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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/amt-2019-427
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-2019-427
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 04 Dec 2019

Submitted as: research article | 04 Dec 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

Real-time pollen monitoring using digital holography

Eric Sauvageat1,2, Yanick Zeder3,4, Kevin Auderset5, Bertrand Calpini1, Bernard Clot1, Benoît Crouzy1, Thomas Konzelmann1, Gian Lieberherr1, Fiona Tummon1, and Konstantina Vasilatou5 Eric Sauvageat et al.
  • 1Federal Office of Meteorology and Climatology MeteoSwiss, Payerne, Switzerland
  • 2University of Bern, Bern, Switzerland
  • 3Lucerne University of Applied Sciences and Arts, Lucerne, Switzerland
  • 4Swisens AG, Horw, Switzerland
  • 5Swiss Federal Institute of Metrology METAS, Bern-Wabern, Switzerland

Abstract. We present the first validation of the Swisens Poleno, currently the only operational automatic pollen monitoring system based on digital holography. The device provides in-flight images of all coarse aerosols and here we develop a two-step classification algorithm that uses these images to identify a range of pollen taxa. Deterministic criteria based on the shape of the particle are applied to initially distinguish between intact pollen grains and other coarse particulate matter. This first level of discrimination identifies pollen with an accuracy of 96 %. Thereafter, individual pollen taxa are recognised using supervised learning techniques. The algorithm is trained using data obtained by inserting known pollen types into the device and out of eight pollen taxa six can be identified with an accuracy of above 90 %. In addition to the ability to correctly identify aerosols, an automatic pollen monitoring system needs to be able to correctly determine particle concentrations. To further verify the device, controlled chamber experiments using polystyrene latex beads were performed. This provided reference aerosols with traceable particle size and number concentrations to ensure particle size and sampling volume were correctly characterised.

Eric Sauvageat et al.
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Short summary
We present the first validation of the only operational automatic pollen monitoring system based on holography, the Swisens Poleno. The device produces real-time images of coarse aerosols and by applying a machine learning algorithm we identify a range of pollen taxa with accuracy > 90 %. The device was further validated in controlled chamber experiments to verify the counting ability and the performance of additional fluorescence measurements, which can further be used for pollen identification.
We present the first validation of the only operational automatic pollen monitoring system based...
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