Evolution of Pollution Levels from COVID-19 Lockdown to Post-Lockdown over India
Abstract
:1. Introduction
2. Materials and Methods
3. Results
3.1. Understanding the Difference between Monsoon and Pre-Monsoon Variation
3.2. Rainfall Anomaly for the Year 2020
3.3. AOD, NO2 and SO2 Anomalies for Lockdown and Unlock Phases of the Year 2020
3.4. Relationships between Meteorological and Air Quality Factors
4. Discussion
5. Conclusions
- The difference between monsoon and pre-monsoon months climatology revealed that the north region has higher AOD values. During the COVID-19 unlock/lockdown phases, the extent of the higher AOD region extended to the total Indo-Gangetic plain. The AOD dipole existed during the anomaly of the lockdown and unlock stages of 2020. However, NO2 variations were lower for the whole of India during unlock months and not for the lockdown period. The increased rainfall amounts in 2020 may be the reason. The SO2 variations show hotspot emission regions over eastern India during both the lockdown and unlock phases compared to climatological variations;
- The NO2 reduction during monsoon months to pre-monsoon months was evident in the whole of India except north region (Haryana, Punjab, Delhi, Himachal Pradesh, Rajasthan, Uttarakhand and parts of Uttar Pradesh) for climatological variations. However, during the COVID-19 times of 2020, the unlock months show a positive change (increase in values) over these states, which is different from the climatological variation. The SO2 variations, however, move in line with climatological variations during 2020, except for higher emission SO2 sites identified during the unlock–lockdown phase over Odisha, West Bengal and Jharkhand;
- The COVID-19 lockdown and unlock months received higher rainfall than climatological variations over India. The surface temperature anomalies show reduced temperature for lockdown and unlock phases, more prominently during the lockdown. During the lockdown phase, there was an increase in cloud cover over northern and eastern India. In contrast, the unlock phases show a dipole pattern—a decrease over the northwestern part and an increase over east India.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- World Health Organization. Rolling Updates on Coronavirus Disease (COVID-19). 2020. Available online: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/events-as-they-happen (accessed on 24 August 2020).
- Koh, D. COVID-19 lockdowns throughout the world. Occup. Med. 2020, 70, 322. [Google Scholar] [CrossRef]
- Lancet, T. India under COVID-19 lockdown. Lancet 2020, 395, 1315. [Google Scholar] [CrossRef]
- Coibion, O.; Gorodnichenko, Y.; Weber, M. The Cost of the COVID-19 Crisis: Lockdowns, Macroeconomic Expectations, and Consumer Spending (No. W27141) (No. w27141). Natl. Bur. Econ. Res. 2020, 27141. Available online: https://www.nber.org/system/files/working_papers/w27141/w27141.pdf (accessed on 15 August 2020).
- Sibley, C.G.; Greaves, L.M.; Satherley, N.; Wilson, M.S.; Overall, N.C.; Lee, C.H.; Milojev, P.; Bulbulia, J.; Osborne, D.; Milfont, T.L.; et al. Effects of the COVID-19 pandemic and nationwide lockdown on trust, attitudes toward government, and well-being. Am. Psychol. 2020, 75, 618–630. [Google Scholar] [CrossRef] [PubMed]
- Webb, L. COVID-19 lockdown: A perfect storm for older people’s mental health. J. Psychiatr. Ment. Health Nurs. 2020, 28, 300. [Google Scholar] [CrossRef] [PubMed]
- Paital, B. Nurture to nature via COVID-19, a self-regenerating environmental strategy of environment in global context. Sci. Total Environ. 2020, 729, 139088. [Google Scholar] [CrossRef]
- Barcelo, D. An environmental and health perspective for COVID-19 outbreak: Meteorology and air quality influence, sewage epidemiology indicator, hospitals disinfection, drug therapies and recommendations. J. Environ. Chem. Eng. 2020, 8, 104006. [Google Scholar] [CrossRef]
- Zambrano-Monserrate, M.A.; Ruano, M.A.; Sanchez-Alcalde, L. Indirect effects of COVID-19 on the environment. Sci. Total Environ. 2020, 728, 138813. [Google Scholar] [CrossRef]
- Muhammad, S.; Long, X.; Salman, M. COVID-19 pandemic and environmental pollution: A blessing in disguise? Sci. Total Environ. 2020, 728, 138820. [Google Scholar] [CrossRef]
- Sahu, S.K.; Tyagi, B.; Beig, G.; Mangaraj, P.; Pradhan, C.; Khuntia, S.; Singh, V. Significant change in air quality parameters during the year 2020 over 1st smart city of India: Bhubaneswar. SN Appl. Sci. 2020, 2, 1990. [Google Scholar] [CrossRef]
- Rodríguez-Urrego, D.; Rodríguez-Urrego, L. Air quality during the COVID-19: PM2. 5 analysis in the 50 most polluted capital cities in the world. Environ. Pollut. 2020, 266, 115042. [Google Scholar] [CrossRef]
- Collivignarelli, M.C.; Abbà, A.; Bertanza, G.; Pedrazzani, R.; Ricciardi, P.; Miino, M.C. Lockdown for COVID-2019 in Milan: What are the effects on air quality? Sci. Total Environ. 2020, 732, 139280. [Google Scholar] [CrossRef] [PubMed]
- Otmani, A.; Benchrif, A.; Tahri, M.; Bounakhla, M.; El Bouch, M.; Krombi, M.H. Impact of COVID-19 lockdown on PM10, SO2 and NO2 concentrations in Salé City (Morocco). Sci. Total Environ. 2020, 735, 139541. [Google Scholar] [CrossRef] [PubMed]
- Tobías, A.; Carnerero, C.; Reche, C.; Massagué, J.; Via, M.; Minguillón, M.C.; Alastuey, A.; Querol, X. Changes in air quality during the lockdown in Barcelona (Spain) one month into the SARS-CoV-2 epidemic. Sci. Total Environ. 2020, 726, 138540. [Google Scholar] [CrossRef] [PubMed]
- Kerimray, A.; Baimatova, N.; Ibragimova, O.P.; Bukenov, B.; Kenessov, B.; Plotitsyn, P.; Karaca, F. Assessing air quality changes in large cities during COVID-19 lockdowns: The impacts of traffic-free urban conditions in Almaty, Kazakhstan. Sci. Total Environ. 2020, 730, 139179. [Google Scholar] [CrossRef] [PubMed]
- Sicard, P.; De Marco, A.; Agathokleous, E.; Feng, Z.; Xu, X.; Paoletti, E.; Rodriguez, J.J.D.; Calatayud, V. Amplified ozone pollution in cities during the COVID-19 lockdown. Sci. Total Environ. 2020, 735, 139542. [Google Scholar] [CrossRef]
- Zalakeviciute, R.; Vasquez, R.; Bayas, D.; Buenano, A.; Mejia, D.; Zegarra, R.; Diaz, A.; Lamb, B. Drastic Improvements in Air Quality in Ecuador during the COVID-19 Outbreak. Aerosol Air Qual. Res. 2020, 20, 1530–1540. [Google Scholar] [CrossRef]
- Broomandi, P.; Karaca, F.; Nikfal, A.; Jahanbakhshi, A.; Tamjidi, M.; Kim, J.R. Impact of COVID-19 Event on the Air Quality in Iran. Aerosol Air Qual. Res. 2020, 20, 0205. [Google Scholar] [CrossRef]
- Filonchyk, M.; Hurynovich, V.; Yan, H.; Gusev, A.; Shpilevskaya, N. Impact Assessment of COVID-19 on Variations of SO2, NO2, CO and AOD over East China. Aerosol Air Qual. Res. 2020, 20, 0226. [Google Scholar] [CrossRef]
- Bao, R.; Zhang, A. Does lockdown reduce air pollution? Evidence from 44 cities in northern China. Sci. Total Environ. 2020, 731, 139052. [Google Scholar] [CrossRef]
- Navinya, C.; Patidar, G.; Phuleria, H.C. Examining Effects of the COVID-19 National Lockdown on Ambient Air Quality across Urban India. Aerosol Air Qual. Res. 2020, 20, 0256. [Google Scholar] [CrossRef]
- Singh, J.; Tyagi, B. Transformation of air quality over a coastal tropical station Chennai during covid-19 lockdown in India. Aerosol Air Qual. Res. 2020, 21, 200490. [Google Scholar] [CrossRef]
- Mahato, S.; Pal, S.; Ghosh, K.G. Effect of lockdown amid COVID-19 pandemic on air quality of the megacity Delhi, India. Sci. Total Environ. 2020, 730, 139086. [Google Scholar] [CrossRef]
- Tyagi, B.; Choudhury, G.; Vissa, N.K.; Singh, J.; Tesche, M. Changing air pollution scenario during COVID-19: Redefining the hotspot regions over India. Environ. Pollut. 2021, 271, 116354. [Google Scholar] [CrossRef] [PubMed]
- Singh, V.; Singh, S.; Biswal, A.; Kesarkar, A.P.; Mor, S.; Ravindra, K. Diurnal and temporal changes in air pollution during COVID-19 strict lockdown over different regions of India. Environ. Pollut. 2020, 266, 115368. [Google Scholar] [CrossRef]
- Ravindra, K.; Singh, T.; Biswal, A.; Singh, V.; Mor, S. Impact of COVID-19 lockdown on ambient air quality in megacities of India and implication for air pollution control strategies. Environ. Sci. Pollut. Res. 2021, 28, 21621–21632. [Google Scholar] [CrossRef] [PubMed]
- Tibrewal, K.; Venkataraman, C. COVID-19 lockdown closures of emissions sources in India: Lessons for air quality and climate policy. J. Environ. Manag. 2022, 302, 114079. [Google Scholar] [CrossRef]
- Nadzir, M.S.M.; Ooi, M.C.G.; Alhasa, K.M.; Bakar, M.A.A.; Mohtar, A.A.A.; Nor, M.F.F.M.; Latif, M.T.; Abd Hamid, H.H.; Ali, S.H.M.; Ariff, N.M.; et al. The Impact of Movement Control Order (MCO) during Pandemic COVID-19 on Local Air Quality in an Urban Area of Klang Valley, Malaysia. Aerosol Air Qual. Res. 2020, 20, 1237–1248. [Google Scholar] [CrossRef]
- Ranjan, A.K.; Patra, A.K.; Gorai, A.K. Effect of lockdown due to SARS COVID-19 on aerosol optical depth (AOD) over urban and mining regions in India. Sci. Total Environ. 2020, 745, 141024. [Google Scholar] [CrossRef]
- Hari, M.; Sahu, R.K.; Sunder, M.S.; Tyagi, B. Then and Now: COVID-19 Pandemic Lockdown Misfire Atmospheric Methane over India. Aerosol Air Qual. Res. 2022, 22, 210354. [Google Scholar] [CrossRef]
- Kundu, B.; Panda, D.; Vissa, N.K.; Tyagi, B. Novel 2019 Coronavirus Outbreak” through the Eyes of GNSS Signal. J. Geol. Soc. India 2022, 98, 83–87. [Google Scholar] [CrossRef]
- Hari, M.; Sahu, R.K.; Tyagi, B.; Kaushik, R. Reviewing the crop residual burning and aerosol variations during the COVID-19 pandemic hit year 2020 over North India. Pollutants 2021, 1, 127–140. [Google Scholar] [CrossRef]
- Sahu, S.K.; Mangaraj, P.; Beig, G.; Tyagi, B.; Tikle, S.; Vinoj, V. Establishing a link between fine particulate matter (PM2. 5) zones and COVID-19 over India based on anthropogenic emission sources and air quality data. Urban Clim. 2021, 38, 100883. [Google Scholar] [CrossRef] [PubMed]
- Platnick, S.; Hubanks, P.; Meyer, K.; King, M.D. MODIS Atmosphere L3 Monthly Product (08_L3). NASA MODIS Adapt. Process. Syst. Goddard Space Flight Cent. 2015. Available online: https://doi.org/10.5067/MODIS/MOD08_M3.006 (accessed on 15 August 2020).
- Levy, R.C.; Remer, L.A.; Mattoo, S.; Vermote, E.F.; Kaufman, Y.J. Second-generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance. J. Geophys. Res. Atmos. 2007, 112, 7811. [Google Scholar] [CrossRef] [Green Version]
- Levy, R.C.; Mattoo, S.; Munchak, L.A.; Remer, L.A.; Sayer, A.M.; Patadia, F.; Hsu, N.C. The Collection 6 MODIS aerosol products over land and ocean. Atmos. Meas. Tech. 2013, 6, 2989–3034. [Google Scholar] [CrossRef] [Green Version]
- Hsu, N.C.; Jeong, M.J.; Bettenhausen, C.; Sayer, A.M.; Hansell, R.; Seftor, C.S.; Huang, J.; Tsay, S.C. Enhanced Deep Blue aerosol retrieval algorithm: The second generation. J. Geophys. Res. Atmos. 2013, 118, 9296–9315. [Google Scholar] [CrossRef]
- Bilal, M.; Qiu, Z.; Campbell, J.R.; Spak, S.N.; Shen, X.; Nazeer, M. A new MODIS C6 Dark Target and Deep Blue merged aerosol product on a 3 km spatial grid. Remote Sens. 2018, 10, 463. [Google Scholar] [CrossRef] [Green Version]
- Wei, J.; Peng, Y.; Guo, J.; Sun, L. Performance of MODIS Collection 6.1 Level 3 aerosol products in spatial-temporal variations over land. Atmos. Environ. 2019, 206, 30–44. [Google Scholar] [CrossRef]
- Krotkov, N.A.; Lamsal, L.N.; Celarier, E.A.; Swartz, W.H.; Marchenko, S.V.; Bucsela, E.J.; Chan, K.L.; Wenig, M.; Zara, M. The version 3 OMI NO2 standard product. Atmos. Meas. Tech. 2017, 9, 3133–3149. [Google Scholar] [CrossRef] [Green Version]
- Krotkov, N.A.; Lamsal, L.N.; Marchenko, S.V.; Celarier, E.A.; Bucsela, E.J.; Swartz, W.H.; Joiner, J.; the OMI core team. OMI/Aura NO2 Cloud-Screened Total and Tropospheric Column L3 Global Gridded 0.25 Degree × 0.25 Degree V3; NASA Goddard Space Flight Center, Goddard Earth Sciences Data and Information Services Center (GES DISC): Greenbelt, MD, USA, 2019. Available online: https://disc.gsfc.nasa.gov/datasets/OMNO2d_003/summary (accessed on 15 August 2020).
- Krotkov, N.A.; Li, C.; Leonard, P. OMI/Aura Sulfur Dioxide (SO2) Total Column L3 1 day Best Pixel in 0.25 Degree × 0.25 Degree V3; Goddard Earth Sciences Data and Information Services Center (GES DISC): Greenbelt, MD, USA, 2015. Available online: https://disc.gsfc.nasa.gov/datasets/OMSO2e_003/summary (accessed on 15 August 2020).
- Hersbach, H.; Bell, B.; Berrisford, P.; Hor´anyi, A.J.M.S.; Sabater, J.M.; Nicolas, J.; Radu, R.; Schepers, D.; Simmons, A.; Soci, C.; et al. Global reanalysis: Goodbye ERA-Interim, hello ERA5. ECMWF Newsl. 2019, 159, 17–24. [Google Scholar] [CrossRef]
- Guttikunda, S.K.; Jawahar, P. Evaluation of particulate pollution and health impacts from planned expansion of coal-fired thermal power plants in India using WRF-CAMx modeling system. Aerosol Air Qual. Res. 2018, 18, 3187–3202. [Google Scholar] [CrossRef]
- Guhathakurta, P.; Rajeevan, M. Trends in the rainfall pattern over India. Int. J. Climatol. 2008, 28, 1453–1469. [Google Scholar] [CrossRef]
- Karplus, V.J.; Zhang, S.; Almond, D. Quantifying coal power plant responses to tighter SO2 emissions standards in China. Proc. Natl. Acad. Sci. USA 2018, 115, 7004–7009. [Google Scholar] [CrossRef] [Green Version]
- Guttikunda, S.K.; Jawahar, P. Atmospheric emissions and pollution from the coal-fired thermal power plants in India. Atmos. Environ. 2014, 92, 449–460. [Google Scholar] [CrossRef]
- Cheng, N.; Zhang, D.; Li, Y.; Xie, X.; Chen, Z.; Meng, F.; Gao, B.; He, B. Spatiotemporal variations of PM2.5 concentrations and the evaluation of emission reduction measures during two red air pollution alerts in Beijing. Sci. Rep. 2017, 7, 8220. [Google Scholar] [CrossRef] [Green Version]
- Vissa, N.K. and Tyagi, B. Aerosol dipole pattern over India: Consequences on rainfall and relation with wind circulations. Acta Geophys. 2021, 69, 2475–2482. [Google Scholar] [CrossRef]
- Sharma, S.; Zhang, M.; Gao, J.; Zhang, H.; Kota, S.H. Effect of restricted emissions during COVID-19 on air quality in India. Sci. Total Environ. 2020, 728, 138878. [Google Scholar] [CrossRef] [PubMed]
- Biswal, A.; Singh, V.; Singh, S.; Kesarkar, A.P.; Ravindra, K.; Sokhi, R.S.; Chipperfield, M.P.; Dhomse, S.S.; Pope, R.J.; Singh, T. and Mor, S. COVID-19 lockdown-induced changes in NO2 levels across India observed by multi-satellite and surface observations. Atmos. Chem. Phys. 2021, 21, 5235–5251. [Google Scholar] [CrossRef]
- Pal, S.C.; Chowdhuri, I.; Saha, A.; Chakrabortty, R.; Roy, P.; Ghosh, M.; Shit, M. Improvement in ambient-air-quality reduced temperature during the COVID-19 lockdown period in India. Environ. Develop. Sustain. 2021, 23, 9581–9608. [Google Scholar] [CrossRef]
- Madineni, V.R.; Dasari, H.P.; Karumuri, R.; Viswanadhapalli, Y.; Perumal, P.; Hoteit, I. Natural processes dominate the pollution levels during COVID-19 lockdown over India. Sci. Rep. 2021, 11, 15110. [Google Scholar] [CrossRef]
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Tyagi, B.; Vissa, N.K.; Ghude, S.D. Evolution of Pollution Levels from COVID-19 Lockdown to Post-Lockdown over India. Toxics 2022, 10, 653. https://doi.org/10.3390/toxics10110653
Tyagi B, Vissa NK, Ghude SD. Evolution of Pollution Levels from COVID-19 Lockdown to Post-Lockdown over India. Toxics. 2022; 10(11):653. https://doi.org/10.3390/toxics10110653
Chicago/Turabian StyleTyagi, Bhishma, Naresh Krishna Vissa, and Sachin D. Ghude. 2022. "Evolution of Pollution Levels from COVID-19 Lockdown to Post-Lockdown over India" Toxics 10, no. 11: 653. https://doi.org/10.3390/toxics10110653
APA StyleTyagi, B., Vissa, N. K., & Ghude, S. D. (2022). Evolution of Pollution Levels from COVID-19 Lockdown to Post-Lockdown over India. Toxics, 10(11), 653. https://doi.org/10.3390/toxics10110653