Forecast Urban Air Pollution in Mexico City by Using Support Vector Machines: A Kernel Performance Approach

The development of forecasting models for pollution particles shows a nonlinear dynamic behavior; hence, implementation is a non-trivial process. In the literature, there have been multiple models of particulate pollutants, which use softcomputing techniques and machine learning such as: multilayer perceptrons, neural networks, support vector machines, kernel algorithms, and so on. This paper presents a prediction pollution model using support vector machines and kernel functions, which are: Gaussian, Polynomial and Spline. Finally, the prediction results of ozone (O3), particulate matter (PM10) and nitrogen dioxide (NO2) at Mexico City are presented as a case study using these techniques.

[1]  K. P. Moustris,et al.  3-Day-Ahead Forecasting of Regional Pollution Index for the Pollutants NO2, CO, SO2, and O3 Using Artificial Neural Networks in Athens, Greece , 2010 .

[2]  A. Stern.  Fundamentals of air pollution , 1973 .

[3]  X. Querol,et al.  Monitoring of sources and atmospheric processes controlling air quality in an urban Mediterranean environment , 2010 .

[4]  M PokrovskyOleg,et al.  Fuzzy logic approach for description of meteorological impacts on urban air pollution species , 2002 .

[5]  R. H. Maryon,et al.  Modelling the long-range transport of secondary PM10 to the UK , 2000 .

[6]  R. Harrison,et al.  Source apportionment of fine particles at urban background and rural sites in the UK atmosphere , 2010 .

[7]  Mikko Kolehmainen,et al.  Forecasting Air Quality Parameters Using Hybrid Neural Network Modelling , 2000 .

[8]  F. Murray,et al.  Air pollution impacts on crops and forests: a global assessment. , 2003 .

[9]  J. Chow,et al.  A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: The case of Temuco, Chile , 2008 .

[10]  M. O'Neill,et al.  Ozone, area social conditions, and mortality in Mexico City. , 2004, Environmental research.

[11]  Giovanna Berti,et al.  Short-Term Effects of Nitrogen Dioxide on Mortality and Susceptibility Factors in 10 Italian Cities: The EpiAir Study , 2011, Environmental health perspectives.

[12]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[13]  Ravi Sankar,et al.  Time Series Prediction Using Support Vector Machines: A Survey , 2009, IEEE Computational Intelligence Magazine.

[14]  Eliane R. Rodrigues,et al.  Comparing the Number of Ozone Exceedances in Different Seasons of the Year in Mexico City , 2011 .

[15]  Oleg M. Pokrovsky,et al.  Fuzzy logic approach for description of meteorological impacts on urban air pollution species: a Hong Kong case study , 2002 .

[16]  Jesús Carlos Pedraza Ortega,et al.  Performance assessment of fuzzy clustering models applied to urban airborne pollution , 2011, CONIELECOMP 2011, 21st International Conference on Electrical Communications and Computers.

[17]  L. Zhong,et al.  Characteristics of particulate matter pollution in the Pearl River Delta region, China: an observational-based analysis of two monitoring sites. , 2011, Journal of environmental monitoring : JEM.

[18]  B. Schölkopf,et al.  Advances in kernel methods: support vector learning , 1999 .

[19]  Robert F. Phalen,et al.  Introduction to Air Pollution Science: A Public Health Perspective , 2011 .

[20]  P. Viotti,et al.  Atmospheric urban pollution: applications of an artificial neural network (ANN) to the city of Perugia , 2002 .

[21]  Wei-Zhen Lu,et al.  Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends. , 2005, Chemosphere.

[22]  Monitoring Nitrogen Dioxide Levels in Urban Areas in Rawalpindi, Pakistan , 2011 .

[23]  Federico Girosi,et al.  Support Vector Machines: Training and Applications , 1997 .

[24]  H.-J. Abraham,et al.  Some ideas about the sources of PM 10 P . Lenschow , 2001 .

[25]  Chia-Wen Lu,et al.  Effects of ambient air pollution on pulmonary function among schoolchildren. , 2011, International journal of hygiene and environmental health.

[26]  Stanislaw Osowski,et al.  Forecasting of the daily meteorological pollution using wavelets and support vector machine , 2007, Eng. Appl. Artif. Intell..

[27]  Antonella Zanobetti,et al.  Chemical Properties of Air Pollutants and Cause-Specific Hospital Admissions among the Elderly in Atlanta, Georgia , 2011, Environmental health perspectives.

[28]  H. Takizawa.  Impact of Air Pollution on Allergic Diseases , 2011, The Korean journal of internal medicine.

[29]  E Ortiz,et al.  Basic statistics of PM2.5 and PM10 in the atmosphere of Mexico City. , 2002, The Science of the total environment.

[30]  Jesús Carlos Pedraza Ortega,et al.  Evaluating trends of airborne contaminants by using support vector regression techniques , 2011, CONIELECOMP 2011, 21st International Conference on Electrical Communications and Computers.

[31]  Jianbing Li,et al.  Identification of regional atmospheric PM10 transport pathways using HYSPLIT, MM5-CMAQ and synoptic pressure pattern analysis , 2010, Environ. Model. Softw..

[32]  S. Nakai,et al.  Air Quality and Acute Respiratory Illness in Biomass Fuel using homes in Bagamoyo, Tanzania , 2007, International journal of environmental research and public health.

[33]  V. Preedy,et al.  Environmental Burden of Disease , 2010 .

[34]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[35]  G. Gennaro,et al.  A Simple Feedforward Neural Network for the PM10 Forecasting: Comparison with a Radial Basis Function Network and a Multivariate Linear Regression Model , 2009 .

[36]  Giorgio Corani,et al.  Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning , 2005 .

[37]  W. Phipatanakul,et al.  Impact of Environmental Controls on Childhood Asthma , 2011, Current allergy and asthma reports.

[38]  Md. Rafiul Hassan,et al.  Forecasting Urban Air Pollution Using HMM-Fuzzy Model , 2008, PAKDD.