Modelling XCO2 for Peninsular Malaysia using Satellite Data and Atmospheric Parameters

This study aims to develop an algorithm for calculating the column-averaged dry air mole fraction of carbon dioxide (XCO2) over peninsular Malaysia using statistical methods. Data from five atmospheric variables consisting of the aerosol asymmetry factor (AAF), aerosol optical thickness (AOT), temperature (temperature), water vapor (H2O vapor) and aerosol single scattering albedo (SSA) were utilized to develop a predictive XCO2 regression model using multiple linear regression (MLR) for examining the impacts of the atmospheric variables on the XCO2. The predictive XCO2 regression model highly correlates with atmospheric variables (R2 = 0.68 for Northeast Monsoon and R2 = 0.64 for Southwest Monsoon). The validation results show that XCO2 yielded a strong R2 for the Northeast Monsoon and Southwest Monsoon seasons, i.e., 0.84 and 0.83, respectively. The proposed regression model exhibited excellent agreement under different monsoon seasons in Peninsular Malaysia.