Prediction of the Air Quality by Artificial Neural Network Using Instability Indices in the City of Tehran-Iran

Document Type : Research Article

Authors

1 Graduated Student, Environmental Sciences Department, Hakim Sabzevari University, Sabzevar, Iran

2 Assistant Professor, Environmental Sciences Department, Hakim Sabzevari University, Sabzevar, Iran

3 Assistant Professor, Environmental Sciences Department, University of Tehran, Tehran, Iran

4 Assistant Professor, Faculty of Mathematics and Computer Science, Hakim Sabzevari University, Sabzevar, Iran

Abstract

Today, air pollution is a serious environmental problem becoming a global concern for human beings Air quality is influenced by emissions, meteorological parameters, and topography. The effect of these parameters can be predicted using statistical methods. In the current study, the data in the period of March 2012 to October 2013 are used. These data have been gathered from the stations of the Department of Environment and Air Quality Control Organization (Azadi and Sharif stations) in Tehran city. The main purpose was to predict the air quality of the next day and emissions of carbon monoxide and suspended particles under the influence of instability indices and meteorological parameters using the Artificial Neural Network. Results of the modeling process showed that the concentration of pollutants is strongly influenced by meteorological parameters. Also, prediction of the PM10 concentration of the next day using meteorological parameters (RMSE=29.03, R=0.76), instability indices and meteorological parameters (RMSE=28.13, R=0.76) were better than those obtained for AQI predicted by meteorological parameters (RMSE=20.81, R=0.50) and instability indices and meteorological parameters (RMSE=19.23, R=0.47). In general, the predicted values of PM10 and CO were better compared to AQI. It can be concluded that an artificial neural network couldn’t load the model properly for AQI compared to PM10.

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References

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AUT Journal of Civil Engineering
Volume 4, Issue 4
December 2020
Pages 517-528

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