Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 17, issue 4
Articles | Volume 17, issue 4
https://doi.org/10.5194/amt-17-1251-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-1251-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 4
Research article
 | 
22 Feb 2024
Research article |  | 22 Feb 2024

A novel probabilistic source apportionment approach: Bayesian auto-correlated matrix factorization

Anton Rusanen, Anton Björklund, Manousos I. Manousakas, Jianhui Jiang, Markku T. Kulmala, Kai Puolamäki, and Kaspar R. Daellenbach

Viewed

Total article views: 5,539 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,609 1,795 135 5,539 157 192
  • HTML: 3,609
  • PDF: 1,795
  • XML: 135
  • Total: 5,539
  • BibTeX: 157
  • EndNote: 192
Views and downloads (calculated since 12 May 2023)
Cumulative views and downloads (calculated since 12 May 2023)

Viewed (geographical distribution)

Total article views: 5,539 (including HTML, PDF, and XML) Thereof 5,199 with geography defined and 340 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 26 Aug 2026
Download
Short summary
We present a Bayesian non-negative matrix factorization model that performs better on our test datasets than currently widely used models. Its advantages are better use of time information and providing a direct error estimation. We believe this could lead to better estimates of emission sources from measurements.
Read more
Share