Abstract
To benefit allergy patients and 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 from March to July 2021.
The automatic systems, including hardware and identification algorithms, were compared with manual Hirst-type traps. Measurements were aggregated into 3-hourly or daily values to allow comparison across all devices. The study reports 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 for 3-hour measurements and R2 > 0.75 for daily measurements compared with Hirst-type traps, while other systems were not suitable to sample total pollen efficiently.
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. 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 manual Hirst-type measurements.
Keywords: Aerobiology; Automatic monitoring; Intercomparison campaign; Pollen; Pollen classification; Real-time.