Articles | Volume 17, issue 4
https://doi.org/10.5194/amt-17-1251-2024
https://doi.org/10.5194/amt-17-1251-2024
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

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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.