Hourly predictive artificial neural network and multivariate regression trees models of Ganoderma spore concentrations in Rzeszów and Szczecin (Poland)

Abstract

Ganoderma spores are one of the most airspora abundant taxa in many regions of the world, and are considered to be important allergens. The aerobiology of Ganoderma basidiospores in two cities in Poland was examined using the volumetric method, (Burkard and Lanzonii Spore Traps), from selected days in 2004, 2005 and 2006. Spores of Ganoderma were present in the atmosphere from June to November, with peak concentrations generally occurring from late July to mid-October. ANN (artificial neural network) and MRT (multivariate regression trees), models indicated that atmospheric phenomenon, hour and relative humidity were the most important variables influencing spore content. The remaining variables (air temperature, dew point, air pressure, wind speed and wind direction), also contributed to the high network performance, (ratio above 1), but their impact was less distinct. Those results are consistent with the Spearman's rank correlation analysis.