Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 26, issue 5
Articles | Volume 26, issue 5
https://doi.org/10.5194/acp-26-3697-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-26-3697-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 26, issue 5
Research article
 | 
16 Mar 2026
Research article |  | 16 Mar 2026

Cloud condensation nuclei phenomenology: predictions based on aerosol chemical and optical properties

Inés Zabala, Juan Andrés Casquero-Vera, Elisabeth Andrews, Andrea Casans, Gerardo Carrillo-Cardenas, Anna Gannet Hallar, and Gloria Titos

Viewed

Total article views: 1,273 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
811 412 50 1,273 126 43 42
  • HTML: 811
  • PDF: 412
  • XML: 50
  • Total: 1,273
  • Supplement: 126
  • BibTeX: 43
  • EndNote: 42
Views and downloads (calculated since 07 Nov 2025)
Cumulative views and downloads (calculated since 07 Nov 2025)

Viewed (geographical distribution)

Total article views: 1,273 (including HTML, PDF, and XML) Thereof 1,273 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 09 Apr 2026
Download
Short summary
This study presents a comprehensive analysis of cloud condensation nuclei (CCN) phenomenology across nine observatories in diverse environments. We evaluate CCN prediction methods based on aerosol chemical composition and optical properties, including empirical and machine learning approaches. While simplified chemical schemes provide first-order estimates, incorporating optical data substantially improves CCN prediction accuracy in regions without direct measurements.
Read more
Share
Altmetrics
Final-revised paper
Preprint