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.

Fungal spores make up a significant portion of Primary Biological Aerosol Particles (PBAPs) with large quantities of such particles noted in the air. Fungal particles are of interest because of their potential to affect the health of both plants and humans. They are omnipresent in the atmosphere year-round, with concentrations varying due to meteorological parameters and location. Equally, differences between indoor and outdoor fungal spore concentrations and dispersal play an important role in occupational health. This review attempts to summarise the different spore sampling methods, identify the most important spore types in terms of negative effects on crops and the public, the factors affecting their growth/dispersal, and different methods of predicting fungal spore concentrations currently in use.

The real-time monitoring of primary biological aerosol particles (PBAP) such as pollen and fungal spores has received much attention in recent years as a result of their health and climatic effects. In this study, the Wideband Integrated Bioaerosol Sensor (WIBS) 4+ model was evaluated for its ability to sample and detect ambient fungal spore and pollen concentrations, compared to the traditional Hirst volumetric method. Although the determination of total pollen and fungal spore ambient concentrations are of interest, the selective detection of individual pollen/fungal spore types are often of greater allergenic/agricultural concern. To aid in this endeavour, modifications were made to the WIBS-4 instrument to target chlorophyll fluorescence. Two additional fluorescence channels (FL4 and FL5 channels) were combined with the standard WIBS channels (FL1, FL2, FL3). The purpose of this modification is to help discriminate between grass and herb pollen from other pollen. The WIBS-4+ was able to successfully detect and differentiate between different bioaerosol classes. The addition of the FL4 and FL5 channels also allowed for the improved differentiation between tree (R² = 0.8), herbaceous (R² = 0.6) and grass (R² = 0.4) pollen and fungal spores (R² = 0.8). Both grass and herbaceous pollen types showed a high correlation with D type particles, showing strong fluorescence in the FL4 channel. The additional fluorescent data that were introduced also improved clustering attempts, making k-means clustering a comparable solution for this high-resolution data.

Respiratory allergies triggered by pollen allergens represent a significant health concern to the Irish public. Up to now, Ireland has largely refrained from participating in long-term aerobiological studies. Recently, pollen monitoring has commenced in several sampling locations around Ireland. The first results of the pollen monitoring campaigns for Dublin (urban) and Carlow (rural) concerning the period 2017–2019 and 2018–2019, respectively, are presented herein. Additional unpublished pollen data from 1978–1980 and, 2010–2011 were also incorporated in creating the first pollen calendar for Dublin. During the monitoring period over 60 pollen types were identified with an average Annual Pollen Integral (APIn) of 32,217 Pollen × day/m³ for Dublin and 78,411 Pollen × day/m³ for Carlow. The most prevalent pollen types in Dublin were: Poaceae (32%), Urticaceae (29%), Cupressaceae/Taxaceae (11%), Betula (10%), Quercus (4%), Pinus (3%), Fraxinus (2%), Alnus (2%) and Platanus (1%). The predominant pollen types in Carlow were identified as Poaceae (70%), Urticaceae (12%), Betula (10%), Quercus (2%), Fraxinus (1%) and Pinus (1%). These prevalent pollen types increased in annual pollen concentration in both locations from 2018 to 2019 except for Fraxinus. Although higher pollen concentrations were observed for the Carlow (rural) site a greater variety of pollen types were identified for the Dublin (urban) site. The general annual trend in the pollen season began with the release of tree pollen in early spring, followed by the release of grass and herbaceous pollen which dominated the summer months with the annual pollen season coming to an end in October. This behaviour was illustrated for 21 different pollen types in the Dublin pollen calendar. The correlation between ambient pollen concentration and meteorological parameters was also examined and differed greatly depending on the location and study year. A striking feature was a substantial fraction of the recorded pollen sampled in Dublin did not correlate with the prevailing wind directions. However, using non-parametric wind regression, specific source regions could be determined such as Alnus originating from the Southeast, Betula originating from the East and Poaceae originating from the Southwest.