Mediating Airborne Birch Modelling and Forecasting

Air pollution contributes to increased mortality and lower quality of life. It imposes additional distress on people suffering from respiratory diseases such as pollinosis. A quarter of the adult population and a third of all children in Europe are estimated to suffer from airborne allergenic pollen. In the future even more people might be subjected to pollen allergies since changes in climate and land-use tend to increase the amount of allergenic airborne pollen and prolong the pollen seasons. Good pollen mitigation measures may ease the symptoms but it requires proper knowledge on the modelling and forecasting of allergenic pollen in the air.

We start from the setup of the pollen transport model SILAM (System for Integrated modeLling of Atmospheric coMposition), driven by ECMWF ERA5 meteorology in a bottom-up emission approach for the period 1982-2019 for the Belgian territory. The dynamic vegetation component in the pollen transport model is determined by pollen emission source maps which have to be ingested in the model for every pollen season. The used maps are derived by merging multi-decadal datasets of spaceborne NDVI with forest inventory data in a Random Forest statistical framework.

Here we study the impact of spatially-varying pollen emission sources on the modelled airborne birch pollen levels compared with in-situ observations in a Monte-Carlo approach. Preliminary analysis indicates that by selecting the model scenario corresponding with median pollen levels, the correlation between the modelled and observed pollen levels increases with 16%. We show the importance of ingesting the appropriate pollen emission source map when the modelling and forecasting of airborne birch pollen levels is aimed for. Finally, we review methods to relate the pre-pollen season meteorology or vegetation state on the birch pollen loads of the up-coming season as tool for selecting the best emission source map in the forecasting framework.