Articles | Volume 14, issue 2
Atmos. Meas. Tech., 14, 923–943, 2021
https://doi.org/10.5194/amt-14-923-2021
Atmos. Meas. Tech., 14, 923–943, 2021
https://doi.org/10.5194/amt-14-923-2021

Research article 08 Feb 2021

Research article | 08 Feb 2021

A new method for long-term source apportionment with time-dependent factor profiles and uncertainty assessment using SoFi Pro: application to 1 year of organic aerosol data

Francesco Canonaco et al.

Data sets

A new method for long-term source apportionment with time-dependent factor profiles and uncertainty assessment using SoFi Pro: application to 1 year of organic aerosol data Francesco Canonaco, Anna Tobler, Gang Chen, Yulia Sosedova, Jay Gates Slowik, Carlo Bozzetti, Kaspar Rudolf Daellenbach, Imad El Haddad, Monica Crippa, Ru-Jin Huang, Markus Furger, Urs Baltensperger, and André Stephan Henry Prévôt https://doi.org/10.5281/zenodo.4456562

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Short summary
Long-term ambient aerosol mass spectrometric data were analyzed with a statistical model (PMF) to obtain source contributions and fingerprints. The new aspects of this paper involve time-dependent source fingerprints by a rolling technique and the replacement of the full visual inspection of each run by a user-defined set of criteria to monitor the quality of each of these runs more efficiently. More reliable sources will finally provide better instruments for political mitigation strategies.