Single imputation method of missing values in environmental pollution data sets

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A review of artificial neural network models for ambient air pollution prediction (2019, methodology): “Missing data imputation techniques are categorised as univariate, multivariate, nearest neighbour, and the hybrid of the previous approaches (Junninen et al., 2004; Plaia & Bondı, 2006).”
Filling Missing Data Using Interpolation Methods: Study on the Effect of Fitting Distribution (2013, methodology): “Decision on selecting an appropriate method for handling missing observations on time series depends on the missing data pattern and on the missing-data mechanism [2].”
'Effortless Perfection:' Do Chinese Cities Manipulate Air Pollution Data? (2014, background): “Third, atmospheric scientists and environmental engineers also assume that the distribution of ambient levels is continuous (Junninen et al., 2004; Plaia and Bondi, 2006; Md Yusof et al., 2010).”