Combining Short- and Long-Read Sequencing Technologies to Identify SARS-CoV-2 Variants in Wastewater.

During the COVID-19 pandemic, the monitoring of SARS-CoV-2 RNA in wastewater was used to track the evolution and emergence of variant lineages and gauge infection levels in the community, informing appropriate public health responses without relying solely on clinical testing. As more sublineages were discovered, it increased the difficulty in identifying distinct variants in a mixed population sample, particularly those without a known lineage. Here, we compare the sequencing technology from Illumina and from Oxford Nanopore Technologies, in order to determine their efficacy at detecting variants of differing abundance, using 248 wastewater samples from various Quebec and Ontario cities.

Towards a modelling framework for nature-based solutions in wastewater treatment.

This article presents the authors' perspectives on modelling best practices for nature-based solutions (NBS). The authors led a workshop on NBS modelling as part of the 8th IWA Water Resource Recovery Modelling Seminar (WRRmod2022+) in January 2023, where the discussion centred around the design, use cases, and potential applications of NBS models. Four real-world case studies, encompassing an aerated lagoon, a biofilm-enhanced aerated lagoon, a stormwater basin, and a constructed wetland were reviewed to demonstrate practical applications and challenges in modelling NBS systems.

Towards good modelling practice for parallel hybrid models for wastewater treatment processes.

This study explores various approaches to formulating a parallel hybrid model (HM) for Water and Resource Recovery Facilities (WRRFs) merging a mechanistic and a data-driven model. In the study, the HM is constructed by training a neural network (NN) on the residual of the mechanistic model for effluent nitrate. In an initial experiment using the Benchmark Simulation Model no.

A comprehensive, open-source data model for wastewater-based epidemiology.

The recent SARS-COV-2 pandemic has sparked the adoption of wastewater-based epidemiology (WBE) as a low-cost way to monitor the health of populations. In parallel, the pandemic has encouraged researchers to openly share their data to serve the public better and accelerate science. However, environmental surveillance data are highly dependent on context and are difficult to interpret meaningfully across sites.

Machine learning for modeling NO emissions from wastewater treatment plants: Aligning model performance, complexity, and interpretability.

Nitrous oxide (NO) emissions may account for up to 80 % of a wastewater treatment plant's (WWTP) total carbon footprint. Given the complexity of the pathways involved, estimating NO emissions through mechanistic models still often fails to precisely depict process dynamics. Alternatively, data-driven methods for predicting NO emissions hold substantial potential.

Assessing the equivalence of WRRF regulations using dynamic model simulations.

A wide diversity of regulatory practices for wastewater resource recovery plants exists throughout the world. This contribution aims to highlight the implications of choosing particular permitting structures and investigate the equivalence of effluent standards in terms of limit values and compliance assessment specifications. These factors heavily affect the true performance that a treatment plant has to attain and thus the required plant capacity and operation.

Wastewater-based epidemiology: the crucial role of viral shedding dynamics in small communities.

Background: Wastewater surveillance (WWS) of pathogens is a rapidly evolving field owing to the 2019 coronavirus disease pandemic, which brought about a paradigm shift in public health authorities for the management of pathogen outbreaks. However, the interpretation of WWS in terms of clinical cases remains a challenge, particularly in small communities where large variations in pathogen concentrations are routinely observed without a clear relation to clinical incident cases.

Methods: Results are presented for WWS from six municipalities in the eastern part of Canada during the spring of 2021.

Sidestream bio-P and mainstream anammox in a BNR process with upstream carbon capture.

The integration of biological phosphorus removal (bio-P) and shortcut nitrogen removal (SNR) processes is challenging because of the conflicting demands on influent carbon: SNR allows for upstream carbon diversion, but this reduction of influent carbon (especially volatile fatty acids [VFAs]) prevents or limits bio-P. The objective of this study was to achieve SNR, either via partial nitritation/anammox (PNA) or partial denitrification/anammox (PdNA), simultaneously with biological phosphorus removal in a process with upstream carbon capture. This study took place in a pilot scale A/B process with a sidestream bio-P reactor and tertiary anammox polishing.

The ASM2d model with two-step nitrification can better simulate biological nutrient removal systems enriched with complete ammonia oxidizing bacteria (comammox Nitrospira).

The discovery of comammox Nitrospira, a complete ammonia-oxidizing microorganism belonging to the genus Nitrospira, has brought new insights into the nitrification process in wastewater treatment plants (WWTPs). The applicability of Activated Sludge Model No. 2 d with one-step nitrification (ASM2d-OSN) or two-step nitrification (ASM2d-TSN) for the simulation of the biological nutrient removal (BNR) processes of a full-scale WWTP in the presence of comammox Nitrospira was studied.

An improved 1D reactive Bürger-Diehl settler model for secondary settling tank denitrification.

An improved 1D reactive settler model is pursued in order to increase the understanding of reactive settling processes and obtain a better prediction of the nitrogen mass balance in wastewater treatment systems. The developed model is based on the 1D Bürger-Diehl settler model with compression function and the Activated Sludge Model No. 1 biological reactions.

Artificial intelligence techniques in electrochemical processes for water and wastewater treatment: a review.

Unlabelled: In recent years, artificial intelligence (AI) techniques have been recognized as powerful techniques. In this work, AI techniques such as artificial neural networks (ANNs), support vector machines (SVM), adaptive neuro-fuzzy inference system (ANFIS), genetic algorithms (GA), and particle swarm optimization (PSO), used in water and wastewater treatment processes, are reviewed. This paper describes applications of the mentioned AI techniques for the modelling and optimization of electrochemical processes for water and wastewater treatment processes.

Influence of MBBR carrier geometrical properties and biofilm thickness restraint on biofilm properties, effluent particle size distribution, settling velocity distribution, and settling behaviour.

The relatively poor settling characteristics of particles produced in moving bed biofilm reactor (MBBR) outline the importance of developing a fundamental understanding of the characterization and settleability of MBBR-produced solids. The influence of carrier geometric properties and different levels of biofilm thickness on biofilm characteristics, solids production, particle size distribution (PSD), and particle settling velocity distribution (PSVD) is evaluated in this study. The analytical ViCAs method is applied to the MBBR effluent to assess the distribution of particle settling velocities.

The transition of WRRF models to digital twin applications.

Digital Twins (DTs) are on the rise as innovative, powerful technologies to harness the power of digitalisation in the WRRF sector. The lack of consensus and understanding when it comes to the definition, perceived benefits and technological needs of DTs is hampering their widespread development and application. Transitioning from traditional WRRF modelling practice into DT applications raises a number of important questions: When is a model's predictive power acceptable for a DT? Which modelling frameworks are most suited for DT applications? Which data structures are needed to efficiently feed data to a DT? How do we keep the DT up to date and relevant? Who will be the main users of DTs and how to get them involved? How do DTs push the water sector to evolve? This paper provides an overview of the state-of-the-art, challenges, good practices, development needs and transformative capacity of DTs for WRRF applications.

An essential tool for WRRF modelling: a realistic and complete influent generator for flow rate and water quality based on data-driven methods.

Modelling, automation, and control are widely used for water resource recovery facility (WRRF) optimization. An influent generator (IG) is a model, aiming to provide the flowrate and pollutant concentration dynamics at the inlet of a WRRF for a range of modelling applications. In this study, a new data-driven IG model is proposed, only using routine data and weather information, and without need for any additional data collection.

Mainstream short-cut N removal modelling: current status and perspectives.

This work gives an overview of the state-of-the-art in modelling of short-cut processes for nitrogen removal in mainstream wastewater treatment and presents future perspectives for directing research efforts in line with the needs of practice. The modelling status for deammonification (i.e.

An influent generator for WRRF design and operation based on a recurrent neural network with multi-objective optimization using a genetic algorithm.

Nowadays, modelling, automation and control are widely used for Water Resource Recovery Facilities (WRRF) upgrading and optimization. Influent generator (IG) models are used to provide relevant input time series for dynamic WRRF simulations used in these applications. Current IG models found in literature are calibrated on the basis of a single performance criterion, such as the mean percentage error or the root mean square error.

Carbonaceous vs. total biochemical oxygen demand as a basis for WRRF design and performance monitoring.

The standard 5-day biochemical oxygen demand (BOD ) measurement of water quality is used widely as a design parameter for water resource recovery facilities (WRRFs). This measure usually includes a component of nitrogenous oxygen demand (NOD) that can cause oversizing of biological processes and under-evaluation of process capacity. Carbonaceous BOD (CBOD ) more closely represents oxygen demand associated with biodegradation of organic constituents of a wastewater than does BOD and therefore should be used as a basis for sizing aerobic treatment processes.

Effects of ferric-phosphate forms on phosphorus release and the performance of anaerobic fermentation of waste activated sludge.

Five ferric-phosphate (Fe(III)Ps) with amorphous or crystalline structures were added to waste activated sludge (WAS) for anaerobic fermentation, aiming to investigate effects of Fe(III)Ps forms on phosphorus (P) release and the performance of WAS fermentation. The results revealed that the Fe(III) reduction rate of hexagonal-FePO was faster than that of monoclinic-FePO·2HO, thanks to its lower crystal field stabilization energy. FePO·nHO was reduced to vivianite and part of the phosphate was released as orthophosphate (PO-P).

A critical review of the data pipeline: how wastewater system operation flows from data to intelligence.

Faced with an unprecedented amount of data coming from evermore ubiquitous sensors, the wastewater treatment community has been hard at work to develop new monitoring systems, models and controllers to bridge the gap between current practice and data-driven, smart water systems. For additional sensor data and models to have an appreciable impact, however, they must be relevant enough to be looked at by busy water professionals; be clear enough to be understood; be reliable enough to be believed and be convincing enough to be acted upon. Failure to attain any one of those aspects can be a fatal blow to the adoption of even the most promising new measurement technology.

Dynamic grit chamber modelling: dealing with particle settling velocity distributions.

Grit chambers are meant to reduce the impact of inorganic particles on equipment and processes downstream. Despite their important role, characterization and modelling studies of these process units are scarce, leading to a lack of knowledge and suboptimal operation. Thus, this study presents the first dynamic model, based on mass balances and particle settling velocity distributions, for use in a water resource recovery facility (WRRF) simulator for design and optimization of grit removal units.

Characterizing the settleability of grit particles.

Grit chambers are installed at the headworks of a water resource recovery facility (WRRF) to reduce the impact of grit particles to the equipment and processes downstream. This settling process should thus be designed and operated in an efficient way. Despite the importance of knowing settling characteristics for design and operation of grit chambers, previous grit definitions have been based only on particle size characteristics, and not on settling velocities.

Modelling hydrolysis: Simultaneous versus sequential biodegradation of the hydrolysable fractions.

Hydrolysis is considered the limiting step during solid waste anaerobic digestion (including co-digestion of sludge and biosolids). Mechanisms of hydrolysis are mechanistically not well understood with detrimental impact on model predictive capability. The common approach to multiple substrates is to consider simultaneous degradation of the substrates.

Nitrification in a biofilm-enhanced highly loaded aerated lagoon.

A full-scale biofilm-enhanced aerated lagoon using fixed submerged media was monitored using automated water quality monitoring stations over the span of one year to quantify its nitrification performance. The system was operating at a high organic loading rate averaging 5.8 g total CBOD /m of media per day (23.

How Urban Storm- and Wastewater Management Prepares for Emerging Opportunities and Threats: Digital Transformation, Ubiquitous Sensing, New Data Sources, and Beyond - A Horizon Scan.

Ubiquitous sensing will create many opportunities and threats for urban water management, which are only poorly understood today. To identify the most relevant trends, we conducted a horizon scan regarding how ubiquitous sensing will shape the future of urban drainage and wastewater management. Our survey of the international urban water community received an active response from both the academics and the professionals from the water industry.