Background: Airborne pollen is ranked as the world’s most harmful aeroallergen, with pollen allergy presenting a significant socioeconomic burden. Contributing factors influencing allergenic reactions include the aeroallergen concentration and the taxonomic origin of pollen grains. In the UK, current pollen monitoring protocols aim to reduce this burden by monitoring and forecasting regional atmospheric pollen concentrations. However, due to morphological features being largely conserved across taxa, traditional palynological techniques are unable to discriminate between species of grass and tree pollen, and the ability to accurately characterise the taxonomic composition of airborne pollen remains an Achilles heel of pollen
forecasting. Results: Using aerial environmental DNA (eDNA) sampling and DNA metabarcoding (with two complementary DNA barcode markers, rbcL and ITS2), we aim to explore the composition of airborne pollen across the tree allergy season, at two sites in Great Britain. We will compare traditional palynological techniques and
targeted high-throughput sequencing to assess the accuracy of pollen identification via DNA metabarcoding and the ability to detect rare species. Furthermore, we will investigate the influences of bias involved in pollen DNA metabarcoding and test measures of association between species’ abundances to explore cross-utility of the different approaches for measuring pollen abundance. Significance: We anticipate that this research will increase our understanding of the ecology of airborne pollen in time and space and demonstrate the usefulness of DNA metabarcoding in pollen monitoring and aerobiology research.