Time series analysis of COVID-19 stringency measures on the spatiotemporal dynamics of air pollution
,
Abstract
Introduction: Execution of COVID-19 lockdown measures caused variations in air pollution worldwide. This paper investigates the impact of COVID-19 stringency measures on the spatio-temporal dynamics of air pollution in Mumbai, India, using a comprehensive two-and-a-half-year pandemic period dataset.
Materials and methods: We classified the pandemic period into 7 phases and 21 sub-phases based on the severity of the Oxford COVID-19 Government Response Tracker (OxCGRT) Stringency Index (SI). Optimized Hotspot analysis (OHS) and Ordinary Least Square Regression models explored the spatio-temporal fluctuations and the effect of stringency measures on air quality.
Results: The R2 value varied; with the best model R2 of 0.61 for Particulate Matters (PM10) and Nitrogen dioxide (NO2) and lowest of 0.23 for Sulfur dioxide (SO2). A 10-point increase in SI caused a 3-7% reduction in air pollutants. Substantial reduction in average PM10, PM2.5, NO2, and Carbon monoxide (CO) was observed throughout the COVID-19 phases. Meteorology and SI collectively caused maximum reduction of 82.6%, 72.7%, 53.8%, 52.2%, 49.1%, 28.4% for NO2, PM2.5, PM10, NH3, CO, and SO2 respectively, during complete or extreme lockdown phases. Except SO2, seasonality significantly influenced the pollutant concentrations. Winter was the worst period while monsoon was the best. OHS identified central Mumbai wards as hotspots and areas close to the national park as coldspots.
Conclusion: PM10, NO2 and CO were more affected by SI measures than NH3 and SO2. For a rapid emergency response to high PM10, implementation of SI, very high (≥ 80 score) and above is advised. Findings of this study have significant public health policy implications, especially among global south nations.
2. Murray CJL, Aravkin AY, Zheng P, Abbafati C, Abbas KM, Abbasi-Kangevari M, et al. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet [Internet]. 2020;396(10258):1223–49.Available from: http://dx.doi.org/10.1016/s0140-6736(20)30752-2.
3. Liu F, Wang M, Zheng M. Effects of COVID-19 lockdown on global air quality and health. Sci Total Environ [Internet]. 2021;755(142533):142533. Available from: http://dx.doi.org/10.1016/j.scitotenv.2020.142533.
4. Hale T, Angrist N, Goldszmidt R, Kira B, Petherick A, Phillips T, et al. A global panel database of pandemic policies (Oxford COVID-19 Government Response Tracker). Nat Hum Behav [Internet]. 2021;5(4):529–38. Available from: http://dx.doi.org/10.1038/s41562-021-01079-8.
5. Skirienė AF, Stasiškienė Ž. COVID-19 and air pollution: Measuring pandemic impact to air quality in five European countries. Atmosphere (Basel) [Internet]. 2021;12(3):290. Available from: http://dx.doi.org/10.3390/atmos12030290.
6. Aix ML, Petit P, Bicout DJ. Air pollution and health impacts during the COVID-19 lockdowns in Grenoble, France. Environ Pollut [Internet]. 2022;303(119134):119134. Available from: http://dx.doi.org/10.1016/j.envpol.2022.119134.
7. Kumar S, Managi S. Does stringency of lockdown affect air quality? Evidence from Indian cities. Econ Disaster Clim Chang [Internet]. 2020;4(3):481–502. Available from: http://dx.doi.org/10.1007/s41885-020-00072-1.
8. Goel A, Saxena P, Sonwani S, Rathi S, Srivastava A, Bharti AK, et al. Health benefits due to reduction in respirable particulates during COVID-19 lockdown in India. Aerosol Air Qual Res [Internet]. 2021;21(5):200460. Available from: http://dx.doi.org/10.4209/aaqr.200460.
9. Saadat MN, Das S, Nandy S, Pandey D, Chakraborty M, Mina U, Sarkar A. Can the nation-wide COVID-19 lockdown help India identify region-specific strategies for air pollution?. Spatial Information Research. 2021 Dec 18:1-5.
10. Singh V, Singh S, Biswal A, Kesarkar AP, Mor S, Ravindra K. Diurnal and temporal changes in air pollution during COVID-19 strict lockdown over different regions of India. Environ Pollut [Internet]. 2020;266(115368):115368. Available from: http://dx.doi.org/10.1016/j.envpol.2020.115368
11. Sharma S, Zhang M, Anshika, Gao J, Zhang H, Kota SH. Effect of restricted emissions during COVID-19 on air quality in India. Sci Total Environ [Internet]. 2020;728(138878):138878. Available from: http://dx.doi.org/10.1016/j.scitotenv.2020.138878.
12. Mahato S, Pal S, Ghosh KG. Effect of lockdown amid COVID-19 pandemic on air quality of the megacity Delhi, India. Sci Total Environ [Internet]. 2020;730(139086):139086. Available from: http://dx.doi.org/10.1016/j.scitotenv.2020.139086.
13. Taloor AK, Singh AK, Kumar P, Kumar A, Tripathi JN, Kumari M, et al. Geospatial technology-based analysis of air quality in India during the COVID-19 pandemic. Remote Sens (Basel) [Internet]. 2022;14(18):4650. Available from: http://dx.doi.org/10.3390/rs14184650.
14. Patankar AM, Trivedi PL. Monetary burden of health impacts of air pollution in Mumbai, India: Implications for public health policy. Public Health [Internet]. 2011;125(3):157–64. Available from: http://dx.doi.org/10.1016/j.puhe.2010.11.009.
15. Xie Q, Sun Q. Monitoring the spatial variation of aerosol optical depth and its correlation with land use/land cover in Wuhan, China: A perspective of urban planning. Int J Environ Res Public Health [Internet]. 2021;18(3):1132. Available from: http://dx.doi.org/10.3390/ijerph18031132.
16. Dadhich AP, Goyal R, Dadhich PN. Assessment of spatio-temporal variations in air quality of Jaipur city, Rajasthan, India. Egypt J Remote Sens Space Sci [Internet]. 2018;21(2):173–81. Available from: http://dx.doi.org/10.1016/j.ejrs.2017.04.002.
17. Gov.in. [cited 2023 Nov 21]. Available from: https://portal.mcgm.gov.in/irj/go/km/docs/documents/MCGM.
18. Home [Internet]. Gov.in. [cited 2023 Nov 21]. Available from: https://censusindia.gov.in.
19. CCR [Internet]. Cpcbccr.com. [cited 2023 Nov 21]. Available from: https://app.cpcbccr.com/ccr/#/caaqm-dashboard-all/caaqm-landing.
20. Oxford COVID-19 Government Response Tracker (OxCGRT,2022) https://github.com/OxCGRT/covid-policy-tracker/tree/master/data/India
21. Kumar A, Gupta I, Brandt J, Kumar R, Dikshit AK, Patil RS. Air quality mapping using GIS and economic evaluation of health impact for Mumbai City, India. J Air Waste Manag Assoc [Internet]. 2016;66(5):470–81. Available from: http://dx.doi.org/10.1080/10962247.2016.1143887.
22. Jha DK, Sabesan M, Das A, Vinithkumar NV, Kirubagaran R. Evaluation of Interpolation Technique for Air Quality Parameters in Port Blair, India. Universal journal of environmental research & technology. 2011 Oct 1;1(3).
23. Shukla K, Kumar P, Mann GS, Khare M. Mapping spatial distribution of particulate matter using Kriging and Inverse Distance Weighting at supersites of megacity Delhi. Sustain Cities Soc [Internet]. 2020;54(101997):101997. Available from: http://dx.doi.org/10.1016/j.scs.2019.101997.
24. Abhijith KV., Kumar P, Gallagher J, McNabola A, Baldauf R, Pilla F, et al. Air pollution abatement performances of green infrastructure in open road and built-up street canyon environments – A review. Atmos Environ [Internet]. 2017;162:71–86. Available from: http://dx.doi.org/10.1016/j.atmosenv.2017.05.014
25. Upadhyay A, Dey S, Chowdhury S, Goyal P. Expected health benefits from mitigation of emissions from major anthropogenic PM2.5 sources in India: Statistics at state level. Environ Pollut [Internet]. 2018;242:1817–26. Available from: http://dx.doi.org/10.1016/j.envpol.2018.07.085.
26. Guttikunda SK, Goel R, Pant P. Nature of air pollution, emission sources, and management in the Indian cities. Atmos Environ (1994) [Internet]. 2014;95:501–10. Available from: http://dx.doi.org/10.1016/j.atmosenv.2014.07.006.
| Files | ||
| Issue | Vol 9 No 2 (2024): Spring 2024 | |
| Section | Original Research | |
| DOI | https://doi.org/10.18502/japh.v9i2.15922 | |
| Keywords | ||
| COVID-19 Air pollution Stringency index Regression models Hotspot analysis | ||
| Rights and permissions | |
|
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
