Water Environment Research最新文献

A novel approach to sewer network assessment is presented that uses artificial intelligence (AI)/machine learning (ML) to predict infiltration and structural defect occurrences in each pipe instead of estimating the traditional criteria-based overall pipe condition or likelihood of failure. A comparative analysis of four decision tree-based ML models, and their use in predicting the defect locations in sewer networks, is presented. The models are developed using data from closed-circuit television (CCTV) inspections coupled with additional pipe information and inspection reports. The ML approach uses such information from two utilities to create utility-specific defect prediction models. The class imbalance in the data, due to more defects than nondefects, is addressed with three methods, and the hyperparameters, settings that define the model architecture, are optimized via a repeated stratified k-fold cross-validation grid search. The performance of the models is assessed using the area under the receiver operating characteristics (AUC-ROC) and precision recall (AUC-PR) curves. LightGBM-based models, with the cost-sensitive learning method for addressing class imbalance, show the best performance overall when predicting either types of defects for both utilities. The best performing model achieves an AUC-ROC of 0.79 and an AUC-PR of 0.62. For the two utilities investigated, an application of SHapley Additive exPlanations (SHAP) shows that the most important features for indicating both types of defects are "pipe location" and "pipe age."

Well-continuous zirconia (Zr)-based metal-organic framework (UiO-66) grown onto alumina hollow fiber (AHF) remains challenging. By introducing Zr oxide (ZrO₂) nanoparticles as coat-seeded particles, the hydrogen bond between UiO-66 and ZrO₂ nanoparticles can be enhanced. This study aims to modify AHF by coat-seeding ZrO₂ nanoparticles using the sol-gel Pechini method prior to in situ solvothermal deposition of UiO-66. The prepared samples were characterized using scanning electron microscopy (SEM), field emission SEM (FESEM), atomic force microscopy (AFM), and contact angle measurement. The residual UiO-66 samples were examined using X-ray diffraction (XRD), Brunauer-Emmett-Teller (BET), and Fourier transform infrared (FTIR) spectroscopy. The study of humic acid (HA) removal revealed that the AHF/ZrO₂/Zr-B24 sample showed excellent performance in HA removal with 99.8% rejection and 42.4 ± 1.7 L·m^-2·h^-1 of solute permeation. This result was attributed to charge repulsion between the negative charge of carboxyl groups belonging to UiO-66 and HA.

Nitrification has a pivotal role in water resource recovery facilities, forming also the rate-limiting step. Packed bed biofilm reactors-including biological aerated filter (BAF) and submerged aerated filter (SAF)-facilitate high-rate nitrification with volumetric rates > 1 kg N·m^-3·day^-1 (due to high specific surface areas) and are attractive for tertiary nitrification. Compared to SAFs, BAFs are susceptible to clogging, incur larger head losses, and demand frequent backwashing. While few works investigated the effect of water upflow velocity on nitrification in BAFs (using a single reactor), no such study has been performed with SAFs. This work attempted to systematically characterize the effect of water upflow velocity in nitrifying SAFs. Trials were performed in two test beds, each having five nitrifying SAFs (10 reactors in total) operated at water upflow velocities of about 1, 5, 10, 15, and 20 m/h. Volumetric ammonia loading rates (vALRs) from about 200-2000 and 300-1400 g N·m^-3·day^-1 were applied (in the test beds) over a period of about 230 and 157 days, respectively. While an increase in water upflow velocity from 4 to 15 m/h was observed to positively influence the nitrification rate in the previous studies using BAFs, water upflow velocity did not show any effect on nitrification rates in SAFs in this study. Nearly complete nitrification could be obtained at all five water upflow velocities even at vALR of about 2 kg N·m^-3·day^-1 (surface-specific loading rate ≈2.1 g N·m^-2·day^-1). Water velocity did not show any effect on the biofilm community structure (diversity, richness, and dominant organisms).

The escalating frequency of harmful cyanobacterial blooms (HCBs), driven by climate change and eutrophication, poses risks to ecosystems, water resources, and public health. Given South Korea's heavy reliance on surface waters, increasingly affected by HCBs producing microcystins and taste and odor compounds (geosmin and 2-methylisoborneol), this study used machine learning to predict cyanobacterial proliferation by 2100 under climate scenarios. It also estimates increases in treatment costs, assuming water treatment plants (WTPs) respond to increased bloom intensity solely by modifying their usage of powdered activated carbon (PAC). A random forest (RF) model trained on 28 years of Nakdong River data projected HCB occurrences under Shared Socioeconomic Pathway 5-8.5. The RF indicated significant increases in HCB magnitude and variability (cyanobacteria densities from 1.6 × 10⁴ to 6.3 × 10⁴ cells/mL; coefficient of variation from 1.60 to 1.77), corresponding to a 6.7°C increase in mean annual air temperature. Analysis of WTP operational data and prior studies revealed a correlation between PAC use and HCB events, suggesting the increase in HCBs necessitates significantly higher PAC doses to treat projected secondary metabolites, particularly microcystins. Under the worst-case scenario, the projected cost burden for water treatment could triple from current levels, potentially reaching $22.1/month/household by 2100, supporting proactive implementation of advanced treatment facilities in high-risk regions. These findings underscore the need for enhanced preparedness to address more complex HCB patterns under climate change, ensuring water safety, economic stability, and human health. Additionally, this study provides a methodological blueprint for other countries facing similar climatic and environmental challenges.

This study unravels the intermolecular mechanistic degradation of palm oil mill effluent (POME) in sulfate removal, utilizing a distinctive coagulant derived from naturally abundant limestone (CaCO₃), which was activated into calcium hydroxide [Ca(OH)₂] through calcination and exothermic reactions. Sulfate was reduced from POME by 88.76% with the optimal conditions (pH 5, 200 g/L Ca(OH)₂ dosage, and 135 min settling time) with strong correlation (r = 0.8237) and statistically significant (p = 0.0064) of Pearson's correlation. Scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) revealed an ideal morphology and elemental composition with reduction of 4.4753 m²/g surface area in Ca(OH)₂ to slurry. Kinetic studies evaluated that sulfate removal at 4400 mg/L strongly followed the second-order model with a high coefficient of determination (R² = 0.9883). Analysis using Fourier-transform infrared spectroscopy (FTIR) and gas chromatography-mass spectrometry (GC-MS) detected the formation of 2-isopropyl-5-methyl-1-heptanol (C₁₁H₂₄O). Overall, this study demonstrates the potential of Ca(OH)₂ from CaCO₃ as an efficient material for POME additional treatment, helping reduce VOCs and improve the value of industrial wastewater.

Advanced treatment methods for removing antibiotics are cost-intensive. Subsequently, the goal of environmental and economic sustainability has switched attention towards bio-adsorbents. This study evaluated the effectiveness of raw and alkali-modified peanut shell powder as a cost-effective, novel adsorbent for removing azithromycin, one of the most widely used drugs worldwide. Prepared adsorbents were characterized by FTIR and SEM equipped with EDX. Experiments designed using a Taguchi-based approach were performed with a synthetic azithromycin solution to optimize initial concentrations, adsorbent dose, pH, and time. The results showed 63% removal with raw adsorbent at pH 11, an initial concentration of 20 mg/L, a time of 45 min, and an adsorbent dose of 0.4 g/L. With the modified adsorbent, an attractive 85% (maximum) removal was achieved at pH 11, an initial concentration of 30 mg/L, a time of 60 min, and an adsorbent dose of 0.4 g/L. Based on analysis of variance (ANOVA), pH and initial concentration are identified as the most influential factors for azithromycin removal. The improved adsorption performance of modified peanut shells (q_max = 192.1 mg/g compared to 159.2 mg/g for raw PS) was due to increased surface heterogeneity, enhanced electrostatic interactions, and greater accessibility of oxygen-containing functional groups, as confirmed by kinetic, isotherm, and surface analysis.

Johkasou and biogeofilter hybrids have been developed to remove nutrients from domestic wastewater. Granulated coal ash, a by-product of coal-fired power plants, was used as the base material for the biogeofilter installed at the rear stage of the Johkasou system. The biogeofilter showed stable nutrient removal performance, which was attributed to both uptake by plants and adsorption by granulated coal ash. Flowering plants were best at removing phosphorus, while green manure crops were better at removing nitrogen. The phosphorus adsorbed onto the granulated coal ash could easily be used for plant growth. Hence, the regeneration of phosphate adsorption sites on granulated coal ash by plants is advantageous for extending the operational lifespan of the phosphate adsorption process and lowering maintenance costs. The proposed system is able to respond to fluctuations in concentrations of wastewater and seasonal changes, making it a viable decentralized wastewater treatment system for regions without established sewerage networks.

The increasing number of artificial dyes from industrial processes contaminating water sources requires more efficient and sustainable techniques for wastewater remediation. This study involves the utilization of litchi (Litchi chinensis) fruit peels in the green synthesis of magnesium oxide nanoparticles (MgO-NPs). Further, for the characterization of eco-friendly MgO-NPs, ultraviolet-visible spectra, Fourier transform infrared (FTIR) spectroscopy, dynamic light scattering, scanning electron microscopy (SEM), and X-ray diffraction (XRD) spectroscopy were utilized. An absorption peak at 274 nm from UV-visible spectroscopy indicates the development of MgO-NPs. The particle average size was found to be 96.33 nm with a polydispersity index of 0.32. The application of the synthesized nanoparticle was evaluated for the removal of malachite green and Eriochrome Black T. The biosynthesized nanoparticles demonstrated an enhanced photocatalytic activity, effectively removing malachite green (95.66%) and Eriochrome Black T (92.69%) from contaminated water under solar light irradiation. These results reveal that the green-synthesized MgO-NPs achieved significant efficiency in dye removal, highlighting their potential as a cost-effective and sustainable approach for wastewater treatment applications.

This study aimed to evaluate the hydrodynamic behavior of a vertical subsurface flow constructed wetland (VSSF-CW) treating domestic sewage by applying a saline tracer, comparing system performance in operational Years 3 (NR-3) and 5 (NR-5), and assessing the influence of a rainfall event (R-5). Electrical conductivity monitoring was used to construct residence time distribution (RTD) curves for all tests, enabling detailed characterization of hydraulic behavior. As a result, the system exhibited highly dispersed flow (d > 1.21; N < 2.07) with a tendency toward continuous stirred tank reactor (CSTR) behavior. A comparison between NR-3 and NR-5 tests revealed significant differences (p < 0.05, t test) in the hydrodynamic parameters. The rainfall event (R-5) had a statistically significant effect (p < 0.05, t test), decreasing hydraulic retention time, increasing dilution, and enhancing dispersive flow within the treatment unit. These findings highlight the importance of long-term hydrodynamic monitoring in VSSF-CW systems and demonstrate how operational conditions and external factors such as rainfall can influence treatment performance.

The limitation of membrane filtration is that it only filters out pollutants but does not treat them thoroughly; therefore, catalytic membranes are a synergistic combination of membrane filtration and catalytic decomposition of pollutants. This study focuses on synthesizing mixed oxides Cu-Mn-O with different ratios and coating the optimal onto a PET membrane using a dip-coating method. The materials were thoroughly characterized through FTIR, XRD, TGA, zeta potential, EDS, BET, and SEM. The 3:1CuO/MnO₂ showed a low-order structure and seems to be amorphous through XRD results. SEM results showed that the mixed oxide nanoparticles have a porous structure, ultra-small size, and uniform distribution. The large specific surface area of the 3:1CuO/MnO₂ (64.09 m²/g) enhances the surface-active sites. In adsorption and degradation tests, the 3:1CuO/MnO₂ consistently showed the highest efficiency for both Congo red (CR) and methylene blue (MB). Specifically, the CR degradation reaction followed pseudo-second-order kinetics (PSO), while the MB degradation process conformed to pseudo-first-order kinetics (PFO). In membrane filtration, the water flux reached 170.5 L/m².h for the PET membrane and decreased to 124.7 L/m².h for the 3:1CuO/MnO₂/PET membrane due to the filling of the catalytic particles. Notably, the CR rejection of the 3:1CuO/MnO₂/PET membrane surged from 74.3% to 96.7% in the presence of peroxydisulfate (PDS). ROS (reactive oxygen species) trapping tests identified singlet oxygen as the primary oxidizing agent. Finally, the catalytic membrane exhibited impressive durability with stable performance after 4 cycles, opening up potential practical applications in textile wastewater treatment.