Real-time pollen monitoring using digital holography

We present the first validation of the SwisensPoleno, currently the only operational automatic pollen mon-itoring system based on digital holography. The device pro-vides in-flight images of all coarse aerosols, and here wedevelop a two-step classification algorithm that uses theseimages to identify a range of pollen taxa. Deterministiccriteria based on the shape of the particle are applied toinitially distinguish between intact pollen grains and othercoarse particulate matter. This first level of discriminationidentifies pollen with an accuracy of 96 %. Thereafter, in-dividual pollen taxa are recognized using supervised learn-ing techniques. The algorithm is trained using data obtainedby inserting known pollen types into the device, and out ofeight pollen taxa six can be identified with an accuracy ofabove 90 %. In addition to the ability to correctly identifyaerosols, an automatic pollen monitoring system needs to beable to correctly determine particle concentrations. To fur-ther verify the device, controlled chamber experiments us-ing polystyrene latex beads were performed. This providedreference aerosols with traceable particle size and numberconcentrations in order to ensure particle size and samplingvolume were correctly characterized

Towards European automatic bioaerosol monitoring:comparison of 9 automatic pollen observational instruments with classic Hirst-type traps

To benefit allergy patients and the medical practitioners, pollen information should be available in both a reliable and timely manner; the latter is only recently possible due to automatic monitoring. To evaluate the performance of all currently available automatic instruments, an international intercomparison campaign was jointly organised by the EUMETNET AutoPollen Programme and the ADOPT COST Action in Munich, Germany (March–July 2021).The automatic systems (hardware plus identification algorithms) were compared with manual Hirst-type traps. Measurements were aggregated into 3-hourly or daily values to allow comparison across all devices. We report results for total pollen as well as for Betula, Fraxinus, Poaceae, and Quercus, for all instruments that provided these data. The results for daily averages compared better with Hirst observations than the 3-hourly values. For total pollen, there was a considerable spread among systems, with some reaching R2 > 0.6 (3 h) and R2 > 0.75 (daily) compared with Hirst-type traps, whilst other systems were not suitable to sample total pollen efficiently (R2 < 0.3). For individual pollen types, results similar to the Hirst were frequently shown by a small group of systems. For Betula, almost all systems performed well (R2 > 0.75 for 9 systems for 3-hourly data). Results for Fraxinus and Quercus were not as good for most systems, while for Poaceae (with some exceptions), the performance was weakest. For all pollen types and for most measurement systems, false positive classifications were observed outside of the main pollen season. Different algorithms applied to the same device also showed different results, highlighting the importance of this aspect of the measurement system. Overall, given the 30 % error on daily concentrations that is currently accepted for Hirst-type traps, several automatic systems are currently capable of being used operationally to provide real-time observations at high temporal resolutions. They provide distinct advantages compared to the manual Hirst-type measurements