Water Research X最新文献

Water disinfection by copper vessels has been prevalent over thousands of years. Unfortunately, people are still suffering from the bacterial pollution in drinking water. Here we show that, only through steeping with tiny amounts of common plant leaves, the room-temperature water in copper pots has unexpectedly high antibacterial ability. Remarkably, copper ions released from copper pots into water are in concentrations lower than the WHO safety threshold for drinking water, and have effective antibacterial ability when water contains specific leave components (polyphenols and/or lignin). Our computations show that the key to enhance antibacterial ability is the great increase in the proportion of Cu⁺ induced by aromatic rings in these leave components, which has been demonstrated by our experiments. The findings may disclose the mystery of copper vessels for water disinfection, and more importantly, provide effective antibacterial applications in industries and daily lives, by safely using copper ions together with biocompatible natural substances.

Nitrite-oxidizing bacteria (NOB) are undesirable in the anaerobic ammonium oxidation (anammox)-driven nitrogen removal technologies in the modern wastewater treatment plants (WWTPs). Diverse strategies have been developed to suppress NOB based on their physiological properties that we have understood. But our knowledge of the diversity and mechanisms employed by NOB for survival in the modern WWTPs remains limited. Here, Three NOB species (NOB01–03) were recovered from the metagenomic datasets of a full-scale WWTP treating duck breeding wastewater. Among them, NOB01 and NOB02 were classified as newly identified lineage VII, tentatively named Candidatus (Ca.) Nitrospira NOB01 and Ca. Nitrospira NOB02. Analyses of genomes and in situ transcriptomes revealed that these two novel NOB were active and showed a high metabolic versatility. The transcriptional activity of Ca. Nitrospira could be detected in all tanks with quite different dissolved oxygen (DO) (0.01–5.01 mg/L), illustrating Ca. Nitrospira can survive in fluctuating DO conditions. The much lower Ca. Nitrospira abundance on the anammox bacteria-enriched sponge carrier likely originated from the intensification substrate (NO₂⁻) competition from anammox and denitrifying bacteria. In particular, a highlight is that Ca. Nitrospira encoded and treanscribed cyanate hydratase (CynS), amine oxidase, urease (UreC), and copper-containing nitrite reductase (NirK) related to ammonium and NO production, driving NOB to interact with the co-existed AOB and anammox bacteria. Ca. Nitrospira strains NOB01 and NOB02 showed quite different niche preference in the same aerobic tank, which dominanted the NOB communities in activated sludge and biofilm, respectively. In addition to the common rTCA cycle for CO₂ fixation, a reductive glycine pathway (RGP) was encoded and transcribed by NOB02 likely for CO₂ fixation purpose. Additionally, a 3b group hydrogenase and respiratory nitrate reductase were uniquely encoded and transcribed by NOB02, which likely confer a survival advantage to this strain in the fluctuant activated sludge niche. The discovery of this new genus significantly broadens our understanding of the ecophysiology of NOB. Furthermore, the impressive metabolic versatility of the novel NOB revealed in this study advances our understanding of the survival strategy of NOB and provides valuable insight for suppressing NOB in the anammox-based WWTP.

The world's first full-scale, 280 nm UV LED reactor for wastewater disinfection was tested at flows of 545 and 817 m³ day⁻¹. The system achieved a > 3 average log reduction of total coliform at 545 m³ day⁻¹ and the 817 m³ day⁻¹ flow rate achieved over a > 2.5 average log reduction for all operational conditions. The delivered fluence of the full-scale system ranged from 28 to 148 mJ cm⁻² and aligns with a UV auditing study that was conducted prior to the installation of the wastewater reactor. These results benchmark the performance that can be achieved by UV LED disinfection and further connect bench-scale disinfection results with full-scale performance. The approach established in this manuscript provides a novel tool for utilities when considering emerging UV disinfection technologies. In summary, this study establishes that UV LEDs are an effective wastewater disinfectant at-scale and are comparable to conventional low-pressure UV systems. This is the first instance where the efficacy of UV LEDs for municipal wastewater disinfection has been demonstrated using a large-scale installation at a functioning wastewater facility.

Biofiltration processes are often part of advanced wastewater treatment (aWWT) technologies for the removal of organic micropollutants (OMP) from conventional wastewater treatment plant (WWTP) effluents. Although biological effects are not always the main focus of these technologies (e.g. filtration through granular activated carbon), they have been shown to contribute significantly to total OMP removal. While OMP biodegradation kinetics in conventional biological wastewater treatment are well researched, no systematic comparison to biomass from aWWT is available. This biomass faces different growth conditions and higher OMP concentrations relative to the background organic matter. Adaptation to these conditions could be possible and could lead to faster OMP biodegradation kinetics, which would show in a larger pseudo first-order biodegradation kinetic constant k_biol. In this work, k_biol values for biomass obtained from aWWT biofilters were determined by evaluating OMP removals measured in lab-scale biofilters using a mechanistic model of the experimental setup. A comparison to k_biol values from literature for conventional wastewater treatment (with nutrient removal) revealed similar OMP biodegradation kinetics without any advantages of biomass from aWWT. A conceptual evaluation of influencing factors on OMP removal in biofilters showed that operational parameters (such as the biomass concentration or the empty bed contact time) and the affinity of OMPs to adsorb on biomass have a significant additional effect on biological OMP removal. Therefore, k_biol values alone are not sufficient to estimate biological OMP removal in biofilters and further information about the system is required.

Mathematical modeling plays a crucial role in understanding and managing urban water systems (UWS), with mechanistic models often serving as the foundation for their design and operations. Despite the wide adoptions, mechanistic models are challenged by the complexity of dynamic processes and high computational demands. Data-driven models bring opportunities to capture system complexities and reduce computational cost, by leveraging the abundant data made available by recent advance in sensor technologies. However, the interpretability and data availability hinder their wider adoption. This paper advocates for a paradigm shift in the application of data-driven models within the context of UWS. Integrating existing mechanistic knowledge into data-driven modeling offers a unique solution that reduces data requirements and enhances model interpretability. The knowledge-informed approach balances model complexity with dataset size, enabling more efficient and interpretable modeling in UWS. Furthermore, the integration of mechanistic and data-driven models offers a more accurate representation of UWS dynamics, addressing lingering uncertainties and advancing modelling capabilities. This paper presents perspectives and conceptual framework on developing and implementing knowledge-informed data-driven modeling, highlighting their potential to improve UWS management in the digital era.

Xanthates, derivatives of xanthic acid, are widely utilized across industries such as agrochemicals, rubber processing, pharmaceuticals, metallurgical, paper and mining to help separate metals from ore. Despite their prevalent use, many registered xanthates lack comprehensive information on potential risks to human health and the environment. The mining sector, a significant consumer of xanthates, drives demand. However, emissions into the environment remain poorly understood, especially concerning water quality. A recent EU parliamentary voting on water legislation highlights the urgency to address water pollution and the potential toxicity of xanthates. While limited data exist on xanthate presence in the environment, existing studies indicate their toxicity and contribution to environmental pollution, primarily due to carbon disulfide, a decomposition product. Concerns are mounting over the release of xanthates and carbon disulfide, particularly in mining areas near populated regions and river tributaries, raising questions about downstream impacts and public health risks. Proposed expansions of xanthate-reliant mining activities in Europe, heighten concerns about emissions and water quality impacts. Current databases lack xanthate-related monitoring data, hindering environmental and health risk assessments. Addressing this gap requires water sampling and chemical analysis and investigations into the use, occurrence, and potential impacts of xanthates from industrial activities on water bodies, including those used for drinking water production is imperative.

Determining the reliability of nanofiltration (NF) membranes for the removal of contaminants of emerging concern, including polyfluoroalkyl substances (PFASs), pharmaceuticals, and personal care products (PPCPs), is important for ensuring drinking water safety. This study aimed to clarify the factors that influence the removal of nine major PFASs during submerged NF treatment via extrapolation based on the factors that influence PPCP removal. The rejection of nine PFASs in ultra-filtered dam water by a polypiperazine-amide (NF270) membrane increased from 71 % to 94 % at a low permeate flux of 5 L/m² h as the PFAS molecular dimensions increased. PFASs with a carboxylic acid (-CO₂H) were rejected to a greater extent than PFASs with a sulfo group (-SO₃H). Further, negatively charged PFASs or PPCPs were rejected to a greater extent than uncharged and positively charged PPCPs. Our findings suggest that the rejection of PFASs can vary because of the (i) clearance distance between the PFASs’ molecular dimensions and NF membrane pore diameter and (ii) intensity of electrostatic repulsion between the PFASs’ functional groups and NF membrane surface. Our study indicates that submerged NF can achieve high PFAS rejection; however, variations in rejection among PFASs can become more prominent owing to a low permeate flux.

Chemicals are commonly dosed in sewer systems to reduce the emission of hydrogen sulfide (H₂S) and methane (CH₄), incurring high costs and environmental concerns. Nitrite dosing is a promising approach as nitrite can be produced from urine wastewater, which is a feasible integrated water management strategy. However, nitrite dosing usually requires strict conditions, e.g., relatively high nitrite concentration (e.g., ∼200 mg N/L) and acidic environment, to inhibit microorganisms. In contrast to “microbial inhibition”, this study proposes “microbial utilization” concept, i.e., utilizing nitrite as a substrate for H₂S and CH₄ consumption in sewer. In a laboratory-scale sewer reactor, nitrite at a relatively low concentrations of 25–48 mg N/L was continuously dosed. Two nitrite-dependent microbial utilization processes, i.e., nitrite-dependent anaerobic methane oxidation (n-DAMO) and microbial sulfide oxidation, successfully occurred in conjunction with nitrite reduction. The occurrence of both processes achieved a 58 % reduction in dissolved methane and over 90 % sulfide removal in the sewer reactor, with microbial activities measured as 15.6 mg CH₄/(L·h) and 29.4 mg S/(L·h), respectively. High copy numbers of n-DAMO bacteria and sulfide-oxidizing bacteria (SOB) were detected in both sewer biofilms and sediments. Mechanism analysis confirmed that the dosed nitrite at a relatively low level did not cause the inhibition of sulfidogenic process due to the downward migration of activity zones in sewer sediments. Therefore, the proposed “microbial utilization” concept offers a new alternative for simultaneous removal of sulfide and methane in sewers.

The ongoing evolution of SARS-CoV-2 is a significant concern, especially with the decrease in clinical sequencing efforts, which impedes the ability of public health sectors to prepare for the emergence of new variants and potential COVID-19 outbreaks. Wastewater-based epidemiology (WBE) has been proposed as a surveillance program to detect and monitor the SARS-CoV-2 variants being transmitted in communities. However, research is limited in evaluating the effectiveness of wastewater collection at sentinel sites for monitoring disease prevalence and variant dynamics, especially in terms of inferring the epidemic patterns on a broader scale, such as at the state/province level. This study utilized a multiplexed tiling amplicon-based sequencing (ATOPlex) to track the longitudinal dynamics of variant of concern (VOC) in wastewater collected from municipalities in Queensland, Australia, spanning from 2020 to 2022. We demonstrated that wastewater epidemiology measured by ATOPlex exhibited a strong and consistent correlation with the number of daily confirmed cases. The VOC dynamics observed in wastewater closely aligned with the dynamic profile reported by clinical sequencing. Wastewater sequencing has the potential to provide early warning information for emerging variants. These findings suggest that WBE at sentinel sites, coupled with sensitive sequencing methods, provides a reliable and long-term disease surveillance strategy.

The frequent occurrence of safety incidents in sewer systems due to the emergency toxicity of hydrogen sulfide (H₂S) necessitate timely and efficient prediction, early warning and real-time control. However, various factors influencing H₂S generation and emission leads to a substantial computational burden for the existing dynamic sewer process models and fails to timely control the H₂S exposure risk. The present study proposed a swift prediction model (SPM) that combined the validated dynamic sewer process model (the biofilm-initiated sewer process model, BISM) with a high-speed machine learning algorithm (MLA), achieving accurately and swiftly predict the dissolved sulfide (DS) concentration and H₂S concentration in a specific sewer network. Based on Gradient Boosting Decision Tree-based SPM, the simulated concentrations of DS and H₂S are 1.95 mg S/L and 214 ppm, respectively, which are closely to the field-measured values of 1.82 mg S/L and 219 ppm. Notably, SPM achieved a computation time of less than 0.3 s, and a significant improvement over BISM (> 5000 s) for the same task. Moreover, the real-time and dynamic dosing scheme facilitated by SPM outperformed the conventional constant dosing scheme provided by dynamic sewer process model, which significantly improved the H₂S control completion rate from 69 % to 100 %, and achieved a significant reduction in chemical dosage. In conclusion, the integration of dynamic sewer process models with MLA addresses the inadequacy of monitoring data for MLA training, and thus pursues swift prediction of H₂S generation and emission, and achieving real-time, effective, and economic control of H₂S in complex sewer networks.