Prediction of column ozone concentrations using multiple regression analysis and principal component analysis techniques: A case study in peninsular Malaysia

• We develop a new algorithm to predict column ozone (O3) in Peninsular Malaysia.
• Multiple linear regression and principle component analysis techniques have been employed.
• Regression equations for column O3 returned similar values of R (≈0.83) for both the NEM and SWM seasons.
• There was close agreement between the predicted and observed data for column O3.

The aim of this study is to develop new algorithms of the column ozone (O3) in Peninsular Malaysia using statistical 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 component analysis (PCA) methods were utilized to achieve the objectives of the study. MRA was used to generate regression equations for O3 NEM and O3 SWM, whereas a combination of the MRA and PCA methods were used to generate regression equations for PCA1 and PCA2. The results of the best regression equations for the column O3 through MRA by using four of the independent variables were highly correlated (R = 0.811 for SWM, R = 0.803 for NEM) for the six-year (2003–2008) data. However, the result of fitting the best equations for the O3 data using four of the independent variables gave approximately the same R values (≈0.83) for both the NEM and SWM seasons using the combined MRA and PCA methods. The common variables that appeared in both regression equations were H2O vapor and NO2. This result was expected because NO2 is a precursor of O3. The correlation coefficients (R) of the validation for the NEM and SWM seasons were 0.877–0.888 and 0.837–0.896, respectively. These statistical values indicated a very good agreement between the monthly predicted and observed O3 for Peninsular Malaysia.