Journal of Environmental Management最新文献

Although considerable effort has been devoted to have an in-depth understanding on the role of hydrochar on anaerobic co-digestion of food waste and waste activated sludge in enhancing biogas production, few studies have investigated on how it affects the digestate dewaterability. This work investigated the impact of different dosages of hydrochar (0, 2, 5, and 10 g L^-1) on the digestate dewatering performance and its physicochemical characteristics during the anaerobic co-digestion of food waste and waste activated sludge, as well as underlying mechanism. Experimental results indicated that adding hydrochar obviously improved digestate dewaterability, with the specific resistance to filtration reducing by 62.0%-65.6% and the capillary suction time decreasing by 6.9%-24.0%. Mechanistic studies revealed that 10 g L^-1 hydrochar reduced the protein content and enhanced the removal of tyrosine-like and tryptophan-like protein in extracellular polymeric substances. Fourier-transform infrared spectroscopy analysis demonstrated that adding hydrochar not only enhanced the removal of hydrophilic functional groups in extracellular polymeric substances, but also reduced the ratio of α-helix to (β-sheet + random coil). Consequently, extracellular polymeric substances hydrophobicity was preferred, resulting in better digestate dewaterability. Further microbial exploration indicated that 10 g L^-1 hydrochar enriched the protein-hydrolyzing microbes, which probably contributed to the lower hydrophilic extracellular polymeric substances content and components, as well as weaker protein secondary structure. Further dewatering test demonstrated that hydrochar (especially at high-dose) still caused a positive effect on the subsequent dewatering of digestate when adding the regulator FeCl_3. The current findings not only increased the appeal for the application of hydrochar in the anaerobic co-digestion of food waste and waste activated sludge, but also provided forward-looking insights on the neglected benefit of hydrochar on the subsequent dewatering process of the digestate.

Limiting biological invasions is essential for conserving biodiversity and sustainable future. Preventing human-vectored introductions, especially into ecologically important areas, is recognized as critical; however, quantitative evidence on effective interventions, including those targeting behavioral change, is scarce. Moreover, beyond preventing biological invasion, the impact of the medium through which behavioral interventions are delivered on their effectiveness remains poorly understood in conservation. Here using the case of seed introduction into mountainous regions via footwear, we compared the effectiveness of behavioral interventions through cleaning stations equipped with five types of footwear-cleaning tools with overall participants. Specifically, we adopted the RE-AIM framework and evaluate effectiveness of the tools from three dimensions. While the same behavioral messages and intervention devices were used in all cases, the proportion of seeds removed using the cleaning station and the proportion of visitors who spontaneously used the cleaning station varied substantially across the tools used in the cleaning station. This resulted in significant differences in the proportion of seed interception by visitors' voluntary use of the cleaning station. Notably, cleaning stations equipped with a side-brushed scrubber showed the highest performance, intercepting an estimated 55% of seeds that would otherwise be introduced, even without enforcement. Moreover, the intention to use cleaning tools differed among tools, and was primarily constrained by perceived temporal and mental burdens. Our results highlighted the importance of medium in the conservation behavioral interventions. We also suggest utility of the framework in conservation for evaluating and reporting intervention effectiveness, and identifying priority areas for improvement.

In recent years, nitrous oxide (N₂O) has emerged as a critical target for mitigation owing to its exceptional stability and significant potential to cause global warming. Conventional abatement technologies, including thermal, catalytic, and plasma-based technologies, are currently limited by economic and scalability constraints, hindering their practical deployment. In this study, we developed an electron beam (EB) radiolysis approach to decompose N₂O under ambient conditions. Utilizing an absorbed dose of 50 kGy, we achieved over 90 % decomposition of the 100 ppm N₂O in both N₂ and Ar backgrounds. However, oxygen inhibits radiolytic N₂O decomposition by facilitating the reformation of N₂O through reactions involving N₂- and O₂-derived reactive species. While net N₂O formation was observed from N₂ and O₂ mixtures, the introduction of initial N₂O effectively suppressed this process, demonstrating a distinct kinetic advantage for N₂O decomposition. Therefore, net N₂O decomposition was achieved when N₂O and O₂ concentrations were equivalent. The inhibitory effect of oxygen diminished at higher absorbed doses, indicating that elevated irradiation energies favor direct electron impact decomposition of N₂O over indirect pathways mediated by reactive species. These findings establish EB radiolysis as an efficient and scalable strategy for N₂O conversion. Moreover, they provide a clear scientific basis for addressing the challenges posed by oxygen in practical applications.

The development of bifunctional catalysts that combine photocatalytic pollutant degradation and hydrogen peroxide synthesis provides an effective strategy for dealing with environmental pollution and energy problems. Herein, a S-doped graphitic carbon nitride photocatalyst containing N-vacancies was synthesized by hydrothermal and calcination methods. The introduction of sulfur effectively modulated the valence band position of graphitic carbon nitride, thus enhancing its absorption efficiency of visible light. N vacancies in g-C₃N₄ not only effectively trap localized electrons to promote photogenerated electron-hole (e^--h⁺) separation (forming localized states) but also introduce impurity energy levels in the band structure to enhance the adsorption and activation of reactive oxygen species. PL spectroscopy confirms that S doping and N vacancies generate active sites for charge trapping, suppressing e^--h⁺ recombination. The synthesized SCN-E materials exhibit excellent bifunctional photocatalytic performance: (1) photocatalytic degradation of tetracycline antibiotics-degradation rates of tetracycline hydrochloride (TC), oxytetracycline (OTC), and chlortetracycline (CTC) reach ∼100%, 92%, and 94% within 30 min under visible light irradiation; (2) photocatalytic H₂O₂ synthesis-a yield of 1183.54 μmol L^-1·h^-1 is achieved after 30 min of O₂ bubbling. In addition, the biotoxicity of the catalytically degraded antibiotic solutions to E. coli DH5α was basically eliminated.

Rising carbon emissions have intensified global climate change, creating an urgent need for innovative solutions that generate value while also reducing emissions. Carbon capture, conversion, and utilization (CCCU) is a transformational technique that captures and converts CO₂ from energy and industrial sources into valuable fuels, chemicals, and materials. This review examines the current state of CCCU technologies, highlighting innovative materials including solvents, solid sorbents, and membranes, as well as main CO₂ capture methodologies like pre-combustion, post-combustion, and oxy-fuel combustion. Emerging conversion technologies include photocatalysis, electrocatalysis, and biochemical pathways, with an emphasis on the synthesis of methanol, dimethyl carbonate (DMC), dimethyl ether (DME), urea, and formic acid. The role of nanomaterials and bio-inspired systems in enhancing conversion efficiency is also explored. Industrial case studies and life-cycle assessments demonstrate the economic and environmental viability of CCCU, particularly when paired with renewable energy sources such as green hydrogen. Despite promising progress, CCCU still faces technical, economic, and infrastructural challenges related to energy consumption, scalability, and policy support. Looking to the future, research should focus on creating hybrid systems that can combine capture and conversion in a single process, developing more advanced catalysts, designing flexible modular reactors, and improving efficiency using machine learning. CCCU can be unlocked to its full potential by integrating it into circular economy frameworks and industrial symbiosis models. CCCU promotes decarbonization by transforming CO₂ waste into a valuable resource. This aligns economic growth with environmental responsibility and fosters sustainable development. This review focuses on the commercial viability of CCCU. The conference emphasized the critical importance of technological innovation and strategic implementation in establishing renewable energy as the foundation for a low-carbon, climate-resilient future.

Methane is a potent greenhouse gas with significant climate implications, being approximately 84 times more impactful than CO₂ over a 20-year timeframe. Accurately predicting the spatiotemporal distribution of methane concentrations, particularly near industrial sources, is essential for effective environmental monitoring and provides a critical foundation for subsequent emission source identification. This study introduces a novel Spatial-Temporal Cross-Attention Network (ST-CAN) to address the challenge of fusing sparse, high-frequency ground-based observations with spatially extensive but temporally infrequent satellite imagery. Using the Athabasca oil sands region as a case study, ST-CAN incorporates three synergistic innovations that work together to address data fusion challenges: (1) wavelet decomposition transforming high-frequency ground measurements into multi-scale temporal features capturing both long-term trends and short-term emission events; (2) environmental semantic clustering that identifies distinct atmospheric patterns from these wavelet features, providing interpretable contextual labels; and (3) a bidirectional cross-attention mechanism where these semantic cluster labels dynamically guide how ground temporal features query and fuse with satellite spatial information, adaptively prioritizing relevant features based on identified environmental states. The model is designed to leverage time-dense ground data to enhance the temporal resolution of weekly satellite-derived concentration maps, generating high-fidelity spatially representative methane concentration predictions by integrating information from four spatially distributed monitoring stations and satellite imagery, capturing regional-scale atmospheric dynamics. Extensive evaluations demonstrate ST-CAN significantly outperforms all the baseline models in predictive accuracy and robustness. The bidirectional mechanism notably improves interpolation during satellite data gaps, mitigating cloud cover and data sparsity challenges. By combining interpretability with advanced AI techniques, ST-CAN provides a transparent and scalable framework for high-resolution methane concentration modelling, advancing environmental monitoring capabilities and supporting targeted climate mitigation efforts.

Deteriorating asbestos-cement (AC) roofs in urban areas pose environmental and public health risks from carcinogenic asbestos fibers. Accurate identification of these structures is crucial for hazard assessment and mitigation strategies. This study compared four supervised classification algorithms for AC roof detection using multispectral remote sensing data: Minimum Distance (MiD), Mahalanobis Distance (MhD), Spectral Angle Mapper (SAM), and Support Vector Machine (SVM). The dataset includes RGB, four- and eight-band multispectral, and hyperspectral images from an urban test site. Using ENVI® software, each classifier's performance was assessed across five land cover classes: AC, steel roofs, clay roofs, roads, and vegetation. A key novelty is the comparative analysis of these methods across a multi-scale dataset. High spatial resolution (0.18 m RGB and four-band imagery) achieved overall accuracies of 95% and 96%, respectively. Results indicate that readily available optical data with high spatial detail can outperform spectrally richer but coarser-resolution (1.2 m) satellite data in accuracy. This study emphasizes the discriminatory power of visible-near-infrared (VNIR) spectral information for AC identification across image types, contrasting with literature that relies on costly Shortwave Infrared (SWIR) data. This finding suggests a more accessible pathway for AC roof detection, beneficial for low- and middle-income countries transitioning from asbestos. A conceptual digital twin framework for urban roofing is proposed, offering authorities a tool to monitor AC roof remediation and curb illicit removal in low-to middle-income economies.

Due to the excessive chemical-energy consumption, conventional treatments (e.g. Fenton or catalytic ozonation) have faced great challenges when treating chemical wastewater containing tetrabromobisphenol S (TBBPS), a prevalent brominated flame retardant. This study proposes an electrocatalytic active atomic hydrogen (H*)-mediated electroreduction strategy, enabling enhanced degradation of TBBPS into intermediates with lower toxicity and higher biodegradability. A palladium-modified electrocatalytic microfiltration membrane (EMM) was developed to efficiently generate H*, which played a dominant role in the reductive debromination of TBBPS. By conducting quenching experiments, it was found that free H* and adsorbed H* co-dominate the TBBPS removal process. Under optimized conditions, the EMM achieved nearly complete removal (∼100%) of TBBPS (100 mg L^-1) within 60 min, with a low energy consumption of 9.53 kWh/m³. The degradation pathway of TBBPS was proposed on the results of calculations and LC-MS analysis, and the evidence demonstrated the lower toxicity of the degradation intermediates. These findings emphasize the prospects of H*-mediated electroreduction strategy as a green and environmentally friendly treatment for industrial wastewater treatment.

This preliminary study examines how beaver engineering affects river condition in England and can be measured in relation to Biodiversity Net Gain (BNG) using the River Condition Assessment (RCA) methodology. Paired river reaches with (impact) and without (control) beaver engineering were compared at eight sites across southern England. At two sites, beavers were enclosed; at six, they were free-living. Two additional sites supported free-living beavers but without beaver engineering, so impact and control reaches could not be distinguished. Three hypotheses were tested: (i) in lowland English rivers, beaver engineering improves river condition as measured by the RCA; (ii) the magnitude of improvement depends on baseline river condition; and (iii) improvements in river condition are linked to increases in the presence and abundance of specific physical habitats. Findings from paired sites indicated that beaver-engineered reaches were generally assessed by the RCA to be in better condition than control reaches, supporting hypothesis (i), especially where control reaches were assessed to be overdeep (due to past management). Furthermore, the degree to which condition improved across paired reaches was inversely related to the condition of the control reach, supporting hypothesis (ii). Ten physical features showed significantly higher abundances in beaver-impacted reaches, while other physical features showed similar abundances in control and impact reaches, supporting hypothesis (iii). Although conducted in lowland, predominantly pastoral landscapes, the sites captured a typical range of environmental settings for beaver engineering and this preliminary study demonstrates that the RCA can effectively measure the influence of beaver engineering on river condition. These results provide a foundation for future research and offer practical guidance for considering beaver reintroduction as a nature-based tool for achieving Biodiversity Net Gain in development planning.

Combined sewer overflows (CSOs) represent a major challenge for urban water management, as they intermittently discharge untreated wastewater and stormwater to the receiving water bodies. Nature-based solutions (NbS), such as treatment wetlands (TWs), offer a sustainable alternative, but full-scale evidence, particularly aerated TWs operating under highly variable CSO conditions, remains extremely limited. The present study addresses this gap through a two-year evaluation of a full-scale aerated TW treating CSOs upstream of the Merone WWTP (Italy). The system was evaluated for conventional pollutants, nutrients, heavy metals, organic micropollutants, pathogens, and microplastics. Results demonstrate that forced aeration enables stable treatment performance under high hydraulic and pollutant loading rates, achieving median removals of 85.3% for COD, 89.0% for BOD₅, 95.6% for TSS, and 66.6% for ammonium nitrogen. High retention of particle-associated metals (Pb, Cu, Zn, Al) and microplastics (70-90%) highlights the dominant role of filtration and sorption processes, while compound-specific behavior governed organic micropollutant removal. Pathogen reductions ranged from 0.5 to 2 log units, indicating effective attenuation but confirming that additional disinfection would be required for water reuse applications. Additionally, an adaptive aeration strategy based on real-time COD monitoring is proposed, showing the potential to reduce aeration demand to approximately 10% of CSO event duration. This approach could lower aeration energy consumption by about 43 MWh y^-1, without compromising treatment reliability. Overall, the findings confirm the feasibility of aerated TWs as robust NbS for CSO management, highlighting the potential of sensor-supported, demand-based aeration to enhance wetland performance and operational sustainability in future large-scale applications.