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

Area of Science:

• Aerobiology
• Environmental Mycology
• Allergen Monitoring

Background:

• Ganoderma spp. basidiospores are allergenic and can cause respiratory sensitization.
• High spore concentrations were detected in Worcester, UK (2006-2010).
• Linking sensitization to spore levels is difficult due to limited aerobiological data.

Purpose of the Study:

• To conduct aerobiological monitoring of Ganoderma spp. spores in Worcester.
• To investigate relationships between spore concentration and meteorological factors.
• To develop predictive models for Ganoderma spp. spore levels.

Main Methods:

• Utilized a 7-day volumetric spore sampler for five years of daily mean concentration measurements.
• Employed multivariate regression trees and artificial neural networks for model construction.
• Analyzed correlations between spore presence and weather parameters, focusing on temperature and dew point.

Main Results:

• Dew point and maximum temperature were identified as key factors influencing Ganoderma spore presence.
• Established concentration thresholds: low (0-49), moderate (50-99), high (100-149), and very high (>150 s m(-3)).
• Developed a forecasting model with observed-predicted correlations ranging from r=0.57 to r=0.68.

Conclusions:

• Temperature-based thresholds can define Ganoderma spore concentration levels.
• A reliable forecasting model for Ganoderma spores was successfully developed.
• Aerobiological monitoring and forecasting are crucial for managing spore-related respiratory issues.