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