Characterization of Surface Ozone Behavior at Different Regimes

Abstract

Previous studies showed that the influence of meteorological variables and concentrations of other air pollutants on O-3 concentrations changes at different O-3 concentration levels. In this study, threshold models with artificial neural networks (ANNs) were applied to characterize the O-3 behavior at an urban site (Porto, Portugal), describing the effect of environmental and meteorological variables on O-3 concentrations. ANN characteristics, and the threshold variable and value, were defined by genetic algorithms (GAs). The considered predictors were hourly average concentrations of NO, NO2, and O-3, and meteorological variables (temperature, relative humidity, and wind speed) measured from January 2012 to December 2013. Seven simulations were performed and the achieved models considered wind speed (at 4.9 m.s(-1)), temperature (at 17.5 degrees C) and NO2 (at 26.6 mu g.m(-3)) as the variables that determine the change of O-3 behavior. All the achieved models presented a similar fitting performance: R-2 = 0.71-0.72, RMSE = 14.5-14.7 mu g.m(-3), and the index of agreement of the second order of 0.91. The combined effect of these variables on O-3 concentration was also analyzed. This statistical model was shown to be a powerful tool for interpreting O-3 behavior, which is useful for defining policy strategies for human health protection concerning this air pollutant.