Articles | Volume 24, issue 4
https://doi.org/10.5194/acp-24-2239-2024
© Author(s) 2024. 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-24-2239-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evaluation of WRF-Chem-simulated meteorology and aerosols over northern India during the severe pollution episode of 2016
School of GeoSciences, University of Edinburgh, Crew Building, Edinburgh, EH9 3FF, UK
David S. Stevenson
CORRESPONDING AUTHOR
School of GeoSciences, University of Edinburgh, Crew Building, Edinburgh, EH9 3FF, UK
Mathew R. Heal
School of Chemistry, University of Edinburgh, Joseph Black Building, Edinburgh, EH9 3FJ, UK
Related authors
No articles found.
Jize Jiang, David S. Stevenson, Aimable Uwizeye, Giuseppe Tempio, Alessandra Falcucci, Flavia Casu, and Mark A. Sutton
Geosci. Model Dev., 18, 5051–5099, https://doi.org/10.5194/gmd-18-5051-2025, https://doi.org/10.5194/gmd-18-5051-2025, 2025
Short summary
Short summary
A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from livestock farming. It is estimated that about 30 % of excreted N from livestock is lost due to NH3 emissions from housing, manure management and land application of manure. High NH3 volatilization often occurs in hot regions, while poor management practices also result in significant N losses through NH3 emissions.
Alexander K. Tardito Chaudhri and David S. Stevenson
Atmos. Chem. Phys., 25, 7369–7385, https://doi.org/10.5194/acp-25-7369-2025, https://doi.org/10.5194/acp-25-7369-2025, 2025
Short summary
Short summary
There remains a large uncertainty in the global warming potential of atmospheric hydrogen due to poor constraints on its soil deposition and, therefore, its lifetime. A new analysis of the latitudinal variation in the observed seasonality of hydrogen is used to constrain its surface fluxes. This is complemented with a simple latitude–height model where surface fluxes are adjusted from a prototype deposition scheme.
Alok K. Pandey, David S. Stevenson, Alcide Zhao, Richard J. Pope, Ryan Hossaini, Krishan Kumar, and Martyn P. Chipperfield
Atmos. Chem. Phys., 25, 4785–4802, https://doi.org/10.5194/acp-25-4785-2025, https://doi.org/10.5194/acp-25-4785-2025, 2025
Short summary
Short summary
Nitrogen dioxide is an air pollutant largely controlled by human activity that affects ozone, methane, and aerosols. Satellite instruments can quantify column NO2 and, by carefully matching the time and location of measurements, enable evaluation of model simulations. NO2 over south and east Asia is assessed, showing that the model captures not only many features of the measurements, but also important differences that suggest model deficiencies in representing several aspects of the atmospheric chemistry of NO2.
Jize Jiang, David S. Stevenson, and Mark A. Sutton
Geosci. Model Dev., 17, 8181–8222, https://doi.org/10.5194/gmd-17-8181-2024, https://doi.org/10.5194/gmd-17-8181-2024, 2024
Short summary
Short summary
A special model called AMmonia–CLIMate (AMCLIM) has been developed to understand and calculate NH3 emissions from fertilizer use and also taking into account how the environment influences these NH3 emissions. It is estimated that about 17 % of applied N in fertilizers was lost due to NH3 emissions. Hot and dry conditions and regions with high-pH soils can expect higher NH3 emissions.
Yao Ge, Sverre Solberg, Mathew R. Heal, Stefan Reimann, Willem van Caspel, Bryan Hellack, Thérèse Salameh, and David Simpson
Atmos. Chem. Phys., 24, 7699–7729, https://doi.org/10.5194/acp-24-7699-2024, https://doi.org/10.5194/acp-24-7699-2024, 2024
Short summary
Short summary
Atmospheric volatile organic compounds (VOCs) constitute many species, acting as precursors to ozone and aerosol. Given the uncertainties in VOC emissions, lack of evaluation studies, and recent changes in emissions, this work adapts the EMEP MSC-W to evaluate emission inventories in Europe. We focus on the varying agreement between modelled and measured VOCs across different species and underscore potential inaccuracies in total and sector-specific emission estimates.
Willem E. van Caspel, David Simpson, Jan Eiof Jonson, Anna M. K. Benedictow, Yao Ge, Alcide di Sarra, Giandomenico Pace, Massimo Vieno, Hannah L. Walker, and Mathew R. Heal
Geosci. Model Dev., 16, 7433–7459, https://doi.org/10.5194/gmd-16-7433-2023, https://doi.org/10.5194/gmd-16-7433-2023, 2023
Short summary
Short summary
Radiation coming from the sun is essential to atmospheric chemistry, driving the breakup, or photodissociation, of atmospheric molecules. This in turn affects the chemical composition and reactivity of the atmosphere. The representation of photodissociation effects is therefore essential in atmospheric chemistry modeling. One such model is the EMEP MSC-W model, for which a new way of calculating the photodissociation rates is tested and evaluated in this paper.
Gemma Purser, Mathew R. Heal, Edward J. Carnell, Stephen Bathgate, Julia Drewer, James I. L. Morison, and Massimo Vieno
Atmos. Chem. Phys., 23, 13713–13733, https://doi.org/10.5194/acp-23-13713-2023, https://doi.org/10.5194/acp-23-13713-2023, 2023
Short summary
Short summary
Forest expansion is a ″net-zero“ pathway, but change in land cover alters air quality in many ways. This study combines tree planting suitability data with UK measured emissions of biogenic volatile organic compounds to simulate spatial and temporal changes in atmospheric composition for planting scenarios of four species. Decreases in fine particulate matter are relatively larger than increases in ozone, which may indicate a net benefit of tree planting on human health aspects of air quality.
Yao Ge, Massimo Vieno, David S. Stevenson, Peter Wind, and Mathew R. Heal
Atmos. Chem. Phys., 23, 6083–6112, https://doi.org/10.5194/acp-23-6083-2023, https://doi.org/10.5194/acp-23-6083-2023, 2023
Short summary
Short summary
The sensitivity of fine particles and reactive N and S species to reductions in precursor emissions is investigated using the EMEP MSC-W (European Monitoring and Evaluation Programme Meteorological Synthesizing Centre – West) atmospheric chemistry transport model. This study reveals that the individual emissions reduction has multiple and geographically varying co-benefits and small disbenefits on different species, demonstrating the importance of prioritizing regional emissions controls.
David S. Stevenson, Richard G. Derwent, Oliver Wild, and William J. Collins
Atmos. Chem. Phys., 22, 14243–14252, https://doi.org/10.5194/acp-22-14243-2022, https://doi.org/10.5194/acp-22-14243-2022, 2022
Short summary
Short summary
Atmospheric methane’s growth rate rose by 50 % in 2020 relative to 2019. Lower nitrogen oxide (NOx) emissions tend to increase methane’s atmospheric residence time; lower carbon monoxide (CO) and non-methane volatile organic compound (NMVOC) emissions decrease its lifetime. Combining model sensitivities with emission changes, we find that COVID-19 lockdown emission reductions can explain over half the observed increases in methane in 2020.
Yao Ge, Massimo Vieno, David S. Stevenson, Peter Wind, and Mathew R. Heal
Atmos. Chem. Phys., 22, 8343–8368, https://doi.org/10.5194/acp-22-8343-2022, https://doi.org/10.5194/acp-22-8343-2022, 2022
Short summary
Short summary
Reactive N and S gases and aerosols are critical determinants of air quality. We report a comprehensive analysis of the concentrations, wet and dry deposition, fluxes, and lifetimes of these species globally as well as for 10 world regions. We used the EMEP MSC-W model coupled with WRF meteorology and 2015 global emissions. Our work demonstrates the substantial regional variation in these quantities and the need for modelling to simulate atmospheric responses to precursor emissions.
Fanlei Meng, Yibo Zhang, Jiahui Kang, Mathew R. Heal, Stefan Reis, Mengru Wang, Lei Liu, Kai Wang, Shaocai Yu, Pengfei Li, Jing Wei, Yong Hou, Ying Zhang, Xuejun Liu, Zhenling Cui, Wen Xu, and Fusuo Zhang
Atmos. Chem. Phys., 22, 6291–6308, https://doi.org/10.5194/acp-22-6291-2022, https://doi.org/10.5194/acp-22-6291-2022, 2022
Short summary
Short summary
PM2.5 pollution is a pressing environmental issue threatening human health and food security globally. We combined a meta-analysis of nationwide measurements and air quality modeling to identify efficiency gains by striking a balance between controlling NH3 and acid gas emissions. Persistent secondary inorganic aerosol pollution in China is limited by acid gas emissions, while an additional control on NH3 emissions would become more important as reductions in SO2 and NOx emissions progress.
Yao Ge, Mathew R. Heal, David S. Stevenson, Peter Wind, and Massimo Vieno
Geosci. Model Dev., 14, 7021–7046, https://doi.org/10.5194/gmd-14-7021-2021, https://doi.org/10.5194/gmd-14-7021-2021, 2021
Short summary
Short summary
This study reports the first evaluation of the global EMEP MSC-W ACTM driven by WRF meteorology, with a focus on surface concentrations and wet deposition of reactive N and S species. The model–measurement comparison is conducted both spatially and temporally, covering 10 monitoring networks worldwide. The statistics from the comprehensive evaluations presented in this study support the application of this model framework for global analysis of the budgets and fluxes of reactive N and SIA.
Ernesto Reyes-Villegas, Upasana Panda, Eoghan Darbyshire, James M. Cash, Rutambhara Joshi, Ben Langford, Chiara F. Di Marco, Neil J. Mullinger, Mohammed S. Alam, Leigh R. Crilley, Daniel J. Rooney, W. Joe F. Acton, Will Drysdale, Eiko Nemitz, Michael Flynn, Aristeidis Voliotis, Gordon McFiggans, Hugh Coe, James Lee, C. Nicholas Hewitt, Mathew R. Heal, Sachin S. Gunthe, Tuhin K. Mandal, Bhola R. Gurjar, Shivani, Ranu Gadi, Siddhartha Singh, Vijay Soni, and James D. Allan
Atmos. Chem. Phys., 21, 11655–11667, https://doi.org/10.5194/acp-21-11655-2021, https://doi.org/10.5194/acp-21-11655-2021, 2021
Short summary
Short summary
This paper shows the first multisite online measurements of PM1 in Delhi, India, with measurements over different seasons in Old Delhi and New Delhi in 2018. Organic aerosol (OA) source apportionment was performed using positive matrix factorisation (PMF). Traffic was the main primary aerosol source for both OAs and black carbon, seen with PMF and Aethalometer model analysis, indicating that control of primary traffic exhaust emissions would make a significant reduction to Delhi air pollution.
James M. Cash, Ben Langford, Chiara Di Marco, Neil J. Mullinger, James Allan, Ernesto Reyes-Villegas, Ruthambara Joshi, Mathew R. Heal, W. Joe F. Acton, C. Nicholas Hewitt, Pawel K. Misztal, Will Drysdale, Tuhin K. Mandal, Shivani, Ranu Gadi, Bhola Ram Gurjar, and Eiko Nemitz
Atmos. Chem. Phys., 21, 10133–10158, https://doi.org/10.5194/acp-21-10133-2021, https://doi.org/10.5194/acp-21-10133-2021, 2021
Short summary
Short summary
We present the first real-time composition of submicron particulate matter (PM1) in Old Delhi using high-resolution aerosol mass spectrometry. Seasonal analysis shows peak concentrations occur during the post-monsoon, and novel-tracers reveal the largest sources are a combination of local open and regional crop residue burning. Strong links between increased chloride aerosol concentrations and burning sources of PM1 suggest burning sources are responsible for the post-monsoon chloride peak.
Robbie Ramsay, Chiara F. Di Marco, Mathew R. Heal, Matthias Sörgel, Paulo Artaxo, Meinrat O. Andreae, and Eiko Nemitz
Biogeosciences, 18, 2809–2825, https://doi.org/10.5194/bg-18-2809-2021, https://doi.org/10.5194/bg-18-2809-2021, 2021
Short summary
Short summary
The exchange of the gas ammonia between the atmosphere and the surface is an important biogeochemical process, but little is known of this exchange for certain ecosystems, such as the Amazon rainforest. This study took measurements of ammonia exchange over an Amazon rainforest site and subsequently modelled the observed deposition and emission patterns. We observed emissions of ammonia from the rainforest, which can be simulated accurately by using a canopy resistance modelling approach.
Gemma Purser, Julia Drewer, Mathew R. Heal, Robert A. S. Sircus, Lara K. Dunn, and James I. L. Morison
Biogeosciences, 18, 2487–2510, https://doi.org/10.5194/bg-18-2487-2021, https://doi.org/10.5194/bg-18-2487-2021, 2021
Short summary
Short summary
Short-rotation forest plantations could help reduce greenhouse gases but can emit biogenic volatile organic compounds. Emissions were measured at a plantation trial in Scotland. Standardised emissions of isoprene from foliage were higher from hybrid aspen than from Sitka spruce and low from Italian alder. Emissions of total monoterpene were lower. The forest floor was only a small source. Model estimates suggest an SRF expansion of 0.7 Mha could increase total UK emissions between < 1 %–35 %.
Y. Sim Tang, Chris R. Flechard, Ulrich Dämmgen, Sonja Vidic, Vesna Djuricic, Marta Mitosinkova, Hilde T. Uggerud, Maria J. Sanz, Ivan Simmons, Ulrike Dragosits, Eiko Nemitz, Marsailidh Twigg, Netty van Dijk, Yannick Fauvel, Francisco Sanz, Martin Ferm, Cinzia Perrino, Maria Catrambone, David Leaver, Christine F. Braban, J. Neil Cape, Mathew R. Heal, and Mark A. Sutton
Atmos. Chem. Phys., 21, 875–914, https://doi.org/10.5194/acp-21-875-2021, https://doi.org/10.5194/acp-21-875-2021, 2021
Short summary
Short summary
The DELTA® approach provided speciated, monthly data on reactive gases (NH3, HNO3, SO2, HCl) and aerosols (NH4+, NO3−, SO42−, Cl−, Na+) across Europe (2006–2010). Differences in spatial and temporal concentrations and patterns between geographic regions and four ecosystem types were captured. NH3 and NH4NO3 were dominant components, highlighting their growing relative importance in ecosystem impacts (acidification, eutrophication) and human health effects (NH3 as a precursor to PM2.5) in Europe.
Jize Jiang, David S. Stevenson, Aimable Uwizeye, Giuseppe Tempio, and Mark A. Sutton
Biogeosciences, 18, 135–158, https://doi.org/10.5194/bg-18-135-2021, https://doi.org/10.5194/bg-18-135-2021, 2021
Short summary
Short summary
Ammonia is a key water and air pollutant and impacts human health and climate change. Ammonia emissions mainly originate from agriculture. We find that chicken agriculture contributes to large ammonia emissions, especially in hot and wet regions. These emissions can be greatly affected by the local environment, i.e. temperature and humidity, and also by human management. We develop a model that suggests ammonia emissions from chicken farming are likely to increase under a warming climate.
Robbie Ramsay, Chiara F. Di Marco, Matthias Sörgel, Mathew R. Heal, Samara Carbone, Paulo Artaxo, Alessandro C. de Araùjo, Marta Sá, Christopher Pöhlker, Jost Lavric, Meinrat O. Andreae, and Eiko Nemitz
Atmos. Chem. Phys., 20, 15551–15584, https://doi.org/10.5194/acp-20-15551-2020, https://doi.org/10.5194/acp-20-15551-2020, 2020
Short summary
Short summary
The Amazon rainforest is a unique
laboratoryto study the processes which govern the exchange of gases and aerosols to and from the atmosphere. This study investigated these processes by measuring the atmospheric concentrations of trace gases and particles at the Amazon Tall Tower Observatory. We found that the long-range transport of pollutants can affect the atmospheric composition above the Amazon rainforest and that the gases ammonia and nitrous acid can be emitted from the rainforest.
David S. Stevenson, Alcide Zhao, Vaishali Naik, Fiona M. O'Connor, Simone Tilmes, Guang Zeng, Lee T. Murray, William J. Collins, Paul T. Griffiths, Sungbo Shim, Larry W. Horowitz, Lori T. Sentman, and Louisa Emmons
Atmos. Chem. Phys., 20, 12905–12920, https://doi.org/10.5194/acp-20-12905-2020, https://doi.org/10.5194/acp-20-12905-2020, 2020
Short summary
Short summary
We present historical trends in atmospheric oxidizing capacity (OC) since 1850 from the latest generation of global climate models and compare these with estimates from measurements. OC controls levels of many key reactive gases, including methane (CH4). We find small model trends up to 1980, then increases of about 9 % up to 2014, disagreeing with (uncertain) measurement-based trends. Major drivers of OC trends are emissions of CH4, NOx, and CO; these will be important for future CH4 trends.
Cited articles
Acharja, P., Ghude, S. D., Sinha, B., Barth, M., Govardhan, G., Kulkarni, R., Sinha, V., Kumar, R., Ali, K., Gultepe, I., Petit, J.-E., and Rajeevan, M. N.: Thermodynamical framework for effective mitigation of high aerosol loading in the Indo-Gangetic Plain during winter, Sci. Rep., 13, 13667, https://doi.org/10.1038/s41598-023-40657-w, 2023.
Akagi, S. K., Yokelson, R. J., Wiedinmyer, C., Alvarado, M. J., Reid, J. S., Karl, T., Crounse, J. D., and Wennberg, P. O.: Emission factors for open and domestic biomass burning for use in atmospheric models, Atmos. Chem. Phys., 11, 4039–4072, https://doi.org/10.5194/acp-11-4039-2011, 2011.
Ångström, A.: The parameters of atmospheric turbidity, Tellus, 16, 64–75, https://doi.org/10.3402/tellusa.v16i1.8885, 1964.
Babu, S. S., Moorthy, K. K., Manchanda, R. K., Sinha, P. R., Satheesh, S. K., Vajja, D. P., Srinivasan, S., and Kumar, V. H. A.: Free tropospheric black carbon aerosol measurements using high altitude balloon: Do BC layers build “their own homes” up in the atmosphere?: Free Tropospheric Black Carbon Aerosol, Geophys. Res. Lett., 38, L08803, https://doi.org/10.1029/2011GL046654, 2011.
Bali, K., Dey, S., and Ganguly, D.: Diurnal patterns in ambient PM2.5 exposure over India using MERRA-2 reanalysis data, Atmos. Environ., 248, 118180, https://doi.org/10.1016/j.atmosenv.2020.118180, 2021.
Balzarini, A., Pirovano, G., Honzak, L., Žabkar, R., Curci, G., Forkel, R., Hirtl, M., San José, R., Tuccella, P., and Grell, G. A.: WRF-Chem model sensitivity to chemical mechanisms choice in reconstructing aerosol optical properties, Atmos. Environ., 115, 604–619, https://doi.org/10.1016/j.atmosenv.2014.12.033, 2015.
Beig, G., Srinivas, R., Parkhi, N. S., Carmichael, G. R., Singh, S., Sahu, S. K., Rathod, A., and Maji, S.: Anatomy of the winter 2017 air quality emergency in Delhi, Sci. Total Environ., 681, 305–311, https://doi.org/10.1016/j.scitotenv.2019.04.347, 2019.
Beig, G., Sahu, S. K., Rathod, A., Tikle, S., Singh, V., and Sandeepan, B. S.: Role of meteorological regime in mitigating biomass induced extreme air pollution events, Urban Clim., 35, 100756, https://doi.org/10.1016/j.uclim.2020.100756, 2021.
Bharali, C., Nair, V. S., Chutia, L., and Babu, S. S.: Modeling of the Effects of Wintertime Aerosols on Boundary Layer Properties Over the Indo Gangetic Plain, J. Geophys. Res.-Atmos., 124, 4141–4157, https://doi.org/10.1029/2018JD029758, 2019.
Bhardwaj, P., Naja, M., Kumar, R., and Chandola, H. C.: Seasonal, interannual, and long-term variabilities in biomass burning activity over South Asia, Environ. Sci. Pollut. Res., 23, 4397–4410, https://doi.org/10.1007/s11356-015-5629-6, 2016.
Bisht, D. S., Tiwari, S., Dumka, U. C., Srivastava, A. K., Safai, P. D., Ghude, S. D., Chate, D. M., Rao, P. S. P., Ali, K., Prabhakaran, T., Panickar, A. S., Soni, V. K., Attri, S. D., Tunved, P., Chakrabarty, R. K., and Hopke, P. K.: Tethered balloon-born and ground-based measurements of black carbon and particulate profiles within the lower troposphere during the foggy period in Delhi, India, Sci. Total Environ., 573, 894–905, https://doi.org/10.1016/j.scitotenv.2016.08.185, 2016.
Bond, T. C., Doherty, S. J., Fahey, D. W., Forster, P. M., Berntsen, T., DeAngelo, B. J., Flanner, M. G., Ghan, S., Kärcher, B., Koch, D., Kinne, S., Kondo, Y., Quinn, P. K., Sarofim, M. C., Schultz, M. G., Schulz, M., Venkataraman, C., Zhang, H., Zhang, S., Bellouin, N., Guttikunda, S. K., Hopke, P. K., Jacobson, M. Z., Kaiser, J. W., Klimont, Z., Lohmann, U., Schwarz, J. P., Shindell, D., Storelvmo, T., Warren, S. G., and Zender, C. S.: Bounding the role of black carbon in the climate system: A scientific assessment: Black Carbon In The Climate System, J. Geophys. Res.-Atmos., 118, 5380–5552, https://doi.org/10.1002/jgrd.50171, 2013.
Brooks, J., Liu, D., Allan, J. D., Williams, P. I., Haywood, J., Highwood, E. J., Kompalli, S. K., Babu, S. S., Satheesh, S. K., Turner, A. G., and Coe, H.: Black carbon physical and optical properties across northern India during pre-monsoon and monsoon seasons, Atmos. Chem. Phys., 19, 13079–13096, https://doi.org/10.5194/acp-19-13079-2019, 2019.
Buchard, V., da Silva, A. M., Randles, C. A., Colarco, P., Ferrare, R., Hair, J., Hostetler, C., Tackett, J., and Winker, D.: Evaluation of the surface PM2.5 in Version 1 of the NASA MERRA Aerosol Reanalysis over the United States, Atmos. Environ., 125, 100–111, https://doi.org/10.1016/j.atmosenv.2015.11.004, 2016.
Buchard, V., Randles, C. A., Da Silva, A. M., Darmenov, A., Colarco, P. R., Govindaraju, R., Ferrare, R., Hair, J., Beyersdorf, A. J., Ziemba, L. D., and Yu, H.: The MERRA-2 Aerosol Reanalysis, 1980 nward. Part II: Evaluation and Case Studies, J. Climate, 30, 6851–6872, https://doi.org/10.1175/JCLI-D-16-0613.1, 2017.
Cash, J. M., Langford, B., Di Marco, C., Mullinger, N. J., Allan, J., Reyes-Villegas, E., Joshi, R., Heal, M. R., Acton, W. J. F., Hewitt, C. N., Misztal, P. K., Drysdale, W., Mandal, T. K., Shivani, Gadi, R., Gurjar, B. R., and Nemitz, E.: Seasonal analysis of submicron aerosol in Old Delhi using high-resolution aerosol mass spectrometry: chemical characterisation, source apportionment and new marker identification, Atmos. Chem. Phys., 21, 10133–10158, https://doi.org/10.5194/acp-21-10133-2021, 2021.
Chhabra, A., Sehgal, V. K., Dhakar, R., Jain, N., and Verma, R.: Monitoring Of Active Fire Events Due To Paddy Residue Burning In Indo-Gangetic Plains Using Thermal Remote Sensing, Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., XLII-3/W6, 649–657, https://doi.org/10.5194/isprs-archives-XLII-3-W6-649-2019, 2019.
Chin, M., Ginoux, P., Kinne, S., Torres, O., Holben, B. N., Duncan, B. N., Martin, R. V., Logan, J. A., Higurashi, A., and Nakajima, T.: Tropospheric Aerosol Optical Thickness from the GOCART Model and Comparisons with Satellite and Sun Photometer Measurements, J. Atmos. Sci., 59, 461–483, 2002.
Chow, J. C., Lowenthal, D. H., Chen, L.-W. A., Wang, X., and Watson, J. G.: Mass reconstruction methods for PM2.5: a review, Air Qual. Atmos. Health, 8, 243–263, https://doi.org/10.1007/s11869-015-0338-3, 2015.
Conibear, L., Butt, E. W., Knote, C., Arnold, S. R., and Spracklen, D. V.: Residential energy use emissions dominate health impacts from exposure to ambient particulate matter in India, Nat. Commun., 9, 617, https://doi.org/10.1038/s41467-018-02986-7, 2018.
Cusworth, D. H., Mickley, L. J., Sfulprizio, M. P., Liu, T., Marlier, M. E., DeFries, R. S., Guttikunda, S. K., and Gupta, P.: Quantifying the influence of agricultural fires in northwest India on urban air pollution in Delhi, India, Environ. Res. Lett., 13, 044018, https://doi.org/10.1088/1748-9326/aab303, 2018.
Dekker, I. N., Houweling, S., Pandey, S., Krol, M., Röckmann, T., Borsdorff, T., Landgraf, J., and Aben, I.: What caused the extreme CO concentrations during the 2017 high-pollution episode in India?, Atmos. Chem. Phys., 19, 3433–3445, https://doi.org/10.5194/acp-19-3433-2019, 2019.
Dhaka, S. K., Chetna, Kumar, V., Panwar, V., Dimri, A. P., Singh, N., Patra, P. K., Matsumi, Y., Takigawa, M., Nakayama, T., Yamaji, K., Kajino, M., Misra, P., and Hayashida, S.: PM2.5 diminution and haze events over Delhi during the COVID-19 lockdown period: an interplay between the baseline pollution and meteorology, Sci. Rep., 10, 13442, https://doi.org/10.1038/s41598-020-70179-8, 2020.
Durre, I., Vose, R. S., and Wuertz, D. B.: Overview of the Integrated Global Radiosonde Archive, J. Climate, 19, 53–68, https://doi.org/10.1175/JCLI3594.1, 2006.
Emery, C. and Tai, E.: Enhanced Meteorological Modeling and Performance Evaluation for Two Texas Ozone Episodes, https://www.semanticscholar.org/paper/Enhanced-Meteorological-Modeling-and-Performance-Emery-Tai/3faa521b77acb7158769d9523be8f33e1d7e7ec6 (last access: 14 February 2024), 2001.
Emmons, L. K., Walters, S., Hess, P. G., Lamarque, J.-F., Pfister, G. G., Fillmore, D., Granier, C., Guenther, A., Kinnison, D., Laepple, T., Orlando, J., Tie, X., Tyndall, G., Wiedinmyer, C., Baughcum, S. L., and Kloster, S.: Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4), Geosci. Model Dev., 3, 43–67, https://doi.org/10.5194/gmd-3-43-2010, 2010.
Emmons, L. K., Schwantes, R. H., Orlando, J. J., Tyndall, G., Kinnison, D., Lamarque, J.-F., Marsh, D., Mills, M. J., Tilmes, S., Bardeen, C., Buchholz, R. R., Conley, A., Gettelman, A., Garcia, R., Simpson, I., Blake, D. R., Meinardi, S., and Pétron, G.: The Chemistry Mechanism in the Community Earth System Model Version 2 (CESM2), J. Adv. Model. Earth Syst., 12, e2019MS001882, https://doi.org/10.1029/2019MS001882, 2020.
Fast, J. D., Gustafson Jr., W. I., Easter, R. C., Zaveri, R. A., Barnard, J. C., Chapman, E. G., Grell, G. A., and Peckham, S. E.: Evolution of ozone, particulates, and aerosol direct radiative forcing in the vicinity of Houston using a fully coupled meteorology-chemistry-aerosol model, J. Geophys. Res.-Atmos., 111, D21305, https://doi.org/10.1029/2005JD006721, 2006.
Friedl, M. A., McIver, D. K., Hodges, J. C. F., Zhang, X. Y., Muchoney, D., Strahler, A. H., Woodcock, C. E., Gopal, S., Schneider, A., Cooper, A., Baccini, A., Gao, F., and Schaaf, C.: Global land cover mapping from MODIS: algorithms and early results, Remote Sens. Environ., 83, 287–302, https://doi.org/10.1016/S0034-4257(02)00078-0, 2002.
Gani, S., Bhandari, S., Seraj, S., Wang, D. S., Patel, K., Soni, P., Arub, Z., Habib, G., Hildebrandt Ruiz, L., and Apte, J. S.: Submicron aerosol composition in the world's most polluted megacity: the Delhi Aerosol Supersite study, Atmos. Chem. Phys., 19, 6843–6859, https://doi.org/10.5194/acp-19-6843-2019, 2019.
Gelaro, R., McCarty, W., Suárez, M. J., Todling, R., Molod, A., Takacs, L., Randles, C. A., Darmenov, A., Bosilovich, M. G., Reichle, R., Wargan, K., Coy, L., Cullather, R., Draper, C., Akella, S., Buchard, V., Conaty, A., da Silva, A. M., Gu, W., Kim, G.-K., Koster, R., Lucchesi, R., Merkova, D., Nielsen, J. E., Partyka, G., Pawson, S., Putman, W., Rienecker, M., Schubert, S. D., Sienkiewicz, M., and Zhao, B.: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), J. Climate, 30, 5419–5454, https://doi.org/10.1175/JCLI-D-16-0758.1, 2017.
Georgiou, G. K., Christoudias, T., Proestos, Y., Kushta, J., Hadjinicolaou, P., and Lelieveld, J.: Air quality modelling in the summer over the eastern Mediterranean using WRF-Chem: chemistry and aerosol mechanism intercomparison, Atmos. Chem. Phys., 18, 1555–1571, https://doi.org/10.5194/acp-18-1555-2018, 2018.
Ghosh, S., Dey, S., Das, S., Riemer, N., Giuliani, G., Ganguly, D., Venkataraman, C., Giorgi, F., Tripathi, S. N., Ramachandran, S., Rajesh, T. A., Gadhavi, H., and Srivastava, A. K.: Towards an improved representation of carbonaceous aerosols over the Indian monsoon region in a regional climate model: RegCM, Geosci. Model Dev., 16, 1–15, https://doi.org/10.5194/gmd-16-1-2023, 2023.
Ginoux, P., Chin, M., Tegen, I., Prospero, J. M., Holben, B., Dubovik, O., and Lin, S.-J.: Sources and distributions of dust aerosols simulated with the GOCART model, J. Geophys. Res.-Atmos., 106, 20255–20273, https://doi.org/10.1029/2000JD000053, 2001.
Govardhan, G., Satheesh, S. K., Moorthy, K. K., and Nanjundiah, R.: Simulations of black carbon over the Indian region: improvements and implications of diurnality in emissions, Atmos. Chem. Phys., 19, 8229–8241, https://doi.org/10.5194/acp-19-8229-2019, 2019.
Govardhan, G., Ghude, S. D., Kumar, R., Sharma, S., Gunwani, P., Jena, C., Yadav, P., Ingle, S., Debnath, S., Pawar, P., Acharja, P., Jat, R., Kalita, G., Ambulkar, R., Kulkarni, S., Kaginalkar, A., Soni, V. K., Nanjundiah, R. S., and Rajeevan, M.: Decision Support System version 1.0 (DSS v1.0) for air quality management in Delhi, India, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2022-300, in review, 2023a.
Govardhan, G., Ambulkar, R., Kulkarni, S., Vishnoi, A., Yadav, P., Choudhury, B. A., Khare, M., and Ghude, S. D.: Stubble-burning activities in north-western India in 2021: Contribution to air pollution in Delhi, Heliyon, 9, e16939, https://doi.org/10.1016/j.heliyon.2023.e16939, 2023b.
Greenstone, M. and Fan, C.: Air Quality Life Index, Annual Update, https://aqli.epic.uchicago.edu/wp-content/uploads/2021/08/AQLI_2020_Report_2021-spring-update.pdf (last access: 14 February 2024), 2020.
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock, W. C., and Eder, B.: Fully coupled “online” chemistry within the WRF model, Atmos. Environ., 39, 6957–6975, https://doi.org/10.1016/j.atmosenv.2005.04.027, 2005.
Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P. I., and Geron, C.: Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature), Atmos. Chem. Phys., 6, 3181–3210, https://doi.org/10.5194/acp-6-3181-2006, 2006.
Gunwani, P. and Mohan, M.: Sensitivity of WRF model estimates to various PBL parameterizations in different climatic zones over India, Atmos. Res., 194, 43–65, https://doi.org/10.1016/j.atmosres.2017.04.026, 2017.
Gupta, P., Christopher, S. A., Patadia, F., and Rastogi, N.: The unusual stubble burning season of 2020 in northern India: a satellite perspective, Int. J. Remote Sens., 44, 6882–6896, https://doi.org/10.1080/01431161.2023.2277160, 2023.
Gupta, T., Rajeev, P., and Rajput, R.: Emerging Major Role of Organic Aerosols in Explaining the Occurrence, Frequency, and Magnitude of Haze and Fog Episodes during Wintertime in the Indo Gangetic Plain, ACS Omega, 7, 1575–1584, https://doi.org/10.1021/acsomega.1c05467, 2022.
HEI – Health Effects Institute: How Does Your Air Measure Up Against the WHO Air Quality Guidelines? A State of Global Air Special Analysis, Health Effects Institute, Boston, MA, https://www.healtheffects.org/announcements/heis-state-global-air-releases-two-special-reports (last access: 21 February 2024), 2022.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023.
Hodzic, A. and Jimenez, J. L.: Modeling anthropogenically controlled secondary organic aerosols in a megacity: a simplified framework for global and climate models, Geosci. Model Dev., 4, 901–917, https://doi.org/10.5194/gmd-4-901-2011, 2011.
Hodzic, A. and Knote, C.: WRF-Chem 3.6.1: MOZART gas-phase chemistry with MOSAIC aerosols, https://www2.acom.ucar.edu/sites/default/files/documents/MOZART_MOSAIC_V3.6.readme_dec2016.pdf (last access: 14 February 2024), 2014.
Holben, B. N., Eck, T. F., Slutsker, I., Tanré, D., Buis, J. P., Setzer, A., Vermote, E., Reagan, J. A., Kaufman, Y. J., Nakajima, T., Lavenu, F., Jankowiak, I., and Smirnov, A.: AERONET – A Federated Instrument Network and Data Archive for Aerosol Characterization, Remote Sens. Environ. 66, 1–16, https://doi.org/10.1016/S0034-4257(98)00031-5, 1998.
India Meteorological Department: Annual Report 2016, Govt. of India Ministry of Earth Sciences, https://metnet.imd.gov.in/docs/imdnews/ar2017.pdf (last access: 14 February 2024), 2017.
Janssens-Maenhout, G., Crippa, M., Guizzardi, D., Dentener, F., Muntean, M., Pouliot, G., Keating, T., Zhang, Q., Kurokawa, J., Wankmüller, R., Denier van der Gon, H., Kuenen, J. J. P., Klimont, Z., Frost, G., Darras, S., Koffi, B., and Li, M.: HTAP_v2.2: a mosaic of regional and global emission grid maps for 2008 and 2010 to study hemispheric transport of air pollution, Atmos. Chem. Phys., 15, 11411–11432, https://doi.org/10.5194/acp-15-11411-2015, 2015.
Jena, C., Ghude, S. D., Kulkarni, R., Debnath, S., Kumar, R., Soni, V. K., Acharja, P., Kulkarni, S. H., Khare, M., Kaginalkar, A. J., Chate, D. M., Ali, K., Nanjundiah, R. S., and Rajeevan, M. N.: Evaluating the sensitivity of fine particulate matter (PM2.5) simulations to chemical mechanism in Delhi, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2020-673, 2020.
Jena, C., Ghude, S. D., Kumar, R., Debnath, S., Govardhan, G., Soni, V. K., Kulkarni, S. H., Beig, G., Nanjundiah, R. S., and Rajeevan, M.: Performance of high resolution (400 m) PM2.5 forecast over Delhi, Sci. Rep., 11, 4104, https://doi.org/10.1038/s41598-021-83467-8, 2021.
Jethva, H.: Assessing predictability of post-monsoon crop residue fires in Northwestern India, Front. Earth Sci., 10, 1047278, https://doi.org/10.3389/feart.2022.1047278, 2022.
Jethva, H., Torres, O., Field, R. D., Lyapustin, A., Gautam, R., and Kayetha, V.: Connecting Crop Productivity, Residue Fires, and Air Quality over Northern India, Sci. Rep., 9, 16594, https://doi.org/10.1038/s41598-019-52799-x, 2019.
Kalenderski, S., Stenchikov, G., and Zhao, C.: Modeling a typical winter-time dust event over the Arabian Peninsula and the Red Sea, Atmos. Chem. Phys., 13, 1999–2014, https://doi.org/10.5194/acp-13-1999-2013, 2013.
Kanawade, V. P., Srivastava, A. K., Ram, K., Asmi, E., Vakkari, V., Soni, V. K., Varaprasad, V., and Sarangi, C.: What caused severe air pollution episode of November 2016 in New Delhi?, Atmos. Environ., 222, 117125, https://doi.org/10.1016/j.atmosenv.2019.117125, 2020.
Kaskaoutis, D. G., Kumar, S., Sharma, D., Singh, R. P., Kharol, S. K., Sharma, M., Singh, A. K., Singh, S., Singh, A., and Singh, D.: Effects of crop residue burning on aerosol properties, plume characteristics, and long-range transport over northern India: Effects of crop residue burning, J. Geophys. Res.-Atmos., 119, 5424–5444, https://doi.org/10.1002/2013JD021357, 2014.
Knote, C., Hodzic, A., Jimenez, J. L., Volkamer, R., Orlando, J. J., Baidar, S., Brioude, J., Fast, J., Gentner, D. R., Goldstein, A. H., Hayes, P. L., Knighton, W. B., Oetjen, H., Setyan, A., Stark, H., Thalman, R., Tyndall, G., Washenfelder, R., Waxman, E., and Zhang, Q.: Simulation of semi-explicit mechanisms of SOA formation from glyoxal in aerosol in a 3-D model, Atmos. Chem. Phys., 14, 6213–6239, https://doi.org/10.5194/acp-14-6213-2014, 2014.
Kulkarni, S. H., Ghude, S. D., Jena, C., Karumuri, R. K., Sinha, B., Sinha, V., Kumar, R., Soni, V. K., and Khare, M.: How Much Does Large-Scale Crop Residue Burning Affect the Air Quality in Delhi?, Environ. Sci. Technol., 54, 4790–4799, https://doi.org/10.1021/acs.est.0c00329, 2020.
Kumar, A., Sinha, V., Shabin, M., Hakkim, H., Bonsang, B., and Gros, V.: Non-methane hydrocarbon (NMHC) fingerprints of major urban and agricultural emission sources for use in source apportionment studies, Atmos. Chem. Phys., 20, 12133–12152, https://doi.org/10.5194/acp-20-12133-2020, 2020.
Kumar, A., Hakkim, H., Sinha, B., and Sinha, V.: Gridded 1 km × 1 km emission inventory for paddy stubble burning emissions over north-west India constrained by measured emission factors of 77 VOCs and district-wise crop yield data, Sci. Total Environ., 789, 148064, https://doi.org/10.1016/j.scitotenv.2021.148064, 2021.
Kumar, M., Parmar, K. S., Kumar, D. B., Mhawish, A., Broday, D. M., Mall, R. K., and Banerjee, T.: Long-term aerosol climatology over Indo-Gangetic Plain: Trend, prediction and potential source fields, Atmos. Environ., 180, 37–50, https://doi.org/10.1016/j.atmosenv.2018.02.027, 2018.
Kumar, R., Naja, M., Pfister, G. G., Barth, M. C., Wiedinmyer, C., and Brasseur, G. P.: Simulations over South Asia using the Weather Research and Forecasting model with Chemistry (WRF-Chem): chemistry evaluation and initial results, Geosci. Model Dev., 5, 619–648, https://doi.org/10.5194/gmd-5-619-2012, 2012a.
Kumar, R., Naja, M., Pfister, G. G., Barth, M. C., and Brasseur, G. P.: Simulations over South Asia using the Weather Research and Forecasting model with Chemistry (WRF-Chem): set-up and meteorological evaluation, Geosci. Model Dev., 5, 321–343, https://doi.org/10.5194/gmd-5-321-2012, 2012b.
Kumar, R., Barth, M. C., Pfister, G. G., Naja, M., and Brasseur, G. P.: WRF-Chem simulations of a typical pre-monsoon dust storm in northern India: influences on aerosol optical properties and radiation budget, Atmos. Chem. Phys. 14, 2431–2446, https://doi.org/10.5194/acp-14-2431-2014, 2014.
Kumar, R., Barth, M. C., Pfister, G. G., Nair, V. S., Ghude, S. D., and Ojha, N.: What controls the seasonal cycle of black carbon aerosols in India?, J. Geophys. Res.-Atmos., 120, 7788–7812, https://doi.org/10.1002/2015JD023298, 2015.
Kumar, R., Barth, M. C., Pfister, G. G., Monache, L. D., Lamarque, J. F., Archer-Nicholls, S., Tilmes, S., Ghude, S. D., Wiedinmyer, C., Naja, M., and Walters, S.: How Will Air Quality Change in South Asia by 2050?, J. Geophys. Res.-Atmos., 123, 1840–1864, https://doi.org/10.1002/2017JD027357, 2018.
Kumar, R., Ghude, S. D., Biswas, M., Jena, C., Alessandrini, S., Debnath, S., Kulkarni, S., Sperati, S., Soni, V. K., Nanjundiah, R. S., and Rajeevan, M.: Enhancing Accuracy of Air Quality and Temperature Forecasts During Paddy Crop Residue Burning Season in Delhi Via Chemical Data Assimilation, J. Geophys. Res.-Atmos., 125, e2020JD033019, https://doi.org/10.1029/2020JD033019, 2020.
Kumari, S., Verma, N., Lakhani, A., and Kumari, K. M.: Severe haze events in the Indo-Gangetic Plain during post-monsoon: Synergetic effect of synoptic meteorology and crop residue burning emission, Sci. Total Environ., 768, 145479, https://doi.org/10.1016/j.scitotenv.2021.145479, 2021.
Lack, D. A. and Cappa, C. D.: Impact of brown and clear carbon on light absorption enhancement, single scatter albedo and absorption wavelength dependence of black carbon, Atmos. Chem. Phys., 10, 4207–4220, https://doi.org/10.5194/acp-10-4207-2010, 2010.
Lalchandani, V., Srivastava, D., Dave, J., Mishra, S., Tripathi, N., Shukla, A. K., Sahu, R., Thamban, N. M., Gaddamidi, S., Dixit, K., Ganguly, D., Tiwari, S., Srivastava, A. K., Sahu, L., Rastogi, N., Gargava, P., and Tripathi, S. N.: Effect of Biomass Burning on PM 2.5 Composition and Secondary Aerosol Formation During Post-Monsoon and Winter Haze Episodes in Delhi, J. Geophys. Res.-Atmos., 127, e2021JD035232, https://doi.org/10.1029/2021JD035232, 2022.
Li, M., Zhang, Q., Kurokawa, J., Woo, J.-H., He, K., Lu, Z., Ohara, T., Song, Y., Streets, D. G., Carmichael, G. R., Cheng, Y., Hong, C., Huo, H., Jiang, X., Kang, S., Liu, F., Su, H., and Zheng, B.: MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP, Atmos. Chem. Phys., 17, 935–963, https://doi.org/10.5194/acp-17-935-2017, 2017.
Liu, N., Zhou, S., Liu, C., and Guo, J.: Synoptic circulation pattern and boundary layer structure associated with PM2.5 during wintertime haze pollution episodes in Shanghai, Atmos. Res., 228, 186–195, https://doi.org/10.1016/j.atmosres.2019.06.001, 2019.
Liu, T., Mickley, L. J., Gautam, R., Singh, M. K., DeFries, R. S., and Marlier, M. E.: Detection of delay in post-monsoon agricultural burning across Punjab, India: potential drivers and consequences for air quality, Environ. Res. Lett., 16, 014014, https://doi.org/10.1088/1748-9326/abcc28, 2021.
Lu, Z., Zhang, Q., and Streets, D. G.: Sulfur dioxide and primary carbonaceous aerosol emissions in China and India, 1996–2010, Atmos. Chem. Phys., 11, 9839–9864, https://doi.org/10.5194/acp-11-9839-2011, 2011.
McDuffie, E. E., Smith, S. J., O'Rourke, P., Tibrewal, K., Venkataraman, C., Marais, E. A., Zheng, B., Crippa, M., Brauer, M., and Martin, R. V.: A global anthropogenic emission inventory of atmospheric pollutants from sector- and fuel-specific sources (1970–2017): an application of the Community Emissions Data System (CEDS), Earth Syst. Sci. Data, 12, 3413–3442, https://doi.org/10.5194/essd-12-3413-2020, 2020.
Mhawish, A., Banerjee, T., Broday, D. M., Misra, A., and Tripathi, S. N.: Evaluation of MODIS Collection 6 aerosol retrieval algorithms over Indo-Gangetic Plain: Implications of aerosols types and mass loading, Remote Sens. Environ., 201, 297–313, https://doi.org/10.1016/j.rse.2017.09.016, 2017.
Mhawish, A., Sorek-Hamer, M., Chatfield, R., Banerjee, T., Bilal, M., Kumar, M., Sarangi, C., Franklin, M., Chau, K., Garay, M., and Kalashnikova, O.: Aerosol characteristics from earth observation systems: A comprehensive investigation over South Asia (2000–2019), Remote Sens. Environ., 259, 112410, https://doi.org/10.1016/j.rse.2021.112410, 2021.
Mhawish, A., Sarangi, C., Babu, P., Kumar, M., Bilal, M., and Qiu, Z.: Observational evidence of elevated smoke layers during crop residue burning season over Delhi: Potential implications on associated heterogeneous PM2.5 enhancements, Remote Sens. Environ., 280, 113167, https://doi.org/10.1016/j.rse.2022.113167, 2022.
Mogno, C., Palmer, P. I., Knote, C., Yao, F., and Wallington, T. J.: Seasonal distribution and drivers of surface fine particulate matter and organic aerosol over the Indo-Gangetic Plain, Atmos. Chem. Phys., 21, 10881–10909, https://doi.org/10.5194/acp-21-10881-2021, 2021.
Mohan, M. and Bhati, S.: Analysis of WRF Model Performance over Subtropical Region of Delhi, India, Adv. Meteorol., 2011, 1–13, https://doi.org/10.1155/2011/621235, 2011.
Moorthy, K. K., Beegum, S. N., Srivastava, N., Satheesh, S. K., Chin, M., Blond, N., Babu, S. S., and Singh, S.: Performance evaluation of chemistry transport models over India, Atmos. Environ., 71, 210–225, https://doi.org/10.1016/j.atmosenv.2013.01.056, 2013.
Mues, A., Lauer, A., Lupascu, A., Rupakheti, M., Kuik, F., and Lawrence, M. G.: WRF and WRF-Chem v3.5.1 simulations of meteorology and black carbon concentrations in the Kathmandu Valley, Geosci. Model Dev., 11, 2067–2091, https://doi.org/10.5194/gmd-11-2067-2018, 2018.
Mukherjee, T., Asutosh, A., Pandey, S. K., Yang, L., Gogoi, P. P., Panwar, A., and Vinoj, V.: Increasing Potential for Air Pollution over Megacity New Delhi: A Study Based on 2016 Diwali Episode, Aerosol Air Qual. Res., 18, 2510–2518, https://doi.org/10.4209/aaqr.2017.11.0440, 2018.
Mukherjee, T., Vinoj, V., Midya, S. K., Puppala, S. P., and Adhikary, B.: Numerical simulations of different sectoral contributions to post monsoon pollution over Delhi, Heliyon, 6, e03548, https://doi.org/10.1016/j.heliyon.2020.e03548, 2020.
Nair, V. S., Solmon, F., Giorgi, F., Mariotti, L., Babu, S. S., and Moorthy, K. K.: Simulation of South Asian aerosols for regional climate studies, J. Geophys. Res.-Atmos., 117, D04209, https://doi.org/10.1029/2011JD016711, 2012.
Navinya, C. D., Vinoj, V., and Pandey, S. K.: Evaluation of PM2.5 Surface Concentrations Simulated by NASA's MERRA Version 2 Aerosol Reanalysis over India and its Relation to the Air Quality Index, Aerosol Air Qual. Res., 20, 1329–1339, https://doi.org/10.4209/aaqr.2019.12.0615, 2020.
NCAR – National Center for Atmospheric Research: ACOM MOZART-4/GEOS-5 global model output UCAR National, https://www.acom.ucar.edu/gctm/mozart/subset (last access: 13 February 2024), 2016.
NCAR – National Center for Atmospheric Research: WRF-Chem Tools for the Community | Atmospheric Chemistry Observations & Modeling, https://www2.acom.ucar.edu/wrf-chem/wrf-chem-tools-community (last access: 14 February 2024), 2024.
Nelli, N. R., Temimi, M., Fonseca, R. M., Weston, M. J., Thota, M. S., Valappil, V. K., Branch, O., Wulfmeyer, V., Wehbe, Y., Al Hosary, T., Shalaby, A., Al Shamsi, N., and Al Naqbi, H.: Impact of Roughness Length on WRF Simulated Land-Atmosphere Interactions Over a Hyper-Arid Region, Earth Space Sci., 7, e2020EA001165, https://doi.org/10.1029/2020EA001165, 2020.
Ojha, N., Sharma, A., Kumar, M., Girach, I., Ansari, T. U., Sharma, S. K., Singh, N., Pozzer, A., and Gunthe, S. S.: On the widespread enhancement in fine particulate matter across the Indo-Gangetic Plain towards winter, Sci. Rep., 10, 5862, https://doi.org/10.1038/s41598-020-62710-8, 2020.
Pan, X., Chin, M., Gautam, R., Bian, H., Kim, D., Colarco, P. R., Diehl, T. L., Takemura, T., Pozzoli, L., Tsigaridis, K., Bauer, S., and Bellouin, N.: A multi-model evaluation of aerosols over South Asia: common problems and possible causes, Atmos. Chem. Phys., 15, 5903–5928, https://doi.org/10.5194/acp-15-5903-2015, 2015.
Pandey, A., Brauer, M., Cropper, M. L., Balakrishnan, K., Mathur, P., Dey, S., Turkgulu, B., Kumar, G. A., Khare, M., Beig, G., Gupta, T., Krishnankutty, R. P., Causey, K., Cohen, A. J., Bhargava, S., Aggarwal, A. N., Agrawal, A., Awasthi, S., Bennitt, F., Bhagwat, S., Bhanumati, P., Burkart, K., Chakma, J. K., Chiles, T. C., Chowdhury, S., Christopher, D. J., Dey, S., Fisher, S., Fraumeni, B., Fuller, R., Ghoshal, A. G., Golechha, M. J., Gupta, P. C., Gupta, R., Gupta, R., Gupta, S., Guttikunda, S., Hanrahan, D., Harikrishnan, S., Jeemon, P., Joshi, T. K., Kant, R., Kant, S., Kaur, T., Koul, P. A., Kumar, P., Kumar, R., Larson, S. L., Lodha, R., Madhipatla, K. K., Mahesh, P. A., Malhotra, R., Managi, S., Martin, K., Mathai, M., Mathew, J. L., Mehrotra, R., Mohan, B. V. M., Mohan, V., Mukhopadhyay, S., Mutreja, P., Naik, N., Nair, S., Pandian, J. D., Pant, P., Perianayagam, A., Prabhakaran, D., Prabhakaran, P., Rath, G. K., Ravi, S., Roy, A., Sabde, Y. D., Salvi, S., Sambandam, S., Sharma, B., Sharma, M., Sharma, S., Sharma, R. S., Shrivastava, A., Singh, S., Singh, V., Smith, R., Stanaway, J. D., Taghian, G., Tandon, N., Thakur, J. S., Thomas, N. J., Toteja, G. S., Varghese, C. M., Venkataraman, C., Venugopal, K. N., Walker, K. D., Watson, A. Y., Wozniak, S., Xavier, D., Yadama, G. N., Yadav, G., Shukla, D. K., Bekedam, H. J., Reddy, K. S., Guleria, R., Vos, T., Lim, S. S., Dandona, R., Kumar, S., Kumar, P., Landrigan, P. J., and Dandona, L.: Health and economic impact of air pollution in the states of India: the Global Burden of Disease Study 2019, Lancet Planet. Health, 5, e25–e38, https://doi.org/10.1016/S2542-5196(20)30298-9, 2021.
Patel, K., Bhandari, S., Gani, S., Campmier, M. J., Kumar, P., Habib, G., Apte, J., and Hildebrandt Ruiz, L.: Sources and Dynamics of Submicron Aerosol during the Autumn Onset of the Air Pollution Season in Delhi, India, ACS Earth Space Chem., 5, 118–128, https://doi.org/10.1021/acsearthspacechem.0c00340, 2021.
Paulot, F., Naik, V., and W. Horowitz, L.: Reduction in Near-Surface Wind Speeds With Increasing CO2 May Worsen Winter Air Quality in the Indo-Gangetic Plain, Geophys. Res. Lett., 49, e2022GL099039, https://doi.org/10.1029/2022GL099039, 2022.
Pawar, P. V., Ghude, S. D., Govardhan, G., Acharja, P., Kulkarni, R., Kumar, R., Sinha, B., Sinha, V., Jena, C., Gunwani, P., Adhya, T. K., Nemitz, E., and Sutton, M. A.: Chloride (HCl/Cl−) dominates inorganic aerosol formation from ammonia in the Indo-Gangetic Plain during winter: modeling and comparison with observations, Atmos. Chem. Phys., 23, 41–59, https://doi.org/10.5194/acp-23-41-2023, 2023.
Provencal, S., Buchard, V., Silva, A. M. da, Leduc, R., Barrette, N., Elhacham, E., and Wang, S.-H.: Evaluation of PM2.5 Surface Concentrations Simulated by Version 1 of NASA's MERRA Aerosol Reanalysis over Israel and Taiwan, Aerosol Air Qual. Res., 17, 253–261, https://doi.org/10.4209/aaqr.2016.04.0145, 2017.
Ram, K. and Sarin, M. M.: Day–night variability of EC, OC, WSOC and inorganic ions in urban environment of Indo-Gangetic Plain: Implications to secondary aerosol formation, Atmos. Environ., 45, 460–468, https://doi.org/10.1016/j.atmosenv.2010.09.055, 2011.
Ram, K., Singh, S., Sarin, M. M., Srivastava, A. K., and Tripathi, S. N.: Variability in aerosol optical properties over an urban site, Kanpur, in the Indo-Gangetic Plain: A case study of haze and dust events, Atmos. Res., 174–175, 52–61, https://doi.org/10.1016/j.atmosres.2016.01.014, 2016.
Ramanathan, V., Crutzen, P. J., Kiehl, J. T., and Rosenfeld, D.: Aerosols, climate, and the hydrological cycle, Science, 294, 2119–2124, https://doi.org/10.1126/science.1064034, 2001.
Randles, C. A., Da Silva, A. M., Buchard, V., Colarco, P. R., Darmenov, A., Govindaraju, R., Smirnov, A., Holben, B., Ferrare, R., Hair, J., Shinozuka, Y., and Flynn, C. J.: The MERRA-2 Aerosol Reanalysis, 1980–onward, Part I: System Description and Data Assimilation Evaluation, J. Climate, 30, 6823–6850, https://doi.org/10.1175/JCLI-D-16-0609.1, 2017.
Ratnam, J. V. and Kumar, K. K.: Sensitivity of the Simulated Monsoons of 1987 and 1988 to Convective Parameterization Schemes in MM5, J. Climate, 18, 2724–2743, https://doi.org/10.1175/JCLI3390.1, 2005.
Riemer, N., Ault, A. P., West, M., Craig, R. L., and Curtis, J. H.: Aerosol Mixing State: Measurements, Modeling, and Impacts, Rev. Geophys., 57, 187–249, https://doi.org/10.1029/2018RG000615, 2019.
Rooney, B., Zhao, R., Wang, Y., Bates, K. H., Pillarisetti, A., Sharma, S., Kundu, S., Bond, T. C., Lam, N. L., Ozaltun, B., Xu, L., Goel, V., Fleming, L. T., Weltman, R., Meinardi, S., Blake, D. R., Nizkorodov, S. A., Edwards, R. D., Yadav, A., Arora, N. K., Smith, K. R., and Seinfeld, J. H.: Impacts of household sources on air pollution at village and regional scales in India, Atmos. Chem. Phys., 19, 7719–7742, https://doi.org/10.5194/acp-19-7719-2019, 2019.
Roozitalab, B., Carmichael, G. R., and Guttikunda, S. K.: Improving regional air quality predictions in the Indo-Gangetic Plain – case study of an intensive pollution episode in November 2017, Atmos. Chem. Phys., 21, 2837–2860, https://doi.org/10.5194/acp-21-2837-2021, 2021.
Sarkar, S., Singh, R. P., and Chauhan, A.: Crop Residue Burning in Northern India: Increasing Threat to Greater India, J. Geophys. Res.-Atmos., 123, 6920–6934, https://doi.org/10.1029/2018JD028428, 2018.
Sawlani, R., Agnihotri, R., Sharma, C., Patra, P. K., Dimri, A. P., Ram, K., and Verma, R. L.: The severe Delhi SMOG of 2016: A case of delayed crop residue burning, coincident firecracker emissions, and atypical meteorology, Atmos. Pollut. Res., 10, 868–879, https://doi.org/10.1016/j.apr.2018.12.015, 2019.
Sayer, A. M., Munchak, L. A., Hsu, N. C., Levy, R. C., Bettenhausen, C., and Jeong, M.-J.: MODIS Collection 6 aerosol products: Comparison between Aqua's e-Deep Blue, Dark Target, and “merged” data sets, and usage recommendations, J. Geophys. Res.-Atmos., 119, 13965–13989, https://doi.org/10.1002/2014JD022453, 2014.
Schiavina, M., Melchiorri, M., and Freire, S.: GHS-DUC R2022A – GHS Degree of Urbanisation Classification, application of the Degree of Urbanisation methodology (stage II) to GADM 3.6 layer, multitemporal (1975–2030) – OBSOLETE RELEASE.European Commission, JRC – Joint Research Centre [data set], https://doi.org/10.2905/F5224214-6B66-43DF-A9C6-CC974F17D803, 2022.
Schnell, J. L., Naik, V., Horowitz, L. W., Paulot, F., Mao, J., Ginoux, P., Zhao, M., and Ram, K.: Exploring the relationship between surface PM2.5 and meteorology in Northern India, Atmos. Chem. Phys., 18, 10157–10175, https://doi.org/10.5194/acp-18-10157-2018, 2018.
Sembhi, H., Wooster, M., Zhang, T., Sharma, S., Singh, N., Agarwal, S., Boesch, H., Gupta, S., Misra, A., Tripathi, S. N., Mor, S., and Khaiwal, R.: Post-monsoon air quality degradation across Northern India: assessing the impact of policy-related shifts in timing and amount of crop residue burnt, Environ. Res. Lett., 15, 104067, https://doi.org/10.1088/1748-9326/aba714, 2020.
Sengupta, A., Govardhan, G., Debnath, S., Yadav, P., Kulkarni, S. H., Parde, A. N., Lonkar, P., Dhangar, N., Gunwani, P., Wagh, S., Nivdange, S., Jena, C., Kumar, R., and Ghude, S. D.: Probing into the wintertime meteorology and particulate matter (PM2.5 and PM10) forecast over Delhi, Atmos. Pollut. Res., 13, 101426, https://doi.org/10.1016/j.apr.2022.101426, 2022.
Shaik, D. S., Kant, Y., Mitra, D., Singh, A., Chandola, H. C., Sateesh, M., Babu, S. S., and Chauhan, P.: Impact of biomass burning on regional aerosol optical properties: A case study over northern India, J. Environ. Manage., 244, 328–343, https://doi.org/10.1016/j.jenvman.2019.04.025, 2019.
Sharma, S. K., Mandal, T. K., Sharma, A., Jain, S., and Saraswati: Carbonaceous Species of PM2.5 in Megacity Delhi, India During 2012–2016, Bull. Environ. Contam. Toxicol., 100, 695–701, https://doi.org/10.1007/s00128-018-2313-9, 2018.
Shen, C., Liu, Y., Shen, A., Cui, Y., Chen, X., Fan, Q., Chan, P., Tian, C., Xie, Z., Wang, C., Lan, J., Li, X., Wu, J., and Yang, Y.: Spatializing the roughness length of heterogeneous urban surfaces to improve the WRF simulation – Part 2: Impacts on the thermodynamic environment, Atmos. Environ., 294, 119464, https://doi.org/10.1016/j.atmosenv.2022.119464, 2023.
Singh, N., Murari, V., Kumar, M., Barman, S. C., and Banerjee, T.: Fine particulates over South Asia: Review and meta-analysis of PM2.5 source apportionment through receptor model, Environ. Pollut., 223, 121–136, https://doi.org/10.1016/j.envpol.2016.12.071, 2017.
Singh, N., Agarwal, S., Sharma, S., Chatani, S., and Ramanathan, V.: Air Pollution Over India: Causal Factors for the High Pollution with Implications for Mitigation, ACS Earth Space Chem., 5, 3297–3312, https://doi.org/10.1021/acsearthspacechem.1c00170, 2021.
Singh, R., Kumar, S., and Singh, A.: Elevated Black Carbon Concentrations and Atmospheric Pollution around Singrauli Coal-Fired Thermal Power Plants (India) Using Ground and Satellite Data, Int. J. Environ. Res. Publ. Health, 15, 2472, https://doi.org/10.3390/ijerph15112472, 2018.
Singh, T., Biswal, A., Mor, S., Ravindra, K., Singh, V., and Mor, S.: A high-resolution emission inventory of air pollutants from primary crop residue burning over Northern India based on VIIRS thermal anomalies, Environ. Pollut., 266, 115132, https://doi.org/10.1016/j.envpol.2020.115132, 2020.
Skamarock, W. C. and Klemp, J. B.: A time-split nonhydrostatic atmospheric model for weather research and forecasting applications, J. Comput. Phys., 227, 3465–3485, https://doi.org/10.1016/j.jcp.2007.01.037, 2008.
Song, Z., Fu, D., Zhang, X., Wu, Y., Xia, X., He, J., Han, X., Zhang, R., and Che, H.: Diurnal and seasonal variability of PM2.5 and AOD in North China plain: Comparison of MERRA-2 products and ground measurements, Atmos. Environ., 191, 70–78, https://doi.org/10.1016/j.atmosenv.2018.08.012, 2018.
Srivastava, N., Satheesh, S. K., Blond, N., and Krishna Moorthy, K.: Simulation of Aerosol Fields over South Asia Using CHIMERE – Part-II: Performance Evaluation, Curr. Sci., 111, 83–92, 2016.
Stauffer, D. R. and Seaman, N. L.: Multiscale Four-Dimensional Data Assimilation, J. Appl. Meteorol., 33, 416–434, https://doi.org/10.1175/1520-0450(1994)033<0416:MFDDA>2.0.CO;2, 1994.
Takigawa, M., Patra, P. K., Matsumi, Y., Dhaka, S. K., Nakayama, T., Yamaji, K., Kajino, M., and Hayashida, S.: Can Delhi's Pollution be Affected by Crop Fires in the Punjab Region?, SOLA, 16, 86–91, https://doi.org/10.2151/sola.2020-015, 2020.
Talukdar, S., Tripathi, S. N., Lalchandani, V., Rupakheti, M., Bhowmik, H. S., Shukla, A. K., Murari, V., Sahu, R., Jain, V., Tripathi, N., Dave, J., Rastogi, N., and Sahu, L.: Air Pollution in New Delhi during Late Winter: An Overview of a Group of Campaign Studies Focusing on Composition and Sources, Atmosphere, 12, 1432, https://doi.org/10.3390/atmos12111432, 2021.
Thomas, A., Sarangi, C., and Kanawade, V. P.: Recent Increase in Winter Hazy Days over Central India and the Arabian Sea, Sci. Rep., 9, 17406, https://doi.org/10.1038/s41598-019-53630-3, 2019.
Tie, X., Madronich, S., Walters, S., Zhang, R., Rasch, P., and Collins, W.: Effect of clouds on photolysis and oxidants in the troposphere, J. Geophys. Res.-Atmos., 108, 4642, https://doi.org/10.1029/2003JD003659, 2003.
Tuccella, P., Curci, G., Visconti, G., Bessagnet, B., Menut, L., and Park, R. J.: Modeling of gas and aerosol with WRF/Chem over Europe: Evaluation and sensitivity study, J. Geophys. Res.-Atmos., 117, D03303, https://doi.org/10.1029/2011JD016302, 2012.
Upadhyay, A., Dey, S., and Goyal, P.: A comparative assessment of regional representativeness of EDGAR and ECLIPSE emission inventories for air quality studies in India, Atmos. Environ., 223, 117182, https://doi.org/10.1016/j.atmosenv.2019.117182, 2020.
Venkataraman, C., Brauer, M., Tibrewal, K., Sadavarte, P., Ma, Q., Cohen, A., Chaliyakunnel, S., Frostad, J., Klimont, Z., Martin, R. V., Millet, D. B., Philip, S., Walker, K., and Wang, S.: Source influence on emission pathways and ambient PM2.5 pollution over India (2015–2050), Atmos. Chem. Phys., 18, 8017–8039, https://doi.org/10.5194/acp-18-8017-2018, 2018.
Vogelezang, D. H. P. and Holtslag, A. A. M.: Evaluation and model impacts of alternative boundary-layer height formulations, Bound.-Lay. Meteorol., 81, 245–269, https://doi.org/10.1007/BF02430331, 1996.
Voiland, A. and Jethva, H.: Earth Matters – Using Satellites to Size Up the Severity of Crop Fires in Northern India, https://earthobservatory.nasa.gov/blogs/earthmatters/2017/02/08/ (last access: 20 February 2024), 2017.
Wang, K., Zhang, Y., and Yahya, K.: Decadal application of WRF/Chem over the continental US: Simulation design, sensitivity simulations, and climatological model evaluation, Atmos. Environ., 253, 118331, https://doi.org/10.1016/j.atmosenv.2021.118331, 2021.
Wang, R., Tao, S., Shen, H., Huang, Y., Chen, H., Balkanski, Y., Boucher, O., Ciais, P., Shen, G., Li, W., Zhang, Y., Chen, Y., Lin, N., Su, S., Li, B., Liu, J., and Liu, W.: Trend in Global Black Carbon Emissions from 1960 to 2007, Environ. Sci. Technol., 48, 6780–6787, https://doi.org/10.1021/es5021422, 2014.
WHO – World Health Organization: WHO global air quality guidelines, Particulate matter (PM2.5 and PM10), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide, World Health Organization, Geneva, https://www.who.int/publications/i/item/9789240034228 (last access: 21 February 2024), 2021.
Wiedinmyer, C., Akagi, S. K., Yokelson, R. J., Emmons, L. K., Al-Saadi, J. A., Orlando, J. J., and Soja, A. J.: The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning, Geosci. Model Dev., 4, 625–641, https://doi.org/10.5194/gmd-4-625-2011, 2011.
WRF: WRF Modeling System Download, https://www2.mmm.ucar.edu/wrf/users/download/get_source.html (last access: 13 February 2024), 2024.
Zaveri, R. A., Easter, R. C., Fast, J. D., and Peters, L. K.: Model for Simulating Aerosol Interactions and Chemistry (MOSAIC), J. Geophys. Res.-Atmos., 113, D13204, https://doi.org/10.1029/2007JD008782, 2008.
Zhang, Y., Zhang, X., Wang, L., Zhang, Q., Duan, F., and He, K.: Application of WRF/Chem over East Asia: Part I. Model evaluation and intercomparison with MM5/CMAQ, Atmos. Environ., 124, 285–300, https://doi.org/10.1016/j.atmosenv.2015.07.022, 2016.
Zhao, C., Liu, X., Leung, L. R., Johnson, B., McFarlane, S. A., Gustafson, W. I. J., Fast, J. D., and Easter, R.: The spatial distribution of mineral dust and its shortwave radiative forcing over North Africa: modeling sensitivities to dust emissions and aerosol size treatments, Atmos. Chem. Phys., 10, 8821–8838, https://doi.org/10.5194/acp-10-8821-2010, 2010.
Zhao, C., Chen, S., Leung, L. R., Qian, Y., Kok, J. F., Zaveri, R. A., and Huang, J.: Uncertainty in modeling dust mass balance and radiative forcing from size parameterization, Atmos. Chem. Phys., 13, 10733–10753, https://doi.org/10.5194/acp-13-10733-2013, 2013.
Short summary
Air pollution levels across northern India are amongst some of the worst in the world, with episodic and hazardous haze events. Here, the ability of the WRF-Chem model to predict air quality over northern India is assessed against several datasets. Whilst surface wind speed and particle pollution peaks are over- and underestimated, respectively, meteorology and aerosol trends are adequately captured, and we conclude it is suitable for investigating severe particle pollution events.
Air pollution levels across northern India are amongst some of the worst in the world, with...
Altmetrics
Final-revised paper
Preprint