2D Mapping by Kohonen Networks of the Air Quality Data From a Large City

The 15-variable environmental data (7 concentrations: CO, SO2, O3, NOx, NO, NO2, particulate matter smaller than 10 micron (PM10), and 8 weather data: cloudiness, rainfall, insolation factor (Isfi), temperature, pressure at two locations, and wind intensity with direction) in a period of 45 days with 1-h intervals were extracted from a larger database of concentrations recorded in minute intervals for the same time period. The monitoring site was located in the City of Buenos Aires in a relatively heavy traffic crossroad of two avenues. The data required special pretreatment where the hourly content of rain, wind intensity, wind velocity, and cloudiness were concerned. The new variable named insolation factor (relative UV radiation) calculated on the basis of the general meteorological data, the geographic position of the monitoring site, cloudiness, date, and the time of the recording was composed. The relative intensity of UV radiation was modeled by a Gaussian function, multiplied by a cloudiness factor. Based on the 14-variable input and the 1-variable output (ozone) data, first, the clustering of all 980 data records was made. The top map clustering showing the ozone concentration was related to the maps of all 14 variables. The link between O3 clusters, NO2, and Isfi weight levels is shown and discussed. As a preliminary result of this study some of the most interesting correlations between the maps and remaining variables are given.

[1]  Ari Karppinen,et al.  A modelling system for predicting urban air pollution: model description and applications in the Helsinki metropolitan area , 2000 .

[2]  Short-Range Prediction of Tropospheric Ozone Concentrations and Exceedances for Baton Rouge, Louisiana , 2003 .

[3]  D. Fahey,et al.  Ozone production in the rural troposphere and the implications for regional and global ozone distributions , 1987 .

[4]  Mahesh A Rege,et al.  A Simple Neural Network for Estimating Emission Rates of Hydrogen Sulfide and Ammonia from Single Point Sources. , 1996, Journal of the Air & Waste Management Association.

[5]  Jure Zupan,et al.  The Mean Angular Distance Among Objects and Its Relationships with Kohonen Artificial Neural Networks , 2003, J. Chem. Inf. Comput. Sci..

[6]  Paul Geladi,et al.  A comparison of modeling nonlinear systems with artificial neural networks and partial least squares , 1999 .

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

[8]  J Schwartz,et al.  Short-term effects of particulate air pollution on cardiovascular diseases in eight European cities , 2002, Journal of epidemiology and community health.

[9]  M. W Gardner,et al.  Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences , 1998 .

[10]  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 .

[11]  V. Prybutok,et al.  A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area. , 1996, Environmental pollution.

[12]  G. Hoogenboom,et al.  Development of a neural network model to predict daily solar radiation , 1994 .

[13]  M. Jenkin,et al.  Analysis of the relationship between ambient levels of O3, NO2 and NO as a function of NOx in the UK , 2001 .

[14]  Saleh M. Al-Alawi,et al.  Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks , 2002, Environ. Model. Softw..

[15]  A. Comrie.  Comparing Neural Networks and Regression Models for Ozone Forecasting , 1997 .

[16]  Jure Zupan,et al.  Kohonen and counterpropagation artificial neural networks in analytical chemistry , 1997 .

[17]  David T. Mage,et al.  Urban air pollution in megacities of the world , 1996 .

[18]  M. Kolehmainen,et al.  Neural networks and periodic components used in air quality forecasting , 2001 .

[19]  John Powell,et al.  Summary of the history and status of the system development for the Environmental Chemicals Data and Information Network (ECDIN) , 1983, J. Chem. Inf. Comput. Sci..

[20]  Francesca Dominici,et al.  National maps of the effects of particulate matter on mortality: exploring geographical variation. , 2002, Environmental health perspectives.

[21]  Dominic Palmer-Brown,et al.  Modeling complex environmental data , 1997, IEEE Trans. Neural Networks.