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Prediction of mortality resulted from NO2 concentration in Tehran by Air Q+ software and artificial neural network

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Abstract

In this study, for the first time, the combination of Air Q+ software and wavelet neural network was used to predict the mortality rate caused by the increase in NO2 concentration in Tehran. In the combination of these two softwares, the wavelet neural network software was used to predict daily NO2 concentration based on 12 effective parameters, and then the annual concentration of NO2 was calculated using the daily concentration of wavelet neural network output. Then, annual concentration of NO2 was used as the input of Air Q+ software. The mortality rate was calculated by Air Q+ software. In this research, the most appropriate predictive algorithm for neural network was studied and layer recurrent algorithm was the most appropriate algorithm. Then, capability of this network was enhanced to predict future NO2 concentration by wavelet transformation, and wavelet neural network was designed. Also, NO2 concentration is predicted for future 47 months by using of the time series of the previous data and the wavelet neural network. Analyzing the sensitivity of mortality resulted from NO2 concentration was done by using of wavelet neural network and Air Q+ software, and it was concluded that the increase or decrease in the parameters affecting NO2 concentration will affect the mortality rate. This research has identified petrol consumption as the most influential parameter. The conclusion is that by lowering the 10% of petrol consumption, the mortality based on NO2 concentration in ambient air will decrease about 50%.

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Acknowledgements

The authors would like to thank Tehran Weather Bureau and Tehran Municipality.

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Correspondence to F. Qaderi.

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Editorial responsibility: M. Abbaspour.

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Ebrahimi Ghadi, M., Qaderi, F. & Babanezhad, E. Prediction of mortality resulted from NO2 concentration in Tehran by Air Q+ software and artificial neural network. Int. J. Environ. Sci. Technol. 16, 1351–1368 (2019). https://doi.org/10.1007/s13762-018-1818-4

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  • DOI: https://doi.org/10.1007/s13762-018-1818-4

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