# Characterization of Surface Ozone Behavior at Different Regimes
[33] mentioned that high-ozone episodes can be explained by several factors: (i) atmospheric stagnation; (ii) horizontal transport by the wind of ozone-rich air masses; (iii) high solar radiation and temperature; and (iv) the influence of local winds (sea breezes and valley winds). [PERSON] et al. [19] observed a positive correlation between O\({}_{3}\) concentrations with temperature and a negative correlation with relative humidity. Regarding the effect of wind field, the northeast flow from Spain (Galicia and Asturias) was observed, and this can be associated with the long-range transport of atmospheric pollutants to Portugal. [PERSON] et al. [34] compared O\({}_{3}\) trends at urban and rural sites. At the rural site, O\({}_{3}\) concentrations were mainly influenced by the wind (transport), showing low variability with the concentrations of other pollutants. On the other hand, at the urban site, most of the variance was explained by the NO\({}_{2}\)/NO\({}_{\rm x}\) ratio. Several research studies can be found where ANN models were applied to determine O\({}_{3}\) trends and to predict its concentration (to provide early warning to the population when high O\({}_{3}\) concentration episodes occur). [PERSON] [35] compared the performance of an ANN model with a multiple linear regression (MLR) model to predict daily average O\({}_{3}\) concentrations in different cities with distinct climate and O\({}_{3}\) regimes. ANN models presented slightly better performance than MLR. [PERSON] and [PERSON] [36] developed ANN models to predict O\({}_{3}\) concentrations through meteorological and environmental data. The contribution of the meteorological data was defined between 33% and 41%, while the remaining variation was attributed to chemical pollutants. NO, SO\({}_{2}\), relative humidity (the highest contribution), non-methane hydrocarbon, and NO\({}_{2}\) were the variables that most influenced the O\({}_{3}\) concentrations. Additionally, temperature also presents an important role, while solar radiation had a lower effect than expected. [PERSON] et al. [37] compared threshold autoregressive (TAR) models, autoregressive (AR) models, and ANN in the prediction of the next day hourly average O\({}_{3}\) concentrations. In the training period, ANN presented a higher performance. However, in the test period, TAR models presented more accurate results and the distinction became greater when the evaluation was performed for the prediction of extreme values. In recent studies, O\({}_{3}\) concentrations have shown different behaviors regarding certain explanatory variables [25; 37], which can be classified as O\({}_{3}\) regimes. This observation can be justified by the chemical reactions associated with O\({}_{3}\) formation/destruction that are influenced by certain variables, such as temperature, solar radiation, and wind speed [32]. To take these regimes account, threshold regression models were considered in this study [38]. Thus, GAs were used to define the threshold variable and value (the value of the explanatory variable corresponding to the change of the regime; two regimes were selected), the number of hidden neurons, and the activation function in the hidden and output layers. In this study, hourly average O\({}_{3}\) concentrations were modeled using threshold models with an ANN, whose structure was iteratively optimized by GAs. The achieved models enable the characterization of O\({}_{3}\) variability with selected meteorological and environmental variables in different regimes.