Ragweed pollen forecasting

Abstract

Rationale Pollen from Ambrosia species are the most important pollen allergens in North America, and the ability to accurately predict day to day pollen levels could provide important benefit to sensitive individuals. Methods Daily ragweed pollen concentrations from 1987 to 2001 collected with a Burkard spore trap were analyzed to determine pollen season characteristics. Daily concentrations were also compared with data from the National Weather Service to determine the effects of meteorological conditions on airborne pollen levels and to develop an empirical forecasting model. The model was used to predict daily pollen levels during the 2002 ragweed season. Results Pollen data from 1987 to 2001 showed that the mean start date (5% of season total) was 27 Aug; the mean date of the season peak was 10 Sep; and the mean ending date (95% of season total) was 11 Oct. Meteorological parameters used in model development include temperature, relative humidity, wind speed, and rainfall. Forecasts were generated each morning from 1 Sep to 31 Oct and published on the internet at http://pollen.utulsa.edu. Pollen levels were predicted as Low, Moderate, High, and Very High as defined by the National Allergy Bureau. In addition, intermediate levels were also used. Following the ragweed season, forecasts were compared with the daily pollen concentration. Analysis showed that the model successfully predicted pollen severity on 84% of the days during the 2002 ragweed season. Conclusions Pollen forecasting can provide predictions of daily pollen levels and enable sensitive individuals to avoid exposure to high pollen levels or take prophylactic medication.