The relationships between air pollutants, meteorological parameters and concentration of airborne fungal spores

• Published in: Environmental pollution (1987) Vol. 159; no. 2; pp. 602 - 608
• Main Authors: Grinn-Gofroń, Agnieszka, Strzelczak, Agnieszka, Wolski, Tomasz
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.02.2011; Elsevier
• Subjects: Air Microbiology; Air pollutants; Air Pollutants - analysis; Air pollution; Alternaria; Alternaria - chemistry; Alternaria - growth & development; Applied sciences; Artificial neural networks; Cladding; Cladosporium; Cladosporium - chemistry; Cladosporium - growth & development; Environmental Monitoring; Exact sciences and technology; Fungal spores; Fungi; Learning theory; Mathematical models; Meteorological Concepts; Meteorological parameters; Neural networks; Neural Networks (Computer); Pollutants; Pollution; Spores; Spores, Fungal - chemistry; Spores, Fungal - growth & development; Temperature; Air pollutants; Meteorological parameters; Fungal spores; Artificial neural networks; Atmospheric condition; Prediction; Neural network; Forecast model; Modeling; Indicator; Spores; Fungi; Bioaerosol; Correlation analysis; Aerosols; Air pollution; Fungi Imperfecti; Performance; Aerial survey
• ISSN: 0269-7491; 1873-6424
• DOI: 10.1016/j.envpol.2010.10.002

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. ANN models predict airspore contents from weather conditions and air pollutant.