Articles | Volume 15, issue 12
https://doi.org/10.5194/acp-15-6929-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/acp-15-6929-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Estimates of non-traditional secondary organic aerosols from aircraft SVOC and IVOC emissions using CMAQ
M. C. Woody
Institute for the Environment, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
J. J. West
Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
S. H. Jathar
Department of Civil and Environmental Engineering, University of California, Davis, CA, USA
A. L. Robinson
Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
Institute for the Environment, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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Joshua Singleton, Hantao Wang, Jerry R. Ziemke, Marc L. Serre, and J. Jason West
EGUsphere, https://doi.org/10.5194/egusphere-2026-4798, https://doi.org/10.5194/egusphere-2026-4798, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Satellite ozone observations have been considered poor predictors of ozone at ground level. Here we evaluate the ability of one satellite product to estimate ground-level ozone globally over 18 years. The satellite product shows a modest correlation with ground-level ozone that does not vary strongly with year, season, world region, or ozone level. Using a linear regression, we translate the satellite product into a global monthly ground-level ozone dataset for use in future data fusion studies.
Hantao Wang, Marc L. Serre, Kazuyuki Miyazaki, Juan Cuesta, Jerry R. Ziemke, and J. Jason West
EGUsphere, https://doi.org/10.5194/egusphere-2026-2812, https://doi.org/10.5194/egusphere-2026-2812, 2026
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
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Ground-level ozone poses a significant health risk, yet ground monitors are sparse and satellites lack surface sensitivity. Here we develop a framework to infer surface ozone directly from satellite observations. By leveraging vertical profiles from balloons and aircraft and using chemical reanalysis vertical ratios, we significantly improved the accuracy of ozone estimates. Our 2005–2022 global dataset provides a valuable ground-level ozone background field for regions lacking ground networks.
Sebastian H. M. Hickman, Makoto M. Kelp, Paul T. Griffiths, Kelsey Doerksen, Kazuyuki Miyazaki, Elyse A. Pennington, Gerbrand Koren, Fernando Iglesias-Suarez, Martin G. Schultz, Kai-Lan Chang, Owen R. Cooper, Alex Archibald, Roberto Sommariva, David Carlson, Hantao Wang, J. Jason West, and Zhenze Liu
Geosci. Model Dev., 18, 8777–8800, https://doi.org/10.5194/gmd-18-8777-2025, https://doi.org/10.5194/gmd-18-8777-2025, 2025
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Machine learning is being more widely used across environmental and climate science. This work reviews the use of machine learning in tropospheric ozone research, focusing on three main application areas in which significant progress has been made. Common challenges in using machine learning across the three areas are highlighted, and future directions for the field are indicated.
Hantao Wang, Kazuyuki Miyazaki, Haitong Zhe Sun, Zhen Qu, Xiang Liu, Antje Inness, Martin Schultz, Sabine Schröder, Marc Serre, and J. Jason West
Atmos. Chem. Phys., 25, 15969–15990, https://doi.org/10.5194/acp-25-15969-2025, https://doi.org/10.5194/acp-25-15969-2025, 2025
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We compare six datasets of global ground-level ozone, developed using geostatistical, machine learning, or reanalysis methods. The datasets show important differences from one another in ozone magnitude, greater than 5 ppb, and trends, globally and regionally. Compared with measurements, performance varies among datasets, and most overestimate ozone, particularly at lower concentrations. These differences among datasets highlight uncertainties for applications to health and other impacts.
Christos I. Efstathiou, Elizabeth Adams, Carlie J. Coats, Robert Zelt, Mark Reed, John McGee, Kristen M. Foley, Fahim I. Sidi, David C. Wong, Steven Fine, and Saravanan Arunachalam
Geosci. Model Dev., 17, 7001–7027, https://doi.org/10.5194/gmd-17-7001-2024, https://doi.org/10.5194/gmd-17-7001-2024, 2024
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We present a summary of enabling high-performance computing of the Community Multiscale Air Quality Model (CMAQ) – a state-of-the-science community multiscale air quality model – on two cloud computing platforms through documenting the technologies, model performance, scaling and relative merits. This may be a new paradigm for computationally intense future model applications. We initiated this work due to a need to leverage cloud computing advances and to ease the learning curve for new users.
Tianle Pan, Andrew T. Lambe, Weiwei Hu, Yicong He, Minghao Hu, Huaishan Zhou, Xinming Wang, Qingqing Hu, Hui Chen, Yue Zhao, Yuanlong Huang, Doug R. Worsnop, Zhe Peng, Melissa A. Morris, Douglas A. Day, Pedro Campuzano-Jost, Jose-Luis Jimenez, and Shantanu H. Jathar
Atmos. Meas. Tech., 17, 4915–4939, https://doi.org/10.5194/amt-17-4915-2024, https://doi.org/10.5194/amt-17-4915-2024, 2024
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This study systematically characterizes the temperature enhancement in the lamp-enclosed oxidation flow reactor (OFR). The enhancement varied multiple dimensional factors, emphasizing the complexity of temperature inside of OFR. The effects of temperature on the flow field and gas- or particle-phase reaction inside OFR were also evaluated with experiments and model simulations. Finally, multiple mitigation strategies were demonstrated to minimize this temperature increase.
Benjamin N. Murphy, Darrell Sonntag, Karl M. Seltzer, Havala O. T. Pye, Christine Allen, Evan Murray, Claudia Toro, Drew R. Gentner, Cheng Huang, Shantanu Jathar, Li Li, Andrew A. May, and Allen L. Robinson
Atmos. Chem. Phys., 23, 13469–13483, https://doi.org/10.5194/acp-23-13469-2023, https://doi.org/10.5194/acp-23-13469-2023, 2023
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We update methods for calculating organic particle and vapor emissions from mobile sources in the USA. Conventionally, particulate matter (PM) and volatile organic carbon (VOC) are speciated without consideration of primary semivolatile emissions. Our methods integrate state-of-the-science speciation profiles and correct for common artifacts when sampling emissions in a laboratory. We quantify impacts of the emission updates on ambient pollution with the Community Multiscale Air Quality model.
Nicole A. June, Anna L. Hodshire, Elizabeth B. Wiggins, Edward L. Winstead, Claire E. Robinson, K. Lee Thornhill, Kevin J. Sanchez, Richard H. Moore, Demetrios Pagonis, Hongyu Guo, Pedro Campuzano-Jost, Jose L. Jimenez, Matthew M. Coggon, Jonathan M. Dean-Day, T. Paul Bui, Jeff Peischl, Robert J. Yokelson, Matthew J. Alvarado, Sonia M. Kreidenweis, Shantanu H. Jathar, and Jeffrey R. Pierce
Atmos. Chem. Phys., 22, 12803–12825, https://doi.org/10.5194/acp-22-12803-2022, https://doi.org/10.5194/acp-22-12803-2022, 2022
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The evolution of organic aerosol composition and size is uncertain due to variability within and between smoke plumes. We examine the impact of plume concentration on smoke evolution from smoke plumes sampled by the NASA DC-8 during FIREX-AQ. We find that observed organic aerosol and size distribution changes are correlated to plume aerosol mass concentrations. Additionally, coagulation explains the majority of the observed growth.
Michael Cheeseman, Bonne Ford, Zoey Rosen, Eric Wendt, Alex DesRosiers, Aaron J. Hill, Christian L'Orange, Casey Quinn, Marilee Long, Shantanu H. Jathar, John Volckens, and Jeffrey R. Pierce
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-751, https://doi.org/10.5194/acp-2021-751, 2021
Revised manuscript not accepted
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This article predicts concentrations of airborne particulate matter over wintertime Denver, CO, USA, using meteorological and geographic information, as well as low-cost aerosol optical depth (AOD) measurements captured by citizen scientists. Machine learning methods revealed that low boundary layer heights and stagnant air were the best predictors of poor air quality, while AOD provided little skill in predicting particulate matter for this location and time period.
Shuping Zhang, Golam Sarwar, Jia Xing, Biwu Chu, Chaoyang Xue, Arunachalam Sarav, Dian Ding, Haotian Zheng, Yujing Mu, Fengkui Duan, Tao Ma, and Hong He
Atmos. Chem. Phys., 21, 15809–15826, https://doi.org/10.5194/acp-21-15809-2021, https://doi.org/10.5194/acp-21-15809-2021, 2021
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Six heterogeneous HONO chemistry updates in CMAQ significantly improve HONO concentration. HONO production is primarily controlled by the heterogeneous reactions on ground and aerosol surfaces during haze. Additional HONO chemistry updates increase OH and production of secondary aerosols: sulfate, nitrate, and SOA.
Eric A. Wendt, Casey Quinn, Christian L'Orange, Daniel D. Miller-Lionberg, Bonne Ford, Jeffrey R. Pierce, John Mehaffy, Michael Cheeseman, Shantanu H. Jathar, David H. Hagan, Zoey Rosen, Marilee Long, and John Volckens
Atmos. Meas. Tech., 14, 6023–6038, https://doi.org/10.5194/amt-14-6023-2021, https://doi.org/10.5194/amt-14-6023-2021, 2021
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Fine particulate matter air pollution is one of the leading contributors to adverse health outcomes on the planet. Here, we describe the design and validation of a low-cost, compact, and autonomous instrument capable of measuring particulate matter levels directly, via mass sampling, and optically, via mass and sunlight extinction measurements. We demonstrate the instrument's accuracy relative to reference measurements and its potential for community-level sampling.
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Short summary
Utilizing an aircraft-specific parameterization based on smog chamber data in a regional AQM, contributions of non-traditional secondary organic aerosols (NTSOA) from aircraft emissions of semi-volatile and intermediate volatility organic compounds were assessed. NTSOA, a previously unaccounted component of PM2.5 in most AQMs, contributed up to 7.4% of aviation-attributable PM2.5 at the airport and rose to 17.9% downwind, suggesting its significance in aviation-attributed PM2.5 at all scales.
Utilizing an aircraft-specific parameterization based on smog chamber data in a regional AQM,...
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