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A statistical model for predicting the inter-annual variability of birch pollen abundance in Northern and North-Eastern Europe

Artikel i vetenskaplig tidskrift
Författare O. Ritenberga
M. Sofiev
P. Siljamo
A. Saarto
Åslög Dahl
A. Ekebom
I. Sauliene
V. Shalaboda
E. Severova
L. Hoebeke
H. Ramfjord
Publicerad i Science of the Total Environment
Volym 615
Sidor 228-239
ISSN 0048-9697
Publiceringsår 2018
Publicerad vid Institutionen för biologi och miljövetenskap
Sidor 228-239
Språk English
Länkar doi.org/10.1016/j.scitotenv.2017.09...
Ämnesord Birch pollen, Inter-annual variability, Pollen forecasting, Seasonal pollen index, Cluster analysis, Annual variability, Biological characteristic, Correlation coefficient, Interannual variability, Pollen indexes, Similar pattern, Statistical modeling, Forecasting
Ämneskategorier Geovetenskap och miljövetenskap

Sammanfattning

The paper suggests a methodology for predicting next-year seasonal pollen index (SPI, a sum of daily-mean pollen concentrations) over large regions and demonstrates its performance for birch in Northern and North-Eastern Europe. A statistical 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 in Europe, where the observed SPI exhibits similar patterns of the multi-annual variability. We built the model for the northern cluster of stations, which covers Finland, Sweden, Baltic States, part of Belarus, and, probably, Russia and Norway, where the lack of data did not allow for conclusive analysis. The constructed model was capable of predicting the SPI with correlation coefficient reaching up to 0.9 for some stations, odds ratio is infinitely high for 50% of sites inside the region and the fraction of prediction falling within 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. © 2017 Elsevier B.V.

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