Search Results (5,068)

The presence of endocrine-disrupting compounds (EDCs) in aquatic environments has raised significant concerns, particularly regarding their impact on marine biota. Among these compounds, 17α-ethinylestradiol (EE2), a synthetic estrogen widely used in oral contraceptives, is highly persistent and biologically active at very low concentrations. This study evaluated the effects of EE2 exposure on oxidative stress responses and endocrine disruption in Mytilus galloprovincialis, exposed for 28 days to three EE2 concentrations (10, 30, and 300 ng·L⁻¹). Biomarkers of oxidative stress, including the enzymatic activities of superoxide dismutase (SOD), catalase (CAT), and glutathione-S-transferase (GST), as well as Lipid Peroxidation (MDA levels), total ubiquitin (UBI) and the endocrine disruption marker, vitellogenin-like protein (VTG) were assessed. Results showed significant increase in GST and a decrease in CAT activities followed by an elevation at 300 ng·L⁻¹, slightly higher than control values. Overall, results suggest an enhanced oxidative challenge. No significant changes were detected in MDA and UBI levels. VTG-like protein levels increased according to the EE2 concentrations tested, suggesting an effect on the mussel’s endocrine system. These results show the activation of detoxification and antioxidant defense mechanisms in exposed mussels as a response to mitigate oxidative stress damage. Furthermore, it highlights the importance of using biomarkers in pollution monitoring studies and environmental risk assessment. Full article

Emissions from transportation are rapidly increasing, representing the second-largest source within the energy sector. Switching to biofuels is a promising strategy to mitigate these environmental impacts. The main aim of this study is to evaluate and compare the environmental performance of fossil gasoline and bioethanol blends in a high-performance Formula SAE race car using a comprehensive well-to-wheel (WTW) life cycle assessment (LCA) approach. The vehicle was tested under three fuel scenarios: (i) 100% fossil gasoline, (ii) a blend of 85% first-generation bioethanol (1G-pure bioethanol) derived from corn and 15% fossil gasoline (E85-1G), and (iii) a blend of 85% second-generation bioethanol (2G-pure bioethanol) derived from grape pomace, a winemaking waste product, and 15% fossil gasoline (E85-2G). The novelty of this work lies in the combined experimental and LCA-based comparison of crop-based and waste-derived bioethanol under identical high-performance operating conditions, enabling a direct assessment of feedstock influence on environmental impacts. The well-to-tank (WTT) results show that 2G bioethanol achieves the lowest environmental burdens across all impact categories, while 1G-pure bioethanol is significantly affected by emissions from corn cultivation. Fossil gasoline exhibits the highest impacts in terms of global warming potential (GWP) and Abiotic Resource Depletion (ARD). The tank-to-wheel (TTW) analysis confirms the superior environmental performance of the E85-2G blend. Despite requiring 6–16% more fuel to complete the race, E85-2G maintains its environmental advantage, and both biofuel blends produce lower air emissions than conventional gasoline. Full article

Sustainable land use policy planning requires integrated approaches that account for environmental and socio-economic trade-offs of greenhouse gas (GHG) mitigation measures. This study presents a spatial decision-support tool developed to support the evaluation of policy scenarios in non-urban land-use sectors, with application to the land use, land-use change, and forestry (LULUCF) sector in Latvia. The tool enables users to select predefined mitigation measures, apply spatial selection criteria, and generate quantitative and spatially explicit outputs. In addition to estimating GHG mitigation potential, it evaluates impacts on profitability, employment, and habitat quality, allowing the assessment of trade-offs and synergies across multiple dimensions. Scenario results are reported as both absolute and relative impacts, improving transparency and comparability. Developed in Python 3.10 and supported by a PostgreSQL 17/PostGIS 3.5 database, the tool operates through a web-based interface and supports efficient scenario construction and evaluation. While results depend on underlying data and assumptions, the tool provides a transparent framework for exploring policy options and supports evidence-based decision-making in land-use and climate policy planning. Full article

Background/Objectives: The accumulation of unused and expired pharmaceuticals in households is a growing public health concern with implications for patient safety, rational drug use, and environmental health. However, systematic risk characterization integrating clinical and environmental perspectives at the community level remains limited, particularly in low- and middle-income settings. This study aimed to develop and apply a composite risk index, grounded in an eco-pharmacovigilance framework, for the assessment of health risks associated with accumulated household pharmaceuticals in southeastern Mexico. Methods: A cross-sectional study was conducted in 526 randomly selected households using stratified sampling. Guided in-home medication inventories were performed with participant collaboration, and pharmaceuticals were classified according to the Anatomical Therapeutic Chemical (ATC) system. A composite risk index (CRI = Fr × PR) was developed within an eco-pharmacovigilance framework. The frequency of accumulation (Fr) for each therapeutic group was multiplied by a potential risk score (PR) derived through a structured multidisciplinary expert consensus process integrating clinical toxicity, environmental persistence, and antimicrobial resistance potential. Results: A total of 2184 pharmaceutical units were recorded during the household inventories, of which 28.7% were expired. Expired medications were primarily retained rather than actively used, representing a latent risk for inappropriate self-medication and accidental exposure. The therapeutic groups with the highest CRI values were antihypertensives (CRI = 42.3), antidiabetics (CRI = 37.8), and antibiotics (CRI = 31.5), indicating a relatively higher contribution within the composite risk index framework to overall household pharmaceutical risk. These findings highlight priority therapeutic groups driven by the combined effect of high accumulation frequency, distinct accumulation patterns, and intrinsic hazard. Conclusions: Household pharmaceutical accumulation can be characterized using a composite, eco-pharmacovigilance-based approach that integrates exposure and hazard dimensions. The proposed framework functions as a prioritization tool rather than a precise quantitative measure, enabling the identification of therapeutic groups requiring targeted intervention. Findings should be interpreted as indicative of relative risk patterns rather than precise estimates, given the exploratory design and guided data collection approach. The proposed framework provides a practical tool for prioritizing interventions aimed at improving rational drug use, reducing accumulation, and mitigating environmental impact. Further validation in diverse settings is warranted to strengthen its applicability. Full article

The construction industry, a global economic pillar and carbon emission giant, faces a critical gap between digital transformation and risk management, which ultimately undermines the sector’s capacity for risk management. This study combines social technical systems theory with the technology–organization–environment framework, using panel data from Chinese listed construction firms to explore how digital transformation affects project risk management. Key findings reveal that digital transformation significantly boosts risk management through two distinct pathways. While environmental governance capacity and green innovation efficiency both serve as significant mediators, the study identifies a notable disparity in the driving forces: digital transformation exerts a stronger impact on green innovation efficiency (17.8%) compared to environmental governance (4.4%). However, the resulting mediating effects of these two paths are found to be remarkably similar (0.0060 vs. 0.0068). Furthermore, labor investment efficiency is identified as a critical boundary condition, with a threshold effect (−0.385) below which the benefits of digital transformation weaken. These findings provide empirical evidence from Chinese context regarding the “technology-institution” co-evolution mechanism in construction. While centered on China, the study offers valuable insights for global stakeholders on how to harness digitalization to mitigate project risks and enhance sustainability. Full article

This study evaluates the potential of using Granulated Waste Tire Rubber (GWTR) as an alternative raw material in geopolymer mortars an eco-friendly, low-carbon alternative to traditional cement-based systems. The research investigates the synergistic effect of industrial by-products, such as slag (from ferrochrome plants) and fly ash (from thermal power plants), combined with varying proportions of GWTR (1/4, 1/3, and 1/2 by volume). A total of 22 mixtures were prepared using diverse binder pastes, including pure cement, slag-based, and fly ash-based geopolymer systems, alongside their cement-substituted derivatives. The mechanical and physical performances were assessed through compressive strength, flexural strength, and Ultrasonic Pulse Velocity (UPV) tests at 3, 7, 28, and 180 days, complemented by SEM microstructural analyses. The findings indicate that while GWTR significantly reduces the mechanical properties of pure cement matrices, this negative impact is substantially mitigated in geopolymer mortars supplemented with 5–10% cement. Mixtures containing 1/4 GWTR with 90–95% slag or fly ash (M6, M7, M15, M16) yielded the most successful results in terms of both strength and sustainability, specifically, mixtures M7 and M16 because the hybrid binder synergy effectively compensated for the rubber-induced porosity, ensuring a denser matrix and structural-grade compressive strength alongside high sustainability. Significant decreases in performance were observed at higher GWTR ratios, particularly at the 1/2 level. Overall, the study demonstrates that integrating GWTR into optimized geopolymer systems offers a viable pathway for the valorization of environmental waste and minimizing the ecological footprint of the construction industry. Full article

This study examines the association between tourism intensity, the free movement of people, and water quality outcomes across the European Union (EU-27) over the period 2012–2024. By integrating open-access datasets from Eurostat, the European Environment Agency (EEA), and the EXIOBASE input–output framework, the analysis estimates the direct (blue), indirect, and grey components of the tourism-related water footprint and explores their relationship with bathing water quality indicators using panel econometric models. The results indicate that tourism activity increased substantially during the study period, while the share of bathing waters classified as “excellent” also improved. The findings further show that the gray water footprint is strongly associated with variations in water quality, whereas higher wastewater treatment coverage is positively associated with improved environmental outcomes. These results highlight the importance of wastewater management and governance capacity in moderating the relationship between tourism and water quality across diverse European contexts. We find that tourism activity rose by approximately 28% during the study period; yet, through improvements in wastewater treatment infrastructure and governance, the share of bathing waters rated “excellent” also increased. Notably, the grey water footprint emerged as the strongest predictor of water quality deterioration, while wastewater treatment coverage significantly mitigated negative impacts. Comparative case studies of Spain, Greece, Croatia and Romania highlight how institutional and technological capacity are associated with differences in tourism–water relationships across diverse hydro-climatic contexts. Our findings underscore that sustainable tourism in Europe is less a matter of visitor numbers and more a question of effective water management systems. The study supports a policy shift towards integrated water-tourism planning and circular water-use strategies to support more sustainable management of tourism-related environmental pressures. Full article

Afghanistan is highly vulnerable to the effects of climate change, which poses significant challenges to food security and environmental systems. To mitigate these challenges and promote sustainable development, it is important to adopt an integrated method that promotes food production and climate resilience for environmental sustainability. This manuscript aims to estimate the decoupling impact of green energy on CO₂ emissions and food crop production in Afghanistan, with a focus on promoting Sustainable food production. In this research article, the Nonlinear Auto Regressive Distributed Lag (NARDL) model was used to estimate data from 1996 to 2021 in Afghanistan. The NARDL bounds test confirms a stable long-run equilibrium relationship between climatic factors and food crop production. The long-run results reveal an asymmetric decoupling impact of green energy on CO₂ emission and food crop production. Specifically, a 1% positive or negative shock in the interaction between green energy and CO₂ emissions produces different outcomes for food crop production. Increasing temperature tends to decrease food production, while precipitation increases food production over the long term. Furthermore, raising CO₂ emissions negatively affects long-term food production, while greater use of green energy contributes to food production in the future. These findings underscore the need to adopt climate-resilient technologies, including climate-smart agriculture, to help farmers withstand the adverse effects of climate change. In addition, to ensure long-term stability in food production, Afghanistan should prioritize the development of green technologies. This approach would reduce agriculture’s dependence on fossil fuels and foster the growth of sustainable agricultural industries. Full article

Currently, Large Language Models (LLMs), exemplified by ChatGPT, are accelerating technological development across various domains, including the environmental domain, owing to their powerful text-generation and information-processing capabilities. With changes in global climate and environmental conditions, environmental sustainability has emerged as a major global challenge. Leveraging LLMs to advance environmental sustainability and mitigate current environmental problems is considered a valuable and effective approach. This study aims to systematically synthesize research progress and core challenges in current LLMs for promoting sustainability-related fields, and to comprehensively analyze the application contexts, impacts, and development potential of various LLMs within the environmental sector. Following the PRISMA-ScR guidelines, a comprehensive search was conducted across six databases: Web of Science (WOS), Scopus, ACM Digital Library, IEEE Xplore, ScienceDirect, and Google Scholar. A total of 20 articles were ultimately included for analysis. The findings indicate that LLMs play a positive role in maintaining environmental sustainability and promoting the low-carbon energy transition. The applications of LLMs span six core domains: the green transition, carbon emission management, air quality assessment, smart city operations, map analysis, and human cognition and behavioral observation. However, the training and operation of current LLMs consume considerable resources, which creates an inherent conflict with the goals of sustainable development. Future efforts must focus on developing a secure, equitable, and scalable LLM support system to advance environmental sustainability. This requires optimizing model energy efficiency and ensuring a balance between performance, reliability, and environmental impact. These endeavors are crucial for addressing environmental problems and guaranteeing the sustainable progression of LLMs across diverse environmental contexts. Full article

The effective closure of the Strait of Hormuz on 28 February 2026 disrupted approximately 20 million barrels (bbl) per day of crude oil transit, constituting the largest supply shock in global oil market history. This study quantifies the resulting economic losses under three blockade-duration scenarios and evaluates the Northern Sea Route (NSR) as a partial mitigation mechanism through a novel framework integrating sustainable supply chain resilience (SSCR), the Triple Bottom Line (TBL), and the United Nations Sustainable Development Goals (SDGs). A 3 × 3 scenario matrix crossing three blockade durations with three NSR utilization levels estimates global and country-level impacts using data from the International Energy Agency (IEA), the International Monetary Fund (IMF), and the Centre for High North Logistics (CHNL). Even under maximum feasible NSR utilization, net environmentally adjustment mitigation offsets only 1.1–3.6% of total global losses, demonstrating that the Northern Sea Route functions as marginal insurance rather than a viable substitute for Hormuz-dependent supply chains. Global Gross Domestic Product (GDP) losses range from USD 330 billion to USD 2.2 trillion, with South Korea (68–70% Middle East crude dependency) and Japan (approximately 95%) disproportionately affected. After TBL environmentally adjustment monetizing CO₂, black-carbon, and icebreaker costs, the NSR mitigates 1.1–3.6% of total losses, functioning as insurance rather than substitution. The SDG assessment reveals a fundamental trade-off: the NSR offsets energy-security losses (SDGs 7, 9) but worsens climate and marine outcomes (SDGs 13, 14). Theoretically, this study proposes “alternative maritime route availability” as a conceptual extension of supply chain resilience (SCRes) capabilities and outlines a sustainability-adjusted resilience score (SARS) framework that, pending further validation, could serve as a replicable assessment tool. These findings underscore that accelerating the energy transition remains the most effective long-term response to chokepoint vulnerability. Full article

This study examines agricultural and rural carbon emission efficiency and the underlying “low-carbon efficiency illusion” in China, where measured efficiency gains fail to translate into genuine environmental improvements. Using panel data from 30 Chinese provinces spanning 2000 to 2022, this study employs a meta-frontier slack-based measure (SBM) model to assess agricultural and rural carbon emission efficiency across meta-frontier and group-frontier benchmarks and investigates the efficiency illusion from the perspective of carbon emission reduction cost constraints. We further combine the Extreme Gradient Boosting (XGBoost) model and Shapley Additive Explanations (SHAP) explainability methods to identify core drivers of agricultural carbon emission reduction costs. We find that technical inefficiency is the primary constraint on China’s agricultural and rural carbon emission efficiency; the number of provinces with an efficiency illusion shows an initial increase followed by a decrease between 2005 and 2022; and core drivers of emission reduction costs exhibit heterogeneous impacts and significant threshold effects across the two frontier frameworks. These findings offer evidence-based guidance for designing differentiated, targeted emission reduction strategies to mitigate the efficiency illusion, advance low-carbon agricultural transition, and support the sustainable development of agricultural and rural systems in the context of the United Nations Sustainable Development Goals. Full article

Water scarcity is becoming an increasingly critical global challenge, driven by climate change, rapid population growth, pollution, and unsustainable water use. Drought further intensifies this crisis by reducing water availability across agricultural, environmental, and socio-economic systems. In this context, nanotechnology has emerged as a promising tool for improving water management and enhancing drought resilience. This review examines the role of nanotechnology in drought mitigation and water conservation through multiple pathways, including the enhancement of plant drought tolerance, improvement in soil water retention, the development of smart irrigation and nano-sensing systems, and the expansion of water resources through purification, desalination, and wastewater reuse. In addition, the broader drought–water nexus is discussed to position nano-enabled approaches within existing water management strategies. While numerous studies report improvements in water-use efficiency, stress tolerance, and treatment performance under controlled conditions, significant limitations remain. These include concerns related to environmental safety, nanotoxicity, scalability, cost, and the gap between laboratory findings and field-level applications. Overall, nanotechnology should be considered a complementary approach rather than a stand-alone solution for addressing water scarcity under drought conditions. Future research should focus on long-term environmental impacts, techno-economic feasibility, and large-scale field validation to support the safe and effective integration of nanotechnology into sustainable water management systems. Full article

The primary productivity of phytoplankton (PP_eu) is critical to the carbon cycle in aquatic ecosystems. However, in complex lakes covered by ice, the estimation of PP_eu using remote sensing techniques is constrained. To address this limitation, this study developed an estimation model for ice-covered PP_eu by incorporating optical parameters such as the ice surface refractive index and the extinction coefficient of the ice layer into the vertical generalized production model (VGPM). This approach overcomes the challenges associated with remote sensing-based estimation of PP_eu during ice-covered periods. The results indicate that the annual carbon sequestration of the WLSHL is 1.72 × 10⁴ t C, with an average annual PP_eu of 316.96 mg C·m⁻²·d⁻¹. In addition to the indicators that are directly involved in the estimation of PP_eu, the environmental factors that affect PP_eu include water temperature (WT), ice thickness (IT), snow, water depth (D), total dissolved solids (TDSs), salinity (S), ammonia nitrogen (NH₄⁺-N), nitrate nitrogen (NO₃⁻-N), and oxidation–reduction potential (ORP). The PP_eu in the ice period is found to be only 17% lower than that in the ice-free period. However, the PP_eu during the ice period is considerably higher than that during the ice + snow period. The findings indicate that the impact of freezing on PP_eu during the winter is relatively limited, whereas the influence of snowfall is more pronounced. In order to mitigate the elevated PP_eu and the occurrence of algal blooms during the summer, the intensity of underwater radiation can be regulated on a periodic basis. To optimize the function of the carbon sink in winter lakes, the PP_eu can be enhanced through initiatives such as water replenishment prior to freezing and snow removal following freezing. Full article

Whether industrial artificial intelligence (industrial AI) contributes to environmental sustainability remains an open empirical and theoretical question. While digital and intelligent technologies are widely promoted as drivers of green transformation, their net impact on carbon emissions is ambiguous due to potentially offsetting efficiency gains and rebound effects. This study examines how industrial AI influences urban carbon emissions using panel data for 260 Chinese cities from 2005 to 2019. We construct a novel city-level industrial AI development index by integrating information on data infrastructure, AI-related talent supply and intelligent manufacturing services using the entropy weight method. Employing two-way fixed-effects models, instrumental-variable estimations, lag structures, and multiple robustness checks, we identify the causal impact of industrial AI on carbon emissions. The results indicate that industrial AI significantly reduces urban carbon emissions. Mechanism analyses suggest that this effect operates primarily through improvements in energy efficiency and green technological innovation, while being partially offset by scale expansion. Furthermore, a higher share of secondary industry mitigates the emission-reducing effect of industrial AI. Heterogeneity analysis further indicates stronger emission-reduction effects in eastern regions, large cities, and areas with higher human capital and stronger environmental regulation. The findings suggest that intelligent industrial upgrading can simultaneously enhance productivity and support climate mitigation, but this effect is highly context-dependent, offering policy insights for achieving sustainable industrial modernization and carbon neutrality in emerging economies. Full article

This review comprehensively examines the incorporation of nanoparticles (NPs) into biopolymers for 3D printing in biomedical applications, integrating material design, processing strategies, and translational considerations within a unified framework. Different types of NPs are analyzed regarding their effects on mechanical reinforcement, rheological modulation, and structural organization of biopolymeric matrices. The discussion covers principal additive manufacturing technologies, including extrusion-based systems such as fused deposition modeling (FDM) and direct ink writing (DIW), vat photopolymerization, powder-bed fusion (SLS), and emerging in situ nanoparticle formation approaches, emphasizing how nanoparticle loading and surface functionalization govern yield stress, shear-thinning behavior, viscoelastic recovery, and dimensional fidelity while mitigating agglomeration and optimizing interfacial interactions. Comparative evaluation of compressive modulus, strength, toughness, crystallinity, and porosity establishes structure–property–processing relationships directly linked to printability and functional performance. Biomedical applications are addressed in tissue engineering, biosensing, controlled and targeted drug delivery, and bioimaging, highlighting the balance between bioactivity and manufacturability. Finally, critical challenges—including compatibility, reproducibility, biological safety, long-term stability, regulatory adaptation, and environmental impact—are discussed, alongside future perspectives focused on green nanomaterials, AI-driven predictive formulation design, and digital twins for real-time monitoring and quality control in nano-enabled additive manufacturing. Full article