Can artificial neural networks be used to predict the origin of ozone episodes?
Tropospheric ozone is a secondary pollutant having a negative impact on health and environment. To control and minimize such impact the European Community established regulations to promote a clean air all over Europe. However, when an episode is related with natural mechanisms as Stratosphere-Troposphere Exchanges (STE), the benefits of an action plan to minimize precursor emissions are inefficient. Therefore, this work aims to develop a tool to identify the sources of ozone episodes in order to minimize misclassification and thus avoid the implementation of inappropriate air quality plans. For this purpose, an artificial neural network model - the Multilayer Perceptron - is used as a binary classifier of the source of an ozone episode. Long data series, between 2001 and 2010, considering the ozone precursors, (7)Be activity and meteorological conditions were used. With this model, 2-7% of a mean error was achieved, which is considered as a good generalization. Accuracy measures for imbalanced data are also discussed. The MCC values show a good performance of the model (0.65-0.92). Precision and F1-measure indicate that the model specifies a little better the rare class. Thus, the results demonstrate that such a tool can be used to help authorities in the management of ozone, namely when its thresholds are exceeded due natural causes, as the above mentioned STE. Therefore, the resources used to implement an action plan to minimize ozone precursors could be better managed avoiding the implementation of inappropriate measures.
University Fernando Pessoa, Global Change, Energy, Environment and Bioengineering Center (CIAGEB), Praça 9 de Abril, 349, 4249-004 Porto, Portugal; University of Aveiro, Department of Mechanical Engineering/Centre for Mechanical Technology and Automation, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal. Electronic address: trfontes@ua.pt.
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Fontes
University of Aveiro, Department of Mathematics, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal; INEB - Instituto de Engenharia Biomédica, Rua do Campo Alegre, 823, 4150-180 Porto, Portugal.
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Silva
University Fernando Pessoa, Global Change, Energy, Environment and Bioengineering Center (CIAGEB), Praça 9 de Abril, 349, 4249-004 Porto, Portugal.
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Silva
University Fernando Pessoa, Global Change, Energy, Environment and Bioengineering Center (CIAGEB), Praça 9 de Abril, 349, 4249-004 Porto, Portugal.
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Barros
New University of Lisbon, Faculty of Sciences and Technology/Center for Environmental and Sustainability Research (CENSE), Quinta da Torre, 2829-516 Caparica, Portugal.
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Carvalho
D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000706:statistics & numerical data*; D001272:Atmosphere / Q000737:chemistry; D004784:Environmental Monitoring / Q000379:methods*; D005060:Europe; D016571:Neural Networks, Computer; D010126:Ozone / Q000032:analysis*
D000393:Air Pollutants; D010126:Ozone
Artificial neural network; Classification; Human health; Ozone; Stratosphere; Troposphere