Environmental Science: Water Research & Technology最新文献

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.

Water quality prediction is a highly important task for the anticipation and management of a polluted environment. Accurate prediction can assist in making better decisions in the area of environmental water quality. The WQI (water quality index) is the best method of measuring water quality. However, previous research has suffered from limitations, such as ambiguity and eclipsing. Machine learning algorithms are considered effective methods to rectify the limitations of conventional WQIs. The proposed model aims to detect the main water quality parameters, which include biochemical and physical features. It is also used to determine the usability of water for irrigation purposes. The proposed model uses federated learning to train optimized RPART (recursive partitioning) on water quality data such as pH, turbidity, dissolved oxygen and temperature. These data are distributed across different geographical or organizational locations without transferring raw data to a central server. The proposed algorithm demonstrates a shorter search time compared to RPART, achieving O(1) in the best case and O(log N·2^d) in the worst case for completing the search operation. The dataset partitioning of 15% for testing, 70% for training, and 15% for validation indicates the robust classification and prediction performance of the WQI model for Indian reservoirs. ORPART gives 92% data accuracy, requires less search time for keys, and has high data capability with a lower error rate. The integration of the federated learning and optimized RPART techniques can lead to more efficient, sustainable, and data-driven management of irrigation water quality, benefiting agriculture, the environment, and local communities.

Fluoxetine (FLX), a widely prescribed antidepressant and one of the most prevalent pharmaceuticals detected in the environment, has piqued significant interest recently due to its persistence and potential ecological effects. Despite its widespread detection, no comprehensive review currently exists that focuses specifically on FLX's environmental behaviour. As a polyfluorinated synthetic organic compound, FLX serves as an ideal model for understanding the broader challenges faced by fluorinated pharmaceuticals. This review presents a critical and integrative assessment of FLX, beginning with its molecular structure and the role of the C–F bond in enhancing the chemical stability and recalcitrance. The review then explores its environmental fate, including its behaviour towards hydrolysis, photolysis, partitioning, susceptibility to microbial attack, potential for bioaccumulation, and interactions and joint toxicity with other co-existing pollutants. This is followed by a comprehensive and critical discussion of existing advanced removal techniques currently investigated for FLX removal. Despite some promising approaches, challenges remain due to the inherent stability of the C–F bond, the toxicity of by-products, and the complexity of the matrix. The review proposes treatment chains, such as adsorption (AC, biochar, nano-adsorbents), followed by chemical (AOPs, electro-Fenton, UVC/solar irradiation) and biological (MBBR, biofilters) as recommendations for future studies. In addition, the review also aims to highlight the need for environmental management of FLX, not only to mitigate its ecological footprint but also to offer broader insights into the class of polyfluorinated pharmaceuticals.

This review aims to provide a comprehensive overview of the electrochemical advanced oxidation processes (EAOPs) for the removal of underwater microplastics. First, we analyze the sources of various microplastic contaminants, such as personal hygiene products, synthetic textiles, industrial processes, plastic waste, fishing nets, and road wear, and the complexity of underwater microplastic distribution, including spatial, vertical, and temporal distributions. Then, the types, principles and reaction mechanisms of EAOPs for underwater microplastic removal are described in detail, and their applications to microplastic removal are discussed, including electrode materials and parameter optimization. The unique contribution of this review lies in its critical synthesis that bridges the gap between fundamental electrochemistry and applied water treatment, offering a dedicated focus on the operational parameters and implementation challenges specific to microplastic degradation, which have not been comprehensively addressed in the literature. In addition, the advantages and limitations of EAOPs are analyzed, such as their efficient decomposition ability, low risk of secondary pollution and easy control, along with the problems such as high energy consumption, high electrode cost and complicated operation. Finally, to promote the sustainable application of EAOPs in wastewater treatment, ways to overcome these limitations, including the development of new electrode materials, optimization of operating parameters, integration of other technologies, and resource and energy recovery, are suggested.