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).
Highlights
Clean air is a basic requirement for human health and on the other hand, Air pollutants impose a wide-ranging adverse impacts on biological, physical and natural systems especially on human health [1]
One of these factors is the existence of numerous lines and areas which act as sources of the atmospheric pollution [37]
Results showed that the highest CO concentration occurred in cold seasons (R2= 0.78) and that the lowest concentration of pollutant was seen in warm season (R2= 0.66)
Summary
Clean air is a basic requirement for human health and on the other hand, Air pollutants impose a wide-ranging adverse impacts on biological, physical and natural systems especially on human health [1]. Neural networks are analytical and educational tools that try to mimic the patterns of information processing in the human brain They have high flexibility and examine nonlinear relationships between parameters. Models were trained, validated and tested based on backpropagation neural network using the gathered data It showed that the models had ability to produce a truthful forecasting of hourly concentrations of the pollutants more than 10 h in advance. This comparative study showed that the neural network model describes the data more accurate than the multiple linear regression-based models and the California line source dispersion model. Prediction of pollutant concentration with meteorological parameters and secondly, the effect of instability indices on air pollution were investigated
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call