In order to exactly explain the relationship between O₃and influencing factors, providing a scientific basis of the relationship is essential. This research has been conducted to analyze of the effects of O₃precursors and meteorological factors on ambient O₃Concentrations. In order to achieve the pu...
In order to exactly explain the relationship between O₃and influencing factors, providing a scientific basis of the relationship is essential. This research has been conducted to analyze of the effects of O₃precursors and meteorological factors on ambient O₃Concentrations. In order to achieve the purpose, this research was conducted the following analysis: a. Through the previous studies discussion, this research identified correctly the relationship between O₃and influencing factors. NO₂and air temperature were chosen as the influencing factors for Ozone. b. This research set the expected model with the form which combined of precursors and meteorological factors. c. Using the expected model, double-log multiple regression analysis was performed. d. The structural stability of regression models were verified by Chow test. e. This research has conducted an Spatial Auto-correlation Analysis(SAR) for the effects of precursor and meteorological factors on ambient O₃concentrations. The research concludes as follows: Firstly, Except for the regression models for August, the level of significance for all the regression models were accepted. Also, regression coefficients for all variables were consistent with the expected sign. Secondly, The sign of NO₂coefficients showed a negative direction. It means O₃concentrations are reduced with an increase of NO₂concentrations. However, in order to reduce O₃, increasing the NO₂is not correct, because NO₂is also one of the air pollutants. Therefore, it is essential to find a way to reduce O₃and NO₂at the same time. Thirdly, The sign of Air Temperature coefficients showed a positive direction. It means O₃concentrations are increased with an increase of air temperature. Also, it means higher temperatures make the creation of O₃faster. Therefore, the reduction of high temperature on summer is essential to reduce of higher O₃concentrations. Fourthly, The results of temporal stability analysis for regression model, structural stability of regression model of consecutive 2 months is confirmed on 9 Air Pollution Monitoring Stations(APMS) from January to May. Fifthly, The results of spatial stability analysis for regression model, structural stability of regression model of adjacent 2 APMS is mainly confirmed on the northern districts in Seoul. Finally, The results of SAR, the average of O₃has positive spatial auto-correlation on some periods. But the correlation coefficient of independent variables rarely have spatial auto-correlation. The results of this research can be served as the basic data for establishment of effective air pollution management policy in future.
In order to exactly explain the relationship between O₃and influencing factors, providing a scientific basis of the relationship is essential. This research has been conducted to analyze of the effects of O₃precursors and meteorological factors on ambient O₃Concentrations. In order to achieve the purpose, this research was conducted the following analysis: a. Through the previous studies discussion, this research identified correctly the relationship between O₃and influencing factors. NO₂and air temperature were chosen as the influencing factors for Ozone. b. This research set the expected model with the form which combined of precursors and meteorological factors. c. Using the expected model, double-log multiple regression analysis was performed. d. The structural stability of regression models were verified by Chow test. e. This research has conducted an Spatial Auto-correlation Analysis(SAR) for the effects of precursor and meteorological factors on ambient O₃concentrations. The research concludes as follows: Firstly, Except for the regression models for August, the level of significance for all the regression models were accepted. Also, regression coefficients for all variables were consistent with the expected sign. Secondly, The sign of NO₂coefficients showed a negative direction. It means O₃concentrations are reduced with an increase of NO₂concentrations. However, in order to reduce O₃, increasing the NO₂is not correct, because NO₂is also one of the air pollutants. Therefore, it is essential to find a way to reduce O₃and NO₂at the same time. Thirdly, The sign of Air Temperature coefficients showed a positive direction. It means O₃concentrations are increased with an increase of air temperature. Also, it means higher temperatures make the creation of O₃faster. Therefore, the reduction of high temperature on summer is essential to reduce of higher O₃concentrations. Fourthly, The results of temporal stability analysis for regression model, structural stability of regression model of consecutive 2 months is confirmed on 9 Air Pollution Monitoring Stations(APMS) from January to May. Fifthly, The results of spatial stability analysis for regression model, structural stability of regression model of adjacent 2 APMS is mainly confirmed on the northern districts in Seoul. Finally, The results of SAR, the average of O₃has positive spatial auto-correlation on some periods. But the correlation coefficient of independent variables rarely have spatial auto-correlation. The results of this research can be served as the basic data for establishment of effective air pollution management policy in future.
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