Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 19, issue 8
Articles | Volume 19, issue 8
https://doi.org/10.5194/amt-19-2817-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-19-2817-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 8
Research article
 | 
28 Apr 2026
Research article |  | 28 Apr 2026

Flow cytometry and machine learning enable identification of allergenic urban tree pollen

Sarah Tardif, Maria Raquel Kanieski, Gauthier Lapa, Grégoire Bonnamour, Rita Sousa-Silva, Isabelle Laforest-Lapointe, and Alain Paquette

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Short summary
We developed a high-throughput method combining flow cytometry and machine learning to identify urban pollen. Using a reference database of 97 species across 34 genera, with values of particle size, granularity, and multi-channel fluorescence for each pollen grains, our method enables rapid species- and genus-level pollen identification. It provides an efficient alternative to microscopy, with potential for large-scale urban pollen monitoring and allergy management.
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