Articles | Volume 24, issue 11
https://doi.org/10.5194/acp-24-6635-2024
© 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-24-6635-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Role of atmospheric aerosols in severe winter fog over the Indo-Gangetic Plain of India: a case study
Chandrakala Bharali
CORRESPONDING AUTHOR
Department of Physics, Dibrugarh University, Dibrugarh, Assam, India
Mary Barth
CORRESPONDING AUTHOR
NSF National Center for Atmospheric Research, Boulder, CO, US
Rajesh Kumar
NSF National Center for Atmospheric Research, Boulder, CO, US
Sachin D. Ghude
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, India
Vinayak Sinha
Department of Earth and Environmental Sciences, Indian Institute of Science Education and Research, Mohali, Punjab, India
Baerbel Sinha
Department of Earth and Environmental Sciences, Indian Institute of Science Education and Research, Mohali, Punjab, India
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Chayan Roychoudhury, Rajesh Kumar, Cenlin He, William Y. Y. Cheng, Kirpa Ram, Naoki Mizukami, and Avelino F. Arellano
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-275, https://doi.org/10.5194/essd-2025-275, 2025
Preprint under review for ESSD
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We present a 17-year, 12 km regional dataset for Asia that uniquely captures aerosol–weather–snow interactions. By assimilating satellite data into a chemistry–climate model, it provides hourly to three-hourly fields of meteorology, air quality, and snow-related variables. Evaluations show good agreement with observations, and source attribution of black carbon is also provided to quantify pollution pathways to Asia’s glaciers, major freshwater source for over a billion people.
Chayan Roychoudhury, Cenlin He, Rajesh Kumar, and Avelino F. Arellano Jr.
Earth Syst. Dynam., 16, 1237–1266, https://doi.org/10.5194/esd-16-1237-2025, https://doi.org/10.5194/esd-16-1237-2025, 2025
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We present a novel data-driven approach to understand how pollution and weather processes interact to influence snowmelt in Asian glaciers and how these interactions are represented in three climate models. Our findings show where models need improvement in predicting snowmelt, particularly dust and its transport. This method can support future model development for reliable predictions in climate-vulnerable regions.
Genevieve Rose Lorenzo, Luke D. Ziemba, Avelino F. Arellano, Mary C. Barth, Ewan C. Crosbie, Joshua P. DiGangi, Glenn S. Diskin, Richard Ferrare, Miguel Ricardo A. Hilario, Michael A. Shook, Simone Tilmes, Jian Wang, Qian Xiao, Jun Zhang, and Armin Sorooshian
Atmos. Chem. Phys., 25, 5469–5495, https://doi.org/10.5194/acp-25-5469-2025, https://doi.org/10.5194/acp-25-5469-2025, 2025
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Novel aerosol hygroscopicity analyses of CAMP2Ex (Cloud, Aerosol, and Monsoon Processes Philippines Experiment) field campaign data show low aerosol hygroscopicity values in Southeast Asia. Organic carbon from smoke decreases hygroscopicity to levels more like those in continental than in polluted marine regions. Hygroscopicity changes at cloud level demonstrate how surface particles impact clouds in the region, affecting model representation of aerosol and cloud interactions in similar polluted marine regions with high organic carbon emissions.
Rajesh Kumar, Piyush Bhardwaj, Cenlin He, Jennifer Boehnert, Forrest Lacey, Stefano Alessandrini, Kevin Sampson, Matthew Casali, Scott Swerdlin, Olga Wilhelmi, Gabriele G. Pfister, Benjamin Gaubert, and Helen Worden
Earth Syst. Sci. Data, 17, 1807–1834, https://doi.org/10.5194/essd-17-1807-2025, https://doi.org/10.5194/essd-17-1807-2025, 2025
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We have created a 14-year hourly air quality dataset at 12 km resolution by combining satellite observations of atmospheric composition with air quality models over the contiguous United States (CONUS). The dataset has been found to reproduce key observed features of air quality over the CONUS. To enable easy visualization and interpretation of county-level air quality measures and trends by stakeholders, an ArcGIS air quality dashboard has also been developed.
Yuhang Zhang, Huan Yu, Isabelle De Smedt, Jintai Lin, Nicolas Theys, Michel Van Roozendael, Gaia Pinardi, Steven Compernolle, Ruijing Ni, Fangxuan Ren, Sijie Wang, Lulu Chen, Jos Van Geffen, Mengyao Liu, Alexander M. Cede, Martin Tiefengraber, Alexis Merlaud, Martina M. Friedrich, Andreas Richter, Ankie Piters, Vinod Kumar, Vinayak Sinha, Thomas Wagner, Yongjoo Choi, Hisahiro Takashima, Yugo Kanaya, Hitoshi Irie, Robert Spurr, Wenfu Sun, and Lorenzo Fabris
Atmos. Meas. Tech., 18, 1561–1589, https://doi.org/10.5194/amt-18-1561-2025, https://doi.org/10.5194/amt-18-1561-2025, 2025
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We developed an advanced algorithm for global retrieval of TROPOspheric Monitoring Instrument (TROPOMI) HCHO and NO2 vertical column densities with much improved consistency. Sensitivity tests demonstrate the complexity and nonlinear interactions of auxiliary parameters in the air mass factor calculation. An improved agreement is found with measurements from a global ground-based instrument network. The scientific retrieval provides a useful source of information for studies combining HCHO and NO2.
Dylan Jones, Lucas Prates, Zhen Qu, William Cheng, Kazuyuki Miyazaki, Takashi Sekiya, Antje Inness, Rajesh Kumar, Xiao Tang, Helen Worden, Gerbrand Koren, and Vincent Huijen
EGUsphere, https://doi.org/10.5194/egusphere-2024-3759, https://doi.org/10.5194/egusphere-2024-3759, 2025
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We evaluate five chemical reanalysis products to assess their potential to provide useful information on tropospheric ozone variability. We find that the reanalyses produce consistent information on ozone variations in the free troposphere, but have large discrepancies at the surface. The results suggests that improvements in the reanalyses are needed to better exploit the assimilated observations to enhance the utility of the reanalysis products at the surface.
Christopher Lawrence, Mary Barth, John Orlando, Paul Casson, Richard Brandt, Daniel Kelting, Elizabeth Yerger, and Sara Lance
Atmos. Chem. Phys., 24, 13693–13713, https://doi.org/10.5194/acp-24-13693-2024, https://doi.org/10.5194/acp-24-13693-2024, 2024
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This work uses chemical transport and box modeling to study the gas- and aqueous-phase production of organic acid concentrations measured in cloud water at the summit of Whiteface Mountain on 1 July 2018. Isoprene was the major source of formic, acetic, and oxalic acid. Gas-phase chemistry greatly underestimated formic and acetic acid, indicating missing sources, while cloud chemistry was a key source of oxalic acid. More studies of organic acids are required to better constrain their sources.
Sachin Mishra, Vinayak Sinha, Haseeb Hakkim, Arpit Awasthi, Sachin D. Ghude, Vijay Kumar Soni, Narendra Nigam, Baerbel Sinha, and Madhavan N. Rajeevan
Atmos. Chem. Phys., 24, 13129–13150, https://doi.org/10.5194/acp-24-13129-2024, https://doi.org/10.5194/acp-24-13129-2024, 2024
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We quantified 111 gases using mass spectrometry to understand how seasonal and emission changes lead from clean air in the monsoon season to extremely polluted air in the post-monsoon season in Delhi. Averaged total mass concentrations (260 µg m-3) were > 4 times in polluted periods, driven by biomass burning emissions and reduced atmospheric ventilation. Reactive gaseous nitrogen, chlorine, and sulfur compounds hitherto unreported from such a polluted environment were discovered.
Connor J. Clayton, Daniel R. Marsh, Steven T. Turnock, Ailish M. Graham, Kirsty J. Pringle, Carly L. Reddington, Rajesh Kumar, and James B. McQuaid
Atmos. Chem. Phys., 24, 10717–10740, https://doi.org/10.5194/acp-24-10717-2024, https://doi.org/10.5194/acp-24-10717-2024, 2024
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We demonstrate that strong climate mitigation could improve air quality in Europe; however, less ambitious mitigation does not result in these co-benefits. We use a high-resolution atmospheric chemistry model. This allows us to demonstrate how this varies across European countries and analyse the underlying chemistry. This may help policy-facing researchers understand which sectors and regions need to be prioritised to achieve strong air quality co-benefits of climate mitigation.
Arpit Awasthi, Baerbel Sinha, Haseeb Hakkim, Sachin Mishra, Varkrishna Mummidivarapu, Gurmanjot Singh, Sachin D. Ghude, Vijay Kumar Soni, Narendra Nigam, Vinayak Sinha, and Madhavan N. Rajeevan
Atmos. Chem. Phys., 24, 10279–10304, https://doi.org/10.5194/acp-24-10279-2024, https://doi.org/10.5194/acp-24-10279-2024, 2024
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We use 111 volatile organic compounds (VOCs), PM10, and PM2.5 in a positive matrix factorization (PMF) model to resolve 11 pollution sources validated with chemical fingerprints. Crop residue burning and heating account for ~ 50 % of the PM, while traffic and industrial emissions dominate the gas-phase VOC burden and formation potential of secondary organic aerosols (> 60 %). Non-tailpipe emissions from compressed-natural-gas-fuelled commercial vehicles dominate the transport sector's PM burden.
Matthew S. Johnson, Sajeev Philip, Scott Meech, Rajesh Kumar, Meytar Sorek-Hamer, Yoichi P. Shiga, and Jia Jung
Atmos. Chem. Phys., 24, 10363–10384, https://doi.org/10.5194/acp-24-10363-2024, https://doi.org/10.5194/acp-24-10363-2024, 2024
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Satellites, like the Ozone Monitoring Instrument (OMI), retrieve proxy species of ozone (O3) formation (formaldehyde and nitrogen dioxide) and the ratios (FNRs) which can define O3 production sensitivity regimes. Here we investigate trends of OMI FNRs from 2005 to 2021, and they have increased in major cities, suggesting a transition from radical- to NOx-limited regimes. OMI also observed the impact of reduced emissions during the 2020 COVID-19 lockdown that resulted in increased FNRs.
Andrew O. Langford, Raul J. Alvarez II, Kenneth C. Aikin, Sunil Baidar, W. Alan Brewer, Steven S. Brown, Matthew M. Coggan, Patrick D. Cullis, Jessica Gilman, Georgios I. Gkatzelis, Detlev Helmig, Bryan J. Johnson, K. Emma Knowland, Rajesh Kumar, Aaron D. Lamplugh, Audra McClure-Begley, Brandi J. McCarty, Ann M. Middlebrook, Gabriele Pfister, Jeff Peischl, Irina Petropavlovskikh, Pamela S. Rickley, Andrew W. Rollins, Scott P. Sandberg, Christoph J. Senff, and Carsten Warneke
EGUsphere, https://doi.org/10.5194/egusphere-2024-1938, https://doi.org/10.5194/egusphere-2024-1938, 2024
Preprint withdrawn
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High ozone (O3) formed by reactions of nitrogen oxides (NOx) and volatile organic compounds (VOCs) can harm human health and welfare. High O3 is usually associated with hot summer days, but under certain conditions, high O3 can also form under winter conditions. In this study, we describe a high O3 event that occurred in Colorado during the COVID-19 quarantine that was caused in part by the decrease in traffic, and in part by a shallow inversion created by descent of stratospheric air.
Yafang Guo, Chayan Roychoudhury, Mohammad Amin Mirrezaei, Rajesh Kumar, Armin Sorooshian, and Avelino F. Arellano
Geosci. Model Dev., 17, 4331–4353, https://doi.org/10.5194/gmd-17-4331-2024, https://doi.org/10.5194/gmd-17-4331-2024, 2024
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This research focuses on surface ozone (O3) pollution in Arizona, a historically air-quality-challenged arid and semi-arid region in the US. The unique characteristics of this kind of region, e.g., intense heat, minimal moisture, and persistent desert shrubs, play a vital role in comprehending O3 exceedances. Using the WRF-Chem model, we analyzed O3 levels in the pre-monsoon month, revealing the model's skill in capturing diurnal and MDA8 O3 levels.
Vishnu Thilakan, Dhanyalekshmi Pillai, Jithin Sukumaran, Christoph Gerbig, Haseeb Hakkim, Vinayak Sinha, Yukio Terao, Manish Naja, and Monish Vijay Deshpande
Atmos. Chem. Phys., 24, 5315–5335, https://doi.org/10.5194/acp-24-5315-2024, https://doi.org/10.5194/acp-24-5315-2024, 2024
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This study investigates the usability of CO2 mixing ratio observations over India to infer regional carbon sources and sinks. We demonstrate that a high-resolution modelling system can represent the observed CO2 variations reasonably well by improving the transport and flux variations at a fine scale. Future carbon data assimilation systems can thus benefit from these recently available CO2 observations when fine-scale variations are adequately represented in the models.
Gaurav Govardhan, Sachin D. Ghude, Rajesh Kumar, Sumit Sharma, Preeti Gunwani, Chinmay Jena, Prafull Yadav, Shubhangi Ingle, Sreyashi Debnath, Pooja Pawar, Prodip Acharja, Rajmal Jat, Gayatry Kalita, Rupal Ambulkar, Santosh Kulkarni, Akshara Kaginalkar, Vijay K. Soni, Ravi S. Nanjundiah, and Madhavan Rajeevan
Geosci. Model Dev., 17, 2617–2640, https://doi.org/10.5194/gmd-17-2617-2024, https://doi.org/10.5194/gmd-17-2617-2024, 2024
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A newly developed air quality forecasting framework, Decision Support System (DSS), for air quality management in Delhi, India, provides source attribution with numerous emission reduction scenarios besides forecasts. DSS shows that during post-monsoon and winter seasons, Delhi and its neighboring districts contribute to 30 %–40 % each to pollution in Delhi. On average, a 40 % reduction in the emissions in Delhi and the surrounding districts would result in a 24 % reduction in Delhi's pollution.
Leon Kuhn, Steffen Beirle, Vinod Kumar, Sergey Osipov, Andrea Pozzer, Tim Bösch, Rajesh Kumar, and Thomas Wagner
Atmos. Chem. Phys., 24, 185–217, https://doi.org/10.5194/acp-24-185-2024, https://doi.org/10.5194/acp-24-185-2024, 2024
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NO₂ is an important air pollutant. It was observed that the WRF-Chem model shows significant deviations in NO₂ abundance when compared to measurements. We use a 1-month simulation over central Europe to show that these deviations can be mostly resolved by reparameterization of the vertical mixing routine. In order to validate our results, they are compared to in situ, satellite, and MAX-DOAS measurements.
Wenfu Tang, Louisa K. Emmons, Helen M. Worden, Rajesh Kumar, Cenlin He, Benjamin Gaubert, Zhonghua Zheng, Simone Tilmes, Rebecca R. Buchholz, Sara-Eva Martinez-Alonso, Claire Granier, Antonin Soulie, Kathryn McKain, Bruce C. Daube, Jeff Peischl, Chelsea Thompson, and Pieternel Levelt
Geosci. Model Dev., 16, 6001–6028, https://doi.org/10.5194/gmd-16-6001-2023, https://doi.org/10.5194/gmd-16-6001-2023, 2023
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The new MUSICAv0 model enables the study of atmospheric chemistry across all relevant scales. We develop a MUSICAv0 grid for Africa. We evaluate MUSICAv0 with observations and compare it with a previously used model – WRF-Chem. Overall, the performance of MUSICAv0 is comparable to WRF-Chem. Based on model–satellite discrepancies, we find that future field campaigns in an eastern African region (30°E–45°E, 5°S–5°N) could substantially improve the predictive skill of air quality models.
Manu Goudar, Juliëtte C. S. Anema, Rajesh Kumar, Tobias Borsdorff, and Jochen Landgraf
Geosci. Model Dev., 16, 4835–4852, https://doi.org/10.5194/gmd-16-4835-2023, https://doi.org/10.5194/gmd-16-4835-2023, 2023
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A framework was developed to automatically detect plumes and compute emission estimates with cross-sectional flux method (CFM) for biomass burning events in TROPOMI CO datasets using Visible Infrared Imaging Radiometer Suite active fire data. The emissions were more reliable when changing plume height in downwind direction was used instead of constant injection height. The CFM had uncertainty even when the meteorological conditions were accurate; thus there is a need for better inversion models.
Matthew S. Johnson, Amir H. Souri, Sajeev Philip, Rajesh Kumar, Aaron Naeger, Jeffrey Geddes, Laura Judd, Scott Janz, Heesung Chong, and John Sullivan
Atmos. Meas. Tech., 16, 2431–2454, https://doi.org/10.5194/amt-16-2431-2023, https://doi.org/10.5194/amt-16-2431-2023, 2023
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Satellites provide vital information for studying the processes controlling ozone formation. Based on the abundance of particular gases in the atmosphere, ozone formation is sensitive to specific human-induced and natural emission sources. However, errors and biases in satellite retrievals hinder this data source’s application for studying ozone formation sensitivity. We conducted a thorough statistical evaluation of two commonly applied satellites for investigating ozone formation sensitivity.
Prajjwal Rawat, Manish Naja, Evan Fishbein, Pradeep K. Thapliyal, Rajesh Kumar, Piyush Bhardwaj, Aditya Jaiswal, Sugriva N. Tiwari, Sethuraman Venkataramani, and Shyam Lal
Atmos. Meas. Tech., 16, 889–909, https://doi.org/10.5194/amt-16-889-2023, https://doi.org/10.5194/amt-16-889-2023, 2023
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Satellite-based ozone observations have gained importance due to their global coverage. However, satellite-retrieved products are indirect and need to be validated, particularly over mountains. Ozonesondes launched from a Himalayan site are used to assess the Atmospheric Infrared Sounder (AIRS) ozone retrieval. AIRS is shown to overestimate ozone in the upper troposphere and lower stratosphere, while the differences from ozonesondes are more minor in the middle troposphere and stratosphere.
Pooja V. Pawar, Sachin D. Ghude, Gaurav Govardhan, Prodip Acharja, Rachana Kulkarni, Rajesh Kumar, Baerbel Sinha, Vinayak Sinha, Chinmay Jena, Preeti Gunwani, Tapan Kumar Adhya, Eiko Nemitz, and Mark A. Sutton
Atmos. Chem. Phys., 23, 41–59, https://doi.org/10.5194/acp-23-41-2023, https://doi.org/10.5194/acp-23-41-2023, 2023
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In this study, for the first time in South Asia we compare simulated ammonia, ammonium, and total ammonia using the WRF-Chem model and MARGA measurements during winter in the Indo-Gangetic Plain region. Since observations show HCl promotes the fraction of high chlorides in Delhi, we added HCl / Cl emissions to the model. We conducted three sensitivity experiments with changes in HCl emissions, and improvements are reported in accurately simulating ammonia, ammonium, and total ammonia.
Mauro Morichetti, Sasha Madronich, Giorgio Passerini, Umberto Rizza, Enrico Mancinelli, Simone Virgili, and Mary Barth
Geosci. Model Dev., 15, 6311–6339, https://doi.org/10.5194/gmd-15-6311-2022, https://doi.org/10.5194/gmd-15-6311-2022, 2022
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In the present study, we explore the effect of making simple changes to the existing WRF-Chem MEGAN v2.04 emissions to provide MEGAN updates that can be used independently of the land surface model chosen. The changes made to the MEGAN algorithm implemented in WRF-Chem were the following: (i) update of the emission activity factors, (ii) update of emission factor values for each plant functional type (PFT), and (iii) the assignment of the emission factor by PFT to isoprene.
Chaman Gul, Shichang Kang, Siva Praveen Puppala, Xiaokang Wu, Cenlin He, Yangyang Xu, Inka Koch, Sher Muhammad, Rajesh Kumar, and Getachew Dubache
Atmos. Chem. Phys., 22, 8725–8737, https://doi.org/10.5194/acp-22-8725-2022, https://doi.org/10.5194/acp-22-8725-2022, 2022
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This work aims to understand concentrations, spatial variability, and potential source regions of light-absorbing impurities (black carbon aerosols, dust particles, and organic carbon) in the surface snow of central and western Himalayan glaciers and their impact on snow albedo and radiative forcing.
Christophe Lerot, François Hendrick, Michel Van Roozendael, Leonardo M. A. Alvarado, Andreas Richter, Isabelle De Smedt, Nicolas Theys, Jonas Vlietinck, Huan Yu, Jeroen Van Gent, Trissevgeni Stavrakou, Jean-François Müller, Pieter Valks, Diego Loyola, Hitoshi Irie, Vinod Kumar, Thomas Wagner, Stefan F. Schreier, Vinayak Sinha, Ting Wang, Pucai Wang, and Christian Retscher
Atmos. Meas. Tech., 14, 7775–7807, https://doi.org/10.5194/amt-14-7775-2021, https://doi.org/10.5194/amt-14-7775-2021, 2021
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Global measurements of glyoxal tropospheric columns from the satellite instrument TROPOMI are presented. Such measurements can contribute to the estimation of atmospheric emissions of volatile organic compounds. This new glyoxal product has been fully characterized with a comprehensive error budget, with comparison with other satellite data sets as well as with validation based on independent ground-based remote sensing glyoxal observations.
Liji M. David, Mary Barth, Lena Höglund-Isaksson, Pallav Purohit, Guus J. M. Velders, Sam Glaser, and A. R. Ravishankara
Atmos. Chem. Phys., 21, 14833–14849, https://doi.org/10.5194/acp-21-14833-2021, https://doi.org/10.5194/acp-21-14833-2021, 2021
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We calculated the expected concentrations of trifluoroacetic acid (TFA) from the atmospheric breakdown of HFO-1234yf (CF3CF=CH2), a substitute for global warming hydrofluorocarbons, emitted now and in the future by India, China, and the Middle East. We used two chemical transport models. We conclude that the projected emissions through 2040 would not be detrimental, given the current knowledge of the effects of TFA on humans and ecosystems.
Xinxin Ye, Pargoal Arab, Ravan Ahmadov, Eric James, Georg A. Grell, Bradley Pierce, Aditya Kumar, Paul Makar, Jack Chen, Didier Davignon, Greg R. Carmichael, Gonzalo Ferrada, Jeff McQueen, Jianping Huang, Rajesh Kumar, Louisa Emmons, Farren L. Herron-Thorpe, Mark Parrington, Richard Engelen, Vincent-Henri Peuch, Arlindo da Silva, Amber Soja, Emily Gargulinski, Elizabeth Wiggins, Johnathan W. Hair, Marta Fenn, Taylor Shingler, Shobha Kondragunta, Alexei Lyapustin, Yujie Wang, Brent Holben, David M. Giles, and Pablo E. Saide
Atmos. Chem. Phys., 21, 14427–14469, https://doi.org/10.5194/acp-21-14427-2021, https://doi.org/10.5194/acp-21-14427-2021, 2021
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Wildfire smoke has crucial impacts on air quality, while uncertainties in the numerical forecasts remain significant. We present an evaluation of 12 real-time forecasting systems. Comparison of predicted smoke emissions suggests a large spread in magnitudes, with temporal patterns deviating from satellite detections. The performance for AOD and surface PM2.5 and their discrepancies highlighted the role of accurately represented spatiotemporal emission profiles in improving smoke forecasts.
Andreas Tilgner, Thomas Schaefer, Becky Alexander, Mary Barth, Jeffrey L. Collett Jr., Kathleen M. Fahey, Athanasios Nenes, Havala O. T. Pye, Hartmut Herrmann, and V. Faye McNeill
Atmos. Chem. Phys., 21, 13483–13536, https://doi.org/10.5194/acp-21-13483-2021, https://doi.org/10.5194/acp-21-13483-2021, 2021
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Feedbacks of acidity and atmospheric multiphase chemistry in deliquesced particles and clouds are crucial for the tropospheric composition, depositions, climate, and human health. This review synthesizes the current scientific knowledge on these feedbacks using both inorganic and organic aqueous-phase chemistry. Finally, this review outlines atmospheric implications and highlights the need for future investigations with respect to reducing emissions of key acid precursors in a changing world.
Isabelle De Smedt, Gaia Pinardi, Corinne Vigouroux, Steven Compernolle, Alkis Bais, Nuria Benavent, Folkert Boersma, Ka-Lok Chan, Sebastian Donner, Kai-Uwe Eichmann, Pascal Hedelt, François Hendrick, Hitoshi Irie, Vinod Kumar, Jean-Christopher Lambert, Bavo Langerock, Christophe Lerot, Cheng Liu, Diego Loyola, Ankie Piters, Andreas Richter, Claudia Rivera Cárdenas, Fabian Romahn, Robert George Ryan, Vinayak Sinha, Nicolas Theys, Jonas Vlietinck, Thomas Wagner, Ting Wang, Huan Yu, and Michel Van Roozendael
Atmos. Chem. Phys., 21, 12561–12593, https://doi.org/10.5194/acp-21-12561-2021, https://doi.org/10.5194/acp-21-12561-2021, 2021
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This paper assess the performances of the TROPOMI formaldehyde observations compared to its predecessor OMI at different spatial and temporal scales. We also use a global network of MAX-DOAS instruments to validate both satellite datasets for a large range of HCHO columns. The precision obtained with daily TROPOMI observations is comparable to monthly OMI observations. We present clear detection of weak HCHO column enhancements related to shipping emissions in the Indian Ocean.
Pooja V. Pawar, Sachin D. Ghude, Chinmay Jena, Andrea Móring, Mark A. Sutton, Santosh Kulkarni, Deen Mani Lal, Divya Surendran, Martin Van Damme, Lieven Clarisse, Pierre-François Coheur, Xuejun Liu, Gaurav Govardhan, Wen Xu, Jize Jiang, and Tapan Kumar Adhya
Atmos. Chem. Phys., 21, 6389–6409, https://doi.org/10.5194/acp-21-6389-2021, https://doi.org/10.5194/acp-21-6389-2021, 2021
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In this study, simulations of atmospheric ammonia (NH3) with MOZART-4 and HTAP-v2 are compared with satellite (IASI) and ground-based measurements to understand the spatial and temporal variability of NH3 over two emission hotspot regions of Asia, the IGP and the NCP. Our simulations indicate that the formation of ammonium aerosols is quicker over the NCP than the IGP, leading to smaller NH3 columns over the higher NH3-emitting NCP compared to the IGP region for comparable emissions.
Fernando Chouza, Thierry Leblanc, Mark Brewer, Patrick Wang, Sabino Piazzolla, Gabriele Pfister, Rajesh Kumar, Carl Drews, Simone Tilmes, Louisa Emmons, and Matthew Johnson
Atmos. Chem. Phys., 21, 6129–6153, https://doi.org/10.5194/acp-21-6129-2021, https://doi.org/10.5194/acp-21-6129-2021, 2021
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The tropospheric ozone lidar at the JPL Table Mountain Facility (TMF) was used to investigate the impact of Los Angeles (LA) Basin pollution transport and stratospheric intrusions in the planetary boundary layer on the San Gabriel Mountains. The results of this study indicate a dominant role of the LA Basin pollution on days when high ozone levels were observed at TMF (March–October period).
Wenjie Wang, Jipeng Qi, Jun Zhou, Bin Yuan, Yuwen Peng, Sihang Wang, Suxia Yang, Jonathan Williams, Vinayak Sinha, and Min Shao
Atmos. Meas. Tech., 14, 2285–2298, https://doi.org/10.5194/amt-14-2285-2021, https://doi.org/10.5194/amt-14-2285-2021, 2021
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We designed a new reactor for measurements of OH reactivity (i.e., OH radical loss frequency) based on the comparative reactivity method under
high-NOx conditions, such as in cities. We performed a series of laboratory tests to evaluate the new reactor. The new reactor was used in the field and performed well in measuring OH reactivity in air influenced by upwind cities.
Yuting Wang, Yong-Feng Ma, Domingo Muñoz-Esparza, Cathy W. Y. Li, Mary Barth, Tao Wang, and Guy P. Brasseur
Atmos. Chem. Phys., 21, 3531–3553, https://doi.org/10.5194/acp-21-3531-2021, https://doi.org/10.5194/acp-21-3531-2021, 2021
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Large-eddy simulations (LESs) were performed in the mountainous region of the island of Hong Kong to investigate the degree to which the rates of chemical reactions between two reactive species are reduced due to the segregation of species within the convective boundary layer. We show that the inhomogeneity in emissions plays an important role in the segregation effect. Topography also has a significant influence on the segregation locally.
Vinod Kumar, Steffen Beirle, Steffen Dörner, Abhishek Kumar Mishra, Sebastian Donner, Yang Wang, Vinayak Sinha, and Thomas Wagner
Atmos. Chem. Phys., 20, 14183–14235, https://doi.org/10.5194/acp-20-14183-2020, https://doi.org/10.5194/acp-20-14183-2020, 2020
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We present the first long-term MAX-DOAS measurements of aerosols, nitrogen dioxide and formaldehyde tropospheric columns, vertical distributions, and temporal variation from Mohali in the Indo-Gangetic Plain. We investigate the effect of various emission sources and meteorological conditions on the measured pollutants and how they control ozone formation. These measurements are also used to validate the corresponding satellite observations and are also compared against in situ observations.
Ashish Kumar, Vinayak Sinha, Muhammed Shabin, Haseeb Hakkim, Bernard Bonsang, and Valerie Gros
Atmos. Chem. Phys., 20, 12133–12152, https://doi.org/10.5194/acp-20-12133-2020, https://doi.org/10.5194/acp-20-12133-2020, 2020
Short summary
Short summary
Source apportionment studies require information on the chemical fingerprints of pollution sources to correctly quantify source contributions to ambient composition. These chemical fingerprints vary from region to region, depending on fuel composition and combustion conditions, and are poorly constrained over developing regions such as South Asia. This work characterises the chemical fingerprints of urban and agricultural sources using 49 non-methane hydrocarbons and their environmental impacts.
Cited articles
Abdul-Razzak, H. and Ghan, S. J.: A parameterization of aerosol activation 3. Sectional representation, J. Geophys. Res.-Atmos., 107, AAC 1-1–AAC 1-6, https://doi.org/10.1029/2001JD000483, 2002.
Acharja, P., Ali, K., Ghude, S. D., Sinha, V., Sinha, B., Kulkarni, R., Gultepe, I., and Rajeevan, M. N.: Enhanced secondary aerosol formation driven by excess ammonia during fog episodes in Delhi, India, Chemosphere, 289, 133155, https://doi.org/10.1016/j.chemosphere.2021.133155, 2022.
Anu Rani Sharma, Shailesh Kumar Kharol, Badarinath, K. V. S., and Darshan Singh: Impact of agriculture crop residue burning on atmospheric aerosol loading – a study over Punjab State, India, Ann. Geophys., 28, 367–379, https://doi.org/10.5194/angeo-28-367-2010, 2010.
Arun, S. H., Sharma, S. K., Chaurasia, S., Vaishnav, R., and Kumar, R.: Fog/low clouds detection over the delhi earth station using the ceilometer and the insat-3d/3dr satellite data, Int. J. Remote Sens., 39, 4130–4144, https://doi.org/10.1080/01431161.2018.1454624, 2018.
Badarinath, K. V. S., Kumar Kharol, S., and Rani Sharma, A.: Long-range transport of aerosols from agriculture crop residue burning in Indo-Gangetic Plains—A study using LIDAR, ground measurements and satellite data, J. Atmos. Sol.-Terr. Phy., 71, 112–120, https://doi.org/10.1016/j.jastp.2008.09.035, 2009.
Banerjee, S. and Padmakumari, B.: Spatiotemporal variability and evolution of day and night winter fog over the Indo Gangetic Basin using INSAT-3D and comparison with surface visibility and aerosol optical depth, Sci. Total Environ., 745, 140962, https://doi.org/10.1016/j.scitotenv.2020.140962, 2020.
Behera, S. N. and Sharma, M.: Reconstructing primary and secondary components of PM2.5 composition for an Urban Atmosphere, Aerosol Sci. Tech., 44, 983–992, https://doi.org/10.1080/02786826.2010.504245, 2010.
Bergot, T. and Guedalia, D.: Numerical Forecasting of Radiation Fog. Part I: Numerical Model and Sensitivity Tests, Mon. Weather Rev., 122, 1218–1230, https://doi.org/10.1175/1520-0493(1994)122<1218:NFORFP>2.0.CO;2, 1994.
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.
Bodaballa, J. K., Geresdi, I., Ghude, S. D., and Salma, I.: Numerical simulation of the microphysics and liquid chemical processes occur in fog using size resolving bin scheme, Atmos. Res., 266, 105972, https://doi.org/10.1016/j.atmosres.2021.105972, 2022.
Boutle, I., Price, J., Kudzotsa, I., Kokkola, H., and Romakkaniemi, S.: Aerosol–fog interaction and the transition to well-mixed radiation fog, Atmos. Chem. Phys., 18, 7827–7840, https://doi.org/10.5194/acp-18-7827-2018, 2018.
Bran, S. H. and Srivastava, R.: Investigation of PM2.5 mass concentration over India using a regional climate model, Environ. Pollut., 224, 484–493, https://doi.org/10.1016/j.envpol.2017.02.030, 2017.
Central Pollution Control Board (CPCB): https://airquality.cpcb.gov.in/ccr/#/caaqm-dashboard-all/caaqm-landing, last access: 27 March 2014.
Chapman, E. G., Gustafson Jr., W. I., Easter, R. C., Barnard, J. C., Ghan, S. J., Pekour, M. S., and Fast, J. D.: Coupling aerosol-cloud-radiative processes in the WRF-Chem model: Investigating the radiative impact of elevated point sources, Atmos. Chem. Phys., 9, 945–964, https://doi.org/10.5194/acp-9-945-2009, 2009.
Chaudhary, P., Garg, S., George, T., Shabin, M., Saha, S., Subodh, S., and Sinha, B.: Underreporting and open burning – the two largest challenges for sustainable waste management in India, Resour. Conserv. Recy., 175, 105865, https://doi.org/10.1016/j.resconrec.2021.105865, 2021.
Chaurasia, S. and Gohil, B. S.: Detection of Day Time Fog over India Using INSAT-3D Data, IEEE J. Sel. Top. Appl., 8, 4524–4530, https://doi.org/10.1109/JSTARS.2015.2493000, 2015.
Chaurasia, S. and Jenamani, R. K.: Detection of Fog Using Temporally Consistent, IEEE J. Sel. Top. Appl., 10, 5307–5313, https://doi.org/10.1109/JSTARS.2017.2759197, 2017.
Chen, C., Zhang, M., Perrie, W., Chang, R., Chen, X., Duplessis, P., and Wheeler, M.: Boundary Layer Parameterizations to Simulate Fog Over Atlantic Canada Waters, Earth and Space Science, 7, e2019EA000703, https://doi.org/10.1029/2019EA000703, 2020.
Chen, F. and Dudhia, J.: Coupling and advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system. Part I: Model implementation and sensitivity, Mon. Weather Rev., 129, 569–585, https://doi.org/10.1175/1520-0493(2001)129<0569:CAALSH>2.0.CO;2, 2001.
Computational and Information Systems Laboratory: Cheyenne: HPE/SGI ICE XA System (NCAR Community Computing), National Center for Atmospheric Research, Boulder, CO, https://doi.org/10.5065/D6RX99HX, 2019.
David, L. M., Ravishankara, A. R., Kodros, J. K., Venkataraman, C., Sadavarte, P., Pierce, J. R., Chaliyakunnel, S., and Millet, D. B.: Aerosol Optical Depth Over India, J. Geophys. Res.-Atmos., 123, 3688–3703, https://doi.org/10.1002/2017JD027719, 2018.
Debnath, S., Karumuri, R. K., Govardhan, G., Jat, R., Saini, H., Vispute, A., Kulkarni, S. H., Jena, C., Kumar, R., Chate, D. M., and Ghude, S. D.: Implications of Implementing Promulgated and Prospective Emission Regulations on Air Quality and Health in India during 2030, Aerosol Air Qual. Res., 22, 220112, https://doi.org/10.4209/aaqr.220112, 2022.
Deshpande, P., Meena, D., Tripathi, S., Bhattacharya, A., and Verma, M. K.: Event-based fog climatology and typology for cities in Indo-Gangetic plains, Urban Climate, 51, 101642, https://doi.org/10.1016/j.uclim.2023.101642, 2023.
Dey, S. and Di Girolamo, L.: A decade of change in aerosol properties over the Indian subcontinent, Geophys. Res. Lett., 38, L14811, https://doi.org/10.1029/2011GL048153, 2011.
Dey, S. and Tripathi, S. N.: Estimation of aerosol optical properties and radiative effects in the Ganga basin, northern India, during the wintertime, J. Geophys. Res.-Atmos., 112, D03203, https://doi.org/10.1029/2006JD007267, 2007.
Dhangar, N. G., Lal, D. M., Ghude, S. D., Kulkarni, R., Parde, A. N., Pithani, P., Niranjan, K., Prasad, D. S. V. V. D., Jena, C., Sajjan, V. S., Prabhakaran, T., Karipot, A. K., Jenamani, R. K., Singh, S., and Rajeevan, M.: On the Conditions for Onset and Development of Fog Over New Delhi: An Observational Study from the WiFEX, Pure Appl. Geophys., 178, 3727–3746, https://doi.org/10.1007/s00024-021-02800-4, 2021.
Ding, A. J., Huang, X., Nie, W., Sun, J. N., Kerminen, V. M., Petäjä, T., Su, H., Cheng, Y. F., Yang, X. Q., Wang, M. H., Chi, X. G., Wang, J. P., Virkkula, A., Guo, W. D., Yuan, J., Wang, S. Y., Zhang, R. J., Wu, Y. F., Song, Y., Zhu, T., Zilitinkevich, S., Kulmala, M., and Fu, C. B.: Enhanced haze pollution by black carbon in megacities in China, Geophys. Res. Lett., 43, 2873–2879, https://doi.org/10.1002/2016GL067745, 2016.
Easter, R. C., Ghan, S. J., Zhang, Y., Saylor, R. D., Chapman, E. G., Laulainen, N. S., Abdul-Razzak, H., Leung, L. R., Bian, X., and Zaveri, R. A.: MIRAGE: Model description and evaluation of aerosols and trace gases, J. Geophys. Res.-Atmos., 109, D20210, https://doi.org/10.1029/2004JD004571, 2004.
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 Sy., 12, e2019MS001882, https://doi.org/10.1029/2019MS001882, 2020.
Fahey, K. M. and Pandis, S. N.: Optimizing model performance: variable size resolution in cloud chemistry modeling, Atmos. Environ., 35, 4471–4478, https://doi.org/10.1016/S1352-2310(01)00224-2, 2001.
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 forcing in an urban area using a new fully-coupled meteorology, chemistry, and aerosol model, J. Geophys. Res, 111, D21305, https://doi.org/10.1029/2005JD006721, 2006.
Gautam, R., Hsu, N. C., Kafatos, M., and Tsay, S.: Influences of winter haze on fog/low cloud over the Indo-Gangetic plains, J. Geophys. Res., 112, D05207, https://doi.org/10.1029/2005JD007036, 2007.
Ghude, S. D., Kulkarni, S. H., Jena, C., Pfister, G. G., Beig, G., Fadnavis, S., and Van Der, R. J.: Application of satellite observations for identifying regions of dominant sources of nitrogen oxides over the indian subcontinent, J. Geophys. Res.-Atmos., 118, 1075–1089, https://doi.org/10.1029/2012JD017811, 2013.
Ghude, S. D., Chate, D. M., Jena, C., Beig, G., Kumar, R., Barth, M. C., Pfister, G. G., Fadnavis, S., and Pithani, P.: Premature mortality in India due to PM2.5 and ozone exposure, Geophys. Res. Lett., 43, 4650–4658, https://doi.org/10.1002/2016GL068949, 2016.
Ghude, S. D., Bhat, G. S., Prabhakaran, T., Jenamani, R. K., Chate, D. M., Safai, P. D., Karipot, A. K., Konwar, M., Pithani, P., Sinha, V., Rao, P. S. P., Dixit, S. A., Tiwari, S., Todekar, K., Varpe, S., Srivastava, A. K., Bisht, D. S., Murugavel, P., Ali, K., Mina, U., Dharua, M., Jaya Rao, Y., Padmakumari, B., Hazra, A., Nigam, N., Shende, U., Lal, D. M., Chandra, B. P., Mishra, A. K., Kumar, A., Hakkim, H., Pawar, H., Acharja, P., Kulkarni, R., Subharthi, C., Balaji, B., Varghese, M., Bera, S., and Rajeevan, M.: Winter fog experiment over the Indo-Gangetic plains of India, Curr. Sci., 112, 767–784, https://api.semanticscholar.org/CorpusID:99884123 (last access: 3 June 2024), 2017.
Ghude, S. D., Kumar, R., Jena, C., Debnath, S., Kulkarni, R. G., Alessandrini, S., Biswas, M., Kulkrani, S., Pithani, P., Kelkar, S., Sajjan, V., Chate, D. M., Soni, V. K., Singh, S., Nanjundiah, R. S., and Rajeevan, M.: Evaluation of PM2.5 forecast using chemical data assimilation in the WRF-Chem model: A novel initiative under the Ministry of Earth Sciences Air Quality Early Warning System for Delhi, India, Curr. Sci., 118, 1803–1815, https://api.semanticscholar.org/CorpusID:221212334 (last access: 3 June 2024), 2020.
Ghude, S. D., Jenamani, R. K., Kulkarni, R., Wagh, S., Dhangar, N. G., Parde, A. N., Acharja, P., Lonkar, P., Govardhan, G., Yadav, P., Vispute, A., Debnath, S., Lal, D. M., Bisht, D. S., Jena, C., Pawar, P. V., Dhankhar, S. S., Sinha, V., Chate, D. M., Safai, P. D., Nigam, N., Konwar, M., Hazra, A., Dharmaraj, T., Gopalkrishnan, V., Padmakumari, B., Gultepe, I., Biswas, M., Karipot, A. K., Prabhakaran, T., Nanjundiah, R. S., and Rajeevan, M.: WiFEX Walk into the Warm Fog over Indo-Gangetic Plain Region, B. Am. Meteorol. Soc., 104, E980–E1005, https://doi.org/10.1175/BAMS-D-21-0197.1, 2023.
Govardhan, G., Nanjundiah, R. S., Satheesh, S. K., Krishnamoorthy, K., and Kotamarthi, V. R.: Performance of WRF-chem over indian region: Comparison with measurements, J. Earth Syst. Sci., 124, 875–896, https://doi.org/10.1007/s12040-015-0576-7, 2015.
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.
Gundel, L. A., Benner, W. H., and Hansen, A. D. A.: Chemical composition of fog water and interstitial aerosol in Berkeley, California, Atmos. Environ., 28, 2715–2725, https://doi.org/10.1016/1352-2310(94)90443-X, 1994.
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, T. and Mandariya, A.: Sources of submicron aerosol during fog-dominated wintertime at Kanpur, Environ. Sci. Pollut. R., 20, 5615–5629, https://doi.org/10.1007/s11356-013-1580-6, 2013.
Hakkim, H., Sinha, V., Chandra, B. P., Kumar, A., Mishra, A. K., Sinha, B., Sharma, G., Pawar, H., Sohpaul, B., Ghude, S. D., Pithani, P., Kulkarni, R., Jenamani, R. K., and Rajeevan, M.: Volatile organic compound measurements point to fog-induced biomass burning feedback to air quality in the megacity of Delhi, Sci. Total Environ., 689, 295–304, https://doi.org/10.1016/j.scitotenv.2019.06.438, 2019.
Hong, S. Y., Noh, Y., and Dudhia, J.: A new vertical diffusion package with an explicit treatment of entrainment processes, Mon. Weather Rev., 134, 2318–2341, https://doi.org/10.1175/MWR3199.1, 2006.
Jacobson, M. Z., Turco, R. P., Jensen, E. J., and Toon, O. B.: Modeling coagulation among particles of different composition and size, Atmos. Environ., 28, 1327–1338, https://doi.org/10.1016/1352-2310(94)90280-1, 1994.
Jain, S., Sharma, S. K., Vijayan, N., and Mandal, T. K.: Seasonal characteristics of aerosols (PM2.5 and PM10) and their source apportionment using PMF: A four year study over Delhi, India, Environ. Pollut., 262, 114337, https://doi.org/10.1016/j.envpol.2020.114337, 2020.
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, 1–9, https://doi.org/10.1038/s41598-021-83467-8, 2021.
Jenamani, R. K.: Alarming rise in fog and pollution causing a fall in maximum temperature over Delhi, Curr. Sci., 93, 314–322, 2007.
Jethva, H., Chand, D., Torres, O., Gupta, P., Lyapustin, A., and Patadia, F.: Agricultural burning and air quality over northern india: A synergistic analysis using nasa's a-train satellite data and ground measurements, Aerosol Air Qual. Res., 18, 1756–1773, https://doi.org/10.4209/aaqr.2017.12.0583, 2018.
Katata, G., Chino, M., Kobayashi, T., Terada, H., Ota, M., Nagai, H., Kajino, M., Draxler, R., Hort, M. C., Malo, A., Torii, T., and Sanada, Y.: Detailed source term estimation of the atmospheric release for the Fukushima Daiichi Nuclear Power Station accident by coupling simulations of an atmospheric dispersion model with an improved deposition scheme and oceanic dispersion model, Atmos. Chem. Phys., 15, 1029–1070, https://doi.org/10.5194/acp-15-1029-2015, 2015.
Kaul, D. S., Gupta, T., Tripathi, S. N., Tare, V., and Collett, J. L.: Secondary organic aerosol: A comparison between foggy and nonfoggy days, Environ. Sci. Technol., 45, 7307–7313, https://doi.org/10.1021/es201081d, 2011.
Kedia, S., Ramachandran, S., Holben, B. N., and Tripathi, S. N.: Quantification of aerosol type, and sources of aerosols over the Indo-Gangetic Plain, Atmos. Environ., 98, 607–619, https://doi.org/10.1016/j.atmosenv.2014.09.022, 2014.
Knote, C., Tuccella, P., Curci, G., Emmons, L., Orlando, J. J., Madronich, S., Baró, R., Jiménez-Guerrero, P., Luecken, D., Hogrefe, C., Forkel, R., Werhahn, J., Hirtl, M., Pérez, J. L., San José, R., Giordano, L., Brunner, D., Yahya, K., and Zhang, Y.: Influence of the choice of gas-phase mechanism on predictions of key gaseous pollutants during the AQMEII phase-2 intercomparison, Atmos. Environ., 115, 553–568, https://doi.org/10.1016/j.atmosenv.2014.11.066, 2014.
Krishna, R. K., Panicker, A. S., Yusuf, A. M., and Ullah, B. G.: On the contribution of particulate matter (PM2.5) to direct radiative forcing over two urban environments in India, Aerosol Air Qual. Res., 19, 399–410, https://doi.org/10.4209/aaqr.2018.04.0128, 2019.
Krishna Moorthy, K., Suresh Babu, S., Manoj, M. R., and Satheesh, S. K.: Buildup of aerosols over the Indian Region, Geophys. Res. Lett., 40, 1011–1014, https://doi.org/10.1002/grl.50165, 2013.
Kulkarni, R., Jenamani, R. K., Pithani, P., and Konwar, M.: Loss to Aviation Economy Due to Winter Fog in New Delhi during the Winter of 2011–2016, Atmosphere, 10, 198, https://doi.org/10.3390/atmos10040198, 2019.
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. and Sarin, M. M.: Aerosol iron solubility in a semi-arid region: temporal trend and impact of anthropogenic sources, Tellus B, 62, 125–132, https://doi.org/10.1111/j.1600-0889.2009.00448.x, 2010.
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., 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., 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.
Kumar, R., Mishra, V., Buzan, J., Kumar, R., Shindell, D., and Huber, M.: Dominant control of agriculture and irrigation on urban heat island in India, Sci. Rep., 7, 14054, https://doi.org/10.1038/s41598-017-14213-2, 2017.
Lalchandani, V., Kumar, V., Tobler, A., M. Thamban, N., Mishra, S., Slowik, J. G., Bhattu, D., Rai, P., Satish, R., Ganguly, D., Tiwari, S., Rastogi, N., Tiwari, S., Močnik, G., Prévôt, A. S. H., and Tripathi, S. N.: Real-time characterization and source apportionment of fine particulate matter in the Delhi megacity area during late winter, Sci. Total Environ., 770, 145324, https://doi.org/10.1016/j.scitotenv.2021.145324, 2021.
Lelieveld, J., Evans, J. S., Fnais, M., Giannadaki, D., and Pozzer, A.: The contribution of outdoor air pollution sources to premature mortality on a global scale, Nature, 525, 367–371, https://doi.org/10.1038/nature15371, 2015.
Levy, R., Hsu, C., et al.: MODIS Atmosphere L2 Aerosol Product, NASA MODIS Adaptive Processing System, Goddard Space Flight Center [data set], USA, https://doi.org/10.5067/MODIS/MOD04_L2.061, 2015a.
Levy, R., Hsu, C., et al.: MODIS Atmosphere L2 Aerosol Product, NASA MODIS Adaptive Processing System, Goddard Space Flight Center [data set], USA, https://doi.org/10.5067/MODIS/MYD04_L2.061, 2015b.
Ma, Y., Brooks, S. D., Vidaurre, G., Khalizov, A. F., Wang, L., and Zhang, R.: Rapid modification of cloud-nucleating ability of aerosols by biogenic emissions, Geophys. Res. Lett., 40, 6293–6297, https://doi.org/10.1002/2013GL057895, 2013.
Maalick, Z., Kühn, T., Korhonen, H., Kokkola, H., Laaksonen, A., and Romakkaniemi, S.: Effect of aerosol concentration and absorbing aerosol on the radiation fog life cycle, Atmos. Environ., 133, 26–33, https://doi.org/10.1016/j.atmosenv.2016.03.018, 2016.
Mandariya, A. K., Gupta, T., and Tripathi, S. N.: Effect of aqueous-phase processing on the formation and evolution of organic aerosol (OA) under different stages of fog life cycles, Atmos. Environ., 206, 60–71, https://doi.org/10.1016/j.atmosenv.2019.02.047, 2019.
Martin, L. R. and Good, T. W.: Catalyzed oxidation of sulfur dioxide in solution: The iron-manganese synergism, Atmos. Environ. A-Gen., 25, 2395–2399, https://doi.org/10.1016/0960-1686(91)90113-L, 1991.
Mishra, V., Ambika, A. K., Asoka, A., Aadhar, S., Buzan, J., Kumar, R., and Huber, M.: Moist heat stress extremes in India enhanced by irrigation, Nat. Geosci., 13, 722–728, https://doi.org/10.1038/s41561-020-00650-8, 2020.
Mohan, M. and Bhati, S.: Analysis of WRF Model Performance over Subtropical Region of Delhi, India, Adv. Meteorol., 2011, 621235, https://doi.org/10.1155/2011/621235, 2011.
Mohan, M. and Gupta, M.: Sensitivity of PBL parameterizations on PM10 and ozone simulation using chemical transport model WRF-Chem over a sub-tropical urban airshed in India, Atmos. Environ., 185, 53–63, https://doi.org/10.1016/j.atmosenv.2018.04.054, 2018.
Moore, R. H., Cerully, K., Bahreini, R., Brock, C. A., Middlebrook, A. M., and Nenes, A.: Hygroscopicity and composition of California CCN during summer 2010, J. Geophys. Res.-Atmos., 117, 1–14, https://doi.org/10.1029/2011JD017352, 2012.
Nagar, P. K., Singh, D., Sharma, M., Kumar, A., Aneja, V. P., George, M. P., Agarwal, N., and Shukla, S. P.: Characterization of PM2.5 in Delhi: role and impact of secondary aerosol, burning of biomass, and municipal solid waste and crustal matter, Environ. Sci. Pollut. R., 24, 25179–25189, https://doi.org/10.1007/s11356-017-0171-3, 2017.
Nagpure, A. S., Ramaswami, A., and Russell, A.: Characterizing the Spatial and Temporal Patterns of Open Burning of Municipal Solid Waste (MSW) in Indian Cities, Environ. Sci. Technol., 49, 12904–12912, https://doi.org/10.1021/acs.est.5b03243, 2015.
Neu, J. L. and Prather, M. J.: Toward a more physical representation of precipitation scavenging in global chemistry models: cloud overlap and ice physics and their impact on tropospheric ozone, Atmos. Chem. Phys., 12, 3289–3310, https://doi.org/10.5194/acp-12-3289-2012, 2012.
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, 1–9, 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.
Pant, P., Shukla, A., Kohl, S. D., Chow, J. C., Watson, J. G., and Harrison, R. M.: Characterization of ambient PM2.5 at a pollution hotspot in New Delhi, India and inference of sources, Atmos. Environ., 109, 178–189, https://doi.org/10.1016/j.atmosenv.2015.02.074, 2015.
Patil, R. S., Kumar, R., Menon, R., Shah, M. K., and Sethi, V.: Development of particulate matter speciation profiles for major sources in six cities in India, Atmos. Res., 132–133, 1–11, https://doi.org/10.1016/j.atmosres.2013.04.012, 2013.
Pawar, H. and Sinha, B.: Residential heating emissions (can) exceed paddy-residue burning emissions in rural northwest India, Atmos. Environ., 269, 118846, https://doi.org/10.1016/j.atmosenv.2021.118846, 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.
Pithani, P., Ghude, S. D., Chennu, V. N., Kulkarni, R. G., Steeneveld, G. J., Sharma, A., Prabhakaran, T., Chate, D. M., Gultepe, I., Jenamani, R. K., and Madhavan, R.: WRF Model Prediction of a Dense Fog Event Occurred During the Winter Fog Experiment (WIFEX), Pure Appl. Geophys., 176, 1827–1846, https://doi.org/10.1007/s00024-018-2053-0, 2019.
Pithani, P., Ghude, S. D., Jenamani, R. K., Biswas, M., Naidu, C. V., Debnath, S., Kulkarni, R., Dhangar, N. G., Jena, C., Hazra, A., Phani, R., Mukhopadhyay, P., Prabhakaran, T., Nanjundiah, R. S., and Rajeevan, M.: Real-time forecast of dense fog events over Delhi: The performance of the wrf model during the wifex field campaign, Weather Forecast., 35, 739–756, https://doi.org/10.1175/WAF-D-19-0104.1, 2020.
Pleim, J. E.: A combined local and nonlocal closure model for the atmospheric boundary layer. Part I: Model description and testing, J. Appl. Meteorol. Clim., 46, 1383–1395, https://doi.org/10.1175/JAM2539.1, 2007a.
Pleim, J. E.: A combined local and nonlocal closure model for the atmospheric boundary layer. Part II: Application and evaluation in a mesoscale meteorological model, J. Appl. Meteorol. Clim., 46, 1396–1409, https://doi.org/10.1175/JAM2534.1, 2007b.
Pleim, J. E. and Chang, J. S.: A non-local closure model for vertical mixing in the convective boundary layer, Atmos. Environ. A.-Gen., 26, 965–981, https://doi.org/10.1016/0960-1686(92)90028-J, 1992.
Pleim, J. E. and Gilliam, R.: An indirect data assimilation scheme for deep soil temperature in the Pleim-Xiu land surface model, J. Appl. Meteorol. Clim., 48, 1362–1376, https://doi.org/10.1175/2009JAMC2053.1, 2009.
Pleim, J. E. and Xiu, A.: Development of a land surface model. Part II: Data assimilation, J. Appl. Meteorol., 42, 1811–1822, https://doi.org/10.1175/1520-0450(2003)042<1811:DOALSM>2.0.CO;2, 2003.
Powers, J. G., Klemp, J. B., Skamarock, W. C., Davis, C. A., Dudhia, J., Gill, D. O., Coen, J. L., Gochis, D. J., Ahmadov, R., Peckham, S. E., Grell, G. A., Michalakes, J., Trahan, S., Benjamin, S. G., Alexander, C. R., Dimego, G. J., Wang, W., Schwartz, C. S., Romine, G. S., Liu, Z., Snyder, C., Chen, F., Barlage, M. J., Yu, W., and Duda, M. G.: The weather research and forecasting model: Overview, system efforts, and future directions, B. Am. Meteorol. Soc., 98, 1717–1737, https://doi.org/10.1175/BAMS-D-15-00308.1, 2017.
Pye, H. O. T., Nenes, A., Alexander, B., Ault, A. P., Barth, M. C., Clegg, S. L., Collett Jr., J. L., Fahey, K. M., Hennigan, C. J., Herrmann, H., Kanakidou, M., Kelly, J. T., Ku, I.-T., McNeill, V. F., Riemer, N., Schaefer, T., Shi, G., Tilgner, A., Walker, J. T., Wang, T., Weber, R., Xing, J., Zaveri, R. A., and Zuend, A.: The acidity of atmospheric particles and clouds, Atmos. Chem. Phys., 20, 4809–4888, https://doi.org/10.5194/acp-20-4809-2020, 2020.
Ram, K., Sarin, M. M., Sudheer, A. K., and Rengarajan, R.: Carbonaceous and secondary inorganic aerosols during wintertime fog and haze over urban sites in the Indo-Gangetic plain, Aerosol Air Qual. Res., 12, 355–366, https://doi.org/10.4209/aaqr.2011.07.0105, 2012a.
Ram, K., Sarin, M. M., and Tripathi, S. N.: Temporal trends in atmospheric PM2.5, PM10, elemental carbon, organic carbon, water-soluble organic carbon, and optical properties: Impact of biomass burning emissions in the Indo-Gangetic Plain, Environ. Sci. Technol., 46, 686–695, https://doi.org/10.1021/es202857w, 2012b.
Ramachandran, S., Rupakheti, M., and Lawrence, M. G.: Aerosol-induced atmospheric heating rate decreases over South and East Asia as a result of changing content and composition, Sci. Rep., 10, 1–17, https://doi.org/10.1038/s41598-020-76936-z, 2020.
Rengarajan, R., Sarin, M. M., and Sudheer, A. K.: Carbonaceous and inorganic species in atmospheric aerosols during wintertime over urban and high-altitude sites in North India, J. Geophys. Res.-Atmos., 112, D21307, https://doi.org/10.1029/2006JD008150, 2007.
Ruan, X., Zhao, C., Zaveri, R. A., He, P., Wang, X., Shao, J., and Geng, L.: Simulations of aerosol pH in China using WRF-Chem (v4.0): sensitivities of aerosol pH and its temporal variations during haze episodes, Geosci. Model Dev., 15, 6143–6164, https://doi.org/10.5194/gmd-15-6143-2022, 2022.
Safai, P. D., Ghude, S., Pithani, P., Varpe, S., Kulkarni, R., Todekar, K., Tiwari, S., Chate, D. M., Prabhakaran, T., Jenamani, R. K., and Rajeevan, M. N.: Two-way relationship between aerosols and fog: A case study at IGI airport, New Delhi, Aerosol Air Qual. Res., 19, 71–79, https://doi.org/10.4209/aaqr.2017.11.0542, 2019.
Sarkar, C., Roy, A., Chatterjee, A., Ghosh, S. K., and Raha, S.: Factors controlling the long-term (2009–2015) trend of PM2.5 and black carbon aerosols at eastern Himalaya, India, Sci. Total Environ., 656, 280–296, https://doi.org/10.1016/j.scitotenv.2018.11.367, 2019.
Sarkar, S., Chokngamwong, R., Cervone, G., Singh, R. P., and Kafatos, M.: Variability of aerosol optical depth and aerosol forcing over India, Adv. Space. Res., 37, 2153–2159, https://doi.org/10.1016/j.asr.2005.09.043, 2006.
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.
Shao, N., Lu, C., Jia, X., Wang, Y., Li, Y., Yin, Y., Zhu, B., Zhao, T., Liu, D., Niu, S., Fan, S., Yan, S., and Lv, J.: Radiation fog properties in two consecutive events under polluted and clean conditions in the Yangtze River Delta, China: a simulation study, Atmos. Chem. Phys., 23, 9873–9890, https://doi.org/10.5194/acp-23-9873-2023, 2023.
Sharma, G., Annadate, S., and Sinha, B.: Will open waste burning become India's largest air pollution source?, Environ. Pollut., 292, 118310, https://doi.org/10.1016/j.envpol.2021.118310, 2022.
Sharma, S. K. and Mandal, T. K.: Chemical composition of fine mode particulate matter (PM2.5) in an urban area of Delhi, India and its source apportionment, Urban Climate, 21, 106–122, https://doi.org/10.1016/j.uclim.2017.05.009, 2017.
Sharma, S. K. and Mandal, T. K.: Elemental Composition and Sources of Fine Particulate Matter (PM2.5) in Delhi, India, B. Environ. Contam. Tox., 110, 1–8, https://doi.org/10.1007/s00128-023-03707-7, 2023.
Shin, H. H. and Hong, S. Y.: Intercomparison of Planetary Boundary-Layer Parametrizations in the WRF Model for a Single Day from CASES-99, Bound.-Lay. Meteorol., 139, 261–281, https://doi.org/10.1007/s10546-010-9583-z, 2011.
Singh, A. and Dey, S.: Influence of aerosol composition on visibility in megacity Delhi, Atmos. Environ., 62, 367–373, https://doi.org/10.1016/j.atmosenv.2012.08.048, 2012.
Singh, N., Banerjee, T., Raju, M. P., Deboudt, K., Sorek-Hamer, M., Singh, R. S., and Mall, R. K.: Aerosol chemistry, transport, and climatic implications during extreme biomass burning emissions over the Indo-Gangetic Plain, Atmos. Chem. Phys., 18, 14197–14215, https://doi.org/10.5194/acp-18-14197-2018, 2018.
Sinha, B., Garg, S., George, T., Chaudhary, P., Shabin, M., Saha, S., and Subodh, S.: OWBEII 2020, Version 1, Mendeley Data [data set], https://doi.org/10.17632/t2tn4t9473.1, 2021.
Srinivas, B. and Sarin, M. M.: PM2.5, EC and OC in atmospheric outflow from the Indo-Gangetic Plain: Temporal variability and aerosol organic carbon-to-organic mass conversion factor, Sci. Total Environ., 487, 196–205, https://doi.org/10.1016/j.scitotenv.2014.04.002, 2014.
Srivastava, P., Dey, S., Srivastava, A. K., Singh, S., and Tiwari, S.: Most probable mixing state of aerosols in Delhi NCR, northern India, Atmos. Res., 200, 88–96, https://doi.org/10.1016/j.atmosres.2017.09.018, 2018.
Steeneveld, G. J., Ronda, R. J., and Holtslag, A. A. M.: The Challenge of Forecasting the Onset and Development of Radiation Fog Using Mesoscale Atmospheric Models, Bound.-Lay. Meteorol., 154, 265–289, https://doi.org/10.1007/s10546-014-9973-8, 2015.
Stolaki, S., Haeffelin, M., Lac, C., Dupont, J. C., Elias, T., and Masson, V.: Influence of aerosols on the life cycle of a radiation fog event. A numerical and observational study, Atmos. Res., 151, 146–161, https://doi.org/10.1016/j.atmosres.2014.04.013, 2015.
Syed, F. S., Körnich, H., and Tjernström, M.: On the fog variability over south Asia, Clim. Dynam., 39, 2993–3005, https://doi.org/10.1007/s00382-012-1414-0, 2012.
Tare, V., Tripathi, S. N., Chinnam, N., Srivastava, A. K., Dey, S., Manar, M., Kanawade, V. P., Agarwal, A., Kishore, S., Lal, R. B., and Sharma, M.: Measurements of atmospheric parameters during Indian Space Research Organization Geosphere Biosphere Program Land Campaign II at a typical location in the Ganga basin: 2. Chemical properties, J. Geophys. Res.-Atmos., 111, D23210, https://doi.org/10.1029/2006JD007279, 2006.
Tav, J., Masson, O., Burnet, F., Paulat, P., Bourrianne, T., Conil, S., and Pourcelot, L.: Determination of fog-droplet deposition velocity from a simple weighing method, Aerosol Air Qual. Res., 18, 103–113, https://doi.org/10.4209/aaqr.2016.11.0519, 2018.
Taylor, K. E.: Summarizing multiple aspects of model performance in a single diagram, J. Geophys. Res., 106, 7183–7192, https://doi.org/10.1029/2000JD900719, 2001.
Van Der Velde, I. R., Steeneveld, G. J., Wichers Schreur, B. G. J., and Holtslag, A. A. M.: Modeling and forecasting the onset and duration of severe radiation fog under frost conditions, Mon. Weather Rev., 138, 4237–4253, https://doi.org/10.1175/2010MWR3427.1, 2010.
Verma, S., Ramana, M. V., and Kumar, R.: Atmospheric rivers fueling the intensification of fog and haze over Indo-Gangetic Plains, Sci. Rep., 12, 1–9, https://doi.org/10.1038/s41598-022-09206-9, 2022.
Wang, T., Liu, M., Liu, M., Song, Y., Xu, Z., Shang, F., Huang, X., Liao, W., Wang, W., Ge, M., Cao, J., Hu, J., Tang, G., Pan, Y., Hu, M., and Zhu, T.: Sulfate Formation Apportionment during Winter Haze Events in North China, Environ. Sci. Technol., 56, 7771–7778, https://doi.org/10.1021/acs.est.2c02533, 2022.
Wexler, A. S., Lurmann, F. W., and Seinfeld, J. H.: Modelling urban and regional aerosols – I. model development, Atmos. Environ., 28, 531–546, https://doi.org/10.1016/1352-2310(94)90129-5, 1994.
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.
Xie, B., Fung, J. C. H., Chan, A., and Lau, A.: Evaluation of nonlocal and local planetary boundary layer schemes in the WRF model, J. Geophys. Res.-Atmos., 117, 1–26, https://doi.org/10.1029/2011JD017080, 2012.
Xiu, A. and Pleim, J. E.: Development of a land surface model. Part I: Application in a mesoscale meteorological model, J. Appl. Meteorol., 40, 192–209, https://doi.org/10.1175/1520-0450(2001)040<0192:DOALSM>2.0.CO;2, 2001.
Yadav, P., Parde, A. N., Dhangar, N. G., Govardhan, G., Lal, D. M., Wagh, S., Prasad, D. S. V. V. D., Ahmed, R., and Ghude, S. D.: Understanding the genesis of a dense fog event over Delhi using observations and high-resolution model experiments, Model. Earth Syst. Environ., 8, 5011–5022, https://doi.org/10.1007/s40808-022-01463-x, 2022.
Yadav, R., Bhatti, M. S., Kansal, S. K., Das, L., Gilhotra, V., Sugha, A., Hingmire, D., Yadav, S., Tandon, A., Bhatti, R., Goel, A., and Mandal, T. K.: Comparison of ambient air pollution levels of Amritsar during foggy conditions with that of five major north Indian cities: multivariate analysis and air mass back trajectories, SN Appl. Sci., 2, 1–11, https://doi.org/10.1007/s42452-020-03569-2, 2020.
Yan, S., Zhu, B., Zhu, T., Shi, C., and Liu, D.: The Effect of Aerosols on Fog Lifetime: Observational Evidence and Model Simulations, Geophys. Res. Lett., 48, e2020GL61803, https://doi.org/10.1029/2020GL091156, 2021.
Yu, H., Liu, S. C., and Dickinson, R. E.: Radiative effects of aerosols on the evolution of the atmospheric boundary layer, J. Geophys. Res.-Atmos., 107, AAC 3-1–AAC 3-14, https://doi.org/10.1029/2001jd000754, 2002.
Zaveri, R. A., Easter, R. C., and Peters, L. K.: A computationally efficient Multicomponent Equilibrium Solver for Aerosols (MESA), J. Geophys. Res.-Atmos., 110, 1–22, https://doi.org/10.1029/2004JD005618, 2005.
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, 1–29, https://doi.org/10.1029/2007JD008782, 2008.
Zhang, D. and Anthes, R. A.: A High-Resolution Model of the Planetary Boundary Layer–Sensitivity Tests and Comparisons with SESAME-79 Data, J. Appl. Meteorol. Clim., 21, 1594–1609, https://doi.org/10.1175/1520-0450(1982)021<1594:AHRMOT>2.0.CO;2, 1982.
Zhang, F., Li, Y., Li, Z., Sun, L., Li, R., Zhao, C., Wang, P., Sun, Y., Liu, X., Li, J., Li, P., Ren, G., and Fan, T.: Aerosol hygroscopicity and cloud condensation nuclei activity during the AC3Exp campaign: implications for cloud condensation nuclei parameterization, Atmos. Chem. Phys., 14, 13423–13437, https://doi.org/10.5194/acp-14-13423-2014, 2014.
Zhang, X., Musson-Genon, L., Dupont, E., Milliez, M., and Carissimo, B.: On the Influence of a Simple Microphysics Parametrization on Radiation Fog Modelling: A Case Study During ParisFog, Bound.-Lay. Meteorol., 151, 293–315, https://doi.org/10.1007/s10546-013-9894-y, 2014.
Short summary
This study examines the role of atmospheric aerosols in winter fog over the Indo-Gangetic Plains of India using WRF-Chem. The increase in RH with aerosol–radiation feedback (ARF) is found to be important for fog formation as it promotes the growth of aerosols in the polluted environment. Aqueous-phase chemistry in the fog increases PM2.5 concentration, further affecting ARF. ARF and aqueous-phase chemistry affect the fog intensity and the timing of fog formation by ~1–2 h.
This study examines the role of atmospheric aerosols in winter fog over the Indo-Gangetic Plains...
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