ACS ES&T water最新文献

As a complementary or alternative approach to experiments, theoretical computation of adsorption between carbon materials and emerging aromatic organic contaminants (AOCs) is increasingly important in elucidating adsorption mechanisms and characteristics, as well as their predictions. In this study, the adsorption energies between graphene and 112 AOCs were first analyzed by density functional theory (DFT-D). By the use of quantum molecular descriptors, different machine learning (ML) algorithms were developed. EXtreme gradient boosting exhibited the best performance among the four ML algorithms investigated, showing the lowest root-mean-square percentage error of 4.5% for the test data set. Accordingly, the interpretable ML technique (i.e., SHAP) assessed the importance and dependence of descriptors in the adsorption mechanisms of AOCs to graphene. The global interpretation confirmed that the molecular-volume-induced van der Waals interactions including π–π stacking are dominant, whereas the other interactions (e.g., induced hydrogen and electrostatic interactions) are comparably less significant in the adsorption of most AOCs on graphene. In contrast, using local interpretation, hydrogen bonds and induced dipole interactions with surrounding water were identified as important explanatory variables in the adsorption of AOCs containing carbonyl and sulfur functional groups. Therefore, the developed DFT-D-based ML models could be a reference model for theoretical and experimental studies.

Denitrification (DNF) and dissimilatory nitrate reduction to ammonium (DNRA) compete in reducing sediment conditions where DNF permanently removes nitrogen (N), while DNRA retains N with the conversion of nitrate (NO₃^–) to ammonium (NH₄⁺). Thus, an increase in the level of DNRA can undermine permanent N removal. We investigated the relative magnitude and controls of these two processes at two milldam-affected riparian sites. DNRA (5.2–37.6 μg L^–1 h^–1) accounted for 10–79% of total NO₃^– reduction and was highest in riparian sediments with higher iron (Fe) and sodium (Na⁺) in groundwater. DNF was the primary mechanism for NO₃^– reduction when Fe and Na⁺ concentrations were low but when NO₃^– was elevated. DNRA rates were higher for treatments with higher dissolved organic carbon (DOC):NO₃^– and Fe:NO₃^– ratios, indicating the stimulation of both heterotrophic and Fe²⁺ driven autotrophic DNRA. DNF and DNRA rates and their microbial functional genes decreased with increasing sediment depths. These findings imply that hydrologically stagnant and persistently reducing conditions associated with relict milldams and similar anthropogenic structures may enhance DNRA at the expense of DNF and undermine permanent N removal in riparian zones. Thus, the effects of such structures need to be accounted for in watershed N management strategies.

Photocatalysis studies were performed using graphene oxide–TiO₂ (GOT) nanocomposite irradiated using 125 W UV and visible irradiation to investigate the effect of various water matrices, i.e., distilled water (DW), secondary treated wastewater (WWE), and lake water (LW) on the removal of organophosphorus pesticides from binary mixtures formulated using a 2² full factorial design. The EC₆₀ and EC₄₀ values of individual pesticides, determined from the dose response profile using the Ellman assay were used as the high and low concentrations, respectively. Photocatalysis was conducted at a GOT dose of 60 mg/L. For both Mixture-I, comprised of dichlorvos and malathion, and Mixture-II, comprised of parathion and phorate, degradation followed the order, DW > WWE > LW. After 80 min, the highest degradation of ∼80% was observed for Mixture I in DW under UV irradiation when the concentration of both pesticides was at EC₄₀. Malathion displayed a higher rate and extent of degradation and mineralization compared to dichlorvos in all of the mixture combinations. Under similar reaction conditions, phorate and parathion demonstrated similar values of the first-order degradation rate constant. Dissolved organic matter (DOM) had a detrimental effect on pesticide degradation by blocking the active sites on the catalyst and by scavenging the oxidative radicals generated during irradiation. A decrease in SUVA₂₅₄ in both WWE and LW during photocatalysis indicated the decomposition of aromatic moieties in DOM. After UV/visible photocatalysis, the lowest residual toxic effect, as measured in the Ellman assay, was observed in mixtures containing low initial concentration of both the pesticides.

Suspended sediment is a critical water quality parameter and an indicator of geomorphic processes, but suspended sediment dynamics in urban streams may not conform to the first-flush model widely used for other pollutants. We analyzed discharge and turbidity data for 367 events from three urban watersheds (impervious cover 16–45%) in Cleveland, Ohio (USA). Less intensely urbanized watersheds exhibit higher turbidity compared to that of the most highly urbanized watershed. Proportionally, more counterclockwise hysteresis is observed in the two less urbanized watersheds, and more clockwise hysteresis occurs in the highly urbanized watershed. However, hysteresis patterns are driven by different mechanisms in each watershed, and geomorphic analysis was critical to identifying the underlying mechanisms. In the least urbanized watershed, spatial rainfall variability controls sediment hysteresis. In the intermediate watershed, the erosion of upstream weathered shale banks during dry periods plays a significant role in the sediment supply and shaping hysteresis. In the most urbanized watershed, high eroding banks in downstream reaches lead to more frequent clockwise hysteresis. Overall, we suggest that as the impervious surfaces increase, the availability of instream sediments (bed and banks) plays an increased role in suspended sediment dynamics, and geomorphology remains essential for guiding management decisions.

On-site analysis of bacteria is important, but high-practicality methods are challenging due to their limited sensitivity and weak anti-interference. Herein, nitrocellulose (NC) membranes with hydrophilic characteristics and porous structures are utilized to filter those small coexisting substances in samples and selectively enrich target bacteria at the NC surfaces. To visualize bacteria with enhanced stability and sensitivity, the in situ growth of plasmonic Au nanocrystals on bacteria is implemented via the incubation of the bacterium-loaded NC membranes in Au reaction solutions. The bacteria are remarkably stained by the plasmonic Au nanocrystals, exhibiting responsive color changes for quantitative analysis. First, bacteria are visualized by in situ grown Au nanocrystals. In comparison with the presynthetic Au nanocrystals, those chemicals in Au reaction solutions are more stable, which ensures comparable stability and reproductivity. Second, the bacterium responses are amplified via plasmonic chemical reactions. Those Au nanocrystals are considered not only visual probes but also signal amplifiers. Overall, the protocol of Au-stained bacteria on NC membranes facilitates the on-site microbial analysis with characteristics of high simplicity, speediness, sensitivity, stability, and anti-interference, largely contributing to the progress of nanotechnologies from fundamental research to practical applications.

The contamination of natural water bodies with dyes and other refractory compounds is a menacing issue in developing nations. Despite stringent laws, industrial effluent is not managed efficiently, as it incurs additional cost. Hence, the present research focuses on sustainable mitigation of refractory contaminants using a self-driven bioelectro-Fenton (BEF) system. The iron-activated charcoal (Gt-Fe/AC) cathode-cum-Fenton catalyst used in this investigation was synthesized using waste green tea extract as a biogenic agent. The green catalyst-driven BEF system (Gt-Fe/AC-MFC) achieved a maximum power density of 111.7 ± 3.1 mW/m² and a maximum operating voltage of 108 ± 3 mV, while parallelly degrading 20 mg/L of Coomassie Brilliant Blue (CBB) dye almost entirely in 300 min at a neutral pH. Additionally, high removal of Congo red dye (96.8 ± 1.2%) and methylparaben (90.9 ± 0.6%) was attained under similar operating conditions. Moreover, the Fe-AC-catalyzed BEF performed fairly well in treating spiked real wastewater and exhibited remarkable stability, with only a 3% decrease in CBB removal efficiency after 10 continuous cycles and 0.11% drop in cathodic current per cycle. Hence, this BEF system can be a sustainable oxidative technology to tackle refractory wastewater in resource-constricted regions.

Current research on advanced oxidation processes often focuses on removing individual organic contaminants, sometimes overlooking the impact of microplastics (MPs) on mass transfer. Real-time and precise monitoring through experimental measurements is challenging. In this study, we used computational fluid dynamics simulations to examine the effect of MPs on mass transfer in a flow-through electro-peroxone process. Our findings revealed that MPs decreased the concentration of hydroxyl radicals at the electrochemical cathode/solution interface. However, there was no significant impact on the concentrations and diffusion pathways of O₃ in the inlet gas phase and hydrogen peroxide on the electrochemical cathode surface. Additionally, the average size of MPs increased from 135.0 to 750.0 μm, and their count rose from 7474 to 10,924 particles/L. This was accompanied by increases in average turbulent kinetic energy and turbulent dissipation rate by 0.027 and 0.018 km²/s², and 0.041 and 0.702 m²/s³, respectively. These changes suggested that the enlargement and increased count of MPs hindered liquid flow, reducing the efficiency of converting gaseous O₃ to aqueous O₃. Consequently, this diminished the removal efficiency of pollutants in the electro-peroxone process. These insights are crucial for developing more efficient advanced oxidation processes for the simultaneous removal of MPs and pollutants.

Effective leakage detection is crucial for ensuring operational efficiency, reducing water loss, and maintaining infrastructure integrity in water distribution systems (WDSs). This study presents a specialized leakage detection approach enhanced by spatial–temporal graph convolutional networks (ST-GCN). This method combines large-scale network partition, optimized sensor placement, pilot-scale network partition, and the ST-GCN model, which captures both spatial and temporal dependencies. Then, two case studies are employed to evaluate the effectiveness of this method. The model achieved an average accuracy, precision, recall, and F1-score of 98.38, 98.89, 97.95, and 98.41% across multiple tests for Network A and of 98.51, 98.51, 98.56, and 98.53% for Network B, respectively, which demonstrate the model’s high performance. Furthermore, it compares the model’s simulation results with three existing methods. The enhanced ST-GCN model is superior to those of the other models in terms of accuracy, confirming its superior effectiveness in detecting leakages.

Nitrogen is a component of many fundamental biomolecules and also participates in environmental redox chemistry. Nitrogen pollution is a serious environmental problem. Biological nitrogen removal is one of the most significant issues in wastewater treatment. Microbial-driven nitrogen transformations are carried out through metabolic pathways. Wastewater treatment systems using microalgal-bacterial cocultures can improve nutrient removal efficiency through interspecies synergistic interactions. However, relevant studies on the nitrogen metabolism and microbial characteristics of microalgal-bacterial systems have not been systematically reviewed and discussed. This Review comprehensively analyzes nitrogen contaminants in various biological nitrogen removal microalgal-bacterial composite systems and summarizes the microbial characteristics and nitrogen-metabolizing enzymes present. Nitrogen metabolism regulation methods involving quorum sensing and genetic regulation are described, and methods of enhancing microbial nitrogen removal through microbial community interactions, enhancing enzyme activity, and promoting electron transfer are introduced in detail. This Review provides a perspective on the improvement and optimization of microalgal-bacterial wastewater treatment technology through analyzing the nitrogen metabolism and increasing process performance.

Nitrate is accumulated in the groundwater, modified through nitrification/denitrification, and exchanged with coastal/estuarine water bodies. To examine the sources and modifications of nitrate, the concentrations and isotopic composition of nitrate (δ ¹⁵N and δ ¹⁸O_NO3) in the groundwater was monitored at 5 locations along the bank of Godavari estuary and in the estuarine waters for 7 months during wet (August–November) and dry (March–May) periods. Though the concentration of nitrate (NO₃^–) was higher during the wet than dry periods in both the groundwater and estuary, insignificant seasonal variability was observed in δ¹⁵N and δ¹⁸O_NO3 indicating homogenization through mineralization–immobilization turnover of NO₃^–. The range of δ¹⁵N to δ¹⁸O of NO₃^– indicates soil, manure, and septic waste may be the major source of NO₃^–. The mean ratio of δ¹⁵N/δ¹⁸O of 1.1 ± 0.3 indicates the occurrence of denitrification in the groundwater. Concentrations δ¹⁵N_NO3 and δ¹⁸O_NO3 of NO₃^– displayed a significant relation between groundwater and estuarine water suggesting that NO₃^– is possibly denitrified. This study suggests that denitrified NO₃^– (enriched δ¹⁵N and δ¹⁸O) reported in the Godavari estuary may be contributed through submarine groundwater discharge than it is hypothesized to flux from the watershed.