npj Clean Water最新文献

Ampicillin (AMP) poses a significant environmental hazard to aquatic ecosystems, and previous research has been inadequate in addressing the ecological toxicity of its byproducts. A novel photocatalyst, WO₃-BiFeO₃/digestate biochar (DSB), denoted WBD, was synthesized for efficient photocatalytic degradation of AMP. In our study, 150 mg WBD achieved 99.93% AMP degradation in 3 h at a concentration of 100 mg/L. WBD maintained a stable degradation performance under various environmental stressors, including pH, and the presence of Cl⁻, NO₃⁻, and HA. •O₂⁻, h⁺, •OH, and ¹O₂ were identified as primary active oxygen species. WBD effectively targeted the -NH₂, -SH, and β-lactam ring-forming -COOH of AMP, facilitating its transformation into low-toxic or non-toxic degradation products, and establishing a detoxification pathway. Overall, this study introduces a novel environmentally material that demonstrates a high degradation efficiency, long-lasting effectiveness, and low product toxicity, offering a promising approach for the effective management of emerging pollutants.

Artificial intelligence has been extensively used to predict surface water quality to assess the health of aquatic ecosystems proactively. However, water quality prediction in data-scarce conditions is a challenge, especially with heterogeneous data from monitoring sites that lack similarity in water quality, hindering the information transfer. A deep learning model is proposed that utilizes representation learning to capture knowledge from source river basins during the pre-training stage, and incorporates meteorological data to accurately predict water quality. This model is successfully implemented and validated using data from 149 monitoring sites across inland China. The results show that the model has outstanding prediction accuracy across all sites, with a mean Nash-Sutcliffe efficiency of 0.80, and has a significant advantage in multi-indicator prediction. The model maintains its excellent performance even when trained with only half of the data. This can be attributed to the representation learning used in the pre-training stage, which enables extensive and accurate prediction under data-scarce conditions. The developed model holds significant potential for cross-basin water quality prediction, which could substantially advance the development of water environment system management.

Water scarcity drives water-stressed regions to make use of all available resources. Consequently, urban stormwater is gaining recognition as a valuable resource, for instance to replenish aquifers; thus, enhancing water supply. However, it carries contaminants that could limit its potential uses, highlighting those recently categorized as persistent, mobile, and toxic (PMT) compounds. In order to conduct broad screening for the presence of PMT compounds in stormwater first-flush samples and rainwater, two instruments based on Gas and Liquid Chromatography coupled to Quadrupole-Time-of-Flight Mass Spectrometry were used, and both suspect and target screening were performed. After prioritization 42 PMTs were detected, of which 24 PMTs were quantified. The results showed that 66% of the quantified PMTs were present in more than 50% of the samples, with average concentrations ranging from 2 ng L^-1 to 2.78 µg L^-1. Of the target PMTs, 11 were quantified for the first time in runoff samples.

The anoxic-oxic (A/O) process is the most common biological method for removing nitrogen (N) from wastewater, but the antibiotic resistance profile of N-metabolizing microbes in A/O processes remains largely underexplored. Here we demonstrated a significant positive correlation between various types of N-metabolizing genes and antibiotic resistance genes (ARGs) in swine wastewater A/O processes across China. We assembled 180 high-quality genomes of dominant N-metabolizing microbes (12.6% of the total metagenome-assembled genomes), all harboring transcriptionally active ARGs. And Pseudomonas was identified as the primary N-metabolizing genus and major ARG host. Among 1110 culturable N-metabolizing isolates, 22.34% were Pseudomonas strains showing high N removal capacity and multi-antibiotic resistance. Moreover, plasmid-mediated ARG transfer further heightened resistance risks. Overall, these findings highlight a significant ARG risk among predominant N-metabolizing microbes in A/O treatment processes, underscoring the urgency of balancing N removal performance with resistance control in wastewater treatment processes.

A pilot-scale batch reverse osmosis (RO) system with a flexible bladder was designed to recover additional water from RO concentrate. The sulfate-rich, ~6400-ppm concentrate was sourced from the Yuma Desalting Plant (Arizona, USA), which desalinates agricultural drainage water. The pilot produced 4.4 m³/day of permeate with 150 ppm total dissolved solids from the facility’s concentrate stream with a recovery ratio of 82.6%. Despite producing supersaturated brine, there was no performance deterioration due to scaling. Using a bladder for retentate pressurization limited average power to 633 W and the specific energy consumption to 3.3 kWh/m³. The pilot’s energy data informed a model of large-scale batch RO, which has the potential to desalinate the same water for less than 1 kWh/m³. Additionally, a model was developed to predict scaling likelihood in batch RO. This investigation demonstrates that batch RO is a viable technology for low-energy brine concentration beyond saturation limits.

In recent years, microplastic contamination in freshwater lakes has become a significant environmental concern. Despite this, there remains a lack of comprehensive understanding of the distribution patterns and regional characteristics of microplastic loads in global lacustrine environments under a unified standard. To address this gap, our study utilizes Machine Learning (the random forest algorithm), combined with number-to-mass transformation techniques to generate a global prediction. The results indicate an average microplastic concentration of 0.57 items/m³ in lakes and reservoirs worldwide, with an accumulated microplastic load of 10167 tons within top 20 m of water—equivalent to 508 million plastic bottles. The primary sources of microplastics are linked to agricultural land use and the proportion of urban areas within watersheds. Notably, the highest microplastic loads are observed in North America, Africa, and Asia, though the contributing factors vary, including concentration-dependent and area-dependent influences, as well as differences in shape composition. These findings provide valuable insights that can guide the development of targeted policies to effectively mitigate microplastic pollution in freshwater ecosystems.

Predicting new unseen data using only wastewater process inputs remains an open challenge. This paper proposes lifelong learning approaches that integrate long short-term memory (LSTM), gated recurrent unit (GRU) and tree-based machine learning models with knowledge-based dictionaries for real-time viral prediction across various wastewater treatment plants (WWTPs) in Saudi Arabia. Limited data prompted the use of a Wasserstein generative adversarial network to generate synthetic data from physicochemical parameters (e.g., pH, chemical oxygen demand, total dissolved solids, total suspended solids, turbidity, conductivity, NO₂-N, NO₃-N, NH₄-N), virometry, and PCR-based methods. The input features and predictors are combined into a coupled dictionary learning framework, enabling knowledge transfer for new WWTP batches. We tested the framework for predicting total virus, adenovirus, and pepper mild mottle virus from WWTP stages, including conventional activated sludge, sand filter, and ultrafiltration effluents. The LSTM and GRU models adapted well to new data, maintaining robust performance. Tests on total viral prediction across four municipal WWTPs in Saudi Arabia showed the lifelong learning model’s value for adaptive viral particle prediction and performance enhancement.

Global climate change has exacerbated water scarcity, while traditional water treatment technologies are often unsustainable due to high energy consumption and negative environmental impacts, posing an urgent need for a sustainable solution. This study developed a novel wood-based flexible Janus membrane coupled with a spine structure for efficient oil-water emulsion separation and fog harvesting. The Janus wood membrane showed high separation efficiency (> 99.6%), high filtration flux (water-in-oil and oil-in-water emulsions exceeded 810 L/m²·h and 747 L/m²·h, respectively), and good reusability. Additionally, the introduction of spine and conical pores significantly enhanced fog collection efficiency (19.23 kg/m²·h), expanding the application potential of Janus membranes. Moreover, this Janus wood membrane offered excellent mechanical properties, dimensional stability, mildew resistance, and environmental benefits. This study underscored the potential of Janus membranes in water management and liquid separation, providing a sustainable solution to water scarcity.

We examined oil-water displacement under constant pressure difference conditions, simulating natural aquifer environments using microfluidic chips with different wettability and pore geometries. The results showed lower oil retention in hydrophobic chips than hydrophilic ones, contrary to previous microfluidic chip experiments conducted under constant flow rate conditions. This is because hydrophobic surfaces reduce capillary pressure drop but increase viscous pressure drop for compensation, leading to higher flow rate and displacement. Additionally, complex pore geometries in hydrophilic chips cause oil clusters to break into smaller blobs, reducing retention and enhancing the relative permeability of water. These findings suggest that relying solely on hydrophobicity may be ineffective in retaining oil in porous materials under constant pressure difference conditions, highlighting the need for more careful consideration in groundwater remediation design.

Microwave (MW)-enhanced catalytic oxidation processes are emerging and effective techniques for the degradation of organic compounds in water and wastewater treatment processes. In this study, through applied MW irradiation, monodisperse, superparamagnetic iron oxide nanocrystals (IONCs) with thin, amorphous silica coatings are demonstrated to rapidly catalyze the degradation of organic compounds in water through a thermally enhanced, Fenton−type process. For this, we precisely synthesize amorphous silica-coated various metal oxide (single domain) nanocrystals, and then evaluate the degradation of methyl orange (MO) and benzoic acid (BA), chosen as model organic molecules. We examine (and optimize) the effects of core (nanocrystal) composition, size, and concentration, along with solution pH and hydrogen peroxide (H₂O₂) concentration. Further, we describe the catalytic degradation of BA with IONCs under MW irradiation through radical scavenger controls and electron paramagnetic resonance (EPR) analysis, which support the proposed reaction mechanism. For materials evaluated, the amorphous silica coating not only prevents the loss of nanocrystal integrity but also provides a reactive, yet stable, interface between nanocrystals and bulk solutions, where the degradation of organic compounds can occur. Synthesized IONCs show high performance, which is repeatable for over five cycles without any deterioration of the nanocrystals core or metal leaching. Taken together, this research highlights the potential of enhanced MW-enhanced oxidation processes appropriately coated (i.e., designed) MW absorbers (here as superparamagnetic IONCs) for advanced water treatment.