Articles | Volume 17, issue 2
https://doi.org/10.5194/amt-17-441-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-441-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Jan 2024
Research article |
| 23 Jan 2024
| 23 Jan 2024
Real-time pollen identification using holographic imaging and fluorescence measurements
Sophie Erb
CORRESPONDING AUTHOR
Federal Office of Meteorology and Climatology MeteoSwiss, 1530 Payerne, Switzerland
Environmental Remote Sensing Laboratory (LTE), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
Elias Graf
Swisens AG, 6048 Horw, Switzerland
Yanick Zeder
Swisens AG, 6048 Horw, Switzerland
Simone Lionetti
Algorithmic Business Research Lab (ABIZ), Lucerne University of Applied Sciences and Arts, 6002 Lucerne, Switzerland
Alexis Berne
Environmental Remote Sensing Laboratory (LTE), École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
Bernard Clot
Federal Office of Meteorology and Climatology MeteoSwiss, 1530 Payerne, Switzerland
Gian Lieberherr
Federal Office of Meteorology and Climatology MeteoSwiss, 1530 Payerne, Switzerland
Fiona Tummon
Federal Office of Meteorology and Climatology MeteoSwiss, 1530 Payerne, Switzerland
Pascal Wullschleger
Algorithmic Business Research Lab (ABIZ), Lucerne University of Applied Sciences and Arts, 6002 Lucerne, Switzerland
Benoît Crouzy
Federal Office of Meteorology and Climatology MeteoSwiss, 1530 Payerne, Switzerland
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Cited
18 citations as recorded by crossref.
- Scaling number concentration measurements from bioaerosol monitors using Hirst-type samplers S. Horender et al. https://doi.org/10.1007/s10453-025-09889-3
- Flow cytometry and machine learning enable identification of allergenic urban tree pollen S. Tardif et al. https://doi.org/10.5194/amt-19-2817-2026
- Operational pollen classification using digital holography and fluorescence B. Crouzy et al. https://doi.org/10.1007/s10453-025-09882-w
- Merging holography, fluorescence, and machine learning for in situ continuous characterization and classification of airborne microplastics N. Beres et al. https://doi.org/10.5194/amt-17-6945-2024
- Identification of Biological Aerosols By its Fluorescence Spectra and Comparison With Volumetric Data E. Illarionov et al. https://doi.org/10.24057/2071-9388-2026-4053
- Airborne DNA: State of the art – Established methods and missing pieces in the molecular genetic detection of airborne microorganisms, viruses and plant particles C. Pogner et al. https://doi.org/10.1016/j.scitotenv.2024.177439
- Review article: how emerging technologies could reshape pollen monitoring for epidemic thunderstorm asthma K. Hanoun et al. https://doi.org/10.1007/s10453-025-09874-w
- The ability of automated fluorometry and holography to discern airborne grass pollen beyond family level I. Ullah et al. https://doi.org/10.1007/s10453-026-09902-3
- Automatic monitoring and imaging-based identification of airborne Olea pollen Q. Farooq et al. https://doi.org/10.1016/j.envres.2026.124268
- A metrological framework for uncertainty evaluation in machine learning classification models S. Bilson et al. https://doi.org/10.1088/1681-7575/ae1bae
- Visual classification of allergenic pollen in iteratively reconstructed lens-less DIHM images B. Cugmas et al. https://doi.org/10.1038/s41598-026-36618-8
- Toward Accurate Real-Time Bioaerosol Monitoring in the Particle Size Range 1 μm–70 μm K. Vasilatou et al. https://doi.org/10.1021/acsestair.5c00282
- Monitoring of Airborne Pollen: A Patent Review D. Cuevas-González et al. https://doi.org/10.3390/atmos15101217
- Pollen holographic images and light-induced fluorescence measurements at the species level S. Erb et al. https://doi.org/10.1038/s41597-025-05139-w
- Pollen morphology and species differentiation in selected species of Inuleae (Asteraceae) T. Qu et al. https://doi.org/10.3897/phytokeys.263.165364
- A methodological framework for evaluating real-time bioaerosol classification algorithms M. Meurville et al. https://doi.org/10.5194/amt-19-3427-2026
- Comparative Analysis of Real-Time Fluorescence-Based Spectroscopic Instruments: Bioaerosol Detection in the Urban Environment of Dublin City, Ireland J. Clancy et al. https://doi.org/10.3390/atmos16030275
- Size-selective particle separation combined with fluorescence excitation module for real-time detection of airborne microorganisms: a feasibility study L. Himics et al. https://doi.org/10.1007/s10453-025-09867-9
18 citations as recorded by crossref.
- Scaling number concentration measurements from bioaerosol monitors using Hirst-type samplers S. Horender et al. https://doi.org/10.1007/s10453-025-09889-3
- Flow cytometry and machine learning enable identification of allergenic urban tree pollen S. Tardif et al. https://doi.org/10.5194/amt-19-2817-2026
- Operational pollen classification using digital holography and fluorescence B. Crouzy et al. https://doi.org/10.1007/s10453-025-09882-w
- Merging holography, fluorescence, and machine learning for in situ continuous characterization and classification of airborne microplastics N. Beres et al. https://doi.org/10.5194/amt-17-6945-2024
- Identification of Biological Aerosols By its Fluorescence Spectra and Comparison With Volumetric Data E. Illarionov et al. https://doi.org/10.24057/2071-9388-2026-4053
- Airborne DNA: State of the art – Established methods and missing pieces in the molecular genetic detection of airborne microorganisms, viruses and plant particles C. Pogner et al. https://doi.org/10.1016/j.scitotenv.2024.177439
- Review article: how emerging technologies could reshape pollen monitoring for epidemic thunderstorm asthma K. Hanoun et al. https://doi.org/10.1007/s10453-025-09874-w
- The ability of automated fluorometry and holography to discern airborne grass pollen beyond family level I. Ullah et al. https://doi.org/10.1007/s10453-026-09902-3
- Automatic monitoring and imaging-based identification of airborne Olea pollen Q. Farooq et al. https://doi.org/10.1016/j.envres.2026.124268
- A metrological framework for uncertainty evaluation in machine learning classification models S. Bilson et al. https://doi.org/10.1088/1681-7575/ae1bae
- Visual classification of allergenic pollen in iteratively reconstructed lens-less DIHM images B. Cugmas et al. https://doi.org/10.1038/s41598-026-36618-8
- Toward Accurate Real-Time Bioaerosol Monitoring in the Particle Size Range 1 μm–70 μm K. Vasilatou et al. https://doi.org/10.1021/acsestair.5c00282
- Monitoring of Airborne Pollen: A Patent Review D. Cuevas-González et al. https://doi.org/10.3390/atmos15101217
- Pollen holographic images and light-induced fluorescence measurements at the species level S. Erb et al. https://doi.org/10.1038/s41597-025-05139-w
- Pollen morphology and species differentiation in selected species of Inuleae (Asteraceae) T. Qu et al. https://doi.org/10.3897/phytokeys.263.165364
- A methodological framework for evaluating real-time bioaerosol classification algorithms M. Meurville et al. https://doi.org/10.5194/amt-19-3427-2026
- Comparative Analysis of Real-Time Fluorescence-Based Spectroscopic Instruments: Bioaerosol Detection in the Urban Environment of Dublin City, Ireland J. Clancy et al. https://doi.org/10.3390/atmos16030275
- Size-selective particle separation combined with fluorescence excitation module for real-time detection of airborne microorganisms: a feasibility study L. Himics et al. https://doi.org/10.1007/s10453-025-09867-9
Saved (final revised paper)
Latest update: 17 Jul 2026
Short summary
In this study, we focus on an automatic bioaerosol measurement instrument and investigate the impact of using its fluorescence measurement for pollen identification. The fluorescence signal is used together with a pair of images from the same instrument to identify single pollen grains via neural networks. We test whether considering fluorescence as a supplementary input improves the pollen identification performance by comparing three different neural networks.
In this study, we focus on an automatic bioaerosol measurement instrument and investigate the...
