Articles | Volume 26, issue 15
https://doi.org/10.5194/acp-26-10965-2026
© Author(s) 2026. 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-26-10965-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 Aug 2026
Research article |
| 06 Aug 2026
| 06 Aug 2026
From κ to χ: evaluating hygroscopicity-based mixing state estimates with a particle-resolved model
Department of Civil and Environmental Engineering, Grainger College of Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
Jian Wang
Department of Energy, Environmental, and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, USA
Department of Climate, Meteorology, and Atmospheric Science, University of Illinois Urbana-Champaign, Urbana, IL, USA
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Jeffrey H. Curtis, Nicole Riemer, and Matthew West
Geosci. Model Dev., 17, 8399–8420, https://doi.org/10.5194/gmd-17-8399-2024, https://doi.org/10.5194/gmd-17-8399-2024, 2024
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Yang Wang, Chanakya Bagya Ramesh, Scott E. Giangrande, Jerome Fast, Xianda Gong, Jiaoshi Zhang, Ahmet Tolga Odabasi, Marcus Vinicius Batista Oliveira, Alyssa Matthews, Fan Mei, John E. Shilling, Jason Tomlinson, Die Wang, and Jian Wang
Atmos. Chem. Phys., 23, 15671–15691, https://doi.org/10.5194/acp-23-15671-2023, https://doi.org/10.5194/acp-23-15671-2023, 2023
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Qian Xiao, Jiaoshi Zhang, Yang Wang, Luke D. Ziemba, Ewan Crosbie, Edward L. Winstead, Claire E. Robinson, Joshua P. DiGangi, Glenn S. Diskin, Jeffrey S. Reid, K. Sebastian Schmidt, Armin Sorooshian, Miguel Ricardo A. Hilario, Sarah Woods, Paul Lawson, Snorre A. Stamnes, and Jian Wang
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Daniel A. Knopf, Peiwen Wang, Benny Wong, Jay M. Tomlin, Daniel P. Veghte, Nurun N. Lata, Swarup China, Alexander Laskin, Ryan C. Moffet, Josephine Y. Aller, Matthew A. Marcus, and Jian Wang
Atmos. Chem. Phys., 23, 8659–8681, https://doi.org/10.5194/acp-23-8659-2023, https://doi.org/10.5194/acp-23-8659-2023, 2023
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Ambient particle populations and associated ice-nucleating particles (INPs)
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boundary layer and free troposphere in the eastern North Atlantic during
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particle populations seasonally and with sampling altitudes, which are
reflected in the INP types. Freezing parameterizations are derived for
implementation in cloud-resolving and climate models.
Francesca Gallo, Janek Uin, Kevin J. Sanchez, Richard H. Moore, Jian Wang, Robert Wood, Fan Mei, Connor Flynn, Stephen Springston, Eduardo B. Azevedo, Chongai Kuang, and Allison C. Aiken
Atmos. Chem. Phys., 23, 4221–4246, https://doi.org/10.5194/acp-23-4221-2023, https://doi.org/10.5194/acp-23-4221-2023, 2023
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Sudipta Ghosh, Sagnik Dey, Sushant Das, Nicole Riemer, Graziano Giuliani, Dilip Ganguly, Chandra Venkataraman, Filippo Giorgi, Sachchida Nand Tripathi, Srikanthan Ramachandran, Thazhathakal Ayyappen Rajesh, Harish Gadhavi, and Atul Kumar Srivastava
Geosci. Model Dev., 16, 1–15, https://doi.org/10.5194/gmd-16-1-2023, https://doi.org/10.5194/gmd-16-1-2023, 2023
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Accurate representation of aerosols in climate models is critical for minimizing the uncertainty in climate projections. Here, we implement region-specific emission fluxes and a more accurate scheme for carbonaceous aerosol ageing processes in a regional climate model (RegCM4) and show that it improves model performance significantly against in situ, reanalysis, and satellite data over the Indian subcontinent. We recommend improving the model performance before using them for climate studies.
Jerome D. Fast, David M. Bell, Gourihar Kulkarni, Jiumeng Liu, Fan Mei, Georges Saliba, John E. Shilling, Kaitlyn Suski, Jason Tomlinson, Jian Wang, Rahul Zaveri, and Alla Zelenyuk
Atmos. Chem. Phys., 22, 11217–11238, https://doi.org/10.5194/acp-22-11217-2022, https://doi.org/10.5194/acp-22-11217-2022, 2022
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Recent aircraft measurements from the HI-SCALE campaign conducted over the Southern Great Plains (SGP) site in Oklahoma are used to quantify spatial variability of aerosol properties in terms of grid spacings typically used by weather and climate models. Surprisingly large horizontal gradients in aerosol properties were frequently observed in this rural area. This spatial variability can be used as an uncertainty range when comparing surface point measurements with model predictions.
Ivo Beck, Hélène Angot, Andrea Baccarini, Lubna Dada, Lauriane Quéléver, Tuija Jokinen, Tiia Laurila, Markus Lampimäki, Nicolas Bukowiecki, Matthew Boyer, Xianda Gong, Martin Gysel-Beer, Tuukka Petäjä, Jian Wang, and Julia Schmale
Atmos. Meas. Tech., 15, 4195–4224, https://doi.org/10.5194/amt-15-4195-2022, https://doi.org/10.5194/amt-15-4195-2022, 2022
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We present the pollution detection algorithm (PDA), a new method to identify local primary pollution in remote atmospheric aerosol and trace gas time series. The PDA identifies periods of contaminated data and relies only on the target dataset itself; i.e., it is independent of ancillary data such as meteorological variables. The parameters of all pollution identification steps are adjustable so that the PDA can be tuned to different locations and situations. It is available as open-access code.
Yu Yao, Jeffrey H. Curtis, Joseph Ching, Zhonghua Zheng, and Nicole Riemer
Atmos. Chem. Phys., 22, 9265–9282, https://doi.org/10.5194/acp-22-9265-2022, https://doi.org/10.5194/acp-22-9265-2022, 2022
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Investigating the impacts of aerosol mixing state on aerosol optical properties has a long history from both the modeling and experimental perspective. In this study, we used particle-resolved simulations as a benchmark to determine the error in optical properties when using simplified aerosol representations. We found that errors in single scattering albedo due to the internal mixture assumptions can have substantial effects on calculating aerosol direct radiative forcing.
Matthew L. Dawson, Christian Guzman, Jeffrey H. Curtis, Mario Acosta, Shupeng Zhu, Donald Dabdub, Andrew Conley, Matthew West, Nicole Riemer, and Oriol Jorba
Geosci. Model Dev., 15, 3663–3689, https://doi.org/10.5194/gmd-15-3663-2022, https://doi.org/10.5194/gmd-15-3663-2022, 2022
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Progress in identifying complex, mixed-phase physicochemical processes has resulted in an advanced understanding of the evolution of atmospheric systems but has also introduced a level of complexity that few atmospheric models were designed to handle. We present a flexible treatment for multiphase chemical processes for models of diverse scale, from box up to global models. This enables users to build a customized multiphase mechanism that is accessible to a much wider community.
Jiaoshi Zhang, Yang Wang, Steven Spielman, Susanne Hering, and Jian Wang
Atmos. Meas. Tech., 15, 2579–2590, https://doi.org/10.5194/amt-15-2579-2022, https://doi.org/10.5194/amt-15-2579-2022, 2022
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New nonparametric, regularized methods are developed to invert the growth factor probability density function (GF-PDF) from humidity-controlled fast integrated mobility spectrometer measurements. These algorithms are computationally efficient, require no prior assumptions of the GF-PDF distribution, and reduce the error in inverted GF-PDF. They can be applied to humidified tandem differential mobility analyzer data. Among all algorithms, Twomey’s method retrieves GF-PDF with the smallest error.
Daniel A. Knopf, Joseph C. Charnawskas, Peiwen Wang, Benny Wong, Jay M. Tomlin, Kevin A. Jankowski, Matthew Fraund, Daniel P. Veghte, Swarup China, Alexander Laskin, Ryan C. Moffet, Mary K. Gilles, Josephine Y. Aller, Matthew A. Marcus, Shira Raveh-Rubin, and Jian Wang
Atmos. Chem. Phys., 22, 5377–5398, https://doi.org/10.5194/acp-22-5377-2022, https://doi.org/10.5194/acp-22-5377-2022, 2022
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Marine boundary layer aerosols collected in the remote region of the eastern North Atlantic induce immersion freezing and deposition ice nucleation under typical mixed-phase and cirrus cloud conditions. Corresponding ice nucleation parameterizations for model applications have been derived. Chemical imaging of ambient aerosol and ice-nucleating particles demonstrates that the latter is dominated by sea salt and organics while also representing a major particle type in the particle population.
Jay M. Tomlin, Kevin A. Jankowski, Daniel P. Veghte, Swarup China, Peiwen Wang, Matthew Fraund, Johannes Weis, Guangjie Zheng, Yang Wang, Felipe Rivera-Adorno, Shira Raveh-Rubin, Daniel A. Knopf, Jian Wang, Mary K. Gilles, Ryan C. Moffet, and Alexander Laskin
Atmos. Chem. Phys., 21, 18123–18146, https://doi.org/10.5194/acp-21-18123-2021, https://doi.org/10.5194/acp-21-18123-2021, 2021
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Analysis of individual atmospheric particles shows that aerosol transported from North America during meteorological dry intrusion episodes may have a substantial impact on the mixing state and particle-type population over the mid-Atlantic, as organic contribution and particle-type diversity are significantly enhanced during these periods. These observations need to be considered in current atmospheric models.
Zhonghua Zheng, Matthew West, Lei Zhao, Po-Lun Ma, Xiaohong Liu, and Nicole Riemer
Atmos. Chem. Phys., 21, 17727–17741, https://doi.org/10.5194/acp-21-17727-2021, https://doi.org/10.5194/acp-21-17727-2021, 2021
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Aerosol mixing state is an important emergent property that affects aerosol radiative forcing and aerosol–cloud interactions, but it has not been easy to constrain this property globally. We present a framework for evaluating the error in aerosol mixing state induced by aerosol representation assumptions, which is one of the important contributors to structural uncertainty in aerosol models. Our study provides insights into potential improvements to model process representation for aerosols.
Fan Mei, Steven Spielman, Susanne Hering, Jian Wang, Mikhail S. Pekour, Gregory Lewis, Beat Schmid, Jason Tomlinson, and Maynard Havlicek
Atmos. Meas. Tech., 14, 7329–7340, https://doi.org/10.5194/amt-14-7329-2021, https://doi.org/10.5194/amt-14-7329-2021, 2021
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This study focuses on understanding a versatile water-based condensation particle counter (vWCPC 3789) performance under various ambient pressure conditions (500–1000 hPa). A vWCPC has the advantage of avoiding health and safety concerns. However, its performance characterization under low pressure is rare but crucial for ensuring successful airborne deployment. This paper provides advanced knowledge of operating a vWCPC 3789 to capture the spatial variations of atmospheric aerosols.
Fan Mei, Jian Wang, Shan Zhou, Qi Zhang, Sonya Collier, and Jianzhong Xu
Atmos. Chem. Phys., 21, 13019–13029, https://doi.org/10.5194/acp-21-13019-2021, https://doi.org/10.5194/acp-21-13019-2021, 2021
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This work focuses on understanding aerosol's ability to act as cloud condensation nuclei (CCN) and its variations with organic oxidation level and volatility using measurements at a rural site. Aerosol properties were examined from four air mass sources. The results help improve the accurate representation of aerosol from different ambient aerosol emissions, transformation pathways, and atmospheric processes in a climate model.
Jiaoshi Zhang, Steven Spielman, Yang Wang, Guangjie Zheng, Xianda Gong, Susanne Hering, and Jian Wang
Atmos. Meas. Tech., 14, 5625–5635, https://doi.org/10.5194/amt-14-5625-2021, https://doi.org/10.5194/amt-14-5625-2021, 2021
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In this study, we present a newly developed instrument, the humidity-controlled fast integrated mobility spectrometer (HFIMS), for fast measurements of aerosol hygroscopic growth. The HFIMS can measure the distributions of particle hygroscopic growth factors at six diameters from 35 to 265 nm under five RH levels from 20 to 85 % within 25 min. The HFIMS significantly advances our capability of characterizing the hygroscopic growth of atmospheric aerosols over a wide range of relative humidities.
Cited articles
Adachi, K., Tobo, Y., Koike, M., Freitas, G., Zieger, P., and Krejci, R.: Composition and mixing state of Arctic aerosol and cloud residual particles from long-term single-particle observations at Zeppelin Observatory, Svalbard, Atmos. Chem. Phys., 22, 14421–14439, https://doi.org/10.5194/acp-22-14421-2022, 2022. a
Adam, M., Putaud, J. P., Martins dos Santos, S., Dell'Acqua, A., and Gruening, C.: Aerosol hygroscopicity at a regional background site (Ispra) in Northern Italy, Atmos. Chem. Phys., 12, 5703–5717, https://doi.org/10.5194/acp-12-5703-2012, 2012. a
Baustian, K. J., Cziczo, D. J., Wise, M. E., Pratt, K. A., Kulkarni, G., Hallar, A. G., and Tolbert, M. A.: Importance of Aerosol Composition, Mixing State, and Morphology for Heterogeneous Ice Nucleation: A Combined Field and Laboratory Approach, J. Geophys. Res.-Atmos., 117, https://doi.org/10.1029/2011JD016784, 2012. a
Bertram, T. H., Cochran, R. E., Grassian, V. H., and Stone, E. A.: Sea Spray Aerosol Chemical Composition: Elemental and Molecular Mimics for Laboratory Studies of Heterogeneous and Multiphase Reactions, Chem. Soc. Rev., 47, 2374–2400, https://doi.org/10.1039/C7CS00008A, 2018. a
Bian, H., Froyd, K., Murphy, D. M., Dibb, J., Darmenov, A., Chin, M., Colarco, P. R., da Silva, A., Kucsera, T. L., Schill, G., Yu, H., Bui, P., Dollner, M., Weinzierl, B., and Smirnov, A.: Observationally constrained analysis of sea salt aerosol in the marine atmosphere, Atmos. Chem. Phys., 19, 10773–10785, https://doi.org/10.5194/acp-19-10773-2019, 2019. a
Bondy, A. L., Bonanno, D., Moffet, R. C., Wang, B., Laskin, A., and Ault, A. P.: The diverse chemical mixing state of aerosol particles in the southeastern United States, Atmos. Chem. Phys., 18, 12595–12612, https://doi.org/10.5194/acp-18-12595-2018, 2018. a
Brechtel, F. J. and Kreidenweis, S. M.: Predicting Particle Critical Supersaturation from Hygroscopic Growth Measurements in the Humidified TDMA. Part I: Theory and Sensitivity Studies, J. Atmos. Sci., 57, 1854–1871, https://doi.org/10.1175/1520-0469(2000)057<1854:PPCSFH>2.0.CO;2, 2000. a
Cheng, Z., Morgenstern, M., Henning, S., Zhang, B., Roberts, G. C., Fraund, M., Marcus, M. A., Lata, N. N., Fialho, P., Mazzoleni, L., Wehner, B., Mazzoleni, C., and China, S.: Cloud Condensation Nuclei Activity of Internally Mixed Particle Populations at a Remote Marine Free Troposphere Site in the North Atlantic Ocean, Sci. Total Environ., 904, 166865, https://doi.org/10.1016/j.scitotenv.2023.166865, 2023. a
Collins, D. R., Cocker, D. R., Flagan, R. C., and Seinfeld, J. H.: The Scanning DMA Transfer Function, Aerosol Sci. Tech., 38, 833–850, https://doi.org/10.1080/027868290503082, 2004. a
Cubison, M. J., Ervens, B., Feingold, G., Docherty, K. S., Ulbrich, I. M., Shields, L., Prather, K., Hering, S., and Jimenez, J. L.: The influence of chemical composition and mixing state of Los Angeles urban aerosol on CCN number and cloud properties, Atmos. Chem. Phys., 8, 5649–5667, https://doi.org/10.5194/acp-8-5649-2008, 2008. a
Deshmukh, S., Poulain, L., Wehner, B., Henning, S., Petit, J.-E., Fombelle, P., Favez, O., Herrmann, H., and Pöhlker, M.: External particle mixing influences hygroscopicity in a sub-urban area, Atmos. Chem. Phys., 25, 741–758, https://doi.org/10.5194/acp-25-741-2025, 2025. a
Drame, M. S., Ceamanos, X., Roujean, J. L., Boone, A., Lafore, J. P., Carrer, D., and Geoffroy, O.: On the Importance of Aerosol Composition for Estimating Incoming Solar Radiation: Focus on the Western African Stations of Dakar and Niamey during the Dry Season, Atmosphere, 6, 1608–1632, https://doi.org/10.3390/atmos6111608, 2015. a
Ebert, M., Weinbruch, S., Hoffmann, P., and Ortner, H. M.: The Chemical Composition and Complex Refractive Index of Rural and Urban Influenced Aerosols Determined by Individual Particle Analysis, Atmos. Environ., 38, 6531–6545, https://doi.org/10.1016/j.atmosenv.2004.08.048, 2004. a
Farley, R. N., Lee, J. E., Rivellini, L.-H., Lee, A. K. Y., Dal Porto, R., Cappa, C. D., Gorkowski, K., Shawon, A. S. M., Benedict, K. B., Aiken, A. C., Dubey, M. K., and Zhang, Q.: Chemical properties and single-particle mixing state of soot aerosol in Houston during the TRACER campaign, Atmos. Chem. Phys., 24, 3953–3971, https://doi.org/10.5194/acp-24-3953-2024, 2024. a
Fraund, M., Pham, D. Q., Bonanno, D., Harder, T. H., Wang, B., Brito, J., De Sá, S. S., Carbone, S., China, S., Artaxo, P., Martin, S. T., Pöhlker, C., Andreae, M. O., Laskin, A., Gilles, M. K., and Moffet, R. C.: Elemental Mixing State of Aerosol Particles Collected in Central Amazonia during GoAmazon2014/15, Atmosphere, 8, 173, https://doi.org/10.3390/atmos8090173, 2017. a
Gasparik, J. T., Ye, Q., Curtis, J. H., Presto, A. A., Donahue, N. M., Sullivan, R. C., West, M., and Riemer, N.: Quantifying Errors in the Aerosol Mixing-State Index Based on Limited Particle Sample Size, Aerosol Sci. Tech., 54, 1527–1541, https://doi.org/10.1080/02786826.2020.1804523, 2020. a
George, I. J. and Abbatt, J. P. D.: Heterogeneous Oxidation of Atmospheric Aerosol Particles by Gas-Phase Radicals, Nat. Chem., 2, 713–722, https://doi.org/10.1038/nchem.806, 2010. a
Hallberg, A., Ogren, J. A., Noone, K. J., Okada, K., Heintzenberg, J., and Svenningsson, I. B.: The Influence of Aerosol Particle Composition on Cloud Droplet Formation, J. Atmos. Chem., 19, 153–171, https://doi.org/10.1007/BF00696587, 1994. a
Jiang, F., Zheng, Z., Coe, H., Healy, R. M., Poulain, L., Gros, V., Zhang, H., Li, W., Liu, D., West, M., Topping, D., and Riemer, N.: Integrating Simulations and Observations: A Foundation Model for Estimating the Aerosol Mixing State Index, ACS ES&T Air, 2, 877–890, https://doi.org/10.1021/acsestair.4c00329, 2025. a
Kleinman, L. I., Springston, S. R., Daum, P. H., Lee, Y.-N., Nunnermacker, L. J., Senum, G. I., Wang, J., Weinstein-Lloyd, J., Alexander, M. L., Hubbe, J., Ortega, J., Canagaratna, M. R., and Jayne, J.: The time evolution of aerosol composition over the Mexico City plateau, Atmos. Chem. Phys., 8, 1559–1575, https://doi.org/10.5194/acp-8-1559-2008, 2008. a
Laskin, A., Iedema, M. J., and Cowin, J. P.: Quantitative Time-Resolved Monitoring of Nitrate Formation in Sea Salt Particles Using a CCSEM/EDX Single Particle Analysis, Environ. Sci. Technol., 36, 4948–4955, https://doi.org/10.1021/es020551k, 2002. a
Lata, N. N., Zhang, B., Schum, S., Mazzoleni, L., Brimberry, R., Marcus, M. A., Cantrell, W. H., Fialho, P., Mazzoleni, C., and China, S.: Aerosol Composition, Mixing State, and Phase State of Free Tropospheric Particles and Their Role in Ice Cloud Formation, ACS Earth and Space Chemistry, 5, 3499–3510, https://doi.org/10.1021/acsearthspacechem.1c00315, 2021. a
Leck, C. and Svensson, E.: Importance of aerosol composition and mixing state for cloud droplet activation over the Arctic pack ice in summer, Atmos. Chem. Phys., 15, 2545–2568, https://doi.org/10.5194/acp-15-2545-2015, 2015. a
Leck, C., Norman, M., Bigg, E. K., and Hillamo, R.: Chemical Composition and Sources of the High Arctic Aerosol Relevant for Cloud Formation, J. Geophys. Res.-Atmos., 107, AAC 1-1–AAC 1-17, https://doi.org/10.1029/2001JD001463, 2002. a
Li, W., Shao, L., Wang, Z., Shen, R., Yang, S., and Tang, U.: Size, Composition, and Mixing State of Individual Aerosol Particles in a South China Coastal City, J. Environ. Sci., 22, 561–569, https://doi.org/10.1016/S1001-0742(09)60146-7, 2010. a
Li, W., Sun, J., Xu, L., Shi, Z., Riemer, N., Sun, Y., Fu, P., Zhang, J., Lin, Y., Wang, X., Shao, L., Chen, J., Zhang, X., Wang, Z., and Wang, W.: A Conceptual Framework for Mixing Structures in Individual Aerosol Particles, J. Geophys. Res.-Atmos., 121, 13784–13798, https://doi.org/10.1002/2016JD025252, 2016. a
Liu, Y., Yao, Y., Curtis, J. H., West, M., and Riemer, N.: The Impacts of Aerosol Mixing State on Heterogeneous N2O5 Hydrolysis, Aerosol Sci. Tech., 59, 402–423, https://doi.org/10.1080/02786826.2024.2443587, 2025. a
Liu, Y., Wang, J., and Riemer, N.: Data for From κ to χ: Evaluating Hygroscopicity-Based Mixing State Estimates with a Particle-Resolved Model, University of Illinois Urbana-Champaign [data set], https://doi.org/10.13012/B2IDB-8214798_V1, 2026. a
Mochida, M., Nishita-Hara, C., Kitamori, Y., Aggarwal, S. G., Kawamura, K., Miura, K., and Takami, A.: Size-Segregated Measurements of Cloud Condensation Nucleus Activity and Hygroscopic Growth for Aerosols at Cape Hedo, Japan, in Spring 2008, J. Geophys. Res.-Atmos., 115, https://doi.org/10.1029/2009JD013216, 2010. a
Murphy, D. M., Anderson, J. R., Quinn, P. K., McInnes, L. M., Brechtel, F. J., Kreidenweis, S. M., Middlebrook, A. M., Pósfai, M., Thomson, D. S., and Buseck, P. R.: Influence of Sea-Salt on Aerosol Radiative Properties in the Southern Ocean Marine Boundary Layer, Nature, 392, 62–65, https://doi.org/10.1038/32138, 1998. a
Murphy, D. M., Froyd, K. D., Bian, H., Brock, C. A., Dibb, J. E., DiGangi, J. P., Diskin, G., Dollner, M., Kupc, A., Scheuer, E. M., Schill, G. P., Weinzierl, B., Williamson, C. J., and Yu, P.: The distribution of sea-salt aerosol in the global troposphere, Atmos. Chem. Phys., 19, 4093–4104, https://doi.org/10.5194/acp-19-4093-2019, 2019. a
O'Brien, R. E., Wang, B., Laskin, A., Riemer, N., West, M., Zhang, Q., Sun, Y., Yu, X.-Y., Alpert, P., Knopf, D. A., Gilles, M. K., and Moffet, R. C.: Chemical Imaging of Ambient Aerosol Particles: Observational Constraints on Mixing State Parameterization, J. Geophys. Res.-Atmos., 120, 9591–9605, https://doi.org/10.1002/2015JD023480, 2015. a
O'Dowd, C. D. and Smith, M. H.: Physicochemical Properties of Aerosols over the Northeast Atlantic: Evidence for Wind-Speed-Related Submicron Sea-Salt Aerosol Production, J. Geophys. Res.-Atmos., 98, 1137–1149, https://doi.org/10.1029/92JD02302, 1993. a
Patterson, E. M.: Optical Properties of the Crustal Aerosol: Relation to Chemical and Physical Characteristics, J. Geophys. Res.-Oceans, 86, 3236–3246, https://doi.org/10.1029/JC086iC04p03236, 1981. a
Petters, M. D. and Kreidenweis, S. M.: A single parameter representation of hygroscopic growth and cloud condensation nucleus activity, Atmos. Chem. Phys., 7, 1961–1971, https://doi.org/10.5194/acp-7-1961-2007, 2007. a, b
Phillips, B. N., Royalty, T. M., Dawson, K. W., Reed, R., Petters, M. D., and Meskhidze, N.: Hygroscopicity- and Size-Resolved Measurements of Submicron Aerosol on the East Coast of the United States, J. Geophys. Res.-Atmos., 123, 1826–1839, https://doi.org/10.1002/2017JD027702, 2018. a
Rader, D. J. and McMurry, P. H.: Application of the Tandem Differential Mobility Analyzer to Studies of Droplet Growth or Evaporation, J. Aerosol Sci., 17, 771–787, https://doi.org/10.1016/0021-8502(86)90031-5, 1986. a
Ravishankara, A. R., Rudich, Y., and Wuebbles, D. J.: Physical Chemistry of Climate Metrics, Chem. Rev., 115, 3682–3703, https://doi.org/10.1021/acs.chemrev.5b00010, 2015. a
Riemer, N. and West, M.: Quantifying aerosol mixing state with entropy and diversity measures, Atmos. Chem. Phys., 13, 11423–11439, https://doi.org/10.5194/acp-13-11423-2013, 2013. a, b, c
Riemer, N., Vogel, H., and Vogel, B.: Soot aging time scales in polluted regions during day and night, Atmos. Chem. Phys., 4, 1885–1893, https://doi.org/10.5194/acp-4-1885-2004, 2004. a
Riemer, N., West, M., Zaveri, R. A., and Easter, R. C.: Simulating the Evolution of Soot Mixing State with a Particle-resolved Aerosol Model, J. Geophys. Res., 114, D09202, https://doi.org/10.1029/2008JD011073, 2009. a
Rivera-Adorno, F. A., Tomlin, J. M., Lata, N. N., Azzarello, L., Robinson, M. A., Washenfelder, R. A., Franchin, A., Middlebrook, A. M., China, S., Brown, S. S., Young, C. J., Fraund, M., Moffet, R. C., and Laskin, A.: Chemical Imaging of Atmospheric Biomass Burning Particles from North American Wildfires, ACS ES&T Air, 2, 508–521, https://doi.org/10.1021/acsestair.4c00242, 2025. a
Schell, B., Ackermann, I. J., Hass, H., Binkowski, F. S., and Ebel, A.: Modeling the Formation of Secondary Organic Aerosol within a Comprehensive Air Quality Model System, J. Geophys. Res.-Atmos., 106, 28275–28293, https://doi.org/10.1029/2001JD000384, 2001. a
Sharpe, S., Li, Y., Benjemia, S., Rivera-Adorno, F., Olayemi, T., Ese, J., Shen, X., Fraund, M., Moffet, R., Nahar Lata, N., Cheng, Z., China, S., Homeyer, C. R., Dykema, J., Marcus, M. A., Wang, J., Cziczo, D., Keutsch, F., and Laskin, A.: Chemical Imaging of Individual Stratospheric Particles Sampled over North America, Environmental Science: Atmospheres, 6, 47–60, https://doi.org/10.1039/D5EA00127G, 2026. a
Shou, C., Riemer, N., Onasch, T. B., Sedlacek, A. J., Lambe, A. T., Lewis, E. R., Davidovits, P., and West, M.: Mixing State Evolution of Agglomerating Particles in an Aerosol Chamber: Comparison of Measurements and Particle-Resolved Simulations, Aerosol Sci. Tech., 53, 1229–1243, https://doi.org/10.1080/02786826.2019.1661959, 2019. a
Singh, N., Banerjee, T., Deboudt, K., Chakraborty, A., Khan, M. F., and Latif, M. T.: Sources, Composition, and Mixing State of Submicron Particulates over the Central Indo-Gangetic Plain, ACS Earth and Space Chemistry, 5, 2052–2065, https://doi.org/10.1021/acsearthspacechem.1c00130, 2021. a
Sun, Y., Wang, Z., Fu, P., Jiang, Q., Yang, T., Li, J., and Ge, X.: The Impact of Relative Humidity on Aerosol Composition and Evolution Processes during Wintertime in Beijing, China, Atmos. Environ., 77, 927–934, https://doi.org/10.1016/j.atmosenv.2013.06.019, 2013. a
Tao, J., Kuang, Y., Luo, B., Liu, L., Xu, H., Ma, N., Liu, P., Xue, B., Zhai, M., Xu, W., Xu, W., and Sun, Y.: Kinetic Limitations Affect Cloud Condensation Nuclei Activity Measurements Under Low Supersaturation, Geophys. Res. Lett., 50, e2022GL101603, https://doi.org/10.1029/2022GL101603, 2023. a
Tomlin, M. J., Weis, J., Veghte, D. P., China, S., Fraund, M., He, Q., Reicher, N., Li, C., Jankowski, A. K., Rivera-Adorno, F. A., Morales, A. C., Rudich, Y., Moffet, R. C., Gilles, M. K., and Laskin, A.: Chemical Composition and Morphological Analysis of Atmospheric Particles from an Intensive Bonfire Burning Festival, Environmental Science: Atmospheres, 2, 616–633, https://doi.org/10.1039/D2EA00037G, 2022. a
Uin, J., Cromwell, E., Hayes, C., and Salwen, C.: Atmospheric Radiation Measurement (ARM) User Facility. Humidified Tandem Differential Mobility Analyzer (AOSHTDMA), updated daily. 2025-03-13 to 2025-11-30, ARM Mobile Facility (CRG), Baltimore, MD: Supplemental Facility 2 in rural setting (S2); 2023-02-15 to 2024-02-13, ARM Mobile Facility (EPC), La Jolla, CA: AMF1 (main site for EPCAPE on Scripps Pier) (M1); 2021-10-01 to 2022-09-30, ARM Mobile Facility (HOU), Houston, TX: AMF1 (main site for TRACER) (M1); 2021-04-27 to 2023-10-24, Southern Great Plains (SGP), Lamont, OK (Extended and Co-located with C1) (E13), Atmospheric Radiation Measurement (ARM) user facility [data set], https://doi.org/10.5439/1776643, 2012. a
Wang, J., Cubison, M. J., Aiken, A. C., Jimenez, J. L., and Collins, D. R.: The importance of aerosol mixing state and size-resolved composition on CCN concentration and the variation of the importance with atmospheric aging of aerosols, Atmos. Chem. Phys., 10, 7267–7283, https://doi.org/10.5194/acp-10-7267-2010, 2010. a
Wang, Y. and Chen, Y.: Significant Climate Impact of Highly Hygroscopic Atmospheric Aerosols in Delhi, India, Geophys. Res. Lett., 46, 5535–5545, https://doi.org/10.1029/2019GL082339, 2019. a
West, M., Riemer, N., Curtis, J., Michelotti, M., Tian, J., and Arabas, S.: compdyn/partmc: Version 2.6.1, Zenodo [code],https://doi.org/10.5281/zenodo.6144610, 2022. a
Wu, J., Liu, J., Gunsch, M. J., Mirrielees, J. A., Moffett, C. E., Zhang, Q., Sheesley, R. J., and Pratt, K. A.: Quantifying the Diversity of an Atmospheric Aerosol Population in an Arctic Oil Field on a Single-Particle Level, J. Geophys. Res.-Atmos., 129, e2024JD041001, https://doi.org/10.1029/2024JD041001, 2024. a
Xue, J., Zhang, T., Park, K., Yan, J., Yoon, Y. J., Park, J., and Wang, B.: Diverse sources and aging change the mixing state and ice nucleation properties of aerosol particles over the western Pacific and Southern Ocean, Atmos. Chem. Phys., 24, 7731–7754, https://doi.org/10.5194/acp-24-7731-2024, 2024. a, b
Yao, X., Fang, M., and Chan, C. K.: The Size Dependence of Chloride Depletion in Fine and Coarse Sea-Salt Particles, Atmos. Environ., 37, 743–751, https://doi.org/10.1016/S1352-2310(02)00955-X, 2003. a
Yao, Y., Curtis, J. H., Ching, J., Zheng, Z., and Riemer, N.: Quantifying the effects of mixing state on aerosol optical properties, Atmos. Chem. Phys., 22, 9265–9282, https://doi.org/10.5194/acp-22-9265-2022, 2022. a
Yeung, M. C., Lee, B. P., Li, Y. J., and Chan, C. K.: Simultaneous HTDMA and HR-ToF-AMS measurements at the HKUST Supersite in Hong Kong in 2011, J. Geophys. Res.-Atmos., 119, 9864–9883, https://doi.org/10.1002/2013JD021146, 2014. a
Yoo, H., Wu, L., Geng, H., and Ro, C.-U.: Physicochemical and temporal characteristics of individual atmospheric aerosol particles in urban Seoul during KORUS-AQ campaign: insights from single-particle analysis, Atmos. Chem. Phys., 24, 853–867, https://doi.org/10.5194/acp-24-853-2024, 2024. a
Yu, C., Liu, D., Broda, K., Joshi, R., Olfert, J., Sun, Y., Fu, P., Coe, H., and Allan, J. D.: Characterising mass-resolved mixing state of black carbon in Beijing using a morphology-independent measurement method, Atmos. Chem. Phys., 20, 3645–3661, https://doi.org/10.5194/acp-20-3645-2020, 2020. a
Zaveri, R. A. and Peters, L. K.: A New Lumped Structure Photochemical Mechanism for Large-Scale Applications, J. Geophys. Res.-Atmos., 104, 30387–30415, https://doi.org/10.1029/1999JD900876, 1999. a
Zaveri, R. A., Easter, R. C., and Peters, L. K.: A Computationally Efficient Multicomponent Equilibrium Solver for Aerosols (MESA), J. Geophys. Res.-Atmos., 110, https://doi.org/10.1029/2004JD005618, 2005a. a
Zaveri, R. A., Easter, R. C., and Wexler, A. S.: A New Method for Multicomponent Activity Coefficients of Electrolytes in Aqueous Atmospheric Aerosols, J. Geophys. Res.-Atmos., 110, https://doi.org/10.1029/2004JD004681, 2005b. a
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, https://doi.org/10.1029/2007JD008782, 2008. a, b
Zeng, L., Tan, T., Zhao, G., Du, Z., Hu, S., Shang, D., and Hu, M.: Overestimation of Black Carbon Light Absorption Due to Mixing State Heterogeneity, npj Clim. Atmos. Sci., 7, 2, https://doi.org/10.1038/s41612-023-00535-8, 2024. a
Zhang, F., Wang, Y., Peng, J., Ren, J., Collins, D., Zhang, R., Sun, Y., Yang, X., and Li, Z.: Uncertainty in Predicting CCN Activity of Aged and Primary Aerosols, J. Geophys. Res.-Atmos., 122, 11723–11736, https://doi.org/10.1002/2017JD027058, 2017. a
Zhang, Z., Wang, J., Wang, J., Riemer, N., Liu, C., Jin, Y., Tian, Z., Cai, J., Cheng, Y., Chen, G., Wang, B., Wang, S., and Ding, A.: Steady-state mixing state of black carbon aerosols from a particle-resolved model, Atmos. Chem. Phys., 25, 1869–1881, https://doi.org/10.5194/acp-25-1869-2025, 2025. a
Zhao, G., Tan, T., Zhu, Y., Hu, M., and Zhao, C.: Method to quantify black carbon aerosol light absorption enhancement with a mixing state index, Atmos. Chem. Phys., 21, 18055–18063, https://doi.org/10.5194/acp-21-18055-2021, 2021. a, b
Zheng, Z., West, M., Zhao, L., Ma, P.-L., Liu, X., and Riemer, N.: Quantifying the structural uncertainty of the aerosol mixing state representation in a modal model, Atmos. Chem. Phys., 21, 17727–17741, https://doi.org/10.5194/acp-21-17727-2021, 2021. a, b
Zhu, S., Sartelet, K. N., Healy, R. M., and Wenger, J. C.: Simulation of Particle Diversity and Mixing State over Greater Paris: A Model–Measurement Inter-Comparison, Faraday Discuss., 189, 547–566, https://doi.org/10.1039/C5FD00175G, 2016. a
Short summary
Airborne particles affect clouds, climate, and air quality, but it is difficult to determine how their chemical components are mixed within individual particles. We tested a method that estimates this mixing from water-uptake measurements using detailed computer simulations. The method works well in many cases, but can overestimate particle mixing when moderately water-attracting material exists in separate particle types. We then applied this uncertainty framework to long-term observations.
Airborne particles affect clouds, climate, and air quality, but it is difficult to determine how...
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