Smith M, Emberlin J
National Pollen and Aerobiology Research Unit, University College, Worcester, UK.
Clin Exp Allergy. 2005 Oct;35(10):1400-6. doi: 10.1111/j.1365-2222.2005.02349.x.
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.
The objective of this study is to construct a medium-range (</=7 day) forecast model for grass pollen at north London.
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.
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.
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.
现在许多媒体定期发布中期（约7天）天气预报。因此，空气生物学家尝试针对致敏花粉制作覆盖相同时间段的中期预报是合乎逻辑的。
本研究的目的是构建伦敦北部草花粉的中期（≤7天）预报模型。
基于1990年至1999年的草花粉和气象数据，采用回归分析生成预报模型，并在2000年和2002年的数据上进行测试。通过将草花粉季节分为三个时期来改进建模过程；草花粉释放的峰前期、高峰期和峰后期。预报由五个回归模型组成：两个简单线性回归模型预测高峰期的开始和结束日期，以及三个多元回归模型预测峰前期、高峰期和峰后期的每日平均草花粉计数。
总体而言，预报模型在2000年的准确率为62%，在2002年为47%，这反映出2002年草花粉季节的强度高于分析中包含的任何其他季节这一事实。
本研究有潜力对空气生物学领域做出显著贡献。利用北大西洋涛动的冬季平均值来预测草花粉季节的某些特征，这是空气生物学工作中的一项重要进展。预测五到七天内致敏花粉计数的能力将使过敏患者受益。此外，致敏花粉的中期预报将有助于医疗行业，包括计划治疗的过敏症专科医生和安排临床试验的医生。
