A SVM-based regression model to study the air quality at local scale in Oviedo urban area (Northern Spain): A case study

Abstract

This research work presents a method of daily air pollution modeling by using support vector machine (SVM) technique in Oviedo urban area (Northern Spain) at local scale. Hazardous air pollutants or toxic air contaminants refer to any substances that may cause or contribute to an increase in mortality or in serious illness, or that may pose a present or potential hazard to human health. In this work, based on the observed data of NO, NO2, CO, SO2, O3 and dust (PM10) for the years 2006, 2007 and 2008, the support vector regression (SVR) technique is used to build the nonlinear dynamic model of the air quality in the urban area of the city of Oviedo (Spain). One main aim of this model was to make an initial preliminary estimate of the dependence between primary and secondary pollutants in the city of Oviedo. A second main aim was to determine the factors with the greatest bearing on air quality with a view to proposing health and lifestyle improvements. It is well-known that the United States National Ambient Air Quality Standards (NAAQS) establishes the limit values of the main pollutants in the atmosphere in order to ensure the health of healthy people. They are known as the criteria pollutants. This SVR fit captures the prime idea of statistical learning theory in order to obtain a good forecasting of the dependence among the main pollutants in the city of Oviedo. Finally, on the basis of these numerical calculations using SVR technique, from the experimental data, conclusions of this study are exposed.