Pollen-related respiratory allergies affect up to 30% of the world's population, especially children. These allergies cause high medical costs, lead to absences from work and school and result in early deaths. Climate change will further exacerbate the pollen problem in the coming years, as more and more aggressive pollen is expected over longer periods. PollenNet pursues the following goals using and further developing AI methods:
(1) accurate analysis and prediction of the distribution of allergenic plants and in particular their growth phases (phenology),
(2) better characterization of pollen properties, especially with regard to allergenicity and dispersal, by means of cytometer analyses and fluid mechanics experiments, (3) development of pollen transport and dispersal models for high-resolution local, temporal and taxonomic prediction of pollen loads, and
(4) Research into objective individual markers in the EEG for allergy sufferers in the domestic environment. By integrating these findings, an approach is to be developed that enables significantly more accurate and up-to-date predictions of local pollen exposure.
