npj Clean Water最新文献

Anaerobic digestion (AD) is a crucial bioenergy source widely applied in wastewater treatment. However, its efficiency improvement is hindered by complex microbial communities and sensitivity to feedstock properties. We, thus, propose a hybrid quantum-classical machine learning (Q-CML) regression algorithm using a quantum circuit learning (QCL) strategy. Combining a variational quantum circuit with a classical optimiser, this approach predicts biogas production from operational data of 18 full-scale mesophilic AD sites in the UK. Tailored for noisy intermediate-scale quantum (NISQ) devices, the low-depth QCL model outperforms conventional regression methods (R²: 0.53) and matches the performance of a classical multi-layer perceptron (MLP) regressor (R²: 0.959) with significantly fewer parameters and better scalability. Comparative analysis highlights the advantages of quantum superposition and entanglement in capturing intricate correlations in AD data. This study positions Q-CML as a cutting-edge solution for optimising AD processes, boosting energy recovery, and driving the circular economy.

Anionic dyes contaminate water and severely disrupt aquatic ecosystems, urgently demanding effective treatment solutions for safety. This study explores the synthesis of unsaturated MIL-101(Cr) and its exceptional performance in removing anionic dyes from polluted water systems. The synthesized MIL-101(Cr) exhibits medium Lewis’s acid and strong Brønsted acid sites, a high specific surface area (>3000 m²/g), and a Zeta potential of 30 mV, contributing to its strong adsorption capability. Adsorption studies reveal Langmuir isotherm model fitting, with maximum adsorption capacities of 4231, 1266, and 568 mg/g for Acid Blue 92, Congo Red, and Acid Blue 90, respectively. The chemisorption process follows pseudo-second-order kinetics and is spontaneous and exothermic. MIL-101(Cr) demonstrates chemical and water stability, maintaining over 80% removal efficiency after five recycling cycles. This research provides valuable insights into treating anionic dye-contaminated wastewater using MIL-101(Cr) as an efficient adsorbent.

Disposal of antibiotics and antimicrobial-resistant enteric bacteria (ARB) into water from various sources is responsible for maintaining ARB in the environment. Relative prevalence and circulation of ARB may vary across water sources. We hypothesized that these ARBs with different resistance genes are distributed in various freshwater sources and are related to each other. We screened 155 enteric bacterial isolates from eight different water sources in Dhaka. The prevalence of ARB and MDR enteric bacteria in water was significantly associated (p value < 0.05) with the sources. The genotypic analysis of bla_TEM, qnrB, tetA, mcr-1, and sul-1 revealed higher similarity of the isolates from freshwater with previously reported isolates from clinical samples. Water sources with direct exposure to antibiotics had a significantly higher frequency of genotypic and phenotypic resistance. This study calls for continuous monitoring of water sources and strengthening the treatment of antibiotic and ARB-containing effluents in Bangladesh.

Load reduction is essential for mitigating the transmission risk of antibiotic resistance genes (ARGs) from livestock wastewater. This study examined the potential and mechanisms of Myriophyllum elatinoide-planted system in reducing ARGs in swine wastewater. Field experiment showed a progressive decline in ARG diversity and abundance as pond number increased, which was attributed to bacterial community shift and mobile genetic element-mediated horizontal transfer. This was corroborated by hydroponic experiment, where removal rates of antibiotics, antibiotic-resistant bacteria, and ARGs reached 64.82–87.83%, 66.27–98.39%, and 93.63–99.82%. Mechanistically, such a reduction could be achieved through direct uptake by plant roots and shoots, and indirectly by alleviating the selection pressure from antibiotic residues. Furthermore, M. elatinoides treatment substantially decreased ARG burdens in wastewater-receiving water (71.40–96.68%) and soils (36.81–85.69%). Our findings present a feasible and sustainable strategy for mitigating swine wastewater-borne ARGs, aiding in the fight against the spread of antibiotic resistance.

In light of escalating nitrogen pollution in aquatic systems, this study presents a comprehensive machine learning (ML) approach to predict ammonia nitrogen adsorption capacity of biochar and identify optimal conditions. Twelve ML models, including tree-based ensembles, kernel-based methods, and deep learning, were evaluated using Bayesian optimization and cross-validation. Results show tree-based ensemble models excel, with CatBoost performing best (R² = 0.9329, RMSE = 0.5378) and demonstrating strong generalization. Using SHAP and Partial Dependence Plots, we found experimental conditions (67.2%) and biochar’s chemical properties (18.2%) most influenced adsorption capacity. Moreover, under these experimental conditions (C₀ > 50 mg/L and pH 6–9), a higher adsorption capacity could achieved. A Python-based GUI incorporating CatBoost facilitates practical applications in designing efficient biochar adsorption systems. By merging advanced ML techniques and interpretability tools, this study deepens understanding of biochar’s ammonia adsorption and supports sustainable strategies for mitigating nitrogen pollution.

Peroxymonosulfate (PMS) activation by cobalt manganese spinel (Co-Mn) is always unsatisfactory due to the interference of co-existing anions in water. In this study, we used a sulfate-modification strategy to prepare a sea urchin-like Co-Mn catalyst (CoMn₂O₄-S) with abundant oxygen vacancies for counteracting the interference of anions in pollutant degradation. Compared with the conventional Co-Mn catalyst (CoMn₂O₄), CoMn₂O₄-S exhibited higher resistance to poisoning of NO₃⁻, Cl⁻, and SO₄²⁻ in PMS activation involved phenol degradation. Additionally, H₂PO₄⁻ could even enhance phenol degradation by 150.2% for CoMn₂O₄-S/PMS system, in contrast to its induced 18.5% inhibition to CoMn₂O₄/PMS system. It was demonstrated that vacancies and sea urchin structure alleviated catalyst agglomeration for preserving catalytic sites and promoted catalyst surface modulation for radical diffusion, contributing to the enhanced stability in saline water. This work provides a facile strategy for overcoming the negative effects of co-existing anions on heterogeneous PMS-activation based water treatment.

A compact three-in-one water treatment process, combining a flocculant, a fibrous super-bridging agent, and a screen-based floc retention system, simultaneously improves water treatment and sludge dewatering. The presence of fibrous materials allows for the formation of very large flocs, efficient floc separation via screening (without settling), and sludge dewatering through a compact press-filter system. The implementation of this three-in-one process is possible due to the formation of very large fiber-based flocs. The sludge containing fibers was subsequently dewatered using a screen-based press filter without further chemical addition. The use of fibers also significantly improved the removal of total organic carbon, nanoplastics, and microplastics. This three-in-one process could be used for decentralized water treatment in drinking water and wastewater applications in small cities, marginalized communities, and developing countries. The compact process, which also performs sludge dewatering, would reduce the risks associated with mismanaged sludge to the environment and human health.

Heavy metal measurement is vital for ecological risk assessment and regulatory compliance. This study reports a sensor using gold nanoparticle-modified carbon thread electrodes for the simultaneous detection of Cd²⁺, Pb²⁺, Cu²⁺, and Hg²⁺ in water samples. Differential pulse voltammetry (DPV) was employed, achieving detection limits of 0.99 µM, 0.62 µM, 1.38 µM, and 0.72 µM, respectively, with a linear span of 1–100 µM. The sensor operated effectively in acidic conditions, with excellent selectivity, repeatability, and reproducibility. Real water samples from various lakes in Hyderabad, India, were analyzed to validate their practical application. To extract the sensing features a convolutional neural network (CNN) model was used to process DPV signals, enhancing heavy metal ion classification with high accuracy. Performance metrics such as precision, recall, and F1 score were evaluated. Integration with IoT technology has improved the user experience, advanced heavy metal quantification capabilities, and further enabled remote monitoring.

Thiram is a widely used fungicide in agricultural practices as spraying on crops and grains storage but its potential adverse effects on non-target aquatic organisms have raised serious concerns. In this study, firstly LC₅₀ value of thiram has been determined as 0.744 mg/L for the first time in Labeo rohita and then, the fish were exposed to different sub-lethal concentrations of thiram (40 μg/L, 80 μg/L and 120 μg/L) to evaluate the effects on tissue growth, oxidative stress, anti-oxidant enzymes and histo-pathological parameters at days-20, 40 and 60 of the experiment. There observed a significant (p < 0.05) decrease in the body weight while a significant increase in the relative (p < 0.05) and absolute (p < 0.05) weights of kidneys, heart and brain was found. Hematological analysis showed a significant (p < 0.05) increase in leukocytes and neutrophils while lymphocytes and monocytes, RBCs and hemoglobin concentration were significantly (p < 0.05) decreased. Serum biochemical parameters revealed a significant (p < 0.05) increase in urea and hepatic enzymes while a significant (p < 0.05) decrease in total proteins, albumin and creatinine were observed at higher doses (80 μg/L and 120 μg/L) of thiram. Oxidative stress parameters; ROS and TBARS were increased significantly (p < 0.05) while antioxidant enzymes (CAT, SOD, POD, GSH) showed a significant (p < 0.05) decrease in the treated groups compared to the control. Moreover, comet assay revealed significant (p < 0.05) DNA damage in the isolated cells of kidneys, heart and brain at higher doses (80 μg/L and 120 μg/L) of thiram that was further confirmed by histo-pathological alterations in these tissues. The findings demonstrate that thiram severely pollute the marine eco-system and is quite hazardous for aquatic species by causing severe health effects even at sub-lethal concentrations and thus, render the apparently clean water to be unfit for animal and human consumption.

This study investigated the changes in water quality and microbial risks resulting from trace metal pollutants in stagnant drinking water conditions using a 168-h experimental simulation and a metagenomic approach. The results showed that Fe(III) increased the water turbidity. Stagnation also caused significant biofilm growth, which was increased by trace metal pollutants, resulting in a higher production of extracellular polymeric substances (EPS). Adaptive mechanisms of bacterial communities dominated by Pseudomonadota in response to trace metal pollutant stress were discovered. Pathogenic bacteria, particularly Salmonella enterica and Pseudomonas aeruginosa, were found in stagnant drinking water, potentially exacerbated by Al(III). The overall exposure risk of antibiotic resistance genes (ARGs) increased, whereas Fe(III) enhanced the co-occurrence of ARGs and pathogens, potentially leading to serious hidden microbial risks. This study reveals imperceptible microbial risks posed by trace metal pollutants in stagnant drinking water, providing scientific warning and advice for drinking water safety.