Astray Gonzalo, Rodríguez-Rajo F Javier, Ferreiro-Lage J Angel, Fernández-González María, Jato Victoria, Mejuto J Carlos
Department of Physical Chemistry, Faculty of Sciences, University of Vigo, 32004, Ourense, Spain.
J Environ Monit. 2010 Nov;12(11):2145-52. doi: 10.1039/c0em00248h. Epub 2010 Oct 5.
The monitoring of atmospheric Alternaria spores is of major importance due to their adverse effects on crops and their role as human allergens. Most species act as plant pathogens, prompting considerable economic losses worldwide on important crops such as potato, tomato or wheat. Fungal spores can also have serious detrimental effects on human health, triggering respiratory diseases and allergenic processes. The aim of this study was not only to examine the relationship between the atmospheric Alternaria spore content and the prevailing meteorological parameters, but also to predict the atmospheric Alternaria spore content in the Northwest Spain using a novel data analysis technique, ANNs (Artificial Neural Networks). A Hirst-type LANZONI VPPS 2000 volumetric 7-day recording sampler was used to collect the airborne spores from 1997 to 2008. Neural networks provided us with a good tool for forecasting Alternaria airborne spore concentration, and thus could help the automation of the prediction system in the aerobiological information diffusion to the population suffering from allergic problems or the prevention of considerable economic worldwide losses on important crops. Our proposed model would be applied to different geographical areas; nevertheless, the adjustment of the model, by using the available and adequate variables, would be realised in each case.
由于链格孢属孢子对作物有不利影响且作为人类过敏原，对大气中链格孢属孢子的监测至关重要。大多数链格孢属物种是植物病原体，在全球范围内给马铃薯、番茄或小麦等重要作物造成了可观的经济损失。真菌孢子也会对人类健康产生严重的有害影响，引发呼吸道疾病和过敏反应。本研究的目的不仅是研究大气中链格孢属孢子含量与主要气象参数之间的关系，还使用一种新的数据分析技术——人工神经网络（ANNs）来预测西班牙西北部大气中链格孢属孢子的含量。使用一台Hirst型LANZONI VPPS 2000容积式7天记录采样器在1997年至2008年期间收集空气中的孢子。神经网络为预测链格孢属空气传播孢子浓度提供了一个很好的工具，从而有助于预测系统的自动化，将空气生物学信息传播给患有过敏问题的人群，或防止重要作物在全球范围内遭受巨大经济损失。我们提出的模型将应用于不同的地理区域；然而，在每种情况下，都将通过使用可用的适当变量来实现模型的调整。
