, Volume 35, Issue 4, pp 703–717 | Cite as

Spatio-temporal monitoring and modelling of birch pollen levels in Belgium

  • Willem W. VerstraetenEmail author
  • Sébastien Dujardin
  • Lucie Hoebeke
  • Nicolas Bruffaerts
  • Rostislav Kouznetsov
  • Nicolas Dendoncker
  • Rafiq Hamdi
  • Catherine Linard
  • Marijke Hendrickx
  • Mikhail Sofiev
  • Andy W. Delcloo
Original Paper


In Belgium, ~ 10% of the people is estimated to suffer from allergies due to pollen emitted by the birch family trees. Timely information on forthcoming pollen exposure episodes using a forecasting system can allow patients to take preventive measures. To date, the only available information on pollen concentrations in Belgium comes from five stations that monitor daily airborne birch pollen concentrations, but real-time and detailed spatial information is lacking. Pollen transport models can both quantify and forecast the spatial and temporal distribution of airborne birch pollen concentrations if accurate and updated maps of birch pollen emission sources are available and if the large inter-seasonal variability of birch pollen is considered. Here we show that the SILAM model driven by ECMWF ERA5 meteorological data is able to determine airborne birch pollen levels using updated maps of areal fractions of birch trees, as compared to the pollen observations of the monitoring stations in Belgium. Forest inventory data of the Flemish and Walloon regions were used to update the default MACCIII birch map. Spaceborne MODIS vegetation activity combined with an updated birch fraction map and updated start and end dates of the birch pollen season were integrated into SILAM. The correlation (R2) between SILAM modelled and observed time series of daily birch pollen levels of 50 birch pollen seasons increased up to ~ 50%. The slopes of the linear correlation increased on average with ~ 60%. Finally, SILAM is able to capture the threshold of 80 pollen grains m−3 exposure from the observations.


Birch pollen Birch fraction maps Pollen observations SILAM model Time series 



This research was partly funded by the Belgian Science Policy Office (BELSPO) in the frame of the Belgian Research Action through Interdisciplinary Networks Brain ( programme—project RespirIT (BR/154/A1/RespirIT) and partly funded by the Royal Meteorological Institute of Belgium. We acknowledge the discussions with the other members of the RespirIT project.


This research was partly funded by the Belgian Science Policy Office (BELSPO) in the frame of the Belgian Research Action through Interdisciplinary Networks Brain ( programme—project RespirIT (BR/154/A1/RespirIT) and partly funded by the Royal Meteorological Institute of Belgium.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Royal Meteorological Institute of BelgiumUkkel, BrusselsBelgium
  2. 2.Department of GeographyUniversity of NamurNamurBelgium
  3. 3.Mycology and Aerobiology UnitSciensanoBrusselsBelgium
  4. 4.Finnish Meteorological InstituteHelsinkiFinland
  5. 5.Department of Physics and AstronomyGhent UniversityGhentBelgium

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