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  • 1.
    Ekebom, Agneta
    et al.
    Swedish Museum of Natural History, Department of Environmental research and monitoring.
    Dahl, Åslög
    Göteborgs universitet.
    Pollensäsongen 2017: Sammanställning av pollenförekomsten i Sverige2018Report (Other academic)
  • 2.
    Ekebom, Agneta
    et al.
    Swedish Museum of Natural History, Department of Environmental research and monitoring.
    Dahl, Åslög
    Göteborgs universitet.
    Pollensäsongen 2018: Sammanställning av pollenförekomsten i Sverige2019Report (Other academic)
  • 3. Lind, Tomas
    et al.
    Ekebom, Agneta
    Swedish Museum of Natural History, Department of Environmental research and monitoring.
    Alm Kübler, Kerstin
    Swedish Museum of Natural History, Department of Environmental research and monitoring.
    Östensson, Pia
    Swedish Museum of Natural History, Department of Environmental research and monitoring.
    Bellander, Tom
    Lõhmus, Mare
    Pollen Season Trends (1973-2013) in Stockholm Area, Sweden2016In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 11, no 11, p. 1-12Article in journal (Refereed)
  • 4.
    Ritenberga, Olga
    et al.
    University of Latvia Faculty of Geography and Earth Sciences.
    Sofiev, Mikhail
    Finnish Meteorological Institute.
    Siljamo, Pilvi
    Finnish Meteorological Institute.
    Saarto, Annika
    Unit of Aerobiology, University of Turku.
    Dahl, Aslog
    Department of Biological and Environmental Sciences, University of Gothenburg.
    Ekebom, Agneta
    Swedish Museum of Natural History, Department of Environmental research and monitoring.
    Sauliene, Ingrida
    Research Institute, Siauliai University.
    Shalaboda, Valentina
    Institute for Experimental Botany of the NAS of Belarus.
    Severova, Elena
    Moscow State University.
    Hoebeke, Lucie
    Belgian Aerobiological Network, Mycology and Aerobiology service, Scientific Institute of Public Health.
    Ramfjord, Hallvard
    Department of Biology, Norwegian University of Science and Technology.
    A statistical model for predicting the inter-annual variability of birchpollen abundance in Northern and North-Eastern Europe2018In: Science of the Total Environment, ISSN 0048-9697, E-ISSN 1879-1026, Vol. 615, p. 228-239Article in journal (Refereed)
    Abstract [en]

    The paper suggests amethodology for predicting next-year seasonal pollen index (SPI, a sumof daily-mean pollen concentrations)over large regions and demonstrates its performance for birch in Northern andNorth-Eastern Europe. Astatistical model is constructed using meteorological, geophysical and biological characteristics of the previous year).A cluster analysis of multi-annual data of European Aeroallergen Network (EAN) revealed several large regions inEurope, where the observed SPI exhibits similar patterns of the multi-annual variability.We built the model for thenorthern cluster of stations, which covers Finland, Sweden, Baltic States, part of Belarus, and, probably, Russia andNorway,where the lack of data did not allow for conclusive analysis. The constructed modelwas capable of predictingthe SPI with correlation coefficient reaching up to 0.9 for somestations, odds ratio is infinitely high for 50% of sites insidethe region and the fraction of prediction fallingwithin factor of 2 from observations, stays within 40–70%. In particular,model successfully reproduced both the bi-annual cycle of the SPI and years when this cycle breaks down.

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