Predictive models in aerobiology: data transformation

This paper attempts to evaluate the effect of mathematical transformations of pollen and meteorogical data used in aerobiological forecasting models. Stepwise multiple regression equations were developed in order to facilitate short term forecasts during the pre-peak period. The daily mean pollen data (xi) expressed as number of pollen grains per cubic metre of air were used directly and transformed into different scales: log(xi + 1), ln((x11000/Σp) + 1) and √xi, where Σp is the sum of the daily mean values throughout the season. Thirteen meteorological parameters and the variable time were used as forecasting variables. The most reliable forecasts were obtained with data transformed by ‘square root’ and with untransformed data. Based on the results obtained, we recommend that the data be transformed by means of the square root if they do not show a normal distribution and that non-linear statistics be used in this kind of study.