12 May 2023
 | 12 May 2023
Status: this preprint is currently under review for the journal AMT.

A novel probabilistic source apportionment approach: Bayesian Auto-correlated Matrix Factorization

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

Abstract. The concentrations and sources of particulate matter in the atmosphere are temporally auto-correlated. Here, we present a Bayesian matrix factorization model (BAMF) that considers the temporal auto-correlation of the components (sources) and provides a direct error estimation. The performance of BAMF is compared to positive matrix factorization (PMF) using synthetic Time-of-Flight Aerosol Chemical Speciation Monitor data, representing different urban environments from typical European towns to megacities. We find that BAMF resolves sources better than PMF on all datasets with auto-correlated components, but highly cross-correlated components continue to be challenging. However, we demonstrate that adding even partial prior information about the chemical composition of the components to BAMF improves the factorization. Overall, BAMF-type models are promising tools for source apportionment and merit further research.

Anton Rusanen et al.

Status: open (until 27 Jun 2023)

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  • RC1: 'Comment on amt-2023-70', Anonymous Referee #1, 26 May 2023 reply

Anton Rusanen et al.

Anton Rusanen et al.


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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.