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Modelling grass pollen levels in Belgium.

Willem W Verstraeten1, Rostislav Kouznetsov2, Lucie Hoebeke3

  • 1Royal Meteorological Institute of Belgium, Ukkel, Brussels, Belgium.

The Science of the Total Environment
|September 8, 2020
PubMed
Summary
This summary is machine-generated.

This study models airborne grass pollen in Belgium using the SILAM model, improving forecasts by optimizing seasonal pollen release dates. This enhances public health by better predicting pollen exposure for those with respiratory issues.

Keywords:
Airborne grass pollenBiogenic aerosolsChemistry Transport Model

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Area of Science:

  • Environmental Science
  • Atmospheric Science
  • Public Health

Background:

  • Biogenic aerosols like airborne grass pollen significantly impact public health, exacerbating conditions for individuals with cardiovascular and respiratory diseases.
  • Current monitoring of airborne pollen in Belgium is limited to offline measurements at a few sites, hindering timely national coverage and short-term forecasting.

Purpose of the Study:

  • To model spatio-temporal airborne grass pollen levels in Belgium using the Chemistry Transport Model SILAM.
  • To optimize the grass pollen season's start and end dates within the SILAM model by comparing simulations with observational data.
  • To quantify the impact of spatial grass pollen source distribution on airborne concentrations.

Main Methods:

  • Applied the SILAM model to Belgium, utilizing a 2015 grassland map for bottom-up emission inventories and ERA5 meteorological data for aerosol transport.
  • Optimized grass pollen seasonal onset and offset dates by comparing model simulations (2008-2018) with data from the Belgian aerobiological surveillance network.
  • Quantified source-receptor relationships by simulating varying grass pollen emissions across different model domain areas.

Main Results:

  • Adjusting the grass pollen season's start and end dates significantly improved model performance, nearly doubling the correlation with local observations.
  • Simulations revealed that up to 33% of airborne grass pollen in a specific area originated from other regions within Belgium.
  • Incorporating temporal scaling of inter-seasonal pollen amounts further increased model performance, achieving an additional R² increase of up to 22%.

Conclusions:

  • Optimizing seasonal parameters and source-receptor dynamics in the SILAM model substantially enhances the accuracy of airborne grass pollen level predictions in Belgium.
  • Improved modeling capabilities can aid in better public health management by providing more reliable short-term forecasts of grass pollen exposure.
  • Future enhancements could involve more detailed grass pollen source data and dynamic adjustments to the pollen season's start and end dates.