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Appl. Sci. 2017, 7(9), 944; doi:10.3390/app7090944

Characterization of Surface Ozone Behavior at Different Regimes

Laboratório de Engenharia de Processos, Ambiente Biotecnologia e Energia (LEPABE), Departamento de Engenharia Química, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
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Received: 25 July 2017 / Revised: 31 August 2017 / Accepted: 12 September 2017 / Published: 14 September 2017
(This article belongs to the Special Issue Application of Artificial Neural Networks in Geoinformatics)
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Abstract

Previous studies showed that the influence of meteorological variables and concentrations of other air pollutants on O3 concentrations changes at different O3 concentration levels. In this study, threshold models with artificial neural networks (ANNs) were applied to characterize the O3 behavior at an urban site (Porto, Portugal), describing the effect of environmental and meteorological variables on O3 concentrations. ANN characteristics, and the threshold variable and value, were defined by genetic algorithms (GAs). The considered predictors were hourly average concentrations of NO, NO2, and O3, 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 NO2 (at 26.6 μg·m−3) as the variables that determine the change of O3 behavior. All the achieved models presented a similar fitting performance: R2 = 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 O3 concentration was also analyzed. This statistical model was shown to be a powerful tool for interpreting O3 behavior, which is useful for defining policy strategies for human health protection concerning this air pollutant. View Full-Text
Keywords: air pollution; artificial neural network; genetic algorithms; surface ozone; threshold models air pollution; artificial neural network; genetic algorithms; surface ozone; threshold models
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MDPI and ACS Style

Afonso, N.F.; Pires, J.C.M. Characterization of Surface Ozone Behavior at Different Regimes. Appl. Sci. 2017, 7, 944.

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