Regression Analysis Of Column Ozone And Selected Atmospheric Parameters In Peninsular Malaysia From Sciamachy Data

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dc.contributor.authorKOK CHOOI, TAN
dc.date.accessioned2016-06-21T03:36:41Z
dc.date.available2016-06-21T03:36:41Z
dc.date.issued2015-04
dc.description.abstractOzone (O3) is unique among pollutants because it is not emitted directly into the air, and its results from complex chemical reactions in the atmosphere. O3 can bring different effects for all the living on earth (either harm or protect), depending on where O3 resides. This is the main reason why O3 is such a serious environmental problem that is difficult to control and predict. The aim of this study is to develop new algorithms for column O3 in Peninsular Malaysia using statistical methods. In the end, four regression equations – denoted as O3 NEM, O3 SWM, PCA1 (O3 NEM season) and PCA2 (O3 SWM season) – were developed. In addition, the study focused on the analysis and investigation of how the selected atmospheric parameters affect column O3 values in Peninsular Malaysia. Multiple regression analysis (MRA) and principal component analysis (PCA) methods have been utilized to achieve these study objectives. MRA was used to generate regression equations for O3 NEM and O3 SWM, while a combination of MRA and PCA methods were used to generate regression equations for PCA1 and PCA2. Statistical analysis has been carried out to test the performance of the MRA method and the combined MRA/PCA method, in terms of root mean square errors (RMSE), index of agreement (IA), and fractional bias (FB). The results of the best column O3 regression equations, using MRA with four independent variables, were highly correlated (R = 0.811 for SWM, and R = 0.803 for NEM; R2 ≈ 0.658 for SWM, and R2 ≈ 0.645 for NEM for the six-year (2003-2008) data). The correlation coefficients (R) of validation for the NEM and SWM seasons were 0.783 to 0.799 and 0.752 to 0.802, respectively. On the other hand, using the combined MRA/PCA method with four independent variables, the results of fitting the best O3 data equations gave about the same values of R (≈ 0.83) for both the NEM and SWM seasons. The common variables that appeared in both regression equations were H2O vapor and NO2. This result was expected, as NO2 is a precursor of O3. The correlation coefficients (R) of validation for the NEM and SWM seasons were 0.877 to 0.888 and 0.837 to 0.896, respectively. Using MRA methods, the RMSE, IA, and FB for the predicted O3 were found to be 15.5 DU, 0.57, and -0.05, respectively. On the other hand, using the combined MRA/PCA method, the RMSE, IA, and FB for the predicted O3 were found to be 10.93 DU, 0.68, and -0.03, respectively. These statistical values indicated a very good agreement between the predicted and observed monthly O3 for Peninsular Malaysia. The obtained results demonstrate that the combined MRA/PCA method performs slightly better than the MRA method in predicting the O3 value in Peninsular Malaysia. Overall, these results clearly indicate the advantage of using satellite Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) and correlation analysis to investigate the impact of atmospheric parameters on total column O3 over Peninsular Malaysia. The validation and comparison conducted in this study demonstrate the high accuracy of the regression equations and hence has successfully accomplished the objectives of this research.en_US
dc.identifier.urihttp://hdl.handle.net/123456789/2153
dc.subjectRegression Analysis Of Column Ozone And Selected Atmospheric Parametersen_US
dc.subjectIn Peninsular Malaysia From Sciamachy Dataen_US
dc.titleRegression Analysis Of Column Ozone And Selected Atmospheric Parameters In Peninsular Malaysia From Sciamachy Dataen_US
dc.typeThesisen_US
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