Department of Statistics and Operations Research, Faculty of Pharmacy, University of Granada, 18071 Granada, Spain.
Int J Biometeorol. 2013 May;57(3):483-6. doi: 10.1007/s00484-012-0527-9. Epub 2012 Feb 22.
The problem of developing a 2-week-on ahead forecast of atmospheric cypress pollen levels is tackled in this paper by developing a principal component multiple regression model involving several climatic variables. The efficacy of the proposed model is validated by means of an application to real data of Cupressaceae pollen concentration in the city of Granada (southeast of Spain). The model was applied to data from 11 consecutive years (1995-2005), with 2006 being used to validate the forecasts. Based on the work of different authors, factors as temperature, humidity, hours of sun and wind speed were incorporated in the model. This methodology explains approximately 75-80% of the variability in the airborne Cupressaceae pollen concentration.
本文通过建立一个涉及多个气候变量的主成分多元回归模型，解决了提前两周预测大气柏科花粉水平的问题。通过将柏科花粉浓度的实际数据应用于西班牙东南部格拉纳达市的研究，验证了所提出模型的有效性。该模型应用于 11 年的连续数据（1995-2005 年），2006 年的数据用于验证预测。根据不同作者的研究，该方法纳入了温度、湿度、日照时间和风速等因素。该方法可以解释空气中柏科花粉浓度变化的 75-80%左右。
