# Characterization of Surface Ozone Behavior at Different Regimes  
[PERSON]  
Laboratorio de Engenharia de Processos, Ambiente Biotecnologia e Energia (LEPABE), Departamento de Engenharia Quimica, Faculdade de Engenharia, Universidade do Porto, [PERSON], 4200-465 Porto, Portugal; [EMAIL_ADDRESS]  
[PERSON]  
Correspondence:  
In urban areas, O\({}_{3}\) concentrations are usually lower than the values observed in rural areas [17; 18; 19; 20]. This phenomenon occurs mainly due to high NO\({}_{\rm x}\) concentrations. Photochemical equilibrium is defined by the following equations [21; 22; 23]:  
\[{\rm NO}_{2}+hv\ (\lambda\textless 420\ nm)\to{\rm NO}+{\rm O}\] (R1)  
\[{\rm O}+{\rm O}_{2}+M\to{\rm O}_{3}+M\] (R2)  
\[{\rm NO}+{\rm O}_{3}\to{\rm NO}_{2}+{\rm O}_{2}.\] (R3)  
The photochemical reaction of NO\({}_{2}\) decomposition (chemical reaction R1) leads to the production of NO molecules and oxygen atoms that combine with molecular oxygen to produce ozone (chemical reaction R2). O\({}_{3}\) can also react with NO, forming NO\({}_{2}\) (chemical reaction R3). The complexity of phenomena associated with O\({}_{3}\) formation makes it hard to understand and predict its concentration in ambient air. Moreover, there are insufficient data (e.g., inventories of air pollutant emissions) to develop consistent phenomenological models to describe O\({}_{3}\) behavior. Alternatively, statistical models have the capability of characterizing the relationship between variables using collected data and they involve mere pattern-recognition using mathematical operations. One of the most applied statistical models is the artificial neural network (ANN). ANNs are nonlinear models, which are inspired in the biological neural processing system [24; 25]. These models are composed by artificial neurons (grouped in layers; three layers--input, hidden, and output--are often applied) that receive an input value and converts to an output through a selected function (activation function). Additionally, ANNs are characterized by a high fitting performance. The rapid development of computer hardware has increased the processing capabilities, which have led to achievement of ANN models with less computation time [26]. Therefore, these models have been used in a wide range of applications, including classification, regression, and mapping [27; 28; 29]. However, there are too many variables that need to be defined before the model parameters can be determined, including (i) the number of processing neurons in the hidden layer, and (ii) the activation function for each neuron. In recent years, genetic algorithms (GAs) have been applied to help in the definition of these variables. GAs are commonly applied to generate high-quality solutions for optimization and search problems, based on bio-inspired operators, such as mutation, crossover, and selection [30; 31]. In GAs, a set of candidate solutions (called population--a group of individuals) are iteratively modified though the mentioned genetic operators in order to find a group of better solutions for the next generation (new iteration). GAs present the following advantages: (i) continuous or discrete variables can be optimized; (ii) a derivative function is not required; (iii) multivariable problems can be optimized; (iv) extremely complex cost surfaces can be dealt with; and (v) a list of optimal solutions (and not just a single one) is provided. In Porto (selected area in this study--Portugal), an increasing trend of O\({}_{3}\) concentrations (147% higher) has been observed since the 19 th century due to the photochemical production of this pollutant, associated with the increase in anthropogenic emissions mainly due to traffic [32]. Additionally, in the north of Portugal, Lamas d'Olo is a rural site where the highest O\({}_{3}\) concentrations are usually measured. Consequently, this site is often selected to evaluate O\({}_{3}\) behavior. [PERSON] et al.