The control of nitrite-oxidizing bacteria (NOB) challenges the implementation of partial nitritation and anammox (PN/A) processes under mainstream conditions. The aim of the present study was to understand how operating conditions impact microbial competition and the control of NOB in hybrid PN/A systems, where biofilm and flocs coexist. A hybrid PN/A moving-bed biofilm reactor (MBBR; also referred to as integrated fixed film activated sludge or IFAS) was operated at 15 °C on aerobically pre-treated municipal wastewater (23 mgNH4-N L-1). Ammonium-oxidizing bacteria (AOB) and NOB were enriched primarily in the flocs, and anammox bacteria (AMX) in the biofilm. After decreasing the dissolved oxygen concentration (DO) from 1.2 to 0.17 mgO2 L-1 - with all other operating conditions unchanged - washout of NOB from the flocs was observed. The activity of the minor NOB fraction remaining in the biofilm was suppressed at low DO. As a result, low effluent NO3- concentrations (0.5 mgN L-1) were consistently achieved at aerobic nitrogen removal rates (80 mgN L-1 d-1) comparable to those of conventional treatment plants. A simple dynamic mathematical model, assuming perfect biomass segregation with AOB and NOB in the flocs and AMX in the biofilm, was able to qualitatively reproduce the selective washout of NOB from the flocs in response to the decrease in DO-setpoint. Similarly, numerical simulations indicated that flocs removal is an effective operational strategy to achieve the selective washout of NOB. The direct competition for NO2- between NOB and AMX - the latter retained in the biofilm and acting as a "NO2-sink" - was identified by the model as key mechanism leading to a difference in the actual growth rates of AOB and NOB (i.e., μNOB < μAOB in flocs) and allowing for the selective NOB washout over a broad range of simulated sludge retention times (SRT = 6.8-24.5 d). Experimental results and model predictions demonstrate the increased operational flexibility, in terms of variables that can be easily controlled by operators, offered by hybrid systems as compared to solely biofilm systems for the control of NOB in mainstream PN/A applications.

Nitrous oxide (N2O) emissions from wastewater treatment contribute significantly to greenhouse gas emissions. They have been shown to exhibit a strong seasonal and daily profile in previously conducted monitoring campaigns. However, only two year-long online monitoring campaigns have been published to date. Based on three monitoring campaigns on three full-scale wastewater treatment plants (WWTPs) with different activated sludge configurations, each of which lasted at least one year, we propose a refined monitoring strategy for long-term emission monitoring with multiple flux chambers on open tanks. Our monitoring campaigns confirm that the N2O emissions exhibited a strong seasonal profile and were substantial on all three plants (1-2.4% of the total nitrogen load). These results confirm that N2O is the most important greenhouse gas emission from wastewater treatment. The temporal variation was more distinct than the spatial variation within aeration tanks. Nevertheless, multiple monitoring spots along a single lane are crucial to assess representative emission factors in flow-through systems. Sequencing batch reactor systems were shown to exhibit comparable emissions within one reactor but significant variation between parallel reactors. The results indicate that considerable emission differences between lanes are to be expected in cases of inhomogeneous loading and discontinuous feeding. For example, N2O emission could be shown to depend on the amount of treated reject water: lanes without emitted <1% of the influent load, while parallel lanes emitted around 3%. In case of inhomogeneous loading, monitoring of multiple lanes is required. Our study enables robust planning of monitoring campaigns on WWTPs with open tanks. Extensive full-scale emission monitoring campaigns are important as a basis for reliable decisions about reducing the climate impact of wastewater treatment. More specifically, such data sets help us to define general emission factors for wastewater treatment plants and to construct and critically evaluate N2O emission models.

As cities develop and resource demands rise, the water sector faces crucial challenges to deliver reliable, sustainable, and efficient services. Digital Twins (DTs), virtual replicas of physical systems, offer a promising tool to transform how we manage water infrastructure. Originally developed in the aerospace industry, DTs are now gaining traction in the water sector, enabling real-time monitoring, simulation, and predictive control of water and wastewater treatment, collection and distribution networks, and water reclamation and reuse systems. While still emerging in the water sector, DTs have shown potential to enhance operational efficiency, reduce environmental impacts, and support smarter, more resilient water management. This review study provides a comprehensive overview of current DT applications in the water sector, highlighting successful case studies, technical challenges, and knowledge gaps. It also explores how DTs can help bridge the water–energy nexus by optimizing resources utilized across interconnected systems. By synthesizing recent advances and identifying future research directions, this paper illustrates how DTs can play a central role in building sustainable, adaptive, and digitally-enabled water infrastructure.

This paper serves as a problem statement of the issues surrounding uncertainty in wastewater treatment modelling. The paper proposes a structure for identifying the sources of uncertainty introduced during each step of an engineering project concerned with model-based design or optimisation of a wastewater treatment system. It briefly references the methods currently used to evaluate prediction accuracy and uncertainty and discusses the relevance of uncertainty evaluations in model applications. The paper aims to raise awareness and initiate a comprehensive discussion among professionals on model prediction accuracy and uncertainty issues. It also aims to identify future research needs. Ultimately the goal of such a discussion would be to generate transparent and objective methods of explicitly evaluating the reliability of model results, before they are implemented in an engineering decision-making context.