Environmental Science: Water Research & Technology最新文献

This study examines the potential of Purolite Shallow Shell™ SSTA63 anion exchange resin for mitigating nitrate ion (NO₃⁻) contamination in aqueous environments. Through systematic experimentation, including dosage optimization, pH dependency, kinetic and desorption studies, we investigate the sorption behavior and practical applications of the resin. Results indicate that the resin effectively removes NO₃⁻ ions, with maximum efficiency achieved within 10 minutes. When 0.025 g of resin was used, 75% of NO₃⁻ was removed, whereas with 0.05 g, 89% was removed, and with 0.1 g of resin, 95% was removed. At pH 1, approximately 50% of NO₃⁻ ions were removed, with removal efficiency reaching 97% between pH 4 and 10. Sorption isotherms affirm the suitability of the Langmuir model for the current investigation. The monolayer maximum sorption capacity (q_max) value was found to be 53.65 mg g⁻¹. The resin demonstrates robust desorption capabilities using 0.1 M hydrochloric acid (HCl), effectively desorbing NO₃⁻ above 99%, indicating easy NO₃⁻ desorption and resin regeneration. The presence of coexisting ions such as chloride (Cl⁻), sulfate (SO₄²⁻), and phosphate (PO₄³⁻) showed a minimal impact on NO₃⁻ removal in individual binary mixtures, with efficiencies exceeding 93%, suggesting a strong selectivity of the resin towards NO₃⁻. Purolite SSTA63 anion exchange resin exhibited a high affinity for NO₃⁻ ions, even over other competing ions, despite the general trend of ion exchange resins to favor ions with a higher atomic number and valence. Overall, this resin presents a promising solution for NO₃⁻ removal, with implications for water treatment and environmental remediation.

This study investigates the effect of ferrous sulfate (FeSO₄) treatment on protective film formation and subsequent microbially influenced corrosion (MIC) of CuNi 70/30 pipeline alloy, a material commonly used in maritime platforms. CuNi 70/30 coupons were treated with FeSO₄ solution in potable water and seawater simulating a flow speed of 0.94 m s⁻¹ for 5 d. The treated coupons exhibited a protective iron oxyhydroxide, likely lepidocrocite (γ FeOOH), film on the surface. MIC performance was evaluated in modified Baar's medium with SRB for 28 d. Results revealed thicker SRB biofilm and increased MIC pitting attack on FeSO₄ treated coupons compared to untreated coupons. These findings suggest that FeSO₄ treatment may exacerbate MIC susceptibility in MIC-prone environments, highlighting the importance of carefully considering corrosion mitigation strategies in maritime platform applications.

In this study, a hybrid bionanomaterial (GO@Di) composed of Dictyosphaerium sp. microalgae and graphene oxide (GO) was synthesized for the first time to be used as an adsorbent for the removal of pentavalent arsenic (As(V)) from aqueous solutions. GO@Di was characterized by analytical techniques including Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), pH at point of zero charge (pH_PZC), and BET surface analysis. Solution pH, adsorbent mass, initial concentration of the pollutant, and ionic strength were evaluated and optimized to identify the best conditions for As(V) removal using GO@Di. A removal efficiency of 69% and an adsorption capacity of 885 mg g⁻¹ were obtained under the optimal conditions of pH 3, 1 mg of GO@Di and initial As(V) concentration of 50 mg L⁻¹. The adsorption kinetics were also analyzed, reaching the equilibrium at 120 min. The experimental kinetic results were correlated with the pseudo-second order model. Equilibrium data were fitted with the Brunauer–Emmett–Teller (BET) isotherm model. Regeneration studies indicated that GO@Di could be re-used efficiently up to 4 adsorption/desorption cycles. Finally, GO@Di was applied to real samples of natural waters and industrial effluents, obtaining removal percentages between 52 and 95%, which demonstrated the promising potential of GO@Di to depollute complex aqueous matrices containing As(V). Future studies will focus on the removal of other arsenical species using GO@Di and its implementation in dynamic adsorption systems.

The objective of this study was to assess the impacts of biochar and iron-enhanced sand (IES) on the comprehensive contaminant retention performance of a field-scale stormwater filtration system. The system distributed runoff from a parking lot into three filters containing sand, sand amended with biochar (custom-produced via pyrolysis of red pine wood chips at 550 °C), or IES. Over the first two field seasons of operation flow into the testbed and out of each filter were continuously monitored, and influent and effluent samples were collected during 21 precipitation events and analyzed for various contaminants and water quality parameters. To account for variations in flow distribution between the filters, long-term filter performance was assessed based on comparison of apparent cumulative input and output contaminant loads over the study duration (i.e., apparent cumulative contaminant retention). The IES filter showed the most effective phosphorous retention performance (>90% net retention of total phosphorus, TP), reflecting results from previous studies. The biochar-amended filter showed improved retention of zinc and total inorganic nitrogen (TIN) relative to the sand filter, which may be attributed to: (i) enhanced electrostatic interactions between zinc and oxygen-containing functional groups on the biochar surface, and (ii) improved attenuation of ammonia-N due to reduced nitrification and/or enhanced adsorption of ammonium. The biochar-amended filter did not show improved retention of total organic carbon or Escherichia coli, in contrast to some previous studies, potentially due to differences in biochar material properties (e.g., reduced hydrophobic interactions due to the custom biochar's relatively polar surface chemistry) or operational conditions (e.g., differences in flow rate or biofilm development between the filters). These findings demonstrate the complexities surrounding the application of biochar as a stormwater filter material for broad contaminant removal, and warrant the development of best practice recommendations for biochar selection and performance testing.

Wastewater-based epidemiology can track infectious diseases and COVID-19 surges. There is variability in viral signals from wastewater owing to numerous sample processing and virus detection methods, and many factors including characteristics of wastewater treatment plants (WWTPs) should be considered to consistently associate the signals with COVID-19 prevalence. This study optimized the virus detection method, validated the use of a process-control virus, investigated 22 WWTPs across South Korea (covering approximately 20% of the population) during two periods (24.8 versus 2027.4 weekly COVID-19 cases per 100 000 people), tested the infectivity of SARS-CoV-2 in wastewater, and characterized the environmental factors influencing wastewater-bound SARS-CoV-2 and local COVID-19 using data-driven models (DDMs). The most sensitive virus quantification methods were selected (PEG precipitation and commercial kits for RT-qPCR detection, approximately 39% more sensitive) by comparing various methods. Using a surrogate virus showed reduced variation (approximately 24%) between the intra- and inter-laboratory results. The number of WWTPs with positive detection of SARS-CoV-2 in raw wastewater increased (four to twenty) as the national COVID-19 cases peaked. SARS-CoV-2 is more likely to be detected in moderately sized facilities located in populated areas with sanitary sewer systems. In addition, results of infectivity testing suggested no potential for COVID-19 transmission through wastewater. The DDMs indicated that the air temperature, water quality, and number of COVID-19 cases were related to the SARS-CoV-2 in wastewater. Community COVID-19 cases were predicted (test performance: 0.703–0.970) with the data on wastewater viral load and other variables implying that these factors should be monitored for wastewater surveillance.

Urban domestic wastewater is a significant source of dissolved organic matter (DOM) in aquatic environments, critically impacting urban water quality. This study integrates the optical properties and molecular features of DOM, providing a comprehensive understanding of its behavior in urban sanitary sewage. Utilizing ultraviolet-visible (UV-vis) spectroscopy, three-dimensional synchronous fluorescence spectroscopy, and ultra-high-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS), we establish a robust bidirectional correlation between optical properties and molecular characteristics. Our findings reveal that urban domestic wastewater is predominantly composed of protein-like substances and microbial humic components, rich in heteroatoms and homologous compounds. The established correlations between optical and molecular features validate the DOM characterization system, demonstrating consistency between photochemical properties and molecular characteristics. Molecules related to photochemical parameters align with high H/C and low O/C ratio regions. The correlation analysis indicates that the highly associated areas are the fluorescent domains of protein-like materials and microbially derived humic-like substances. This innovative approach provides actionable insights for urban water quality management, highlighting the critical role of these methods in effective environmental monitoring.

A year-long removal of dissolved organic matter (DOM) from Lake Kinneret water, the main reservoir of surface drinking water in Israel, was studied by adsorption pilot plant columns with media which included new (virgin) granular activated carbon (GAC), regenerated GAC (rGAC), a clay–polymer nanocomposite (PD–MMT), and a combined media (COMB) of PD–MMT composite followed by rGAC at the same volumes. Lake Kinneret water is characterized by low specific absorption of UV at 254 nm (SUVA₂₅₄), high ionic strength and high bromide content. We studied DOM removal mechanisms by each adsorbent and their combination, via monitoring their emerging concentrations through the columns. The effect of DOM removal on trihalomethanes formation (THMF) was also elucidated. Simulated and predicted DOM adsorption in GAC columns by developing an extended model including adsorption and biodegradation is presented. The best yield of DOM removal results (expressed as UV₂₅₄ and DOC) was by the COMB and GAC columns. The COMB presents a synergistic result by the combination of two removal mechanisms, electrostatic by PD–MMT and hydrophobic by rGAC. The analysis along the columns shows that whereas the removal by GAC and rGAC was carried out through all layers, the removal by PD–MMT was preferentially by the upper and middle layers. Emerging SUVA₂₅₄ values decreased for all media throughout the pilot run. The humic matter (HM) compounds comprising hydrophobic characteristics were more efficiently removed than the non-absorbing fractions at 254 nm (NABS₂₅₄) with more hydrophilic characteristics. THM precursors' removal by COMB as well as GAC satisfied the THM regulations. The removal of hydrophilic matter in the presence of bromide should improve the reduction of THM formation in treated water. Modeling of DOM removal at the laboratory and pilot plant, which focused on removal by GAC column, could fit the data only by considering DOM biodegradation. When a steady state during pilot operation was reached, biodegradation yields, the main contribution to DOM removal, improved the overall capacity of GAC removal beyond the adsorption process.

Membrane processes are an established barrier technology for water reclamation from wastewater. Applied at a household scale to improve sanitation practice, membrane technology can disrupt the source–receptor pathway, alleviate water scarcity through eliminating flush water and recover clean water for reuse. However, blackwater comprises a distinct composition compared to municipal wastewater, and there is only limited understanding on whether membrane selectivity is sufficient to produce water of sufficient quality for reuse. In this study, pressure driven and thermally driven membranes are evaluated for their potential to treat blackwater, by relating selectivity to relevant water quality standards (ISO 30500) and the transmission of volatile organic compounds (VOCs) that are primarily associated with faecal odour, and thus constitute a critical challenge to water reuse. Both pressure driven (reverse osmosis) and thermally driven (membrane distillation and pervaporation) membranes were able to produce water that conformed to category B of the ISO 30500 standard for the majority of determinants. A critical limiting factor was in the selectivity for ammonia and odorous VOCs which were generally poorly removed by reverse osmosis and membrane distillation. The high ammonia transmission was accounted for by the elevated pH of blackwater which shifted the ammonium equilibria toward volatile ammonia which is poorly separated by RO polymers, and is free to diffuse through the gas-filled micropores of the membrane distillation membrane. In contrast, greater ammonia and VOC separation was evidenced for the pervaporation membrane due to advanced polymer–solute interactions. In a preliminary assessment, the hydrophilicity exhibited by the membrane was also advantageous to withstanding fouling. If complemented with a polishing step to target the residual COD and VOCs (that may be of similar origin), pervaporation could deliver to category A standard for non-potable reuse. This is particularly advantageous for water scarce regions where solar or liquified fuels may be applied in favour of electricity for off-grid sanitation.

With the widespread use of last-resort antibiotics, carbapenems, clinical reports of infections associated with carbapenem-resistant Enterobacterales (CRE) have increased. Clinical surveillance for CRE involves susceptibility testing and/or whole genome sequencing of resistant isolates, which is laborious, resource intensive, and requires expertise. Wastewater surveillance can potentially complement clinical surveillance of CRE, and population-level antibiotic resistance (AR) surveillance more broadly. In this study, we quantitatively and qualitatively compared two widely used methods for AR wastewater surveillance: (1) a culture-based approach for quantifying carbapenem-resistant bacteria and (2) a digital droplet PCR (ddPCR) assay targeting five major carbapenemase-encoding genes. We developed a new multiplexed ddPCR assay to detect five carbapenemase-encoding genes and applied it to wastewater samples from three sites over 12 weeks. In parallel, we quantified carbapenem resistant bacteria and carbapenemase-producing bacteria using culture-based methods. We assessed associations between the concentrations of carbapenemase-encoding genes and resistant bacteria. Although both approaches showed similar trends in the overall abundance of dominant carbapenem-resistant bacteria and genes, there were weak correlations between the quantitative levels of resistance. Nanopore sequencing of the resistome of the carbapenem-resistant bacteria revealed that discrepancies arose from differences in the sensitivity and specificity of the methods. This study highlights tradeoffs between methods: culture-based methods offer detailed phenotypic data on carbapenem-resistant bacteria but have longer turnaround times and lower throughput, whereas ddPCR offers rapid, sensitive detection but may miss some resistance mechanisms. Integrating these methods with sequencing provides sensitive, quantitative AR information and their clinical relevance.

Biochemical oxygen demand (BOD) is one of the most sensitive and essential indicators of wastewater quality. However, today, BOD detection methods require considerable effort and time, resulting in management and operational errors during the wastewater-treatment process which leads to the production of poor-quality effluent that poses a threat to public health and safety. Using advanced machine learning (ML) methods, we developed generalizable BOD prediction model based on a unique, centrally integrated database from 30 wastewater-treatment plants (WWTP) across Israel. The model is based on easily retrieved water parameters measured by on-site sensors or conventional analytical devices. In this work, three different ML algorithms were examined and compared, random forest (RF), support vector machine, and gradient tree boosting. The optimized RF model reached the best results, R² of 0.91 and RMSE of 8.58 in predicting the total BOD at different stages of the treatment process. The three key features for modeling were chemical oxygen demand, total suspended solids, and total Kjeldahl nitrogen. We then present an approach to predict BOD in effluent, focusing on binary classification predictions for regulatory compliance. For a prediction threshold of BOD > 9 mg L⁻¹, a recall of 0.89 was achieved. These results demonstrate the potential of the model to be a generalized solution for BOD predictions in WWTP across Israel, and possibly worldwide. This method can be used as a part of a sensor for BOD monitoring and management in wastewater, effectively minimizing the time gaps between routine lab testing. The fundamental challenge addressed herein has important global relevance, especially in an era in which the demand for high-quality wastewater reuse is expected to increase dramatically.