Alireza Ehsanzadeh; Farhad Nejadkoorki; Sattar Khodadoostan
Abstract
Background & objectives: Air particulate matters which have natural and human made origins have significant effects on the climate, the environment and human health. Several epidemiological studies have shown a direct relationship among the concentrations of suspended particles with different ...
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Background & objectives: Air particulate matters which have natural and human made origins have significant effects on the climate, the environment and human health. Several epidemiological studies have shown a direct relationship among the concentrations of suspended particles with different adverse health effects. The general purpose of this research was to determine the most important parameters affecting on the concentration of PM10 in Tajrish station (Tehran) and develop an estimator model for PM10. Materials & methods: In this study, a model is constructed using principal component regression (PCR) for the relationship between the hourly concentration of particulate matter smaller than 10 microns with meteorological parameters (WD, WS, T, P, H) and air pollution parameters (CO, NO2, SO2, NOx, NMHC, THC) in Tajrish station (Tehran). The results of the performance evaluation of PCR model were measured in training and testing stages using RMSE, MAE, R and IA as statistical indicators. Results: The results of principal components import into multiple regression model showed that the most important variable affecting on the concentrations of PM10, are air temperature and the wind speed. Also, CO and SO2 emissions were known as synergic factors for PM10 concentration. The results showed that PCR model is able to estimate 41% of PM10 concentrations in the testing. Conclusion: The principal components regression analysis showed that meteorological parameters are one of most important factors affecting on the reduction of PM10 concentration in Tajrish station (Tehran).
Alireza Ehsanzadeh; Farhad Nejadkoorki; Ali Taleb
Abstract
Background and objective: 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. Materials & Methods: Air Quality Index ...
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Background and objective: 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. Materials & Methods: 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. Results: 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. Conclusion: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.