International Journal of Environmental Science and Technology最新文献

Water contamination caused by pharmaceutical pollutants is a growing environmental concern. Caffeine, a widely consumed psychoactive substance, has been identified as an emerging contaminant due to its persistence in aquatic environments and resistance to conventional wastewater treatment methods. In this study, the photocatalytic degradation of caffeine was investigated using silver-doped titanium dioxide (A-TO) nanoparticles under UV-A irradiation. The 0.5 A-TO nanocatalyst was synthesized via an impregnation method and characterized using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR-ATR), scanning electron microscopy (SEM–EDS), and diffuse reflectance spectroscopy (DRS). Photodegradation experiments demonstrated that 0.5% A-TO achieved a 95% degradation rate of caffeine within 120 min, outperforming pure TiO₂. The enhanced efficiency is attributed to improved charge carrier separation and reduced electron–hole recombination due to Ag doping. Kinetic modeling confirmed that the photodegradation follows a pseudo-first-order reaction. Additionally, scavenger studies identified hydroxyl radicals (OH^·) and superoxide radicals (O₂^·⁻) as the primary reactive species responsible for caffeine degradation. Total organic carbon (TOC) analysis revealed a 72% mineralization rate, indicating effective breakdown of caffeine into less harmful byproducts. A phytotoxicity test using lentil seedlings confirmed the environmental safety of the treated water, with the germination index increasing from 32.81% (high toxicity) to 91.67% (non-toxic) after photocatalysis. Finally, a decision tree coupled with bootstrap aggregation (DT_Bootstrap) was employed to optimize process parameters, with a MATLAB-based interface developed for predictive modeling. These findings highlight the potential of A-TO as an efficient photocatalyst for pharmaceutical pollutant remediation in water treatment applications.

One of the key challenges of developing economies is environmental sustainability, as a fast industrialization process frequently leads to the diminishing of ecological strength. Therefore, the present study explores both the mean based estimations (linear) and heterogeneous (nonlinear) effect of technological innovation, social growth (specifically human development), financial development and key macroeconomic indicators on ecological footprint and clean energy. In addition, the study examines the reciprocal relationship between clean energy and ecological footprint by uses a panel dataset consist of 41 developing belt and road countries from 1995 to 2022. Data were obtained from the world development indicators (WDI), world intellectual property organization (WIPO), international energy agency (IEA), and the united nations population division (UNPD). This study deploys simple mean based regression, quantile-on-quantile regression (QQR), quantile process estimation (QPE), and hurlin causality to analyze dynamic and heterogeneous relationship respectively, while the panel fully modified ordinary least square (FMOLS) and panel dynamic ordinary least square (DOLS) models provide the robust estimates of linear dynamic relationship. The findings indicate technological innovation, social growth, financial development and natural resources have a significantly direct positive impact on both clean energy (CE) and ecological footprint (EFP). It shows innovation, cleaner technologies, digital connectivity, awareness and financial system promotes access to green investments but also suggests this dual outcome reflects innovation transition stage which is common in developing countries. At the same time, all regions have negative significant reciprocal relationship between CE and EFP except Asia, highlighting coal and oil dependence still dominate in energy structure. Populations weaken CE adoption substantially, because population pressure competes with the need for clean energy infrastructure investment in Latin America, Europe and Asia. Comparing the results of pre- and post-BRI reveals that clean energy was more effective before the BRI, indicates a continue reliance on fossil fuels based investment. This study acknowledges certain data limitations, particularly the limited number of observations for R&D and High-tech exports, which may affect the precision of cross-country comparisons. Policymaker are encourage to adopt region specific actions, such as fostering innovation through R&D investment, public–private collaboration, and educational partnerships is crucial to reducing ecological pressure, while financial institutions must actively promote financing green bonds or green funds to achieve long term ecological resilience.

The operational efficiency of photovoltaic (PV) systems directly impacts their economic viability and environmental benefits. This study presents a novel hybrid intelligent controller for Maximum Power Point Tracking (MPPT) that improves energy harvesting and sustainability by integrating Support Vector Regression (SVR) predictive capabilities with Q-Learning adaptive control. The SVR component, trained on comprehensive simulation data covering irradiance ranges of 200–1000 W/m² and temperature ranges of 15–45°C, accurately predicts maximum power points based on environmental inputs. The Q-Learning component refines these predictions in real time, demonstrating superior adaptability to dynamic operating conditions and system aging. Extensive validation confirms exceptional performance: SVR models achieve high predictive accuracy (R² = 0.9823 for voltage; R² = 0.9889 for power), while the hybrid controller attains 99.81% tracking efficiency, exhibiting 59% better degradation resistance compared to conventional approaches. For a representative 10 kW PV installation, the method yields substantial benefits: 738 kWh/year of additional energy generation (a 4.4% improvement), 516 kg of annual CO₂ reduction (12.9 metric tons over 25 years), a rapid 3.6-year payback period, and a net lifetime profit of $2,963. The conservative base-case Levelized Cost of Energy (LCOE) analysis demonstrates minimal change (-0.42%). In contrast, sensitivity analysis confirms that efficiency gains exceeding 5% result in substantially reduced LCOE. These findings establish the hybrid SVR-Q-Learning approach as a commercially viable and scalable solution that addresses the dual imperatives of maximizing renewable energy yield while ensuring robust long-term economic returns, positioning the approach as a practical advancement for enhancing global PV infrastructure efficiency and reducing environmental impact.

This study sheds light on the complex mechanisms of heterogeneous photocatalysis induced by UV–TiO₂ irradiation, revealing the key reactive species responsible for the decomposition of persistent organic pollutants in water. By employing a range of scavengers and evaluating 50 diverse organic pollutants, including pesticides, pharmaceuticals, and dyes, we uncover the crucial roles of holes, electrons and different reactive oxygen species in the photodegradation process. Our findings highlight the importance of molecular structure, electronic nature, and redox potential in determining the decomposition pathway, and demonstrate the potential for tailored photocatalytic approaches to target specific pollutants. This research also provides valuable insights into the dynamic interactions between scavengers and photoreactive species, illuminating how key external factors—including pH, adsorption catalyst capacity, and light source characteristics—could alter the selectivity towards the target pollutant. These finding lay the groundwork for developing optimized water treatment strategies, enabling more precise and efficient pollutant degradation.

Accurate prediction of carbon emissions is fundamental to effective climate governance, yet it is inherently challenging due to the complex, multi-scale, and uncertain nature of emission data. To address these challenges, this study introduces a novel carbon emission prediction framework that integrates advanced computational techniques. The framework first employs a fuzzy entropy-constrained variational mode decomposition method for sophisticated signal denoising and pattern preservation. It then utilizes a temporally reinforced inductive graph attention network to capture intricate short and long term spatial–temporal dependencies. A pattern recognition system that combines clustering with adversarial training is incorporated to extract and leverage shared knowledge, while a forest copula architecture models nonlinear dependencies to generate robust probabilistic predictions. Experimental results demonstrate that this integrated approach achieves a 40.88% improvement in point prediction accuracy and a 45.82% enhancement in the reliability of interval predictions, significantly outperforming existing benchmark models. Furthermore, interpretability analyses validate the framework's capability in pinpointing the primary spatial–temporal drivers of carbon fluctuations and in disentangling the interactive relationships among various emission sources. This provides actionable insights for policymakers, establishing a new paradigm for reliable carbon emission prediction that successfully balances predictive accuracy, uncertainty quantification, and decision-making relevance.

Qualitative assessment of ecosystem health and the factors influencing it is important to ensure the selection of effective, sustainable territorial development measures. The study aimed to assess spatiotemporal changes in land use, land urbanization, and ecosystem health across Lithuania during 2000–2018 and determine the main driving forces. The ecosystem health was evaluated using the three-dimensional Vigor–Organization–Resilience (VOR) framework. Significant changes in land use were related to decreased agricultural areas and increased semi-natural vegetation areas, with a slight increase in artificial surfaces and an increase in land urbanization. Land urbanization increased slightly from 3.17 to 3.27%, particularly around cities. Ecosystem vigor and organization were generally high across the country, yet ecosystem resilience remained low to medium in most areas. Between 2000 and 2018, the ecosystem health index (EHI) declined from 0.79 to 0.75, indicating a gradual reduction in overall ecosystem condition. Hot spot and cold spot analyses revealed increasing spatial heterogeneity: cold spots expanded throughout the central lowlands and major cities, while hot spots concentrated in the eastern uplands. Spatial autocorrelation (Moran’s I) confirmed significant clustering of ecosystem health, though the degree of clustering slightly decreased over time. Geodetector analysis identified land use intensity, population density, and the proportion of urban and natural land as dominant drivers of ecosystem health. Among them, land use intensity exerted the strongest and increasing influence over time. These findings highlight the growing spatial unevenness of ecosystem health in Lithuania and underscore the need for integrative land management policies that balance urban development with ecosystem sustainability.

This study presents a novel MgSn(OH)₆/4CuS@HC photocatalyst developed via hydrothermal treatment followed by chemical precipitation. MgSn(OH)₆/4CuS@HC was applied for wastewater treatment, targeting eight pollutants: Tetracycline, Doxycycline, Ofloxacin, 3-Nitroaniline, 2-Nitrophenol, Methyl red, Congo red, and Methylene blue. The novel photocatalyst was studied using FTIR, EDX, DRS, SEM, and PL techniques. To gain further insight into the reaction mechanism, scavenger experiments were conducted in addition to operational parameters such as pH, dosage, CuS loading on MgSn(OH)₆@HC, pollutant initial concentration, selectivity, reusability, water source, and kinetic studies. The performance test highlighted the outstanding photocatalytic activity of templated MgSn(OH)₆/4CuS@HC, exhibiting remarkable stability over five cycles. This performance is attributed to the direct Z-scheme mechanism and the hydrochar template, which improve charge separation, extend photocharge lifetime, and reduce the agglomeration of nanosemiconductors, thereby enhancing overall stability. This study offers a new perspective on green photocatalysts for diverse environmental applications.

Urbanization and anthropogenic activities are continuously deteriorating urban air quality in metropolitan cities of the world. Lahore is consistently ranked among top cities globally with the worst air quality. Based on the significance, the current study aims to address a critical gap in existing research by analyzing land use- based urban air quality of Lahore, integrating ground-based Particulate Matter (PM_2.5 and PM₁₀) measurements and Sentinel-5P-derived air pollutants data. The Sentinel-5P derived measurements were used to predict and evaluate the significance of their relationships with ground-based PM observations. Geospatial patterns of the PM_2.5 and PM₁₀ were analyzed using Inverse Distance Weighting (IDW) interpolation and Moran’s I spatial autocorrelation. Two General Linear Models (GLM) were employed to evaluate the satellite-derived pollutants and land use categories as significant predictors of the PM_2.5 and PM₁₀. Research findings indicated high concentrations of PM_2.5 and PM₁₀ were recorded on 1st November 2019 as 416 and 1906 µg/m³ respectively, in northern and northeastern parts of the city across the transportational land use. PM_2.5 exhibited high correlation with CH₄ (r = 0.69), AAI (r = 0.61), and SO₂ (r = 0.57). The GLM results showed LULC (F = 4.72, η² = 0.167) and CH₄ (F = 9.49, η² = 0.084) with a significant (p < 0.05) and large effect in predicting PM_2.5. The research findings possess significant policy implications for urban air quality management, urban planning, and sustainable land use management. The research findings also support health policy makers emphasizing the significance of land-use-based targeted interventions to reduce harmful air pollutants.

Addressing pharmaceutical pollution on a global scale is crucial, with amoxicillin (AMX) being among the most extensively used antibiotics in both human and veterinary medicine. This study presents an innovative photocatalyst for AMX degradation using Zn-incorporated α-Bi₂O₃ thin films under visible light. The optimization of process parameters was carried out using Central Composite Design (CCD) under Response Surface Methodology (RSM), considering three variables (a) temperature (210–490 °C), (b) Zn concentration (0.17–5.83%wt), and (c) antibiotic dose (14.65–85.35 mg/L). The optimized catalyst (Zn-αBO-357–3.19–25) demonstrated a high degradation efficiency of 70.06% and total organic carbon (TOC) removal of 49.28% after 3 h of visible light irradiation. A quadratic model showed strong correlation, with coefficient of determination (R²) for degradation efficiency and TOC removal was 0.9925 and 0.9593, respectively. The Zn- αBO thin films were characterized using UV–Vis, XRD, FTIR, SEM, AFM, XPS, and TGA techniques. The measured band gap energy of 2.42 eV indicated strong visible light absorption. The catalyst maintained over 65% degradation efficiency across five reuse cycles. When applied to real pharmaceutical wastewater, the Zn-αBO thin films achieved 64.23% COD removal, 51.52% BOD removal, and 57.58% reduction in TSS. These results highlight a promising strategy for mitigating pharmaceutical contamination in wastewater through optimized photocatalytic degradation using CCD.

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This work investigates the effects of hydrochar (HC) derived from Cuscuta (dodder), an invasive plant, on the textural, structural, morphological, and porosity properties, as well as the adsorptive performance of pozzolan-based alkali-activated geopolymers for the removal of crystal violet (CV) dye from saline water. The geopolymer-hydrochar composites GP₀, GP_2.5-HC, and GP_5-HC were prepared by replacing 0%, 2.5%, and 5% of the pozzolan with hydrochar, respectively. X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), N₂ adsorption, and scanning electron microscopy (SEM) analyses were used to assess the effects of HC on the mineralogical profile, functional groups, structure, morphology, and texture of the pozzolan-based geopolymers. Batch adsorption experiments were conducted to evaluate CV removal by these composites. Incorporation of 2.5% and 5% HC produced morphologically distinct composites but did not affect the geopolymerization reaction, as no new mineralogical phases were observed. The addition of 5% HC resulted in ~ 27% decrease in specific surface area, from 20.00 to 14.66 m²/g, while achieving ultrahigh adsorption capacities ranging from 24 to 5896 mg/g in saline water. Salinity enhanced the adsorption capacity 7–11-fold compared with non-saline water, attributed to decreased CV solubility, shifts in ion-exchange equilibrium favoring cation uptake, and electrostatic shielding in saline media. R² values ≥ 0.90 and low χ² error function values indicated that the Langmuir–Freundlich and Genuine Halsey isotherms best fit the equilibrium data in both non-saline and saline water. Overall, the results demonstrate that geopolymer–hydrochar composites are promising candidates for the removal of CV from saline water.

Graphical abstract