Articles | Volume 18, issue 13
Atmos. Chem. Phys., 18, 9597–9615, 2018
https://doi.org/10.5194/acp-18-9597-2018
Atmos. Chem. Phys., 18, 9597–9615, 2018
https://doi.org/10.5194/acp-18-9597-2018

Research article 09 Jul 2018

Research article | 09 Jul 2018

Identification of new particle formation events with deep learning

Jorma Joutsensaari et al.

Related authors

Combined effects of boundary layer dynamics and atmospheric chemistry on aerosol composition during new particle formation periods
Liqing Hao, Olga Garmash, Mikael Ehn, Pasi Miettinen, Paola Massoli, Santtu Mikkonen, Tuija Jokinen, Pontus Roldin, Pasi Aalto, Taina Yli-Juuti, Jorma Joutsensaari, Tuukka Petäjä, Markku Kulmala, Kari E. J. Lehtinen, Douglas R. Worsnop, and Annele Virtanen
Atmos. Chem. Phys., 18, 17705–17716, https://doi.org/10.5194/acp-18-17705-2018,https://doi.org/10.5194/acp-18-17705-2018, 2018
Short summary
Biotic stress accelerates formation of climate-relevant aerosols in boreal forests
J. Joutsensaari, P. Yli-Pirilä, H. Korhonen, A. Arola, J. D. Blande, J. Heijari, M. Kivimäenpää, S. Mikkonen, L. Hao, P. Miettinen, P. Lyytikäinen-Saarenmaa, C. L. Faiola, A. Laaksonen, and J. K. Holopainen
Atmos. Chem. Phys., 15, 12139–12157, https://doi.org/10.5194/acp-15-12139-2015,https://doi.org/10.5194/acp-15-12139-2015, 2015
Short summary
The role of low volatile organics on secondary organic aerosol formation
H. Kokkola, P. Yli-Pirilä, M. Vesterinen, H. Korhonen, H. Keskinen, S. Romakkaniemi, L. Hao, A. Kortelainen, J. Joutsensaari, D. R. Worsnop, A. Virtanen, and K. E. J. Lehtinen
Atmos. Chem. Phys., 14, 1689–1700, https://doi.org/10.5194/acp-14-1689-2014,https://doi.org/10.5194/acp-14-1689-2014, 2014
Evolution of particle composition in CLOUD nucleation experiments
H. Keskinen, A. Virtanen, J. Joutsensaari, G. Tsagkogeorgas, J. Duplissy, S. Schobesberger, M. Gysel, F. Riccobono, J. G. Slowik, F. Bianchi, T. Yli-Juuti, K. Lehtipalo, L. Rondo, M. Breitenlechner, A. Kupc, J. Almeida, A. Amorim, E. M. Dunne, A. J. Downard, S. Ehrhart, A. Franchin, M.K. Kajos, J. Kirkby, A. Kürten, T. Nieminen, V. Makhmutov, S. Mathot, P. Miettinen, A. Onnela, T. Petäjä, A. Praplan, F. D. Santos, S. Schallhart, M. Sipilä, Y. Stozhkov, A. Tomé, P. Vaattovaara, D. Wimmer, A. Prevot, J. Dommen, N. M. Donahue, R.C. Flagan, E. Weingartner, Y. Viisanen, I. Riipinen, A. Hansel, J. Curtius, M. Kulmala, D. R. Worsnop, U. Baltensperger, H. Wex, F. Stratmann, and A. Laaksonen
Atmos. Chem. Phys., 13, 5587–5600, https://doi.org/10.5194/acp-13-5587-2013,https://doi.org/10.5194/acp-13-5587-2013, 2013

Related subject area

Subject: Aerosols | Research Activity: Field Measurements | Altitude Range: Troposphere | Science Focus: Physics (physical properties and processes)
The impact threshold of the aerosol radiative forcing on the boundary layer structure in the pollution region
Dandan Zhao, Jinyuan Xin, Chongshui Gong, Jiannong Quan, Yuesi Wang, Guiqian Tang, Yongxiang Ma, Lindong Dai, Xiaoyan Wu, Guangjing Liu, and Yongjing Ma
Atmos. Chem. Phys., 21, 5739–5753, https://doi.org/10.5194/acp-21-5739-2021,https://doi.org/10.5194/acp-21-5739-2021, 2021
Short summary
Technical note: Measurement of chemically resolved volume equivalent diameter and effective density of particles by AAC-SPAMS
Long Peng, Lei Li, Guohua Zhang, Xubing Du, Xinming Wang, Ping'an Peng, Guoying Sheng, and Xinhui Bi
Atmos. Chem. Phys., 21, 5605–5613, https://doi.org/10.5194/acp-21-5605-2021,https://doi.org/10.5194/acp-21-5605-2021, 2021
Short summary
The impact of cloudiness and cloud type on the atmospheric heating rate of black and brown carbon in the Po Valley
Luca Ferrero, Asta Gregorič, Griša Močnik, Martin Rigler, Sergio Cogliati, Francesca Barnaba, Luca Di Liberto, Gian Paolo Gobbi, Niccolò Losi, and Ezio Bolzacchini
Atmos. Chem. Phys., 21, 4869–4897, https://doi.org/10.5194/acp-21-4869-2021,https://doi.org/10.5194/acp-21-4869-2021, 2021
Short summary
Meteorology-driven variability of air pollution (PM1) revealed with explainable machine learning
Roland Stirnberg, Jan Cermak, Simone Kotthaus, Martial Haeffelin, Hendrik Andersen, Julia Fuchs, Miae Kim, Jean-Eudes Petit, and Olivier Favez
Atmos. Chem. Phys., 21, 3919–3948, https://doi.org/10.5194/acp-21-3919-2021,https://doi.org/10.5194/acp-21-3919-2021, 2021
Short summary
The seasonal cycle of ice-nucleating particles linked to the abundance of biogenic aerosol in boreal forests
Julia Schneider, Kristina Höhler, Paavo Heikkilä, Jorma Keskinen, Barbara Bertozzi, Pia Bogert, Tobias Schorr, Nsikanabasi Silas Umo, Franziska Vogel, Zoé Brasseur, Yusheng Wu, Simo Hakala, Jonathan Duplissy, Dmitri Moisseev, Markku Kulmala, Michael P. Adams, Benjamin J. Murray, Kimmo Korhonen, Liqing Hao, Erik S. Thomson, Dimitri Castarède, Thomas Leisner, Tuukka Petäjä, and Ottmar Möhler
Atmos. Chem. Phys., 21, 3899–3918, https://doi.org/10.5194/acp-21-3899-2021,https://doi.org/10.5194/acp-21-3899-2021, 2021
Short summary

Cited articles

Alam, A., Shi Ji, P., and Harrison Roy, M.: Observations of new particle formation in urban air, J. Geophys. Res.-Atmos., 108, https://doi.org/10.1029/2001JD001417, 2003. 
Albrecht, B. A.: Aerosols, Cloud Microphysics, and Fractional Cloudiness, Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989. 
Amari, S.-I.: Natural Gradient Works Efficiently in Learning, Neural Comput., 10, 251–276, https://doi.org/10.1162/089976698300017746, 1998. 
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, https://doi.org/10.1016/j.earscirev.2008.03.001, 2008. 
Asmi, E., Kivekäs, N., Kerminen, V.-M., Komppula, M., Hyvärinen, A.-P., Hatakka, J., Viisanen, Y., and Lihavainen, H.: Secondary new particle formation in Northern Finland Pallas site between the years 2000 and 2010, Atmos. Chem. Phys., 11, 12959–12972, https://doi.org/10.5194/acp-11-12959-2011, 2011. 
Download
Short summary
New particle formation (NPF) in the atmosphere is globally an important source of aerosol particles. NPF events are typically identified and analyzed manually by researchers from particle size distribution data day by day, which is time consuming and might be inconsistent. We have developed an automatic analysis method based on deep learning for NPF event identification. The developed method can be easily utilized to analyze any long-term datasets more accurately and consistently.
Altmetrics
Final-revised paper
Preprint