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.
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.
<p>The continued increase in global plastic production and poor waste management ensures that plastic pollution is a serious environmental concern for years to come. Because of their size, shape, and relatively low density, primary or secondary plastic particles in the environment between 1-1000 &#181;m in size (known as microplastics, or MPs) may be entrained (and/or re-entrained) into the atmosphere through processes similar to other coarse-mode particles, such as mineral dust. MPs can thus be advected over great distances, reaching even the most pristine and remote areas of the Earth, and may have significant negative consequences for humans and the environment. The detection and analysis of MPs once airborne, however, remains a challenge because most observational methods are offline and resource-intensive, and, therefore, are not capable of providing continuous quantitative information.</p><p>In this study, we present results using an online, in situ airflow cytometer (SwisensPoleno Jupiter; Swisens AG; Horw, Switzerland) &#8211; coupled with machine learning &#8211; to detect, analyze, and classify airborne, single-particle MPs in near real time. The performance of the instrument to differentiate single-particle MPs of five common polymer types was investigated under laboratory conditions using combined information about their size and shape (determined using holographic imaging) and intrinsic fluorescence, known as autofluorescence, measured using three excitation wavelengths and five emission detection windows. The classification capability using these methods was determined alongside other coarse-mode aerosol with similar morphology or autofluorescence characteristics, such as a mineral dust and several pollen taxa.</p><p>The tested MPs exhibit a measurable autofluorescence signal that not only allows them to be distinguished from the other particles in this study demonstrating autofluorescence properties, such as pollen, but can also be differentiated from each other, with high (>90%) classification accuracy based on their multispectral autofluorescence signatures and morphology. The results using the presented novel methods are expected to provide a foundation towards significantly improving the understanding of properties and types of MPs present in the atmosphere.</p>
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