Articles | Volume 19, issue 7
https://doi.org/10.5194/amt-19-2575-2026
https://doi.org/10.5194/amt-19-2575-2026
Research article
 | 
17 Apr 2026
Research article |  | 17 Apr 2026

Leveraging machine learning to enhance aerosol classification using Single-Particle Mass Spectrometry

Jose A. Perez Chavez, Maria A. Zawadowicz, Joseph Wilkins, and Christopher Blaszczak-Boxe

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Cited articles

Anderson, B. J., Musicant, D. R., Ritz, A. M., Ault, A., Gross, D., Yuen, M., and Gälli, M.: User-Friendly Clustering for Atmospheric Data Analysis, Carleton College Computer Science Technical Report, Department of Computer Science at Brown University, 9 pp., https://cs.brown.edu/people/aritz/files/tech2005a.pdf (last access: 20 March 2026), 2005. 
Andreae, M. O. and Rosenfeld, D.: Aerosol–cloud–precipitation interactions. Part 1. The nature and sources of cloud-active aerosols, Earth Sci. Rev., 89, 13–41, 2008. 
Andreae, M. O., Rosenfeld, D., Artaxo, P., Costa, A. A., Frank, G. P., Longo, K. M., and Silva-Dias, M. A. F.: Smoking rain clouds over the Amazon, Science, 303, 1337–1342, 2004. 
Ansel, J., Yang, E., He, H., Gimelshein, N., Jain, A., Voznesensky, M., Bao, B., Bell, P., Berard, D., Burovski, E., Chauhan, G., Chourdia, A., Constable, W., Desmaison, A., DeVito, Z., Ellison, E., Feng, W., Gong, J., Gschwind, M., Hirsh, B., Huang, S., Kalambarkar, K., Kirsch, L., Lazos, M., Lezcano, M., Liang, Y., Liang, J., Lu, Y., Luk, C. K., Maher, B., Pan, Y., Puhrsch, C., Reso, M., Saroufim, M., Siraichi, M. Y., Suk, H., Zhang, S., Suo, M., Tillet, P., Zhao, X., Wang, E., Zhou, K., Zou, R., Wang, X., Mathews, A., Wen, W., Chanan, G., Wu, P., and Chintala, S.: PyTorch 2: Faster machine learning through dynamic python bytecode transformation and graph compilation, in: ASPLOS '24: Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, La Jolla, CA, USA, 27 April–1 May 2024, Association for Computing Machinery (ACM), New York, NY, United States, 2, 929–947, https://doi.org/10.1145/3620665.3640366, 2024. 
Arndt, J., Sciare, J., Mallet, M., Roberts, G. C., Marchand, N., Sartelet, K., Sellegri, K., Dulac, F., Healy, R. M., and Wenger, J. C.: Sources and mixing state of summertime background aerosol in the north-western Mediterranean basin, Atmos. Chem. Phys., 17, 6975–7001, https://doi.org/10.5194/acp-17-6975-2017, 2017. 
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In this study, we leverage the power of machine learning to develop classifiers using a comprehensive dataset of SPMS spectra. These classifiers enable automatic differentiation of aerosol particles based on their chemistry and size, facilitating more accurate and efficient aerosol classification. Our results show increased accuracy when including unlabeled data in a semi-supervised framework.
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