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Atmospheric SO2 concentrations at sampling stations near the fossil fuel fired power station at As Pontes (La Coruña, Spain) were predicted using a model for the corresponding time series consisting of a self‐explicative term and a linear combination of exogenous variables. In a supplementary simulation study, models of this kind behaved better than the corresponding pure self‐explicative or pure linear regression models. Copyright © 2000 John Wiley & Sons, Ltd.

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.

Every time, the travel patterns are becoming more di erentiated, in uenced by new variables resulting from changes in lifestyle. The relevance of the senior segment for the industry, with the continuous population aging, and their economic status, made this segment very attractive group for the sector, and more in a country as Spain characterized by its aging. The spatial e ects are being considered as a key element to understand this process, but there are only a few number of researches focusing on cross-cultural in uences and the neighbourhood context. For this purpose, the technique of Geographically Weighted Principal Component Analysis (GWPCA) is applied in a novel way for the sector, showing di erent behaviour patterns according to area of origin. The GWPCA is a localized version of Principal Component Analysis (PCA) used when there is a certain spatial heterogeneity in the structure of a multivariate data set. The results con rm that GWPCA is an e ective statistic methodology to research the spatial heterogeneity for travel behaviour, with clearly di erentiated scenarios for the north, centre and south of Spain.