A hybrid membrane bioreactor (HMBR) enhances treatment performance by simultaneously utilizing organisms on both suspended and attached sludge, yet the microbial mechanisms underpinning their efficiency remain poorly understood. In this study, we investigate spatial variability within microbial communities in HMBRs and correlate this factor with pollutant removal capacity. High-throughput sequencing results revealed significant differences in community structure between suspended sludge, suspended media surfaces, and membrane module surfaces. Suspended sludge exhibited the highest species richness, whereas microbial communities on suspended media resembled those within the sludge, contrasting markedly with membrane surface communities. Key functional groups were enriched at specific locations: Pseudomonas and Comamonas dominate the surface of the suspension culture medium and participate in nitrification; phosphorus-accumulating organisms (PAOs), primarily from the Flavobacteriales and Planctomycetaceae phyla, were most abundant on suspended media surfaces. This spatial partitioning of functional microbes indicates cooperative division of labor. Media surfaces serve as primary sites for nitrification and phosphorus removal, whilst suspended sludge flocs and membrane module surfaces are the principal contributors to denitrification. The results of this study provide microbiological evidence for optimizing HMBR design and operation, confirming that spatial community structure is a key factor influencing performance.

The uncertainty of natural inflows and market behavior challenges ensuring a reliable power balance in hydropower-dominated electricity markets. This study proposes a novel framework integrating hourly load balancing on typical days into a monthly scheduling model solved with Gurobi11.0.1 to evaluate demand-met reliability across storage and inflow states. By employing total storage as a system state to reduce dimensional complexity and simulating future runoff scenarios based on current inflows, the method performs multi-year statistical simulations to assess reliability over the following year. Applied to a system of 39 hydropower reservoirs in China, the case studies of present models and procedures suggest: (1) controlling reservoir storage levels during the dry season is crucial for ensuring the power demand-met rate in the following year, with May being the most critical month; (2) the power demand-met rate does not monotonically increase with higher storage levels—there is an optimal storage level that maximizes the demand-met rate; and (3) June and October offer the greatest flexibility in storage adjustment to achieve the highest demand-met reliability.