Background: Ambrosia pollen is an important aeroallergen in North America; the ability to predict daily pollen levels may provide an important benefit for sensitive individuals.
Objective: To analyze the long-term Ambrosia pollen counts and develop a forecasting model to predict the next day's pollen concentration.
Methods: Airborne pollen has been collected since December 1986 with a Burkard spore trap at the University of Tulsa. Summary statistics and season metrics were calculated for the 27 years of data. Concentration and previous-day meteorologic data from 1987 to 2011 were used to develop a multiple regression model to predict pollen levels for the following day. Model output was compared to 2012 and 2013 ragweed pollen data.
Results: The Tulsa ragweed season extends from the middle of August to late October. The mean start date is August 22, the mean peak date is September 10, and the mean end date is October 20. The mean cumulative season total is 11,599 pollen/m(3), and the mean daily concentration is 197 pollen/m(3). Previous-day meteorologic and phenologic data were positively related to pollen concentration (P < .001). Precipitation was modeled as a dichotomous variable. The final model included minimum temperature, dichotomous precipitation, dew point, and phenology variable (R = 0.7146, P < .001). Analysis of the model's accuracy revealed that the model was highly representative of the 2012 and 2013 seasons (R = 0.680, P < .001).
Conclusion: Multiple regression models may be useful in explaining the variability of Ambrosia pollen levels. Further testing of the modeling parameters in different geographical areas is needed.
Copyright © 2014 American College of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.