Articles | Volume 20, issue 1
https://doi.org/10.5194/acp-20-499-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-20-499-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Jan 2020
Research article |
| 14 Jan 2020
| 14 Jan 2020
Mitigation of PM2.5 and ozone pollution in Delhi: a sensitivity study during the pre-monsoon period
Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK
Data Science Institute, Lancaster University, Lancaster, LA1 4YW, UK
Oliver Wild
Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK
Data Science Institute, Lancaster University, Lancaster, LA1 4YW, UK
Edmund Ryan
Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK
Department of Mathematics, University of Manchester, Manchester, M13 9PL, UK
Saroj Kumar Sahu
Environmental Science, Department of Botany, Utkal University, Bhubaneswar, India
Douglas Lowe
Centre for Atmospheric Sciences, School of Earth, Atmospheric and
Environmental Sciences, University of Manchester, Manchester, M13 9PL, UK
Scott Archer-Nicholls
NCAS Climate, Department of Chemistry, University of Cambridge,
Cambridge, CB2 1EW, UK
Centre for Atmospheric Sciences, School of Earth, Atmospheric and
Environmental Sciences, University of Manchester, Manchester, M13 9PL, UK
Gordon McFiggans
Centre for Atmospheric Sciences, School of Earth, Atmospheric and
Environmental Sciences, University of Manchester, Manchester, M13 9PL, UK
Tabish Ansari
Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK
Vikas Singh
National Atmospheric Research Laboratory, Gadanki, Andhra Pradesh, India
Ranjeet S. Sokhi
Centre for Atmospheric and Climate Physics Research, University of
Hertfordshire, Hatfield, Hertfordshire, UK
Alex Archibald
NCAS Climate, Department of Chemistry, University of Cambridge,
Cambridge, CB2 1EW, UK
Gufran Beig
Indian Institute of Tropical Meteorology, Pune, India
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- PM2.5 diminution and haze events over Delhi during the COVID-19 lockdown period: an interplay between the baseline pollution and meteorology S. Dhaka et al. 10.1038/s41598-020-70179-8
- Strategies for the coordinated control of particulate matter and carbon dioxide under multiple combined pollution conditions Z. Wang et al. 10.1016/j.scitotenv.2023.165679
- Temporary reduction in fine particulate matter due to ‘anthropogenic emissions switch-off’ during COVID-19 lockdown in Indian cities P. Kumar et al. 10.1016/j.scs.2020.102382
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- Role of meteorological regime in mitigating biomass induced extreme air pollution events G. Beig et al. 10.1016/j.uclim.2020.100756
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- Avoiding high ozone pollution in Delhi, India Y. Chen et al. 10.1039/D0FD00079E
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- Exacerbation of Fatality Rates Induced by Poor Air Quality Due to Open-Air Mass Funeral Pyre Cremation during the Second Wave of COVID-19 M. Manoj et al. 10.3390/toxics10060306
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- Statistical Emulation of Winter Ambient Fine Particulate Matter Concentrations From Emission Changes in China L. Conibear et al. 10.1029/2021GH000391
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- A study of cross-correlations between PM2.5 and O3 based on Copula and Multifractal methods J. Zhang et al. 10.1016/j.physa.2021.126651
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- Using Short‐Term Data to Quickly and Quantitatively Determine Formation Mechanisms of PM2.5 and Ozone E. Zhao & Y. Kuo 10.1002/clen.202100184
- Cross-evaluating WRF-Chem v4.1.2, TROPOMI, APEX, and in situ NO2 measurements over Antwerp, Belgium C. Poraicu et al. 10.5194/gmd-16-479-2023
- Ensemble source apportionment of air pollutants and carbon dioxide based on online measurements Z. Wang et al. 10.1016/j.jclepro.2022.133468
- A Multi-model Air Quality System for Health Research: Road model development and evaluation M. Seaton et al. 10.1016/j.envsoft.2022.105455
- Road transport impact on PM2.5 pollution over Delhi during the post-monsoon season C. Mogno et al. 10.1016/j.aeaoa.2022.100200
- Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation L. Conibear et al. 10.1029/2021GH000570
- Editorial: Machine learning in studies of atmospheric environment and climate change Y. Chen et al. 10.3389/fenvs.2023.1280733
- Association of retreating monsoon and extreme air pollution in a megacity G. Beig et al. 10.1016/j.jes.2021.01.004
44 citations as recorded by crossref.
- Quantifying the high resolution seasonal emission of air pollutants from crop residue burning in India S. Sahu et al. 10.1016/j.envpol.2021.117165
- Trends in Summer-Time Tropospheric Ozone during COVID-19 Lockdown in Indian Cities Might Forecast a Higher Future Risk S. Das et al. 10.3390/atmos13071115
- PM2.5 diminution and haze events over Delhi during the COVID-19 lockdown period: an interplay between the baseline pollution and meteorology S. Dhaka et al. 10.1038/s41598-020-70179-8
- Strategies for the coordinated control of particulate matter and carbon dioxide under multiple combined pollution conditions Z. Wang et al. 10.1016/j.scitotenv.2023.165679
- Temporary reduction in fine particulate matter due to ‘anthropogenic emissions switch-off’ during COVID-19 lockdown in Indian cities P. Kumar et al. 10.1016/j.scs.2020.102382
- Implications for ozone control by understanding the survivor bias in observed ozone-volatile organic compounds system Z. Wang et al. 10.1038/s41612-022-00261-7
- India’s Maiden air quality forecasting framework for megacities of divergent environments: The SAFAR-project G. Beig et al. 10.1016/j.envsoft.2021.105204
- Role of meteorological regime in mitigating biomass induced extreme air pollution events G. Beig et al. 10.1016/j.uclim.2020.100756
- Analysis of various transport modes to evaluate personal exposure to PM2.5 pollution in Delhi K. Maji et al. 10.1016/j.apr.2020.12.003
- Avoiding high ozone pollution in Delhi, India Y. Chen et al. 10.1039/D0FD00079E
- Regional pollution loading in winter months over India using high resolution WRF-Chem simulation R. Jat et al. 10.1016/j.atmosres.2020.105326
- Anomalous behaviour of ozone under COVID-19 and explicit diagnosis of O3-NOx-VOCs mechanism A. Rathod et al. 10.1016/j.heliyon.2021.e06142
- Exacerbation of Fatality Rates Induced by Poor Air Quality Due to Open-Air Mass Funeral Pyre Cremation during the Second Wave of COVID-19 M. Manoj et al. 10.3390/toxics10060306
- Fractal analysis of impact of PM2.5 on surface O3 sensitivity regime based on field observations C. Liu et al. 10.1016/j.scitotenv.2022.160136
- Temporally resolved sectoral and regional contributions to air pollution in Beijing: informing short-term emission controls T. Ansari et al. 10.5194/acp-21-4471-2021
- Unprecedented reduction in air pollution and corresponding short-term premature mortality associated with COVID-19 lockdown in Delhi, India K. Maji et al. 10.1080/10962247.2021.1905104
- On the transition of major pollutant and O3 production regime during Covid-19 lockdowns L. R et al. 10.1016/j.jenvman.2022.116907
- Health risk assessment of workers’ exposure to BTEX and PM during refueling in an urban fuel station S. Jayaraj & S. Shiva Nagendra 10.1007/s10661-023-12130-8
- Relationship between summertime concurring PM2.5 and O3 pollution and boundary layer height differs between Beijing and Shanghai, China Y. Miao et al. 10.1016/j.envpol.2020.115775
- Ammonium Chloride Associated Aerosol Liquid Water Enhances Haze in Delhi, India Y. Chen et al. 10.1021/acs.est.2c00650
- Process-based diagnostics of extreme pollution trail using numerical modelling during fatal second COVID-19 wave in the Indian capital G. Beig et al. 10.1016/j.chemosphere.2022.134271
- COVID-19 lockdown and air quality of SAFAR-India metro cities R. Yadav et al. 10.1016/j.uclim.2020.100729
- Statistical Emulation of Winter Ambient Fine Particulate Matter Concentrations From Emission Changes in China L. Conibear et al. 10.1029/2021GH000391
- Emission Sector Impacts on Air Quality and Public Health in China From 2010 to 2020 L. Conibear et al. 10.1029/2021GH000567
- Sources of PM2.5 and its responses to emission reduction strategies in the Central Plains Economic Region in China: Implications for the impacts of COVID-19 H. Du et al. 10.1016/j.envpol.2021.117783
- Insights on Air Pollution During COVID-19: A Review S. Kumar 10.1007/s41810-023-00173-w
- Impacts of anthropogenic aerosols on orographic precipitation in Arizona J. Henao et al. 10.1016/j.uclim.2023.101561
- Source apportionment of particulate matter based on numerical simulation during a severe pollution period in Tangshan, North China J. He et al. 10.1016/j.envpol.2020.115133
- Investigating the importance of sub-grid particle formation in point source plumes over eastern China using IAP-AACM v1.0 with a sub-grid parameterization Y. Wei et al. 10.5194/gmd-14-4411-2021
- A study of cross-correlations between PM2.5 and O3 based on Copula and Multifractal methods J. Zhang et al. 10.1016/j.physa.2021.126651
- Ozone sensitivity factor: NOX or NMHCs?: A case study over an urban site in Delhi, India A. Sharma et al. 10.1016/j.uclim.2021.100980
- Machine learning reveals the effects of drivers on PM2.5 and CO2 based on ensemble source apportionment method H. Xu et al. 10.1016/j.atmosres.2023.107019
- Investigating the background and local contribution of the oxidants in London and Bangkok M. Khan et al. 10.1039/D0FD00086H
- MODIS-Derived Fire Characteristics and Greenhouse Gas Emissions from Cropland Residue Burning in Central India T. Ray et al. 10.3390/su142416612
- Simulating organic aerosol in Delhi with WRF-Chem using the volatility-basis-set approach: exploring model uncertainty with a Gaussian process emulator E. Reyes-Villegas et al. 10.5194/acp-23-5763-2023
- Đánh giá sự phát thải khí nhà kính tại thành phố Cần Thơ - Trường hợp nghiên cứu tại ba quận Ninh Kiều, Cái Răng và Bình Thủy T. Nguyễn et al. 10.22144/ctu.jvn.2023.103
- Using Short‐Term Data to Quickly and Quantitatively Determine Formation Mechanisms of PM2.5 and Ozone E. Zhao & Y. Kuo 10.1002/clen.202100184
- Cross-evaluating WRF-Chem v4.1.2, TROPOMI, APEX, and in situ NO2 measurements over Antwerp, Belgium C. Poraicu et al. 10.5194/gmd-16-479-2023
- Ensemble source apportionment of air pollutants and carbon dioxide based on online measurements Z. Wang et al. 10.1016/j.jclepro.2022.133468
- A Multi-model Air Quality System for Health Research: Road model development and evaluation M. Seaton et al. 10.1016/j.envsoft.2022.105455
- Road transport impact on PM2.5 pollution over Delhi during the post-monsoon season C. Mogno et al. 10.1016/j.aeaoa.2022.100200
- Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation L. Conibear et al. 10.1029/2021GH000570
- Editorial: Machine learning in studies of atmospheric environment and climate change Y. Chen et al. 10.3389/fenvs.2023.1280733
- Association of retreating monsoon and extreme air pollution in a megacity G. Beig et al. 10.1016/j.jes.2021.01.004
Latest update: 29 Nov 2023
Short summary
PM2.5 and O3 are two major air pollutants. Some mitigation strategies focusing on reducing PM2.5 may lead to substantial increase in O3. We use statistical emulation combined with atmospheric transport model to perform thousands of sensitivity numerical studies to identify the major sources of PM2.5 and O3 and to develop strategies targeted at both pollutants. Our scientific evidence suggests that regional coordinated emission control is required to mitigate PM2.5 whilst preventing O3 increase.
PM2.5 and O3 are two major air pollutants. Some mitigation strategies focusing on reducing PM2.5...
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