Environmental Science: Water Research & Technology最新文献

This communication highlights several modeling issues in the paper referenced in the title, including: (1) the improper application of the pseudo-first-order kinetic model, (2) the use of incorrect formulations of the Temkin and Dubinin–Radushkevich isotherm models, and (3) methodological errors in the calculation of thermodynamic parameters. Taken together, these issues call into question the scientific rigor of the study and emphasize the importance of adopting sound modeling practices in sorption research.

N-Nitrosodimethylamine (NDMA) is a probable human carcinogen that can be formed in drinking water treatment systems as a byproduct of chloramination and chlorination. Occurrence of NDMA and other N-nitrosamines in the United States has been previously assessed using a variety of techniques, but few studies have been able to distinguish between concentrations above and below suggested screening levels (e.g., 0.7 ng L⁻¹ for NDMA). This study evaluated the presence of NDMA and seven other N-nitrosamines in two drinking water distribution systems in the northeastern United States (n = 42 locations) and assessed factors influencing its occurrence. NDMA was present in 98% of water samples across both systems (MDL 0.15 ng L⁻¹) with higher concentrations in the system utilizing chloramination (0.39–1.32 ng L⁻¹) than the system utilizing chlorination (0.20–0.54 ng L⁻¹). Samples were collected before and after flushing taps, and higher concentrations of NDMA were observed in samples collected prior to flushing, suggesting increased formation due to temporary stagnation. N-Nitrosomorpholine was the only other N-nitrosamine detected in samples taken after tap flushing (5% detection rate; MDL 0.21 ng L⁻¹), though four additional nitrosamines were detected before flushing in at least one sample. Water quality parameters (i.e., chlorine residual, dissolved organic carbon, total dissolved nitrogen, specific UV absorbance, pH, temperature, specific conductance) and other disinfection byproducts (trihalomethanes) were measured to assess correlations with NDMA occurrence, and NDMA concentrations were negatively correlated with residual chlorine in both distribution systems. These observations illustrate the potential prevalence of low-level nitrosamine occurrence in disinfected drinking water and provide a framework for system-specific understanding of NDMA occurrence, which can aid in prioritizing locations where further investigation may be needed to mitigate potential exposure risks.

This study systematically compared four advanced oxidation processes (AOPs) for norfloxacin (NOR) degradation in aquatic systems: O₃, O₃/UV, O₃/H₂O₂, and UV–H₂O₂/O₃. The UV–H₂O₂/O₃ system demonstrated the highest degradation efficiency, achieving 83.85% NOR removal within 1.5 minutes with a reaction rate constant 125.09% higher than O₃ alone. Considering both economic feasibility and efficiency, the O₃/UV system showed superior practical value, exhibiting an 82.56% higher pseudo-first-order reaction rate than standalone O₃ treatment. The optimal operating conditions were determined to be an ozone concentration of 0.5 mg L⁻¹ and UV fluence of 44 mJ cm⁻². Radical scavenging experiments revealed that direct O₃ oxidation contributed 31.47% to overall degradation. The ·OH exposure in the O₃/UV system reached 3 × 10⁻¹⁰ mol L⁻¹ s⁻¹, representing an 8.98-fold increase over ozone treatment alone. LC–MS analysis coupled with DFT calculations identified four primary degradation pathways: piperazine ring cleavage, quinolone core ring-opening, decarboxylation/defluorination, and direct aromatic defluorination. Cytotoxicity assessment using CHO cells confirmed safety with >90% cell viability across all systems. Pilot-scale validation using real water matrices was conducted, achieving 84.21–90.34% NOR removal and significant UV₂₅₄ (52.71–58.36%) and total organic carbon (TOC) (27.47–31.34%) reduction. Background water matrix composition, reactive radical generations, significantly influenced performance, with Yellow River water getting the highest treatment efficiency due to lower dissolved organic carbon and turbidity. This investigation bridges the gap between laboratory research and practical application, providing both mechanistic insights and practical solutions for antibiotic contamination control in aquatic environments.

Quantification of copies of double stranded RNA using RT-PCR methods may require denaturation of the double stranded structure using an initial high temperature incubation followed by rapid cooling, herein called “heat snap”. Papers in the literature that report rotavirus RNA concentrations in fecal and environmental samples do not consistently report the use of such a “heat snap”. In this study, we quantified rotavirus RNA in diverse environmental samples (wastewater solids, wastewater, and drainage samples) using digital RT-PCR methods with and without a heatsnap. Concentrations were higher in samples by a factor of 125 when a heat snap was applied. This was consistent across sample types, and across laboratories and PCR instrumentation. We recommend a heat snap be used when enumerating double stranded RNA from rotavirus and other double stranded RNA viruses in environmental samples.

Water resource recovery facilities (WRRFs) are sinks of legacy and replacement per- and polyfluoroalkyl substances (PFAS). This study evaluates the potential biotransformation, bioaccumulation, and adsorption of PFAS in wastewater sludge. Individual partitioning of parent PFAS and transformation products were measured in aqueous and solid phases of aerobic and anaerobic bacterial cultures for five structurally variable legacy and replacement PFAS using independent tests: perfluorooctanoic acid (PFOA), perfluorooctane sulfonic acid (PFOS), perfluorobutane sulfonic acid (PFBS), 6:2 fluorotelomer sulfonate (6:2 FTS), and hexafluoropropylene oxide dimer acid (GenX). Anaerobic cultures (anaerobic digestate and dehalogenating KB-1®) showed only adsorption (10.9–38.3%) with no transformation of the parent PFAS, irrespective of structural variances, in 90 days. Aerobic cultures from activated and nitrification sludge resulted in adsorption (26.9 ± 1.2–55.8 ± 1.4%), biotic accumulation (13.35–17.55%), and transformation (28.96–47.87%) of long-chain PFAS in 21 days. Notably, PFOA, PFOS, and 6:2 FTS were rapidly transformed 47.87 ± 1.6%, 28.96 ± 0.6%, and 43.1 ± 1.0%, respectively, after a shift occurred in microbial community structure under batch growth after 6 days, with the generation of shorter-chain compounds (carboxylates and sulfonates) and limited defluorination. Aerobic wastewater microbial communities converged, with Methylophilus, Acidomonas, Pseudomonas, Clostridium, Klebsiella, and Acinetobacter positively correlated with PFAS degradation. This study highlights the importance of unit processes and microbial community structure in controlling the fate and transport of select PFAS.

Tyre wear particles (TWP) are a major microplastic pollutant in road runoff, yet their transport dynamics in stormwater remain poorly understood. This study investigates the abundance and dynamic behaviour of TWP during rain events in a highway stormwater system between March and May 2023. Road runoff was collected from gully pots and stormwater wells using automatic samplers during rain events and analysed for TWP, elements, total suspended solids (TSS), volatile suspended solids (VSS) and turbidity. Quantification of TWP was performed using pyrolysis-gas chromatography/mass spectrometry for size fractions of 1.6–20 μm and 1.6–500 μm. Results show that TWP concentrations ranged from 9–170 mg L⁻¹ for the larger size fraction, and 8–150 mg L⁻¹ for the fine size fraction, with higher concentrations at the beginning of the rain event, suggesting a first-flush effect or sediment resuspension. The majority, 87 ± 13% on average, of TWP were quantified in the fine size fraction (1.6–20 μm). The findings indicate that TWP are mobilised from road surfaces and resuspend from gully pot sediments, thus resulting in low retention of TWP in the stormwater system. Additionally, high concentrations of metals, such as Cr, Cu, and Zn, were measured. Strong correlations were observed between TWP, TSS, VSS, and metals, suggesting shared transport pathways. These findings contribute to understanding the dynamic TWP transport behaviour during rain events, supporting better design of stormwater treatment systems targeting this emerging contaminant.

The presence of emerging pharmaceutical contaminants, particularly carbamazepine (CBZ), in wastewater has become a significant environmental concern due to its persistence in traditional treatment systems and potential adverse effects on aquatic ecosystems and human health. This study explores the efficacy of biochar, a carbon-rich material derived from biomass pyrolysis, as an adsorbent for removing CBZ from wastewater. A rough set machine learning (RSML) model was developed to predict CBZ removal efficiency. The model considered multiple operational parameters known to influence adsorption processes, including adsorption time, initial CBZ concentration, solution pH, adsorbent dosage, temperature, and adsorption type. The dataset was discretized to facilitate rough set analysis, allowing for identifying influential parameters and generating clear decision rules that link input conditions to removal efficiency. The results demonstrate that the RSML model attained a high classification accuracy of 93.15%, outperforming traditional classifiers. The model produced 49 scientifically coherent decision rules, providing valuable insights into the optimal conditions for maximising CBZ removal. This research highlights the potential of biochar as a sustainable solution for addressing pharmaceutical contaminants in wastewater and emphasises the importance of interpretable machine learning models in environmental engineering. The developed RSML tool offers practical guidance for real-time practitioners, enabling efficient and effective wastewater treatment strategies that can mitigate the ecological impacts of emerging contaminants like CBZ.

Heavy metal contamination in aquatic bodies presents a critical challenge to both environmental sustainability and public health, particularly due to the toxic, non-biodegradable and persistent nature of metals such as zinc. This study presents a sustainable approach for the effective removal of zinc ions (Zn²⁺) from aqueous solutions using a newly developed adsorbent material. In this approach, layered double hydroxides (LDHs) were synthesized using a co-precipitation method and were further characterized using PXRD, SEM, EDX, and BET analyses and FTIR, fluorescence, photoluminescence, and time-resolved photoluminescence spectroscopies. Comprehensive batch adsorption experiments were performed for Zn²⁺ removal, and its residual concentrations were determined by atomic absorption spectroscopy (AAS). The adsorption data were systematically analyzed using adsorption isotherm models as well as kinetics parameters. The maximum adsorption capacity (q_max) was found to be 39.62 mg g⁻¹ for NiAl LDH, 313.40 mg g⁻¹ for NiAl LDH@CD, 303.52 mg g⁻¹ for MgAl LDH and 314.23 mg/g for MgAl LDH@CD. The removal efficiencies were found to be 87% for NiAl LDH, 93% for NiAl LDH@CD, 86.51% for MgAl LDH and 90.83% for MgAl LDH@CD, with reusability up to seven cycles. These results highlight the potential of LDH-based adsorbents, particularly CD-modified LDH adsorbents, as eco-friendly and cost-effective solutions for heavy metal remediation in wastewater treatment.

Coir retting effluent, rich in lignocellulosic and phenolic compounds, poses serious environmental challenges due to its high chemical oxygen demand (COD), conductivity, and salinity. This study evaluates the effectiveness of microfiltration membranes and microwave-assisted advanced oxidation processes in treating coir retting effluent. It explores the potential of combining both methods to achieve enhanced pollutant removal efficiency. An integrated treatment system was developed, combining low-pressure filtration using chitosan/poly(acrylic acid) (CHI/PAA) multilayer membranes with the MW-Fenton (MW-F) process for efficient remediation of coir retting effluent. The CHI/PAA multilayer membranes were fabricated via a layer-by-layer (LbL) assembly method and tested under varying pH conditions and bilayer numbers. At 5.5 bilayers, a COD reduction of 42.30% and a flux of 72.84 m³ m⁻² per day at native pH were achieved along with significant rejection of dissolved pollutants. Attenuated Total Reflectance Fourier-Transform Infrared (ATR-FTIR) analysis confirmed the adsorption of organic compounds on the membrane surface. Most of the phenolic compounds identified in the feed via Ultra-Performance Liquid Chromatography–Quadrupole Time-of-Flight Mass Spectrometry (UPLC-Q-ToF-MS) were effectively removed using this approach. Treatment of the coir retting effluent by MW-F alone, with an optimal H₂O₂ dosage of 200 mM and a fixed Fe²⁺ concentration of 0.18 mM, resulted in a COD reduction of 38.46% along with substantial decreases in conductivity, TDS, and salinity at near-neutral pH. Integration of membrane filtration with MW-F at an optimal H₂O₂ dosage of 200 mM significantly improved performance, resulting in a COD reduction of 76.92%, a colour removal of 97.51%, and a flux of 212.07 m³ m⁻² per day at neutral pH. This combined system offers a sustainable, efficient, and economically viable solution for treating complex lignocellulosic wastewater without the need for pH adjustment, making it particularly suitable for decentralized applications in the coir processing industry.

Reverse osmosis (RO) technology, as a key support for modern water treatment systems, is significantly affected by membrane fouling in its long-term operational performance. Accurately predicting the permeability of membrane elements is of great significance for determining the fouling status of membrane elements. Although traditional data-driven methods have achieved modeling of membrane fouling trends to some extent, they generally suffer from problems such as poor model monotonicity and insufficient ability to model physical laws. To overcome the above limitations, this paper proposes a Physics-Informed Neural Network (PINN) framework that integrates physical knowledge. It innovatively introduces the physical monotonicity reflected by the variation of reverse osmosis membrane permeability with operating conditions as a constraint, and constructs a predictive model with physical consistency and data-driven capabilities. The model is developed based on the experimentally measured data obtained from the test bench of the pure water special station. It selects operating time, inlet salt content, concentrated water salt content, inlet pressure, concentrated water pressure and temperature as inputs, and membrane permeability coefficients as outputs. The results indicate that the constructed PINN model outperforms traditional data-driven methods in both error evaluation metrics and coefficient of determination evaluation metrics, and partial dependency analysis shows that its prediction results have high consistency at the physical trend level. This study provides an effective paradigm for embedding physical constraints into reverse osmosis performance prediction models, and offers a more universal and interpretable modeling approach for state monitoring and performance optimization of reverse osmosis systems.