Filling the Missing Data of Air Pollutant Concentration Using Single Imputation Methods

Abstract

Hourly measured PM10 concentration at eight monitoring stations within peninsular Malaysia in 2006 was used to conduct the simulated missing data. The gap lengths of the simulated missing values are limited to 12 hours since the actual trend of missingness is considered short. Two percentages of simulated missing gaps were generated that are 5 % and 15 %. A number of single imputation methods (linear interpolation (LI), nearest neighbour interpolation (NN), mean above below (MAB), daily mean (DM), mean 12-hour (12M), mean 6-hour (6M), row mean (RM) and previous year (PY)) were calculated to fill in the simulated missing data. In addition, multiple imputation (MI) was also conducted to compare between the single imputation methods. The performances were evaluated using four statistical criteria namely mean absolute error, root mean squared error, prediction accuracy and index of agreement. The results show that 6M perform comparably well to LI. Thus, this show that the effect of smaller averaging time gives better prediction. Other single imputation methods predict the missing data well except for PY. RM and MI performs moderately with the increasing performance in higher fraction of missing gaps whereas LR makes the worst methods for both simulated missing data percentages.