title: Prediction of ozone concentrations in Oporto city with statistical approaches.
abstract: The performance of three statistical methods: time-series, multiple linear regression and feedforward artificial neural networks models were compared to predict the daily mean ozone concentrations. The study here reported was based on data from one urban site with traffic influences and one rural background site. The studies were performed for the year 2002 and the respective four trimesters separately. In the multiple linear regression and feedforward artificial neural network models, the concentrations of ozone, the concentrations of its precursors (nitrogen oxides) and some meteorological variables for one and two days before the prediction day were used as predictors. For the application of these models in the validation step, the inputs of ozone concentration for one and two days before were replaced by the ozone concentrations predicted by the models. The results showed that time-series modelling was not profitable. In the development step, similar performances were obtained with multiple linear regression and feedforward artificial neural network. Better performance indexes were achieved with feedforward artificial neural network models in validation step. Concluding, feedforward artificial neural network models were more efficient to predict ozone concentrations.
authors.affiliation: LEPAE, Departamento de Engenharia Química, Faculdade de Engenharia, Universidade do Porto, R. Dr. Roberto Frias, 4200-465 Porto, Portugal.
authors.forename: S I V
authors.initials: SI
authors.lastname: Sousa
authors.forename: F G
authors.initials: FG
authors.lastname: Martins
authors.forename: M C
authors.initials: MC
authors.lastname: Pereira
authors.forename: M C M
authors.initials: MC
authors.lastname: Alvim-Ferraz
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D002248:Carbon Monoxide / Q000032:analysis; D002947:Cities; D004391:Dust / Q000032:analysis; D004784:Environmental Monitoring / Q000706:statistics & numerical data; D005544:Forecasting; D006813:Humidity; D016014:Linear Models; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D009569:Nitric Oxide / Q000032:analysis; D009585:Nitrogen Dioxide / Q000032:analysis; D010126:Ozone / Q000032:analysis*; D011174:Portugal; D015203:Reproducibility of Results; D013458:Sulfur Dioxide / Q000032:analysis; D013696:Temperature; D014919:Wind
chemical_list: D000393:Air Pollutants; D004391:Dust; D013458:Sulfur Dioxide; D009569:Nitric Oxide; D010126:Ozone; D002248:Carbon Monoxide; D009585:Nitrogen Dioxide