A study on the Simulation and Estimation of the urban air quality by using artificial intelligence (case study: Gholhak Station of Tehran (

Air pollution in Tehran, because of high concentration of pollutants, has caused various diseases and many problems concerning the public health and welfare of citizens and also damages to the environment and living organisms.
Air Quality Index (AQI) is a key tool to monitor the air quality, to realize the effects of air pollution on health and to choose methods against air pollution. This study aimed at modeling and estimation AQI by CART algorithm and adaptive boosting algorithm (AdaBoost). Hourly data on concentration of air pollutants and meteorological parameters related to Gholhak stations in Tehran was used for modeling and estimation of AQI.
The results showed that CART model had better performance than AdaBoost model. To evaluate these models, root mean square error (RMSE), mean absolute error (MAE), mean squared error (MSE) and correlation coefficient (R) of the CART model for the test, were respectively, 0.75, 0.101, 0.563, and 0.99 when compared to the AdaBoost model (RMSE=7.1, MAE=5.11, MSE=50.52 and R=0.95) which implies the absolute superiority of the CART model than the AdaBoost model.
The results of this study showed that regression decision tree model can be used as an efficient model for modelling and estimation of urban air quality index.