Nonlinear Air Quality Modeling Using Support Vector Machines in Gijón Urban Area (Northern Spain) at Local Scale

Abstract

Abstract Due to the health impacts caused by exposures to air pollutants in urban areas, monitoring and forecasting of air quality parameters have become popular as an important topic in atmospheric and environmental research today. The knowledge on the dynamics and complexity of air pollutants behaviour has made artificial intelligence and statistical learning models as a useful tool for a more accurate pollutant concentration prediction. The aim of this study is to build a regression model of daily air quality prediction by using support vector machine (SVM) technique in Gijón urban area (Northern Spain) at local scale. To accomplish the objective, the observed data of nitrogen oxides, carbon monoxide, sulphur dioxide, ozone and dust for years 2006 to 2008 are used to create a highly nonlinear model of the air quality in the city based on SVM techniques. One objective of this model was to make a preliminary estimate of the dependence between primary and secondary pollutants in Gijón urban area at local scale. A second aim was to determine the factors with the greatest bearing on air quality in order to propose health and lifestyle improvements. This support vector regression model captures the main insight of statistical learning theory to obtain a good prediction of the dependence among the main pollutants in Gijón urban area since it shows a good agreement between the observed and predicted values of pollutants using statistical estimators as correlation coefficients, mean errors and root mean squared errors. Finally, conclusions of this study are exposed.