Analysis of surface ozone using a recurrent neural network

• DOI: 10.1016/j.scitotenv.2015.01.106
• Publication Date: 2015-02-11T06:30:37Z
• Authors: Fabio Biancofiore; Marco Verdecchia; Piero Di Carlo; Barbara Tomassetti; Eleonora Aruffo; Marcella Busilacchio; Sebastiano Bianco; Sinibaldo Di Tommaso; Carlo Colangeli
• Keywords: 13. Climate action; ozone; neural network; urban pollution; 01 natural sciences; Boundary layer; Model comparison; Neural network; Ozone; Troposphere; Urban pollution; Environmental Chemistry; Pollution; Waste Management and Disposal; Environmental Engineering; 0105 earth and related environmental sciences

Hourly concentrations of ozone (O₃) and nitrogen dioxide (NO₂) have been measured for 16 years, from 1998 to 2013, in a seaside town in central Italy. The seasonal trends of O₃ and NO₂ recorded in this period have been studied. Furthermore, we used the data collected during one year (2005), to define the characteristics of a multiple linear regression model and a neural network model. Both models are used to model the hourly O₃ concentration, using, two scenarios: 1) in the first as inputs, only meteorological parameters and 2) in the second adding photochemical parameters at those of the first scenario. In order to evaluate the performance of the model four statistical criteria are used: correlation coefficient, fractional bias, normalized mean squared error and a factor of two. All the criteria show that the neural network gives better results, compared to the regression model, in all the model scenarios. Predictions of O₃ have been carried out by many authors using a feed forward neural architecture. In this paper we show that a recurrent architecture significantly improves the performances of neural predictors. Using only the meteorological parameters as input, the recurrent architecture shows performance better than the multiple linear regression model that uses meteorological and photochemical data as input, making the neural network model with recurrent architecture a more useful tool in areas where only weather measurements are available. Finally, we used the neural network model to forecast the O₃ hourly concentrations 1, 3, 6, 12, 24 and 48 h ahead. The performances of the model in predicting O₃ levels are discussed. Emphasis is given to the possibility of using the neural network model in operational ways in areas where only meteorological data are available, in order to predict O₃ also in sites where it has not been measured yet.