Beig, Gufran and et al, . (2022) Process-based Diagnostics of Extreme Pollution Trail using Numerical Modelling during Fatal Second COVID-19 wave in the Indian Capital. Chemosphere, 298 (134271).
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| Abstract: | The world's worst outbreak, the second COVID-19 wave, not only unleashed unprecedented devastation of human life, but also made an impact of lockdown in the Indian capital, New Delhi, in particulate matter (PM: PM2.5 and PM10) virtually ineffective during April to May 2021. The air quality remained not only unabated but also was marred by some unusual extreme pollution events. SAFAR-framework model simulations with different sensitivity experiments were conducted using the newly developed lockdown emission inventory to understand various processes responsible for these anomalies in PM. Model results well captured the magnitude and variations of the observed PM before and after the lockdown but significantly underestimated their levels in the initial period of lockdown followed by the first high pollution event when the mortality counts were at their peak (∼400 deaths/day). It is believed that an unaccounted emission source was playing a leading role after balancing off the impact of curtailed lockdown emissions. The model suggests that the unprecedented surge in PM10 (690 μg/m3) on May 23, 2021, though Delhi was still under lockdown, was associated with large-scale dust transport originating from the north west part of India combined with the thunderstorm. The rainfall and local dust lifting played decisive roles in other unusual events. Obtained results and the proposed interpretation are likely to enhance our understanding and envisaged to help policymakers to frame suitable strategies in such kinds of emergencies in the future. |
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| Item Type: | Journal Paper |
| Subjects: | School of Natural and Engineering Sciences > Energy School of Natural and Engineering Sciences > Energy and Environment |
| Divisions: | Schools > Natural Sciences and Engineering |
| Date Deposited: | 27 Mar 2023 11:34 |
| Last Modified: | 27 Mar 2023 11:34 |
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| Funders: | * |
| Projects: | * |
| DOI: | https://doi.org/10.1016/j.chemosphere.2022.134271 |
| URI: | http://eprints.nias.res.in/id/eprint/2470 |
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