More than 90% of the sulfur dioxide in the air comes from human sources. Because of the adverse health effects of high levels of sulfur dioxide, specific regulations have been adopted to manage and reduce the amount of sulfur dioxide produced. However, some SO2 emission incidents (i.e. emission exceeding the limits established by law) still occur. The aim of this paper is to predict time series of SO2 concentrations in order to estimate in advance high emission episodes and analyse the influence of previous series in the prediction. Previous studies aimed to forecast SO2 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 polynomial 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 located in northern Spain.