Ambient air quality monitoring is a governmental duty that is widely carried out in order to detect non-biological (“chemical”) components in ambient air, such as particles of < 10 µm (PM10, PM2.5), ozone, sulphur dioxide, and nitrogen oxides. These monitoring networks are publicly funded and air quality data are open to the public. The situation for biological particles that have detrimental effects on health, as is the case of pollen and fungal spores, is however very different. Most pollen and spore monitoring networks are not publicly funded and data are not freely available. The information regarding which biological particle is being monitored, where and by whom, is consequently often not known, even by aerobiologists themselves. This is a considerable problem, as local pollen data are an important tool for the prevention of allergic symptoms. The aim of this study was to review pollen monitoring stations throughout the world and to create an interactive visualization of their distribution. The method employed to collect information was based on: (a) a review of the recent and historical bibliography related to pollen and fungal spore monitoring, and (b) personal surveys of the managers of national and regional monitoring networks. The interactive application was developed using the R programming language. We have created an inventory of the active pollen and spore monitoring stations in the world. There are at least 879 active pollen monitoring stations in the world, most of which are in Europe (> 500). The prevalent monitoring method is based on the Hirst principle (> 600 stations). The inventory is visualised as an interactive and on-line map. It can be searched, its appearance can be adjusted to the users’ needs and it is updated regularly, as new stations or changes to those that already exist can be submitted online. The map shows the current situation of pollen and spore monitoring and facilitates collaboration among those individuals who are interested in pollen and spore counts. It might also help to improve the monitoring of biological particles up to the current level employed for non-biological components.

Background: Pollen are monitored in Europe by a network of about 400 pollen traps, all operated manually. To date, automated pollen monitoring has only been feasible in areas with limited variability in pollen species. There is a need for rapid reporting of airborne pollen as well as for alleviating the workload of manual operation. We report our experience with a fully automated, image recognition-based pollen monitoring system, BAA500. Methods: The BAA500 sampled ambient air intermittently with a 3-stage virtual impactor at 60 m3/h in Munich, Germany. Pollen is deposited on a sticky surface that was regularly moved to a microscope equipped with a CCD camera. Images of the pollen were constructed and compared with a library of known samples. A Hirst-type pollen trap was operated simultaneously. Results: Over 480,000 particles sampled with the BAA500 were both manually and automatically identified, of which about 46,000 were pollen. Of the automatically reported pollen, 93.3% were correctly recognized. However, compared with manual identification, 27.8% of the captured pollen were missing in the automatic report, with most reported as unknown pollen. Salix pollen grains were not identified satisfactorily. The daily pollen concentrations reported by a Hirst-type pollen trap and the BAA500 were highly correlated (r = 0.98). Conclusions: The BAA500 is a functional automated pollen counter. Its software can be upgraded, and so we expected its performance to improve upon training. Automated pollen counting has great potential for workload reduction and rapid online pollen reporting.

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.