ABSTRACT: Air pollution data obtained using automated machines often contain missing values which can cause bias due to systematic differences between observed and unobserved data. We used interpolation and mean imputation techniques to replace simulated missing values from annual hourly monitoring data for PM10. The most effective method for generating the missing data points was to replace each missing value with the mean of the two data points before and after the missing value. This approach was referred to as the mean-before-after method.
Noraziana et al. REF studied effective steps for extracting data that is missing by replacing each value with the mean value of the two points that occur before and after (mean-before-after).
Estimation of missing values in air pollution data using single imputation techniques
ScienceAsia
ScienceAsia
Geography