Water Science and Technology最新文献

Introducing microplastics (MPs) into the marine environment is a global problem. Tire-derived microplastics (TMPs) are estimated to account for 60% of all secondary MPs dispersed in aquatic environments. To effectively detect TMPs in environmental samples using micro-Fourier transform infrared (μFTIR) spectroscopy, a high-quality reference library is essential. However, the use of conventional diamond crystals in FTIR presents challenges for the detection of materials containing carbon black, such as rubber and tires. In addition, there is a discrepancy between spectra from standard libraries and spectra from environmental samples, which makes detection difficult. In order to overcome these problems in the detection of TMPs by μFTIR, we developed four reference libraries to improve the detection, and 'The 26 tire wear library' was found to be the best among these four. Furthermore, a comparison of these new libraries revealed the following requirements to improve TMP detection: (i) the reference spectra must be acquired under the same setup used for material observation including prism material, (ii) tires, not rubber, must be used as reference materials, and (iii) tire wear samples must be prepared to replicate the actual generation conditions on roads.

Petroleum hydrocarbons (PHCs) are organic substances that occur naturally on earth. PHCs have emerged as one of the most prevalent and detrimental contaminants in regions comprising soil and water resources. The limitations of conventional physicochemical and biological remediation solutions could be solved by combining remediation techniques. An effective, affordable, and environmentally benign method of reducing petroleum toxins is provided by the advanced idea of bioremediation, which has evolved into nanobioremediation. Environments contaminated with PHCs have been restored through microbe-plant-nanoparticle (NP)-mediated remediation, this review emphasizes how various metallic NPs interact with microbes and plants changing both their activity and that of enzymes, therefore accelerating the remediation process. This work further examines the challenges and possible uses of nanobioremediation, as well as the application of novel technologies in the interactions between bacteria, plants, and NPs for the bioremediation of PHCs. Furthermore, it has been shown that the use of plant-based, microbe-based, microbe-plant-based, and microbe-plant-NP-based techniques to remediate contaminated soils or water bodies is economical and environmentally beneficial. Microbial consortia have been reported as the treasure houses for the cleaning and recovery of hydrocarbon-contaminated environments, and the development of technologies for bioremediation requires an understanding of hydrocarbon degradation mechanisms.

In the present study, bio-citric acid/tungsten oxide (WO₃) (BCAWO) nanoparticles (NPs) were prepared by using Solanum lycopersicum fruit extract as a reducing as well as a capping agent. The photocatalysts were characterized by UV-vis diffuse reflectance spectroscopy, powder X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive X-ray spectrometry (EDS), high-resolution transmission electron microscopy, and photoluminescence spectroscopy techniques. Diffraction peaks in the XRD spectrum were identified as the crystal planes of crystalline tungsten oxide. The BCAWO had an average size of 23.14 nm. For W-O bonds, the Fourier transform infrared spectrum displays the vibrational peak at 671.23 cm^-1. A prominent absorption band was observed at 268 nm, indicating the 1.2 eV bandgap. Under xenon (Xe) lamp irradiation, the synthesized BCAWO nanoparticles showed notable photocatalytic degradation of 2,4-dichlorophenol (2,4-DCP), with a degradation rate of 96%. With BCAWO concentrations of 2.5 g/L, pH of 4, reaction period of 180 min, and 2,4 DCP concentration of 10 mg/L, the degradation of 2,4-DCP had the highest efficacy, 96%. The degradation of phenols in wastewater may be facilitated by using the green WO₃ nanoparticles as a photocatalyst, according to the results.

Wastewater treatment plants (WWTPs) comprise energy-intensive processes, serving as primary contributors to overall WWTP costs. This research study proposes a novel approach that integrates support vector regression (SVR) with the firefly algorithm (FFA) for the prediction of energy consumption in a WWTP in Chlef City, Algeria. The database comprises a comprehensive set of 1,653 samples, capturing diverse information categories. It includes chemical and physical characteristics, encompassing chemical oxygen demand, 5-day biochemical oxygen demand, potential of hydrogen, water temperature, total suspended sediment in water and basin, influent N-NH₃ concentration, number of aerators, and operating time. Additionally, the hydraulic and energy-related parameters are represented by the flow entered at the station and the energy consumed by aerators, respectively. Finally, meteorological data, comprising rainfall, temperature, relative humidity, and the aridity index, are part of the dataset required for analysis. In this regard, 15 different models that correspond to 15 different combinations of input parameters are assessed in this study. The results show that the SVR-FFA-15 can render an improvement in the prediction accuracy of energy consumption in WWTPs. This study provides a useful tool for managing the energy consumption of wastewater treatment and makes insightful recommendations for future energy savings.

This study proposes a novel approach for predicting variations in water quality at wastewater treatment plants (WWTPs), which is crucial for optimizing process management and pollution control. The model combines convolutional bi-directional gated recursive units (CBGRUs) with adaptive bandwidth kernel function density estimation (ABKDE) to address the challenge of multivariate time series interval prediction of WWTP water quality. Initially, wavelet transform (WT) was employed to smooth the water quality data, reducing noise and fluctuations. Linear correlation coefficient (CC) and non-linear mutual information (MI) techniques were then utilized to select input variables. The CBGRU model was applied to capture temporal correlations in the time series, integrating the Multiple Heads of Attention (MHA) mechanism to enhance the model's ability to comprehend complex relationships within the data. ABKDE was employed, supplemented by bootstrap to establish upper and lower bounds of the prediction intervals. Ablation experiments and comparative analyses with benchmark models confirmed the superior performance of the model in point prediction, interval prediction, the analysis of forecast period, and fluctuation detection for water quality data. Also, this study verifies the model's broad applicability and robustness to anomalous data. This study contributes significantly to improved effluent treatment efficiency and water quality control in WWTPs.

Stormwater is recognised as a vector for microplastics (MPs), including tyre wear particles (TWPs) from land-based sources to receiving waterbodies. Before reaching the waterbodies, the stormwater may be treated. In this study, sediments from six treatment facilities (five retention ponds and a subsurface sedimentation tank) were analysed to understand MP occurrence, concentrations, sizes, polymer types and distribution between inlet and outlet. The concentrations of MPs showed large variations between and within different facilities with MP concentrations of 1,440-72,209 items/kg (analysed by μFTIR) corresponding to 120-2,950 μg/kg and TWP concentrations from

This study includes Micractinium reisseri cultivation in artificial saline medium (ASM). With the aim of harvesting the bulk M. reisseri biomass, an experiment was set up at a bench scale to evaluate the best flocculation technique with the least compromising biomass and lipid loss. The flocculation efficiencies for the M. reisseri biomass have been studied using the auto-, bio-, and chemical-flocculation methods. Different concentrations of chitosan for the biological method and alum for the chemical method were added in M. reisseri culture growing in the liquid ASM. The optimal concentration with the highest biomass and oil collection was determined for each method. In the biological method, the highest (96.44%) and lowest (67.88%) flocculation efficiencies were observed by adding 15 and 2 mg of chitosan, respectively, and in the chemical method, the highest (97.2%) and lowest (35.4%) flocculation efficiencies were observed by adding 150 and 50 mg of alum, respectively. The auto-flocculation method shows the highest efficiency (97.8%) among all the tests. The oil yield from the three highest biomasses was 2.60, 1.51, and 1.08% in the auto-, bio-, and chemical-flocculation methods, respectively. The time taken for auto-, bio-, and chemical-flocculation was 48, 4, and 1 h, respectively.

This article provides a comprehensive review of decision support tools for water reuse (DST4WR), focusing on microbiological risk assessment (MRA), life cycle analysis (LCA), life cycle cost (LCC), and multi-criteria decision analysis (MCDA). A systematic review of 35 articles published between 2020 and 2024, plus one from 2019, was conducted. The studies were categorised based on the DST4WR applied, with each tool discussed individually. MRA tools assess public health risks in different case studies. LCA identifies key environmental indicators, and its integration with LCC facilitates comprehensive cost analysis. MCDA, applied in various case studies, uses criteria like environmental, social, economic, technical, public health, and functional aspects. Integrating DST4WR tools identifies synergies and trade-offs between criteria, aiding informed decision-making. Combining MRA, LCA/LCC, and MCDA is especially beneficial, as each tool provides a distinct perspective. Using these tools together offers a holistic view of water reuse management, ensuring that all relevant factors are balanced. This approach enhances decision-making and builds stakeholder confidence and acceptance by transparently addressing public health, environmental, economic, and social concerns.

Due to rapid urbanization and industrialization, combined pollution caused by BTEX (benzene, toluene, ethylbenzene, and xylene) and heavy metals has become ubiquitous in soils, which would pose serious health risks to humans. However, the effects of heavy metals on the sorption and desorption behaviors of BTEX have not been fully elucidated. In this study, the effects of Cu²⁺ and Pb²⁺ ions on the sorption and desorption of benzene onto humic acids and black carbons were investigated. The results showed that Cu²⁺ and Pb²⁺ ions significantly reduced the sorption capacity, slowed down the sorption rate, and made the desorption less hysteretic of benzene on both humic acids and black carbons. Furthermore, the inhibitory effects by Pb²⁺ were significantly stronger than those of Cu²⁺. By combining the results of Fourier transform infrared spectroscopy and the site energy distribution model, it can be speculated that the hydration shells of Cu²⁺ and Pb²⁺ ions partially cover the surface of humic acids and black carbons, blocking their micropores and shielding sorption sites, consequently inhibiting the sorption of benzene. This study highlights that coexisting metal cations can significantly influence the fate of BTEX in soils.

Environmental challenges in low-income countries, such as Haiti, persist due to inadequate sanitation infrastructure. This study assesses the environmental impacts of nine on-site sanitation systems to identify those with the least environmental impacts and explore improvement options. Nine scenarios were developed, each representing different systems for managing 1 ton of fecal sludge over 1 year. The 'Impact World + ' and 'IPCC 2013 GWP 100a' methods evaluated impacts on ecosystems, human health, and climate change. Data sources included interviews, weighing records, and scientific publications. Results show that Scenario 8 (Flush Toilet - Evacuation - Planted Drying Beds) is most impactful on health (1.17 × 10^-2 DALY), while Scenario 1 (Composting Toilet - Evacuation - Unplanted Drying Beds) is least impactful (1.77 × 10^-3 DALY). For ecosystem impacts, Scenario 2 (Container-based Toilet - Evacuation - Planted Drying Beds) is most impactful (3.81 × 10³ PDF·m²·year), while Scenario 6 (VIP latrine - Evacuation - Lagoons) is least impactful (3.52 × 10³ PDF·m²·year). Key hotspots include toilet paper, wood shavings, GHG emissions, and water use. The study recommends an integrated approach combining environmental life cycle assessment (LCA) with life cycle cost assessment and social LCA for sustainable decision-making on sanitation systems in low-income countries.