Tackling climate change through wastewater reuse in agriculture: A prioritization methodology.

Water shortages, exacerbated by climate change, are posing a major global challenge, particularly impacting the agricultural sector. A growing interest is raised towards reclaimed wastewater (RWW) as an alternative irrigation source, capable of exploiting also the nutrient content through the fertigation practice. However, a prioritization methodology for selecting the most appropriate wastewater treatment plants (WWTPs) for implementing direct RWW reuse is currently missing. Such prioritization would benefit water utilities, often managing several WWTPs, and policymakers in optimizing economic asset allocation. In this work, a prioritization framework is proposed to evaluate WWTPs' suitability for implementing direct RWW reuse considering both WWTP and surrounding territory characteristics. This procedure consists of four key steps. Firstly, a techno-economic model was developed, in which monthly mass balances on water and nutrients are solved by matching crop requirements, rainfall conditions, and effluent characteristics. Economic suitability was quantified considering economic benefits due to savings in freshwater resource, mineral fertilizers and avoided greenhouse gases emissions, but also losses in crop yield due to RWW salinity content. Secondly, a classification procedure was coded to select representative WWTPs among a set of WWTPs, based on their size, presence of nutrient removal processes, and type of crops in their surroundings. The techno-economic model was then applied to these selected WWTPs. Thirdly, input parameters' relevance in determining WWTP suitability for RWW reuse was ranked. Finally, scenario analyses were conducted to study the influence of rainfall patterns and nutrient treatment removal on the RWW reuse feasibility. The type of crops surrounding the WWTPs and RWW salinity content resulted to be crucial elements in determining WWTPs suitability for RWW reuse implementation. The proposed methodology proved to be an effective support tool for policymakers and water utilities to assess the techno-economic feasibility of direct RWW reuse, generalizing results to several combinations of WWTPs and crops.

Adsorption on activated carbon combined with ozonation for the removal of contaminants of emerging concern in drinking water.

The presence of Contaminants of Emerging Concern (CECs) in drinking water is raising concern for potential negative effects on human health. Ozonation and adsorption on activated carbon are the most suitable processes for CECs removal in drinking water treatment plants (DWTPs). This study aims at evaluating the performance of ozonation and adsorption as in-series processes compared to those of the stand-alone processes, focusing on 18 compounds representative of various CECs families. No CECs spike was performed to evaluate the effectiveness of these processes towards CECs at their environmental concentrations. Adsorption isotherms were performed on water samples collected before and after the full-scale ozonation in a DWTP, testing different combinations of ozone and activated carbon doses. Generally, the combination of the two processes was beneficial (83% average removal) compared to adsorption and ozonation alone (71% and 34% average removal respectively). The effect of ozonation on adsorption depends on CECs reactivity with ozone, since ozonation improves the adsorption performance of poorly-oxidizable CECs, but worsens that of well-oxidizable compounds. The removal of organic matter, investigated by absorbance at 254 nm and fluorescence, by ozonation reduces competition for the subsequent CECs removal by adsorption (up to 20% increase of total CECs adsorption). Finally, the removal of both absorbance and fluorescence seems to be a good proxy variables for total CECs adsorption, with different relationships depending on the presence of ozonation. Conversely, it is not effective for ozonation, since the relationship depends on the reactivity of the specific CEC with ozone.

An integrated human health risk assessment framework for alkylphenols due to drinking water and crops' food consumption.

The increasing overexploitation and pollution of freshater resources are potential threats for public health, causing cross-contamination among the interconnected environmental compartments (freshwater, soil, crops). In particular, contaminants of emerging concern (CECs) originating from anthropic activities are not completely removed by wastewater treatments plants. This leads to their presence in drinking water (DW) sources, soil and crops intended for human consumption due to discharges of treated wastewater in surface waters and direct wastewater reuse practices. Currently, health risk assessments are limited to single exposure sources without considering the multiple exposure routes to which humans are subjected. For instance, among CECs, bisphenol A (BPA) and nonylphenol (NP), respectively, adversely affect immune and renal systems and have been frequently detected in DW and food, their major exposure sources for humans. Here, an integrated procedure is proposed to quantitatively assess health risk from CECs due to multiple exposure from the consumption of both DW and food, considering the relevant inter-connected environmental compartments. This procedure was applied to BPA and NP to calculate their probabilistic Benchmark Quotient (BQ), showing its potential in quantitatively apportioning the risk between contaminants and exposure sources, and its use as a decision support tool for prioritizing mitigation measures. Our results indicate that, even though the human health risk due to NP is not negligible, the estimated risk due to BPA is significantly higher, and the consumption of food from edible crops determines a higher risk compared to tap water. Hence, BPA is undoubtedly a contaminant to be prioritized, especially through mitigation actions aimed at its prevention and removal from food.

Identifying Eukaryotes and Factors Influencing Their Biogeography in Drinking Water Metagenomes.

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., <1%) of the overall metagenomic data with higher relative abundances in surface water-fed or chlorinated systems with high residuals. The α and ß diversities of eukaryotes were correlated with those of prokaryotic and viral communities, highlighting the common role of environmental/management factors. Finally, a co-occurrence analysis highlighted clusters of eukaryotes whose members' presence and abundance in DWDSs were affected by disinfection strategies, climate conditions, and source water types.

Modelling and prediction of the effect of operational parameters on the fate of contaminants of emerging concern in WWTPs.

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.

Disinfection efficiency prediction under dynamic conditions: Application to peracetic acid disinfection of wastewater.

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.

Integrated Exposure and Algal Ecotoxicological Assessments of Effluents from Secondary and Advanced-Tertiary Wastewater-Treatment Plants.

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.

Quantitative chemical risk assessment for mixtures: Application to alkylphenol mixtures and phthalate mixtures in tap and bottled water.

The occurrence and hazard risks of mixtures of Contaminants of Emerging Concern (CECs) in drinking water (DW) lead to serious consideration regarding the possible impacts on public health. Consequently, there is ongoing research, development and empowerment of risk assessment procedures to get more toxicological insight. For instance, alkylphenols and phthalates have been frequently reported to be present both in bottled and tap water, affecting different human endpoints. Currently, deterministic chemical risk assessment (CRA) is used to evaluate the compounds' mixture health risk. However, CRA deals just qualitatively with sources of uncertainty, which may lead to erroneous assessment of risks. Here, a new procedure for quantitative chemical risk assessment of CEC mixtures (QCRAMIX) is proposed. Its potential is illustrated by a case study where the risks related to the presence of mixtures of alkylphenols or phthalates in tap versus bottled DW are compared. Uncertainties in both exposure and hazard assessment steps of the procedure are included to calculate a probabilistic mixture Benchmark Quotient (BQMIX). The QCRAMIX procedure highlighted the non-negligible health risks posed by those compounds in both DW sources based on overall water consumption. In fact, DW consumers' behaviour in 13 different countries, in terms of total DW consumption and fraction of bottled and tap water consumed, were considered to evaluate the influence on health risk. For alkylphenols, the total water consumption was found to be the most relevant factor in increasing the health risk, while for phthalates the risk was found to be mainly influenced by the percentage of bottled water consumed. Hence, the proposed QCRAMIX procedure can be a valuable tool for prioritization of CECs to be included in DW regulations which aim to minimize the overall risk, accounting for actual DW consumption.

Artificial neural network modeling of full-scale UV disinfection for process control aimed at wastewater reuse.

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.

UVC inactivation of MS2-phage in drinking water - Modelling and field testing.

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 on 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.

Perfluoroalkyl substances (PFAS) adsorption in drinking water by granular activated carbon: Influence of activated carbon and PFAS characteristics.

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.

Bisphenol A leaching from epoxy resins in the drinking water distribution networks as human health risk determinant.

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.

Bacterial dynamics in drinking water distribution systems and flow cytometry monitoring scheme optimization.

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.

Development of a quantitative chemical risk assessment (QCRA) procedure for contaminants of emerging concern in drinking water supply.

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.

Soft sensor predictor of E. coli concentration based on conventional monitoring parameters for wastewater disinfection control.

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.

Modelling the fate of micropollutants in integrated urban wastewater systems: Extending the applicability to pharmaceuticals.

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.

Nanostructured Cellulose-Based Sorbent Materials for Water Decontamination from Organic Dyes.

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.

Ecotoxicity Evaluation of Pure Peracetic Acid (PAA) after Eliminating Hydrogen Peroxide from Commercial PAA.

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 0.78-0.91) mg/L for V. fischeri and 2.46 (2.35-2.58) mg/L for P. subcapitata, whereas the lethal concentration (LC50) was 0.74 (0.55-0.91) 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 (0.41-0.44) and 1.38 (0.96-1.99) mg/L, respectively, which were lower than pure PAA, whilst the LC50 for D. magna was 0.78 (0.58-0.95) 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.