Wastewater-based epidemiology applied at the building-level reveals distinct virome profiles based on the age of the contributing individuals.

BACKGROUND: Human viruses released into the environment can be detected and characterized in wastewater. The study of wastewater virome offers a consolidated perspective on the circulation of viruses within a population. Because the occurrence and severity of viral infections can vary across a person's lifetime, studying the virome in wastewater samples contributed by various demographic segments can provide valuable insights into the prevalence of viral infections within these segments. In our study, targeted enrichment sequencing was employed to characterize the human virome in wastewater at a building-level scale. This was accomplished through passive sampling of wastewater in schools, university settings, and nursing homes in two cities in Catalonia. Additionally, sewage from a large urban wastewater treatment plant was analysed to serve as a reference for examining the collective excreted human virome. RESULTS: The virome obtained from influent wastewater treatment plant samples showcased the combined viral presence from individuals of varying ages, with astroviruses and human bocaviruses being the most prevalent, followed by human adenoviruses, polyomaviruses, and papillomaviruses. Significant variations in the viral profiles were observed among the different types of buildings studied. Mamastrovirus 1 was predominant in school samples, salivirus and human polyomaviruses JC and BK in the university settings while nursing homes showed a more balanced distribution of viral families presenting papillomavirus and picornaviruses and, interestingly, some viruses linked to immunosuppression. CONCLUSIONS: This study shows the utility of building-level wastewater-based epidemiology as an effective tool for monitoring the presence of viruses circulating within specific age groups. It provides valuable insights for public health monitoring and epidemiological studies.

Applying a Digital Twin and wastewater analysis for robust validation of COVID-19 pandemic forecasts: insights from Catalonia.

Monitoring SARS-CoV-2 spread is challenging due to asymptomatic infections, numerous variants, and population behavior changes from non-pharmaceutical interventions. We developed a Digital Twin model to simulate SARS-CoV-2 evolution in Catalonia. Continuous validation ensures our model's accuracy. Our system uses Catalonia Health Service data to quantify cases, hospitalizations, and healthcare impact. These data may be under-reported due to screening policy changes. To improve our model's reliability, we incorporate data from the Catalan Surveillance Network of SARS-CoV-2 in Sewage (SARSAIGUA). This paper shows how we use sewage data in the Digital Twin validation process to identify discrepancies between model predictions and real-time data. This continuous validation approach enables us to generate long-term forecasts, gain insights into SARS-CoV-2 spread, reassess assumptions, and enhance our understanding of the pandemic's behavior in Catalonia.

Profiling wastewater characteristics in intra-urban catchments using online monitoring stations.

This study aims to investigate the differences in intra-urban catchments with different characteristics through real-time wastewater monitoring. Monitoring stations were installed in three neighbourhoods of Barcelona to measure flow, total chemical oxygen demand (COD), pH, conductivity, temperature, and bisulfide (HS-) for 1 year. Typical wastewater profiles were obtained for weekdays, weekends, and holidays in the summer and winter seasons. The results reveal differences in waking up times and evening routines, commuting behaviour during weekends and holidays, and water consumption. The pollutant profiles contribute to a better understanding of pollution generation in households and catchment activities. Flows and COD correlate well at all stations, but there are differences in conductivity and HS- at the station level. The article concludes by discussing the operational experience of the monitoring stations.

The Fourth-Revolution in the Water Sector Encounters the Digital Revolution.

The so-called fourth revolution in the water sector will encounter the Big data and Artificial Intelligence (AI) revolution. The current data surplus stemming from all types of devices together with the relentless increase in computer capacity is revolutionizing almost all existing sectors, and the water sector will not be an exception. Combining the power of Big data analytics (including AI) with existing and future urban water infrastructure represents a significant untapped opportunity for the operation, maintenance, and rehabilitation of urban water infrastructure to achieve economic and environmental sustainability. However, such progress may catalyze socio-economic changes and cross sector boundaries (e.g., water service, health, business) as the appearance of new needs and business models will influence the job market. Such progress will impact the academic sector as new forms of research based on large amounts of data will be possible, and new research needs will be requested by the technology industrial sector. Research and development enabling new technological approaches and more effective management strategies are needed to ensure that the emerging framework for the water sector will meet future societal needs. The feature further elucidates the complexities and possibilities associated with such collaborations.

Assessing stormwater control measures using modelling and a multi-criteria approach.

This paper presents a methodology for assessing the selection of stormwater control measures (SCM) within an urban drainage system that combines hydrological-hydraulic modelling and multi-criteria analysis (MCA). The methodology's utility is illustrated on urban catchment in the city of Girona, Spain. The SWMM model was applied and calibrated to simulate SCM scenarios. Seven scenarios were evaluated consisting of one grey infrastructure measure using underground storage tank and three nature-based SCM i.e. infiltration basins, infiltration trenches, green roofs, and combinations thereof. These scenarios were evaluated with MCA including combined sewer overflow (CSO) reduction, CAPEX, OPEX, amenity, biodiversity, and feasibility regarding ownership. The results show that the scenario that included only infiltration basins was most favourable, followed by the scenario which combined infiltration basins and trenches. The underground storage tank was the least favourable with the lowest grade, due to high CAPEX and OPEX, and due to single functionality.

Dynamic air supply models add realism to the evaluation of control strategies in water resource recovery facilities.

This paper introduces the application of a fully dynamic air distribution model integrated with a biokinetic process model and a detailed process control model. By using a fully dynamic air distribution model, it is possible to understand the relationships between aeration equipment, control algorithms, process performance, and energy consumption, thus leading to a significantly more realistic prediction of water resource recovery facility (WRRF) performance. Consequently, this leads to an improved design of aeration control strategies and equipment. A model-based audit has been performed for the Girona WRRF with the goal of providing a more objective evaluation of energy reduction strategies. Currently, the Girona plant uses dissolved oxygen control and has been manually optimised for energy consumption. Results from a detailed integrated model show that the implementation of an ammonia-based aeration controller, a redistribution of the diffusers, and the installation of a smaller blower lead to energy savings between 12 and 21%, depending on wastewater temperature. The model supported the development of control strategies that counter the effects of current equipment limitations, such as tapered diffuser distribution, or over-sized blowers. The latter causes an intermittent aeration pattern with blowers switching on and off, increasing wear of the equipment.

A robust and accurate surrogate method for monitoring the frequency and duration of combined sewer overflows.

Discharges of untreated wastewater from combined sewer overflows (CSOs) can affect hydraulic stress and have significant environmental impacts on receiving water bodies. Common flow rate and water level sensors for monitoring of CSO events are expensive in terms of investment costs, installation, operation and maintenance. This paper presents a novel surrogate method to detect CSO events by using two low-cost temperature sensors. The novelty is the experimental setup for installation of temperature sensors in CSO structures and an algorithm developed to automatically calculate the duration of CSO events considering the response time of the system. The occurrence and duration of CSO events is computed based on the convergence of the two temperature signals. The method was tested under field conditions in a CSO structure, and the results were compared to the information gathered from a parallel installed flow sensor. The application of two temperature sensors installed inside a CSO structure was proven to be robust and accurate for the automatic detection of the occurrence and duration of CSO events. Within the 7-month test phase, 100% of the 20 CSO events could be detected without false detections. The accuracy of detecting the start and end of the CSO events was 2 min in comparison to the flow sensor.

Assessing Urban Wastewater System Upgrades Using Integrated Modeling, Life Cycle Analysis, and Shadow Pricing.

This study assesses the environmental impacts of four measures proposed for upgrading of the urban wastewater system of Eindhoven and the Dommel River in The Netherlands, against the base case, "do-nothing" option. The measures aim to reduce the overall environmental impact of the Eindhoven urban wastewater system (UWS) by targeting river dissolved oxygen depletion and ammonia peaks, reducing combined sewer overflows, and enhancing nutrient removal. The measures are evaluated using a life cycle analysis with the boundaries including the receiving river section by means of an integrated model of the UWS. An uncertainty analysis of the estimated impacts has been performed to support the outcomes. The study also uses the economic concept of shadow prices to assign relative weights of socio-economic importance to the estimated life cycle impacts. This novel integration of tools complements the assessments of this UWS with the inclusion of long-term global environmental impacts and the investigation of trade-offs between different environmental impacts through a single monetary unit. The results support the selection of deeper clarifiers as the most environmentally beneficial measure for upgrade.

Ecosystem-based management of a Mediterranean urban wastewater system: a sensitivity analysis of the operational degrees of freedom.

Urban wastewater systems discharge organic matter, nutrients and other pollutants (including toxic substances) to receiving waters, even after removing more than 90% of incoming pollutants from human activities. Understanding their interactions with the receiving water bodies is essential for the implementation of ecosystem-based management strategies. Using mathematical modeling and sensitivity analysis we quantified how 19 operational variables of an urban wastewater system affect river water quality. The mathematical model of the Congost system (in the Besòs catchment, Spain) characterizes the dynamic interactions between sewers, storage tanks, wastewater treatment plants and the river. The sensitivity analysis shows that the use of storage tanks for peak shaving and the use of a connection between two neighboring wastewater treatment plants are the most important factors influencing river water quality. We study how the sensitivity of the water quality variables towards changes in the operational variables varies along the river due to discharge locations and river self-purification processes. We demonstrate how to use the approach to identify interactions and how to discard non-influential operational variables.

Seamless integration of sewer system topology and tree location data: An algorithm to diagnose the potential impact of tree roots on pipes and propose rearrangement solutions.

Wastewater networks are subject to several threats leading to wastewater leakages and public health hazards. External elements such as natural factors and human activities are common causes of wastewater leakages and require more in-depth analysis. Prevention and rehabilitation work is essential to secure wastewater networks and avoid pipe failures. This work presents a new algorithm that allows for the seamless integration of sewer topology and tree location data to diagnose the potential impact of tree roots on pipes. The algorithm also proposes tree rearrangement options that balance the cost of tree rearrangement with the cost of pipe repair. The paper also showcases a real-world case study in the city of Girona to evaluate the performance of the presented algorithms for a specific case focusing on tree roots as a natural factor. Results show that it is possible to optimally rearrange a number of the trees with the greatest impact, significantly minimizing pipe failures and wastewater leakages (82% risk reduction with only rearranging a 12% of the most impactful trees). The rearrangement solution not only protects the environment and prevents public health hazards, but also achieves a positive economic payback during the operational period of the pipes, saving up to 1.33M€ for a tree rearrangement of 7%. The presented methodology is applicable to other natural or human factors.

Enhancing reclaimed water distribution network resilience with cost-effective meshing.

Water Distribution Networks (WDNs) are critical infrastructures that ensure a continuous supply of safe water to homes. In the face of challenges, like water scarcity, establishing resilient networks is imperative, especially in regions vulnerable to water crises. This study evaluates the resilience of network designs through graph theory, including its hydraulic feasibility using EPANET software, an aspect often overlooked. Novel mathematical algorithms, including Resilience by Design (RbD) and Resilience-strengthening (RS) algorithms, provide cost-effective and resilient network designs, even with budget constraints. A novel metric, Water Availability (WA), is introduced to offer a comprehensive measure of network resilience, thereby addressing ongoing discrepancies in resilience evaluation methods. Practical benefits are illustrated through a case study in which a resilient-by-design reclaimed water network is created, and an existing equivalent non-resilient network is improved. The resilient-by-design network demonstrates remarkably better results compared to the equivalent non-resilient design, including up to a 36 % reduction in the probability of service disruptions and a nearly 65 % decrease in the annual average unserved water due to service disruptions. These findings underscore the enormous advantages of a resilience-focused network design approach. When compared to the equivalent non-resilient design, the resilient-by-design network generated effectively safeguards up to a significant 91,700m3 of water from the impacts of water disruption events over a 50-year operational period. In addition, the resilient-by-design WDN solution incurs a subtle decrease in overall costs compared to consuming tap water from the drinking WDN baseline over a 50-year operational period. These findings highlight the cost-effectiveness of the approach, even offering financial benefits. This paper builds on our previous research by expanding its scope to include resilience considerations, providing algorithms that can be easily adapted from reclaimed to drinking WDNs. Ultimately, we contribute to the enhancement of water resource management and infrastructure planning in ever-evolving urban environments.

Including Life Cycle Assessment for decision-making in controlling wastewater nutrient removal systems.

This paper focuses on the use of Life Cycle Assessment (LCA) to evaluate the performance of seventeen control strategies in wastewater treatment plants (WWTPs). It tackles the importance of using site-specific factors for nutrient enrichment when decision-makers have to select best operating strategies. Therefore, the LCA evaluation is repeated for three different scenarios depending on the limitation of nitrogen (N), phosphorus (P), or both, when evaluating the nutrient enrichment impact in water bodies. The LCA results indicate that for treated effluent discharged into N-deficient aquatic systems (e.g. open coastal areas) the most eco-friendly strategies differ from the ones dealing with discharging into P-deficient (e.g. lakes and rivers) and N&P-deficient systems (e.g. coastal zones). More particularly, the results suggest that strategies that promote increased nutrient removal and/or energy savings present an environmental benefit for N&P and P-deficient systems. This is not the case when addressing N-deficient systems for which the use of chemicals (even for improving N removal efficiencies) is not always beneficial for the environment. A sensitivity analysis on using weighting of the impact categories is conducted to assess how value choices (policy decisions) may affect the management of WWTPs. For the scenarios with only N-limitation, the LCA-based ranking of the control strategies is sensitive to the choice of weighting factors, whereas this is not the case for N&P or P-deficient aquatic systems.

Modelling and monitoring of integrated urban wastewater systems: review on status and perspectives.

While the general principles and modelling approaches for integrated management/modelling of urban water systems already present a decade ago still hold, in recent years aspects like model interfacing and wastewater treatment plant (WWTP) influent generation as complements to sewer modelling have been investigated and several new or improved systems analysis methods have become available. New/improved software tools coupled with the current high computational capacity have enabled the application of integrated modelling to several practical cases, and advancements in monitoring water quantity and quality have been substantial and now allow the collecting of data in sufficient quality and quantity to permit using integrated models for real-time applications too. Further developments are warranted in the field of data quality assurance and efficient maintenance.

ediblecity: an R package to model and estimate the benefits of urban agriculture.

Urban agriculture is gaining attraction to become one of the pillars of the urban ecological transition and to increase food security in an urbanized planet. However, there is a lack of systematic quantification of the benefits provided by urban agriculture solutions. In this paper, we present an R package to estimate several indicators related to benefits of urban agriculture. The goal is to provide a tool for researchers and practitioners interested in the impacts of urban agriculture. The ediblecity package provides functions to calculate 8 indicators: urban heat island, runoff prevention, green areas accessibility, NO 2 sequestration, jobs created in commercial gardens, volunteers involved in community gardens, green per capita and, finally, food production. Moreover, the package also provides a function to generate scenarios with different implementations of urban agriculture. We illustrate the use of the package by comparing three scenarios in a neighborhood of Girona (Spain), which is included in the package as an example dataset. There, we compare scenarios with an increasing amount of urban agriculture solutions. The ediblecity package is open-source software. This allows other R developers to contribute to the package, providing new functionalities or improving the existing ones.

N-Nitrosamines and their precursors in wastewater effluents from selected industries in Spain.

N-nitrosamines (NAs), and N-nitrosodimethylamine (NDMA) in particular, are hazardous disinfection byproducts (DBPs) relevant when wastewater impacts drinking water sources and, in water reuse practices. Our study investigates the concentrations of NDMA and five additional NAs and their precursors in industrial wastewater effluents. Aiming to identify potential differences between industrial typologies, wastewaters from 38 industries belonging to 11 types of the UN International Standard Industrial Classification of All Economic Activities system (ISIC) were analysed. Results show that the presence of most NAs and their precursors cannot be linked to a specific industry type as these were in general very different within the classes. Nevertheless, N-nitrosomethylethylamine (NMEA) and N-nitrosopiperidine (NPIP) as well as precursors for N-nitrosodiethylamine (NDEA), NPIP and N-nitrosodibuthylamine (NDBA) could be rank with different concentrations between ISIC classes (p-value < 0.05). Specific industrial wastewater with notable high concentrations of NAs and their precursors were identified too. The effluents with the highest concentration of NDMA belong to the ISIC C2011 class (Manufacture of basic chemical), while the effluents with the highest concentration of NDMA precursors were from the ISIC C1511 class (Tanning and dressing of leather; dressing and dyeing of fur). Other relevant NAs found were NDEA in ISIC class B0810 (Quarrying of stone, sand, and clay) and ISIC class C2029 (Manufacture of other chemical products).

Surveillance of SARS-CoV-2 in sewage from buildings housing residents with different vulnerability levels.

During the last three years, various restrictions have been set up to limit the transmission of the Coronavirus Disease (COVID-19). While these rules apply at a large scale (e.g., country-wide level) human-to-human transmission of the virus that causes COVID-19, the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), occurs at a small scale. Different preventive policies and testing protocols were implemented in buildings where COVID-19 poses a threat (e.g., elderly residences) or constitutes a disruptive force (e.g., schools). In this study, we sampled sewage from different buildings (a school, a university campus, a university residence, and an elderly residence) that host residents of different levels of vulnerability. Our main goal was to assess the agreement between the SARS-CoV-2 concentration in wastewater and the policies applied in these buildings. All buildings were sampled using passive samplers while 24 h composite samples were also collected from the elderly residence. Results showed that passive samplers performed comparably well to composite samples while being cost-effective to keep track of COVID-19 prevalence. In the elderly residence, the comparison of sampling protocols (passive vs. active) combined with the strict clinical testing allowed us to compare the sensitivities of the two methods. Active sampling was more sensitive than passive sampling, as the former was able to detect a COVID-19 prevalence of 0.4 %, compared to a prevalence of 2.2 % for passive sampling. The number of COVID-19-positive individuals was tracked clinically in all the monitored buildings. More frequent detection of SARS-CoV-2 in wastewater was observed in residential buildings than in non-residential buildings using passive samplers. In all buildings, sewage surveillance can be used to complement COVID-19 clinical testing regimes, as the detection of SARS-CoV-2 in wastewater remained positive even when no COVID-19-positive individuals were reported. Passive sampling is useful for building managers to adapt their COVID-19 mitigation policies.

Optimal design of water reuse networks in cities through decision support tool development and testing.

Water scarcity and droughts are an increasing issue in many parts of the world. In the context of urban water systems, the transition to circularity may imply wastewater treatment and reuse. Planning and assessment of water reuse projects require decision-makers evaluating the cost and benefits of alternative scenarios. Manual or semi-automatic approaches are still common practice for planning both drinking and reclaimed water distribution networks. This work illustrates a decision support tool that, based on open data sources and graph theory coupled to greedy optimization algorithms, is able to automatically compute the optimal reclaimed water network for a given scenario. The tool provides not only the maximum amount of served reclaimed water per unit of invested cost, but also the length and diameters of the pipes required, the location and size of storage tanks, the population served, and the construction costs, i.e., everything under the same architecture. The usefulness of the tool is illustrated in two different but complementary cities in terms of size, density, and topography. The construction cost of the optimal water reclaimed network for a city of approximately 100,000 inhabitants is estimated to be in the range of €0.17-0.22/m3 (for a payback period of 30 years).

Could we estimate industrial wastewater flows composition using the UN-ISIC classification system?

Although we have extensive datasets on the location and typology of industries, we do not know much on their generated and discharged wastewater. This lack of information compromises the achievement of the sustainable development goals focused on water (Sustainable Development Goal 6) in Europe and globally. Thus, our goal was to assess to which degree the chemical composition of industrial wastewater could be estimated based on the industry's typology according to its International Standard Industrial Classification of All Economic Activities (ISIC) class. We collected wastewater effluent water samples from 60 industrial wastewater effluents (before any wastewater treatment process), accounting for 5 samples each of 12 ISIC classes, analyzed the composition of key contaminants (i.e. European Commission rated priority compounds and watchlist), and statistically assessed the similarities and differences amongst ISIC classes using ordination and random forest analyses. The results showed statistically significant linkages between most ISIC classes and the composition of produced wastewater. Among the analytical parameters measured, the random forest methodology allowed identifying a sub-set particularly relevant for classification or eventual contamination prediction based on ISIC class. This is an important applied research topic with strong management implications to (i) determine pollution emission caps for each individual ISIC class, (ii) define monitoring schemes to sample and analyze industrial wastewater, and (iii) enable predicting pollutant loads discharged in river basins with scarce information. These encouraging results urge us to expand our work into other ISIC classes and water quality parameters to draw a full picture of the relationship between ISIC classes and produced wastewater.

Comparison of different modeling approaches to better evaluate greenhouse gas emissions from whole wastewater treatment plants.

New tools are being developed to estimate greenhouse gas (GHG) emissions from wastewater treatment plants (WWTPs). There is a trend to move from empirical factors to simple comprehensive and more complex process-based models. Thus, the main objective of this study is to demonstrate the importance of using process-based dynamic models to better evaluate GHG emissions. This is tackled by defining a virtual case study based on the whole plant Benchmark Simulation Model Platform No. 2 (BSM2) and estimating GHG emissions using two approaches: (1) a combination of simple comprehensive models based on empirical assumptions and (2) a more sophisticated approach, which describes the mechanistic production of nitrous oxide (N(2) O) in the biological reactor (ASMN) and the generation of carbon dioxide (CO(2) ) and methane (CH(4) ) from the Anaerobic Digestion Model 1 (ADM1). Models already presented in literature are used, but modifications compared to the previously published ASMN model have been made. Also model interfaces between the ASMN and the ADM1 models have been developed. The results show that the use of the different approaches leads to significant differences in the N(2) O emissions (a factor of 3) but not in the CH(4) emissions (about 4%). Estimations of GHG emissions are also compared for steady-state and dynamic simulations. Averaged values for GHG emissions obtained with steady-state and dynamic simulations are rather similar. However, when looking at the dynamics of N(2) O emissions, large variability (3-6 ton CO(2) e day(-1) ) is observed due to changes in the influent wastewater C/N ratio and temperature which would not be captured by a steady-state analysis (4.4 ton CO(2) e day(-1) ). Finally, this study also shows the effect of changing the anaerobic digestion volume on the total GHG emissions. Decreasing the anaerobic digester volume resulted in a slight reduction in CH(4) emissions (about 5%), but significantly decreased N(2) O emissions in the water line (by 14%).

Model-based knowledge acquisition in environmental decision support system for wastewater integrated management.

The main goal of the Water Framework Directive is to achieve good chemical and ecological status of water bodies by 2015. The implementation of integrated river basin management, including sewer systems, wastewater treatment plants and receiving water bodies, is essential to accomplishing this objective. Integrated management is complex and therefore the implementation of control systems and the development of decision support systems are needed to facilitate the work of urban wastewater system (UWS) managers. Within this context, the objective of this paper is to apply integrated modelling of an UWS to simulate and analyse the behaviour of the 'Congost' UWS in Spain, and to optimize its performance against different types of perturbations. This analysis results in optimal operating set-points for each perturbation, improves river water quality, minimizes combined sewer overflows and optimizes flow lamination from storm water tanks. This is achieved by running Monte Carlo simulations and applying global sensitivity analysis. The set-points will become part of the knowledge base composed of a set of IF-THEN rules of the environmental decision support system being developed for this case study.