Title: Ground-level ozone prediction using a neural network model based on meteorological variables and appiled to the metropolitan area of São Paulo
Authors: Alessandro Santos Borges; Maria de Fátima Andrade; Roberto Guardani
Addresses: Department of Atmospheric Sciences, Institute of Astronomy, Geophysics and Atmospheric Science, University of São Paulo, Brazil ' Department of Atmospheric Sciences, Institute of Astronomy, Geophysics and Atmospheric Science, University of São Paulo, Brazil ' Chemical Engineering Department, University of São Paulo, Brazil
Abstract: A neural network model to predict ozone concentration in the São Paulo Metropolitan Area was developed, based on average values of meteorological variables in the morning (8:00-12:00 hr) and afternoon (13:00-17:00 hr) periods. Outputs are the maximum and average ozone concentrations in the afternoon (12:00-17:00 hr). The correlation coefficient between computed and measured values was 0.82 and 0.88 for the maximum and average ozone concentration, respectively. The model presented good performance as a prediction tool for the maximum ozone concentration. For prediction periods from 1 to 5 days 0 to 23% failures (95% confidence) were obtained.
Keywords: ozone forecasting; neural networks; air pollution; megacities; tropospheric ozone; air quality; Brazil; meteorological variables.
International Journal of Environment and Pollution, 2012 Vol.49 No.1/2, pp.1 - 15
Accepted: 22 Sep 2011
Published online: 14 Oct 2012 *