Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 25, issue 1
Articles | Volume 25, issue 1
https://doi.org/10.5194/acp-25-685-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-685-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 1
Technical note
 | 
17 Jan 2025
Technical note |  | 17 Jan 2025

Technical note: Towards atmospheric compound identification in chemical ionization mass spectrometry with pesticide standards and machine learning

Federica Bortolussi, Hilda Sandström, Fariba Partovi, Joona Mikkilä, Patrick Rinke, and Matti Rissanen

Viewed

Total article views: 3,253 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,712 423 118 3,253 226 107 120
  • HTML: 2,712
  • PDF: 423
  • XML: 118
  • Total: 3,253
  • Supplement: 226
  • BibTeX: 107
  • EndNote: 120
Views and downloads (calculated since 18 Jul 2024)
Cumulative views and downloads (calculated since 18 Jul 2024)

Viewed (geographical distribution)

Total article views: 3,253 (including HTML, PDF, and XML) Thereof 3,253 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 08 Mar 2026
Download
Short summary
Chemical ionization mass spectrometry (CIMS) is widely used in atmospheric chemistry studies. We still have a limited understanding of the complex functioning of the instrument; therefore, we applied machine learning to provide insights from CIMS analyses. We were able to predict both detection and signal intensity with a fair error, and we found out the most important structural fragments for negative ionization schemes (NH and OH) and positive ones (nitrogen-containing groups).
Read more
Share
Altmetrics
Final-revised paper
Preprint