Short-term prediction of Betula airborne pollen concentration in Vigo (NW Spain) using logistic additive models and partially linear models.
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.
Cotos-Yáñez
Tomas R
TR
Department of Statistics and Operation Research, University of Vigo, Campus Universitario As Lagoas, 32004-Ourense, Spain. cotos@uvigo.es
Rodríguez-Rajo
F J
FJ
Jato
M V
MV
D000485:Allergens; D029662:Betula; D004784:Environmental Monitoring; D005544:Forecasting; D006801:Humans; D006967:Hypersensitivity; D016014:Linear Models; D011058:Pollen; D011634:Public Health; D013030:Spain; D013696:Temperature; D014919:Wind
D000485:Allergens