Department of Plant Taxonomy and Phytogeography, Faculty of Natural Science, University of Szczecin, Wąska 13 Street, 71-415 Szczecin, Poland.
Environ Pollut. 2011 Feb;159(2):602-8. doi: 10.1016/j.envpol.2010.10.002. Epub 2010 Oct 27.
Fungal spores are an important component of bioaerosol and also considered to act as indicator of the level of atmospheric bio-pollution. Therefore, better understanding of these phenomena demands a detailed survey of airborne particles. The objective of this study was to examine the dependence of two the most important allergenic taxa of airborne fungi--Alternaria and Cladosporium--on meteorological parameters and air pollutant concentrations during three consecutive years (2006-2008). This study is also an attempt to create artificial neural network (ANN) forecasting models useful in the prediction of aeroallergen abundance. There were statistically significant relationships between spore concentration and environmental parameters as well as pollutants, confirmed by the Spearman's correlation rank analysis and high performance of the ANN models obtained. The concentrations of Cladosporium and Alternaria spores can be predicted with quite good accuracy from meteorological conditions and air pollution recorded three days earlier.
真菌孢子是生物气溶胶的重要组成部分,也被认为是大气生物污染水平的指标。因此,要更好地了解这些现象,就需要对空气传播颗粒进行详细调查。本研究的目的是研究在连续三年(2006-2008 年)期间,两种最重要的空气传播真菌过敏原——交链孢霉和半知菌属——对气象参数和空气污染物浓度的依赖性。本研究还试图创建有用的人工神经网络(ANN)预测模型,以预测空气过敏原的丰度。孢子浓度与环境参数和污染物之间存在统计学上的显著关系,这一点得到了 Spearman 等级相关分析的证实,并且得到的 ANN 模型性能也很高。可以从三天前记录的气象条件和空气污染数据中相当准确地预测出半知菌属和交链孢霉孢子的浓度。