AI Summary & Key Takeaways
AI InsightThese statisticalvalues indicated a very good agreement between the monthly predicted and observed O3 forPeninsular Malaysia.
📌 3 Key Takeaways
Özet
The aim of this study is to develop new algorithms of the column ozone (O3) in Peninsular Malaysia usingstatistical methods. Four regression equations, denoted as O3 NEM, O3 SWM, (PCA1) O3 NEM season, and(PCA2) O3 SWM season, were developed. Multiple regression analysis (MRA) and principal componentanalysis (PCA) methods were utilized to achieve the objectives of the study. MRA was used to generateregression equations for O3 NEM and O3 SWM, whereas a combination of the MRA and PCA methodswere used to generate regression equations for PCA1 and PCA2. The results of the best regressionequations for the column O3 through MRA by using four of the independent variables were highlycorrelated (R ¼ 0.811 for SWM, R ¼ 0.803 for NEM) for the six-year (2003e2008) data. However, theresult of fitting the best equations for the O3 data using four of the independent variables gaveapproximately the same R values (z0.83) for both the NEM and SWM seasons using the combined MRAand PCA methods. The common variables that appeared in both regression equations were H2O vaporand NO2. This result was expected because NO2 is a precursor of O3. The correlation coefficients (R) of thevalidation for the NEM and SWM seasons were 0.877e0.888 and 0.837e0.896, respectively. These statisticalvalues indicated a very good agreement between the monthly predicted and observed O3 forPeninsular Malaysia.