Life cycle assessments of drinking water treatment plants (DWTPs) typically rely on single-year data or hypothetical scenarios, leaving temporal variability and facility-specific trade-offs poorly understood. This study conducted a cradle-to-gate life cycle assessment of two full-scale DWTPs serving Shanghai over 24 consecutive months, evaluating eight ReCiPe 2016 midpoint impact categories. The plants differ structurally: DWTP-A treats estuarine water using CO₂ pretreatment and polyaluminum chloride, while DWTP-B treats degraded inland reservoir water through heavy alum and ammonium sulfate dosing. Results revealed an asymmetric trade-off between the two facilities. DWTP-B achieved a lower climate change burden (0.396 kg CO₂-eq/m³) than DWTP-A (0.411 kg CO₂-eq/m³) and lower carcinogenic human toxicity, yet DWTP-A outperformed DWTP-B across the remaining six categories, with the marine eutrophication gap reaching nearly fourfold. Wilcoxon tests confirmed all plant-level differences were statistically significant, and six of eight categories remained stable across seasons and years. Grid electricity was the dominant driver of climate impact, while divergent coagulant and ammonium sulfate supply chains drove toxicity and eutrophication. A scenario substituting full photovoltaic power cut carbon emissions at DWTP-A by up to 61% but raised ecotoxicity by up to 66%, reflecting the upstream burdens of solar panel manufacturing and illustrating environmental burden shifting between impact categories. These findings show that environmental optimization in urban water treatment cannot follow a single template, and that utilities need facility-specific strategies, tailored to raw water source and treatment chemistry, to avoid solving one problem while creating another.

Due to their high energy and chemical-intensive processes, urban drinking water treatment plants (DWTPs) are considered to have substantial environmental implications. This study employs a scenario-based life-cycle assessment to investigate the upstream impacts of the chemical energy use of two large-scale urban DWTPS in Shanghai. The two DWTPs use comparable treatment methods; however, Scenario 1 incorporates CO2 for pH adjustment, while Scenario 2 uses a hybrid coagulant comprising Al2(SO4)3 and polyaluminium chloride (PAC). Analysis was conducted across six impact categories. Based on the findings, Scenario 1 has a lower global warming potential (GWP) of 0.216 kgCO2-eq, a terrestrial acidification (TEA) of 9.98 x 10-4 kg SO2-eq, a higher human carcinogenic toxicity (HCT) of 3.143 kg 1,4-DCB, and a freshwater ecotoxicity of 0.012 kg 1,4-DCB. Coagulation-sedimentation emerged as the main process in this scenario, accounting for approximately 27 % of GWP, 82 % of HCT, and 37 % of freshwater eutrophication (FWE). Scenario 2 demonstrated lower toxicity impacts; however, it has a higher GWP of 0.227 kg CO2 eq and TEA of 1.07 x 10- 3 kg SO2-eq. The sludge treatment was the main contributor to the overall GWP (0.067 kgCO2-eq), HCT (0.171 kg 1,4-DCB), and FWE (1.30 x 10- 5 kgP eq). PAC was identified as the principal contributor in most categories in both scenarios. Sensitivity analysis indicated that electricity is the main contributor to GWP, whereas chemicals have the most significant impact on toxicity-related categories. These findings demonstrate that no single treatment approach is optimal and highlight the need for a multifaceted approach to sustainable urban water management.

The two-electron oxygen reduction reaction (2e- ORR) offers a promising route for the on-site electrosynthesis of hydrogen peroxide (H2O2). While oxygen-functionalized carbon-based catalysts exhibit favorable ORR activity, their performance is often limited by the simplistic idealization of individual oxygen functional groups (OFGs). This study introduces a gradient engineering strategy of OFGs to precisely modulate the microenvironment surrounding cobalt phthalocyanine (CoPC) molecular interfaces. Based on the theory of density universal function (DFT), the gradient strategy is transferred to the spatial probability distribution of OFGs, and it is clear that the symmetric distribution of carbonyl group (C--O) can spatially isolate its strong electron-withdrawing effect and optimize the binding energy between the electron-deficient Co center and the OOH* intermediate. Integrated into a proton exchange membrane (PEM) reactor, the resulting catalyst achieved continuous and stable operation at a current density of 50 mA cm- 2 for 24 h, delivering a high H2O2 yield of 9.7 mol gcat low cell voltage of 2.26 V. This work provides new mechanistic insights into OFG gradient engineering as an effective strategy to enhance H2O2 electrosynthesis.

The performance of biological wastewater treatment processes directly impacts water resource recycling and ecological safety. This year-long study compared full-scale wastewater treatment plants (WWTPs) using either the anaerobic/anoxic/aerobic (AAO) or modified Bardenpho process. By integrating water quality analysis with 16S rRNA sequencing, we examined how process type, influent quality, and seasonal factors affect microbial communities and treatment performance. Systems with high chemical oxygen demand (COD) and biochemical oxygen demand (BOD)/COD influent exhibited the best pollutant removal performance, with average nitrogen and phosphorus concentrations in the effluent as low as 7.0 mg/L and 0.1 mg/L, respectively. Optimizing a 1:9 influent distribution ratio between the pre-anoxic and first anoxic zones in the modified Bardenpho process increased total nitrogen (TN) removal efficiency by an average of 14 percentage points compared to the AAO process. Additionally, the modified Bardenpho process identified 1100 bacterial genera, indicating a more complex and stable community. Influent water quality had the most significant impact on microbial communities and treatment efficiency, followed by seasonal factors and process type. This study provides theoretical and data support for the optimization of wastewater treatment processes and seasonal regulations.

Wastewater treatment plants (WWTPs) require precise real-time technologies to maintain effluent quality while optimizing the chemical consumption under fluctuating influent conditions. This study proposes a Bayesian optimized ensemble machine learning (BO-EML) framework to predict various contaminants, optimize chemical dosages, and analyze economic costs of chemical dosing. The model employs three ensembles and three baseline models within a structured nested cross-validation (NCV) framework to optimize hyperparameters through Bayesian prediction of chemical oxygen demand (COD), biochemical oxygen demand (BOD), total nitrogen (TN), and total phosphorus (TP). A full-scale WWTP was examined as a case study, resulting in test R2 values of COD (XGBoost R2 = 0.899), BOD (Gradient Boosting R2 = 0.975), TN (XGBoost R2 = 0.837), and TP (Gradient Boosting R2 = 0.737), significantly surpassing the performance of linear and single-tree baseline models, respectively. The SHAP findings indicated that influent indicators and chemicals displayed variable directional effects based on the dosage and connection to additional process parameters. Economic optimization showed a 32.1% cost reduction while maintaining the removal efficiency and regulatory compliance in chemical dosing. Thus, this framework shows the potential for integrating data-driven prediction, interpretation, and cost-efficient optimization, which can serve for stable WWTP operations.

This study addresses the issue of excessive coagulant consumption in drinking water treatment plants (DWTPs) by applying Machine Learning (ML) algorithms for precise process control. Operational data, including influent flow, raw water turbidity, pH, temperature, and effluent turbidity, were collected and analyzed through cross-correlation and feature engineering. Random forest (RF), Backpropagation neural network (BP), Generalized regression neural network (GRNN), and Multiple linear regression (MLR) algorithms were employed to construct prediction models for turbidity and dosage. A dosage optimization model was developed by integrating the best-performing GRNN prediction model into a closed-loop control strategy and validated via offline simulation based on historical operational data. The GRNN model achieved the highest accuracy for both effluent turbidity prediction (R2=0.9347, RMSE=0.0894 NTU; RMSE: Root Mean Square Error) and coagulant dosage estimation (R2=0.9490, RMSE=1.2660 mg/L), outperforming all other tested algorithms. After optimization, the average dosage decreased by 15.7%, and the average effluent turbidity improved from 0.81 to 0.77 NTU. The findings demonstrate that embedding ML algorithms within a closed-loop control framework will effectively reduce coagulant consumption and enhance effluent quality, thereby offering tangible engineering implications for DWTPs.

Microplastic (MP) pollution in the Arctic has recently attracted increasing attention; however, only a few studies have reported on this topic. Herein, we review the distribution, transport mechanisms, and major sources of MPs in the Arctic aquatic environment. We found that microfibers are the dominant MPs in the Arctic, and their main sources are atmospheric transport, ocean currents, and local human activities. Furthermore, MPs are widespread in the Arctic and have infiltrated the food web, posing risks to ecosystem stability. In addition, MPs may affect the climate system through several pathways, such as reducing the albedo of ice and snow, releasing greenhouse gases, disrupting the carbon pump, and altering atmospheric processes. Given the unique environmental conditions of the Arctic, we summarize suitable monitoring methods for multiple media and highlight key quality-control measures. We also evaluate the applicability and limitations of existing regulations and policies. This study calls for enhanced scientific monitoring, policy development, and international collaboration to curb the growing MP pollution in the Arctic. There is an urgent need for a systematic governance framework for MP pollution in the Arctic to address the associated environmental and climate risks.

Under China's Dual Carbon goals, drinking water treatment plants (DWTPs) face critical gaps between refined carbon management and low-carbon operation optimization. To address the critical gap, a closed-loop framework was developed for energy-carbon performance quantification, dynamic prediction, and intelligent optimization. Based on 5-year operational data, the target DWTP had an annual average carbon emission of 7860.81 tCO2-eq and an average emission intensity of 0.1182 kgCO2-eq/m3. The water pumping station, accounting for 57.15% of the total electricity consumption, was identified as the core energy-consuming unit.Three machine learning models (Random Forest, XGBoost, and LightGBM) were developed using water quality and quantity data for carbon emission intensity (CEI) prediction. LightGBM and Random Forest achieved the optimal prediction performance for electricity-related and PAC-related CEI, with test set R2 of 0.938 and 0.759, respectively. SHAP analysis identified effluent permanganate index as the core driving factor of CEI, and cross-validation confirmed model robustness. The improved multi-objective constrained hybrid-variable red fox algorithm optimized pumping station operation, achieving a 24.22% reduction in daily energy consumption, with the strategy fully verified for operational safety and stability. This framework provided robust theoretical support and engineering-ready guidance for DWTP low-carbon transformation.

Bacterial endotoxins originating from Gram-negative biofilm bacteria are emerging concerns in drinking water due to their pyrogenic and inflammatory health effects. This study systematically compared the effects of chlorine and chlorine dioxide (ClO2) on endotoxin release, using planktonic E. coli for mechanistic analysis and biofilms to verify relevance under pipeline conditions. Biofilms were cultivated in a rotating disk reactor under hydrodynamic conditions simulating real distribution systems and exposed to disinfectants at concentrations ranging from 0.2 to 5 mg/L. Results indicated that chlorine induced rapid bacterial inactivation accompanied by significant endotoxin release, reaching about 3.23 EU/mL, while ClO2 produced a similar peak level (about 3.00 EU/mL). The main difference between the two disinfectants lies in the post-release fate of endotoxin, as ClO2 promoted partial attenuation of endotoxin activity, whereas chlorine showed limited subsequent degradation. Bacterial density, pH, and temperature are key factors influencing endotoxin release. Low bacterial density enhanced endotoxin release, while pH affected chlorine and ClO2 differently, with stronger effects under acidic and alkaline conditions, respectively. Flow cytometry and scanning electron microscopy (SEM) revealed distinct bactericidal mechanisms between the two disinfectants, correlating with their endotoxin release profiles. The findings highlight that ClO2 can more effectively control endotoxin accumulation than chlorine, suggesting its potential advantage in drinking water systems serving vulnerable populations or where aerosol exposure is a concern.

Endotoxins derived from Gram-negative bacteria are increasingly recognized as indicators of microbial lysis in drinking water systems. While many studies have examined endotoxin release from suspended bacteria, its implications for biofilm-associated processes in drinking water distribution systems remain insufficiently understood. In this study, planktonic E. coli suspensions were used to characterize disinfection induced endotoxin accumulation, and a rotating disk reactor biofilm system was employed to evaluate biofilm responses to external endotoxin exposure. Direct RDR biofilm disinfection and concentration gradient exposure were further conducted to connect disinfection-derived endotoxin release with biofilm-associated responses. Chloramine and ultraviolet (UV) disinfection were compared under controlled conditions. Measurable endotoxin accumulation in the liquid phase increased with initial bacterial concentration. When the initial concentration reached 1.2 & times; 105 CFU/mL, chloramine at 5 mg/L increased endotoxin to 3.42 EU/mL after 300 mins. Ultraviolet treatment at 360 mJ/cm produced lower detectable endotoxin levels of 1.63 EU/mL. Chloramine caused oxidative membrane damage with rapid potassium leakage, whereas UV caused DNA damage, rapid loss of culturability, and delayed membrane disruption. The concentration gradient exposure experiment showed that low EU/mL endotoxin caused limited short-term HPC changes, whereas clearer responses occurred under medium and high load exposure. Under the 500 EU/mL high load challenge, biofilm alpha diversity decreased by more than 35 %. Community composition also shifted, with decreased Bacillus and enrichment of Gram-negative genera including Acidovorax and Caulobacter. Overall, chloramine caused continuous endotoxin release, while UV showed lower detectable endotoxin levels but did not eliminate endotoxin activity.

Membrane fouling remains the primary bottleneck in treating high-organic-load water systems, such as algae-laden water. This study investigates a novel self-cleaning strategy by coupling the piezoelectric properties of barium titanate (BaTiO3) ceramic membranes with ultrasonic activation. At an optimal ultrasonic frequency of 36 kHz, BaTiO3 membranes achieved a minimal normalized transmembrane pressure (TMP/TMP0) of 1.32, which represents a substantial reduction in fouling compared to alumina membranes. Post-cleaning analyses revealed minimal residual fouling resistances (0.72 & times; 107m-1 for alkaline and 0.07 & times; 107m-1 for acid cleaning), significantly outperforming commercial Al2O3 membranes. Mechanistic studies elucidated that ultrasonic stress amplifies spontaneous polarization, promoting the electrostatic detachment of charged algal cells and triggering a spatial reconfiguration of foulants from the membrane center to edge regions. Furthermore, the ultrasound-piezoelectric synergy enhanced the generation of hydroxyl radicals (& sdot;OH), effectively degrading extracellular polymeric substances (EPS) and restoring permeate flux. A triple-mechanism model-encompassing electrostatic detachment, domain-induced migration to edge regions, and free radical oxidation-was validated. These findings provide a robust theoretical framework for developing sustainable, high-efficiency piezoelectric membrane technologies for complex water treatment applications.

Deploying artificial intelligence (AI) in drinking water treatment plants (DWTPs) requires frameworks that balance algorithmic deployment with operational safety. To explore an offline evaluation approach prior to direct online control, this study developed a Simulated Closed-Loop Optimization (SCLO) framework. This framework features a bidirectional interaction mechanism for iterative dosage adjustment. Multiple machine learning algorithms were evaluated, with the Generalized Regression Neural Network (GRNN) selected to drive the dual surrogate models, yielding predictions for effluent turbidity (R2 = 0.9347, RMSE = 0.0894 NTU) and coagulant dosage (R2 = 0.9490, RMSE = 1.2660 mg/L). To evaluate mechanistic reliability, SHapley Additive exPlanations (SHAP) analysis was integrated to quantify the algorithmic logic. The analysis indicated that dosage decisions were primarily driven by influent pH and flow rate, which aligns with physical-chemical coagulation principles. Operating within this transparent environment, the SCLO strategy computationally reduced average coagulant consumption by 15.7% in offline simulations while maintaining effluent compliance. These findings suggest that the SCLO framework holds the potential to serve as a decision-support baseline for future DWTP operations.

Reclaiming wastewater from drinking water treatment plants (DWTPs) presents significant prospects. This study evaluated the performance, operational stability, and fouling control strategies of flat-sheet ceramic membranes for treating sand filter backwash water (SFBW). Systematic water quality characterization revealed that the SFBW contained high levels of total bacterial counts (30,500-50,700 CFU/mL), metal ions (e.g., Al, Fe, Mn), and dissolved organic matter (DOM). Through a series of pilot-scale experiments, the effects of membrane flux, filtration cycles, and feedwater quality were investigated, and it was determined that the system can operate continuously at 100 LMH (L center dot m-2 center dot h-1). Tests have shown that ceramic membranes were highly effective at removing turbidity and microorganisms. No bacteria were detected in the treated water, and the removal efficiency for CODMnreached 81.4%. Concentrations of Al, Fe, and Mn were consistently maintained below reuse standards of 0.2, 0.3, and 0.1 mg L-1, respectively. To mitigate membrane fouling, a hierarchical cleaning strategy comprising hydraulic backwashing, sodium hypochlorite (NaClO) oxidation, and citric acid chelation successfully achieved complete flux recovery. This research underscores the high reliability and application potential of ceramic membrane technology for SFBW resource reclamation in the DWTPs.

Energy conservation and carbon reduction in pumping systems at urban water supply plants are pivotal for achieving carbon peak and carbon neutrality goals.Over 90%of the electricity consumption in such plants is attributed to pump operation.However,current research faces three interconnected problems.First,static models based on theoretical characteristics fail to represent actual dynamic operating conditions accurately,leading to biased optimization baselines.Second,the coupling between intelligent algorithms and increasingly complex pump-optimization problems remains insufficient,often resulting in suboptimal solutions.Third,the evaluation system is fragmented,and assessment results are not effectively fed back into the optimization process to enable iterative improvement.These disconnections among the Model-Algorithm-Assessment components represent a core scientific challenge that hinders precise and effective decarbonization of water-supply systems.This review systematically examines recent advances in the application of intelligent algorithms to pump energy optimization.It first outlines the key elements of pump energy modeling,including operating-point derivation via curve fitting,objective function formulation,and constraint setting,which together provide a foundation for subsequent algorithmic optimization.It then categorizes and analyzes the application scenarios and technical features of traditional heuristics,data-driven methods,and hybrid algorithms.Literature analysis reveals that traditional heuristics remain the most widely applied algorithms but are prone to premature convergence under dynamic conditions,limiting their practical effectiveness.In contrast,emerging hybrid algorithms that integrate mechanistic models with data-driven techniques have demonstrated additional energy-saving potential:specifically,they can reduce energy consumption by 5%to 10%compared to traditional algorithms.A life-cycle perspective indicates that operational-phase carbon emissions account for 70%to 85%of the total footprint,while the manufacturing and disposal stages contribute 15%to 30%.This finding suggests that life-cycle assessment(LCA)could complement existing evaluation systems and underscores the need for a holistic assessment beyond mere operational energy use.Operational-phase metrics are also detailed,as they are essential for quantifying optimization effects and providing feedback to algorithms.The results indicate that the iterative synergy between intelligent algorithms and assessment systems is central to enhancing performance.To address the identified gaps,we propose a Model-Algorithm-Assessment tripartite framework that focuses on three interrelated aspects:(1)a sufficiently accurate and generalizable mathematical model;(2)intelligent algorithms that overcome algorithm-problem mismatch and achieve efficient optimization under complex,time-varying conditions;and(3)a life-cycle assessment system that provides comprehensive validation and broader evaluation dimensions for optimization strategies.This framework promotes the implementation of intelligent energy-saving and carbon-reduction technologies in urban water-supply plants.

The inadequacy of physicochemical-parameter-based replacement criteria for biologically activated carbon (BAC) in drinking water treatment plants (DWTPs) posed significant risks to organic and emerging contaminant removal. To address this, the study systematically investigated the temporal evolution of BAC characteristics through metagenomic sequencing, while employing Multiple Linear Regression (MLR), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), Gradient Boosting Decision Trees (GBDT), and Random Forest (RF) models to establish predictive machine learning models for BAC replacement from the biodegradation and adsorption of BAC. The results indicated that, with the prolongation of operating time, the iodine value, BET surface area (BET), and t-Plot micropore volume decreased, while the acidic functional groups increased. Although the alpha diversity stabilized, the total functional bacterial abundance and the key genus Bradyrhizobium exhibited marked decline, showing 7.7% and 4.4% reductions respectively. Network analysis revealed that aged BAC harbored stable clusters of antibiotic resistance genes (ARGs) and virulence factors (VFs), highlighting a latent bio-safety risk. For model performance, RF achieved optimal prediction (R2 = 0.994) for t-Plot micropore volume prediction, while XGBoost demonstrated superior biodegradation modeling (R2 = 0.87) for the total relative abundance of functional bacteria, significantly outperforming the linear baseline (MLR, R2 = 0.22). The study supported a dual-indicator strategy that integrated adsorption capacity and microbial functionality, and presented a proof-of-concept framework for the precise lifecycle management of BAC, offering a data-driven decision-support tool for water treatment optimization.

The application of ultrasound has shown potential for improving dewatering efficiency by disrupting the structure of sludge and enhancing the release of water due to its unique physical and chemical effects. Various characterization techniques, including capillary suction time (CST), rheological properties, and adhesion characteristics, were utilized in conjunction with visual analysis of cell rupture to comprehensively evaluate dewatering efficiency and algal cell responses. The results identified 40 kHz as the optimal ultrasonic frequency for morphological shape regulation (with a corresponding energy density of 33.30 J/mL), where the dewatering efficiency reached a peak of 79.50%. This critical threshold enables controlled cell shriveling rather than total lysis, which was quantitatively confirmed by the physical states of water. The interstitial water content was found to increase by 48.24% when treated at this optimal condition. Furthermore, analyses of dissolved organic matter (DOM) fractions revealed that ultrasonic stress enhances the hydrophobicity-hydrophilicity transition of organics by disrupting the hydration shell of proteins. This study elucidates the mechanisms of non-destructive dewatering of algal sludge by focusing on the critical transition from bound water to free water, demonstrating that 40 kHz frequency optimizes water release through targeted gas vacuole rupture and effective regulation of cellular integrity.

Deploying artificial intelligence (AI) in drinking water treatment plants (DWTPs) requires frameworks that balance algorithmic deployment with operational safety. To explore an offline evaluation approach before any online implementation, this study developed a Dual Surrogate Simulated Feedback (DSSF) framework. This framework features a surrogate-based simulated feedback mechanism for iterative dosage adjustment. Multiple machine learning algorithms were evaluated, with the general regression neural network (GRNN) selected to drive the dual surrogate models, yielding predictions for settled-water turbidity (R2 = 0.9347, root mean square error (RMSE) = 0.0894 NTU) and coagulant dosage (R2 = 0.9490, RMSE = 1.2660mg/L). To improve model interpretability, SHapley Additive exPlanations (SHAP) analysis was integrated to quantify feature contributions in the surrogate models. The analysis indicated that influent pH and flow rate were the main contributors to dosage prediction, which was consistent with established coagulation process knowledge. Within this offline surrogate-model evaluation, the DSSF strategy suggested a potential 15.7% simulated reduction in average candidate coagulant dosage while improving surrogate-predicted target-range satisfaction. These findings suggest that the DSSF framework holds the potential to serve as a pre-deployment decision-support baseline for future DWTP operations.

This study examines the release of microplastics from four types of pipe materials polypropylene random copolymer (PPR), polyvinyl chloride (PVC), polyethylene (PE), and stainless steel, assessing their impact on water quality and microbial communities under varying residual chlorine concentrations in stagnant water. Significant differences were found in the amount of microplastic released from each material, with PVC pipes releasing the highest amount, reaching 1.14 x 105 particles/L, while PE pipes released the least, exhibiting no significant difference from stainless steel samples. Microplastic particles in water accelerates chlorine decay, increases turbidity, elevates total organic carbon (TOC) levels, and can lead to the release of smaller microplastic particles into the water. Microplastics facilitated microbial proliferation, as indicated by higher heterotrophic plate counts (HPC), particularly in samples with PPR microplastics. After 360 h of stagnation, the HPC in the PPR-containing sample was still 1.40 x 105 CFU/mL, while chlorine levels in other samples had dropped to near zero. Scanning electron microscopy (SEM) revealed considerable surface degradation of microplastics under higher chlorine concentrations, with distinct bacterial colonization patterns on different microplastic materials. Chlorine concentration influenced microbial composition, with Sphingomonas species dominating at 0.25 mg/L and Pseudomonas species at 1.5 mg/L. Kruskal-Wallis test, identified significant taxonomic and phylogenetic differences in microbial communities among stagnant water, microplastic particles, and tube wall biofilms. Initial chlorine residuals also shaped dominant species, with Sphingomonas prevailing at 0.25 mg/L and Pseudomonas at 1.5 mg/L.