Air pollution is the act of contaminating the cleanliness of the air, which deteriorates the air quality and severely affects human health and the environment. This study has examined the imputation of carbon monoxide (CO) missing data, conducted in the metropolitan area of Petaling Jaya, Malaysia using basic statistical methods. The purpose of this study is to determine the best method among the five basic statistical methods for the imputation of missing CO data in the study area. The five basic statistical methods used to impute the missing data are Linear Interpolation (LI), Top Bottom Mean (TBM), Daily Mean (DM), 12-Hour Mean (M12), and 6-Hour Mean (M6). Annual hourly data for CO gas pollutants from Petaling Jaya were taken in 2017, and the data used is complete and continuous. The percentages of missing data applied in this study are 10%, 20%, and 30%. The performance indices used to evaluate basic statistical methods are Correlation Coefficient (CC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Overall, the best basic statistical method is ; it is hoped that this study can help the Malaysian Department of Environment (DOE) in imputing missing data for CO air pollutants in the future.