Agra and Delhi. There is an increase in O 3 concentration, predicted mainly because of the reduction in NOx and particulate matter (PM 2.5 and PM10) concentrations. The positive correlation between O 3 and Temperature is because the radiation controls the temperature and therefore the photolysis effectiveness will be higher. It points out that apart from the photochemical reaction, some other mechanism might also be contributing to O 3 mixing ratio whereas surface O 3 has negative Pearson correlation with temperature in Noida and Gurugram indicate that Surface O 3 is not directly correlated to air temperature, a lower concentration of NO 2 makes it impossible for the O 3 generated during the day to be further effectively converted (Zhao et al., 2018) . It is seen from For Delhi, the correlations between different air pollutants concentration in Delhi during the study period (2nd March to 14th of April) are shown in Table 4 a-d. The daily (24 h) average concentration of PM 2.5 is highly correlated with the daily average concentration of PM 10 (r = 0.89), NO 2 (r = 0.79) as well as 8 h average concentration of CO (r = 0.72). Likewise, the daily average concentration of PM 10 is also strongly correlated with the daily average concentration of NO 2 (r = 0.8), as well as 8 h average concentration of CO (r = 0.76) and surface ozone (r = 0.24). This visibly implies that the augmented control of regional transport activity compared to local contributions in the megacity is the key responsible factor for the reduction of pollutants concentration as during the lockdown period the regional transportation has been restricted completely. Besides these, CO (r = 0.77) is strongly associated with NO 2 . The correlation between O 3 and NO 2 ; O 3 and CO is not evident (Mahato et al., 2020) .

For Delhi, Noida and Gurugram, there is a very poor or negative correlation between temperature and PBL with O 3 concentrations, but humidity and wind speed correlate negatively with O 3 concentrations. Significant factors influencing the dispersion, aggregation and chemical transition processes of ambient air pollutants are atmospheric conditions, spatial structure, and urban settlement problems (Dandotiya et al., 2020) . A correlation analysis demonstrates a good association between ozone and PBL height only in Agra, indicating that surface ozone enhancement could be due to its mixing in a deeper boundary layer with the ozone-rich air aloft, but rather poor or negative correlation found for all observation sites (Ojha et al., 2012) . For all observation sites, NO 2 and CO concentrations correlated very well, which could indicate that CO and NO 2 generated from similar sources (vehicular activities) (Kerimray et al., 2020) . A correlation analysis demonstrates a good association between ozone and PBL height only in Agra, indicating that surface ozone enhancement could be due to its mixing in a deeper boundary layer with the ozone-rich air aloft, but rather poor or negative correlation found for all observation sites (Ojha et al., 2012) .

A substantial reduction of NO 2 , CO PM 2.5 and PM 10 concentration in the atmosphere for Delhi, Noida and Gurugram due to the restriction of all anthropogenic emissions, thus reducing NO 2 concentrations (Table 2) during the lockdown, could be a reason for the reduction in ozone concentration during lockdown (Mahato et al., 2020) . Correlation coefficients between air pollutants (NO 2 , O 3 , PM 2.5 and PM 10 ) for the observation sites during the study period are presented in Table 4a -d. Air pollutants are correlated significantly with each other. For all observation sites, the concentrations of PM 2.5 and PM 10 correlated well, which may suggest that PM 2.5 and PM 10 emerged from similar sources. But on the other hand, O 3 and NO 2 were negatively or weakly correlated with PM 2.5 and PM 10 for all observing sites. The observations indicate that local transportation control and restricted industrial operations have reduced the overall air pollution burden (Hashim et al., 2020) .

Air temperature, PBL height and wind speed are significant factors that influence air pollutant dispersion. The rise in air temperature due to the start of the summer season directly reduces the atmospheric balance and thus raises the planetary boundary layer height of the pollutants and hence the vertical mixing height of the pollutants in the troposphere. (Ravindra et al., 2019; Cichowicz et al., 2017; Akpinar et al., 2008) . The increase in air temperature attributed to the rise in solar radiation also increases the strength of atmospheric photochemical reactions. During the lockdown period, the average temperature raised by 6 • C to 7 • C compared to the pre-lockdown time due to the start of the summer season. Therefore, the rise in temperature can be linked to a small fraction of the overall decrease in pollution levels during the lockdown time. This rise is due to the presence of powerful solar radiation for the production of photochemical reactions, leading to the formation of O 3 at ground level. The rise in wind speed is also beneficial to air pollutants dispersion and local suspension of the PM 2.5 and PM 10 sources. During the lockdown, the wind speed marginally increased from 4.1 m/s before lockdown to 0.7 m/s. The comparison between the daily mean wind speed and the variance of the air pollutants indicates a decrease in the concentrations of NO 2 , CO, PM and O 3 during the peaks of wind speed during the entire study period (Mor et al., 2020) .

This research analyses the impact of national air quality lockdown during COVID-19 as well as the correlation of contaminants in four northern Indian cities. From 24 March to 14 April 2020 during lockdown the concentration of pollutant highly declined by − 49.6% (Noida), − 43% (Delhi), − 71.8% (Noida) and − 60% (Gurugram) for PM 2.5 , PM 10 , NO 2 and CO respectively while O 3 has increased drastically 98% in Agra. In 2020, during the lockdown, the concentration of pollutant is also reduced significantly in comparison to 2018-2019. Important variables affecting air pollutant dispersion are air temperature, PBL and wind direction. For surface ozone production, the different meteorological conditions such as high temperature, low relative humidity and high solar radiation are favourable. The results are especially important for areas where epidemics of COVID-19 and air pollution are both currently elevated, reinforcing the importance of mitigation and enhancing air quality not just in the short term but also in the long term. The analysis reported here only highlights trends in air quality during the period of lockdown. However, to impose short-term (2-4 day) lockdown as an effective policy mechanism for emissions control, and its effect on the economy needs to be rigorously studied. Therefore, lockdown is the important alternate mechanism to be adopted to control air emissions and the existing study is planned to examine the degree of improvement in air quality during the lockdown.

Abhishek Saxena: Conceptualization; Investigation; Methodology; Software; Writing an original draft. Shani Raj: Writing original draft; Writing -review & editing; Figure preparation ; Formal analysis.

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

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The authors would like to thank the Central Pollution Control Board (CPCB) and NOAA (National Oceanic and Atmospheric Administration) Air Resources Laboratory's (ARL) NASA. Authors also grateful to Mr. Hanwant Singh (M.L. Sukhadia University) for their help in graphical draw.