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Changes in concentration of Alternaria and Cladosporium spores during summer storms

机译:夏季风暴中链格孢和孢子孢子孢子浓度的变化

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摘要

Fungal spores are known to cause allergic sensitization. Recent studies reported a strong association between asthma symptoms and thunderstorms that could be explained by an increase in airborne fungal spore concentrations. Just before and during thunderstorms the values of meteorological parameters rapidly change. Therefore, the goal of this study was to create a predictive model for hourly concentrations of atmospheric Alternaria and Cladosporium spores on days with summer storms in Szczecin (Poland) based on meteorological conditions. For this study we have chosen all days of June, July and August (2004–2009) with convective thunderstorms. There were statistically significant relationships between spore concentration and meteorological parameters: positive for air temperature and ozone content while negative for relative humidity. In general, before a thunderstorm, air temperature and ozone concentration increased, which was accompanied by a considerable increase in spore concentration. During and after a storm, relative humidity increased while both air temperature ozone concentration along with spore concentrations decreased. Artificial neural networks (ANN) were used to assess forecasting possibilities. Good performance of ANN models in this study suggest that it is possible to predict spore concentrations from meteorological variables 2 h in advance and, thus, warn people with spore-related asthma symptoms about the increasing abundance of airborne fungi on days with storms.
机译:已知真菌孢子会引起过敏性致敏。最近的研究报道哮喘症状和雷暴之间有很强的联系,这可以通过空气传播的真菌孢子浓度增加来解释。就在雷暴发生之前和之中,气象参数的值迅速变化。因此,本研究的目的是根据波兰的气象条件,为在什切青(波兰)发生夏季暴风雨的日子建立一个预测模式,用于预测夏季交接日大气中链格孢和孢子孢子的小时浓度。在本研究中,我们选择了6月,7月和8月(2004-2009年)全天为对流雷暴。孢子浓度和气象参数之间存在统计学上的显着关系:空气温度和臭氧含量为正值,而相对湿度为负值。通常,在雷暴发生前,空气温度和臭氧浓度增加,同时孢子浓度也显着增加。在暴风雨期间和之后,相对湿度增加,而空气中的臭氧浓度和孢子浓度均降低。人工神经网络(ANN)用于评估预测可能性。这项研究中ANN模型的良好性能表明,可以提前2小时根据气象变量预测孢子浓度,因此可以向患有孢子相关性哮喘症状的人警告在暴风雨天气中空气传播真菌的数量会增加。

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