Wavelet-based fractal analysis of airborne pollen

The most abundant biological particles in the atmosphere are pollen grains and spores. Self-protection of a pollen allergy is possible through information about future pollen contents in the air. In spite of the importance of airborne pollen concentration forecasting, it has not been possible to predict the pollen concentrations with great accuracy, and about 25% of daily pollen forecasts result in failures. Previous analyses of the dynamic characteristics of atmospheric pollen time series indicate that the system can be described by a low dimensional chaotic map. We apply a wavelet transform to study the multifractal characteristics of an airborne pollen time series. The information and the correlation dimensions correspond to a chaotic system showing a loss of information with time evolution.