Forecasting SO₂ pollution incidents by means of quantile curves based on additive models

Abstract

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 SO 2 emission incidents (i.e. emission exceeding the limits established by law) still occur. The aim of this paper is to predict time series of SO 2 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 SO 2 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 SO 2 time series of a coal-fired power station located in northern Spain.

Comparing quantile regression methods for probabilistic forecasting of NO2 pollution levels

“…Quantile regression has gained an increasing attention from very different scientific disciplines 8 , including financial and economic applications 9 , medical applications 10 , wind power forecasting 11 , electric load forecasting 12 , environmental modelling 13 and meteorological modelling 14 (these references are just examples and the list is not exhaustive). To our knowledge, despite its success in other areas, quantile regression has not been applied in the framework of air quality, with the exception of 15 .…”

Functional Location-Scale Model to Forecast Bivariate Pollution Episodes

“…with pollution data of approximately 12 years, which includes a considerable number of pollution episodes (see [9] for a detailed description of the historical matrix construction). In summary, in the historical matrix not all the data are used, but only part of them, following a quantile-weighted criterion.…”

“…Forecasting air quality and concentrations of pollutants in the atmosphere by means of statistical methods is an active area of research given the transcendence of the problem and the difficulty to find optimal solutions using deterministic mathematical models. Among the different methods that can be found in the literature to tackle this problem, models for time series analysis such as the integrated autoregressive moving average-ARIMA [1][2][3], multivariate regression [4][5][6][7], generalized linear or additive models (GAM) [8][9][10][11] and artificial neural networks (ANN) [12][13][14][15][16][17][18][19] are the most extended. Due to the increased access to continuous data over time, functional data analysis [20,21] was also proposed for air quality forecasting and outlier detection [22][23][24].…”

Modeling conditional reference regions: Application to glycemic markers

“…Unlike existing methods, cited above, our statistical model places no parametric restrictions on the response, and the nonlinear effects of continuous covariates may be estimated using local polynomial regression smoothers. Our proposal is an extension to bivariate data of a previous work, 29 where the authors used a location‐scale model to estimate univariate percentile curves. The final performance of our conditional reference regions depends heavily on a bivariate kernel density estimator.…”