Trihalomethanes (THMs), which may be harmful to human health if ingested or inhaled, are produced when organic matter reacts with chlorine. Hence, their formation during potabilization requires to be controlled to ensure safe drinking water. In this study, the predictive capacity of a Multiple Linear Regression (MLR) and an Artificial Neural Networks (ANN) models have been compared with real-time field-scale data of the THM formation potential (THM FP) from a Spanish Drinking Water Treatment Plant (DWTP). Spectral absorbance data obtained with Spectro::lyser® probes, installed in several treatment steps of the plant were the independent variables used to construct the models. Variable selection was based on the Stepwise Selection (SS) procedure. Following the fitting of the investigated models, ANN demonstrated precise goodness of fit (R2 = 0.92; RMSE = 0.77), clearly outperforming the MLR model (R2 = 0.35; RMSE = 1.65). Severe multicollinearity among wavelengths is responsible for the model's accuracy difference. Even though it was reduced by a prior study on the Variance Inflation Factor (VIF), it was still very high for some of the remaining wavelengths. As a result of this effect, large fictitious correlations were produced, which adversely impacted the MLR model's prediction performance (R2 = 0.30 in the validation set). While R2 reduced, indicating perhaps a slight overtraining of the ANN, the resulting R2 in the validation set (0.72) was still very high This study proved that Machine Learning models such as Artificial Neural Networks based on spectral absorption data can enhance the ability of operators to respond to critical events, becoming a decisive component of the daily management of drinking water in DWTP when needed.

Urban runoff effluents transport multiple pollutants collected from urban surfaces. which ultimately reach freshwater ecosystems. We here collect the existing scientific evidence on the urban runoff impacts on aquatic organisms and ecosystem functions, assessed the potential toxicity of the most common pollutants present in urban runoff, and characterized the ecotoxicological risk for freshwaters. We used the Toxic Units models to estimate the toxicity of individual chemicals to freshwater biota and observed that the highest ecotoxicological risk of urban runoff was associated to metals, polycyclic aromatic hydrocarbons (PAHs) and pesticides and, in a few cases, to phthalates. The potential risk was highest for copper and zinc, as well as for anthracene, fluoranthene, Di(2-ethylhexyl) phthlate (DEHP), imidacloprid, cadmium, mercury, and chromium. These pollutants had contrasting effects on freshwater biological groups, though the risk overall decreased from basal to upper trophic levels. Our analysis evidenced a lack of data on ecotoxicological effects of several pollutants present in urban runoff effluents, caused by lack of toxicity data and by the inadequate representation of biological groups in the ecotoxicological databases. Nevertheless, evidence indicates that urban runoff presents ecotoxicological risk for freshwater biota, which might increase if hydrological patterns become extreme, such as long dry periods and floods. Our study highlights the importance of considering both the acute and chronic toxicity of urban effluent pollutants, as well as recognizing the interplay with other environmental stressors, to design adequate environmental management strategies on urban freshwater ecosystems receiving urban runoff.

Over the past decade, considerable emphasis has been placed on the development of digital twins for water and wastewater applications. However, physical twins – if properly designed - offer a higher quality comparison with the real system that cannot be achieved by a digital twin. This paper focuses on experimental investigations of hydrogen sulfide formation in anaerobic sewer lines through biochemical pathways using a sewer physical twin (SPT). The SPT was conceived to independently control sewage residence time, horizontal pipe velocity, and other important sewer parameters (scouring velocity, well shear, etc.) able to affect hydrogen sulfide production, wastewater characteristics and microbial ecology distribution both in the bulk flow and in the biofilm. The SPT, constructed with three identical parallel lines to allow robust data collection and benchmarking of in-sewer treatment strategies for odor control, was operated for over 200 days using real sewage. Longitudinal wastewater fractionation studies and microbial ecology profiles confirmed the presence of active hydrolysis and fermentation processes, with a mature biofilm. The activity of sulfate-reducing bacteria (SRB) decreased as the biofilm thickness decreased, while the concentration of hydrogen sulfide followed the opposite trend. The addition of ferrous ions effectively controlled the concentration of dissolved sulfide, resulting in the formation of ferrous sulfide. Distinct bacterial populations were identified for the bulk wastewater and the biofilm, with SRB and methanogens being predominant in the biofilm and sulfur-oxidizing and facultative bacteria more abundant in the bulk wastewater. In conclusion, this study successfully demonstrated that the SPT accurately mimics the observed trends in full scale associated with sulfide emissions thus providing data with high reproducibility, satisfactory accuracy, and novel insights into system performance. It is anticipated that the SPT will play a crucial role also in laboratory and pilot studies associated in adjacent areas including sewer epidemiology and GHG emissions to allow high quality data collection under a variety of operating conditions and sewer process dynamics.

A novel scale-down Sewer Physical-Twin (SPT) system has been developed to simulate the dynamic conditions of force mains. The primary objective is to assess the presence and mitigation of hydrogen sulfide in sewer lines, targeting issues related to odor and corrosion. The key feature of this testing system lies in its adaptability to various operating conditions, such as flow velocity, retention time, and chemical dosing at different locations, making it versatile for specific on-site sewer conditions. Three SPT units, each featuring five loops, were operated for a duration of 200 days, both with and without chemical injection. The study successfully demonstrated biofilm formation, consistent sulfide production, and effective sulfide control through the addition of ferrous iron. Microbial ecology studies further unveiled a diverse range of microorganisms in both biofilm and bulk samples. This research underscores the SPT’s potential as a valuable screening tool for investigating hydrogen sulfide generation and control in sewer systems.

The management of urban wastewater systems and the associated modelling of these systems has become indispensable in today's world. In order for these models to represent reality as accurately as possible, a reliable calibration is essential. Water level data is used as a standard, but due to expensive sensors and harsh conditions in the sewer, data can only be collected at a few key points of the system. One novel solution, that has experienced an upswing in recent years, is collecting data using low-cost temperature sensors. Two sensors are needed; one is placed in the stream; the other is placed at the crest of the weir. In the case of dry weather, the sensor measures the air phase, whereas, in the case of Combined Sewer Overflow (CSO), the discharged storm and wastewater is measured. The start and end of a CSO event can be determined via the merging of measured temperature values in both points of the overflow structure. Due to this method, the duration of CSO events in a sewer system can be detected.In this work, the potential benefits of this novel method for model calibration are assessed. Therefore, autocalibration runs with water level data and fictional temperature data were carried out via OSTRICH for a SWMM model located in Berlin. Furthermore, calibration runs with a different number of measuring sites were performed, to evaluate the amount of necessary measuring sites for a reliable calibration. In order to be able to compare the different approaches, a calibration period of 19 events was first required for the respective datatype. Next, a validation period which consisted of 18 events was carried out and evaluated by the R² of three water level measuring sites for both approaches to ensure comparability. It was revealed that the calibration with duration data based on temperature sensors was able to achieve results as good as the conventional approach using water level data. Due to low spatial distribution of the measuring sites in the model, it could not be finally answered if more measuring sites would yield to even better results. However, already with one measuring site, promising calibration outcomes could be achieved and thus, offers an alternative for water utilities and practitioners.

With increased commitment from the international community to reduce greenhouse gas (GHG) emissions from all sectors in accordance with the Paris Agreement, the water sector has never felt the pressure it is now under to transition to a low-carbon water management model. This requires reducing GHG emissions from grid-energy consumption (Scope 2 emissions), which is straightforward; however, it also requires reducing Scope 1 emissions, which include nitrous oxide and methane emissions, predominantly from wastewater handling and treatment.The pathways and factors leading to biological nitrous oxide and methane formation and emissions from wastewater are highly complex and site-specific. Good emission factors for estimating the Scope 1 emissions are lacking, water utilities have little experience in directly measuring these emissions, and the mathematical modelling of these emissions is challenging. Therefore, this book aims to help the water sector address the Scope 1 emissions by breaking down their pathways and influencing factors, and providing guidance on both the use of emission factors, and performing direct measurements of nitrous oxide and methane emissions from sewers and wastewater treatment plants. The book also dives into the mathematical modelling for predicting these emissions and provides guidance on the use of different mathematical models based upon your conditions, as well as an introduction to alternative modelling methods, including metabolic, data-driven, and AI methods. Finally, the book includes guidance on using the modelling tools for assessing different operating strategies and identifying promising mitigation actions.A must-have book for anyone needing to understand, account for, and reduce water utility Scope 1 emissions.ISBN: 9781789060454 (Paperback)ISBN: 9781789060461 (eBook)ISBN: 9781789060478 (ePub)

With increased commitment from the international community to reduce greenhouse gas (GHG) emissions from all sectors in accordance with the Paris Agreement, the water sector has never felt the pressure it is now under to transition to a low-carbon water management model. This requires reducing GHG emissions from grid-energy consumption (Scope 2 emissions), which is straightforward; however, it also requires reducing Scope 1 emissions, which include nitrous oxide and methane emissions, predominantly from wastewater handling and treatment. The pathways and factors leading to biological nitrous oxide and methane formation and emissions from wastewater are highly complex and site-specific. Good emission factors for estimating the Scope 1 emissions are lacking, water utilities have little experience in directly measuring these emissions, and the mathematical modelling of these emissions is challenging. Therefore, this book aims to help the water sector address the Scope 1 emissions by breaking down their pathways and influencing factors, and providing guidance on both the use of emission factors, and performing direct measurements of nitrous oxide and methane emissions from sewers and wastewater treatment plants. The book also dives into the mathematical modelling for predicting these emissions and provides guidance on the use of different mathematical models based upon your conditions, as well as an introduction to alternative modelling methods, including metabolic, data-driven, and AI methods. Finally, the book includes guidance on using the modelling tools for assessing different operating strategies and identifying promising mitigation actions. A must-have book for anyone needing to understand, account for, and reduce water utility Scope 1 emissions. ISBN: 9781789060454 (Paperback) ISBN: 9781789060461 (eBook) ISBN: 9781789060478 (ePub)

The quantification of direct greenhouse gas (GHG) emissions from sewers and wastewater treatment plants is of great importance for urban sustainable development.In fact, the identification and assessment of anthropogenic sources of GHG emissions (mainly nitrous oxide and methane) in these engineered systems represent the first step in establishing effective mitigation strategies.This chapter provides an overview of the currently available nitrous oxide and methane quantification methods applied at full-scale in sewers and wastewater treatment plants.Since the first measurement campaigns in the early 90 s were based on spare grab sampling, quantification methodologies and sampling strategies have evolved significantly, in order to describe the spatio-temporal dynamics of the emissions.The selection of a suitable quantification method is mainly dictated by the objective of the measurement survey and by specific local requirements.Plant-wide quantification methods provide information on the overall emissions of wastewater treatment plants, including unknown sources, which can be used for GHG inventory purposes.To develop on-site mitigation strategies, in-depth analysis of GHG generation pathways and emission patterns is required.In this case, process-unit quantifications can be employed to provide data for developing mechanistic models or to statistically link GHG emissions to operational conditions.With regard to sewers, current available methods are not yet capable of capturing the complexity of these systems due to their geographical extension and variability of conditions and only allow the monitoring of specific locations where hotspots for GHG formation and emission have been identified.

Oxidative chemicals, such as nitrate, are periodically added to sewer systems to mitigate sulfide production and its accumulation but data are lacking on how these treatments affect sewage microbiota and alter their gene expression and mobilization. The present study investigated such effects on the biofilm of a full-scale sewer collected at two different locations, namely a pumping station and at the inlet of an urban wastewater treatment plant (WWTP), before and 15 days after nitrate dosage using a combination of culture-dependent and -independent approaches. Nitrate dosing resulted ineffective on the concentration of antibiotic-resistant Escherichia coli (AR-EC) in biofilms but greatly affected the composition of biofilm bacterial communities and the associated resistome and mobilome, especially at the pumping station where nitrate was dosed. Such responses consisted of a clear reduction on strict anaerobes; an almost twofold increase on the expression of recA gene (stress-response marker); and a significant increase on the relative abundance of most antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs). In turn, the effects of nitrate dosing at the WWTP inlet were barely visible, suggesting that they vanished into the distance from the pumping site (2.4 km). Remarkably, however, the relative abundance of both resistance and mobilization gene biomarkers at the inlet of the studied WWTP clearly oversized that at the pumping station confirming that these facilities are sinks where these resistance determinants accumulate and propagate. Considering the abundance of resistant bacteria and genes in urban sewage, the effects of dosing chemicals should be carefully assessed to lessen the load of such biological pollutants into WWTPs.

With increased commitment from the international community to reduce greenhouse gas (GHG) emissions from all sectors in accordance with the Paris Agreement, the water sector has never felt the pressure it is now under to transition to a low-carbon water management model. This requires reducing GHG emissions from grid-energy consumption (Scope 2 emissions), which is straightforward; however, it also requires reducing Scope 1 emissions, which include nitrous oxide and methane emissions, predominantly from wastewater handling and treatment.The pathways and factors leading to biological nitrous oxide and methane formation and emissions from wastewater are highly complex and site-specific. Good emission factors for estimating the Scope 1 emissions are lacking, water utilities have little experience in directly measuring these emissions, and the mathematical modelling of these emissions is challenging. Therefore, this book aims to help the water sector address the Scope 1 emissions by breaking down their pathways and influencing factors, and providing guidance on both the use of emission factors, and performing direct measurements of nitrous oxide and methane emissions from sewers and wastewater treatment plants. The book also dives into the mathematical modelling for predicting these emissions and provides guidance on the use of different mathematical models based upon your conditions, as well as an introduction to alternative modelling methods, including metabolic, data-driven, and AI methods. Finally, the book includes guidance on using the modelling tools for assessing different operating strategies and identifying promising mitigation actions.A must-have book for anyone needing to understand, account for, and reduce water utility Scope 1 emissions.ISBN: 9781789060454 (Paperback)ISBN: 9781789060461 (eBook)ISBN: 9781789060478 (ePub)

The dosage of free nitrous acid (FNA) is an effective treatment to control emissions of hydrogen sulfide and methane from sewers. It is unclear, however, whether its application could affect the sewer resistome and mobilome (the pool of antibiotic resistance genes (ARGs) and mobile genetic elements (MGEs), respectively). Here, a lab-scale assay for FNA treatment (60 ppm for 6 h) was conducted to explore its effects on the abundance of ARGs and MEGs among sewer compartments (i.e., suspended cells and biofilms) using shotgun metagenomics. FNA treatment caused a significant reduction in viable cells (approximate to 25%, p < 0.01), a 75% and a 83% reduction in the abundance of Aeromonadaceae and Moraxellaceae, respectively, and a 50% increase for Campylobacteraceae in the suspended cell fraction whereas bacterial communities in sewer biofilms remained unaltered. Comparative analyses before and after the FNA treatment also showed a significant reduction (p < 0.01) in the relative abundance of ARGs in the suspended cell fraction, particularly for genes conferring resistance to multidrug, macrolide-lincosamide-streptogramin B, quinolones and bacitracin. Besides, the relative abundance of mobile ISCR8 element significantly decreased (from 0.014% to 0.010%) whereas genes conferring resistance to glycopeptides significantly increased (from 0.0008% to 0.0016%) after 6 h of treatment. A heatmap analysis also showed a clear reduction in the relative abundance of genes conferring resistance to beta-lactam antibiotics and multidrug resistance. The observed reduction in the relative abundance of ARGs and MGEs in suspended cells is a beneficial side effect of a treatment originally intended to mitigate emissions of noxious gases from sewers and may be helpful to guide future policy recommendations to lessen the spread of antibiotic resistance across the urban water cycle.

Combined sewer overflows (CSOs) are of major environmental concern for impacted surface waterbodies. In the last decades, major storm events have become increasingly regular in some areas, and meteorological scenarios predict a further rise in their frequency. Consequently, control and treatment of CSOs with respect to best practice examples, innovative treatment solutions, and management of sewer systems are an inevitable necessity. As a result, the number of publications concerning quality, quantity, and type of treatments has recently increased. This review therefore aims to provide a critical overview on the effects, control, and treatment of CSOs in terms of impact on the environment and public health, strict measures addressed by regulations, and the various treatment alternatives including natural and compact treatments. Drawing together the previous studies, an innovative treatment and control guideline are also proposed for the better management practices.

The H2020 innovation project digital-water.city (DWC) aims at boosting the integrated management of water systems in five major European cities – Berlin, Copenhagen, Milan, Paris and Sofia – by leveraging the potential of data and digital technologies. The goal is to quantify the benefits of a panel of 15 innovative digital solutions and achieve their long-term uptake and successful integration in the existing digital systems and governance processes. One of these promising technologies is a new generation of sensors for measuring combined sewer overflow occurrence, developed by ICRA and IoTsens. Recent EU regulations have correctly identified CSOs as an important source of contamination and promote appropriate monitoring of all CSO structures in order to control and avoid the detrimental effects on receiving waters. Traditionally there has been a lack of reliable data on the occurrence of CSOs, with the main limitations being: i) the high number of CSO structures per municipality or catchment and ii) the high cost of the flow-monitoring equipment available on the market to measure CSO events. These two factors and the technical constraints of accessing and installing monitoring equipment in some CSO structures have delayed the implementation of extensive monitoring of CSOs. As a result, utilities lack information about the behaviour of the network and potential impacts on the local water bodies. The new sensor technology developed by ICRA and IoTsens provides a simple yet robust method for CSO detection based on the deployment of a network of innovative low-cost temperature sensors. The technology reduces CAPEX and OPEX for CSO monitoring, compared to classical flow or water level measurements, and allows utilities to monitor their network extensively. The sensors are installed at the overflows crest and measure air temperature during dry-weather conditions and water temperature when the overflow crest is submerged in case of a CSO event. A CSO event and its duration can be detected by a shift in observed temperature, thanks to the temperature difference between the air and the water phase. Artificial intelligence algorithms further help to convert the continuous measurements into binary information on CSO occurrence. The sensors can quantify the CSO occurrence and duration and remotely provide real-time overflow information through LoRaWAN/2G communication protocols. The solution is being deployed since October 2020 in the cities of Sofia, Bulgaria, and Berlin, Germany, with 10 offline sensors installed in each city to improve knowledge on CSO emissions. Further 36 (Sofia) and 9 (Berlin) online sensors will follow this winter. Besides its main goal of improving knowledge on CSO emissions, data in Sofia will also be used to identify suspected dry-weather overflows due to blockages. In Berlin, data will be used to improve the accuracy of an existing hydrodynamic sewer model for resilience analysis, flood forecasting and efficient investment in stormwater management measures. First results show a good detection accuracy of CSO events with the offline version of the technology. As measurements are ongoing and further sensors will be added, an enhanced set of results will be presented at the conference. Visit us: https://www.digital-water.city/ Follow us: Twitter (@digitalwater_eu); LinkedIn (digital-water.city)

Combined sewer overflows (CSOs) are of major environmental concern for impacted surface waterbodies. In the last decades, major storm events have become increasingly regular in some areas, and meteorological scenarios predict a further rise in their frequency. Consequently, control and treatment of CSOs with respect to best practice examples, innovative treatment solutions, and management of sewer systems are an inevitable necessity. As a result, the number of publications concerning quality, quantity, and type of treatments has recently increased. This review therefore aims to provide a critical overview on the effects, control, and treatment of CSOs in terms of impact on the environment and public health, strict measures addressed by regulations, and the various treatment alternatives including natural and compact treatments. Drawing together the previous studies, an innovative treatment and control guideline are also proposed for the better management practices.

Electrochemical oxidation of hydrogen sulfide and its separation from the waste stream in the form of sulfur was studied at low-cost carbon-based porous materials, activated carbon felt (ACF) and graphite felt (GF). Both materials were capable of selective HS- oxidation to elemental sulfur in low-conductivity solutions (i.e., < 1 mS cm(-1)), as well as in raw sewage. The HS- removal rate was ten times faster at ACF compared with GF electrode due to the higher surface area and chemisorption of HS-1. To address the electrode passivation with the electrodeposited sulfur, different electrochemical recovery strategies were tested. GF could be only partially regenerated (i.e., 30% efficiency) using cathodic polarization. Also, both anodic and cathodic polarization improved the sulfide removal in the subsequent working cycle due to the introduction of new redox-active oxygen containing functional groups. Sulfur deposited at the ACF electrode could not be recovered by any of the investigated strategies. Thus, sulfur was incorporated into the carbon matrix and strongly bonded with the carbon functional groups at both GF and ACF electrodes. Although carbon-based electrodes have been widely investigated for electrochemical sulfide removal, this study demonstrates that their application is limited by low regeneration efficiency of the electrodeposited sulfur.

Wastewater transport along sewers favors the colonization of inner pipe surfaces by wastewater-derived microorganisms that grow forming biofilms. These biofilms are composed of rich and diverse microbial communities that are continuously exposed to antibiotic residues and antibiotic resistant bacteria (ARB) from urban wastewater. Sewer biofilms thus appear as an optimal habitat for the dispersal and accumulation of antibiotic resistance genes (ARGs). In this study, the concentration of antibiotics, integron (intI1) and antibiotic resistance genes (qnrS, sul1, sul2, blaTEM, blaKPC, ermB, tetM and tetW), and potential bacterial pathogens were analyzed in wastewater and biofilm samples collected at the inlet and outlet sections of a pressurized sewer pipe. The most abundant ARGs detected in both wastewater and biofilm samples were sul1 and sul2 with roughly 1 resistance gene for each 10 copies of 16s RNA gene. Significant differences in the relative abundance of gene intI1 and genes conferring resistance to fluoroquinolones (qnrS), sulfonamides (sul1 and sul2) and betalactams (blaTEM) were only measured between inlet and outlet biofilm samples. Composition of bacterial communities also showed spatial differences in biofilms and a higher prevalence of Operational Taxonomic Units (OTUs) with high sequence identity (>98%) to well-known human pathogens was observed in biofilms collected at the inlet pipe section. Our study highlights the role of sewer biofilms as source and sink of ARB and ARGs and supports the idea that community composition rather than antibiotic concentration is the main factor driving the diversity of the sewage resistome.

During heavy rainfall, the capacity of sewer systems and wastewater treatment plants may be surcharged producing uncontrolled wastewater discharges and a depletion of the environmental quality. Therefore there is a need of advanced management tools to tackle with these complex problems. In this paper an environmental decision support system (EDSS), based on the integration of mathematical modeling and knowledge-based systems, has been developed for the coordinated management of urban wastewater systems (UWS) to control and minimize uncontrolled wastewater spills. Effectiveness of the EDSS has been tested in a specially designed virtual UWS, including two sewers systems, two WWTP and one river subjected to typical Mediterranean rain conditions. Results show that sewer systems, retention tanks and wastewater treatment plants improve their performance under wet weather conditions and that EDSS can be very effective tools to improve the management and prevent the system from possible uncontrolled wastewater discharges.