Abstract
In this study, in the first step, three scenarios with different input combinations are created to implement a sensitivity analysis for hourly NO2 prediction in Columbus City, Ohio. Three classes of inputs including concentration-related data (NO2 concentration at previous time steps and NO2 concentration in the suburban monitoring station), meteorology (wind speed, wind direction, and temperature), and traffic-related data (traffic count, hour of the day, and day of the week) are applied to create three scenarios. Also, the support vector regression methodology is employed to perform the sensitivity analysis. Dominant variables determined in the sensitivity analysis are applied as inputs to three models called feed-forward neural network, support vector regression, as well as classification and regression tree. In the last step, ensemble techniques including simple linear averaging, weighted linear averaging, and nonlinear support vector regression ensemble are proposed to improve the performance of sole models. The results indicate that, in the urban area, in addition to NO2 variations in the previous time step, other variables such as hourly traffic count in freeway loop, suburban NO2 concentration, and hour of the day can affect the NO2 concentration. Further, the values of determination coefficient for the individual models, namely classification and regression tree and feed-forward neural network, are 67 and 81% that the ensemble technique as a post-processing approach enhances the performance of them up to 19% and 5% in the verification steps, respectively.
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Acknowledgements
This study was conducted using a grant received by the authors form Research Affairs of University of Tabriz. Also, authors would like to thank EPA, Ohio State University and Ohio Department of Transportation, for providing precious data for the study.
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Nourani, V., Abdollahi, Z. & Sharghi, E. Sensitivity analysis and ensemble artificial intelligence-based model for short-term prediction of NO2 concentration. Int. J. Environ. Sci. Technol. 18, 2703–2722 (2021). https://doi.org/10.1007/s13762-020-03002-6
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DOI: https://doi.org/10.1007/s13762-020-03002-6