CentrEau - Quebec Water Management Research Centre

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.

Abstract Evaporation of intercepted rain by a canopy is an important component of evapotranspiration, particularly in the humid boreal forest, which is subject to frequent precipitation and where conifers have a large surface water storage capacity. Unfortunately, our knowledge of interception processes for this type of environment is limited by the many challenges associated with experimental monitoring of the canopy water balance. The objective of this study is to observe and estimate canopy storage capacity and wet canopy evaporation at the sub‐daily and seasonal time scales in a humid boreal forest. This study relies on field‐based estimates of rainfall interception and evapotranspiration partitioning at the Montmorency Forest, Québec, Canada (mean annual precipitation: 1600 mm, mean annual evapotranspiration: 550 mm), in two balsam fir‐white birch forest stands. Evapotranspiration was monitored using eddy covariance sensors and sap flow systems, whereas rainfall interception was measured using 12 sets of throughfall and six stemflow collectors randomly placed inside six 400‐m2 plots. Changes in the amount of water stored on the canopy were also directly monitored using the stem compression method. The amount of water intercepted by the forest canopy was 11 ± 5% of the total rainfall during the snow‐free (5 July–18 October) measurement periods of 2017 and 2018. The maximum canopy storage estimated from rainfall interception measurements was on average 1.6 ± 0.7 mm, though a higher value was found using the stem compression method (2.2 ± 1.6 mm). Taking the average of the two forest stands studied, evaporation of intercepted water represented 21 ± 8% of evapotranspiration, while the contribution of transpiration and understory evapotranspiration was 36 ± 9% and 18 ± 8%. The observations of each of the evapotranspiration terms underestimated the total evapotranspiration observed, so that 26 ± 12% of it was not attributed. These results highlight the importance to account for the evaporation of rain intercepted by humid boreal forests in hydrological models.

Abstract On-line continuous monitoring of water bodies produces large quantities of high frequency data. Long-term quality control and applicability of these data require rigorous storage and documentation. To carry out these activities successfully, a database has to be built. Such a database should provide the simplicity to store and document all relevant data and should be easy to use for further data evaluation and interpretation. In this paper, a comprehensive database structure for water quality data is proposed. Its goal is to centralize the data, standardize their format, provide easy access, and, especially, document all relevant information (metadata) associated with the measurements in an efficient way. The emphasis on data documentation enables the provision of detailed information not only on the history of the measurements (e.g., where, how, when and by whom was the value measured) but also on the history of the equipment (e.g., sensor maintenance, calibration/validation history), personnel (e.g., experience), projects, sampling sites, etc. As such, the proposed database structure provides a robust and efficient tool for functional data storage and access, allowing future use of data collected at great expense.

The present investigation focused on the photocatalytic degradation of acetamiprid in aqueous solutions under visible light over bare ZnO as well as N- and N-GO-doped photocatalysts. The synthesised materials were characterised using SEM, TEM, XRD, nitrogen sorption, photoluminescence, UV-Vis, FTIR and electrochemical impedance spectroscopy techniques. The obtained results pointed out the high photocatalytic performances of the N-GO-ZnO allowing complete degradation of the acetamiprid after 5 hours of reaction at ambient temperature. Under otherwise the same operating conditions, 12, 38 and 68% conversion were reached in the absence of any photocatalyst, over the bare ZnO and N-doped ZnO materials, respectively.

• Study on micropollutant distribution between biochar, APL and pyrolysis oil. • Determined PFAS in biochar, APL and pyrolysis oil. • Biochar met the guidelines for agricultural valorisation, except for heavy metals. • High methane production was achieved during anaerobic digestion of APL. • CO 2 supply during pyrolysis achieved highest calorific value of pyrolysis oil. Pyrolysis is a suitable process for sewage sludge valorisation while potentially reducing micropollutants. However, previous studies mainly focused on micropollutants in biochar, neglecting their presence of aqueous pyrolysis liquid (APL), pyrolysis oil, and gas. This study analyses the distribution of 66 micropollutants, including PFAS, dioxins and furans (PCDD/Fs), heavy metals (HMs), and polycyclic aromatic hydrocarbons (PAHs) across biochar, APL and pyrolysis oil. Additionally, the impact of a carrier gas supply (N 2 and CO 2 ) on micropollutants distribution was evaluated. In a second stage, the safe use of the pyrolysis end-products for agricultural and energetic purposes was explored. PFAS from biosolids were distributed in biochar (12–13 %), APL (6–7 %), and the oil phase (2–5 %). 63–74 % remained unaccounted for, possibly transferred to the gas phase, or decomposed during pyrolysis. PAHs were predominantly found in the pyrolysis oil, while PCDD/Fs were found in the biochar and pyrolysis oil. HMs were primarily found in biochar. PAH and PCDD/F values in the biochar met the European Biochar Certificate (EBC) guidelines. However, HMs surpassed the thresholds, suggesting either post-treatment or using biochar as a building material instead. Given that pyrolysis oil contains significant quantities of micropollutants, high-temperature combustion could serve for both micropollutant decomposition and energy reclamation. Energetic valorisation of APL assessed by biomethane potential tests, achieved a methane production yield of 432–450 NmL CH4 /g VS . Overall, a combined anaerobic digester and pyrolysis process, including a recirculation of the APL, the valorisation of pyrolysis oil for process heating, and the use of CO 2 from biogas as pyrolysis carrier gas, is suggested for further study.

Abstract In order to avoid eutrophication of freshwater systems, regulations all around the world have become increasingly stringent toward the maximum phosphate concentration allowed in wastewater discharges. Traditional phosphate removal methods such as chemical precipitation and enhanced biological phosphorus removal struggle to lower phosphate levels to the new requirements. Hybrid anion exchange nanotechnology (HAIX-Nano) is composed of a selective adsorption material able to remove phosphate down to levels close to zero. Moreover, HAIX-Nano is not affected by intermittent flow and does not produce sludge making it an interesting alternative. The regeneration process of HAIX-Nano typically requires a chemical solution with a high concentration of sodium hydroxide (NaOH) and sodium chloride (NaCl) (2–5% w/w of each). To lower the environmental impact and the operational cost of the technology, this study aims to enhance the HAIX-Nano regeneration efficiency. Therefore, the backbone of HAIX-Nano, which is normally a strong base anionic (SBA) resin, was changed for a weak base anionic (WBA) resin. The resulting material (WBA-2) exhibited a higher adsorption capacity than the traditional version of HAIX-Nano (SBA-1) under the tested conditions, while also showing a much higher regeneration efficiency. For a desorption solution of only 0.4% NaOH and no NaCl, WBA-2 showed an average regeneration efficiency of 78 ± 1% compared to SBA-1 with 24 ± 1%.

Accurate knowledge of snow depth distributions in forested regions is crucial for applications in hydrology and ecology. In such a context, understanding and assessing the effect of vegetation and topographic conditions on snow depth variability is required. In this study, the spatial distribution of snow depth in two agro-forested sites and one coniferous site in eastern Canada was analyzed for topographic and vegetation effects on snow accumulation. Spatially distributed snow depths were derived by unmanned aerial vehicle light detection and ranging (UAV lidar) surveys conducted in 2019 and 2020. Distinct patterns of snow accumulation and erosion in open areas (fields) versus adjacent forested areas were observed in lidar-derived snow depth maps at all sites. Omnidirectional semi-variogram analysis of snow depths showed the existence of a scale break distance of less than 10 m in the forested area at all three sites, whereas open areas showed comparatively larger scale break distances (i.e., 11–14 m). The effect of vegetation and topographic variables on the spatial variability in snow depths at each site was investigated with random forest models. Results show that the underlying topography and the wind redistribution of snow along forest edges govern the snow depth variability at agro-forested sites, while forest structure variability dominates snow depth variability in the coniferous environment. These results highlight the importance of including and better representing these processes in physically based models for accurate estimates of snowpack dynamics.

Abstract Hydrological projections under future climate change have been shown to be sensitive to the formulation of evapotranspiration. Many hydrological models still rely on empirical formulations of this flux, and hence do not take into account the surface energy budget. On the other hand, land surface schemes, which are used within the climate models to describe land hydrology and associated surface heat fluxes (SHF), rely on the energy conservation. Due to land surface schemes complexity, they are not suitable for integration in traditional hydrological models. A newly developed and relatively simple Maximum Entropy Production model, which operates under the constraint of energy conservation and allows for an appropriate partitioning of available energy into SHF, appears to be a good alternative for integration in hydrological models. However, the MEP's performances still need to be evaluated under various environmental conditions. This study aims to evaluate the SHF simulated by MEP using observations over a multiyear period from several carefully chosen snow‐free sites located in low‐latitude regions. Moreover, simulated fluxes were compared to those derived from the Canadian Land Surface Scheme (CLASS), which was run at the same sites. The analysis of simulated and observed fluxes associated with different water stress conditions suggests that the abilities of MEP and CLASS in estimating sensible and latent heat fluxes are comparable. It was also found that the MEP and CLASS fluxes are in a good agreement with observations. However, the simulated nocturnal fluxes show that both models are in less agreement with the observations.

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.

Rain-on-snow events can cause severe flooding in snow-dominated regions. These are expected to become more frequent in the future as climate change shifts the precipitation from snowfall to rainfall. However, little is known about how winter rainfall interacts with an evergreen canopy and affects the underlying snowpack. In this study, we document 5 years of rain-on-snow events and snowpack observations at two boreal forested sites of eastern Canada. Our observations show that rain-on-snow events over a boreal canopy lead to the formation of melt–freeze layers as rainwater refreezes at the surface of the sub-canopy snowpack. They also generate frozen percolation channels, suggesting that preferential flow is favoured in the sub-canopy snowpack during rain-on-snow events. We then used the multi-layer snow model SNOWPACK to simulate the sub-canopy snowpack at both sites. Although SNOWPACK performs reasonably well in reproducing snow height (RMSE = 17.3 cm), snow surface temperature (RMSE = 1.0 °C), and density profiles (agreement score = 0.79), its performance declines when it comes to simulating snowpack stratigraphy, as it fails to reproduce many of the observed melt–freeze layers. To correct for this, we implemented a densification function of the intercepted snow in the canopy module of SNOWPACK. This new feature allows the model to reproduce 33 % more of the observed melt–freeze layers that are induced by rain-on-snow events. This new model development also delays and reduces the snowpack runoff. In fact, it triggers the unloading of dense snow layers with small rounded grains, which in turn produces fine-over-coarse transitions that limit percolation and favour refreezing. Our results suggest that the boreal vegetation modulates the sub-canopy snowpack structure and runoff from rain-on-snow events. Overall, this study highlights the need for canopy snow property measurements to improve hydrological models in forested snow-covered regions.

Temperature-index (TI) models are commonly used to simulate the volume and occurrence of meltwater in snow-fed catchments. TI models have varying levels of complexity but are all based on air temperature observations. The quality and availability of data that drive these models affect their predictive ability, particularly given that they are frequently applied in remote environments. This study investigates the performance of non-calibrated TI models in simulating the subcanopy snow water equivalent (SWE) of a small watershed located in Eastern Canada, for which some distinctive observations were collected. Among three relatively simple TI algorithms, the model that performed the best was selected based on the average percent bias (Pbias of 24%) and root mean square error (RMSE of 100 mm w.e.), and was designated as the base TI model. Then, a series of supplemental tests were conducted in order to quantify the performance gain that resulted from including the following inputs/processes to the base TI model: subcanopy incoming radiation, canopy interception, snow surface temperature, sublimation, and cold content. As a final test, all the above modifications were performed simultaneously. Our results reveal that, with the exception of snow sublimation (Pbias of 5.4%) and snow surface temperature, the variables mentioned above were unable to improve TI models within our sites. It is therefore worth exploring other feasible alternatives to existing TI models in complex forested environments.

Abstract 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.

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.

Abstract Biomethanation projects across the world struggle with multiple challenges related to the location selection and optimization of the treatment facilities. Important aspects such as treatment plant location and treatment process chain configuration depend on the waste sources to be treated, the required end-product type and quality, as well as its final use destination, all of which are variable in time and space. This research describes the development and use of an integrated decision-support software tool that allows setting up optimal organic waste value chains, named optim-O. Key features of the tool include a multidimensional spatiotemporal database, a model-based decision module for simulation and optimization, as well as a user-friendly interface. The availability of such a software tool will not only allow to save time and money on data collection and calculations, but will also induce more comprehensive decisions by simultaneously taking into account a variety of factors, thereby significantly facilitating and enhancing the decision-making process.

Summary Maple trees repair cold‐induced embolism by generating positive stem pressure during their leafless state, altering sap transport in ways that remain poorly understood. This xylem pressure also drives sap exudation, enabling maple sap harvest for syrup production. This study investigates water source dynamics in leafless maples in early spring and its impact on sap yields. We used heavy water (D 2 O) as a tracer, injected into the soil for root uptake or directly into the stem at various heights of 55 tapped maple trees. Sap isotopic composition was monitored over time and analyzed in relation to weather, sap volume, and sugar content. Our findings show that maple trees absorb soil water gradually in early spring, even when leafless and still under snow cover, with limited sap transport and significant water mixing within the stem. Root‐derived water first appeared around the sixth freeze–thaw event, marking a key rehydration phase coinciding with peak sap yields. Optimal daily maple sap yields also depended on cold nights to enhance sugar concentration, followed by extended thaws at moderate temperatures (3–5°C) to increase sap volume. These results highlight the combined influence of xylem physiology and weather conditions on spring stem rehydration and maple sap yields.

In the alluvial plains of large rivers, annual flooding is essential for numerous ecosystem services, including flood-based agriculture, biodiversity and groundwater recharge . Remote sensing provides increased opportunities to monitor surface water dynamics across large floodplains that are currently poorly captured by local hydrological monitoring and modelling due to data scarcity and the flat, heterogeneous topography. Combining the advances in earth observations with hydrological modelling and extensive in situ fieldwork , this research seeks to improve our understanding of surface water dynamics and associated agricultural practices in the Senegal river floodplain. 2813 mosaics from Landsat , MODIS and Sentinel-2 earth observations are created to map and monitor surface water variations using a site specific MNDWI classification adapted to complex, wetland environments. Validated against ground truth data, the approach is upscaled using cloud computing across this 2250 km 2 floodplain over 1999–2022. Statistical regression models are then developed to estimate flooded and cultivated areas based on upstream flow values since 1950 and analyse trends and exceedance probabilities over time. Results reveal extreme interannual variations in peak flooded areas, ranging from 30,000 ha and 720,000 ha between 1950 and 2022, while annual water modules fluctuate between 210 and 1460 m 3 /s. After 1994, flooded areas show partial recovery, with 95th percentile reaching 89,000 ha during 1994–2022 compared to 37,000 ha in 1972–1993. Flood-based agricultural practices cover between 13,000 ha and 133,000 ha over the same period, highlighting the pronounced variability faced by local rural communities. Occurrence maps and predictive models for annual flooded and cultivated areas based on upstream flows can support early warning tools, helping to prepare for extreme floods and droughts. These outputs are crucial to assess the impact of future climatic and anthropic changes in the region, including planned dams, on the amplitude of annual floods and their associated environmental benefits.

Geochemical tracers have the potential to provide valuable insights for constructing conceptual models of groundwater flow, especially in complex geological contexts. Nevertheless, the reliability of tracer interpretation hinges on its integration into a robust geological framework. In our research, we concentrated on delineating the groundwater flow dynamics in the Innisfil Creek watershed, located in Ontario, Canada. We amalgamated extensive hydrogeological data derived from a comprehensive 3D geological model with the analysis of 61 groundwater samples, encompassing major ions, stable water isotopes, tritium, and radiocarbon. By seamlessly incorporating regional physiographic characteristics, flow pathways, and confinement attributes, we bolstered the efficiency of these tracers, resulting in several notable findings. Firstly, we identified prominent recharge and discharge zones within the watershed. Secondly, we observed the coexistence of relatively shallow and fast-flowing paths with deeper, slower-flowing channels, responsible for transporting groundwater from ancient glacial events. Thirdly, we determined that cation exchange stands as the predominant mechanism governing the geochemical evolution of contemporary water as it migrates toward confined aquifers situated at the base of the Quaternary sequence. Fourthly, we provided evidence of the mixing of modern, low-mineralized water originating from unconfined aquifer units with deep, highly mineralized water within soil–bedrock interface aquifers. These findings not only contribute significantly to the development a conceptual flow model for the sustainable management of groundwater in the Innisfil watershed, but also offer practical insights that hold relevance for analogous geological complexities encountered in other regions.

A data-driven model was proposed for generating the influent flow and water temperature dynamics including the impact of snowmelt under cold climate conditions. The performance was compared with a phenomenological model.

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.

In acute care hospitals, susceptible patients and large, legacy water systems contribute to increased risk of nosocomial infections associated with drinking water pathogens. This study aimed to evaluate the long-term (>1-year) impact of onsite monochloramine treatment on Legionella pneumophila (Lp), nontuberculous mycobacteria (NTMs), Vermamoeba vermiformis (Vv), and physico-chemical water quality in a hospital hot water system. Using an innovative sampling approach, the efficacy of treatment was assessed at 22 distal sites (faucets, showerheads, handwashing stations) and compared to 10 control points representing the main flowing distribution system (return loops, heaters, remote sites). Monochloramine nearly eliminated Lp, achieving up to 3-log reductions in culturability (<24 h) and gene copies (4-week). Mean Vv concentrations decreased by 2-log within 24 h, with no evidence of a shift towards increased NTMs. Optimal reductions in all organisms were observed at monochloramine concentrations of 2-3 mg/L combined with temperatures exceeding 55 °C. However, these conditions were only consistently maintained at control points, where post-treatment mean concentrations were systematically 1-log lower than those at distal sites. The interruption of dosage (5-day and 4-week) also revealed significant and rapid rebounds of Legionella, NTMs, and Vv (>1-2-log), highlighting their persistence in biofilms. Short-term increases in metal release were observed, with mean copper and lead concentrations rising 1.8- and 4.6-fold, respectively. Overall, results confirmed the high and rapid efficacy of onsite monochloramine to control Lp and other organisms. Analysis of water quality, temperature distribution, and usage patterns emphasize the importance of maintaining optimized hydraulic and thermal regimes to ensure effective pathogen control at points of exposure. This study provides actionable insights and practical evidence to support healthcare facilities in implementing robust long-term monitoring and control strategies.