J. Roca-Pardiñas, C. Ordóñez Galán
More than 90% of the sulfur dioxide in the air comes from human sources. Due to the adverse health effects of high levels of sulfur dioxide, some regulations have been adopted to manage and reduce the amount of sulfur dioxide produced. The aim of this work is to predict time series of SO2 concentrations in order to estimate in advance high emission episodes and analyze the influence of previous series in the prediction.
Previous studies aimed to forecast pollution incidents are based on estimating mean values. Instead, we propose the use of quantile curves obtained from additive models as they provide not only the mean but also the whole distribution of the pollution levels. A backfitting algorithm with local kernel smoothers was used to estimate the model, and critical values of the hypothesis test were obtained by means of bootstrapping. The performance of the method was evaluated using simulated data as well as real data drawn from an SO2 time series of a coal-fired power station.
Palabras clave / Keywords: pollution incidents, quantile curve, kernel smoothers, bootstrapping
Programado
Sesión bilateral SEIO-SPE: Statistics in Environmental Sciences and Health (Organizadores: Raquel Menezes y Carmen Cadarso)
30 de mayo de 2018 15:30
Sala Cristal
