Articles | Volume 25, issue 13
https://doi.org/10.5194/acp-25-7431-2025
https://doi.org/10.5194/acp-25-7431-2025
Research article
 | 
15 Jul 2025
Research article |  | 15 Jul 2025

Machine-learning-assisted inference of the particle charge fraction and the ion-induced nucleation rates during new particle formation events

Pan Wang, Yue Zhao, Jiandong Wang, Veli-Matti Kerminen, Jingkun Jiang, and Chenxi Li

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Cited articles

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Chu, B., Kerminen, V.-M., Bianchi, F., Yan, C., Petäjä, T., and Kulmala, M.: Atmospheric new particle formation in China, Atmos. Chem. Phys., 19, 115–138, https://doi.org/10.5194/acp-19-115-2019, 2019. 
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Gagné, S., Leppä, J., Petäjä, T., McGrath, M. J., Vana, M., Kerminen, V.-M., Laakso, L., and Kulmala, M.: Aerosol charging state at an urban site: new analytical approach and implications for ion-induced nucleation, Atmos. Chem. Phys., 12, 4647–4666, https://doi.org/10.5194/acp-12-4647-2012, 2012. 
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We developed a numerical model to investigate the evolution of the charge state of newly formed atmospheric particles. Based on the simulation results, we successfully employed neural networks to predict particle charge states and estimate ion-induced nucleation rates. This study provides new insights into the dynamics of particle charging and introduces advanced methods for evaluating ion-induced nucleation in atmospheric research.
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