We performed a Monte Carlo simulation with Latin Hypercube Sampling (N = 1,000) for each reactor type to propagate the uncertainty or variability of the 18 parameters. An identical set of samples was used across the three reactor types to enable pair-wise comparisons. To elucidate the relative importance of different variables to the sustainability of encapsulated systems, we conducted Monte Carlo filtering for 5 indicators (i.e., COD removal percentage [rCOD]; LCs and CIs for COD removal with and without effluent degassing) using simulation data from the uncertainty analysis. Samples were divided into two groupsthe top 25% ("desirable") and the bottom 75% ("undesirable")based on the indicator value. For example, samples with rCOD higher than the 75th percentile were categorized into the "desirable" group, whereas for LC, samples lower than the 25th percentile were considered desirable. Two-sample Kolmogorov-Smirnov (KS) tests were used to characterize the difference in parameter distributions between the two groups and indicate whether a parameter, among others, plays a statistically significant role in yielding desirable performance for an encapsulated system. The value of the KS test statistics 𝐷 represents the "distance" between the parameter distributions of two sample groups.