Large variation in concentrations of ultrafine (PM1) and coarse (PM10) mode particulate matter during lockdown and unlock phases of Covid-19

IJEP 42(6): 694-702 : Vol. 42 Issue. 6 (June 2022)

Jaspreet Kaur1, Charu Jhamaria1*, Suresh Tiwari2, Harsha Parwani1 and Shivani Sharma1

1. IIS (Deemed to be University), Department of Environmental Science, Jaipur-302 020, Rajasthan, India
2. Indian Institute of Tropical Meteorology, Pune – 411 008, Maharashtra, India

Abstract

Due to the strict enforcement of lockdown, the air quality index improved drastically in the cities across the globe within a few days of lockdown globally. The present study was conducted in Jaipur city to evaluate the effect of lockdown phases on the concentrations of PM10, PM1, NO2, SO2, CO and O3. Among the selected pollutants PM1 (-61.15%) and PM10 (-40.50%) witnessed the maximum reduction in the lockdown phase 1. Among others, gaseous pollutants also showed a declining trend, as NO2 (-69.61%) witnessed maximum reduction followed by CO (-25%) and S O2 (-13.74%). In contrast to this, the O3 (+24.26%) showed the opposite trend. The decreasing trend of pollutant concentrations continued upto the 2nd phase of lockdown, after which conditional relaxations in restrictions led to an increase in pollutants. In comparison to last year (that is 2019) during the same period, the concentration of atmospheric pollutants in 2020 was found to be very low. Ultrafine particulate matter showed a decreasing trend throughout the study whereas coarse mode particles shows a decreasing trend till the 3rd phase of lockdown and increased later on. Whereas, most of the gaseous pollutants show a decreasing trend in almost all phases except O3 showing a reverse trend.

Keywords

COVID-19, Coarse particulate matter, gaseous pollutants, Lockdown phases, Ultrafine particulate matter

References

  1. Bera, B., et al. 2020. Significant impacts of Covid-19 lockdown on urban air pollution in Kolkata (India) and amelioration of environment health. env. develop. sustain., 23(5): 6913-6940. DOI: 10.1007/s10668-020-00898-5.
  2. Tian, S., et al. 2020. Pathological study of the 2019 novel coronavirus disease (COVID-19) through postmortem core biopsies. Modern Pathol., 33:1007-1014.
  3. Zhu, Y., et al. 2020. Association between short-term exposure to air pollution and COVID-19 infection: Evidence from China. Sci. Total Env., 727:138704. DOI: 10.1016/j.scitotenv./2020. 138704.
  4. WHO. 2020. Coronavirus Disease (COVID-19): India situation reports. Available at: https://www.who.int/india /emergencies/coronavirus-disease-(covid-19)/india-situation-report. (Accessed on 2020).
  5. Mahato, S., S. Pal and G.K. Ghosh. 2020. Effect of lockdown amid Covid-19 pandemic on air quality of the megacity Delhi, India. Sci. Total Env., 730:1-22.
  6. Guo, H., et al. 2019. Contributions of local and regional sources to PM2.5 and its health effects in north India. Atmos. Env., 214:116867. DOI: 10.10 16/j.atmosenv.2019.116867.
  7. Tiwari, S., et al. 2012. Assessment of PM1, PM2.5and PM10 concentrations in Delhi at different means cycles. Geofizka. 29(2):125-141.
  8. Chen, Y., et al. 2019. Local characteristics of and exposure to fine particulate matter (PM2.5) in four Indian megacities. Atmos. Env. X.5. DOI: 10.1016/j.aeaoa.2019.100052.
  9. NCAP. 2019. National clean air programme. Ministry of Environmental Forests and Climate Change, Government of India.
  10. CPCB. 2020. Central control room for air quality management – All India. Central Pollution Control Board. Available at: https://app.cpcbccr.com/ccr/#/caaqm-dashboard-all/caaqm-landing. Accessed on 2020.
  11. Mor, S., et al. 2020. Impact of COVID-19 lockdown on air quality in Chandigarh, India: Understanding the emission sources during controlled anthropogenic activities. Chemosphere. 263:127978. DOI: 10.1016/ j.chemosphere.2020.127978.
  12. He, G., Y. Pan and T. Tanaka. 2020. The short-term impacts of COVID-19 lockdown on urban air pollution in China. Nature Sustain, 3:1005-1011.
  13. Berman, J.D. and K. Ebisu. 2020. Changes in U.S. air pollution during the COVID-19 pandemic. Sci. Total Env., 739:139864.
  14. NOAA. 2020. National Oceanic and Atmospheric Administration, U.S. Department of Commerce. Available at: https://ready.arl.noaa.gov/HYSPLIT. php. Accessed in 2020.
  15. Monks, P.S., et al. 2015. Tropospheric ozone and its precursors from the urban to the global scale from air quality to short-lived climate forcer. Atmos. Chem. Phys., 15:8889–8973.
  16. Rodriguez, J.M., M.K. Ko and N.D. Sze. 1991. Role of heterogeneous conversion of N2O5 on sulphate aerosols in global ozone losses. Nature. 352 (6331): 134-137.
  17. Earth null school. 2020. Available at : https:// earth.nullschool.net.
  18. Sandhu, P.S., U. Patel and A.B. Gupta. 2004. Exposure to airborne particles and their physical characterization in ambient environment of Jaipur city. Nat. Env. Poll. Tech., 3(4):509-514.
  19. Verma, S., et al. 2015. A new classification of aerosol sources and types as measured over Jaipur, India. Aerosol Air Qual. Res., 15:985-993. DOI:10.4209/aaqr.2014.07.0143.
  20. Liu, S. and X. Z. Liang. 2010. Observed diurnal cycle climatology of planetary boundary layer height. J. Clim., 23:5790-5809. DOI:10.1175/2010 JC43552.1.
  21. Miao, Y., et al. 2018. Impacts of meteorological conditions on winter time PM2.5 pollution in Taiyuan north, China. Env. Sci. Poll. Res., DOI:10.1007/s11356-018-2327-1.
  22. Sun, Y.L., et al. 2006. Chemical characteristics of PM2.5 and PM10 in haze-fog episodes in Beijing. Env. Sci. Tech., 40:3148-3155.
  23. Yao, L., N. Lu and S. Jiang. 2012. Artificial neural network (ANN) for multi-sources PM2.5 estimation using surface, MODIS and meteorological data. International Conference on Biomedical engineering and biotech-nology. Proceedings, pp 1228-1231.
  24. Zhang, L., et al. 2015. On the severe haze in Beijing during January 2013 : Untraveling the effect of meteorological anomalies with WRF-Chem. Atmos. Env., 104:11-21. DOI:10.1016/j.atmosenv. 2015. 01.001.
  25. Medeiros, B., A. Hall and B. Stevens. 2005. What controls the mean depth of the PBL? J. Clim., 18:3157-3172. DOI:10.1175/JCL 13417.1.
  26. Miao, Y., et al. 2015a. Seasonal variation of local atmospheric circulations and boundary layer structure in the Beijing-Tianjin-Hebei region and implications for air quality. J. Adv. Model. Earth Syst., 7:1602-1626. DOI:10.1002/2015MS000522.
  27. Guo, J., et al. 2016. The climatology of planetary boundary layer height in China derived from radiosonde and reanalysis data. Atmos. Chem. Phys., 16:13309-13319. DOI:10.5194/acp-16-13309-2016.
  28. Miao, S., et al. 2012. Analysis of observation on the urban surface energy balance in Beijing. Sci. China Earth Sci., 55:1881-1890. DOI:10.1007/11430-012-4411-6.
  29. Zhang, Q., et al. 2015. Effects of meteorology and secondary particle formation on visibility during heavy haze events in Beijing, China. Sci. Total Env., 502:578-584.