International Journal of Environmental Science and Technology最新文献

The rising speed of urbanization, together with growing populations and changing consumer behavior, has created excessive waste production that needs sustainable methods of resolution. This research develops a circular economy (CE) system that integrates CNN and IoT technologies into waste elimination by utilizing the VGG16 deep learning algorithm with 97.52% precision for material recognition and IoT devices for immediate surveillance operations. Through their partnership, AI, IoT, and cloud computing systems optimize waste sorting procedures and aid recycling processes, as well as lead to data-driven business decisions. The framework integrates waste sorting done by self-operating systems with vehicle route calculation based on probabilities, together with IoT automated system maintenance, which results in diminished usage of landfills alongside reduced environmental impacts and operating expenses. The AI model analyzes 10,000 waste images during its training process to enhance waste sorting capabilities, while cloud platforms ensure secure, time-sensitive data processing. Studies within the hospitality sector in Singapore show that the approaches work effectively, and a combination of discussions about AI data equivalence alongside ethical AI issues, together with societal acceptance hindrances, demonstrate implementation difficulties. This study introduces advanced technologies that provide innovative waste management solutions, supporting sustainability practices and circular economies. It also delivers essential knowledge to public officials, industrial entities, and community members, helping to strengthen global waste programs, protect ecosystems, and drive economic progress.

Freshwater dam reservoirs are increasingly threatened by land use/land cover changes, leading to eutrophication and degraded water quality. This study presents an integrated remote sensing and object-based image analysis approach to assess the impacts of land-use/land-cover change on eutrophication dynamics in the Sattarkhan dam reservoir, East Azerbaijan, Iran. Multi-temporal Landsat imagery (1994–2024) was employed to classify land-use/land-cover changes and estimate chlorophyll-a concentrations. In-situ chlorophyll-a measurements from seasonal sampling (2023–2024) were used to calibrate empirical models, with the blue/green band ratio power model yielding the best performance (4.44 ± 0.30 (mu text{g}/text{L}) ((RMSE)), 3.22 ± 0.21 (mu text{g}/text{L}) ((MAE)), 36.95% ± 2.80 ((sMPAE)), and 0.70 ± 0.04 ((R^{2}))). The findings suggest a potential rise in eutrophication between 2004 and 2024, though long-term trends are subject to uncertainties from temporal extrapolation of the 2023–2024 calibrated model. Gray relational analysis was employed to investigate the relationship between landcover changes and trophic status, revealing a potential role for increasing built-up land in nutrient loading. These findings highlight the framework’s utility for sustainable watershed management in semi-arid regions. Future work should incorporate advanced modeling for broader applicability.

The integration of advanced oxidation processes (AOPs) for the treatment of laundry wastewater could significantly improve resource efficiency and reduce overall freshwater consumption, thus contributing to sustainable water management strategies. This study therefore demonstrated the effectiveness of different AOPs including O₃, H₂O₂, O₃/H₂O₂, O₃/UV-C, H₂O₂/UV-C, and O₃/H₂O₂/UV-C in the treatment of laundry wastewater to determine the most efficient AOP based on the removal of chemical oxygen demand (COD) and methylene blue active substances (MBAS). The results showed that the O₃/H₂O₂/UV-C method had the highest removal efficiency for both COD (83%) and MBAS (96%). Additionally, a significant reduction in microbial growth was observed in the treated wastewater compared to untreated raw wastewater. Textile tests with the treated wastewater confirmed its potential for reuse in washing machines, as evidenced by achieving a gray scale number of 5, the highest possible without dyeing. These results are further proof of the feasibility of in-situ treatment for the sustainable reuse of laundry wastewater.

This work reports the catalytic activity of a mesoporous material prepared from terephthalic acid obtained via alkaline hydrolysis of recycled polyethylene terephthalate (PET) bottles and niobium salt. PET, commonly found in household waste, is an inexpensive source of terephthalic acid. The synthetic protocol and characterization of the material are thoroughly described. Powder X-ray diffraction (PXRD) confirmed the formation of a crystalline phase, with an average crystallite size of 2.57 µm. Fourier transform infrared spectroscopy (FTIR) and elemental analysis (CHN) confirmed the formation of a new compound with irregular morphology, as observed by scanning electron microscopy (SEM). The structural and electronic properties of the proposed catalyst were further investigated using density functional theory (DFT). Nitrogen adsorption–desorption analysis revealed a type IV isotherm, typical of mesoporous materials. The catalyst was applied in oxidation reactions for the removal of model sulfur- and nitrogen-containing petroleum contaminants. The conditions were optimized, yielding removal efficiencies of 90% for dibenzothiophene and 86% for quinoline, which are suitable models for sulfur- and nitrogen-containing compounds, respectively.

The research aims to use sabiá bark as a biosorbent for removing methylene blue dye from aqueous solutions. The sabiá bark was acquired from Fazenda Ipê, in the municipality of Governador Dix-Sept Rosado, in the state of Rio Grande do Norte, Brazil. It was crushed, macerated, and sieved for particle size standardization. The bark powder underwent extraction removal both hot and cold. The biosorbent was characterized using Scanning Electron Microscopy, Fourier Transform Infrared Spectroscopy, and Zero Charge Potential. Adsorption tests involving pH effects, kinetic studies, and isothermal studies were conducted. The results showed higher efficiency in alkaline medium. Equilibrium was reached in 60 min, and the Pseudo-2nd order kinetic model provided the best fit to the experimental data. Among the isothermal models, the Langmuir isotherm had the best fit at all three investigated temperatures, with 28 ºC providing the best fit with a maximum adsorption capacity of 99.080 mg g⁻¹. Thus, the biosorbent from sabiá bark demonstrated promising results in removing methylene blue dye.

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Atmospheric humidity significantly influences air quality, thermal comfort, and public health outcomes, particularly in arid regions highly impacted by climate change. This study develops innovative environmental monitoring techniques through advanced prediction models for atmospheric humidity to support air quality management and health risk assessment in Kuwait City. Analysis of a 40-year dataset (1984–2023) comprising eleven atmospheric parameters revealed significant correlations between humidity variations and heat stress conditions. Multicollinearity assessment using Variance Inflation Factor, enabled exclusion of highly correlated variables (Tavg: VIF = 92.14; PS: VIF = 8.49), thereby enhancing model reliability. Four ensemble learning models– Random Forest, XGBoost, LightGBM, and GBR were applied to predict daily humidity levels. RF demonstrated superior performance (R² = 0.99329, RMSE = 1.31601) in capturing humidity dynamics. A novel environmental prediction framework comprising five HELMs was developed, with the optimal model (HELM-05) achieving enhanced accuracy (R² = 0.99364, RMSE = 1.28124). These technological improvements in humidity prediction accuracy enable better assessment of health risks associated with high humidity conditions, particularly during extreme heat events. The developed monitoring methodology provides an evidence-based tool for atmospheric monitoring and public health planning in arid regions, facilitating targeted interventions to reduce humidity-related health risks. This research contributes to enhanced understanding of environmental monitoring systems in arid environments and their implications for regional air quality management strategies.

This study aimed to evaluate the biomass and fatty acids production in the newly isolated, acidophilic chlorophyte, namely Tetradesmus obliquus M42, and to valorize the deoiled biomass as an adsorbent to concomitantly reduce the concentration and toxicity of synthetic dyes (methylene blue, crystal violet, and Congo red) in aqueous solutions. To this end, we evaluated: (i) the effects of temperature, pH, nitrogen, phosphate and inorganic carbon on the biomass production and lipid yield; (ii) the dye removal process, as a function of deoiled biomass dose, pH, initial dye concentration, and contact time, and (iii) the dye toxicity reduction, using the inhibition of bioluminescence and viability of Phaeodactylum tricornutum. While initial pH 5 supported rapid growth of M42 and CO₂ consumption, the most promising conditions for both biomass (1.36 g L⁻¹) and lipid yield (680 mg L⁻¹) were at pH 8 under nitrogen limitation and inorganic carbon supplementation. Lipid profile, dominated by monounsaturated fatty acids, met the requirements for biodiesel standards. Deoiled biomass (2.5 g L⁻¹) removed methylene blue faster (62% in 1.3 h) than crystal violet (87% in 1.7 h) and Congo red (61% in 5.5 h) at pH 9, and the adsorption mechanism was mainly attributed to electrostatic interactions. The toxicity of the dyes was significantly reduced, although the two biological models responded differently. From a circular economy perspective within a microalgae-based biorefinery, the use of M42 could offer a dual benefit: high lipid production as a biodiesel feedstock and deoiled biomass reuse to mitigate the environmental impact of toxic dyes.

Accurately quantifying energy consumption and carbon emissions from buildings at the urban scale is fundamental for developing effective energy conservation and emission reduction strategies. However, traditional building-by-building simulation methods are often too computationally prohibitive for city-scale assessments, creating a critical barrier to this essential quantification. This study addresses this challenge by developing and validating an urban building energy consumption proxy model that integrates a three-dimensional geographic information system, physics-based energy simulation, and machine learning. Using the Shanhe Bay Valley in Beijing as a case study, a dataset with data from 600 building simulations was generated to train and select the optimal predictive model from four algorithms. The main findings of this study are threefold. First, the validated model effectively quantified the district's energy performance, predicting an average annual operational carbon emission intensity of 74.40 kg/(m²·a). Second, a sensitivity analysis identified the five most influential drivers—per capita occupied area, ventilation frequency, solar heat gain coefficient, lighting power density, and equipment power density—providing clear targets for energy-saving interventions. Third, the proxy model demonstrated remarkable efficiency, completing the energy assessment for all buildings within the 13.2 km² district in just 4 s, a task that could take days with traditional methods. This integrated approach effectively removes the computational barrier, enabling rapid and reliable quantification of urban energy patterns and providing actionable insights for prioritizing optimization strategies in urban planning and policy.

This study investigates the potential of a bacterial consortium isolated from palm oil mill effluent (POME) to degrade Fats, Oils, and Grease (FOG). Enrichment techniques were employed to isolate a consortium capable of degrading FOG by through the incorporation of used cooking oil (UCO) into the nutrient media. The consortium, composed of three distinct bacterial strains with FOG-degrading abilities, was screened using a lipolytic activity test on Tween 20 agar media. Results identified consortium X3X4 as the most effective consortium, displaying the highest growth and FOG degradation. Optimization experiments, used a 2-level factorial design to explore the impact of bacterial inoculum concentration (2%, 6%, 10% v/v), oil concentration (1%, 3%, 5% v/v), and pH (6, 7, 8) on FOG biodegradation. Statistical analysis revealed that both oil concentration and bacterial inoculum concentration significantly influenced degradation compared to pH. Consortium X3X4, consisting of the Micrococcus lylae strain DSM 20315 and Corynebacterium aurimucosum strain, exhibited optimal FOG degradation, achieving 82.7% degradation after 20 days of incubation. From the kinetic analysis, the consortium’s μ_max and K_s values were calculated as 0.04 h⁻¹ and 4.86% v/v UCO, respectively. This study underscores the efficacy of the bacterial consortium, particularly consortium X3X4, in achieving substantial FOG biodegradation under optimal conditions. The study demonstrates the consortium’s potential for wastewater treatment, though it is limited to laboratory-scale experiments. Practical industrial applications will require additional research to address scaling and operational constraints.