Modelling XCO2 for Peninsular Malaysia using Satellite Data and Atmospheric Parameters

Abstract

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.