Articles | Volume 18, issue 10
https://doi.org/10.5194/gmd-18-2983-2025
© 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/gmd-18-2983-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
21 May 2025
Development and technical paper |
| 21 May 2025
| 21 May 2025
Accounting for effects of coagulation and model uncertainties in particle number concentration estimates based on measurements from sampling lines – a Bayesian inversion approach with SLIC v1.0
Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
Aku Seppänen
Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
Henri Oikarinen
Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
Miska Olin
Department of Atmospheric Sciences, Texas A&M University, College Station, TX, USA
Panu Karjalainen
Aerosol Physics Laboratory, Tampere University, Tampere, Finland
Santtu Mikkonen
Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
Department of Environmental and Biological Sciences, University of Eastern Finland, Kuopio, Finland
Kari Lehtinen
Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
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Miska Olin, David Patoulias, Heino Kuuluvainen, Jarkko V. Niemi, Topi Rönkkö, Spyros N. Pandis, Ilona Riipinen, and Miikka Dal Maso
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An emission factor particle size distribution was determined from the measurements at an urban traffic site. It was used in updating a pre-existing emission inventory, and regional modeling was performed after the update. Emission inventories typically underestimate nanoparticle emissions due to challenges in determining them with high certainty. This update reveals that the simulated aerosol levels have previously been underestimated especially for urban areas and for sub-50 nm particles.
Arto Heitto, Kari Lehtinen, Tuukka Petäjä, Felipe Lopez-Hilfiker, Joel A. Thornton, Markku Kulmala, and Taina Yli-Juuti
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For atmospheric aerosol particles to take part in cloud formation, they need to be at least a few tens of nanometers in diameter. By using a particle condensation model, we investigated how two types of chemical reactions, oligomerization and decomposition, of organic molecules inside the particle may affect the growth of secondary aerosol particles to these sizes. We show that the effect is potentially significant, which highlights the importance of increasing understanding of these processes.
Matthew Ozon, Dominik Stolzenburg, Lubna Dada, Aku Seppänen, and Kari E. J. Lehtinen
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Measuring the rate at which aerosol particles are formed is of importance for understanding climate change. We present an analysis method based on Kalman smoothing, which retrieves new particle formation and growth rates from size-distribution measurements. We apply it to atmospheric simulation chamber experiments and show that it agrees well with traditional methods. In addition, it provides reliable uncertainty estimates, and we suggest instrument design optimisation for signal processing.
Magdalena Okuljar, Heino Kuuluvainen, Jenni Kontkanen, Olga Garmash, Miska Olin, Jarkko V. Niemi, Hilkka Timonen, Juha Kangasluoma, Yee Jun Tham, Rima Baalbaki, Mikko Sipilä, Laura Salo, Henna Lintusaari, Harri Portin, Kimmo Teinilä, Minna Aurela, Miikka Dal Maso, Topi Rönkkö, Tuukka Petäjä, and Pauli Paasonen
Atmos. Chem. Phys., 21, 9931–9953, https://doi.org/10.5194/acp-21-9931-2021, https://doi.org/10.5194/acp-21-9931-2021, 2021
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To estimate the relative contribution of different sources to the particle population in an urban environment, we conducted simultaneous measurements at a street canyon and an urban background station in Helsinki. We investigated the contribution of traffic and new particle formation to particles with a diameter between 1 and 800 nm. We found that during spring traffic does not dominate the particles smaller than 3 nm at either of the stations.
Matthew Ozon, Aku Seppänen, Jari P. Kaipio, and Kari E. J. Lehtinen
Geosci. Model Dev., 14, 3715–3739, https://doi.org/10.5194/gmd-14-3715-2021, https://doi.org/10.5194/gmd-14-3715-2021, 2021
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Experimental research has provided large amounts of high-quality data on aerosol over the last 2 decades. However, inference of the process rates (e.g., the rates at which particles are generated) is still typically done by simple curve-fitting methods and does not assess the credibility of the estimation. The devised method takes advantage of the Bayesian framework to not only retrieve the state of the observed aerosol system but also to estimate the process rates (e.g., growth rate).
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Short summary
Particle size is a key factor determining the properties of aerosol particles which have a major influence on the climate and on human health. When measuring the particle sizes, however, sometimes the sampling lines that transfer the aerosol to the measurement device distort the size distribution, making the measurement unreliable. We propose a method to correct for the distortions and estimate the true particle sizes, improving measurement accuracy.
Particle size is a key factor determining the properties of aerosol particles which have a major...
