International Journal of Biometeorology

, Volume 56, Issue 5, pp 949–958 | Cite as

A method to derive vegetation distribution maps for pollen dispersion models using birch as an example

  • A. Pauling
  • M. W. Rotach
  • R. Gehrig
  • B. Clot
  • Contributors to the European Aeroallergen Network (EAN)
Original Paper


Detailed knowledge of the spatial distribution of sources is a crucial prerequisite for the application of pollen dispersion models such as, for example, COSMO-ART (COnsortium for Small-scale MOdeling - Aerosols and Reactive Trace gases). However, this input is not available for the allergy-relevant species such as hazel, alder, birch, grass or ragweed. Hence, plant distribution datasets need to be derived from suitable sources. We present an approach to produce such a dataset from existing sources using birch as an example. The basic idea is to construct a birch dataset using a region with good data coverage for calibration and then to extrapolate this relationship to a larger area by using land use classes. We use the Swiss forest inventory (1 km resolution) in combination with a 74-category land use dataset that covers the non-forested areas of Switzerland as well (resolution 100 m). Then we assign birch density categories of 0%, 0.1%, 0.5% and 2.5% to each of the 74 land use categories. The combination of this derived dataset with the birch distribution from the forest inventory yields a fairly accurate birch distribution encompassing entire Switzerland. The land use categories of the Global Land Cover 2000 (GLC2000; Global Land Cover 2000 database, 2003, European Commission, Joint Research Centre; resolution 1 km) are then calibrated with the Swiss dataset in order to derive a Europe-wide birch distribution dataset and aggregated onto the 7 km COSMO-ART grid. This procedure thus assumes that a certain GLC2000 land use category has the same birch density wherever it may occur in Europe. In order to reduce the strict application of this crucial assumption, the birch density distribution as obtained from the previous steps is weighted using the mean Seasonal Pollen Index (SPI; yearly sums of daily pollen concentrations). For future improvement, region-specific birch densities for the GLC2000 categories could be integrated into the mapping procedure.


Vegetation distribution Birch pollen Land use data Forest inventory Seasonal Pollen Index 



The authors would like to thank the following institutions for providing their pollen data to the European Aeroallergen Network (EAN):

The Austrian Pollen Information Service; The Scientific Institute of Public Health of Belgium; The German Pollen Information Service; The Spanish Aerobiological Network; the Danish Asthma-Allergy Association; The French Aerobiological Monitoring Network; The Italian Aerobiological Association; the Polish Allergen Research Center; the Spanish Universities of León, the Balearic Islands, Barcelona, Málaga, Vigo, Santiago de Compostela, and Córdoba; the Basque department of Health, Spain; The Free University Berlin, Germany; The Leiden University Medical Center, Netherlands; the University of Gothenburg, Sweden; the University of Novi Sad, Serbia; the Serbian Environmental Protection Agency; The UK Met Office; The Pollen Research Unit in Worcester, UK; Queen's University Belfast, UK; The Scottish Crop Research Institute; the University of Wales Institute, Cardiff, UK; the Hungarian National Institute of Environmental Health; the Ministry of Health, Luxembourg; and The University of Cracow, Poland. Additionally, the authors wish to thank the Swiss Federal Institute for Forest, Snow and Landscape Research and the Swiss Federal Office for Statistics for access to their data.


  1. Brändli UB (1998) Die häufigsten Waldbäume der Schweiz, 2nd ed. Berichte der Eidgenössischen Forschungsanstalt für Wald, Schnee und Landschaft, p 342Google Scholar
  2. Brändli UB (2010) Schweizerisches Landesforstinventar. Ergebnisse der dritten Erhebung 2004–2006. Birmensdorf, Eidgenössische Forschungsanstalt für Wald, Schnee und Landschaft WSL. Bern, Bundesamt für Umwelt, BAFU, p 312Google Scholar
  3. Bricchi E, Frenguelli G, Mincigrucci G (2000) Experimental results about Platanus pollen deposition. Aerobiologia 16:347–352CrossRefGoogle Scholar
  4. CEC (1994) CORINE land cover. Technical guide. CEC, LuxembourgGoogle Scholar
  5. Chamecki M, Meneveau C, Parlange MB (2009) Large eddy simulation of pollen transport in the atmospheric boundary layer. Aerosol Science 40:241–255CrossRefGoogle Scholar
  6. European Commission (2003) Global Land Cover 2000 database. European Commission, Joint Research Centre.
  7. Helbig N, Vogel B, Vogel H, Fiedler F (2004) Numerical modelling of pollen dispersion on the regional scale. Aerobiologia 3:3–19CrossRefGoogle Scholar
  8. Hidalgo P, Manginand A, Galan C, Hembise O, Vazquez L, Sanchez O (2002) An automated system for surveying and forecasting Olea pollen dispersion. Aerobiologia 18:23–31CrossRefGoogle Scholar
  9. Hoffmann F, Janicke L, Janicke U, Wachter R, Kuhn U (2008) Modellrechnungen zur Ausbreitung von Maispollen unter Worst-Case-Annahmen mit Vergleich von Freilandmessdaten. Gutachten Maispollendeposition, Bundesamt für Naturschutz, Ökologiebüro, p 42Google Scholar
  10. Hynynen J, Niemistö P, Viherä-Aarnio A, Brunner A, Hein S, Velling P (2010) Silviculture of birch (Betula pendula Roth and Betula pubescens Ehrh.) in northern Europe. Forestry 83:103–119CrossRefGoogle Scholar
  11. Köble R, Seufert G (2001) Proceedings of the European symposium on the physico-chemical behaviour of atmospheric pollutants: a changing atmosphere. Torino, 17–20 September 2001.Google Scholar
  12. Mandrioli P, Di Cecco M, Andina G (1998) Ragweed pollen: the aeroallergen is spreading in Italy. Aerobiologia 14:13–20CrossRefGoogle Scholar
  13. Mücher CA (ed) (2000) Pan-European Land Cover Mapping (PELCOM) project. Development of a consistent methodology to derive land cover information on a European scale from remote sensing for environmental modeling, PELCOM, p 299Google Scholar
  14. Nilsson S, Persson S (1981) Tree pollen spectra in the Stockholm region (Sweden), 1973–80. Grana 20:179–182CrossRefGoogle Scholar
  15. Päivinen R, Lehikoinen M, Schuck A, Häme T, Väätäinen S, Kennedy P, Folving S (2001) Combining earth observation data and forest statistics. EFI Research Report 14. European Forest Institute, Joint Research Centre - European Commission. EUR 19911 EN, p 101Google Scholar
  16. Schuck A, Van Brusselen J, Päivinen R, Häme T, Kennedy P, Folving S (2002) Compilation of a calibrated European forest map derived from NOAA-AVHRR data. European Forest Institute. Internal Rep. 13, p 44 plus AnnexesGoogle Scholar
  17. Schueler S, Schlünzen KH (2006) Modeling of oak pollen dispersal on the landscape level with a mesoscale atmospheric model. Environ Model Assess 3:179–194CrossRefGoogle Scholar
  18. Skjøth CA, Geles C, Hvidberg M, Hertel O, Brandt J, Frohn LM, Hansen KM, Hedegard GB, Christensen JH, Moseholm L (2008) An inventory of tree species in Europe – An essential data input for air pollution modeling. Ecol Model 217:292–304CrossRefGoogle Scholar
  19. Skjøth CA, Smith M, Sikoparija B, Stach A, Myszkowska D, Kasprzyk I, Radisic P, Stjepanovic B, Hrga I, Apatini D, Magyar D, Paldy A, Ianovici N (2010) A method for producing airborne pollen source inventories: an example of Ambrosia (ragweed) on the Pannonian Plain. Agr Forest Meteorol 150:1203–1210CrossRefGoogle Scholar
  20. Skjøth CA, Sommer J, Stach A, Smith M, Brandt J (2007) The long-range transport of birch (Betula) pollen from Poland and Germany causes significant pre-season concentrations in Denmark. Clin Exp Allergy 37:1204–1212CrossRefGoogle Scholar
  21. Sofiev M, Siljamo P, Ranta H, Rantio-Lehtimäki A (2006) Towards numerical forecasting of long-range air transport of birch pollen: theoretical considerations and a feasibility study. Int J Biometeorol 50:392–402CrossRefGoogle Scholar
  22. Swiss Federal Office for statistics (1997) Arealstatistik (1992/97). BFS GEOSTAT, Bundesamt für Statistik (BFS).Google Scholar
  23. UN-ECE (1998) International Co-operative Programme on Assessment and Monitoring of Air pollution Effects on Forests. Manual on methods and criteria for harmonized sampling, assessment, monitoring and analysis of the effects of air pollution on forests. United Nations Economic Commission for EuropeGoogle Scholar
  24. Vakkari P (2009) EUFORGEN technical guidelines for genetic conservation and use for Silver birch (Betula pendula). Bioversity International, Rome, Italy, p 6Google Scholar
  25. Veriankaite L, Siljamo P, Sofiev M, Sauliene I, Kukkonen J (2010) Modelling analysis of source regions of long-range transported birch pollen that influences allergenic seasons in Lithuania. Aerobiologia 26:47–62CrossRefGoogle Scholar
  26. Vogel H, Pauling A, Vogel B (2008) Numerical simulation of birch pollen dispersion with an operational weather forecast system. Int J Biometeorol 52:805–814CrossRefGoogle Scholar
  27. Vogel B, Vogel H, Bäumer D, Bangert M, Lundgren K, Rinke R, Stanelle T (2009) The comprehensive model system COSMO-ART – Radiative impact of aerosol on the state of the atmosphere on the regional scale. Atmos Chem Phys 9:8661–8680CrossRefGoogle Scholar
  28. WSL (2011) Schweizerisches Landesforstinventar LFI (Swiss national forest inventory). Daten der Erhebung 2004–06. Eidg. Forschungsanstalt WSL, Birmensdorf, SwitzerlandGoogle Scholar

Copyright information

© ISB 2011

Authors and Affiliations

  • A. Pauling
    • 1
  • M. W. Rotach
    • 1
    • 2
  • R. Gehrig
    • 1
  • B. Clot
    • 1
  • Contributors to the European Aeroallergen Network (EAN)
  1. 1.Federal Office of Meteorology and Climatology MeteoSwissZürichSwitzerland
  2. 2.Institute of Meteorology and GeophysicsUniversity of InnsbruckInnsbruckAustria

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