key: cord-0830732-yl5kox73
authors: Mahato, Susanta; Gopal Ghosh, Dr. Krishna
title: Short-term exposure to ambient air quality of the most polluted Indian cities due to lockdown amid SARS-CoV-2
date: 2020-07-02
journal: Environ Res
DOI: 10.1016/j.envres.2020.109835
sha: 26236d2722ad790df46bef6872703a7e880aba1f
doc_id: 830732
cord_uid: yl5kox73

Air pollution has happened to be one of the mounting alarms to be concerned with in many Indian cities. COVID-19 epidemic endow with a unique opportunity to report the degree of air quality improvement due to the nationwide lockdown in 10 most polluted cities across the country. National Air Quality Index (NAQI) based on continuous monitoring records of seven criteria pollutants (i.e. common air pollutants with known health impacts e.g. PM(10), PM(2.5), CO, NO(2), SO(2), NH(3) and O(3)) for a total of 59 stations across the cities, satellite image derived Aerosol Optical Depth (AOD) and few statistical tools are employed to derive the outcomes. NAQI results convey that 8 cities out of the 10 air quality restored to good to satisfactory category during the lockdown period. Within week+1 of the lockdown period, PM(10) and PM(2.5) concentrations have suppressed below the permissible limit in all cities. CO and NO(2) have reduced to about -30% and -57% respectively during the lockdown period. Diurnal concentrations of PM(10) and PM(2.5) have dropped drastically on the very 4(th) day of lockdown and become consistent with minor hourly vacillation. In April 2020 the AOD amount was reduced to about 36% and 18% in contrast to April 2018 and April 2019 respectively. This add-on reporting of the possible recovery extent in air quality may help to guide alternative policy intervention in form of short term lockdown so as to testify whether this type of unconventional policy decisions may be put forward to attain a green environment.. Because, despite numerous restoration plans, air pollution levels have risen unabated in these cities. However, detailed inventory needs to be focused on identifying the localized pollution hotspots (i.e. source contribution).

In many Indian cities air quality is one of the intimidating issues to be concerned with and often has 25 been counted within the world's top polluted city Mukherjee and Agarwal 2018; 26 Garaga et al. 2018 ). Out of the world's top 20 most polluted cities during 2019, 14 are located in India 27 (IQAir 2019). Greenpeace India (2020) identified 231 Indian cities (>80%) having concentration of 28 PM 10 beyond 60µg/m 3 (Permissible limit is 50µg/m 3 m as per WHO (2006) .The Airpocalypse-Report 29 IV (GreenPeace India 2020) of the Central Pollution Control Board (CPCB) has included 102 non-30 extraordinary state of affairs arising out of COVID-19 over the last few months across the globe has 48 led to several unforeseen consequences (Harapan et al. 2020 ) and restoration of environmental health 49 (Gautam and Trivedi 2020; Dutheil et al. 2020) due to imposed restriction on human activities is the 50 most obvious one. Consequently the global concerns for air pollution had escorted momentous 51 attention to the scientific community to examine the level of pollution amid this epidemic. 52 SARS-CoV-2 brought significant health threat particularly for the people having respiratory disorders 53 (Halpin et al. 2020; CDC COVID-19 Response Team 2020) . Study of Coccia (2020) showed that the 54 accelerated diffusion of COVID-19 is to some extent associated with higher air pollution levels in the 55 55 north Italy province capitals. However, the study of Bontempi (2020) suggests that the role of 56 airborne particulate matter (PM) for the virus diffusion is not evident in the case of Lombardy (Italy). 57 Therefore, it is not possible to demonstrate that PM can be a virus carrier. In third world countries air 58 pollution is the leading cause for premature death and disease load globally (Burnett et al. 2014; 59 Cohen et al. 2017; Landrigan et al. 2018 ; State of Global Air 2019). About 3.7 million deaths are 60 ascribed to outdoor air pollution globally of which 88% are in the low-medium income countries 61

The present article has focused on 10 most polluted urban centers in India out of the 20 most polluted 126 cities globally (IQAir 2019) (Figure 1 ). The list includes the megacity Delhi with 10 million plus 127 population; 2 cities (Ghaziabad and Faridabad) with population more than 1 million; 2 cities 128 (Gurugram and Noida) with population above 6 lakh and 5 cities (Muzaffarnagar; Bulandshahr; 129 Greater Noida; Jind and Bhiwadi) having population 1 lakh and above ( Table 1) These four are excluded from the present analysis because of the fact that, daily continuous air quality 137 monitoring data for all the criteria pollutants, particularly for the study period are not available for 138 those cities. 139

All the sample cities are located within the National Capital Region (NCR) which is considered as the 140 largest urban agglomeration in India with a decadal growth rate of >20%. The geographical extension 141 of the NCR within which the cities are located is 27.60°N to 29.30°N latitude and 76.20°E to 78.40°E 142 longitude with a total of twenty three districts (Hazarika et al. 2019) . Physiographicaly the NCR 143 region belongs to the Indo-Gangetic alluvial plain and sandwiched between two states-Haryana and 144

Uttar Pradesh. The average density of the cities are >6000 persons/sq.km in 2011 and a significant 145 portion of population in the region reside in urban areas (about 60%) which is much higher than the 146 national average. All the sample cities experience semi-arid climate having five major seasons: 147

Summer (Mar-May), Monsoon (Jun-Sept), short Post-monsoon (Oct-Nov), Winter (Dec-Feb) and Pre-148 monsoon (March-May). Temperatures stuck between 4°C to 10°C in winter and 42° C to 48°C in 149 summer (Kumar et al. 2017) . Average air temperature during the month of March, 2020 and April, total annual precipitation occurs during the monsoon months (Perrino et al. 2011) . Currently there are 154 a total of 59 ambient air quality monitoring stations across the sample cities (Table 1) all having 155 capacity to monitor and record pollutants. 156

As far as the selection of the urban areas is concerned, the IQAir regularly publishes air quality 157 reports (IQAir 2018; IQAir 2019) every year for regions and cities across the globe based on the 158 concentration of PM 2.5 . PM 2.5 is the prevailing pollutant across the cities of India having its source 159 primarily from traffic, power and automated industry and dust (Guo et al. 2019; Guo et al. 2017) . 160

Higher concentration of PM 2.5 is also the leading cause of human respiratory problem (Xing et al. 161 2016; Li et al. 2018; Hopke et al. 2019) . COVID-19 is more likely to infect people having respiratory 162 diseases and also found to have accelerated spread over the region having higher 163 pollution (Coccia 2020 Aerosol Optical Depth (AOD) data (https://giovanni.gsfc.nasa.gov/giovanni/) has been utilized in this 187 study to show the variation in aerosol concentration in the month of March and April this year from 188 the previous two year. 189

In order to understand the overall improvement in air quality during the lockdown period in 191 comparison to the pre-lockdown period and proceeding years the National Air Quality Index (NAQI) 192 introduced by CPCB (2015) has been utilized. The CPCB also prescribed the Indian National 193 (Ambient) Air Quality Standards (INAQS) for each of the pollutants and are highlighted in Appendix 194

The newly framed, NAQI was launched by CPCB in October 2014 in order to broadcast air quality to 196 the common public in a lucid way. The detailed calculation procedure of NAQI is available at the 197 National Air Quality Index -India Environment Portal (www.indiaenvironmentportal.org.in CPCB 198 2015) . The CPCB has also developed an excel template for calculation of the same and is available at 199 http://www.arthapedia.in/docs/AQI-Calculator-aug15.xls. In the present article we will highlight the 200 method very briefly. 201

The calculation of NAQI is based on data of eight criteria pollutants (namely PM 10 , PM 2.5 , SO 2 , NO 2 , 202 CO, O 3 , Pb and NH 3 ) of which data for at least three pollutants is desirable including one being either 203 PM 10 or/and PM 2.5 . In the present article, we have excluded Pb because of the fact that the data for Pb 204 is rarely available for all the sample stations used in the study. Based on different ambient 205 concentrations range of each of the criteria pollutants corresponding health break points are specified 206 (Appendix 2). Predominantly two steps are involved in NAQI calculation-Formulation of sub-indices 207 for each criteria pollutant and aggregation of the sub-indices to obtain the NAQI. 208 The overall NAQI is the maximum NAQIi, and the corresponding pollutant is the predominant 213 pollutant. The NAQI has six classes of air quality where each category is associated with certain 214 health impacts specified in Appendix 2. 215

The MERRA-2 is a NASA atmospheric reanalysis data released by the NASA Global Modeling and 216

Assimilation Office (GMAO) that was launched in 2017 (Randles et al. 2017) replacing the original 217 MERRA of 1980 (Rienecker et al. 2011 ). The MEERA-2 uses the data assimilation system of the 218 upgraded version of Goddard Earth-observing System Model, Version-5 (GEOS-5) (Randles et al. 219 2017) . In the present analysis the Aerosol optical depth (AOD) products from MERRA-2 are 220 evaluated using available independent ground-based in situ and remote sensing products. MERRA-2 221 AOD has 8 instantaneous values a day that the 8 instantaneous values are taken every 3 h (from 00:00 222 to 21:00 UTC). In the present case MERRA-2 instantaneous AOD at 06:00 (UTC-Coordinated 223

Universal Time) were chosen as it corresponds to 11:30am (Indian Standard Time (IST). The 224

MERRA-2 AOD for the month of March and April from 2018 to 2020 is used to analyze the variation 225 during the two months as well as comparison with the preceding two years. 226

In the present slot we have considered 6 week window period-two weeks before (10 th level. Over 50% improvement in air quality has noticed for Bhiwadi, Noida, Delhi, Faridabad, 249

Muzaffarnagar and Ghaziabad in the 3 weeks of first phase lockdown in comparison to the two weeks 250 pre-lockdown phase (Table 2, column 11) and at all cities in the first phase of lockdown the 251 improvement is >38% than its previous. In the partial lockdown phase (Week+4) air quality standard 252 (Figure 2f inset) became little inferior in comparison to its preceding weeks (Week+1 to Week+3) of 253 the lockdown phase (Figure 2c , 2d, 2e inset). As stated earlier, after three weeks of lockdown due to 254 letting off of certain controlled industrial activity and necessary transportation in some cities is the 255 possible cause for the slight increase in NAQI on the partial lockdown week. Nevertheless, in this 256 partial lockdown phase as much as 40% reduction in NAQI has been noticed in Bhiwadi, Faridabad, 257

Noida, Greater Noida, Ghaziabad and Delhi (Table 2, column 13). However, improvement in air 258 quality during the entire 4 week lockdown period is relatively less in Jind and Bulandshahr. There 259 may be some localized cause for such lesser reduction in NAQI in the two cities. The attenuation in 260 NAQI during the subsequent weeks of the lockdown periods is for the most part coupled with the 261 variation of prevailing pollutants, mainly PM 10 and PM 2.5 discussed afterward. 262 In comparison to the other pollutants, particulate matter (PM) is the foremost one in most of the 274 Indian cities (Guo et al. 2017; Guo et al. 2019 ). It has also been observed that a nearly uniform 275 meteorological condition for pollutants dispersion prevails during the month of March to April in 276

Indian subcontinent (Tiwari et al. 2015; Yadav et al. 2014) . In order to portray the changes in the 277 concentration of PM 10 and PM 2.5 in the most polluted cities 24 hours readings for each day of a week 278 are taken and averaged for respective weeks and subsequently plotted as Box-and-Whisker Plots 279 the cities pertaining to the concentration of primary pollutant PM 10 the reduction was substantial for 298 the city Gurugram, Jind, Bhiwadi, Ghaziabad and Noida during 24 th March to 06 th April (+1 week and 299 +2 Week) (Figure 3 ). Nearly similar patterns can also be noticed in these cities in case of PM 2.5 300 concentration also for the first two weeks of the lockdown period ( Figure 4) . Noticeably, the outskirt 301 cities located within the National Capital Region namely Gurugram, Gaziabad, Noida, Faridabad and 302

Greater Noida the concentration of the two primary pollutants have reduced to an average of 39% 303 during the first phase of lockdown period (+1 week to +3 Week). 

+1 to Week +3 represents During-lockdown phase 1; partial relaxation in lockdown started from Week +4

During the pre-lockdown weeks (10-23 rd March) all cities (except Jind for PM 10 ) exceed the annual 320 PM 10 and PM 2.5 standard of 100µg/m 3 and 60µg/m 3 respectively but during the lockdown weeks (24 th 321

March to 13 th April) pollution level reduced below the INAQS (except Bulandshahr for PM 10 ) ( Figure  322 5a and 5b). Combining all cities, the average PM 10 concentration during the lockdown and partial 323 lockdown period is as much as -49% and -12% respectively (Appendix 3). Whereas the PM 2.5 324 averaged concentrations is reduced by about -46.9% and -31.5% during the consecutive lockdown 325 periods. Exceptionally in Bulandshahr, Jind and Muzaffarnagar PM 10 concentration during the partial 326 lockdown period has increased in comparison to the pre-lockdown period (See Appendix 3, column 9, 327

10 & 11). However, in the case of PM 2.5 in these cities the considerable decrease (-9% to 25.7%) 328 during the partial lockdown period has increased in comparison to the pre-lockdown period. An 329 increase of only PM 10 in these cities may be due to the increase in the typical crustal elements (like-330

Fe, Al, Ti) and dust transport particularly in the urban-traffic sites as evident from several other 331 studies (Chenery et al. 2020; Contini et al. 2014) . In Bulandshahr a similar pattern of increase can also 332 be noticed in case of NO and NH 3 . NH 3 also found to have increased in Noida, Greater Noida and 333

Gurugram during the partial lockdown period. These cities, located in the NCR Delhi region are 334 counted among the fastest growing Indian cities, usually recognized to be dusty due to lots of 335 construction activities. The regional meteorological factor in association with operation of few coal 336 based industries and biomass burning may be some other factor. Apart from the particulate matter 337 pollution, CO and NO 2 are found to have higher concentration in these cities (Figure 5g and 5d ) 338 particularly during the pre-lockdown period which has reduced to about -30% and -57% respectively 339 during the three week lockdown period. In the partial lockdown period the trend continues and their 340 concentration has reduced up to -21% and -51% respectively (Appendix 3). In these cities the two 341 pollutants namely CO and NO 2 are largely emanate from traffic, manufacturing industry and power 342 plants and hence their concentrations remained higher. During the partial lockdown period certain 343 relaxation has been given for restricted public transportation, controlled industrial activity and hence 344 their concentration during the partial lockdown period has increased slightly. The 24h mean 345 concentration of SO 2 and NH 3 for all cities has remained below the permissible limit even during the 346 pre-lockdown period (Figure 5e, 5f) . This is because all of these cities are landlocked located in 347 northern India, but majority of the SO 2 emission source are from shipping activities and NH 3 348 emanating from non-agricultural sources is insignificant (Sutton et al. 1995) . However, slight 349 reduction (>-18%) in mean concentration of these two pollutants can be noticed during the lockdown 350 periods. Concentration of O 3 has increased during the consecutive lockdown period (Figure 5g) . 351

Generally, the month of April to August is considered as the period of high concentration of O 3 352 particularly in south-east Asia due to an increase in Insolation (Goraiet al. 2017) . Increase in the 353 average air temperature during April, 2020 as evident from Figure 1b and Figure 1c supports the fact. 

Real-time hourly concentrations of PM 10 and PM 2.5 for all the 10 cities for three sample dates from prelockdown (10 th March), lockdown (27 th March) and partial lockdown (20 th April) are considered for detecting the pattern of variation (Figure 6; Supplementary 3) . 24 hour mean concentration and standard deviation (SD) considering all stations are calculated for each sample date to show the pattern of variation between sample dates. The daily mean PM 10 in 10 th March (pre-lockdown) was as high as 165.8 (SD 81.8) (Figure 6a ), this has dropped to nearly 70% (avg. 43.2; SD 27.8) only on 4 th day of the lockdown (27 th of March) (Figure 6c ) and remained below the permissible limit. This pattern of variation remains almost the same for PM 2.5 also, however the drop in the mean concentration between 10th of March (pre-lockdown) and

27th of March (lockdown) is almost -72% (Net variation 60.7) (Figure 6b and 6d) . On 20 th of April (partial lockdown) both the PM 10 ( Figure 6e ) and PM 2.5 (Figure 6f ) again attained its pre-disposition when the average concentration has risen to 122.7 (SD 66.4) and 48.4 (SD 34.5) respectively. Interestingly, very good consistency with lesser standard deviation can be observed in both the pollutants on 27 th of March (lockdown), which was less consistent during 10 th of March (pre-lockdown). The hourly concentration of PM 10 and PM 2.5 also shows significant differences among the cities. Particularly, cities like Ghaziabad, Muzaffanagar, Gurugram, Faridabad and Greater Noida are closer to intensive emission sources from manufacturing and power plant industries leading to more concentration of pollutants. However, heavy PM pollution typically associated with sluggish weather conditions with gentle wind and hence site specific climatic conditions is one of the key influencing factors for such variation. 

In aim to supplement the particulate matter concentration during the lockdown and non-lockdown period as Table 7) . The reduction of AOD during the month of April this year in comparison to April 2018 for the cities ranged from -32.81% (Greater Noida) to-40.68% (Bhiwadi). This is a clear indication that the lockdown has lead to up-gradation of air quality as a result of the sizable reduction aerosol optical thickness concentration and also been documented in Gautam (2020) ; Dutheil et al. (2020) etc. 

The lockdown has had significant impacts on the air quality of the 10 most polluted urban centers located within NCR region with enhanced NAQI ratings. The NAQI outcome also conveys that about 70% of the cities across India air quality have restored to either good or satisfactory categories during the lockdown episode. Combining all the selected cities, among the selected pollutants PM 10 and PM 2.5 have witnessed maximum reduction during the three week lockdown period counting as much as -49% and -46.9% respectively. Pollutants like CO and NO 2 have reduced to about -30% and -57% respectively during the fullfledged lockdown period. On the contrary there is insignificant decrease in SO 2 and slight increase in O 3 concentration during the period. Drastic drop in the diurnal concentration of PM 10 and PM 2.5 to almost -70% can be noticed in 27 th of March (Lockdown period) in contrast to that of the 10 th of March (Pre-lockdown phase). Real-time concentration of aerosols as revealed from the profile of the monthly averaged AOD for the month of April in the year 2018 and 2019 show much higher value throughout India than this year. The observation again reveals lessening of the pollutant concentration. The framework adopted in this study can be easily transferred to not only examine similar lockdown effects on the air quality of other urban centers as well as industrial clusters, but also for continuous monitoring and image-extracted datasets, which can extend and improve spatio-temporal air quality assessments.

The policy planning and its implementation in this country in most of the cases are limited to the Tier-1 and Tier-2 cities. Yet, several medium and small urban centers of this country are enlisted in the world's most polluted cities. Therefore, there is a need for a closer look at the state of air pollution, particularly the most polluted Tier-3 cities in order to quantify the extent of pollution. Moreover, with the boosting of population and ever-rising urbanization trend particularly in and around the metropolitan cities, especially over the industrial clusters of the Indo-Gangetic Plain there is a need for revisiting the policy planning to combat the air pollution threat. The lockdown amid the COVID-19 pandemic has given us a rare window when the atmospheric pollution and particles are far less than at any point in the last 2-5 years especially for the industrial sites. Although the air quality have bettered yet, this improvement does not necessarily guarantee sustained purer air quality and there is a high likelihood that the environment shall return to its former degraded status once all lockdown measures are lifted and industrial production is increased to make up the economic losses engendered by such a period of closure. Therefore, integrative plans to recover the air quality are sorely required, together with better regulatory and sound technological interventions. The spate of newspaper articles, blogs and tweets about the cleanlier air quality during this lockdown must be used to press on for coordinated and augmented efforts to truly improve and sustain the environmental health, critically reexamining current management practices, which are quite unsustainable and exclusionary.

Of course lockdown has brought threat to the national economy but certainly the environmental refurbishment process also goes on hand by hand. Consequently if a city lockdown undeniably improved the air quality, it could be taken as an effective unconventional measure to restrain air pollution. The baseline report outlined in the present manuscript will definitely add on the researcher, planners and people at large to think for alternative policy interventions so as to manage the ever rising air pollution. However, an increase in the ground level monitoring network for these most polluted cities may support reporting air quality in finer terms. In these selected cities and all other resembling cities across India urbanization along with population boost is an increasing trend accompanied by ever rising automation and industrialization.

Hence, these cities need to start pollution control planning by anticipating the possible environmental threat.

There is a dearth of study devoted to emission inventory for most of these polluted Tier-3 cities. Therefore, detailed inventory for these medium and small cities having environmental threats need to be focused in order to assess the pollution trend and identify localized pollution hotspots (source contribution).

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Highlights:• Examine the lockdown's effects on air quality of 10 most polluted Indian cities• Lockdown period enhanced the air quality ratings to almost 80% of the cities Declaration of interests √☐ ☐ ☐ ☐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.☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:Dr. Krishna Gopal Ghosh (Corresponding author)