A statistical model for predicting the inter-annual variability of birch pollen abundance in Northern and North-Eastern Europe
| Autor: | Åslög Dahl, I. Sauliene, Pilvi Siljamo, Agneta Ekebom, Mikhail Sofiev, Lucie Hoebeke, Hallvard Ramfjord, Annika Saarto, Elena Severova, Olga Ritenberga, Valentina Shalaboda |
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| Rok vydání: | 2018 |
| Předmět: |
Baltic States
Environmental Engineering Republic of Belarus 010504 meteorology & atmospheric sciences Meteorology Correlation coefficient ta1172 Birch pollen 010501 environmental sciences Seasonal pollen index medicine.disease_cause Disease cluster 01 natural sciences Pollen forecasting Annan biologi Russia Abundance (ecology) Pollen medicine Other Biological Topics Environmental Chemistry Waste Management and Disposal Betula Finland 0105 earth and related environmental sciences Sweden Models Statistical ta114 Norway Statistical model Allergens Pollution Geography ta1181 Seasons Physical geography Inter-annual variability |
| Zdroj: | Science of The Total Environment. 615:228-239 |
| ISSN: | 0048-9697 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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