LAUSR

Forecasting daily airborne pollen concentrations is of great importance for management of seasonal allergies. This paper explores the performance of the pollen calendar as the most basic observation-oriented model for predicting daily concentrations of airborne Ambrosia, Betula and Poaceae pollen. Pollen calendars were calculated as the mean or median value of pollen concentrations on the same date in previous years of the available historic dataset, as well as the mean or median value of pollen concentrations of the smoothed dataset, pre-processed using moving mean and moving median. The performance of the models was evaluated by comparing forecasted to measured pollen concentrations at both daily and 10-day-average resolutions. This research demonstrates that the interpolation of missing data and pre-processing of the calibration dataset yields lower prediction errors. The increase in the number of calibration years corresponds to an improvement in the performance of the calendars in predicting daily pollen concentrations. However, the most significant improvement was obtained using four calibration years. The calendar models correspond well to the shape of the pollen curve. It was also found that daily resolution instead of 10-day averages adds to their value by emphasising variability in pollen exposure, which is important for personal assessment of dose-response for pollen-sensitive individuals.