Artificial intelligence accuracy assessment in NO(2) concentration forecasting of metropolises air

Air quality has been the main concern worldwide and Nitrous oxide (NO(2)) 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 NO(2) 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 NO(2) in the air. The results demonstrate that artificial neural network modeling (R(2) = 0.89, RMSE = 0.32) results in more accurate predictions than MLR analysis (R(2) = 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 NO(2) 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 NO(2) reduction even more than traffic volume.