PM2.5 forecasting in a large city: Comparison of three methods

Abstract There is an increasing awareness for the toxic effects produced by the inhalation of fine particles present in the air. It is important then to provide precise information to the population about the concentrations of this pollutant expected for the incoming hours. We present here a study about the capability of three types of methods for PM2.5 forecasting one day in advance: a multilayer neural network, a linear algorithm and a clustering algorithm. Input variables are past concentrations measured in four monitoring stations and actual and forecasted meteorological information. Outputs are the maxima of the 24 h moving average of PM2.5 concentrations for the following day at the site of the monitoring stations. By training with data from the three previous years, we are able to generate results for the fall–winter period for each year from 2004 to 2007. Although the three methods may be used as operational tools, the clustering algorithm seems more accurate in detecting high concentration situations.

[1]  Joaquín B. Ordieres Meré,et al.  Neural network prediction model for fine particulate matter (PM2.5) on the US-Mexico border in El Paso (Texas) and Ciudad Juárez (Chihuahua) , 2005, Environ. Model. Softw..

[2]  P. J. Silva,et al.  Fine particle concentrations and composition during wintertime inversions in Logan, Utah, USA , 2007 .

[3]  Frances Silverman,et al.  Inhalation of Fine Particulate Air Pollution and Ozone Causes Acute Arterial Vasoconstriction in Healthy Adults , 2002 .

[4]  L. Lave,et al.  Effect of the Fine Fraction of Particulate Matter versus the Coarse Mass and Other Pollutants on Daily Mortality in Santiago, Chile , 2000, Journal of the Air & Waste Management Association.

[5]  R. Burnett,et al.  Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. , 2002, JAMA.

[6]  Jorge Reyes,et al.  An integrated neural network model for PM10 forecasting , 2006 .

[7]  C. Brebbia,et al.  Air Pollution XIV , 2006 .

[8]  Who Europe.  Air Quality Guidelines Global Update 2005: Particulate Matter, ozone, nitrogen dioxide and sulfur dioxide , 2006 .

[9]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[10]  Jorge Reyes,et al.  Prediction of PM2.5 concentrations several hours in advance using neural networks in Santiago, Chile , 2000 .

[11]  J. Schwartz,et al.  Fine particles are more strongly associated than coarse particles with acute respiratory health effects in schoolchildren. , 2000, Epidemiology.

[12]  D. Vlachogiannis,et al.  Time Series Forecasting Of Hourly PM10 Values:Model Intercomparison AndThe Development Of Localized Linear Approaches , 2006 .

[13]  Robert B. Jacko,et al.  Model for Forecasting Expressway Fine Particulate Matter and Carbon Monoxide Concentration: Application of Regression and Neural Network Models , 2007, Journal of the Air & Waste Management Association.

[14]  C. Siriopoulos,et al.  Time series forecasting with a hybrid clustering scheme and pattern recognition , 2004, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[15]  R M Mason,et al.  Is Daily Mortality Associated Specifically with Fine Particles? Data Reconstruction and Replication of Analyses , 2000, Journal of the Air & Waste Management Association.