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Spatio-temporal monitoring and modelling of birch pollen levels in Belgium

机译:比利时桦树花粉水平的时空监测与建模

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In Belgium, ~ 10% of the people is estimated to suffer from allergies due to pollen emitted by the birch family trees. Timely information on forthcoming pollen exposure episodes using a forecasting system can allow patients to take preventive measures. To date, the only available information on pollen concentrations in Belgium comes from five stations that monitor daily airborne birch pollen concentrations, but real-time and detailed spatial information is lacking. Pollen transport models can both quantify and forecast the spatial and temporal distribution of airborne birch pollen concentrations if accurate and updated maps of birch pollen emission sources are available and if the large inter-seasonal variability of birch pollen is considered. Here we show that the SILAM model driven by ECMWF ERA5 meteorological data is able to determine airborne birch pollen levels using updated maps of areal fractions of birch trees, as compared to the pollen observations of the monitoring stations in Belgium. Forest inventory data of the Flemish and Walloon regions were used to update the default MACCIII birch map. Spaceborne MODIS vegetation activity combined with an updated birch fraction map and updated start and end dates of the birch pollen season were integrated into SILAM. The correlation (R-2) between SILAM modelled and observed time series of daily birch pollen levels of 50 birch pollen seasons increased up to ~ 50%. The slopes of the linear correlation increased on average with ~ 60%. Finally, SILAM is able to capture the threshold of 80 pollen grains m(-3) exposure from the observations.
机译:在比利时,估计约有10%的人由于桦树家谱散发出的花粉而患有过敏症。使用预测系统及时了解即将到来的花粉接触事件,可使患者采取预防措施。迄今为止,比利时仅有的有关花粉浓度的可用信息来自五个监测每日机载桦树花粉浓度的站点,但缺乏实时和详细的空间信息。如果可获得准确和更新的桦树花粉排放源图,并且考虑到桦树花粉的季节间较大变异性,那么花粉运输模型可以量化和预测空中桦树花粉浓度的时空分布。在这里,我们表明,与比利时监测站的花粉观测值相比,由ECMWF ERA5气象数据驱动的SILAM模型能够使用更新的桦树面积分数图来确定机载桦树花粉水平。佛兰芒地区和瓦隆地区的森林清单数据用于更新默认的MACCIII桦木图。 SILAM中整合了星空MODIS植被活动,更新的桦树分数图以及桦树花粉季节的更新开始和结束日期。 SILAM模型与观察到的50个桦树花粉季节的每日桦树花粉水平的时间序列之间的相关性(R-2)增加到〜50%。线性相关的斜率平均增加约60%。最后,SILAM能够从观测值中捕获80个花粉粒m(-3)暴露的阈值。

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