Prediction of vertical PM_2.5 concentrations alongside an elevated expressway by using the neural network hybrid model and generalized additive model

Abstract

A study on vertical variation of PM_2.5 concentrations was carried out in this paper. Field measurements were conducted at eight different floor heights outside a building alongside a typical elevated expressway in downtown Shanghai, China. Results show that PM_2.5 concentration decreases significantly with the increase of height from the 3rd to 7th floor or the 8th to 15th floor, and increases suddenly from the 7th to 8th floor which is the same height as the elevated expressway. A non-parametric test indicates that the data of PM_2.5 concentration is statistically different under the 7th floor and above the 8th floor at the 5% significance level. To investigate the relationships between PM_2.5 concentration and influencing factors, the Pearson correlation analysis was performed and the results indicate that both traffic and meteorological factors have crucial impacts on the variation of PM_2.5 concentration, but there is a rather large variation in correlation coefficients under the 7th floor and above the 8th floor. Furthermore, the back propagation neural network based on principal component analysis (PCA-BPNN), as well as generalized additive model (GAM), was applied to predict the vertical PM_2.5 concentration and examined with the field measurement dataset. Experimental results indicated that both models can obtain accurate predictions, while PCA-BPNN model provides more reliable and accurate predictions as it can reduce the complexity and eliminate data co-linearity. These findings reveal the vertical distribution of PM_2.5 concentration and the potential of the proposed model to be applicable to predict the vertical trends of air pollution in similar situations.

Keywords

vertical variations / principal component analysis / back propagation neural network / generalized additive model / urban elevated expressway

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Acknowledgments

The authors would like to acknowledge the support from Shanghai Environmental Protection Bureau, Shanghai Environmental Monitoring Center, Science Technology Department of Zhejiang Province (2014C31028), Peking University-Lincoln Institute (DS20120901), and State Key Laboratory of Ocean Engineering of China (GKZD010059). We thank members from Shanghai Environmental Monitoring Center for their assistance in instrumental calibration. We also appreciate members from the Center for ITS and UAV Applications Research at Shanghai Jiao Tong University for their hard work in data collection and processing.

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2016 Higher Education Press and Springer-Verlag Berlin Heidelberg