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.

Particle tracking was validated against the Inkjet Aerosol Generator at the Japanese National Metrology Institute NMIJ and the reference optical particle counter at METAS using an 11-D spectrometer (Grimm GmbH, Germany) as a transfer standard and size-certified polystyrene particles. Expanded uncertainties (coverage factor k=2) were 24 %, 10 %, and 7 % for particle sizes 15 μm, 20 μm, and 26 μm, respectively. The good agreement among the three methods shows that the shake-the-box particle tracking method can be applied as a reference for particle number concentration.

We used a laboratory-based method for characterising the performance of bioaerosol monitors as a whole unit (hardware plus identification algorithms) using the particle tracking method and, in a separate experiment, freshly sampled pollen. Experiments were carried out with the SwisensPoleno Jupiter, which combines light scattering, inline digital holography and ultraviolet laser-induced fluorescence with machine learning to classify different particles. For a pollen grain to be measured, it must be sampled, detected and correctly classified.  Each stage is subject to particle losses, leading to a measurement efficiency below 100 %. Particle tracking measurements of pollen (Pinus, Zea Mays) resulted in an average counting efficiency of about 43 %, neglecting particle losses in the Sigma-2 sampling head and issues with the classification of particles. The unit-to-unit variability of the Poleno instruments was 30 % based on measurement with three units. Independent experiments with Alnus glutinosa, Betula pendula and Corylus avellana showed that the major source of losses, however, originates from the pollen classification algorithm, which is trimmed to best correlate with Hirst measurements (currently the defacto reference for pollen monitoring). This highlights the need for an independent, standardised method for evaluating classifier losses.