Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 19, issue 6
Articles | Volume 19, issue 6
https://doi.org/10.5194/amt-19-2175-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-19-2175-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 6
Research article
 | 
30 Mar 2026
Research article |  | 30 Mar 2026

Chemical sparsity in Bayesian receptor models for aerosol source apportionment

Marta Via, Jure Demšar, Yufang Hao, Manousos Manousakas, Anton Rusanen, Jianhui Jiang, Stuart K. Grange, Jean-Luc Jaffrezo, Vy Ngoc Thuy Dinh, Gaëlle Uzu, Griša Močnik, and Kaspar R. Daellenbach

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Short summary
We introduce BAMF+HS (Bayesian Autocorrelated Matrix Factorisation+Horseshoe), an enhanced Bayesian receptor model for particulate matter (PM) source apportionment. By applying a regularised horseshoe prior to the chemical composition matrix, BAMF+HS enforces sparsity, filtering out irrelevant species and improving source separation. Tests on synthetic and real datasets show BAMF+HS consistently outperforms previous models in accuracy and clarity.
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