PuSH - Publikationsserver des Helmholtz Zentrums München: Predicting the start, peak and end of the <em>Betula</em> pollen season in Bavaria, Germany.

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Picornell, A.* ; Buters, J.T.M. ; Rojo, J. ; Traidl-Hoffmann, C. ; Damialis, A. ; Menzel, A.* ; Bergmann, K.C.* ; Werchan, M.* ; Schmidt-Weber, C.B. ; Oteros, J.

Predicting the start, peak and end of the Betula pollen season in Bavaria, Germany.

Sci. Total Environ. 690, 1299-1309 (2019)

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Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.

Abstract
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Betula pollen is frequently found in the atmosphere of central and northern Europe. Betula pollen are health relevant as they cause severe allergic reactions in the population. We developed models of thermal requirements to predict start, peak and end dates of the Betula main pollen season for Bavaria (Germany). Betula pollen data of one season from 19 locations were used to train the models. Estimated dates were compared with observed dates, and the errors were spatially represented. External validation was carried out with time series datasets of 3 different locations (36 years in total).Results: The temperature requirements to detonate the main pollen season proved non-linear. For the start date model (error of 8.75 days during external validation), daily mean temperatures above a threshold of 10 degrees C from 28th of February onwards were the most relevant. The peak model (error of 3.58 days) takes into account mean daily temperatures accumulated since the first date of the main pollen season in which the daily average temperature exceeded 11 degrees C. The end model (error of 3.75 days) takes into account all temperatures accumulated since the start of the main pollen season.Conclusion: These models perform predictions that enable the allergic population to better manage their disease. With the established relationship between temperatures and pollen season dates, changes in the phenological behaviour of Betula species due to climate change can be also estimated in future studies by taking into account the different climate scenarios proposed by previous climate change studies.

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