Prediction of CO and PM10 in Cold and Warm Seasons and Survey of the Effect of Instability Indices on Contaminants Using Artificial Neural Network: A Case Study in Tehran City

Abstract

Today, air pollution in urban areas is a major issue that have been affecting human health and the environment. Over the years artificial neural network methods has been used for prediction of pollutants concentration in many metropolitans. In the present study data were obtained from department of environment and air quality controlling stations in city of Tehran from March 2012 to October 2013. Prediction of CO and PM10 contaminations during cold and warm seasons under the influence of instability indices and meteorological parameters was done using the artificial neural network. Results of the modeling process showed that the highest correlation coefficient was obtained 0.84 for PM10 in warm season. On the contrary, the highest correlation coefficient of CO in cold season was 0.78. Also, the effect of instability indices on air pollution was investigated. The highest CO concentration occurred during cold seasons (R2= 0.81), while the lowest concentration was in warm season (R2= 0.72). In case of PM, the highest concentration occurred during warm seasons (R2= 0.84), while the lowest concentration was in cold season (R2=0.75).

Keywords

• artificial neural network
• carbon monoxide
• instability indices
• particle matters
• regression