Method for calibration of bioaerosol monitors based on reference pollen aerosols and imaging-based Lagrangian particle tracking as reference: the case of the Swisens Poleno

Several automated pollen monitors have recently become available, most using conventional and/or machine-learning algorithms to detect pollen and classify their taxa. An international intercomparison campaign was organised by the EUMETNET AutoPollen Programme and the ADOPT COST Action in Munich (March–July 2021) (Maya-Manzano et al. 2023). The study showed that some automatic systems, especially those that have built-in correction factors to compensate for losses of sampling, detection and classification, agreed with the averaged pollen concentration of four manual Hirst-type measurements. The manual pollen counting method, relying on human operators, is still used today due to a lack of traceable metrological standards for pollen monitoring. Additionally, existing reference particle counters have been validated up to particle sizes around 20 µm only (Vasilatou et al. 2022). In this study, we evaluated the shake-the-box particle tracking/detection algorithm (Novara et al. 2023) to serve as a reference measurement of particle number concentration. A volume is illuminated with an expanded light sheet produced by a double-pulse laser, and four calibrated double-frame cameras record images of the particles inside this volume. The algorithm determines the position and velocity of each particle, allowing the measurement of particle number concentration directly in the air without sampling inlets that could influence the measurement.