Articles | Volume 25, issue 9
https://doi.org/10.5194/acp-25-5075-2025
© Author(s) 2025. 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-25-5075-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The evolution of aerosol mixing state derived from a field campaign in Beijing: implications for particle aging timescales in urban atmospheres
Jieyao Liu
School of Geographical Sciences, Hebei Normal University, Shijiazhuang, China
School of Ecology and Environment, College of Artificial Intelligence, Harbin Institute of Technology (Shenzhen), Shenzhen, China
Jingye Ren
Xi'an Institute for Innovative Earth Environment Research, Xi'an, China
Lu Chen
Faculty of Geographical Science, Beijing Normal University, Beijing, China
Anran Zhang
School of Ecology and Environment, College of Artificial Intelligence, Harbin Institute of Technology (Shenzhen), Shenzhen, China
Zhe Wang
School of Ecology and Environment, College of Artificial Intelligence, Harbin Institute of Technology (Shenzhen), Shenzhen, China
Songjian Zou
School of Ecology and Environment, College of Artificial Intelligence, Harbin Institute of Technology (Shenzhen), Shenzhen, China
Honghao Xu
School of Ecology and Environment, College of Artificial Intelligence, Harbin Institute of Technology (Shenzhen), Shenzhen, China
Xingyan Yue
School of Geographical Sciences, Hebei Normal University, Shijiazhuang, China
Related authors
No articles found.
Rou Zhang, Xiaoxiao Huang, Pu Wang, Guiquan Liu, Mengyu Liu, Songjian Zou, Lu Chen, and Fang Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-2472, https://doi.org/10.5194/egusphere-2025-2472, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary
Short summary
This study explores how fine aerosols impact light rain patterns in China, with significant environmental and climatic implications. Data from 2000–2022 show light rain decreased by 1 day/year (2000–2013) but increased by 1.9 days/year post-2013, coinciding with China’s air pollution controls that reduced PM2.5 levels after 2013. Machine learning identified aerosol loading changes as the main driver (explaining 59–63 % of trends), with minor impact from meteorological factors.
Jingye Ren, Wei Xu, Ru-Jin Huang, Fang Zhang, Ying Wang, Lu Chen, Jurgita Ovadnevaite, Darius Ceburnis, and Colin O’Dowd
EGUsphere, https://doi.org/10.5194/egusphere-2025-3284, https://doi.org/10.5194/egusphere-2025-3284, 2025
Short summary
Short summary
Impact of mixing state on cloud condensation nuclei (CCN) activity was incorporated in very limited modeling with typically simplified assumption. This study derived a mixing state index from hygroscopicity and systematically investigated its impacts on CCN activity in inland and coastal air. An entropy-based parameterization proposed here offers a novel approach to reduce model complexity in representing aerosol CCN activation, enabling more accurate simulations of aerosol CCN capacity.
Jingye Ren, Songjian Zou, Honghao Xu, Guiquan Liu, Zhe Wang, Anran Zhang, Chuanfeng Zhao, Min Hu, Dongjie Shang, Lizi Tang, Ru-Jin Huang, Yele Sun, and Fang Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2025-1483, https://doi.org/10.5194/egusphere-2025-1483, 2025
Short summary
Short summary
In this study, a new framework of cloud condensation nuclei (CCN) prediction in polluted region has been developed and it achieves well prediction of hourly-to-yearly scale across North China Plain. The study reveals a significant long-term decreasing trend of CCN concentration at typical supersaturations due to a rapid reduction in aerosol concentrations from 2014 to 2018. This improvement of our new model would be helpful to aerosols climate effect assessment in models.
Jingye Ren, Lu Chen, Jieyao Liu, and Fang Zhang
Atmos. Chem. Phys., 23, 4327–4342, https://doi.org/10.5194/acp-23-4327-2023, https://doi.org/10.5194/acp-23-4327-2023, 2023
Short summary
Short summary
The density of black carbon (BC) is linked to its morphology and mixing state and could cause uncertainty in evaluating cloud condensation nuclei (CCN) activity. A method for retrieving the mixing state and density of BC in the urban atmosphere is developed. The mean retrieval density of internally mixed BC was lower, assuming void-free spherical structures. Our study suggests the importance of accounting for variable BC density in models when assessing its climate effect in urban atmosphere.
Lu Chen, Fang Zhang, Dongmei Zhang, Xinming Wang, Wei Song, Jieyao Liu, Jingye Ren, Sihui Jiang, Xue Li, and Zhanqing Li
Atmos. Chem. Phys., 22, 6773–6786, https://doi.org/10.5194/acp-22-6773-2022, https://doi.org/10.5194/acp-22-6773-2022, 2022
Short summary
Short summary
Aerosol hygroscopicity is critical when evaluating its effect on visibility and climate. Here, the size-resolved particle hygroscopicity at five sites in China is characterized using field measurements. We show the distinct behavior of hygroscopic particles during pollution evolution among the five sites. Moreover, different hygroscopic behavior during NPF events were also observed. The dataset is helpful for understanding the spatial variability in particle composition and formation mechanisms.
Lu Chen, Fang Zhang, Don Collins, Jingye Ren, Jieyao Liu, Sihui Jiang, and Zhanqing Li
Atmos. Chem. Phys., 22, 2293–2307, https://doi.org/10.5194/acp-22-2293-2022, https://doi.org/10.5194/acp-22-2293-2022, 2022
Short summary
Short summary
Understanding the volatility and mixing state of atmospheric aerosols is important for elucidating their formation. Here, the size-resolved volatility of fine particles is characterized using field measurements. On average, the particles are more volatile in the summer. The retrieved mixing state shows that black carbon (BC)-containing particles dominate and contribute 67–77 % toward the total number concentration in the winter, while the non-BC particles accounted for 52–69 % in the summer.
Sihui Jiang, Fang Zhang, Jingye Ren, Lu Chen, Xing Yan, Jieyao Liu, Yele Sun, and Zhanqing Li
Atmos. Chem. Phys., 21, 14293–14308, https://doi.org/10.5194/acp-21-14293-2021, https://doi.org/10.5194/acp-21-14293-2021, 2021
Short summary
Short summary
New particle formation (NPF) can be a large source of CCN and affect weather and climate. Here we show that the NPF contributes largely to cloud droplet number concentration (Nd) but is suppressed at high particle number concentrations in Beijing due to water vapor competition. We also reveal a considerable impact of primary sources on the evaluation in the urban atmosphere. Our study has great significance for assessing NPF-associated effects on climate in polluted regions.
Sarah E. Benish, Hao He, Xinrong Ren, Sandra J. Roberts, Ross J. Salawitch, Zhanqing Li, Fei Wang, Yuying Wang, Fang Zhang, Min Shao, Sihua Lu, and Russell R. Dickerson
Atmos. Chem. Phys., 20, 14523–14545, https://doi.org/10.5194/acp-20-14523-2020, https://doi.org/10.5194/acp-20-14523-2020, 2020
Short summary
Short summary
Airborne observations of ozone and related pollutants show smog was pervasive in spring 2016 over Hebei Province, China. We find high amounts of ozone precursors throughout and even above the PBL, continuing to generate ozone at high rates to be potentially transported downwind. Concentrations even in the rural areas of this highly industrialized province promote widespread ozone production, and we show that to improve air quality over Hebei both NOx and VOCs should be targeted.
Cited articles
Akagi, S. K., Craven, J. S., Taylor, J. W., McMeeking, G. R., Yokelson, R. J., Burling, I. R., Urbanski, S. P., Wold, C. E., Seinfeld, J. H., Coe, H., Alvarado, M. J., and Weise, D. R.: Evolution of trace gases and particles emitted by a chaparral fire in California, Atmos. Chem. Phys., 12, 1397–1421, https://doi.org/10.5194/acp-12-1397-2012, 2012.
Chen, L., Zhang, F., Yan, P., Wang, X., Sun, L., Li, Y., Zhang, X., Sun, Y., and Li, Z.: The large proportion of black carbon (BC)-containing aerosols in the urban atmosphere, Environ. Pollut., 263, 114507, https://doi.org/10.1016/j.envpol.2020.114507, 2020.
Chen, L., Zhang, F., Zhang, D., Wang, X., Song, W., Liu, J., Ren, J., Jiang, S., Li, X., and Li, Z.: Measurement report: Hygroscopic growth of ambient fine particles measured at five sites in China, Atmos. Chem. Phys., 22, 6773–6786, https://doi.org/10.5194/acp-22-6773-2022, 2022.
Chen, X., Wang, Z., Yu, F., Pan, X., Li, J., Ge, B., Wang, Z., Hu, M., Yang, W., and Chen, H.: Estimation of atmospheric aging time of black carbon particles in the polluted atmosphere over central-eastern China using microphysical process analysis in regional chemical transport model, Atmos. Environ., 163, 44–56, https://doi.org/10.1016/j.atmosenv.2017.05.016, 2017.
Chung, S. and Seinfeld, J.: Global distribution and climate forcing of carbonaceous aerosols, J. Geophys. Res.-Atmos., 107, 4407, https://doi.org/10.1029/2001JD001397, 2002.
Colarco, P., Silva, A., Chin, M., and Diehl, T.: Online simulations of global aerosol distributions in the NASA GEOS-4 model and comparisons to satellite and ground-based aerosol optical depth, J. Geophys. Res.-Atmos., 115, D14207, https://doi.org/10.1029/2009JD012820, 2010.
Cooke, W. F., Ramaswamy, V., and Kasibhatla, P.: A general circulation model study of the global carbonaceous aerosol distribution, J. Geophys. Res.-Atmos., 107, ACH 2-1–ACH 2-32, https://doi.org/10.1029/2001JD001274, 2002.
Dai, Q., Bi, X., Song, W., Li, T., Liu, B., Ding, J., Xua, J., Song, C., Yang, N., Schulze, B. C., Zhang, Y., Feng, Y., and Hopke, P. K.: Residential coal combustion as a source of primary sulfate in Xi'an, China, Atmos. Environ., 196, 66–76, https://doi.org/10.1016/j.atmosenv.2018.10.002, 2019.
Enroth, J., Mikkilä, J., Németh, Z., Kulmala, M., and Salma, I.: Wintertime hygroscopicity and volatility of ambient urban aerosol particles, Atmos. Chem. Phys., 18, 4533–4548, https://doi.org/10.5194/acp-18-4533-2018, 2018.
Fan, X., Liu, J., Zhang, F., Chen, L., Collins, D., Xu, W., Jin, X., Ren, J., Wang, Y., Wu, H., Li, S., Sun, Y., and Li, Z.: HTDMA and HR-ToF-AMS Measured in situ Dataset in Winter of 2016 and Summer of 2017 at the Beijing Observation Station[DB/OL], Digital Journal of Global Change Data Repository [data set], https://doi.org/10.3974/geodb.2019.06.11.V1, 2019.
Fan, X., Liu, J., Zhang, F., Chen, L., Collins, D., Xu, W., Jin, X., Ren, J., Wang, Y., Wu, H., Li, S., Sun, Y., and Li, Z.: Contrasting size-resolved hygroscopicity of fine particles derived by HTDMA and HR-ToF-AMS measurements between summer and winter in Beijing: the impacts of aerosol aging and local emissions, Atmos. Chem. Phys., 20, 915–929, https://doi.org/10.5194/acp-20-915-2020, 2020.
Ge, S., Su, J., Zhao, P., Li, J., Liu, S., Qiu, Y., Pu, W., and Ma, Z.: Characteristics of PM2.5 hygroscopicity and the influences of water-soluble ions during haze events in Beijing, Atmos. Environ., 322, 120382, https://doi.org/10.1016/j.atmosenv.2024.120382, 2024.
Ghosh, S., Riemer, N., Giuliani, G., Giorgi, F., Ganguly, D., and Dey, S.: Sensitivity of carbonaceous aerosol properties to the implementation of a dynamic aging parameterization in the regional climate model RegCM, J. Geophys. Res.-Atmos., 126, e2020JD033613, https://doi.org/10.1029/2020JD033613, 2021.
Gysel, M., McFiggans, G. B., and Coe, H.: Inversion of tandem differential mobility analyser (TDMA) measurements, J. Aerosol Sci., 40, 134–151, https://doi.org/10.1016/j.jaerosci.2008.07.013, 2009.
Hersey, S. P., Craven, J. S., Metcalf, A. R., Lin, J., Lathem, T., Suski, K., Cahill, J., Duong, H., Sorooshian, A., Jonsson, H., Shiraiwa, M., Zuend, A., Nenes, A., Prather, K., Flagan, R., and Seinfeld, J.: Composition and hygroscopicity of the los angeles aerosol: CalNex, J. Geophys. Res.-Atmos., 118, 3016–3036, https://doi.org/10.1002/jgrd.50307, 2013.
Hong, J., Xu, H., Tan, H., Yin, C., Hao, L., Li, F., Cai, M., Deng, X., Wang, N., Su, H., Cheng, Y., Wang, L., Petäjä, T., and Kerminen, V.-M.: Mixing state and particle hygroscopicity of organic-dominated aerosols over the Pearl River Delta region in China, Atmos. Chem. Phys., 18, 14079–14094, https://doi.org/10.5194/acp-18-14079-2018, 2018.
Hua, Y., Wang, S., Jiang, J., Zhou, W., Xu, Q., Li, X., Liu, B., Zhang, D., and Zheng, M.: Characteristics and sources of aerosol pollution at a polluted rural site southwest in Beijing, China, Sci. Total Environ., 626, 519–527, https://doi.org/10.1016/j.scitotenv.2018.01.047, 2018.
Huang, Y., Wu, S., Dubey, M. K., and French, N. H. F.: Impact of aging mechanism on model simulated carbonaceous aerosols, Atmos. Chem. Phys., 13, 6329–6343, https://doi.org/10.5194/acp-13-6329-2013, 2013.
Jacobson, M.: Strong radiative heating due to the mixing state of black carbon in atmospheric aerosols, Nature, 409, 695–697, https://doi.org/10.1038/35055518, 2001.
Koch, D. and Hansen, J.: Distant origins of Arctic black carbon: A Goddard Institute for Space Studies Model experiment, J. Geophys. Res.-Atmos., 110, D04204, https://doi.org/10.1029/2004JD005296, 2005.
Krasowsky, T., McMeeking, G., Wang, D., Sioutas, C., and Ban-Weiss, G.: Measurements of the impact of atmospheric aging on physical and optical properties of ambient black carbon particles in Los Angeles, Atmos. Environ., 142, 496–504, https://doi.org/10.1016/j.atmosenv.2016.08.010, 2016.
Liu, D., Joshi, R., Wang, J., Yu, C., Allan, J. D., Coe, H., Flynn, M. J., Xie, C., Lee, J., Squires, F., Kotthaus, S., Grimmond, S., Ge, X., Sun, Y., and Fu, P.: Contrasting physical properties of black carbon in urban Beijing between winter and summer, Atmos. Chem. Phys., 19, 6749–6769, https://doi.org/10.5194/acp-19-6749-2019, 2019.
Liu, J., Fan, S., Horowitz, L., and Levy II, H.: Evaluation of factors controlling long-range transport of black carbon to the Arctic, J. Geophys. Res.-Atmos., 116, D04307, https://doi.org/10.1029/2010JD015145, 2011.
Liu, J., Zhang, F., Xu, W., Sun, Y., Chen, L., Li, S., Ren, J., Hu, B., Wu, H., and Zhang, R.: Hygroscopicity of organic aerosols linked to formation mechanisms, Geophys. Res. Lett., 48, e2020GL091683, https://doi.org/10.1029/2020GL091683, 2021a.
Liu, J., Zhang, F., Xu, W., Chen, L., Ren, J., Jiang, S., Sun, Y., and Li, Z.: A large impact of cooking organic aerosol (COA) on particle hygroscopicity and CCN activity in urban atmosphere, J. Geophys. Res.-Atmos., 126, e2020JD033628, https://doi.org/10.1029/2020JD033628, 2021b.
Li, S., Zhang, F., Jin, X., Sun, Y., Wu, H., Xie, C., Chen, L., Liu, J., Wu, T., Jiang, S., Maureen, C., and Li, Z.: Characterizing the ratio of nitrate to sulfate in ambient fine particles of urban Beijing during 2018–2019, Atmos. Environ., 237, 117662, https://doi.org/10.1016/j.atmosenv.2020.117662, 2020.
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.
Moffet, R. and Prather, K.: In-situ measurements of the mixing state and optical properties of soot with implications for radiative forcing estimates, P. Natl. Acad. Sci. USA, 106, 11872–11877, https://doi.org/10.1073/pnas.0900040106, 2009.
Müller, A., Miyazaki, Y., Aggarwal, S., Kitamori, Y., Boreddy, S., and Kawamura, K.: Effects of chemical composition and mixing state on size-resolved hygroscopicity and cloud condensation nuclei activity of submicron aerosols at a suburban site in northern Japan in summer, J. Geophys. Res.-Atmos., 122, 9301–9318, https://doi.org/10.1002/2017JD027286, 2017.
Park, S. H., Kruis, F. E., Lee, K. W., and Fissan, H.: Evolution of Particle Size Distributions due to Turbulent and Brownian Coagulation, Aerosol Sci. Technol., 36, 419–432, https://doi.org/10.1080/027868202753571241, 2002.
Peng, J. F., Hu, M., Guo, S., Du, Z. F., Zheng, J., Shang, D. J., Zamora, M., Zeng, L. M., Shao, M., Wu, Y. S., Zheng, J., Wang, Y., Glen, C., Collins, D., Molina, M., and Zhang, R. Y.: Markedly enhanced absorption, and direct radiative forcing of black carbon under polluted urban environments, P. Natl. Acad. Sci. USA, 113, 4266–4271, https://doi.org/10.1073/pnas.1602310113, 2016.
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.
Pierce, J. R., Chen, K., and Adams, P. J.: Contribution of primary carbonaceous aerosol to cloud condensation nuclei: processes and uncertainties evaluated with a global aerosol microphysics model, Atmos. Chem. Phys., 7, 5447–5466, https://doi.org/10.5194/acp-7-5447-2007, 2007.
Pöschl, U., Letzel, T., Schauer, C., and Niessner, R.: Interaction of ozone and water vapor with spark discharge soot aerosol particles coated with benzo[a]pyrene : O3 and H2O adsorption, benzo[a]pyrene degradation, and atmospheric implications, J. Phys. Chem. A, 16, 105, https://doi.org/10.1021/jp004137n, 2001.
Ren, J., Zhang, F., Wang, Y., Collins, D., Fan, X., Jin, X., Xu, W., Sun, Y., Cribb, M., and Li, Z.: Using different assumptions of aerosol mixing state and chemical composition to predict CCN concentrations based on field measurements in urban Beijing, Atmos. Chem. Phys., 18, 6907–6921, https://doi.org/10.5194/acp-18-6907-2018, 2018.
Ren, J., Zhang, F., Chen, L., Cao, G., Liu, M., Li, X., Wu, H., Cheng, Y., and Li, Z.: Identifying the hygroscopic properties of fine aerosol particles from diverse sources in urban atmosphere and the applicability in prediction of cloud nuclei, Atmos. Environ., 298, 119615, https://doi.org/10.1016/j.atmosenv.2023.119615, 2023.
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.
Riemer, N., Ault, A. P., West, M., Craig, R. L., and Curtis, J. H.: Aerosol mixing state: Measurements, modeling, and impacts, Rev. Geophys., 57, 187–249, https://doi.org/10.1029/2018RG000615, 2019.
Saha, P. K., Khlystov, A., and Grieshop, A. P.: Downwind evolution of the volatility and mixing state of near-road aerosols near a US interstate highway, Atmos. Chem. Phys., 18, 2139–2154, https://doi.org/10.5194/acp-18-2139-2018, 2018.
Shi, J., Hong, J., Ma, N., Luo, Q., He, Y., Xu, H., Tan, H., Wang, Q., Tao, J., Zhou, Y., Han, S., Peng, L., Xie, L., Zhou, G., Xu, W., Sun, Y., Cheng, Y., and Su, H.: Measurement report: On the difference in aerosol hygroscopicity between high and low relative humidity conditions in the North China Plain, Atmos. Chem. Phys., 22, 4599–4613, https://doi.org/10.5194/acp-22-4599-2022, 2022.
Spitieri, C., Gini, M., Gysel-Beer, M., and Eleftheriadis, K.: Annual cycle of hygroscopic properties and mixing state of the suburban aerosol in Athens, Greece, Atmos. Chem. Phys., 23, 235–249, https://doi.org/10.5194/acp-23-235-2023, 2023.
Stevens, R. and Dastoor, A.: A Review of the Representation of Aerosol Mixing State in Atmospheric Models, Atmosphere, 10, 168, https://doi.org/10.3390/atmos10040168, 2019.
Sun, Y. L., Wang, Z. F., Du, W., Zhang, Q., Wang, Q. Q., Fu, P. Q., Pan, X. L., Li, J., Jayne, J., and Worsnop, D. R.: Long-term real-time measurements of aerosol particle composition in Beijing, China: seasonal variations, meteorological effects, and source analysis, Atmos. Chem. Phys., 15, 10149–10165, https://doi.org/10.5194/acp-15-10149-2015, 2015.
Swietlicki, E., Hansson, H. C., Hameri, K., Svenningsson, B., Massling, A., Mcfiggans, G., P. Mcmurry, H., Petäjä, T., Tunved, P., Gysel, M., Topping, D., Weingartner, E., Baltensperger, U., Rissler, J., Wiedensohler, A., and Kulmala, M.: Hygroscopic properties of submicrometer atmospheric aerosol particles measured with H-TDMA instruments in various environments – a review, Tellus B, 60, 432–469, https://doi.org/10.1111/j.1600-0889.2008.00350.x, 2008.
Wang, Y., Ma, P.-L., Peng, J., Zhang, R., Jiang, J. H., Easter, R. C., and Yung, Y. L.: Constraining aging processes of black carbon in the Community Atmosphere Model using environmental chamber measurements, J. Adv. Model. Earth Sy., 10, 2514–2526. https://doi.org/10.1029/2018MS001387, 2018.
Wang, Y., Li, Z., Zhang, R., Jin, X., Xu, W., Fan, X., Wu, H., Zhang, F., Sun, Y., and Wang, Q.: Distinct ultrafine- and accumulation-mode particle properties in clean and polluted urban environments, Geophys. Res. Lett., 46, 10,918–10,925, https://doi.org/10.1029/2019GL084047, 2019.
Wang, Z., Shi, C., Zhang, H., Chen, Y., Chi, X., Xia, C., Wang, S., Zhu, Y., Zhang, K., Chen, X., Xing, C., and Liu, C.: Measurement report: Dust and anthropogenic aerosols' vertical distributions over northern China dense aerosols gathered at the top of the mixing layer, Atmos. Chem. Phys., 23, 14271–14292, https://doi.org/10.5194/acp-23-14271-2023, 2023.
Winkler, P.: The growth of atmospheric aerosol particles as a function of the relative humidity – II. An improved concept of mixed nuclei, J. Aerosol Sci., 4, 373–387, https://doi.org/10.1016/0021-8502(73)90027-X, 1973.
Xie, C., He, Y., Lei, L., Zhou, W., Liu, J., Wang, Q., Xu, W., Qiu, Y., Zhao, J., Sun, J., Li, L., Li, M., Zhou, Z., Fu, P., Wang, Z., and Sun, Y.: Contrasting mixing state of black carbon-containing particles in summer and winter in Beijing, Environ. Pollut., 263, 114455, https://doi.org/10.1016/j.envpol.2020.114455, 2020.
Xu, W., Xie, C., Karnezi, E., Zhang, Q., Wang, J., Pandis, S. N., Ge, X., Zhang, J., An, J., Wang, Q., Zhao, J., Du, W., Qiu, Y., Zhou, W., He, Y., Li, Y., Li, J., Fu, P., Wang, Z., Worsnop, D. R., and Sun, Y.: Summertime aerosol volatility measurements in Beijing, China, Atmos. Chem. Phys., 19, 10205–10216, https://doi.org/10.5194/acp-19-10205-2019, 2019.
Xu, W., Chen, C., Qiu, Y., Xie, C., Chen,Y., Ma, N., Xu, W., Fu, P., Wang, Z., and Pan, X.: Size-resolved characterization of organic aerosol in the North China Plain: new insights from high resolution spectral analysis, Environ. Sci. Atmos., 1, 346–358, https://doi.org/10.1039/D1EA00025J, 2021.
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.
Yu, F. and Luo, G.: Simulation of particle size distribution with a global aerosol model: contribution of nucleation to aerosol and CCN number concentrations, Atmos. Chem. Phys., 9, 7691–7710, https://doi.org/10.5194/acp-9-7691-2009, 2009.
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.
Zhang, J., Li, W., Wang, Y., Teng, X., Zhang, Y., Xu, L., Yuan, Q., Wu, G., Niu, H., and Shao, L.: Structural collapse and coating composition changes of soot particles during long-range transport, J. Geophys. Res.-Atmos., 128, e2023JD038871, https://doi.org/10.1029/2023JD038871, 2023.
Short summary
Particle mixing states and aging timescales are important for the evaluation of aerosol climate effects, but they are poorly parameterized in current models. We unravel the evolution of real-time mixing states and the aging timescale of size-resolved particles based on field measurements in urban Beijing. This study provides an observational basis for accurately parameterizing the aging timescale of aerosol particles in climate models.
Particle mixing states and aging timescales are important for the evaluation of aerosol climate...
Altmetrics
Final-revised paper
Preprint