Articles | Volume 25, issue 1
https://doi.org/10.5194/acp-25-685-2025
https://doi.org/10.5194/acp-25-685-2025
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: 3,344 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,774 445 125 3,344 260 113 127
  • HTML: 2,774
  • PDF: 445
  • XML: 125
  • Total: 3,344
  • Supplement: 260
  • BibTeX: 113
  • EndNote: 127
Views and downloads (calculated since 18 Jul 2024)
Cumulative views and downloads (calculated since 18 Jul 2024)

Viewed (geographical distribution)

Total article views: 3,344 (including HTML, PDF, and XML) Thereof 3,344 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 02 Apr 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).
Share
Altmetrics
Final-revised paper
Preprint