Forecasting methodologies for Ganoderma spore concentration using combined statistical approaches and model evaluations

• Published in: International journal of biometeorology 2016-04, Vol.60 (4), p.489-498
• Main Authors: Sadyś, Magdalena, Skjøth, Carsten Ambelas, Kennedy, Roy
• Format: Article
• Language: English
• Subjects: air; Air Pollutants - analysis; allergenicity; allergens; Allergens - analysis; Allergies; Animal Physiology; basidiospores; bioclimatology; Biological and Medical Physics; Biophysics; Dew point; dewpoint; Earth and Environmental Science; Environment; Environmental Health; Forecasting; Fungi; Ganoderma; humans; Meteorology; Models, Theoretical; monitoring; Multivariate Analysis; neural networks; Neural Networks (Computer); Original Paper; Plant Physiology; Regression Analysis; respiratory system; Respiratory tract; Spores, Fungal - isolation & purification; statistical analysis; Statistical methods; temperature; United Kingdom; Urban areas; Weather; Weather forecasting
• ISSN: 0020-7128; 1432-1254
• DOI: 10.1007/s00484-015-1045-3

High concentration levels of Ganoderma spp. spores were observed in Worcester, UK, during 2006–2010. These basidiospores are known to cause sensitization due to the allergen content and their small dimensions. This enables them to penetrate the lower part of the respiratory tract in humans. Establishment of a link between occurring symptoms of sensitization to Ganoderma spp. and other basidiospores is challenging due to lack of information regarding spore concentration in the air. Hence, aerobiological monitoring should be conducted, and if possible extended with the construction of forecast models. Daily mean concentration of allergenic Ganoderma spp. spores in the atmosphere of Worcester was measured using 7-day volumetric spore sampler through five consecutive years. The relationships between the presence of spores in the air and the weather parameters were examined. Forecast models were constructed for Ganoderma spp. spores using advanced statistical techniques, i.e. multivariate regression trees and artificial neural networks. Dew point temperature along with maximum temperature was the most important factor influencing the presence of spores in the air of Worcester. Based on these two major factors and several others of lesser importance, thresholds for certain levels of fungal spore concentration, i.e. low (0–49 s m⁻³), moderate (50–99 s m⁻³), high (100–149 s m⁻³) and very high (150