Constructing a 7-day ahead forecast model for grass pollen at north London, United Kingdom.
BACKGROUND
A number of media outlets now issue medium-range ( approximately 7 day) weather forecasts on a regular basis. It is therefore logical that aerobiologists should attempt to produce medium-range forecasts for allergenic pollen that cover the same time period as the weather forecasts.


OBJECTIVE
The objective of this study is to construct a medium-range (</=7 day) forecast model for grass pollen at north London.


METHODS
The forecast models were produced using regression analysis based on grass pollen and meteorological data from 1990 to 1999 and tested on data from 2000 and 2002. The modelling process was improved by dividing the grass pollen season into three periods; the pre-peak, peak and post-peak periods of grass pollen release. The forecast consisted of five regression models: two simple linear regression models predicting the start and end date of the peak period, and three multiple regression models forecasting daily average grass pollen counts in the pre-peak, peak and post-peak periods.


RESULTS
Overall, the forecast models achieved 62% accuracy in 2000 and 47% in 2002, reflecting the fact that the 2002 grass pollen season was of a higher magnitude than any of the other seasons included in the analysis.


CONCLUSIONS
This study has the potential to make a notable contribution to the field of aerobiology. Winter averages of the North Atlantic Oscillation were used to predict certain characteristics of the grass pollen season, which presents an important advance in aerobiological work. The ability to predict allergenic pollen counts for a period between five and seven days will benefit allergy sufferers. Furthermore, medium-range forecasts for allergenic pollen will be of assistance to the medical profession, including allergists planning treatment and physicians scheduling clinical trials.
Smith
M
M
National Pollen and Aerobiology Research Unit, University College, Worcester, UK.
Emberlin
J
J
D000393:Air Pollutants / Q000032:analysis*; D000485:Allergens / Q000032:analysis*; D004784:Environmental Monitoring / Q000379:methods*; D005544:Forecasting; D006801:Humans; D008131:London; D008685:Meteorological Concepts; D006109:Poaceae / Q000276:immunology; D011058:Pollen / Q000276:immunology*; D012044:Regression Analysis; D012621:Seasons; D014504:Urban Health / Q000706:statistics & numerical data
D000393:Air Pollutants; D000485:Allergens