Introduction

Synthetic dyes are widely utilized across numerous industries, but their durability, harmful effects, and resistance to natural degradation processes make them major pollutants, especially in industrial wastewater. Conventional treatment techniques often fail to fully eliminate these substances. Photocatalysis involving semiconductor materials has shown promise as an effective method for degrading the dye under relatively mild conditions. Among the different metal oxide photocatalysts, zinc oxide (ZnO) has attracted considerable interest due to its small band gap, strong stability, affordability, and ability to be activated by visible light.

Methodology

In this study, ZnO nanoparticles (NPs) were synthesised through a green synthesis method using Livistona chinensis leaf extract and utilized for photocatalytic degradation of synthetic dyes under visible light. The NPs were characterized by FT-IR, XRD, UV-vis and FESEM techniques. The band gap was also calculated. The photocatalytic activity was tested against Fast Green FCF azo dye.

Results

The produced NPs showed significant photocatalytic efficacy. The photocatalytic potential of the ZnO NPs against Fast green FCF showed 76 % photodegradation after 90 minutes when exposed to visible light.

Conclusion

The study highlights the advantages of utilizing ZnO NPs as a visible-light-activated, cost-effective, and environmentally friendly photocatalyst. These findings suggest that the NPs could be used as a sustainable wastewater treatment.

Wastewater treatment is one of the major challenges in the reuse of water as a natural resource. Cleaning of water depends on analysing and treating the water for the pollutants that have a significant impact on the quality of the water. Detecting and analysing the surges of these pollutants well before the recycling process is needed to make intelligent decisions for water cleaning. The dynamic changes in pollutants need constant monitoring and effective planning with appropriate treatment strategies. We propose an edge computing-based smart framework that captures data from sensors including ultraviolet, electrochemical, microfluidic and other significant sensor streams. The edge devices send the data from the cluster of sensors to a centralized server that segments anomalies, analyses the data and suggests the treatment plan that is required, which includes aeration, dosing adjustments and other treatment plans. A logic layer is designed at the server level to process the realtime data from the sensor clusters and identifies the discharge of nutrients, metals and emerging contaminants in the water that affects the quality. The platform can make decisions on water treatments using its monitoring, prediction, diagnosis and mitigation measures in a feedback loop. A rule-based Large Language Model (LLM) agent is attached to the server to evaluate data and trigger required actions. A streamlined data pipeline is used to harmonise sensor intervals, flag calibration drift, and store curated features in a local time-series database to run ad-hoc analyses even during critical conditions. An user dashboard has also been designed as part of the system to show the recommendations and actions taken. The proposed system acts as an AI enabled system that makes smart decisions on water treatment, providing an effective cleaning process to improve sustainability.

The composting of grape pomace represents an environmentally sustainable method for valorizing agro-industrial waste by converting it into nutrient-rich organic fertilizer. This study aimed to develop robust predictive models for monitoring microbial population dynamics during composting using near-infrared (NIR) spectroscopy as a rapid, non-destructive analytical tool. A total of nine composting experiments were carried out in laboratory-scale reactors (V = 5 L) over a 30-day period, with the initial substrate moisture content ranging from 50% to 65% and the air flow rates ranging from 0.35 to 2.00 L/min. NIR spectra were recorded within the 904–1699 nm range using an NIR-128-1.7-USB/6.25/50 μm spectrometer. Artificial Neural Network (ANN) models were developed to correlate spectral data with microbiological parameters, including bacterial, fungal, and total microorganism counts. Among various spectral preprocessing techniques, smoothing was identified as the most effective for predicting bacterial counts, yielding the highest Residual-Error-of-Prediction-to-Standard-Deviation ratio (RER = 10.083). In contrast, the combination of the second-order Savitzky–Golay derivative followed by multiplicative scatter correction (SG2D+MSC) proved optimal for modeling fungal (RER = 15.075) and total microorganism counts (RER = 12.040). The use of NIR spectroscopy offers several advantages, including minimal sample preparation, rapid data acquisition, the ability to analyze samples in situ, and simultaneous monitoring of multiple compost parameters. Combined with ANN modeling, this approach enables real-time, accurate prediction of compost microbiology, offering significant potential for process optimization. These findings demonstrate the value of integrating NIR and machine learning tools in advancing sustainable and efficient composting practices.

The European Union aims for carbon neutrality by 2050. To achieve this goal, environmental regulations are being continuously introduced, with the latest Renewable Energy Directive (RED III) set to take effect in 2030. Notably, RED III specifies a minimum 5.5% share of biofuels and a 1% share of renewable fuels of non-biological origin (RFNBO) in the transportation sector. Hydrogen is a crucial component in refineries, and while it is currently produced mainly through reforming processes, they are to be partially replaced with RFNBO to comply with the RED III targets.

This work analyzes various methods to achieve RED III targets, i.e., RFNBO production directly in the refinery or its import in the form of green ammonia to be decomposed in the refinery, biomethane sourced to meet biofuel targets, and low-carbon hydrogen produced via thermal pyrolysis of natural gas. Strategic pathways were compared using Multi-Criteria Decision Analysis (MCDA), evaluating environmental, economic, technological, and implementation criteria to identify viable strategies to integrate renewable fuel solutions into refinery operations. Without predefined criteria preferences, steam biomethane reforming ranks highest in over 50% of consistent preference combinations, primarily due to its cost advantage. Considering environmental or technological preferences, electrolysis is favored due to its zero-emission profile and its ability to produce pure hydrogen at elevated pressures. Under a fixed priority order (economic > technological > implementation > environmental), biomethane reforming remains the top-ranked strategy. However, MCDA results are sensitive to external conditions. If changes in either renewable electricity prices or methane-based gas costs occur, electrolysis can outperform other options in the ranking. Ultimately, the corporate strategy must consider not only techno-economic aspects but also European regulations and market dynamics, which necessitates long-term planning to ensure company's competitiveness.

Numerous economically disadvantaged countries face substantial challenges in water purification due to the absence of centralized wastewater treatment systems. Consequently, this study investigates the optimal degradation of the antibiotic contaminant tetracycline from wastewater utilizing a TiO₂–slag nanocomposite synthesized via an ex situ method under simulated solar irradiation. The morphology and crystalline structure of the synthesized hybrid material were characterized through scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray diffraction (XRD). Fourier-transform infrared spectroscopy (FTIR) was employed to analyze the elemental composition and functional groups. To evaluate photocatalytic performance, a Box–Behnken design (BBD), integrated with response surface methodology, was used to determine the influence of three operational variables: pollutant concentration (20–40 ppm), catalyst loading (10–50 mg/L), and pH level (3–11), with a fixed reaction duration of 60 minutes. The experimental matrix, comprising 17 runs, was designed to capture the interactive effects among these variables on degradation efficiency. A reduced quadratic regression model was established, demonstrating strong predictive capability aligned with empirical results. The coefficient of determination (R²) values for the analysis of variance (ANOVA) were 0.99 and 0.98, indicating statistical significance (p < 0.05) of the proposed model.

Among the tested parameters, catalyst dosage emerged as the most influential factor, exerting a notable effect on the photocatalytic reaction, whereas the synergistic interaction between pollutant concentration and pH showed a positive contribution, culminating in a maximum degradation efficiency of 99.92%. Furthermore, an economic assessment revealed the treatment cost to be $0.9438 per cubic meter, signifying a cost-effective solution. These findings highlight the potential application of TiO₂–slag nanocomposites as an efficient and low-cost material for industrial wastewater remediation, thereby reducing dependence on expensive chemical reagents.

Chronic health and ecological issues stemming from sulfur dioxide (SO₂) emissions due to fossil fuel combustion necessitate the development of more efficient and cost-effective scrubbing methods beyond conventional wet flue gas desulfurization (WFGD). This study explored the synthesis of a calcium vanadium-based nanocomposite (CaV₂O₆) using vanadium pentoxide (V₂O₅) waste and calcium nitrate (Ca(NO₃)₂) as precursors, intended for application as a catalytic adsorbent in dry desulfurization systems. The primary objective was to enhance SO₂ selectivity and catalytic activity by refining the sorbent’s morphological properties to improve thermal stability and redox performance. The formulation approach involved a sol-gel process, rapid thermal treatment, and subsequent hydrogen reduction. A central composite design (CCD) was employed to optimize synthesis parameters, including reaction temperatures ranging from 700 to 1000 °C, reaction times from 1-3 hours, and a V₂O₅:Ca(NO₃)₂ molar ratio of 0.3-1.0. The physicochemical properties of the CaV₂O₆ material were characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), and Brunauer–Emmett–Teller (BET) surface area analysis. SEM revealed a porous nanosheet morphology, while XRD confirmed the formation of an orthorhombic CaV₂O₆ phase. BET analysis showed an average surface area of 25.340 m²/g, representing a 40.6% increase compared to a conventional Ca(OH)₂ sorbent (18.019 m²/g). The developed material suggests that the laboratory-scale synthesis protocol is capable of replacing costly dry flue gas desulfurization reagents with those derived from sustainable precursors.

Recent research trends show interest inovercoming the limitations of wastewater treatment systems and technologies. More researchers are investigating ecologically friendly, low-cost, and low-maintenance alternatives. More focus is being placed on environmentally friendly and sustainable treatment techniques as a result of the growing population and increasing industrialization, which have increased the pollution load on receiving water bodies from both point and non-point sources. Because of its effectiveness, affordability, and sustainability, the phytoremediation process is among the greatest ways to remediate wastewater. The system's advantages and response surface methodology (RSM) are used in this work to make predictions. This process is important for enhancing the efficiency of pollutant removal from wastewater. Phytoremediation uses plants to absorb, degrade, or stabilize contaminants, offering a sustainable and low-cost solution. RSM provides a powerful statistical tool to optimize key operational parameters, identify significant factors, and model the treatment process accurately. Two different plants were placed in phytoremediation system tanks to treat wastewater treatment plant's effluent. Water hyacinth and Salvinia molesta were planted in separate tanks and observed concurrently. The pH and hydraulic retention time (HRT) of the tanks were varied during operation. The results revealed that the removal efficiency of chemical oxygen demand, total dissolved solids, total nitrogen, and turbidity were in the range of 51.84-79% for Salvinia molesta with pH 5, and 52.12-83% with pH7. Water hyacinth recorded lower performances of 13.56-48.81% and 5.29-44.87%, respectively. Accurate predictions were made from the analysis of phytoremediation treatment of wastewater utilizing the response surface approach. Integrating RSM with phytoremediation improves the effectiveness and predictability of effluent treatment.

Global warming and climate change are closely linked to industrial production and fossil fuel consumption, both of which are major contributors to CO₂ emissions. Despite global efforts to promote industrial sustainability, significant CO₂ emissions are expected to persist in the near future. To address this challenge, strategies such as CO₂ capture, storage, and utilization have been widely explored. Among them, CO₂ capture is the critical first step. Membrane technologies—particularly polymeric membranes—are gaining increasing attention for their efficiency and selectivity, offering a competitive edge over conventional CO₂ capture methods. However, scaling these technologies to handle high industrial flow rates remains a major challenge, largely due to the low technology readiness level of current systems [1].

This study developed and evaluated a novel CO₂ capture system using various polymeric hollow fiber membranes (Airrane Co. Ltd., PermSelect®, and UBE Corporation Europe), tested with real industrial gas streams from the textile (CO₂ concentrations ranging from 0.5% to 7%). The system enabled precise control and optimization of parameters such as CO₂ concentration, pressure, and feed flow rate.

Experiments with textile industry gases containing 0.5% CO₂ showed lower permeate fluxes and minimal influence of pressure on separation efficiency. However, when sampling from the textile industry boiler—where CO₂ concentrations reached around 7%—and using the UBE membrane, promising results were obtained. The UBE membrane exhibited excellent CO₂ permeance (100 GPU), along with high selectivity: 33 for CO₂/N₂ and 6 for CO₂/O₂.

In conclusion, this study demonstrated the feasibility of CO₂ capture using commercial polymeric hollow fiber membranes under real industrial conditions. The results highlight the impact of gas impurities on separation performance and underline the need for advanced materials and optimized system configurations to improve CO₂ purity and overall process efficiency. These findings provide a foundation for future research focused on scaling up CO₂ capture systems for industrial applications.

The use of waste materials to stabilize clayey soil could be a viable solution for soil stabilization, but a comprehensive investigation is required before it can be employed. This research aims to investigate the use of waste crushed glass powder as an additive in clayey soil, providing a comprehensive overview of its impacts on the physical and mechanical properties of the soil. This is undertaken by selecting different design mix percentages of clay soil, lime, and waste glass powder. The mix design samples are tested in laboratories using the relevant American Society for Testing and Materials (ASTM) standards, and the results are then analyzed. It was found through analysis that mixing 10% waste glass powder with 5% lime into clayey soil yields the most optimum results in terms of critical mechanical properties. With this combination, the unconfined compressive strength of the soil increased from 1.86 g/cm² to 2.51 g/cm², and the shear strength of the clayey soil increased from 0.93 g/cm² to 1.26 g/cm². The California Bearing Ratio (CBR) values did not show any sign of improvement, although the permeability coefficient (k) improved from 0.000072 cm/sec to 0.000078 cm/sec. On the other hand, no substantial change in the physical properties was observed with the addition of waste glass combined with lime. This research improves our understanding of the behavior of clayey soil reinforced with crushed glass powder; benefits future civil engineers who are studying soil stabilization; provides a way to reuse waste glass and reduce its environmental impact; and helps local communities with stable and more durable structures due to improved soil conditions.

Nowadays, biomass-based materials are gaining prominence in environmental remediation issues due to their affordability, environmental friendliness, and ease of accessibility. Traditional adsorption is the most widely used strategy for removing textile dyes, heavy metals, and hazardous substances from water. Nickel is one of the most extensively studied metals in catalysis research, particularly in gas- and liquid-phase reactions. In this study, the aim was to remove nickel from an aqueous solution using Turkish coffee grounds. For this reason, adsorption experiments were carried out on coffee grounds, an agricultural waste, at room temperature, with different adsorbent amounts (0.05 and 0.07 g), adsorption times (15-60 min), and adsorbent types calcinated at several temperatures (300-700ºC) with a 0.4 g/L initial Ni(II) solution concentration and a 200 rpm shaking rate in a water bath. After that, based on UV-VIS results, Removal Efficiency (RE, %) and Adsorption Capacity (AC, mg/g) values ​​were calculated. Because the coffee waste released its color to the adsorption media, absorbance values were read for both nickel-included and -excluded solutions. The maximum RE value was obtained as being 98% for 0.07 g of adsorbent that was calcinated at 300ºC after 30 min of adsorption at room temperature. In addition, the maximum AC value was found to be 284 mg/g for 0.05 g of adsorbent that was calcinated at 500ºC after 45 min of adsorption at room temperature. In the literature, maximum adsorption capacity values were in the range of 0.13-60.20 mg/g for several biomass-based adsorbents for Ni (II) removal [1]. So, it was thought that the biomass calcination procedure could be effective for Ni (II) removal from water. It is thought that this study creates an alternative green way to clean wastewater using biomass wastes.

[1] Shroff, K. A., & Vaidya, V. K. (2011). Kinetics and equilibrium studies on biosorption of nickel from aqueous solution by dead fungal biomass of Mucor hiemalis. Chemical Engineering Journal, 171(3), 1234-1245.