Atmospheric Measurement Techniques
Atmospheric Measurement Techniques
AMT
Articles | Volume 19, issue 10
Articles | Volume 19, issue 10
https://doi.org/10.5194/amt-19-3427-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-19-3427-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 10
Research article
 | 
26 May 2026
Research article |  | 26 May 2026

A methodological framework for evaluating real-time bioaerosol classification algorithms

Marie-Pierre Meurville, Bernard Clot, Sophie Erb, Maria Lbadaoui-Darvas, Fiona Tummon, Gian-Duri Lieberherr, and Benoît Crouzy

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Cited articles

Adamov, S., Lemonis, N., Clot, B., Crouzy, B., Gehrig, R., Graber, M.-J., Sallin, C., and Tummon, F.: On the measurement uncertainty of Hirst-type volumetric pollen and spore samplers, Aerobiologia, 40, 77–91, https://doi.org/10.1007/s10453-021-09724-5, 2021. a, b, c, d
Bai, J., Lu, F., Zhang, K., et al.: ONNX: Open Neural Network Exchange, GitHub [code], https://github.com/onnx/onnx (last access: 5 August 2025), 2019. a
Bastl, K., Kmenta, M., and Berger, U. E.: Defining pollen seasons: Background and recommendations, Curr. Allergy Asthma Rep., 18, 73, https://doi.org/10.1007/s11882-018-0829-z, 2018. a
Berg, M. J. and Videen, G.: Digital holographic imaging of aerosol particles in flight, Journal of Quantitative Spectroscopy and Radiative Transfer, 112, 1776–1783, https://doi.org/10.1016/j.jqsrt.2011.01.013, 2011. a
Brdar, S., Panić, M., Matavulj, P., Stanković, M., Bartolić, D., and Šikoparija, B.: Explainable AI for unveiling deep learning pollen classification model based on fusion of scattered light patterns and fluorescence spectroscopy, Sci. Rep., 13, https://doi.org/10.1038/s41598-023-30064-6, 2023. a
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Our method evaluates how well automatic devices can classify pollen and other airborne particles in real time. Our goal is to compare different classification systems and understand their strengths and weaknesses. By developing this evaluation process, we aim to enhance the accuracy of bioaerosol forecasts. This research is essential for improving public health and helping people manage allergies more effectively.
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