Department of Plant Taxonomy and Phytogeography, Faculty of Biology, University of Szczecin, Szczecin, Poland.
Space Informatics Lab, University of Cincinnati, 219 Braunstein Hall, Cincinnati, OH 45221, USA; Institute of Geoecology and Geoinformation, Adam Mickiewicz University, Poznan, Poland.
Sci Total Environ. 2019 Feb 25;653:938-946. doi: 10.1016/j.scitotenv.2018.10.419. Epub 2018 Nov 5.
Airborne fungal spores are prevalent components of bioaerosols with a large impact on ecology, economy and health. Their major socioeconomic effects could be reduced by accurate and timely prediction of airborne spore concentrations. The main aim of this study was to create and evaluate models of Alternaria and Cladosporium spore concentrations based on data on a continental scale. Additional goals included assessment of the level of generalization of the models spatially and description of the main meteorological factors influencing fungal spore concentrations. Aerobiological monitoring was carried out at 18 sites in six countries across Europe over 3 to 21 years depending on site. Quantile random forest modelling was used to predict spore concentrations. Generalization of the Alternaria and Cladosporium models was tested using (i) one model for all the sites, (ii) models for groups of sites, and (iii) models for individual sites. The study revealed the possibility of reliable prediction of fungal spore levels using gridded meteorological data. The classification models also showed the capacity for providing larger scale predictions of fungal spore concentrations. Regression models were distinctly less accurate than classification models due to several factors, including measurement errors and distinct day-to-day changes of concentrations. Temperature and vapour pressure proved to be the most important variables in the regression and classification models of Alternaria and Cladosporium spore concentrations. Accurate and operational daily-scale predictive models of bioaerosol abundances contribute to the assessment and evaluation of relevant exposure and consequently more timely and efficient management of phytopathogenic and of human allergic diseases.
空气中的真菌孢子是生物气溶胶的主要成分,对生态、经济和健康都有重大影响。准确、及时地预测空气中孢子浓度,可以降低其主要的社会经济影响。本研究的主要目的是基于大陆尺度的数据,建立和评估拟Alternaria 和 Cladosporium 孢子浓度模型。此外,还评估了模型在空间上的泛化程度,并描述了影响真菌孢子浓度的主要气象因素。在欧洲六个国家的 18 个站点进行了 3 至 21 年的空气生物学监测,具体取决于站点。使用分位数随机森林模型预测孢子浓度。通过以下三种方式测试了 Alternaria 和 Cladosporium 模型的泛化能力:(i) 所有站点使用一个模型,(ii) 站点组使用模型,(iii) 个别站点使用模型。研究表明,使用网格化气象数据可以可靠地预测真菌孢子水平。分类模型还显示了提供更大规模真菌孢子浓度预测的能力。由于包括测量误差和浓度的明显日变化等几个因素,回归模型明显不如分类模型准确。温度和蒸气压被证明是 Alternaria 和 Cladosporium 孢子浓度回归和分类模型中最重要的变量。生物气溶胶丰度的准确和可操作的每日预测模型有助于评估相关暴露情况,并相应地更及时、更有效地管理植物病原和人类过敏疾病。