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

The objective of this study is to build a regression model of air quality by using the support vector machine (SVM) technique in the Aviles 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 (SO"2), ozone (O"3) and dust (PM"1"0) for the years 2006-2008 are used to create a highly nonlinear model of the air quality in the Aviles 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 Aviles 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 Aviles urban area. Finally, on the basis of these numerical calculations, using the support vector regression (SVR) technique, conclusions of this work are drawn.

[1]  Nello Cristianini,et al.  Kernel Methods for Pattern Analysis , 2003, ICTAI.

[2]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[3]  Archontoula Chaloulakou,et al.  Comparative assessment of neural networks and regression models for forecasting summertime ozone in Athens. , 2003, The Science of the total environment.

[4]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[5]  Marija Zlata Boznar,et al.  A neural network-based method for short-term predictions of ambient SO2 concentrations in highly polluted industrial areas of complex terrain , 1993 .

[6]  Yung-Tse Hung,et al.  Air Pollution Control Engineering , 2004 .

[7]  B. Weinhold.  Ozone Nation: EPA Standard Panned by the People , 2008, Environmental health perspectives.

[8]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[9]  Marco Maggini,et al.  A Cyclostationary Neural Network model for the prediction of the NO2 concentration , 2006, ESANN.

[10]  Omar Alagha,et al.  NN-AirPol: a neural-networks-based method for air pollution evaluation and control , 2006 .

[11]  W. T. Davis,et al.  Air Pollution: Its Origin and Control , 1976 .

[12]  L. Buydens,et al.  Facilitating the application of Support Vector Regression by using a universal Pearson VII function based kernel , 2006 .

[13]  P J García-Nieto,et al.  Parametric study of selective removal of atmospheric aerosol by coagulation, condensation and gravitational settling , 2001, International journal of environmental health research.

[14]  Charles Brown,et al.  Air Pollution Control Technology Handbook , 2001 .

[15]  C. N. Hewitt,et al.  Atmospheric Science for Environmental Scientists , 2009 .

[16]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[17]  T. Elbir,et al.  Evaluation of some air pollution indicators in Turkey. , 2000, Environment international.

[18]  P. J. García-Nieto,et al.  Study of the evolution of aerosol emissions from coal-fired power plants due to coagulation, condensation, and gravtitational settling and health impact. , 2006, Journal of environmental management.

[19]  Andreas Christmann,et al.  Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.

[20]  R. Tibshirani,et al.  Improvements on Cross-Validation: The 632+ Bootstrap Method , 1997 .

[21]  Ana Isabel Miranda,et al.  Air pollution forecast in Portugal: a demand from the new air quality framework directive , 2005 .

[22]  M. Gardner,et al.  Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London , 1999 .

[23]  Vojislav Kecman.  Support Vector Machines , 2001 .

[24]  J. Hooyberghs,et al.  A neural network forecast for daily average PM10 concentrations in Belgium , 2005 .

[25]  M Gordon,et al.  Air-Pollution Control. , 1965, Science.

[26]  Lutgarde M. C. Buydens,et al.  SOMPLS: A supervised self-organising map--partial least squares algorithm for multivariate regression problems , 2007 .

[27]  Gavin C. Cawley,et al.  Extensive evaluation of neural network models for the prediction of NO2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki , 2003 .

[28]  Michael C. Ferris,et al.  Interior-Point Methods for Massive Support Vector Machines , 2002, SIAM J. Optim..

[29]  Kazuhiko Ito,et al.  Long-term ozone exposure and mortality. , 2009, The New England journal of medicine.

[30]  Andrew C. Comrie,et al.  Climatology and forecast modeling of ambient carbon monoxide in Phoenix, Arizona , 1999 .

[31]  Lipo Wang.  Support vector machines : theory and applications , 2005 .

[32]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[33]  R. Dennis Cook,et al.  Cross-Validation of Regression Models , 1984 .

[34]  Ferhat Karaca,et al.  Statistical characterization of atmospheric PM10 and PM2.5 concentrations at a non-impacted suburban site of Istanbul, Turkey. , 2005, Chemosphere.

[35]  A Seaton,et al.  Asthma admissions and thunderstorms: a study of pollen, fungal spores, rainfall, and ozone. , 2001, QJM : monthly journal of the Association of Physicians.

[36]  James H. Vincent,et al.  Aerosol Sampling: Science, Standards, Instrumentation and Applications , 1989 .

[37]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[38]  Ferruh Ertürk,et al.  Evaluation of air pollution trends in Istanbul , 2002 .

[39]  Xiugang Li,et al.  Predicting motor vehicle crashes using Support Vector Machine models. , 2008, Accident; analysis and prevention.

[40]  S. Friedlander.  Smoke, Dust, and Haze: Fundamentals of Aerosol Dynamics , 2000 .

[41]  Ian Witten,et al.  Data Mining , 2000 .