Characterization of Surface Ozone Behavior at Different Regimes

Abstract

Previous studies showed that the influence of meteorological variables and concentrations of other air pollutants on O 3 concentrations changes at different O 3 concentration levels. In this study, threshold models with artificial neural networks (ANNs) were applied to characterize the O 3 behavior at an urban site (Porto, Portugal), describing the effect of environmental and meteorological variables on O 3 concentrations. ANN characteristics, and the threshold variable and value, were defined by genetic algorithms (GAs). The considered predictors were hourly average concentrations of NO, NO 2 , and O 3 , and meteorological variables (temperature, relative humidity, and wind speed) measured from January 2012 to December 2013. Seven simulations were performed and the achieved models considered wind speed (at 4.9 m·s −1 ), temperature (at 17.5 • C) and NO 2 (at 26.6 µg·m −3 ) as the variables that determine the change of O 3 behavior. All the achieved models presented a similar fitting performance: R 2 = 0.71-0.72, RMSE = 14.5-14.7 µg·m −3 , and the index of agreement of the second order of 0.91. The combined effect of these variables on O 3 concentration was also analyzed. This statistical model was shown to be a powerful tool for interpreting O 3 behavior, which is useful for defining policy strategies for human health protection concerning this air pollutant.

Analysis and Modelling of PM2.5 Temporal and Spatial Behaviors in European Cities

“…The model applied in this study was structured similarly as defined by Afonso and Pires (2017) [23] and can be characterised by the following equation:…”

“…In this study, GA were used to define the threshold variable and value, the number of hidden neurons in the ANN, the activation function in the hidden layer of the ANN, and to select the explanatory variables to be used in each ANN model for three separate sets of time series (2013-2014; 2015-2016; 2017-2018). The specifications used in the determination of the model were defined as described by Afonso and Pires [23].…”

Editorial for Special Issue: “Application of Artificial Neural Networks in Geoinformatics”

“…[14] applied artificial neural networks and genetic algorithms to characterize surface ozone behavior.…”

A Machine Learning Approach to Investigate the Surface Ozone Behavior

“…The efficiency of the photochemical reactions depends on the concentrations of precursors and on the meteorological parameters. Furthermore, the latter affect the natural emission of precursors and the processes of accumulation, dispersion, transport, and removal connected to the air pollutants [19]. Therefore, the surface O 3 variability is highly dependent on precursors, meteorological parameters, and their interactions through a series of complex and non-linear functions.…”