PREDICTION OF GROUND-LEVEL OZONE CONCENTRATIONS THROUGH STATISTICAL MODELS

This study aims to evaluate the performance of three statistical models: (i) multiple linear regression (MLR), (ii) artificial neural network (ANN) and (iii) multi-gene genetic programming (MGP) for predicting the next day hourly average ozone (O-3) concentrations. O-3 is an important air pollutant that has several negative impacts. Thus, it is important to develop predictive models to prevent the occurrence of air pollution episodes with a time interval enough to take the necessary precautions. The data were collected in an urban site with traffic influences in Oporto Metropolitan Area, Northern Portugal. The air pollutants data (hourly average concentrations of CO, NO, NO2, NOx and O-3), the meteorological data (hourly averages of temperature, relative humidity and wind speed) and the day of week were used as inputs for the models. ANN models presented better results in the training step. However, with regards to the aim of this study, MGP presented the best predictions of O-3 concentrations (test step). The good performances of the models showed that MGP is a useful tool to public health protection as it can provide more trustful early warnings to the population about O-3 concentrations episodes.