Search Results (5,043)

Cultural heritage sites are affected by environmental and anthropogenic pressures that require decision-analysis methods capable of combining heterogeneous datasets while accounting for uncertainty. Multi-Criteria Decision Analysis (MCDA), particularly when integrated with Geographic Information Systems (GIS) and Earth Observation (EO) data, is widely used in geospatial analysis involving multiple, often conflicting criteria. This review examines the evolution, application domains, and methodological challenges of MCDA in cultural heritage risk assessment. The literature indicates a predominant reliance on weighting-based methods, especially the Analytic Hierarchy Process (AHP) combined with GIS-based weighted overlay techniques, while uncertainty treatment, temporal monitoring, validation, and multi-threat applications remain limited. Three illustrative applications show that asset-level, regional susceptibility, and historic-urban frameworks address complementary decision needs but differ in their data, expertise, and institutional requirements. Recent developments show a trend to combine MCDA with fuzzy logic, machine learning, and uncertainty modeling, although methodological consistency across these approaches remains uneven. The findings suggest that multi-criteria risk assessment for cultural heritage may depend less on introducing new analytical techniques and more on improving the integration of existing methods. Incorporating repeatable environmental observations, sensitivity analyses, multi-threat assessment, and stakeholder participation may support a more coherent and reproducible approach to heritage-risk assessment. Full article

Listeria monocytogenes (LM), one of the most virulent foodborne pathogens, poses a serious threat to public health due to its strong environmental adaptability and high pathogenicity. Rapid, sensitive, and real-time detection of LM is of great importance. Chemiresistive gas sensors have attracted enormous attention in LM detection owing to their advantages of low cost, simple structure, fast response, and easy miniaturization, which can achieve indirect detection of LM by recognizing its specific metabolic volatile organic compounds. This review summarizes the recent progress in chemiresistive gas sensors for the detection of LM metabolites. First, the metabolic characteristics of LM and the typical volatile organic compound (3-hydroxy-2-butanone) as its characteristic biomarker are introduced. Then, the performance and sensing mechanisms of different types of chemiresistive gas sensors for LM metabolite detection are summarized and elaborated systematically. The application of chemiresistive gas sensors for the detection of actual samples and the progress in the design of related detection devices are introduced. Finally, the current challenges faced by chemiresistive gas sensors in LM metabolite detection and their future development prospects are discussed. This review provides a comprehensive reference for the research and practical application of chemiresistive gas sensors in Listeria monocytogenes detection. Full article

Heavy metal wastewater, characterized by high toxicity, poor biodegradability, and strong bioaccumulation potential, poses a serious threat to the ecological environment and human health. Conventional chemical treatment methods are costly and prone to secondary pollution. In contrast, using solid waste to prepare environmental functional materials for “waste control by waste” has emerged as a research hotspot in the field of synergistic water pollution control and resource recovery. This review systematically consolidates the sources, physicochemical properties, and compositional characteristics of typical industrial solid wastes—including red mud, electroplating sludge, fly ash, copper slag, and blast furnace slag—and elucidates how compositional variations constrain the selection of functionalization pathways. On this basis, key preparation techniques, namely hydrothermal synthesis, surface modification/impregnation, thermal treatment, geopolymerization, and sol–gel/polymerization, are critically reviewed and compared in terms of product structural regulation, process complexity, and engineering scale-up potential. Subsequently, the adsorption performance of the resultant materials toward heavy metals such as Pb²⁺, Cu²⁺, Cr(VI), As(VI), Cd²⁺, Ni^2+, and Zn²⁺ is discussed, with particular emphasis on the pronounced effects of pH, temperature, initial concentration, coexisting ions, and adsorbent dosage on adsorption capacity. Notably, Pb²⁺ exhibits the highest adsorption capacity, whereas the removal of Cr(VI) and As(V) is strongly governed by the positive charge density and reduction efficiency of the material surface. In quinary heavy metal competitive systems, marked differences in the affinity of adsorption sites toward distinct metal ions have been observed. Mechanistic analysis identifies ion exchange, surface complexation, electrostatic attraction, and redox reactions as the predominant removal pathways. This review further incorporates engineering-oriented assessments, including multi-component competitive adsorption, fixed-bed column operation, and regeneration stability, and identifies the critical bottlenecks currently impeding the transition from laboratory-scale research to practical implementation—namely, performance instability arising from feedstock variability, attenuation of adsorption capacity during prolonged operation (with a 10–40% decline over 5–10 cycles), underdeveloped regeneration and recovery routes, and a systemic deficiency in techno-economic analysis and life-cycle assessment data. Finally, future research directions are proposed, emphasizing the development of low-carbon and energy-efficient preparation technologies, the promotion of synergistic valorization of multiple solid wastes alongside critical metal recovery, and the advancement of this field toward engineering applications through artificial intelligence-assisted design and life-cycle assessment, thereby furnishing theoretical references and technical support for the integrated management of industrial solid waste resource utilization and heavy metal wastewater treatment. Full article

Dyes widely used in industrial sectors like pharmaceuticals and textiles cause a severe environmental threat due to the high stability and toxicity of synthetic dyes, which impair water quality and aquatic ecosystems. Normally, there are used traditional treatment methods, such as adsorption, and membrane filtration, but showed limitations including high costs, energy intensity, and low selectivity in complex matrices. Aqueous Two-Phase Systems (ATPSs) are an sustainable, economic, and versatile alternative for the extraction, separation, and purification of dyes. Therefore, this review examines the thermodynamic fundamentals, formation mechanisms, and the integration of innovative components like Ionic Liquids (ILs) and Deep Eutectic Solvents (DESs). Recent research highlights that ATPS can achieve extraction efficiencies exceeding 95% for dyes; however, the optimization of critical parameters such as pH, temperature, polymer molecular weight, and salt concentration is necessary. Furthermore, this review discusses the potential of these systems within circular economy schemes, emphasizing component recyclability and their alignment with green chemistry principles. Ultimately, ATPSs represent a scalable and eco-friendly platform for managing industrial effluents and recovering valuable compounds. Full article

The hydrogen economy has emerged as a promising pathway to address climate change and ensure long-term global energy security, with water electrolysis powered by renewable energy as a key enabler of sustainable hydrogen production. Recent advances in various electrolyser technologies have enhanced their suitability for industrial applications, creating new opportunities for deploying green hydrogen. To address the gap in integrated, multi-dimensional assessment tools for groundwater-based hydrogen systems in water-scarce developing countries, this study develops and presents a Structured Assessment Framework for Green Hydrogen Production from Groundwater in South Africa—the first framework to simultaneously integrate hydrogeological sustainability screening, electrolyser technology selection under groundwater quality constraints, disaggregated levelised cost of hydrogen (LCOH) analysis including water treatment costs, comparative life cycle assessment (LCA) of green, blue, and grey hydrogen pathways, and policy and governance alignment within a single operationalised architecture. This included integrating five thematic dimensions: groundwater resource assessment, electrolyser technology integration, economic viability, environmental sustainability, and policy and governance considerations. This systematic review was conducted in accordance with the PRISMA 2020 guidelines, drawing on 130 studies retrieved from Scopus and Web of Science (2015–2025). The analysis examines groundwater quality and suitability, the technical feasibility of electrolyser systems, and the comparative implications of grey, blue, and green hydrogen pathways on cost and environmental performance. The framework also provides strategic guidance for deploying renewable-energy-powered hydrogen systems, emphasising life-cycle impacts, regulatory alignment, and the potential for decentralised hydrogen hubs. Findings highlight the significance of strengths, weaknesses, opportunities, and threats (SWOT) for green hydrogen production using groundwater in South Africa, including export potential and strong linkages to the circular economy. The study offers actionable insights for policymakers, planners, and industry stakeholders seeking to advance a sustainable and economically competitive hydrogen landscape. Full article

Human societies depend on prestige hierarchies that confer status on demonstrated competence, alongside the dominance hierarchies that rely on coercion and that humans share with other primates. The same neural processes that support cooperative learning under prestige are hypothesized to participate in rivalry, polarization, and ideological hostility when their operating environments depart from those in which they evolved. This selective narrative review proposes a candidate integrative framework, the Hypothesized Neural Integration Model of Prestige and Pride, drawing on converging but largely indirect evidence from affective neuroscience, neuroeconomics, evolutionary psychology, and social neuroscience. The framework groups five categories of functionally pluripotent processing, each understood as engaging nodes within overlapping large-scale brain networks rather than as a localized regional mechanism: valuation-related processing implicating orbitofrontal and ventromedial prefrontal cortex, reward-learning signals indexed in the ventral striatum, self-referential and identity-relevant processing engaging medial prefrontal cortex within the default-mode network, threat- and salience-related processing involving the amygdala and anterior cingulate cortex within the salience network, and neuroendocrine output regulated through the hypothalamic–pituitary–adrenal axis and modulated by gonadal and neuropeptide systems. Consistent with Poldrack on reverse inference and with Marr on levels of analysis, the framework is presented at an integrative implementational level with candidate algorithmic mappings specified for each category, not as a computational specification of the constructs themselves, and treats high-level constructs such as prestige, status, and sacred value as emergent phenomena arising from dynamic network interactions, bodily states, cultural contexts, and historical contingencies rather than as properties localized to single regions. Because few neuroimaging paradigms isolate freely conferred deference from generic social rank, claims specific to prestige are deliberately restricted throughout, and the evidence base is presented as one concerning social rank unless a paradigm operationalizes prestige directly. Material ownership, sacred values, religious belief, and digital prestige metrics are treated as candidate domains of application, with explicit separation of direct neural evidence, behavioral evidence, and theoretical extrapolation. Symbolic escalation in consumer markets, sacred-value absolutism, and amplified social feedback on digital platforms are interpreted as candidate environmental amplifiers of ancestral status mechanisms rather than demonstrated causal pathways. Nine operational study designs are proposed to guide future preregistered tests, including one designed to establish whether a prestige-specific neural claim can be made at all. Intervention strategies are mapped to candidate processing and network targets using an explicit evidence-grading scheme, with structural and institutional measures weighted equally alongside cognitive and contemplative ones. The framework relies on correlational neuroimaging subject to reverse-inference constraints, draws primarily from samples from Western, educated, industrialized, rich, and democratic populations, and should be evaluated as a hypothesis-generating account rather than as a confirmed neural architecture. Full article

Cyanobacterial harmful algal blooms (CyanoHABs) pose serious threats to inland water ecology and environmental security. Despite advances, accurately capturing their long-term dynamics for effective governance remains challenging due to limited datasets and segmentation model accuracy. This study addresses these gaps by developing a CyanoHABs dataset and a new model, Multi-scale Spatial Attention Network (MSA-Net), to automatically extract cyanobacterial blooms from Landsat imagery using deep learning. With enhanced multi-scale feature extraction and a Hybrid Attention Mechanism in an encoder–decoder framework, MSA-Net outperforms other models, providing robust methods and data support for remote sensing-based CyanoHAB monitoring in complex environments. The proposed MSA-Net achieved an F1-score of 88.34% and a Precision of 88.74%, outperforming all baseline methods. Ablation experiments further verified the effectiveness of the proposed Spatial Feature Enhancement Mechanism (SFEM) and Hybrid Attention Mechanism (HAM) in enhancing segmentation performance. The results highlight MSA-Net’s effectiveness in accurately identifying CyanoHABs, particularly in complex, multi-scale environments. This study provides a transferable deep learning framework and long-term remote sensing dataset for CyanoHAB monitoring, offering valuable insights for the large-scale assessment and management of eutrophic inland waters worldwide. Full article

Occupancy detection is fundamental to the operational intelligence of smart buildings, driving critical functions in energy management, HVAC automation, and physical security. While modern Deep Learning (DL) models have achieved high accuracy in parsing complex environmental sensor data, they remain highly vulnerable to adversarial examples, imperceptibly perturbed inputs designed to deceive neural networks. These vulnerabilities pose severe real-world risks, ranging from energy sabotage, in which systems heat empty rooms, to critical security breaches in which intruders go undetected. To address this security gap, we propose ADS-Guard, a novel Adversarial Detection and Sanitization (ADS) framework rooted in sequence-to-sequence autoencoder purification. Unlike standard denoising techniques, ADS-Guard incorporates a latent consistency regularization mechanism that encourages alignment between clean and adversarial representations in the latent feature space. We evaluated ADS-Guard using a comprehensive experimental pipeline comprising five distinct DL architectures (LSTM, GRU, 1D-CNN, MLP, and Transformer) across three diverse datasets: (1) The UCI Occupancy dataset (20,699 samples) for standard binary detection; (2) Building59 dataset (7200 samples) for three-class occupancy-level classification (Low, Medium, High); and (3) Room Occupancy dataset (10,129 samples), representing a highly imbalanced binary occupancy-detection task. We evaluate ADS-Guard against both Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attacks across diverse occupancy datasets and model architectures. We further assess the framework under adaptive white-box attacks and compare its performance with FGSM-based and PGD-based adversarial training baselines. Our results demonstrate that adversarial attacks can substantially degrade occupancy-detection performance across datasets and model architectures. ADS-Guard consistently improves robustness relative to undefended models against both FGSM and PGD attacks, recovering a substantial portion of the lost performance in binary occupancy tasks and providing meaningful gains in the more challenging multi-class setting. Furthermore, ADS-Guard remains effective under stronger adaptive threat models while providing a practical retraining-free defense that can be integrated with existing occupancy-detection systems without modifying downstream classifiers. Full article

Harmful cyanobacteria blooms (CyanoHABs) have become a common problem in freshwater ecosystems. Monitoring at the federal and state level in response to this threat has increased over the last decade across the United States. In 2018, Pennsylvania environmental agencies began collecting CyanoHAB data to inform state regulatory and management efforts. 998 discrete samples from 138 waterbodies were collected over four years. These data were used to determine if indicators often used in CyanoHAB monitoring co-occurred with toxic conditions. Cyanotoxins were detected in more than half of the samples in Pennsylvania’s monitoring program with anatoxin, saxitoxins, and microcystins detected in similar proportions. It is important to note that the monitoring program is not designed to be representative of all Pennsylvania lakes but instead is targeted toward sites with greater exposure (recreational beaches) or sites with suspected blooms. Our findings suggest that cyanobacteria growth and cyanotoxin production may be driven by independent variables, as only microcystins concentrations were positively correlated with cyanobacteria counts. While increased cyanobacteria counts were related to all environmental variables considered in this study (warmer water temperatures, increased dissolved oxygen concentrations, and reduced water clarity), the relationships between cyanotoxins and environmental variables were mixed. Our analyses highlight the complexities of bloom occurrence and severity, and the challenges of incorporating these factors into monitoring and management strategies. Long-term monitoring approaches like the one analyzed here can inform management strategies that rely on environmental cues and cyanobacteria enumeration to evaluate risk of cyanotoxin presence. Full article

Environmental degradation is no longer a distant threat; it now permeates social media feeds, casual conversations, and daily digital content. However, whether incidental exposure to environmental news leads to meaningful behavioral change remains poorly understood. This study examines how incidental environmental news exposure (IENE) relates to pro-environmental behavior (PEB) among Chinese social media users, with particular attention to the sequential chain-mediating roles of environmental belief (EB) and perceived environmental risk (PER). A theoretical model was developed and tested using survey data from 468 respondents collected in November and December 2024, and analyzed using structural equation modeling (SEM). The results indicate that IENE is significantly and positively associated with EB, PER, and PEB. Notably, EB was not directly associated with PEB; its association operated almost entirely through PER, which accounted for 87.6% of the total EB–PEB association. PER emerged as the principal psychological pathway linking passive news exposure to behavioral engagement, and the structural model explained 42.1% of the variance in PEB. Because the design is cross-sectional, the results establish covariance patterns consistent with the proposed sequence rather than temporal or causal order. These findings indicate that cognitive risk appraisal, rather than environmental belief alone, constitutes the proximal mediating mechanism within the modelled pathway to pro-environmental action. Policymakers and media practitioners should accordingly design communication strategies that extend beyond general awareness-raising to link environmental threats explicitly to perceived personal and societal risk. Full article

SARS-CoV-2 transmission between humans and animals is an important One Health concern, particularly in settings where close human–animal interactions are difficult to avoid. We developed and applied a qualitative likelihood ranking framework for SARS-CoV-2 transmission between people and captive lions (Panthera leo) in an urban zoological setting. Following a structured review, scenario trees were constructed to rank direct and indirect transmission pathways using five domains: activity duration, proximity and frequency, SARS-CoV-2 survival on fomites, and environmental conditions. The framework was applied to a case study of a lion exhibit at an urban zoo in Australia. Pathway ranking depended on the combined effects of baseline exposure attributes and subsequent modifiers reflecting exposure frequency and level of natural disinfection. Although direct pathways, such as veterinary procedures, had the highest likelihood of transmission, they were differentiated by interaction duration and proximity, with frequency modification distinguishing rare, high-intensity interactions from repeated lower-intensity activities. Therefore, routine husbandry or visitor-associated exposures could increase in relative importance. Environmental modification further differentiated pathway likelihood, in particular due to solar exposure and temperature. The framework provides a transparent, practical approach for ranking transmission likelihood when data are scarce, supports consistent prioritisation of biosecurity resources, and is applicable to other emerging infectious disease threats in zoological settings. Full article

Alternanthera philoxeroides (A. philoxeroides) is one of the worst invasive alien species in the world and poses serious threats to both ecological security and agricultural production in China. However, the effects of environmental factors on its growth across different habitat types remain insufficiently understood. To reveal the determining natural factors influencing the growth of A. philoxeroides, fieldwork, including 50 sample quadrats in six field sites, was conducted in the Yangtze River Basin of Hubei Province. In every quadrat, soil properties, soil moisture, pH, soil temperature, soil nutrients (N, P, and K), light intensity, plant cover, and aboveground plant fresh biomass were measured. Cluster analysis showed that A. philoxeroides habitats can be divided into five cluster groups: wetland, grassland, forest understory, farmland and aquatic communities. The results showed that both total community biomass and A. philoxeroides biomass were positively correlated with water content, whereas no significant relationships were found between biomass and soil nutrients, including N, P, and K. The biomass of A. philoxeroides was significantly higher in aquatic habitats, while no significant differences were observed among the other four terrestrial habitats. These findings suggest that hydrological management, together with early control in aquatic habitats, may be an effective strategy to limit the spread of A. philoxeroides. Full article

Sustainable development is seriously hampered by environmental contamination. Heavy metals are perhaps the most significant of these silent pollutants because of the poisoning they inflict on different ecosystems, particularly water systems, as well as the direct threat they pose to the safety of such systems and the ensuing health issues that affect people and other living things. The goal of the current work is to ascertain how effective peanut (Arachis hypogaea) hulls are as easily accessible and reasonably priced adsorbent materials for the ion adsorption of various heavy metals, including lead, copper, zinc, and cadmium. A series of variables, such as pH, temperature, retention time, shape, quantity of adsorbent and particle size, were investigated to reach the optimal conditions for the biosorption process, which were achieved at pH 6 (close to pH-zero charge), a temperature of 40 °C, a retention time of 60 min, an adsorbent dose of 5 g/L, and a particle diameter of 0.1 mm. Experiments showed that lead had the highest treatment rate, approximately 95%, followed by zinc, copper, and cadmium. The results were confirmed and validated using SEM analysis to identify the morphology of the hulls before and after treatment, FTIR spectroscopy to investigate the functional groups responsible for binding to the metal ions, and further supported by kinetic isotherms of the adsorption behavior, which matched the Langmuir and Freundlich models. Nevertheless, the results of the batch experiment were employed by designing a laboratory treatment unit for municipal wastewater containing beads of alginate-immobilized Arachis hypogaea hulls. The designed laboratory unit was effective in treating unacceptable levels of heavy metals in some municipal wastewater from the local drainages of Ramadi City, Iraq. Overall, the study’s findings suggested that peanut hulls could be used to mitigate toxic metal pollution in an eco-friendly manner that guarantees the accomplishment of the Sustainable Development Goals pertaining to future generations’ right to the safe use of water resources. Full article

Aflatoxin B₁ (AFB₁) is a prevalent mycotoxin contaminating food and environmental matrices that can induce hepatic injury, thereby posing a substantial threat to livestock production. Portulaca oleracea L. polysaccharide (POP) possesses diverse biological activities, including antioxidant and anti-inflammatory properties. However, the protective effects of POP against AFB₁-induced hepatic injury have not been fully elucidated. In the present study, a rabbit model of AFB₁-induced hepatic injury was established to investigate the hepatoprotective mechanisms of POP. Histopathological examination and liver function assessments demonstrated that POP markedly ameliorated hepatic histopathological alterations and decreased serum levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), and alkaline phosphatase (ALP). Molecular analyses further demonstrated that POP attenuated AFB₁-induced oxidative stress and hepatocyte apoptosis, effects that were associated with activation of the PI3K/AKT signaling pathway and suppression of the mitochondria-mediated apoptotic pathway. Furthermore, microbiome profiling and targeted short-chain fatty acid (SCFA) metabolomics analyses indicated that POP supplementation contributed to the preservation of intestinal barrier integrity and intestinal microbiota stability, concurrent with the attenuation of AFB₁-induced intestinal and hepatic injury and elevated SCFA production by intestinal microbes. Overall, POP exerts prominent preventive protective effects against AFB₁-induced liver injury, which may be closely associated with alterations in intestinal microbiota composition and microbial short-chain fatty acid metabolism, as well as the activation of the PI3K/AKT signaling pathway. These findings provide novel insights into the mechanisms underlying AFB₁-induced hepatic injury. Full article

Heavy metal contamination in urban topsoil is one of the most serious environmental threats to children’s health, particularly through ingestion, dermal contact, and inhalation exposure routes. The objectives of this study were: (1) to assess the probabilistic exceedance-based priority of eight heavy metals (As, Cd, Co, Cr, Cu, Ni, Pb, and Zn) with respect to regulatory threshold exceedance in Debrecen, Hungary; (2) to map the spatial distribution of exceedance probabilities using sequential indicator simulation (SISIM) with 100 equiprobable realizations per element (1000 for Cr) on a 50 m grid; and (3) to develop a toxicologically weighted composite exceedance index based on the Hungarian regulatory action thresholds and classify the results into priority categories. For Cd, the exceedance probability exceeded p > 0.50 in approximately 98% of the study area, and for Cr, in approximately 82% of the study area (regenerated at $N = 1000$; the Cr threshold lies near the sample median, so the p > 0.50 area is ensemble-size sensitive and was under-converged at $N = 100$). Approximately 86% of the study area fell into the Very Low Priority class, approximately 14% into the Low Priority class, and less than 0.1% of the area exceeded the Moderate Priority threshold. Monte Carlo perturbation of the child exposure relevance factors confirmed strong spatial rank stability of H(x) (median Spearman $\rho = 0.989$), indicating that the priority pattern is robust even though areas close to the Very Low Priority/Low Priority boundary may change class. This paper contributes single-threshold exceedance-probability maps at regulatory limits and a toxicity-weighted exceedance-priority index $H\left(x\right)$—a methodological and interpretive advance over our previous concentration mapping, using the same measurements with no new sampling. By constructing the composite index is toxicity-weighted: arsenic and cadmium carry ≈88% of the child weight, so $H\left(x\right)$ chiefly resolves As- and Cd-driven priority, with the remaining metals refining local class boundaries. Receptor prioritization is a screening output to guide confirmatory sampling, not a definitive risk classification. Full article