Monitoring environmental pollutants is a critical part of exposure sciences research and public health practice. Missing data are often encountered when performing short-term monitoring (<24 h) of air pollutants using real-time monitors, particularly in resource-limited areas. Approaches to handle consecutive periods of missing and incomplete data in this context remain unclear. This work evaluated existing imputation methods to handle missing data for real-time monitors operating at short duration. In a current field study, real-time PM2.5 monitors, placed outside 20 households, were operated for 24-h. Missing data were simulated in these households at four consecutive periods of missingness (20, 40, 60, 80%). Univariate (mean, median, last observation carried forward, Kalman filter, random, Markov) and multivariate time-series (predictive mean matching, row mean method) methods imputed missing concentrations; performance was evaluated using five error metrics (absolute bias, percent absolute error in means, R2 coefficient of determination, root mean square error, mean absolute error). Univariate methods (Markov, random, mean) imputations were the best performing methods yielding 24-h mean concentrations with the lowest error and highest R2 values across all levels of missingness. When evaluating error metrics minute-by-minute, Kalman filters, median, and Markov methods performed well at low levels of missingness (20-40%); however, at higher levels of missingness (60-80%), Markov, random, median, and mean imputation performed best on average Multivariate methods were the worst performing imputation methods across all levels of missingness. Imputation using univariate methods may provide a reasonable solution to address missing data for short term air pollutant monitoring, particularly in resource-limited areas. Addnl. efforts are needed to evaluate imputation methods which are generalizable across a diverse range of study environments.