Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 18, issue 20
Articles | Volume 18, issue 20
https://doi.org/10.5194/amt-18-5763-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-18-5763-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 20
Research article
 | 
24 Oct 2025
Research article |  | 24 Oct 2025

Machine learning-based downscaling of aerosol size distributions from a global climate model

Antti Vartiainen, Santtu Mikkonen, Ville Leinonen, Tuukka Petäjä, Alfred Wiedensohler, Thomas Kühn, and Tuuli Miinalainen

Viewed

Total article views: 1,299 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,099 163 37 1,299 57 44 56
  • HTML: 1,099
  • PDF: 163
  • XML: 37
  • Total: 1,299
  • Supplement: 57
  • BibTeX: 44
  • EndNote: 56
Views and downloads (calculated since 24 Mar 2025)
Cumulative views and downloads (calculated since 24 Mar 2025)

Viewed (geographical distribution)

Total article views: 1,299 (including HTML, PDF, and XML) Thereof 1,255 with geography defined and 44 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 04 Dec 2025
Download
Short summary
Global climate models, commonly used for climate predictions, struggle at capturing local-scale variations in air quality. We have used measurements of ultrafine particles (UFPs), a less understood air pollutant with potentially significant health implications, for training machine learning models that can substantially reduce the inaccuracy in UFP concentrations predicted by a climate model. This approach could aid epidemiological studies of ultrafine particles by extending exposure records.
Read more
Share