Model for forecasting Olea europaea L. airborne pollen in South-West Andalusia, Spain.

TLDR: The results of the multiple regression analysis show that the variables exerting the greatest influence on the pollen index were rainfall in March and temperatures over the months prior to the flowering period, while temperature proved the best variable for this prediction.

Abstract: Data on predicted average and maximum airborne pollen concentrations and the dates on which these maximum values are expected are of undoubted value to allergists and allergy sufferers, as well as to agronomists. This paper reports on the development of predictive models for calculating total annual pollen output, on the basis of pollen and weather data compiled over the last 19 years (1982–2000) for Cordoba (Spain). Models were tested in order to predict the 2000 pollen season; in addition, and in view of the heavy rainfall recorded in spring 2000, the 1982–1998 data set was used to test the model for 1999. The results of the multiple regression analysis show that the variables exerting the greatest influence on the pollen index were rainfall in March and temperatures over the months prior to the flowering period. For prediction of maximum values and dates on which these values might be expected, the start of the pollen season was used as an additional independent variable. Temperature proved the best variable for this prediction. Results improved when the 5-day moving average was taken into account. Testing of the predictive model for 1999 and 2000 yielded fairly similar results. In both cases, the difference between expected and observed pollen data was no greater than 10%. However, significant differences were recorded between forecast and expected maximum and minimum values, owing to the influence of rainfall during the flowering period.