Application of an SVM-based regression model to the air quality study at local scale in the Avilés urban area (Spain)

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• 作者：A. Su&#xe1 ; rez S&#xe1 ; nchez^a ; P.J. Garc&#xed ; a Nieto^b ; ^{lato@orion.ciencias.uniovi.es"" rel=""nofollow} ; P. Riesgo Fern&#xe1 ; ndez^a ; J.J. del Coz D&#xed ; az^c ; F.J. Iglesias-Rodr&#xed ; guez^a
• 关键词：Air quality ; Pollutant substances ; Machine learning ; Support vector regression
• 刊名：Mathematical and Computer Modelling
• 出版年：2011
• 出版时间：September 2011
• 年：2011
• 卷：54
• 期：5-6
• 页码：1453-1466
• 全文大小：1284 K

文摘

The objective of this study is to build a regression model of air quality by using the support vector machine (SVM) technique in the Avilés urban area (Spain) at local scale. Hazardous air pollutants or toxic air contaminants refer to any substance that may cause or contribute to an increase in mortality or serious illness, or that may pose a present or potential hazard to human health. To accomplish the objective of this study, the experimental data of nitrogen oxides (NO_x), carbon monoxide (CO), sulphur dioxide (SO₂), ozone (O₃) and dust (PM₁₀) for the years 2006–2008 are used to create a highly nonlinear model of the air quality in the Avilés urban nucleus (Spain) based on SVM techniques. One aim of this model is to obtain a preliminary estimate of the dependence between primary and secondary pollutants in the Avilés urban area at local scale. A second aim is to determine the factors with the greatest bearing on air quality with a view to proposing health and lifestyle improvements. 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 criteria pollutants. This support vector regression model captures the main insight of statistical learning theory in order to obtain a good prediction of the dependence among the main pollutants in the Avilés urban area. Finally, on the basis of these numerical calculations, using the support vector regression (SVR) technique, conclusions of this work are drawn.