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, NO 2 , 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 °C) and NO 2 (at 26.6 μg·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 μg·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.
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