Water Environment Research最新文献

Cotreatment of mine drainage (MD) in existing wastewater treatment plants (WWTPs) could provide treatment benefits for both waste streams. The alkalinity that is innate to most WW may be sufficient to neutralize MD acidity, elevating pH and correspondingly decreasing concentrations of dissolved metals. Additionally, PO₄ in WW interacts with Fe and Al in MD and is removed from solution, potentially decreasing the need for enhanced biological treatment or chemical precipitation techniques. However, there are concerns about how adding MD to a WWTP may impact the treatment efficiency of the WWTP. Hence, the objective of this study was to determine the impact of AMD addition on the kinetics of BOD removal, in addition to the extent of metals, acidity, and PO₄ removal when WW and MD were mixed in a bench-scale primary clarifier. MD was mixed with primary influent WW in 10:90 and 40:60 MD:WW ratios and allowed to settle for 2 h before the supernatant was transferred to BOD respirometers. The pH remained circumneutral 2 h after mixing, ranging from 7.44 to 8.27 for 10% MD and 6.66 to 7.36 for 40% MD. The BOD oxidation rate was unaffected by MD addition, with first-order kinetic rates ranging from 0.50 to 0.77 day^-1 for raw WW, 0.54 to 0.97 day^-1 for 10% MD, and 0.45 to 0.95 day^-1 for 40% MD. PO₄ removal increased linearly with the molar ratio of ([Fe] + [Al])/[PO₄-P] for the conservative mixture of raw WW and MD and reached > 99% removal when (([Fe] + [Al])/[PO₄-P]) > 2. Overall, this work establishes a practical framework for leveraging mine drainage chemistry within conventional wastewater primary treatment to enhance phosphorus capture without compromising downstream biological performance. The approach offers a scalable pathway for utilities to turn a problematic influent into a controllable unit-process benefit, supporting tighter nutrient limits with minimal new infrastructure and chemical inputs.

The valorization of pretreated waste Salvinia molesta biomass represents a sustainable and circular strategy to address both water contamination and biomass disposal. This study investigated the biosorption performance of pretreated and powdered S. molesta biomass in controlled aqueous solutions of ciprofloxacin (CIP), a widely detected fluoroquinolone antibiotic, under environmentally relevant conditions. The biomass was characterized by a high cell wall fraction (~61%) and moderate protein and polyphenol content, offering a multifunctional surface for biosorption. Batch experiments were conducted to evaluate the effects of pH (4-8) and contact time (up to 60 min) on CIP removal (initial concentration = 1.5 μg/L). The maximum biosorption efficiency (~95%) occurred at pH 6, which aligned with the biomass's point of zero charge ( ${\text{pH}}_p\text{zc}$  = 6.2) and the CIP zwitterionic speciation. Biosorption was rapid in the first 30 min, although equilibrium was rapidly reached within 30 min, consistent with the classical biosorption behavior of dead biomass at low concentrations. The kinetic model followed a pseudo-second-order trend, which was interpreted empirically rather than mechanistically, reflecting surface-controlled adsorption dynamics. Pearson correlations revealed that the protein and polyphenol contents were positively associated (r > 0.85) with biosorption at pH 6-7, highlighting a multi-mechanistic interaction involving electrostatic, hydrogen bonding, and π-π interactions. These findings suggest that S. molesta is a naturally abundant, low-cost biosorbent suitable for decentralized water remediation, particularly in small-scale or proof-of-concept systems using model aqueous matrices, with potential applications in passive treatment units and community-based sanitation systems. Future studies should evaluate isotherm behavior, thermodynamic parameters, and the regeneration potential of the biosorbent to determine scalability under real wastewater conditions.

Water resource recovery facilities often receive landfill leachate (LL), which can disrupt biological processes due to its toxicity and low biodegradability. This study evaluates the anaerobic codigestion (AcoD) of municipal wastewater (MWW), LL, and crude glycerin (CG) as a strategy to enhance organic matter removal and methane yield. Batch reactors were operated under varying conditions defined by a Plackett-Burman screening design, and methane production kinetics were modeled using modified Gompertz and Cone equations. Soluble chemical oxygen demand (sCOD) removal ranged from 67.4% to 94.3%, whereas methane yield varied between 0.076 and 0.349 L _NCH4/g tCOD_add (liters of normalized methane per gram of total COD added). The highest yield was achieved with 2% LL and 1% CG, approaching the theoretical maximum. Statistical analysis revealed that increasing CG content reduced methane yield, and extending the digestion time to 40 days offered limited performance gains. Despite the presence of inhibitory compounds, most conditions showed stable digestion, with short latency phases and effective microbial adaptation. These findings demonstrate the feasibility of codigesting MWW, LL, and CG, especially under optimized proportions, and highlight the potential for energy recovery in wastewater treatment plants using biodiesel by-products.

This work highlights the phenomena that may occur on zinc electrodes used as sacrificial electrodes in a process for treating an effluent containing urea. Zinc ions (Zn²⁺) generated in situ promote coagulation, but in some cases, electrode passivation and localized corrosion can hinder dissolution and reduce treatment efficiency. For this reason, the effect of operational parameters such as current density, initial urea concentration, pH, and supporting electrolyte (NaCl) concentration on urea removal was studied in the first part. While the second part was dedicated to investigating the impact of urea and NaCl electrolyte concentrations on the electrochemical behavior of the zinc electrode, this was done by using electrochemical impedance spectroscopy (EIS), potentiodynamic polarization (PDP), and cyclic voltammetry, while surface changes were analyzed via scanning electron microscopy coupled with energy-dispersive spectroscopy and x-ray diffraction. The obtained results show that the highest urea removal was obtained with the operating conditions: current density of 22 mA/cm², pH 10, 25 mmol/L NaCl, and an initial urea concentration of 20 mmol/L: 12 mmol/L of urea was removed, corresponding to 91 mg/L of dissolved zinc, a faradaic efficiency of 110%. With regard to the surface state of zinc, it was demonstrated that passivation through zinc oxide formation was confirmed by PDP analysis. The results revealed the presence of ZnO crystalline phases as well as surface deposits, both indicative of the development of an oxide layer, which limited further zinc dissolution and floc generation under specific EC operating conditions. Zinc corrosion behavior was strongly influenced by pH, chloride concentration, and urea levels, as evidenced by electrochemical diagnostics (polarization curves and impedance spectra). At alkaline pH and moderate chloride concentrations, enhanced zinc release was observed, while higher urea levels promoted surface degradation and oxide accumulation. These findings highlight the need to balance dissolution and passivation of electrodes to optimize EC performance for nitrogenous pollutant treatment.

Water quality deterioration has intensified the need for rapid and accurate assessment using modern monitoring approaches. Conventional laboratory-based techniques often suffer from delayed analysis and low sampling frequency, limiting timely decision-making. Key quantitative parameters-including pH (optimal 6.5-8.5), dissolved oxygen (DO > 5 mg/L for aquatic health), turbidity (< 1-5 NTU for drinking-water standards), electrical conductivity (250-1500 μS/cm depending on source), and Water Quality Index (WQI < 50 indicating good quality and > 100 reflecting poor conditions)-serve as essential indicators of ecosystem and human health. Recent advancements in Internet of Things (IoT) sensors, artificial intelligence (AI), and machine learning (ML) have enabled high-frequency measurements, predictive forecasting, anomaly detection, and enhanced early warning capabilities. IoT-enabled multiparameter sensing combined with ML models such as random forests, gradient boosting, and deep neural networks significantly improve accuracy in pollutant prediction and trend analysis. This review synthesizes the latest progress in IoT-, AI-, and ML-driven water quality monitoring, outlines quantitative improvements reported in recent literature, and highlights remaining technical, economic, and governance challenges influencing large-scale deployment.

The development of effective and environmentally friendly bacterial attachment media remains a challenge in aquaculture wastewater treatment, particularly for systems with high organic loading such as Clarias macrocephalus ponds. In this study, a bentonite-acrylic acid hydrogel was synthesized by gamma irradiation and evaluated as a bacterial attachment medium for aquaculture wastewater treatment. The effects of composition ratio and irradiation dose on gel-forming ability, swelling behavior, and solubility were investigated to determine optimal preparation conditions. The hydrogel prepared at a bentonite-to-acrylic acid ratio of 10:1 (g/mL) and an irradiation dose of 25 kGy exhibited favorable gel properties and structural stability, making it suitable for bacterial immobilization. Two bacterial strains (B4 and B5) demonstrated strong adhesion to the attachment media and stable immobilization behavior. When applied to wastewater treatment, the combined system achieved high removal efficiencies of 99.44% COD, 99.40% BOD₅, 93.20% TP, 98.14% ammonia, 88.39% SS, and 85.21% color meeting the discharge limits of Vietnamese standards. These results indicate that the bentonite-acrylic acid hydrogel synthesized by irradiation is a promising attachment medium for enhancing biological treatment efficiency in aquaculture wastewater systems.

Microplastics (MPs) are pervasive carriers of aquatic pollutants, yet their adsorption behaviors, especially after environmental aging, remain incompletely understood. This study systematically investigated the adsorption of bisphenol A (BPA) onto four common MPs: polyvinyl chloride (PVC), polyolefin resin (PO), polypropylene (PP), and polyethylene (PE), and their ultraviolet (UV)-aged counterparts. We found that UV aging universally enhanced the adsorption capacity, with increases of up to 19% compared to pristine MPs. Aged PVC (A-PVC) exhibited the highest overall affinity. Adsorption mechanisms diverged: PO, PP, A-PVC, and A-PE followed multilayer chemical adsorption, whereas PE, A-PO, and A-PP exhibited monolayer chemical adsorption; only pristine PVC followed monolayer physical adsorption. Importantly, UV aging altered these adsorption mechanisms by modifying the surface physicochemical properties of MPs. Environmental factors significantly modulated adsorption, which increased with contact time and initial BPA concentration but decreased with higher MPs dosage and pH, peaking at 25°C and remaining unaffected by salinity. These results reveal that UV aging not only intensifies adsorption capacity but can also alter the fundamental adsorption mechanism, thereby reshaping the role of MPs as transport vectors for endocrine-disrupting compounds like BPA in aquatic environments. This study provides crucial insights for ecological risk assessment of coexisting MPs and organic pollutants.

A significant advantage of membrane capacitive deionization (MCDI) lies in its ability to achieve medium to high water recovery rates. A prototype of MCDI unit demonstrated a recovery around 68% while consistently achieving salt removal efficiencies of ≥ 90% from feed water with a total dissolved solids (TDS) concentration of 1490 mg/L. However, the presence of coagulant-derived multivalent ions, particularly Fe²⁺, Fe³⁺, and Al³⁺, poses a challenge to long-term salt rejection efficiency. When Fe³⁺ or Al³⁺ was present at concentrations near 10 mg/L in feed water with a TDS of ~400 mg/L, the residual iron or aluminum concentration in the treated water exceeded the permissible limits defined by drinking water standards. Despite high removal efficiencies (> 90%) for key cations including Na⁺, Ca²⁺, Mg²⁺, Al³⁺, Fe²⁺, and Fe³⁺, regeneration studies revealed a distinct desorption trend: Mg²⁺ > Na⁺ > Ca²⁺ > Al³⁺ > Fe²⁺ ≈ Fe³⁺. This trend indicates that Fe³⁺ and Fe²⁺ are the most strongly retained and thus the most scale-forming ion in MCDI systems, followed by Al³⁺. Salt adsorption capacity of NaCl is 0.66-4.14 mg/g and modeled using the modified Donnan model effectively described the nonlinear adsorption behavior and also for all other systems with and without coagulant ions. Due to the presence of divalent ions, Donnan potential decreased compared to NaCl system without coagulant ions. The presence of coagulant ions further decreased the Donnan potential. Energy consumed 68.2-78.6 kT/ion and mostly increased to 60.6-101.3 kT/ion during partially choked condition. Post-operational surface analyses using x-ray photoelectron spectroscopy (XPS) and energy-dispersive spectroscopy (EDS) confirmed the accumulation of these metal ions on the carbon electrode surfaces. The observed deposition of oxide and hydroxide of coagulant ions significantly impacts long-term MCDI performance, underscoring the need for pretreatment strategies and electrode material optimization to enhance the sustainability and effectiveness of MCDI in domestic water purification applications.

Exergy analysis provides a unified framework for assessing environmental, technical, and economic aspects in energy terms, enabling the identification of irreversibilities that reduce process efficiency. In wastewater treatment, its application remains limited, focusing on municipal effluents and chemical exergy derived from chemical oxygen demand. This study proposes an advanced exergy analysis model for wastewater treatment, structured in three phases: (i) system data, (ii) exergy analysis, and (iii) optimization through exergoenvironmental, exergoeconomic, and exergotechnical indicators, statistically assessed using the desirability function approach (DFA). The model was validated using a case study of a leachate treatment system that combined coagulation-flocculation, three photo-Fenton configurations, and activated sludge. The highest desirability (0.59) in exergy terms for the pretreatment was achieved with 1 g L^-1 iron chloride at pH 5, while DFA was 0.74 in the photo-Fenton process involved pretreated leachate with residual iron (0.08 g L^-1 of iron) and a single 2.5 g L^-1 dose of hydrogen peroxide followed by biological treatment. Irreversibilities were greatest in the biological stage due to electricity demand followed by influent composition, reagent consumption, and sludge generation. The model offers robust criteria for optimizing treatment design and supports the achievement of Sustainable Development Goals 6, 11, and 12.