Articles | Volume 22, issue 2
https://doi.org/10.5194/acp-22-1293-2022
https://doi.org/10.5194/acp-22-1293-2022
Research article
 | 
25 Jan 2022
Research article |  | 25 Jan 2022

New particle formation event detection with Mask R-CNN

Peifeng Su, Jorma Joutsensaari, Lubna Dada, Martha Arbayani Zaidan, Tuomo Nieminen, Xinyang Li, Yusheng Wu, Stefano Decesari, Sasu Tarkoma, Tuukka Petäjä, Markku Kulmala, and Petri Pellikka

Related authors

Aerosol dry deposition fluxes on snow during the ALPACA campaign in Fairbanks, Alaska
Antonio Donateo, Gianluca Pappaccogli, Federico Scoto, Maurizio Busetto, Francesca L. Lovisco, Natalie Brett, Douglas Keller, Brice Barret, Elsa Dieudonné, Roman Pohorsky, Andrea Baccarini, Slimane Bekki, Jean-Christophe Raut, Julia Schmale, Kathy S. Law, Steve R. Arnold, Javier G. Fochesatto, William R. Simpson, and Stefano Decesari
Atmos. Chem. Phys., 25, 18129–18156, https://doi.org/10.5194/acp-25-18129-2025,https://doi.org/10.5194/acp-25-18129-2025, 2025
Short summary
Comparison of particle number concentrations measured with AQ Urban sensors in two different environments in Helsinki, Finland
Kimmo Teinilä, Teemu Lepistö, Jarkko V. Niemi, Harri Portin, Anssi Julkunen, Anu Kousa, Joel Kuula, Hanna E. Manninen, Pasi Aalto, Tuukka Petäjä, Topi Rönkkö, Erkka Saukko, and Hilkka Timonen
EGUsphere, https://doi.org/10.5194/egusphere-2025-5777,https://doi.org/10.5194/egusphere-2025-5777, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Optimizing CCN predictions through inferred modal aerosol composition – a boreal forest case study
Rahul Ranjan, Maura Dewey, Liine Heikkinen, Lauri R. Ahonen, Krista Luoma, Paul Bowen, Tuukka Petäjä, Annica M. L. Ekman, Daniel G. Partridge, and Ilona Riipinen
Atmos. Chem. Phys., 25, 17275–17300, https://doi.org/10.5194/acp-25-17275-2025,https://doi.org/10.5194/acp-25-17275-2025, 2025
Short summary
Global fields of daily accumulation-mode particle number concentrations using in situ observations, reanalysis data, and machine learning
Aino Ovaska, Elio Rauth, Daniel Holmberg, Paulo Artaxo, John Backman, Benjamin Bergmans, Don Collins, Marco Aurélio Franco, Shahzad Gani, Roy M. Harrison, Rakesh K. Hooda, Tareq Hussein, Antti-Pekka Hyvärinen, Kerneels Jaars, Adam Kristensson, Markku Kulmala, Lauri Laakso, Ari Laaksonen, Nikolaos Mihalopoulos, Colin O'Dowd, Jakub Ondracek, Tuukka Petäjä, Kristina Plauškaitė, Mira Pöhlker, Ximeng Qi, Peter Tunved, Ville Vakkari, Alfred Wiedensohler, Kai Puolamäki, Tuomo Nieminen, Veli-Matti Kerminen, Victoria A. Sinclair, and Pauli Paasonen
Aerosol Research, 3, 589–618, https://doi.org/10.5194/ar-3-589-2025,https://doi.org/10.5194/ar-3-589-2025, 2025
Short summary
Measurement report: Optical properties of supermicron aerosol particles in a boreal environment
Sujai Banerji, Krista Luoma, Ilona Ylivinkka, Lauri Ahonen, Veli-Matti Kerminen, and Tuukka Petäjä
Atmos. Chem. Phys., 25, 16895–16914, https://doi.org/10.5194/acp-25-16895-2025,https://doi.org/10.5194/acp-25-16895-2025, 2025
Short summary

Cited articles

Aalto, P., Hämeri, K., Becker, E., Weber, R., Salm, J., Mäkelä, J., Hoell, C., O'Dowd, C., Karlsson, H., Hansson, H.-C., Väkevä, M., Koponen, I., Buzorius, G., and Kulmala, M.: Physical characterization of aerosol particles during nucleation events, Tellus B, 53, 344–358, https://doi.org/10.1034/j.1600-0889.2001.530403.x, 2001. a
Asmi, A., Wiedensohler, A., Laj, P., Fjaeraa, A.-M., Sellegri, K., Birmili, W., Weingartner, E., Baltensperger, U., Zdimal, V., Zikova, N., Putaud, J.-P., Marinoni, A., Tunved, P., Hansson, H.-C., Fiebig, M., Kivekäs, N., Lihavainen, H., Asmi, E., Ulevicius, V., Aalto, P. P., Swietlicki, E., Kristensson, A., Mihalopoulos, N., Kalivitis, N., Kalapov, I., Kiss, G., de Leeuw, G., Henzing, B., Harrison, R. M., Beddows, D., O'Dowd, C., Jennings, S. G., Flentje, H., Weinhold, K., Meinhardt, F., Ries, L., and Kulmala, M.: Number size distributions and seasonality of submicron particles in Europe 2008–2009, Atmos. Chem. Phys., 11, 5505–5538, https://doi.org/10.5194/acp-11-5505-2011, 2011a. a
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, 2011b. a
Choi, S., Kim, T., and Yu, W.: Performance Evaluation of RANSAC Family, in: Proceedings of the British Machine Vision Conference, BMVA Press, 81.1–81.12, https://doi.org/10.5244/C.23.81, 2009. a
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. a, b
Download
Short summary
We regarded the banana shapes in the surface plots as a special kind of object (similar to cats) and applied an instance segmentation technique to automatically identify the new particle formation (NPF) events (especially the strongest ones), in addition to their growth rates, start times, and end times. The automatic method generalized well on datasets collected in different sites, which is useful for long-term data series analysis and obtaining statistical properties of NPF events.
Share
Altmetrics
Final-revised paper
Preprint