Cotos-Yáñez Tomas R, Rodríguez-Rajo F J, Jato M V
Department of Statistics and Operation Research, University of Vigo, Campus Universitario As Lagoas, 32004-Ourense, Spain.
Int J Biometeorol. 2004 May;48(4):179-85. doi: 10.1007/s00484-004-0203-9. Epub 2004 Feb 10.
Betula pollen is a common cause of pollinosis in localities in NW Spain and between 13% and 60% of individuals who are immunosensitive to pollen grains respond positively to its allergens. It is important in the case of all such people to be able to predict pollen concentrations in advance. We therefore undertook an aerobiological study in the city of Vigo (Pontevedra, Spain) from 1995 to 2001, using a Hirst active-impact pollen trap (VPPS 2000) situated in the city centre. Vigo presents a temperate maritime climate with a mean annual temperature of 14.9 degrees C and 1,412 mm annual total precipitation. This paper analyses two ways of quantifying the prediction of pollen concentration: first by means of a generalized additive regression model with the object of predicting whether the series of interest exceeds a certain threshold; second using a partially linear model to obtain specific prediction values for pollen grains. Both models use a self-explicative part and another formed by exogenous meteorological factors. The models were tested with data from 2001 (year in which the total precipitation registered was almost twice the climatological average overall during the flowering period), which were not used in formulating the models. A highly satisfactory classification and good forecasting results were achieved with the first and second approaches respectively. The estimated line taking into account temperature and a calm S-SW wind, corresponds to the real line recorded during 2001, which gives us an idea of the proposed model's validity.
桦树花粉是西班牙西北部地区花粉症的常见病因,13%至60%对花粉粒免疫敏感的个体对其过敏原呈阳性反应。对于所有这类人群而言,能够提前预测花粉浓度非常重要。因此,我们于1995年至2001年在西班牙蓬特韦德拉市的维戈市开展了一项空气生物学研究,使用了一台位于市中心的赫斯特主动撞击式花粉捕捉器(VPPS 2000)。维戈呈现出温带海洋性气候,年平均温度为14.9摄氏度,年总降水量为1412毫米。本文分析了两种量化花粉浓度预测的方法:第一种是通过广义相加回归模型,目的是预测感兴趣的序列是否超过某个阈值;第二种是使用部分线性模型来获取花粉粒的具体预测值。两种模型都使用了一个自解释部分和另一个由外部气象因素构成的部分。使用2001年的数据(该年记录的总降水量几乎是开花期总体气候平均值的两倍)对模型进行了测试,这些数据在模型制定过程中未被使用。分别采用第一种和第二种方法取得了非常令人满意的分类结果和良好的预测结果。考虑到温度和静风的西南偏南风的估计线与2001年记录的实际线相对应,这让我们对所提出模型的有效性有了一定了解。