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

机译:预测德国巴伐利亚桦树花粉季节的开始,峰值和末端

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摘要

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. (C) 2019 Elsevier B.V. All rights reserved.
机译:桦树花粉经常在中央和北欧的气氛中找到。桦木花粉是健康,因为它们导致人口中严重过敏反应。我们开发了用于预测Bavaria(德国)的Betula Main Pollen季节的开始,高峰和结束日期的模型。从19个地点的一个季节的Betula花粉数据用于训练模型。将估计的日期与观察到的日期进行比较,并且在空间上表示误差。外部验证与3个不同地点的时间序列数据集进行(总共36年)。结果:引爆主要花粉季节的温度要求证明了非线性。对于开始日期模型(外部验证期间8.75天的误差),从2月28日起,每日平均温度超过10摄氏度的阈值是最相关的。峰值模型(3.58天的误差)考虑到自定日平均温度的主要花粉季节的第一个日期以来累积的每日温度,其中每日平均温度超过11摄氏度。结局模型(3.75天的误差)考虑了所有温度自主花粉季开始以来累积。结论:这些模型进行预测,使过敏群体能够更好地管理其疾病。随着温度和花粉季节之间的建立关系,通过考虑到以前的气候变化研究提出的不同气候情景,将在未来的研究中估计由于气候变化导致的白桦种类的变化。 (c)2019 Elsevier B.v.保留所有权利。

著录项

  • 来源
    《The Science of the Total Environment》 |2019年第10期|1299-1309|共11页
  • 作者单位

    Univ Malaga Dept Bot & Vegetal Physiol Campus Teatinos S-N E-29071 Malaga Spain;

    Tech Univ Munich German Ctr Lung Res DZL Ctr Allergy & Environm ZAUM Helmholtz Ctr Munich Germany;

    Tech Univ Munich German Ctr Lung Res DZL Ctr Allergy & Environm ZAUM Helmholtz Ctr Munich Germany|Univ Castilla La Mancha Inst Environm Sci Toledo Spain;

    Tech Univ Munich Chair & Inst Environm Med UNIKA T Munich Germany|Helmholtz Zentrum Munchen Munich Germany|German Res Ctr Environm Hlth Augsburg Germany|Ctr Allergy Res & Educ CK CARE Davos Switzerland;

    Tech Univ Munich Chair & Inst Environm Med UNIKA T Munich Germany|Helmholtz Zentrum Munchen Munich Germany|German Res Ctr Environm Hlth Augsburg Germany;

    Tech Univ Munich Dept Ecol & Ecosyst Management Ecoclimatol Freising Weihenstephan Germany;

    Fdn German Pollen Informat Serv PID Berlin Germany;

    Fdn German Pollen Informat Serv PID Berlin Germany;

    Tech Univ Munich German Ctr Lung Res DZL Ctr Allergy & Environm ZAUM Helmholtz Ctr Munich Germany;

    Tech Univ Munich German Ctr Lung Res DZL Ctr Allergy & Environm ZAUM Helmholtz Ctr Munich Germany;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Aerobiology; Phenology; Forecasting; Birch; Meteorological factors;

    机译:健美学;候选;预测;桦木;气象因素;

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