Variation of surface ozone in Campo Grande, Brazil: meteorological effect analysis and prediction

Abstract

The effect of meteorological variables on surface ozone (O-3) concentrations was analysed based on temporal variation of linear correlation and artificial neural network (ANN) models defined by genetic algorithms (GAs). ANN models were also used to predict the daily average concentration of this air pollutant in Campo Grande, Brazil. Three methodologies were applied using GAs, two of them considering threshold models. In these models, the variables selected to define different regimes were daily average O-3 concentration, relative humidity and solar radiation. The threshold model that considers two O-3 regimes was the one that correctly describes the effect of important meteorological variables in O-3 behaviour, presenting also a good predictive performance. Solar radiation, relative humidity and rainfall were considered significant for both O-3 regimes; however, wind speed (dispersion effect) was only significant for high concentrations. According to this model, high O-3 concentrations corresponded to high solar radiation, low relative humidity and wind speed. This model showed to be a powerful tool to interpret the O-3 behaviour, being useful to define policy strategies for human health protection regarding air pollution.