Speaker
Description
Allergic diseases represent a significant global health concern, with the prevalence of allergic rhinitis estimated at approximately 10–30% among adults and around 40% among children (Pawankar et al. 2013). Pollen constitutes one of the predominant factors associated with allergic rhinitis (Pawankar et al. 2013).
Air quality monitoring, particularly with a focus on pollen loads, is currently only possible to a limited extent. Limitations exist both in the spatial and temporal resolution of sampling and in the accuracy of qualitative and quantitative analyses of environmental samples.
Precise classification of pollen loads and particle types is still predominantly performed in a time-delayed manner through manual evaluation of traditional pollen trap samples by trained personnel.
The large number of samples generated in large-scale monitoring networks cannot be handled by manual evaluation alone. Therefore, we are developing a modular pipeline for automated, time-delayed analysis of environmental samples. This approach enables highly accurate quantification and classification of pollen loads (≥ 30 species) while achieving high throughput (≥ 50 samples/day) and can be integrated into existing monitoring networks.
Beyond allergy-related monitoring, this automated analysis framework also opens new avenues for ecological research by enabling large-scale investigations of pollen distribution, biodiversity patterns, and their environmental drivers.
| Status Group | Postdoctoral Researcher |
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