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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. The initial proposed modelling framework was derived from these case studies and encompassed eight sub-models used for these NBS types. The framework was subsequently extended to include eight additional NBS categories, requiring a total of 10 sub-models. In a subsequent step, with a different perspective, the framework was refined to focus on 13 primary use cases of NBS, identifying 10 sub-models needed or potentially required for these specific NBS applications. These frameworks help to identify the necessary sub-models for the NBS system at hand or the use case. This article also discusses the benefits and challenges of applying water resource recovery modelling best practices to NBS, along with recommendations for future research in this area. HIGHLIGHTS Data scarcity is a primary challenge to NBS modelling, with limited data available for model calibration and validation.; The numerous use cases and categories of NBS technologies complicate the development of a broad and flexible modelling framework.; Inspired by four NBS case studies, the paper addresses diverse objectives and challenges in NBS modelling.; The proposed frameworks offer a structured approach to NBS modelling, considering various categories, use cases, and essential sub-models.;

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. 1, a parallel HM effectively addressed limitations in the mechanistic model's representation of autotrophic bacteria growth and the data-driven model's incapability to extrapolate. Next, different versions of a parallel HM of a large pilot-scale WRRF are constructed, using different calibration/training datasets and different versions of the mechanistic model to investigate the balance between the calibration effort for the mechanistic model and the compensation by the NN component. The HM can improve predictions compared to the mechanistic model. Training the NN on an independent validation dataset produced better results than on the calibration dataset. Interestingly, the best performance is achieved for the HM based on a mechanistic model using default (uncalibrated) parameters. Both long short-term memory (LSTM) and convolutional neural network (CNN) are tested as data-driven components, with a CNN HM (root-mean-squared error (RMSE) = 1.58 mg NO3-N/L) outperforming an LSTM HM (RMSE = 4.17 mg NO3-N/L). HIGHLIGHTS In a parallel hybrid model (HM), a data-driven component compensates for structural gaps in a mechanistic model.; A data-driven component of an HM should be trained on an independent dataset.; Integrating an uncalibrated mechanistic model in a parallel HM may lead to better results than applying an overly calibrated mechanistic model.; A convolutional neural network outperforms a long short-term neural network as data-driven component of an HM.;

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. This paper presents the second iteration of the Public Health Environmental Surveillance Open Data Model (PHES-ODM), an open-source dictionary and set of data tools to enhance the interoperability of environmental surveillance data and enable the storage of contextual (meta)data. The data model describes how to store environmental surveillance program data, metadata about measurements taken on various specimens (water, air, surfaces, sites, populations) and data about measurement protocols. The model provides software tools that support the collection and use of PHES-ODM formatted data, including performing PCR calculations and data validation, recording data into input templates, generating wide tables for analysis, and producing SQL database definitions. Fully open-source and already adopted by institutions in Canada, the European Union, and other countries, the PHES-ODM provides a path forward for creating robust, interoperable, open datasets for environmental public health surveillance for SARS-CoV-2 and beyond. HIGHLIGHTS The PHES-ODM supports the collection, storage, and use of environmental public health data.; The PHES-ODM defines a standardized dictionary to describe environmental surveillance programs.; The PHES-ODM provides metadata attributes to facilitate the interpretation of various data, including sampling, protocols, and geolocation.; The PHES-ODM supports open-science principles, allowing its use as a platform for additional software to facilitate data entry, validation and sharing.;

Green spaces and nature-based solutions (NBS) are increasingly considered by land-use planning policies to respond to the multiple challenges related to sustainable development. The multiple benefits brought by NBS make the use of multicriteria decision analysis (MCDA) essential to optimally balance their use. MCDA offers a catalog of methods allowing to structure problems with multiple objectives and to help adopt the optimal solution. However, NBS planning is a recent discipline and research is still ongoing to make this practice more common. We carried out a critical literature review on MCDA-NBS tools and practices, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method on the Web of Science database. We selected 124 papers on the subject between 2000 and 2022. We present a state-of-the-art MCDA approach for NBS and green space planning by looking at where these practices are applied, why and how this process is conducted, and who is involved in it. We found that studies are usually conducted in the global North on a single case study with the help of experts involved in the criteria weighting phase and the help of GIS MCDA tools often integrating a direct ranking method or the AHP method. HIGHLIGHTS MCDA methods help to consider all NBS benefits and evaluate planning alternatives.; Environmental and social criteria are more represented than economic and technical ones.; Stakeholders are rarely involved throughout the entire MCDA process.; MCDA tools for NBS planning are rarely accessible and adaptable to various contexts.; MCDA processes are mainly conducted in countries of the Global North.;

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. The dynamic simulations executed in this work, based on a realistic case study and three selected permits from China, The Netherlands and the USA, show the impact of certain compliance specifications like sampling frequency, averaging and tolerable permit exceedances leading to differences in the required design capacity of more than 250% for the same wastewater to be treated. The results also reveal clear differences between permits in their capacity to handle excess variability. The latter is important to avoid overdesign, i.e., when further investment in treatment capacity would result only in marginal effluent quality gains, as well as to create a safe space for testing innovative technologies or ways of operation that might otherwise trigger compliance issues. HIGHLIGHTS A dynamic modelling procedure to test the equivalence of treatment plant effluent standards has been developed.; The proposed approach allows quantifying the interactions between regulations, plant design and effluent compliance.; Analysis of three actual permit structures covering a variety of global regulatory practices indicates the importance of compliance specifications like sampling frequency, averaging or tolerable permit exceedances.; Strict, not-to-exceed limit values and short averaging periods for effluent parameters tend to lead to costly, overdesigned plants without guaranteeing full compliance under all circumstances.; Permit structures that are able to handle uncertainty and variability can help foster the introduction of new innovative technologies or operational procedures.;

BackgroundWastewater 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.MethodsResults are presented for WWS from six municipalities in the eastern part of Canada during the spring of 2021. We developed a numerical model based on viral kinetics reduction functions to consider both prevalent and incident cases to interpret the WWS data in light of the reported clinical cases in the six surveyed communities.ResultsThe use of the proposed numerical model with a viral kinetics reduction function drastically increased the interpretability of the WWS data in terms of the clinical cases reported for the surveyed community. In line with our working hypothesis, the effects of viral kinetics reduction modeling were more important in small communities than in larger communities. In all but one of the community cases (where it had no effect), the use of the proposed numerical model led to a change from a +1.5% (for the larger urban center, Quebec City) to a +48.8% increase in the case of a smaller community (Drummondville).ConclusionConsideration of prevalent and incident cases through the proposed numerical model increases the correlation between clinical cases and WWS data. This is particularly the case in small communities. Because the proposed model is based on a biological mechanism, we believe it is an inherent part of any wastewater system and, hence, that it should be used in any WWS analysis where the aim is to relate WWS measurement to clinical cases.

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., anammox-based) and nitrite-shunt processes is presented with its challenges and limitations. The importance of mathematical models for considering N2O emissions in the design and operation of short-cut nitrogen removal processes is considered as well. Modelling goals and potential benefits are presented and the needs for new and more advanced approaches are identified. Overall, this contribution presents how existing and future mathematical models can accelerate successful full-scale mainstream short-cut nitrogen removal applications. HIGHLIGHTS Models for mainstream short-cut N removal processes are reviewed by considering their current practice and limitations.; Modelling goals and potential benefits are presented from a modeller perspective to facilitate successful applications.; More advanced modelling approaches are presented to overcome the addressed challenges and limitations.;

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. The model is constructed by an artificial neural network (ANN) and completed with a multivariate regression to generate time series for certain pollutants. The model is able to generate flowrate and quality data (TSS, COD, and nutrients) at different time scales and resolutions (daily or hourly), depending on various user objectives. The model performance is analyzed by a series of statistical criteria. It is shown that the model can generate a very reliable dataset for different model applications. HIGHLIGHTS A data-driven influent generator (IG) to create a long-term time series for flow and pollutant concentration at hourly and daily frequencies.; The IG application only needs WRRF routine data and weather information, without need for any additional data collection.; The IG also generates nutrient concentration time series.; A random walk stochastic process is added to better approximate observed temporal variability.;

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. HIGHLIGHTS A Digital Twin distinguishes itself from a simulation model by a continuous, automated data connection.; Current DT projects in WRRFs focus on operational support and control.; Combining mechanistic models and data-driven techniques into hybrid models can accelerate the adoption of DTs.; Data and information models are key for the implementation and upscaling of DTs.; WRRF staff should be included during the development stages of DTs.;

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. This results in the IG being adequate on average but with a lack of representativeness of, for instance, the observed temporal variability of the dataset. However, adequately capturing influent variability may be important for certain types of WRRF optimization, e.g., reaction to peak loads, control system performance evaluation, etc. Therefore, in this study, a data-driven IG model is developed based on the long short-term memory (LSTM) recurrent neural network and is optimized by a multi-objective genetic algorithm for both mean percentage error and variability. Hence, the influent generator model is able to generate a time series with a probability distribution that better represents reality, thus giving a better influent description for WRRF design and operation. To further increase the variability of the generated time series and in this way approximate the true variability better, the model is extended with a random walk process. HIGHLIGHTS A data-driven influent generator based on the long short-term memory (LSTM) is proposed.; To represent both the average behaviour and variability of the influent, the IG is trained by a multi-objective genetic algorithm.; The model is extended with a random walk stochastic process with a probability distribution that better represents reality, giving a better influent description for model-based WRRF design and operation.;

In this study, artificial intelligence (AI) models including adaptive neuro-fuzzy inference systems (ANFIS), artificial neural networks (ANN), and support vector regression (SVR) were applied to predict the removal efficiency of phosphate from wastewaters using the electrocoagulation process. The five input variables used in this study were current intensity, initial phosphate concentration, initial pH, treatment time, and electrode type. The optimal hyperparameters of the ANN and SVR models were found by integrating metaheuristic algorithms such as genetic algorithms (GA) and particle swarm optimization (PSO) to these models. To increase the reliability and robustness of the developed AI models, a search for optimal hyperparameters was conducted based on repeated random sub-sampling validation instead of a single split approach. The results demonstrated that the effectiveness of the data-driven model depends on how the data is distributed to the training, validation, and test sets. However, hybrid ANN models outperformed other models and PSO-ANN models showed exceptional generalization performance for the different sub-datasets. The average MSE, R2, and MAPE values of the 10 test subsets for PSO-ANN were determined as 7.201, 0.981, and 2.022, respectively. The EC process was interpreted for phosphate removal efficiency using the trained PSO-ANN model. The two input factors with the greatest influence on the effectiveness of phosphate removal, according to the results, are the electrode type and initial phosphate concentration. Additionally, it was found that lowering the pH and initial phosphate concentration and increasing the current intensity and treatment time enhance the removal efficiency.

Wastewater surveillance of pathogens may be a useful tool to help determine whether clinical surveillance of disease is effective or inadequate due to under-reporting and under-detection. In addition, tracking of pathogen concentrations over time could potentially provide a measure of the effectiveness of public health control measures and the impact of the gradual relaxation of these controls. Analysis of wastewater using quantitative molecular methods offers a real-time measure of infections in the community, and thus is expected to provide a more sensitive and rapid indication of changes in infection rates before such effects become detectable by clinical health surveillance. Models may help to back-calculate wastewater prevalence to population prevalence or to correct pathogen counts for wastewater catchment-specific and temporal effects. They may also help to design the wastewater sampling strategy. This article provides a brief summary of the history of pathogen wastewater surveillance to help set the context for the SARS-CoV-2 wastewater-based epidemiology (WBE) programmes currently being undertaken globally. HIGHLIGHTS A brief history of pathogen wastewater surveillance.; Context for current SARS-CoV-2 wastewater-based epidemiology (WBE) programmes.; Recommendations on research and investigation priorities.; Wide potential of modelling is presented together with remaining knowledge gaps.;

Freshwater species are highly impacted by human activities and the consequences on ecosystem functioning are still not well understood. In the literature, a multitrophic perspective appears to be key to advance future biodiversity and ecosystem functioning (BEF) research. This paper aims at studying indirect effects of the synthetic hormone 17α-ethinylestradiol (EE2) on a freshwater food web by creating BEF links, through the interpretation of seasonal cycles and multitrophic interactions. An ecosystem model previously developed using experimental data from a unique whole-ecosystem study on EE2 was extended with the addition of Chaoborus, an omnivorous insect. During the experimental study, a collapse of fathead minnow was measured after one year of exposure. The simulation results showed that EE2 indirect effects on other fishes (horizontal diversity) and lower trophic levels (vertical diversity) were connected to multitrophic interactions with a top-down cascade effect. The results also demonstrated that adding an omnivorous, mid-trophic level group such as Chaoborus enhances resilience. Conversely, missing such a species means that the actual resilience of an ecosystem and its functioning cannot be properly simulated. Thus, the extended ecosystem model offers a tool that can help better understand what is happening after environmental perturbations, such as with EE2.

Modelling flow rates in catchments and sewers with a conceptual, also known as hydrological, approach is widely applied if fast simulations are important. In cases where a detailed hydrodynamic model exists, it is common to start conceptualizing from this detailed counterpart. Unfortunately, no generalized procedure exists, which is surprising as this can be a complex and time-consuming task. This research work proposes a procedure that is validated with two independent combined sewer case studies. The conceptual models provide the targeted results with respect to representation of the flow rates and reduction in the computational time. As the desired performance could be reached for different levels of model aggregation, it is concluded that the conceptual model can be tailored to the points where accurate flow rates need to be predicted. Furthermore, the comparison of the conceptual model results with flow measurements highlights the importance of analyzing and eventually compensating for the limitations of the detailed model.