Title: Neural network-based study for predicting ground-level ozone concentration in large urban areas, applied to the Sao Paulo metropolitan area

Authors: R. Guardani, C.A.O. Nascimento

Addresses: University of Sao Paulo, Engineering School, Chemical Engineering Department, Sao Paulo SP, Brazil. ' University of Sao Paulo, Engineering School, Chemical Engineering Department, Sao Paulo SP, Brazil

Abstract: Events of high concentration of ground-level ozone constitute a matter of major concern in large urban areas in terms of air quality, and public health. In the Sao Paulo Metropolitan Area (SPMA), air quality data generated by a network of air quality measuring stations have been used in a number of studies correlating ozone formation with different variables. A study was carried out on the application of neural network models in the identification of typical sceneries leading to high ground-level ozone concentrations in the SPMA. The results were then applied in the selection of variables, and in the definition of neural network-based models for estimating ozone levels from meteorological variables. When combined with existing weather prediction tools, the models can be applied in the prediction of ozone levels in the SPMA

Keywords: urban air pollution; mathematical modelling; neural networks; ground-level ozone; air quality; Brazil; ozone level prediction

DOI: 10.1504/IJEP.2004.005680

International Journal of Environment and Pollution, 2004 Vol.22 No.4, pp.441 - 459

Available online: 18 Nov 2004 *

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