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The biogeography of eukaryotes in drinking water systems is poorly understood relative to that of prokaryotes or viruses, limiting the understanding of their role and management. A challenge with studying complex eukaryotic communities is that metagenomic analysis workflows are currently not as mature as those that focus on prokaryotes or viruses. In this study, we benchmarked different strategies to recover eukaryotic sequences and genomes from metagenomic data and applied the best-performing workflow to explore the factors affecting the relative abundance and diversity of eukaryotic communities in drinking water distribution systems (DWDSs). We developed an ensemble approach exploiting k-mer- and reference-based strategies to improve eukaryotic sequence identification and identified MetaBAT2 as the best-performing binning approach for their clustering. Applying this workflow to the DWDS metagenomes showed that eukaryotic sequences typically constituted small proportions (i.e.

The great concern over the environmental impact of wastewaters has led to the designing of advanced treatment processes to upgrade conventional treatment plants and achieve a significant reduction of contaminants in receiving waters. In the present study we combined chemical and ecotoxicological analyses, aiming to evaluate the reduction of toxicity effects associated with the removal of micropollutants and to define the contribution of the detected compounds to the overall toxicity of the mixtures in a series of wastewater effluents collected from a secondary treatment (OUT 2) and from a tertiary activated carbon treatment (OUT 3) plant. The target compounds were selected after a screening procedure among pharmaceuticals, musk fragrances, and trace metals. The classical algal growth inhibition test was conducted on the original effluent samples and on different fractions obtained by solid-phase extraction (SPE) treatment. A good accordance was found between the removal of toxicity (30%-80%) and organic compounds (70%-80%) after the tertiary treatment, suggesting its high efficiency to improve the wastewater quality. The discrepancy between the contribution to the overall toxicity of the nonadsorbable compounds (i.e., inorganic or very polar organic compounds) as experimentally measured by the SPE bioassays (18%-76%) and calculated by the concentration addition approach (97%) could be mitigated by including the bioavailability correction in metal-toxicity modeling of wastewater mixtures. For the organic compounds, the toxic equivalency method enabled us to quantify the portion of toxicity explained by the detected chemicals in both OUT 2 (82%-104%) and OUT 3 (5%-57%), validating the selection of the target molecules. The applied integrating approach could be implemented by the inclusion of both additional target chemicals and toxicity endpoints. Environ Toxicol Chem 2022;41:2404-2419. © 2022 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

The biogeography of eukaryotes in drinking water systems is poorly understood relative to prokaryotes or viruses. A common challenge with studying complex eukaryotic communities from natural and engineered systems is that the metagenomic analysis workflows are currently not as mature as those that focus on prokaryotes or even viruses. In this study, we benchmarked different strategies to recover eukaryotic sequences and genomes from metagenomic data and applied the best-performing workflow to explore eukaryotic communities present in drinking water distribution systems (DWDSs). We developed an ensemble approach that exploits k-mer and reference-based strategies to improve eukaryotic sequence identification from metagenomes and identified MetaBAT2 as the best performing binning approach for clustering of eukaryotic sequences. Applying this workflow on the DWDSs metagenomes showed that eukaryotic sequences typically constituted a small proportion (i.e., SynopsisAfter benchmarking tools and developing a dedicated consensus workflow for eukaryotic sequence detection in metagenomes, the experimental, environmental, and engineering factors affecting their biogeography in drinking water distribution systems were investigatedGraphical abstract

Wastewater treatment plants (WWTPs) provide a barrier against the discharge of contaminants of emerging concern (CECs) into the environment. The removal of CECs is highly WWTP-specific and the underlying mechanisms are still poorly understood, hampering the optimization of biological treatment steps for their removal. To fill this knowledge gap, we assessed the influence of four operational parameters of activated sludge biological treatment, namely total suspended solids, temperature, pH and redox conditions, on the sorption and biodegradation of four CECs under controlled laboratory conditions. Design of Experiments was used to better address the factors influencing CECs removal and interactions among operational parameters. The derived statistical models showed results in concordance with previous studies and indicated how sorption and biodegradation of the investigated CECs depend on most tested parameters and few of their interactions. The predictions of the developed models have been compared with literature values, indicating how the tested parameters are responsible for most of the variability of sorption, while they could not reliably generalize biodegradation rates. The developed models were also implemented as an extension of a mechanistic biological treatment model, successfully describing the dynamic behaviour of a large-scale WWTP, which was observed during a three-day continuous monitoring campaign. Compared to a traditional modelling approach, the one including the developed models showed on average almost a three-fold uncertainty reduction, favouring its use to aid WWTP managers and regulators for improved assessment of CEC fate and removal. Finally, the models highlighted that, while higher temperatures and solids concentrations generically favoured CECs removal, removal efficiency vary significantly due to operational parameters and no globally optimum conditions for CECs removal exist. The use of these models opens the door to the combined dynamic management of both traditional contaminants and CECs in WWTPs.

The SARS-CoV-2 Omicron variant emerged in South Africa in November 2021, and has later been identified worldwide, raising serious concerns. A real-time RT-PCR assay was designed for the rapid screening of the Omicron variant, targeting characteristic mutations of the spike gene. The assay was used to test 737 sewage samples collected throughout Italy (19/21 Regions) between 11 November and 25 December 2021, with the aim of assessing the spread of the Omicron variant in the country. Positive samples were also tested with a real-time RT-PCR developed by the European Commission, Joint Research Centre (JRC), and through nested RT-PCR followed by Sanger sequencing. Overall, 115 samples tested positive for Omicron SARS-CoV-2 variant. The first occurrence was detected on 7 December, in Veneto, North Italy. Later on, the variant spread extremely fast in three weeks, with prevalence of positive wastewater samples rising from 1.0% (1/104 samples) in the week 5-11 December, to 17.5% (25/143 samples) in the week 12-18, to 65.9% (89/135 samples) in the week 19-25, in line with the increase in cases of infection with the Omicron variant observed during December in Italy. Similarly, the number of Regions/Autonomous Provinces in which the variant was detected increased from one in the first week, to 11 in the second, and to 17 in the last one. The presence of the Omicron variant was confirmed by the JRC real-time RT-PCR in 79.1% (91/115) of the positive samples, and by Sanger sequencing in 66% (64/97) of PCR amplicons. In conclusion, we designed an RT-qPCR assay capable to detect the Omicron variant, which can be successfully used for the purpose of wastewater-based epidemiology. We also described the history of the introduction and diffusion of the Omicron variant in the Italian population and territory, confirming the effectiveness of sewage monitoring as a powerful surveillance tool.

In this work, a mechanistic dynamic model of continuous flow peracetic acid (PAA) disinfection was developed, calibrated and validated, assuming E. coli as indicator microorganism. The model was conceived as a 1-dimensional dispersion model integrating PAA first order decay and E. coli inactivation rate. Lab-scale batch experiments of PAA decay and E. coli inactivation experiments were performed to calibrate corresponding kinetic models. In each sample, conventional wastewater quality parameters were monitored. A PAA pilot reactor was set up to perform both tracer studies, for dispersion model calibration, and continuous flow disinfection experiments, to validate the integration of hydraulics and kinetics models, under both stationary and dynamic conditions. Linear regression models were calibrated to predict hydrodynamic dispersion, given the flow rate, and PAA decay parameters, given effluent quality and PAA dosage. Successful validation of the PAA disinfection model proved the importance of (i) considering the disinfection process as a dynamic system and (ii) integrating real-time estimation of process disturbances, being the initial E. coli concentration and the impact of effluent quality and PAA dosage on PAA decay kinetics. Importantly, novel inactivation models were proposed, as two different modifications of a literature model for thermal inactivation. These models are suitable for dynamic simulation of Eulerian models and can describe the typical triphasic behavior of inactivation kinetics.

A novel impedance sensor for monitoring the thickness of undesired micrometric deposits on the surfaces of water pipes and tanks is presented. Coplanar 10 μm-spaced gold microelectrodes were fouled in controlled laboratory conditions and the influence of slime on impedance response was measured. Two different sets of experiments were conducted to form slime on the electrodes surface, addressing biofilm growth and calcium carbonate precipitation, respectively. Following the formation, the thickness of the slime was characterized by Atomic Force Microscopy. Experimental tests highlighted that the impedance response is linear with to the thickness of the slime in both cases, in a 2 μm to 10 μm range. Interestingly, the slope of the response (i.e. of the ionic resistance change vs. thickness) is opposite: a decrease in the case of biofilm (-0.6 Ω/μm where the conductivity of the extracellular matrix becomes dominant) and an increase (+1.4 Ω/μm) in the case of inorganic deposits. In conclusion, this simple and robust sensor provides an accurate and selective determination of slime, while fouling and cleaning up cycles have demonstrated the possibility of regeneration. The final goal for the microsensor, once coupled to an electronic system for data processing and transmission, is the installation along the inner surface of pipes and tanks for real-time remote monitoring of long-term formation of slime with sub-micrometric thickness resolution.

Hematite-based photoanodes were used for the photoelectrocatalysed batch oxidation of As(III) in water at pH 7 and 10. Tests were carried out at different As(III) initial concentrations, ranging from 150 μg/L to 30 mg/L. With no pre-treatments, an initial inactive period of 40–50 min was always observed at the beginning of every test. This initial inactive period is completely removed by a surface modification of surface active sites induced by a pre-polarisation at 800 mV (vs. Ag/AgCl). The water splitting active sites related to the Fe(III)-Fe(IV) redox couple were proved to be not active towards As(III) oxidation. The modifications induced by the anodic prepolarisation were deeply studied: no evidence of the formation of surface highly oxidised iron sites (Fe(V) or Fe(VI)) and/or of highly reactive oxygen vacant sites emerges from XPS analysis. Rather, all collected characterisation data support the conclusion that more reactive terminal oxygen species are necessary for As(III) oxydation. The As(III) abatement reaction was modelled by two subsequent first order kinetics in As(III), independently from the initial As(III) concentration: this behaviour was explained suggesting that the highly reactive sites, being formed after prepolarisation, are gradually depleted during the reaction. As this reaction was proved to be very sensitive to the presence of highly reactive iron sites, its utilisation as a probe reaction to study hematite photoanodes is suggested.

UVC disinfection has been recognised by the WHO as an effective disinfection treatment to provide decentralized potable water. Under real conditions there are still unknowns that limit this application including the influence of suspended solids and natural organic matter. This work aims to investigate the influence of two key parameters, suspended solids and natural organic matter, on the efficiency of UVC disinfection of surface water to achieve the drinking water quality requirements established by the WHO for point of use (POU) technologies. Kaolinite (turbidity agent) and humic acids (HA, model of organic matter) were used in a factorial design of experiments (Turbidity from 0 to 5 NTU, and HA from 0 to 3.5 mg/L) to investigate their effect of UVC inactivation of MS2 phage in surface water. A collimated beam (12 W) and a commercial UVC disinfection flow system (16 W) designed to provide drinking water at households were used. The UVC flow system both in the laboratory and in the field was able to achieve the reduction requirements established by WHO (LRV >3.5 for all tested conditions), confirming the good performance of the studied UVC disinfection system. The results found in the lab were used to establish a numerical model that predicts the disinfection rate constant as a function of water turbidity and transmittance at 254 nm (confidence level>95%). The model permitted to elucidate the critical effect of low concentrations of HA in reducing the inactivation rate by 40% for 3.5 mg/L-HA compared with 0, the non-significant detrimental effect of turbidity lower than 5 NTU, and the lack of synergistic effects between both parameters at these levels. The UVC flow system was also tested in the field, in Tzabalho, Chiapas (Mexico), and Antioquia (Colombia), with spiked MS2 into natural surface water. This investigation opens a potential application to monitor the performance of UVC systems with surface water by monitoring transmittance at 254 nm as a tool to control UVC domestic systems to deliver safe drinking water in a household without the need of expensive and laborious biological monitoring tools.

Accurate modeling of wastewater ultraviolet disinfection is fundamental as support for process optimization and control. Detailed modeling of hydrodynamics and fluence rate via computational fluid dynamics, coupled to laboratory studies of inactivation kinetics, are usually the preferred approach for UV disinfection modeling. Despite this approach often provides accurate predictive performance, it requires significantly high computational time, making it unfeasible for real-time process control. In this study, to enable an effective process control, black-box regression models were assessed as a modeling alternative for UV disinfection, synthesizing hydrodynamics, fluence rate and inactivation kinetics. UV disinfection of a full-scale wastewater treatment plant in Italy was monitored for 10 months, measuring influent and effluent E. coli concentration, turbidity, absorbance at 254 nm, temperature and flow rate at different UV doses. Considering the usually observed distribution of effluent E. coli concentration and the zero inflation of the collected dataset, Poisson, zero-inflated Poisson and Hurdle generalized linear models were tested, as well as two-part models coupling a classifier describing the E. coli zero-count events and a regressor estimating the magnitude of E. coli concentrations in positive-count events. The two-part artificial neural network model showed the best predictive performance, being able of both describing nonlinearities and handling the high proportion of null values in the dataset. The deployment of this model to control ultraviolet disinfection was simulated, estimating a plausible 63% energy saving.

Water biostability is desired within drinking water distribution systems (DWDSs) to limit microbiologically-related operational, aesthetic and, eventually, health-related issues. However, variations in microbiological quality can take place both spatially along DWDS pipelines and temporally at single locations due to biofilm detachment, water quality seasonality and other processes. In this study, long- and short-term trends of bacterial concentration and community structure were investigated in a secondary branch of an unchlorinated DWDS for several months using high-frequency flow cytometry (FCM) and traditional laboratory monitoring campaigns. Long-term trends of bacterial concentrations and community structures were likely caused by changes in the water physical-chemical quality (i.e. pH and conductivity). Short-term daily pattern, instead, resulted in significant variations between the bacterial concentrations and community structures at different hours, likely due to biofilm detachment and loose deposits resuspension related to changes in the local water flow. These patterns, however, showed broad variations and did not persist during the entire monitoring campaign presumably due to the stochasticity of local instantaneous demand and seasonal changes in water consumption. During periods without sensible long-term trends, the sampling hours explain a comparable or larger fraction of the bacterial community diversity compared to dates. The variations observed with FCM were poorly or not detected by traditional laboratory analyses, as the correlation between the two were rather weak, highlighting the limited information provided by traditional approaches. On the other hand, FCM data correlated with water pH and conductivity, underlining the relation between physical-chemical and microbiological water quality. Such results suggest that the advanced control of the physical-chemical water quality could minimize the microbiological water quality variations. Moreover, monitoring campaign planning should take into account the sampling time to reduce the noise caused by daily fluctuations and/or assess the overall quality variations. Finally, as monitoring costs are one of the barriers which prevent a more widespread use of FCM, a monitoring scheme optimization strategy was developed. Such strategy employs the data from an initial high-frequency sampling period to select the sampling hours which maximize the observed variations of bacterial concentration and community composition.

Perfluoroalkyl substances (PFAS) persistence in the environment leads to their presence in drinking water, that is of high concern due to their potential human health risk. Adsorption onto activated carbon (AC) has been identified as an effective technique to remove PFAS. Adsorption isotherms and breakthrough curves, determined by rapid small-scale column tests (RSSCTs), were studied for eight PFAS and four granular ACs, characterized by different origins, porosities and numbers of reactivation cycles. Both batch and RSSCT results highlighted the strong interaction of AC and PFAS characteristics in adsorption capacity. The most important factor affecting AC performance is the surface charge: a positively-charged AC showed higher adsorption capacities with greater Freundlich constants (KF) and later 50% breakthroughs compared to the AC with neutral surface. Among the positively-charged ACs, a microporous AC demonstrated higher adsorption capacities for hydrophilic and marginally hydrophobic PFAS, while the mesoporous AC performed better for more hydrophobic PFAS, possibly due to lower pore blockage by organic matter. These results were confirmed at full-scale through a one-year monitoring campaign, in which samples were collected at the inlets and outlets of GAC systems in 17 drinking water treatment plants spread in a wide urban area, where the four analyzed ACs are used.

The uncertainties on the occurrence, fate and hazard of Contaminants of Emerging Concern (CECs) increasingly challenge drinking water (DW) utilities whether additional measures should be taken to reduce the health risk. This has led to the development and evaluation of risk-based approaches by the scientific community. DW guideline values are commonly derived based on deterministic chemical risk assessment (CRA). Here, we propose a new probabilistic procedure, that is a quantitative chemical risk assessment (QCRA), to assess potential health risk related to the occurrence of CECs in DW. The QCRA includes uncertainties in risk calculation in both exposure and hazard assessments. To quantify the health risk in terms of the benchmark quotient probabilistic distribution, the QCRA estimates the probabilistic distribution of CECs concentration in DW based on their concentration in source water and simulating the breakthrough curves of a granular activated carbon (GAC) treatment process. The model inputs and output uncertainties were evaluated by sensitivity and uncertainty analyses for each step of the risk assessment to identify the most relevant factors affecting risk estimation. Dominant factors resulted to be the concentration of CECs in water sources, GAC isotherm parameters and toxicological data. To stress the potential of this new QCRA approach, several case studies are considered with focus on bisphenol A as an example CEC and various GAC management options. QCRA quantifies the probabilistic risk, providing more insight compared to CRA. QCRA proved to be more effective in supporting the intervention prioritization for treatment optimization to pursue health risk minimization.

Real-time acquisition of indicator bacteria concentration at the inlet of disinfection unit is a fundamental support to the control of chemical and ultraviolet wastewater disinfection. Culture-based enumeration methods need time-consuming laboratory analyses, which give results after several hours or days, while newest biosensors rarely provide information about specific strains and outputs are not directly comparable with regulatory limits as a consequence of measurement principles. In this work, a novel soft sensor approach for virtual real-time monitoring of E. coli concentration is proposed. Conventional wastewater physical and chemical indicators (chemical oxygen demand, total nitrogen, nitrate, ammonia, total suspended solids, conductivity, pH, turbidity and absorbance at 254 nm) and flowrate were studied as potential predictors of E. coli concentration relying on data collected from three full-scale wastewater treatment plants. Different methods were compared: (i) linear modeling via ordinary least squares; (ii) ridge regression; (iii) principal component regression and partial least squares; (iv) non-linear modeling through artificial neural networks. Linear soft sensors reached some degree of accuracy, but performances of the artificial neural network based models were by far superior. Sensitivity analysis allowed to prioritize the importance of each predictor and to highlight the site-specific nature of the approach, because of the site-specific nature of relationships between predictors and E. coli concentration. In one case study, pH and conductivity worked as good proxy variables when the occurrence of intense rain events caused sharp increases in E. coli concentration. Differently, in other case studies, chemical oxygen demand, total suspended solids, turbidity and absorbance at 254 nm accounted for the positive correlation between low wastewater quality and E. coli concentration. Moreover, sensitivity analysis of artificial neural network models highlighted the importance of interactions among predictors, contributing to 25 to 30% of the model output variance. This evidence, along with performance results, supported the idea that nonlinear families of models should be preferred in the estimation of E. coli concentration. The artificial neural network based soft sensor deployment for control of peracetic acid disinfectant dosage was simulated over a realistic scenario of wastewater quality recorded by on-line sensors over 2 months. The scenario simulations highlighted the significant benefit of an E. coli soft sensor, which provided up to 57% of disinfectant saving.

Nanostructured materials have been recently proposed in the field of environmental remediation. The use of nanomaterials as building blocks for the design of nano-porous micro-dimensional systems is particularly promising since it can overcome the (eco-)toxicological risks associated with the use of nano-sized technologies. Following this approach, we report here the application of a nanostructured cellulose-based material as sorbent for effective removal of organic dyes from water. It consists of a micro- and nano-porous sponge-like system derived by thermal cross-linking among (2,2,6,6-Tetramethylpiperidin-1-yl)oxyl (TEMPO)-oxidized cellulose nanofibers (TOCNF), branched polyethylenimine 25 kDa (bPEI), and citric acid (CA). The sorbent efficiency was tested for four different organic dyes commonly used for fabric printing (Naphthol Blue Black, Orange II Sodium Salt, Brilliant Blue R, Cibacron Brilliant Yellow), by conducting both thermodynamic and kinetic studies. The material performance was compared with that of an activated carbon, commonly used for this application, in order to highlight the potentialities and limits of this biomass-based new material. The possibility of regeneration and reuse of the sorbent was also investigated.

In recent years, peracetic acid (PAA) has gained a lot of attention as an alternative disinfectant to chlorine-based disinfectants in the water industry. Commercial PAA solutions contain both PAA and hydrogen peroxide (HP), and the degradation of HP is slower than PAA when it is used for disinfection. All previous toxicity studies have been based on commercial PAA, and variance in toxicity values have been observed due to different PAA:HP ratios. In this study, the ecotoxicity of pure PAA was studied, eliminating HP from the commercial PAA mixture using potassium permanganate. Ecotoxicity data were obtained by conducting a battery of ecotoxicity tests: bioassays using Vibrio fischeri (V. fischeri), Daphnia magna (D. magna), and Pseudokirchneriella subcapitata (P. subcapitata). The effect concentration (EC50) of pure PAA was 0.84 (a 95% confidence interval of 0.78&ndash, 0.91) mg/L for V. fischeri and 2.46 (2.35&ndash, 2.58) mg/L for P. subcapitata, whereas the lethal concentration (LC50) was 0.74 (0.55&ndash, 0.91) mg/L for D. magna. Compared to this, our previous study found that the EC50 values of commercial PAA towards V. fischeri and P. subcapitata were 0.42 (0.41&ndash, 0.44) and 1.38 (0.96&ndash, 1.99) mg/L, respectively, which were lower than pure PAA, whilst the LC50 for D. magna was 0.78 (0.58&ndash, 0.95) mg/L. These results showed that pure PAA was less toxic to the most commonly used aquatic species for toxicity tests compared to commercial PAA, except for D. magna.

A smart, safe, and efficient management of water is fundamental for both developed and developing countries. Several wireless sensor networks have been proposed for real-time monitoring of drinking water quantity and quality, both in the environment and in pipelines. However, surface fouling significantly affects the long-term reliability of pipes and sensors installed in-line. To address this relevant issue, we presented a multi-parameter sensing node embedding a miniaturized slime monitor able to estimate the micrometric thickness and type of slime. The measurement of thin deposits in pipes is descriptive of water biological and chemical stability and enables early warning functions, predictive maintenance, and more efficient management processes. After the description of the sensing node, the related electronics, and the data processing strategies, we presented the results of a two-month validation in the field of a three-node pilot network. Furthermore, self-powering by means of direct energy harvesting from the water flowing through the sensing node was also demonstrated. The robustness and low cost of this solution enable its upscaling to larger monitoring networks, paving the way to water monitoring with unprecedented spatio-temporal resolution.

Direct reuse of reclaimed wastewater (RWW) in agriculture has recently received increasing attention as a possible solution to water scarcity. The presence of contaminants of emerging concern (CECs) in RWW can be critical, as these chemicals can be uptaken in irrigated crops and eventually ingested during food consumption.In the present study, an integrated model was developed to predict the fate of CECs in water reuse systems where RWW is used for edible crops irrigation. The model was applied to a case study where RWW (originating from a municipal wastewater treatment plant) is discharged into a water channel, with subsequent irrigation of silage maize, rice, wheat and ryegrass. Environmental and human health risks were assessed for 13 CECs, selected based on their chemical and hazard characteristics. Predicted CEC concentrations in the channel showed good agreement with available measurements, indicating potential ecotoxicity of some CECs (estrogens and biocides) due to their limited attenuation. Plant uptake predictions were in good agreement with existing literature data, indicating higher uptake in leaves and roots than fruits. Notably, high uncertainties were shown for weakly acidic CECs, possibly due to degradation in soil and pH variations inside plants. The human health risk due to the ingestion of wheat and rice was assessed using the threshold of toxicological concern and the hazard quotient. Both approaches predicted negligible risk for most CECs, while sulfamethoxazole and 17α-ethinylestradiol exhibited the highest risk for consumers. Alternative scenarios were evaluated to identify possible risk minimization strategies (e.g., adoption of a more efficient irrigation system).

Pharmaceutical active compounds (PhACs) are a category of micropollutants frequently detected across integrated urban wastewater systems. Existing modelling tools supporting the evaluation of micropollutant fate in such complex systems, such as the IUWS_MP model library (which acronym IUWS stands for Integrated Urban Wastewater System), do not consider fate processes and fractions that are typical for PhACs. This limitation was overcome by extending the existing IUWS_MP model library with new fractions (conjugated metabolites, sequestrated fraction) and processes (consumption-excretion, deconjugation). The performance of the extended library was evaluated for five PhACs (carbamazepine, ibuprofen, diclofenac, paracetamol, furosemide) in two different integrated urban wastewater systems where measurements were available. Despite data uncertainty and the simplicity of the modelling approach, chosen to minimize data requirements, model prediction uncertainty overlapped with the measurements ranges across both systems, stressing the robustness of the proposed modelling approach. Possible applications of the extended IUWS_MP model library are presented, illustrating how this tool can support urban water managers in reducing environmental impacts from PhACs discharges.

The design of water treatment plants requires simultaneous analysis of technical, economic and environmental aspects, identified by multiple conflicting objectives. We demonstrated the advantages of an interactive multiobjective optimization (MOO) method over a posteriori methods in an unexplored field, namely the design of a biological treatment plant for drinking water production, that tackles the process drawbacks, contrarily to what happens in a traditional volumetric-load-driven design procedure. Specifically, we consider a groundwater denitrification biofilter, simulated by the Activated Sludge Model modified with two-stage denitrification kinetics. Three objectives were defined (nitrate removal efficiency, drawbacks on produced water, investment and management costs) and the interactive method NIMBUS applied to identify the best-suited design without any a priori evaluation, as for volumetric-load-driven design procedures. When compared to an evolutionary MOO algorithm, the interactive solution process was faster, more understandable and user-friendly and supported the decision maker well in identifying the most preferred solution (main design/operating parameters) to be implemented. Approach strength has been proved through both sensitivity analysis and positive experimental validation through a pilot scale biofilter operated for three months. In synthesis, without any “a priori” evaluation based on practical experience, the MOO design approach allowed obtaining a preferred Pareto optimal design, characterized by volumetric loading in the range 0.85–2.54 kgN m−3 d−1 (EBCTs: 5–15 min), a carbon dosage of 0.5–0.8 gC,dos/gC,stoich, with SRTs in the range 4–27 d. peerReviewed

Reuse of reclaimed wastewater (RWW) for agricultural irrigation is becoming an essential practice in many arid and semi-arid regions of the world to save fresh water. Additionally, RWW contains essential plant nutrients (e.g., P, N, K), thus limiting the use of chemical fertilizers by farmers, who in the next years would need to substantially intensified their agriculture capacity to meet the increasing food demand. However, as consequences of human activities, other chemicals such as pharmaceuticals and biocides, currently referred to as “contaminants of emerging concern (CECs),” end up in RWW and subsequently in edible part of the crops, posing a risk for the environment and human health. This chapter aims at illustrating how modeling tools can help water managers in the identification of both effective monitoring campaigns and optimal RWW management strategies to minimize risk associated to conventional pollutants and especially to CECs. In fact, models can support the selection among the many hundred chemicals present in wastewater those of highest concern, but also address proper barrier identification once that a desired tolerable risk is set for specific CECs. The chapter illustrates how a RWW reuse system can be conceptualized into mathematical models, with a level of detail allowing to fully understand the processes taking place in each element of the system (wastewater treatment plant, distribution network, soil/plant) and to correctly interpret the simulation results. Two practical examples are also reported, showing the advantages of using modeling tools in risk management in complex RWW reuse systems.

Homogeneous Surface Diffusion Model (HSDM) has been widely used to simulate the breakthrough of organic micropollutants in fixed-bed adsorbers, but its practical applicability in real-scale conditions is not fully established. In this study we proposed a validated methodology to support the assessment of full-scale GAC adsorbers, providing a sound framework for a sustainable management. Specifically, we predicted the breakthrough of volatile organic compounds by the HSDM applied to full-scale granular activated carbon (GAC) adsorbers treating a complex groundwater matrix. Isotherm and short bed adsorber (SBA) tests were conducted to obtain equilibrium and mass-transfer coefficients for two contaminants (chloroform and perchloroethylene, PCE) and two GACs. Isotherm data were well described by Freundlich and Langmuir models, showing that single-component isotherms can be also used in complex water matrices, indirectly taking into account competition phenomena into the estimated parameters. The fitting of SBA data by HSDM was effective for chloroform, while PCE results were not well described, indicating that the combination of isotherm and SBA experiments to estimate HSDM parameters is not always effective, but it can depend on the characteristics of the adsorbate. Breakthrough data from the monitoring of two full-scale adsorbers were finally used to validate HSDM parameters for chloroform: its breakthrough was effectively simulated, without introducing any competition effect in HSDM equations. The model well reproduced also the release of the contaminant (resulting in chromatographic effect) by considering the variation of its influent concentration over time.

Photoelectrocatalytic degradation of target molecules on nanotubular titanium dioxide (TiO 2 ) immobilized on meshed conductive substrate was assessed by measuring the photoelectrochemical response (i.e., generated photocurrent) as indicator of TiO 2 performance. Furthermore, a simple and reliable methodology for degradation modelling and laboratory reactor optimization has been proposed and validated. Nanotubular TiO 2 was grown by anodic oxidation of Ti wire meshes and characterized by ESEM and XRD. Immobilized TiO 2 on Ti wire mesh was used as photo-anode under UV irradiation (254 nm) and subjected to electrical polarization. The photocurrent was monitored in a three-electrode cell, by varying polarization voltage, TiO 2 electrode relative positioning to the UV source (distance), and concentration of a model azo dye compound (Reactive Red 243, RR243). Photoelectrochemical response was modelled as a function of operating parameters and guidelines for photoreactor configuration were identified. Optimized batch photoreactor configuration (1.8 L) was used for degrading a 25 mg L −1 RR243 aqueous solution, achieving 90% decolorization in 45 min and 60% mineralization in 100 min. Decolorization kinetics were effectively described by means of a modified Langmuir-Hinshelwood model based on experimentally measured photocurrents, accounting for the dynamic behaviour of the process due to the change in solution transmittance over time determined by the degradation of target compounds.

N-nitrosamines are a group of potent human carcinogens that can be formed during oxidative treatment of drinking water and wastewater. Many tertiary and quaternary amines present in consumer products (e.g., pharmaceuticals, personal care and household products) are known to be N-nitrosodimethylamine (NDMA) precursors during chloramination, but the formation of other N-nitrosamines has been rarely studied. This study investigates the specific and total N-nitrosamine (TONO) formation potential (FP) of various precursors from nitrogen-containing micropollutants (chlorhexidine, metformin, benzalkonium chloride and cetyltrimethylammonium chloride) and tertiary and quaternary model amines (trimethyl amine, N,N-dimethylbutyl amine, N,N-dimethylbenzyl amine and tetramethyl ammonium). All the studied nitrogenous micropollutants displayed quantifiable TONO FP, with molar yields in the range 0.04-11.92%. However, the observed TONO pools constituted mostly of uncharacterized species, not included in US-EPA 8270 N-nitrosamines standard mix. Only the quaternary ammonium compound benzalkonium chloride showed quantifiable NDMA FP (0.56% molar yield), however, explaining only a minor fraction of the observed TONO FP. The studied model amines showed molar NDMA yields from 0.10% (trimethyl amine) to 5.05% (N,N-dimethylbenzyl amine), very similar to the molar TONO yields. The comparison of the FPs of micropollutants and model compounds showed that the presence of electron donating functional groups (such as a benzyl group) in tertiary and quaternary amine precursors leads to a higher formation of NDMA and uncharacterized N-nitrosamines, respectively. LC-qTOF screening of a list of proposed N-nitrosamine structures has enabled to identify a novel N-nitrosamine (N-nitroso-N-methyldodecylamine) from the chloramination of benzalkonium chloride. This finding supports the hypothesis that different functional groups in quaternary amines can act as leaving groups during chloramination and form differing N-nitrosamine structures at significant yield. Molar TONO yields determined for micropollutants were finally validated under experimental conditions closer to real water matrices, confirming their representativeness also for lower concentration ranges. (C) 2018 Elsevier Ltd. All rights reserved.

The aim of this study was to evaluate the influence of the physical–chemical characteristics of wastewater on PAA decay, in multi-component solutions of inorganic and organic compounds (11 compounds in total) representative of secondary effluents of wastewater treatment plants, disinfected at various PAA concentrations (2–5 mg/L). Batch experiments were defined using the statistical method of the Design of Experiments (DoE) in order to evaluate the effect of each compound and their interaction on PAA decay. Results showed that the organics consumed immediately a considerable amount of PAA, independently from the initial PAA concentration, and consumption dropped rapidly to almost nil after 5 min, whereas PAA consumption due to the inorganics was slow, dependent on the initial PAA concentration and persisted until the end of the experiments (60 min). In detail, inorganics (such as reduced iron and orthophosphate) have shown to be the main drivers of the exponential decay: iron, particularly, has proved to directly consume PAA due to its catalysing capacity, whereas orthophosphate has shown to mainly interact with iron, acting as a chelating compound towards iron and consequently reducing the iron effect in consuming PAA. As for organics, proteins such as, casein and peptone, have been highlighted as the main cause of the initial PAA demand, probably due to the homolytic fission of PAA to generate peroxyl and hydroxyl radicals, which are known to have a high reactivity towards proteins. Finally, a model for predicting the residual PAA concentration was obtained and validated; uncertainty analysis was also performed by a series of Monte Carlo simulations to propagate input uncertainties to the model output.

The presence of antibiotic resistant bacteria (ARB) in wastewater was investigated and the role of wastewater treatment plants (WWTPs) in promoting or limiting antibiotic resistance was assessed. Escherichia coli (E. coli) and total heterotrophic bacteria (THB) resistance to ampicillin, chloramphenicol and tetracycline was monitored in three WWTPs located in Milan urban area (Italy), differing among them for the operating parameters of biological process, for the disinfection processes (based on sodium hypochlorite, UV radiation, peracetic acid) and for the discharge limits to be met. Wastewater was collected from three sampling points along the treatment sequence (WWTP influent, effluent from sand filtration, WWTP effluent). Antibiotic resistance to ampicillin was observed both for E. coli and for THB. Ampicillin resistant bacteria in the WWTP influents were 20–47% of E. coli and 16–25% of THB counts. A limited resistance to chloramphenicol was observed only for E. coli, while neither for E. coli nor for THB tetracycline resistance was observed. The biological treatment and sand filtration led to a decrease in the maximum percentage of ampicillin-resistant bacteria (20–29% for E. coli, 11–21% for THB). However, the conventionally adopted parameters did not seem adequate to support an interpretation of WWTP role in ARB spread. Peracetic acid was effective in selectively acting on antibiotic resistant THB, unlike UV radiation and sodium hypochlorite. The low counts of E. coli in WWTP final effluents in case of agricultural reuse did not allow to compare the effect of the different disinfection processes on antibiotic resistance.

Monitoring and management of drinking water distribution networks (DWDNs), including possible leaching from materials in contact with drinking water, have been stressed as crucial to avoid re-contamination of drinking water leading to a potential increase of human health risk. Recent scientific studies and regulations clearly highlighted the leaching of bisphenol A (BPA) from plastic materials used to renovate DWDNs pipelines as one of the major hazardous source, resulting in severe consequences for human health. In this study, lab migration tests were performed on three commercial epoxy resins, designed with the Design of Experiments (DoE) method in order to build a BPA migration model as a function of water chemical stability, evaluated as aggressivity index (AI), and residual chlorine concentration. Tests lasted about 170 days to account for both short and long-term leaching. BPA migration over time was well described by a combination of two 1st-order kinetic models with an initial peak of leaching, a decrease and, then, a second increase due to resins' deterioration. Initial BPA concentration in the contact water and BPA integral migration over time showed inverse proportionality with both chlorine concentration and AI values. However, measurements of free BPA content in epoxy resins proved that this is due to BPA transformation, not to a reduced leaching. The validated BPA migration model was combined with the hydraulic model of the DWDN in an urban area, through EPANET-MSX software. The model allowed to simulate the propagation of BPA in the DWDN, after the execution of a relining intervention, identifying the most vulnerable areas and permitting to customize a site-specific monitoring and intervention plan to minimize the health risk for final consumers.

A detailed computational fluid dynamics model is presented that integrates reactor hydrodynamics with advanced light models and UV-TiO2 advanced oxidation kinetics to yield the degradation of oxalic acid in a dispersed-phase photoreactor. Model predictions were first compared against experimental data obtained from the literature and subsequently used in a parametric study for investigating scale-up effects associated with both process and photoreactor variables. Investigated variables included: TiO2 concentration (5–400 mg L−1), initial oxalic acid concentration (0.9–32 mg L−1), lamp irradiance (100–10,000 W m−2), background fluid absorbance (0-30 m−1), reactor size (1/4-4 as relative scaling factor), lamp orientation (0-360°) and flowrate (2.5–10 m3 h−1). The analysis revealed that an optimum in oxalic acid degradation is observed when the TiO2 concentration was controlled in the 20–40 mg L−1 range (depending on lamp irradiance). While lamp orientation showed minimal impact, reactor size and flowrate emerged as key variables for photoreactor design. Moreover, an increase in initial oxalic acid concentration substantially reduced oxalic acid degradation performance observed at high loadings. Also, TiO2 activation and photoreactor degradation performance were impacted negatively by light competition with background fluid absorbance.

A safety impedance micro-sensor based on flexible electrodes for continuous monitoring of the surface cleanness of pipes of small diameter (down to few mm) handling drinkable fluids is presented. It allows discrimination between biofilm and limescaling, grants high resolution (few µm in thickness) and enables early warning and predictive (closed-loop) maintenance. It is coupled with a temperature and fluid conductivity probes in order to calibrate for their fluctuations. The current prototypal electronics, measuring impedance in the MHz range, can be scaled towards low-cost and low-power pervasive implementations. Preliminary experimental results showing the consistence with non-curved geometries are presented.

An integrated model was developed, calibrated and validated to predict the fate of micropollutants (MPs) and the human health risks that these substances pose when wastewater is reused for agricultural purposes. Results showed that pharmaceuticals (e.g., the anti-inflammatory drug paracetamol) can form during in-sewer transit from other metabolites and posing a potential risk (Hazard Quotient for infants = 10-2) if wastewater is not treated before irrigation. The model can represent a relevant decision-support tool to plan barriers for reducing the risk associated to MPs.

Conceptual sewer models are useful tools to assess the fate of micropollutants (MPs) in integrated wastewater systems. However, the definition of their model structure is highly subjective, and obtaining a realistic simulation of the in-sewer hydraulic retention time (HRT) is a major challenge without detailed hydrodynamic information or with limited measurements from the sewer network. This study presents an objective approach for defining the structure of conceptual sewer models in view of modelling MP fate in large urban catchments. The proposed approach relies on GIS-based information and a Gaussian mixture model to identify the model optimal structure, providing a multi-catchment conceptual model that accounts for HRT variability across urban catchment. This approach was tested in a catchment located in a highly urbanized Italian city and it was compared against a traditional single-catchment conceptual model (using a single average HRT) for the fate assessment of reactive MPs. Results showed that the multi-catchment model allows for a successful simulation of dry weather flow patterns and for an improved simulation of MP fate compared to the classical single-catchment model. Specifically, results suggested that a multi-catchment model should be preferred for (i) degradable MPs with half-life lower than the average HRT of the catchment and (ii) MPs undergoing formation from other compounds (e.g. human metabolites); or (iii) assessing MP loads entering the wastewater treatment plant from point sources, depending on their location in the catchment. Overall, the proposed approach is expected to ease the building of conceptual sewer models, allowing to properly account for HRT distribution and consequently improving MP fate estimation.

In this work a low-cost and credit-card-sized electronic platform for continuous monitoring of water quality is presented. It simultaneously measures water pH, temperature, conductivity, flow rate, pressure and the micrometric thickness of surface fouling, of both chemical and biological nature. The system includes a GSM transceiver that creates a wireless sensors network for real-time monitoring of these parameters, enabling increased safety and, in perspective, automation and predictive maintenance of the water network. An external watchdog timer increases the robustness and the complete self-diagnostic ability of the solution. Preliminary results from field validation are reported.

The project deals with the environmental problem of water consumption. The aim of this work is to experiment the recycling of dishwasher wastewater through its reuse in growing edible vegetables or ornamental plants; this can also accomplish the valorization of nutrients present in the wastewater. This new process allows to ensure washing functions coupled with vegetables production and to affect users’ environmental awareness and habits, following a user-centered system design approach to understand the users and involve them actively in the system development. The presented work is also aimed to experiment a multidisciplinary approach in order to face environmental problems.