Abstract
Air quality has been the main concern worldwide and Nitrous oxide (NO2) is one of the pollutants that have a significant effect on human health and environment. This study was conducted to compare the regression analysis and neural network model for predicting NO2 pollutants in the air of Tehran metropolis. Data has been collected during a year in the urban area of Tehran and was analyzed using multi-linear regression (MLR) and multilayer perceptron (MLP) neural networks. Meteorological parameters, urban traffic data, urban green space information, and time parameters are applied as input to forecast the daily concentration of NO2 in the air. The results demonstrate that artificial neural network modeling (R2 = 0.89, RMSE = 0.32) results in more accurate predictions than MLR analysis (R2 = 0.81, RMSE = 13.151). According to the result of sensitivity analysis of the model, the value of park area, the average of green space area and one-day time delay are the crucial parameters influencing NO2 concentration of air. Artificial neural network models could be a powerful, effective and suitable tool for analysis and modeling complex and non-linear relation of environmental variables such as ability in forecasting air pollution. Green spaces establishment has a significant role in NO2 reduction even more than traffic volume.
Highlights
Air quality has been the main concern worldwide and Nitrous oxide (NO2) is one of the pollutants that have a significant effect on human health and environment
The comparison between multi-linear regression (MLR) and artificial neural network modeling demonstrates that the neural network model ( R2 = 0.89, root means square error (RMSE) = 0.32) performs more accurately than multiple regression analysis ( R2 = 0.81, RMSE = 13.151)
The results indicated that multilayer perceptron (MLP) has a higher correlation coefficient than MLR in the forecasting of air quality and meteorological factors have an impact upon the NO2 concentration[38]
Summary
Air quality has been the main concern worldwide and Nitrous oxide (NO2) is one of the pollutants that have a significant effect on human health and environment. This study was conducted to compare the regression analysis and neural network model for predicting NO2 pollutants in the air of Tehran metropolis. Epidemiological studies show that high-level exposure to NO2 in the air as a pollutant lead to increasing approximately 5–7% of the lung cancers among people (ex-smokers and non-smokers)[9]. Multi-linear regression (MLR) analysis is an approach to evaluate the relationship between independent and dependent factors The goal of this investigation is the prediction of the concentration levels of NO2 as a factor determining the atmospheric pollution in Tehran city. For this purpose prediction accuracy, MLR and ANN models are selected to be compared. The result of model sensitivity analysis was illustrated to prioritize model variables
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