Abstract
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 recognized 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 in order to ensure particle size and sampling volume were correctly characterized.
Highlights
The incidence of pollinosis and related diseases has increased considerably over the past decades, sparking growing research interest into aeroallergens and pollen monitoring
Most often, sensitized patients exposed to allergenic pollen experience symptoms of allergic rhinitis or hay fever, but exposure to pollen has been shown to exacerbate the development of more severe diseases like asthma, all of which have significant effects on public health and the economy (Greiner et al, 2012; Gamble et al, 2008)
In the category of air-flow cytometers, most existing devices rely on fluorescence and elastic light-scattering measurements combined with machine-learning algorithms to identify and quantify airborne pollen concentrations
Summary
The incidence of pollinosis and related diseases has increased considerably over the past decades, sparking growing research interest into aeroallergens and pollen monitoring. In the category of air-flow cytometers, most existing devices rely on fluorescence and elastic light-scattering measurements combined with machine-learning algorithms to identify and quantify airborne pollen concentrations Some of these systems have already shown promising results and are currently tested in different European countries (Crouzy et al, 2016; Šauliene et al, 2019; Chappuis et al, 2019). In this paper we evaluated a new automated pollen monitoring system based on air-flow cytometry, the Swisens Poleno This device captures holographic images of each airborne particle in addition to measurements of optical properties such as fluorescence intensity, lifetime, and elastic light scattering. The significance of the results for pollen monitoring are discussed, and an overview of the future perspectives for this new technology is provided
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