Tracing biological, anthropogenic, and inorganic sources of coarse aerosols via single-particle fluorescence and optical morphology

Jönsson, Aiden; Fu, Jinglan; Pereira Freitas, Gabriel; Crawford, Ian; Dagsson-Waldhauserová, Pavla; Krejci, Radovan; Tobo, Yutaka; Yttri, Karl Espen; Zieger, Paul

doi:10.5194/acp-26-13617-2026

Articles | Volume 26, issue 18

https://doi.org/10.5194/acp-26-13617-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/acp-26-13617-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 26, issue 18

Research article

|

29 Sep 2026

Research article |

| 29 Sep 2026

Tracing biological, anthropogenic, and inorganic sources of coarse aerosols via single-particle fluorescence and optical morphology

Aiden Jönsson, Jinglan Fu, Gabriel Pereira Freitas, Ian Crawford, Pavla Dagsson-Waldhauserová, Radovan Krejci, Yutaka Tobo, Karl Espen Yttri, and Paul Zieger

Supplement

https://doi.org/10.5194/acp-26-13617-2026-supplement

Data sets

Multiparameter bioaerosol spectrometer (MBS) laboratory characterization of coarse-mode particles — raw data Aiden Jönsson et al. https://doi.org/10.17043/jonsson-2026-aerosol-mbs-raw-1

Multiparameter bioaerosol spectrometer (MBS) laboratory characterization of coarse-mode particles — processed data Aiden Jönsson et al. https://doi.org/10.17043/jonsson-2026-aerosol-mbs-1

Model code and software

TRUFFLE: Trained Recognition of Unique Fluorescence- & Form-based Labels for Environmental aerosols Aiden Jönsson https://doi.org/10.5281/zenodo.22086175

MBS source characterization, machine learning, and algorithm assessment reproduction code Aiden Jönsson https://doi.org/10.5281/zenodo.22086099

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Short summary

Coarse-mode aerosols, like dust and bioaerosols, play important roles in environmental and climate processes. We measured fluorescence and morphological properties of key coarse-mode particle types, compared them with previous characterizations, and trained machine learning models with these data to classify unknown particles. This algorithm improves bioaerosol identification and successfully reproduces the annual bioaerosol cycle previously identified in a year of observations from Svalbard.