Search Results (1,178)

Water pollution is one of the most critical societal and environmental challenges and remains a persisting problem worldwide. The origin of this pollution is diverse, while organic matter occupies a significant portion, originating from different sources. This creates major environmental and health risks, requiring reliable and sensitive analytical tools for effective monitoring. The permanganate index stands as a conventional assessment method for organic pollution, but it demonstrates compound non-specificity toward compounds and limited sensitivity to various contaminant structures. This research introduces cyclic voltammetry as a standalone electrochemical method that provides sensitive detection and characterization of organic oxidizing compounds. Six organic compounds, including gallic acid, phenol, oxalic acid, ascorbic acid, salicylic acid and p-benzoquinone, were used as model compounds and studied in aqueous media. These compounds were analyzed individually, in single-compound mode, to characterize their redox behavior and to identify the voltammetric peaks. Subsequently, a multi-compound analysis was studied to check for the validity of the concept in a more complex matrix. Notably, a strong linear correlation was observed between the measured charge and the theoretical permanganate index, highlighting the quantitative reliability of the electrochemical method. Comparing the obtained results with the permanganate index method confirmed the superiority of cyclic voltammetry in terms of response time and detection capability. The outcomes demonstrate that cyclic voltammetry functions as a robust alternative to the classical chemical oxidation method for environmental water assessment. Full article

The common carp (Cyprinus carpio) kidney uniquely integrates excretory nephrons, renal hematopoietic tissue, and hormonally active thyroid follicles, positioning it as a candidate “multipurpose biomarker organ” for pollutants like perfluorooctanoic acid (PFOA), a prototype long-chain PFAS and persistent organic pollutant exhibiting nephrotoxic, immunotoxic, and thyroid-disrupting effects. Building on prior histological, ultrastructural, and morphometric analyses from carp exposed to waterborne PFOA (0, 200 ng L⁻¹, 2 mg L⁻¹ for 56 days), a hierarchical multipurpose index comprising nephrotoxic, immunotoxic, and thyrotoxic subindices was developed from z-scored light-, electron-microscopy, and morphometric features, enabling cross-scale integration; proximal tubule vesiculations and effete rodlet cells (RCs) were newly quantified from archival electron micrographs. The subindices captured PFOA-induced glomerular hyperfiltration with proximal protein reabsorption and collecting duct RCs recruitment (nephrotoxic); hematopoietic tissue RCs recruitment, clustering, and exocytosis (immunotoxic); and increased thyroid follicle abundance/vesiculation, cross-sectional area, and perimeter (thyrotoxic). Quantification of previously only qualitatively assessed features provided statistical validation, while radar plot integration rendered results more intuitively evident—particularly highlighting the non-monotonic thyroid response—condensing organ-level complexity into a coherent framework supporting carp kidney as a translational One Health model for multi-endpoint waterborne pollutant assessment. Full article

This study investigates the optimization of fabrication and operating parameters for poly(ether sulfone) (PES) ultrafiltration membranes embedded with Bismuth tungstate and multi-walled carbon nanotubes (MWCNTs) Bi₂WO₆/MWCNTs for the removal of dye pollutants from wastewater. Response surface methodology (RSM) coupled with Analysis of Variance (ANOVA) was employed to develop regression models for evaluating membrane performance in terms of dye rejection and permeate flux. A central composite design (CCD) was used to conduct a systematic series of ultrafiltration experiments. The effects of key variables, including Bi₂WO₆/MWCNTs loading (0–0.1 wt.%), operating pressure (5–9) bar, and methyl red (MR) dye concentration (50–150 ppm), on membrane separation performance were comprehensively examined. The developed models demonstrated strong statistical significance and accurately described the experimental data. Optimization results revealed that the operating parameters exerted a more pronounced influence on membrane performance than fabrication variables. The maximum MR rejection of 96.8457% was achieved at an optimal Bi₂WO₆/MWCNTs loading of 0.08 wt.%, dye concentration of 112.6 ppm, and operating pressure of 9 bar. Experimental validation confirmed the reliability and predictive capability of the proposed models. In order to provide high-performance membranes with enhanced permeability, antifouling resistance, and dye removal efficiency for useful wastewater treatment applications, this study attempts to optimize the operating and preparation parameters for adding Bi₂WO₆/MWCNT nanocomposites into PES membranes. Full article

Biomass combustion, as a key technology for achieving a low-carbon transformation of the energy system, faces multiple challenges in its efficient and clean utilization, including the high heterogeneity of fuels, the complex multi-scale coupling of the combustion process, and the attainment of ultra-low emissions. Traditional research methods have significant disconnections between microscopic mechanism understanding, macroscopic performance prediction of reactors, and end-of-pipe pollution control, which restricts the improvement of system performance. This review presents recent advances in advanced numerical simulation, pollutant control strategies, and bioenergy with carbon capture and storage (BECCS) pathways targeting ultra-low emissions in biomass combustion. This work synthesizes progress across three interconnected domains. First, methodologies are examined for integrating detailed chemical kinetics, particle-scale models, and reactor-scale simulations to develop high-fidelity predictive tools. Second, low-nitrogen combustion and synergistic pollutant control strategies for primary furnace types (e.g., grate, fluidized bed) are evaluated, alongside process optimization from fuel pretreatment to flue gas purification. Third, the potential for integrated design of biomass energy systems with carbon capture is assessed, emphasizing that system efficiency hinges on holistic “fuel-combustion-capture” chain optimization rather than isolated unit improvements. Future research directions are highlighted, including the development of physics-informed AI modeling paradigms, deeper co-design of multiple processes, and the establishment of robust life-cycle assessment frameworks. This review aims to provide a structured reference to inform both fundamental research and the practical development of next-generation clean biomass combustion technologies. Full article

Groundwater in the coastal industrial cities of the Guangdong–Hong Kong–Macao Greater Bay Area faces rising pressure from saline–tidal intrusion, multi-source contamination, and intensive abstraction. Effective management therefore requires an integrated view of water quality and resource availability. A total of 369 groundwater samples were collected from Quaternary porous and fractured bedrock aquifers during the wet and dry seasons. Major ions and key pollutants were analyzed, and overall quality was assessed using the improved Nemerow pollution index. A 3D transient FEFLOW model calibrated for 2022–2024 was combined with Nemerow quality classes to quantify season-specific exploitable resources by grade. The results indicate that NO₃⁻, Mn, and NO₃–N are the dominant pollutants (0–202.05 mg/L, 0.001–8.91 mg/L, and 0–108 mg/L, respectively). Nemerow grading shows Class IV prevailing (47.4–54.5%), with higher Class V proportions in fractured groundwater (27.3–34.5%) than in porous groundwater (14.0–15.5%); overall quality deteriorates in the dry season. Annual mean sustainable exploitable resources are 2.72 × 10⁸ m³/a (porous aquifers) and 1.25 × 10⁸ m³/a (fractured aquifers). These results provide a quantitative basis for season- and quality-informed groundwater development and protection in coastal industrial cities. Full article

Volatile organic compounds (VOCs, dominated by xylene, toluene, and benzene) and paint mist emissions from ship painting represent a major environmental and health concern, posing a critical bottleneck to the green transformation of the shipbuilding industry. To tackle this challenge, this study presents an integrated recovery system designed specifically for ship automatic-spraying robots. Guided by the synergistic principle of “air-curtain containment, multi-stage adsorption, and negative-pressure recovery,” the system features a modular design that ensures full compatibility with the robots’ spraying trajectory without operational interference. Core adsorption materials, namely glass fiber filter cotton and honeycomb activated carbon fiber, were selected to suit the high-humidity and high-pollutant-concentration environment typical of ship painting. An appropriately matched axial flow fan maintains stable negative pressure throughout the system. Furthermore, the design integrates an air curtain isolation subsystem and an automated control subsystem, enabling coordinated operation and real-time adjustment. Using ANSYS Fluent, geometric and flow field simulation models were established to analyze airflow distribution and pollutant adsorption behavior, which led to the optimization of key structural and material parameters. Field experiments conducted in shipyard environments demonstrated the system’s superior performance: it achieved a VOC removal efficiency of 88.4% and a paint mist capture efficiency of 85.7% under optimal working conditions, with a maximum simulated paint mist capture efficiency of 86.2%. The system maintained stable performance under complex vertical and overhead spraying conditions, with an efficiency attenuation of less than 1.5%, and its outlet emissions fully complied with the mandatory limits specified in the Emission Standard of Air Pollutants for the Shipbuilding Industry (GB 30981.2-2025). The relative error between experimental data and simulation results is less than 2%, confirming the reliability and practicality of the proposed system. This research provides an efficient and adaptable pollution control solution for green shipbuilding and offers valuable technical insights for the sustainable upgrading of automated painting processes in heavy industries. Full article

Adsorption is a method widely used to remove low-molecular-weight organics from wastewaters, and phenolic compounds from olive mill wastewater are a persistent class of bioactive pollutants of environmental concern. We screened eleven microalgal candidates at 0.10 g·L⁻¹ using batch kinetics fitted with the Lagergren pseudo-first-order model to obtain rate constants (k) and fitted equilibrium capacities (q_e). Cyanobacteria, particularly Anabaena spp. and Limnospira platensis, exhibited the highest adsorptive potential in the screening; wall-less species (e.g., Dunaliella salina, Isochrysis galbana) showed negligible surface adsorption, indicating that the presence and type of cell wall highly influence biosorption. L. platensis was selected for detailed study because of its established industrial cultivation and valorisation potential. Equilibrium experiments with HCl-functionalized L. platensis at four biomass loadings (0.10–1.00 g·L⁻¹; initial phenolic mix 30 mg·L⁻¹) showed that increasing dose reduced equilibrium concentration (C_e) but decreased specific uptake from ≈77 mg·g⁻¹ to ≈18 mg·g⁻¹ while removal rose from ~26% to ~61%. Nonlinear isotherm fitting favoured the Freundlich model (1/n < 1), consistent with heterogeneous, multi-site adsorption. Targeted macromolecular extractions abolished phenol uptake, demonstrating that the intact protein–polysaccharide matrix is essential for binding. L. platensis route delivered higher single-cycle removal (≈61%) compared to the maize-derived activated carbon reference (≈49%) while also incurring a 1.3-fold lower GWP (approximately 3 kg CO₂-eq per treatment) than the activated carbon route (approximately 4 kg CO₂-eq per treatment) in our model. Overall, L. platensis represents a lower-impact alternative for natural phenols remediation, especially when integrated into valorisation pathways that recover algal co-products. Full article

The Chengdu–Chongqing Twin-City Economic Circle (CC-TCEC), located in the Sichuan Basin, frequently experiences persistent winter PM_2.5 pollution due to basin-constrained ventilation and strong meteorology–emission coupling. Using daily PM_2.5 observations from 113 monitoring stations with a strict two-year training and one-year testing split, we develop hybrid spatiotemporal forecasting models that couple a graph neural network (GCN/GAT) for inter-station spatial dependence learning with a temporal backbone (LSTM/Transformer) for evolving concentration dynamics. We adopt a rolling one-day-ahead forecasting scheme using a 7-day look-back window. Across 12-month, 6-month, and 3-month evaluation windows, the meteorology-augmented Multi-GAT-Transformer shows a slight but consistent advantage over the other tested variants, suggesting potential benefits of attention-based spatial weighting and long-range temporal self-attention under nonstationary basin pollution regimes. Spatiotemporal mappings derived from the best-performing configuration suggest that elevated winter PM_2.5 is mainly associated with low-lying areas such as the Chengdu Plain, industry clusters, and dense urban cores, with peaks that also coincide with the New Year and the pre-Lunar New Year period, suggesting a possible contribution from elevated traffic and production activity. These impacts are amplified by winter stagnation (low winds, high humidity, limited precipitation). From a policy perspective, the results support sustainability-oriented winter haze management by enabling early episode warning and hotspot prioritization. Full article

To address global carbon reduction demands, oxy-fuel combustion in circulating fluidized beds (oxy-CFB) has emerged as a highly promising carbon capture technology, offering extensive fuel flexibility and facilitating bioenergy with carbon capture and storage (BECCS). However, its commercialization is hindered by significant energy penalties and complex scale-up challenges. This review comprehensively analyzes the fundamental multiphase mechanisms, heat transfer behaviors, and multi-pollutant emission characteristics of oxy-CFB systems, drawing upon multiscale modeling advancements and operational data from pilot to 30 MW_th industrial demonstrations. Replacing air with an O₂/CO₂/H₂O mixture fundamentally alters gas–solid hydrodynamics and char conversion pathways, necessitating active fluidization state re-specification. Despite shifting optimal desulfurization temperatures and introducing recarbonation risks, the technology demonstrates inherent advantages in synergistic pollutant control, including the complete elimination of thermal NO_x. While atmospheric oxy-CFB is technically viable, transitioning to pressurized operation is critical to minimizing system efficiency penalties. Furthermore, integrating oxygen carrier-aided combustion (OCAC) and developing advanced predictive control strategies are essential to managing multi-module thermal inertia and enabling rapid dynamic responsiveness for modern power grids. Full article

Despite concern over air pollution, fine-scale spatial and demographic disparities in exposure remain largely unquantified in Australian cities due to sparse monitoring and coarse models. In Greater Melbourne, this gap limits neighbourhood-level assessment of PM_2.5 exposure and associated environmental inequalities. To address this gap, we integrated 6-month averaged PM_2.5 observations (October 2023 to March 2024) from 5 regulatory monitoring stations and 13 low-cost sensors (LCSs) to develop a land use regression (LUR) model estimating concentrations at a 100 m resolution. These estimates were used to calculate population-weighted PM_2.5 exposure (PWE) at the mesh block level across Melbourne. To examine factors associated with spatial heterogeneity in PWE, we applied a hybrid modeling framework combining Spatially Explicit Random Forest (Spatial-RF) and Geographically Weighted Regression (GWR), incorporating physical, built-environment, and socio-demographic variables from the Synthesized Multi-Dimensional Environmental Exposure Database (SEED). The Spatial-RF model initially exhibited an R² of 0.56. After multicollinearity diagnostics using the Variance Inflation Factor (VIF), three key explanatory variables were selected for GWR modeling: the Normalized Difference Vegetation Index (NDVI), the Index of Education and Occupation (IEO), and the proportion of culturally and linguistically diverse populations (CALDP). The developed GWR model achieved higher model performance (R² = 0.65) than Spatial-RF and global Ordinary Least Squares (OLS) regression (R² = 0.38), revealing strong spatial non-stationarity. Results show that PWE generally ranged from 5 to 7 µg/m³, exceeding the 2021 WHO air quality guideline, with hotspots in the urban core and along major transport corridors. Elevated exposure occurred in both socioeconomically disadvantaged areas and residents in urban centers with higher socio-economic status, reflecting complex, spatially contingent exposure inequalities. These findings support fine-scale, equity-oriented air quality management. Full article

Fungal endophytes are microorganisms that inhabit plant tissues without causing disease and emerge as critical mediators of plant stress tolerance, nutrient acquisition, and ecosystem resilience under diverse climate change scenarios. Their unique position within the host allows them to modulate physiological responses more closely than external microbiota. This review explores how endophytic fungi contribute to plant adaptation under climate-induced stresses such as heat, salinity, drought, pollution, and nutrient limitation, with a focus on molecular crosstalk, functional trait modules, and metabolic trade-offs. Key findings emphasize multilayered signaling systems, including MAMP/DAMP recognition, phytohormone regulation, immune tuning, ROS dynamics, and effector deployment, while emerging mechanisms such as cross-kingdom RNA and extracellular vesicle (EV)-mediated exchange are discussed as promising but currently limited in empirical validation within many endophytic systems. Endophytes also enhance nutrient exchange through conditional carbon-for-benefit trade and may shape rhizosphere microbiota and soil activities through plant-mediated inputs. Integrative multi-omics approaches provide predominantly correlational insights into the mechanistic basis of these effects, linking molecular function to ecosystem and community outcomes. These insights have potential applications in climate-resilient agriculture, phytoremediation, and ecosystem restoration; however, their large-scale implementation requires further field-based validation and context-specific assessment. Future priorities should focus on trait-based selection, ecological modeling, and biosafety evaluation to translate microbial functions into reliable field-level strategies that support sustainable crop performance under accelerating environmental stress. Full article

The low-carbon city pilot (LCCP) and carbon emissions trading (CET) represent two critical policies for reducing carbon emissions. Accurately evaluating their synergistic effects on the reduction in pollution and carbon emissions (RPCE) is of utmost importance for advancing China’s low-carbon economic growth and achieving the dual-carbon objectives. Utilizing data from 279 prefecture-level cities during 2008 to 2021, this study employed a multi-phase differences-in-differences model to investigate the synergistic effects of the concurrent implementation of LCCP and CET (referred to as the “dual pilot” policy) on RPCE. The findings revealed that (1) the dual pilot policies reduced per capita CO₂ emissions by 0.644% and PM_2.5 concentration by 0.114%, with the dual effect being significantly superior to that of single pilot policies; (2) through mechanism analysis, it was found that technological innovation and clean energy transition served as the principal channels through which the “dual pilot” policy exerted its influence on RPCE; and (3) heterogeneity analysis demonstrated that the “dual pilot” policy was particularly effective in the RPCE in big cities, non-resource-based cities, and highly urbanized cities. This study provides novel empirical evidence supporting the integration of active government intervention with effective market mechanisms to maximize synergies in carbon emission reduction policies and achieve RPCE. Full article

With the rapid development of integrated energy systems (IES) towards integration and marketization, the collaborative planning of multi-energy entities has become a research hotspot. However, in real-world market environments, various energy entities often face information asymmetry and competitive interests, posing significant challenges to the optimal scheduling of the system. To address the incomplete information and competitive constraints among multiple energy hubs (EH) within IES, this paper constructs a multi-entity game planning model that accounts for environmental costs and carbon trading mechanisms. The model employs Bayesian game methods to handle the incomplete information among EH and analyzes the dynamic interactive behaviors of market entities under different strategies through multilateral incomplete information evolutionary game theory. Meanwhile, this paper incorporates carbon trading mechanisms along with the coupling technologies of power-to-gas (P2G) and carbon capture systems (CCS) to balance the economic efficiency and environmental protection. Additionally, in response to investment uncertainty, the real options theory is utilized for evaluation, and then a multi-entity incomplete information planning model is constructed, which is solved by using a nested algorithm proposed in this paper. This approach balances the interests of various entities and enhances the comprehensive long-term investment returns considering options. Simulation results demonstrate that the model effectively reflects the game behaviors among multi-energy entities under incomplete information, yielding optimized scheduling solutions that closely align with real-world scenarios. It improves economic benefits while reducing environmental pollution, providing theoretical foundations and methodological support for the planning of integrated energy systems involving multiple entities in electricity market environments. Full article

The rapid evolution of smart building technologies has transformed the hotel industry, necessitating structured methodologies for evaluating building intelligence. This research, dedicated to engineering problems, proposes an integrated decision-making model that combines fuzzy Shannon entropy and fuzzy multi-objective optimization on the basis of ratio analysis (MOORA) to assess the intelligence level of buildings within the hospitality sector. The model systematically determines the relative importance of intelligence criteria, including engineering, environmental, economic, social and cultural, technological, and energy conservation criteria. By leveraging fuzzy Shannon entropy, the framework objectively assigns weights to criteria based on information distribution, minimizing subjective biases in evaluation. Fuzzy MOORA is then applied to rank alternative intelligent buildings in hotels, ensuring an accurate comparative assessment. The proposed model is tested on real-world hotel data, demonstrating its effectiveness in identifying optimal intelligent building configurations. The results of applying fuzzy Shannon entropy reveal that human comfort, the emission of greenhouse gases (pollution), and system integration are the most important sub-criteria. Finally, by applying the importance of the criteria in the fuzzy MOORA model, the intelligence levels of hotels are evaluated. The results show that the Parsian Kowsar, Piroozy and Sepahan Hotels are the best hotels based on the intelligent building criteria. Full article

Field-scale nitrogen migration mechanisms in small watersheds remain poorly quantified due to insufficient representation of microtopographic heterogeneity. This study investigates nitrogen transport dynamics in a 1.27 km² agricultural watershed in China’s Jianghuai region using unmanned aerial vehicle (UAV) -derived 0.1 m digital elevation models (DEMs) and coupled hydrological–erosion modeling. The Soil Conservation Service Curve Number (SCS-CN) and Modified Universal Soil Loss Equation (MUSLE) models quantified nitrogen output loads, while the multi-flow direction algorithm simulated migration trajectories for total nitrogen (TN), ammonium, and nitrate. Results revealed strong spatial heterogeneity in nitrogen exports (watershed mean: 29.66 kg TN/km²·a), with bare land and greenhouses exhibiting the highest outputs (448.54 and 363.41 kg/km²·a) and forested areas showing minimal export (<6.1 kg/km²·a). Nitrogen migration was predominantly controlled by topographic gradients, with microtopographic features—field ridges, ditches, and buildings—physically redirecting flows and creating critical export nodes at field boundaries. DEM resolution critically affected simulation accuracy: erosion intensity displayed a non-monotonic response with an inflection point near 1 m resolution, corresponding to the median elevation difference (1.2 m) of field ridges. Structural equation modeling confirmed that high-resolution DEMs (0.1–2 m) maintained topographic control over nitrogen migration (~80% contribution), whereas 30 m DEMs reduced this influence to 30%, inducing spurious meteorological dominance. This study demonstrates that decimeter-scale DEMs are essential for accurately capturing microtopographic regulation of nitrogen transport, providing a methodological basis for precision management of agricultural non-point source pollution. Full article