Search Results (6,383)

The persistence and hazardous potential of polycyclic aromatic hydrocarbons (PAHs), with compounds such as anthracene and phenanthrene, raise significant concerns about human health and environmental safety. PAHs are ubiquitous environmental pollutants originating from natural processes and anthropogenic activities, notably fossil fuel combustion. Due to their stability, they tend to accumulate in ecosystems, posing risks to wildlife and human health through bioaccumulation and potential carcinogenicity. Conventional remediation techniques, such as physical adsorption and biological treatment, often fall short in their efficiency and long-term sustainability. Thus, there is an urgent need for innovative methods that can effectively degrade these persistent organic pollutants. Here, we reviewed recent advancements in the photocatalytic degradation of anthracene and phenanthrene, with a focus on metal oxide-based nanocomposites. The major points were: (1) Metal oxides such as TiO₂, ZnO, and CuO, recognized for their photocatalytic properties (they show significantly enhanced efficiency when utilized as a part of nanocomposites, primarily due to the improved charge separation, increased surface area, and numerous active sites); (2) The review of the photocatalytic mechanisms involved in PAH degradation, particularly through the generation of reactive oxygen species that can break down anthracene and phenanthrene into less harmful compounds; and (3) The insights into the formed intermediates and reaction pathways, which can help to deepen the understanding of PAH breakdown and support the design of more efficient catalytic systems for future environmental remediation applications. Full article

Hydraulic engineering structures can threaten freshwater fish by entraining them into hazardous areas. Acoustic barriers have been proposed as a non-physical method to guide fish away from these zones. In this study, we investigated the behavioral responses of juvenile grass carp to different acoustic stimuli under semi-natural conditions using outdoor net cages. Four sound types were tested: a 1000 Hz pure tone and three broadband sounds, including Alligator sinensis hissing, pile-driving noise, and outboard motor noise. Behavioral responses were quantified using response frequency, total midline crossings, first-response time, maximum swimming speed, and average swimming speed. The results showed that Alligator sinensis hissing elicited the highest number of midline crossings, representing the strongest behavioral response among all tested sounds. In addition, both Alligator sinensis hissing and outboard motor noise induced significantly stronger avoidance responses than the pure tone or pile-driving noise, as indicated by higher response frequency and faster swimming speeds. Furthermore, manipulation of pulse repetition intervals in the most effective deterrent sounds generated a novel broadband sound, which altered fish distribution patterns and elicited avoidance behavior. These findings indicate that both sound type and temporal structure influence negative phonotaxis behavior in grass carp and provide experimental evidence for the optimization of acoustic barriers in fish management. Full article

Remote sensing enables building-level flood vulnerability assessment without field surveys, yet existing approaches require site-specific calibration or produce categorical outputs without physical interpretability. We present the Global Flood Vulnerability Model (GFVM), integrating six remotely sensed components (elevation, slope, topographic position index, distance to water, building height, and basement depth) through geographic context classification to quantify vulnerability from terrain and structural characteristics across coastal, fluvial, and pluvial settings. Building heights are extracted primarily from the Global Building Atlas, with gaps filled using a ConvNeXt neural network trained on high-resolution Light Detection and Ranging (LiDAR) ground truth from four cities (within-city MAE 1.35–1.91 m, cross-city MAE 2.05–3.47 m). Terrain metrics are derived from a combination of hierarchical digital elevation models (DEM) (USGS 3DEP 10 m, AHN LiDAR 0.5 m, UK Environment Agency DTM 1 m, Australia 5 m) and global datasets (NASADEM 30 m, Copernicus GLO-30). Hydrographic networks are sourced from OpenStreetMap and Natural Earth. Implementation through Google Earth Engine requires only coordinates as input, returning a five-level vulnerability index with multi-hazard decomposition (fluvial, coastal, pluvial) and SHapley Additive exPlanations (SHAP)-based attribution identifying dominant drivers. Validation across 183 independent locations in Germany, UK, and USA demonstrates robust performance: Area Under Curve 0.855 for separating flooded from non-flooded sites, weighted Cohen’s kappa 0.493 across regulatory zones, and Spearman ρ 0.746 against Federal Emergency Management Agency (FEMA) classifications. Sensitivity analysis across 625 parameter configurations confirms stability, and DEM resolution experiments show that global 30 m elevation data produces category reclassification in only 5.3–8.6% of locations compared to high-resolution sources. Application to the 2024 Kazakhstan floods identifies 118 high-vulnerability locations across 581 assessment points, with vulnerability patterns matching documented inundation. GFVM advances remote sensing applications for disaster risk assessment by demonstrating that multi-source geospatial data fusion enables building-level vulnerability screening without local calibration or field surveys. Full article

Vertical land motion in urban areas is a critical manifestation of groundwater, directly affecting infrastructure stability and groundwater sustainability. While land subsidence caused by groundwater extraction has been widely investigated, the opposite process—surface uplift induced by groundwater recovery—remains poorly documented or understood, particularly regarding its hydrological mechanisms and potential hazards. Here, we integrate InSAR time-series analysis of Sentinel-1 imagery (2017–2025) with groundwater well records to quantify the spatial–temporal characteristics of uplift in Xi’an, China, and to evaluate its hydrogeological drivers. Results reveal a persistent surface uplift zone south of the ancient city in Xi’an, with rates up to 20 mm/yr. The uplift correlates closely with rising groundwater levels in the shallow confined aquifer, indicating a strong coupling between aquifer recharge and surface uplift. Calculated storage coefficients and hydraulic diffusivity values highlight marked spatial variations, constrained by some ground fissures that act as both mechanical discontinuities and hydrological barriers controlling pressure diffusion. Time-series analysis further identifies the eastward propagation of subsidence-to-uplift reversal in Yuhuazhai, an urban village with groundwater injection, which is used to quantify the diffusivity coefficients. Field investigations show that rapid groundwater rebound can lead to uplift-related hazards, such as basement seepage, underscoring that surface uplift must be considered alongside subsidence in urban water management. Full article

Groundwater is a critical lifeline for ecosystems and human settlements in arid and semi-arid regions, yet it is increasingly vulnerable to the dual pressures of extreme climatic conditions and intensifying anthropogenic activities. This study investigated 24 groundwater and 4 river water samples to discuss the hydrogeochemical evolution and water quality suitability in the Tianjun Basin, a typical high-altitude arid basin on the northeastern Tibetan Plateau. The results indicate that groundwater is mildly alkaline (pH: 7.65–8.35) and predominantly fresh (TDS: 233.77–1061.42 mg/L). Hydrochemical facies evolve from HCO₃-Ca type in upstream areas to Mixed HCO₃-Na·Ca and Cl-Na types. Hydrochemical analysis suggests that silicate weathering and carbonate dissolution are the dominant natural processes, while cation exchange further modifies the ionic composition. Notably, anthropogenic nitrogen (NO₃⁻ and NH₄⁺) contamination, primarily from domestic sewage in the Tianjun Basin, has significantly impacted groundwater quality. Health risk assessment shows that infants are the most vulnerable group, with 16.67% of samples posing a non-carcinogenic risk via the oral pathway. Regarding irrigation suitability, while sodium hazards are generally low, a significant salinity hazard is identified due to elevated electrical conductivity in the arid environment. This poses a substantial risk of secondary soil salinization, necessitating strict salt management strategies to preserve long-term land productivity. These findings provide critical insights for the sustainable management of fragile groundwater resources in extreme arid environments. Full article

In sub-Saharan Africa, intensive pesticide use in irrigated agriculture is threatening the quality of soil, water bodies and ecosystem services, yet integrated risk assessments remain limited. This study evaluated the environmental implications and risks of pesticide residues in soils (0–20 cm; n = 15) and irrigation water (n = 15) from off-season irrigation area of the Goulbi Maradi Valley, Niger. Twelve commonly used pesticides in Djiratawa, Maradi 3 and Tibiri, were quantified by High-Performance Liquid Chromatography with Variable Wavelength Detector (HPLC-VWD), revealing Tibiri as a contamination hotspot, where the total pesticide residues in soil and irrigation water reached 6.4 and 19.7 times the respective European Union soil and drinking water benchmarks, dominated by Cypermethrin, Emamectin benzoate and Chlorpyrifos ethyl in soils, and Emamectin benzoate and Dichlorvos in water. Multivariate analysis showed that soil particle size, particularly higher clay content, controlled the retention of strongly sorbing compounds, while pH and salinity governed the occurrence of more soluble residues in irrigation water. While non-carcinogenic risks for Adults and Children via soil and water exposure were acceptable (Hazard Quotient and Hazard Index < 1), ecological risks were unacceptable, with Folsomia candida and Daphnia magna the most affected organisms, driven by Emamectin benzoate (Toxicity Exposure Ratio < 2). Priority actions include phasing out Dichlorvos and Paraquat dichloride, tightening controls on Emamectin benzoate and expanding food-chain monitoring, particularly in vegetables and fish, to support multi-trophic risk assessment and safer irrigation management. Full article

Background: Despite significant advances in diagnosis and treatment, gastric cancer remains a major global malignancy. This study aimed to evaluate the impact of Surveillance, Epidemiology, and End Result (SEER) stages on all-cause and cause-specific mortality in gastric cancer. Methods: This nationwide population-based cohort study analyzed data from the Cancer Public Library Database (CPLD). Patients aged ≥ 30 years diagnosed with gastric cancer between 2012 and 2019 were followed up until 31 December 2020. Cox proportional hazards models and Fine–Gray models were used to compare the risk of all-cause and cause-specific mortality based on SEER stages. The Kaplan–Meier method and cumulative incidence functions were applied to analyze cumulative incidences of all-cause and cause-specific mortality. Statistical significance was assessed using the log-rank test and Gray’s test. Additionally, a subgroup analysis was performed. Results: Among 218,491 individuals, 59,952 died during a median follow-up of 3.62 years. Compared with the localized stage, the risk of all-cause mortality was 4.31 and 24.73 times higher in patients with the regional and distant stages, respectively, after adjusting for sex, age, income, residential area, and comorbidities. The regional stage was associated with an 8.70-, 6.08-, 1.28-, and 1.43-fold higher risk of stomach cancer death, cancer death, cardiovascular death, and respiratory death, respectively. The distant stage was associated with 51.67-, 35.97-, 1.74, and 1.54-fold higher risk of stomach cancer death, cancer death, cardiovascular death, and respiratory death, respectively. Conclusions: Higher SEER stage in gastric cancer is associated with an increased risk of all-cause mortality, gastric cancer-specific mortality, overall cancer mortality, cardiovascular disease-related mortality, and respiratory disease-related mortality. Notably, cardiopulmonary mortality increased with advancing SEER stage, particularly among younger patients, underscoring the need for vigilant monitoring. Full article

Ecological vulnerability assessment serves as a prerequisite for ecological governance, yet evaluating large-scale ecological vulnerability remains challenging. To address this challenge, this study integrates geological elements into ecological vulnerability assessment, taking Ruyuan Area in the Northern Guangdong Mountains, China, as a case study. The area faces ecological hazards such as land desertification and soil erosion, indicating severe governance challenges. This study selected 14 ecological vulnerability factors and constructed assessment models based on Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs). A total of 800 ecological vulnerability sampling points were obtained by combining field survey data with remote sensing imagery. The models were trained using binary vulnerability labels. The resulting continuous probability outputs were then classified into five vulnerability levels using the natural breaks method to generate the final ecological vulnerability map. It should be noted that the multi-level vulnerability map represents graded probability-based differentiation rather than supervised multi-class prediction. Model performance was validated using three metrics: Area Under Receiver Operating Characteristic Curve (AUC–ROC), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The CNN (AUC = 0.916) model outperformed the DNN model (AUC = 0.895). According to the CNN-based classification results, non-vulnerable, slightly vulnerable, mildly vulnerable, moderately vulnerable, and highly vulnerable areas accounted for 36.19%, 22.85%, 14.24%, 12.31%, and 14.41% of the total area, respectively. High ecological vulnerability zones were concentrated in Daqiao, Luoyang, Dabu, and parts of Rucheng towns, with soil parent material and vegetation coverage identified as the main contributing factors, among which parent material was the most important. This finding underscores the notable impact of geological factors on local ecological vulnerability. Based on these results, nine ecological–geological subareas were delineated, and targeted ecological protection and restoration recommendations were proposed. This study, employing machine learning techniques, constructed an ecological vulnerability assessment model incorporating geological elements, thereby providing scientific support for targeted ecological governance in the study area. Full article

Southern New Jersey faces increasing flood risk due to several factors including rapid development, climate change, and aging infrastructure. This study evaluated the flood vulnerability of two municipal solid waste landfills located in Gloucester and Cumberland Counties. These sites are located near rural communities that rely on shallow groundwater for drinking water, which may be contaminated by floods. To assess these challenges, this research applies a hydrologic–hydraulic model to evaluate future flood vulnerability at the Cumberland County Improvement Authority (CCIA) landfill and the Gloucester County Solid Waste Complex (GCSWC) landfill. The method uses HEC-HMS and HEC-RAS 2D model simulations with climate-adjusted precipitation data derived from global climate models. Model performance was evaluated using Hurricane Ida (31 August–2 September 2021) by comparing HEC-RAS-simulated inundation extents with independently derived Sentinel-1 SAR flood maps generated in Google Earth Engine. Climate forcing was developed by deriving climate-adjusted 24 h precipitation–frequency (PF) design depths for 50-year and 100-year design storms under the Shared Socioeconomic Pathway (SSP) emissions pathways SSP2-4.5 (moderate) and SSP5-8.5 (high) for mid-century (2025–2050) and late-century (2070–2100) periods. These PF storm totals were converted to rainfall hyetographs using a fixed alternating variability method (AVM) temporal pattern within the coupled HEC-HMS/HEC-RAS modeling chain. Hazard amplification was primarily expressed through lateral inundation expansion and longer persistence of shallow flooding in low-relief operational zones, rather than uniform increases in peak depth across landfill interiors. Across both facilities, the landfill toe and adjacent access corridors were consistently identified as the most sensitive operational areas. Full article

Post-earthquake emergency response planning requires rapid and adaptive resource allocation under disrupted accessibility, uneven district-level demand, and hazardous field conditions. In large metropolitan areas, these challenges are intensified by spatial differences in social vulnerability, infrastructure disruption, operational feasibility, and responder exposure. Static allocation approaches are often insufficient in such environments because they cannot adequately reflect temporal change or the evolving relationship between urgency, accessibility, and operational risk. This study proposes a dynamic multi-objective model for district-level post-earthquake resource allocation that integrates social vulnerability, occupational safety, and Markov-based updating within a single analytical framework. First, district priority scores are derived through an Analytic Hierarchy Process based on building damage ratio, intervention time, social vulnerability, critical infrastructure damage, secondary hazard risk, team capacity, and occupational safety. Second, a Markov-based updating mechanism is used to represent time-dependent redistribution across response periods. Third, a constrained weighted-sum multi-objective optimization model is formulated to balance district priority, social vulnerability, and responder safety under capacity and accessibility limits. The model is applied to Istanbul using official district-level data from national and local institutional sources. Scenario-based analysis is conducted under balanced, priority-oriented, vulnerability-oriented, and safety-oriented settings, together with static and dynamic model comparisons. The results show that the dynamic structure produces a more adaptive allocation profile than the static structure, with the share of the Very High allocation class declining from 37.66% to 34.95% and the Low allocation class increasing from 12.89% to 16.00% over the response horizon. The findings also indicate that greater emphasis on social vulnerability shifts allocation toward more fragile districts, whereas stronger safety emphasis reduces cumulative operational exposure at the cost of moderate reductions in immediate coverage. Overall, the study contributes to the disaster response literature by linking multi-criteria district prioritization, dynamic redistribution, and safety-aware allocation within a unified district-level decision structure. Beyond the Istanbul application, the proposed framework offers a practical basis for more responsive, equitable, and operationally sustainable post-earthquake planning in complex urban environments. Full article

Community disaster resilience is increasingly guiding risk-reduction investments, but in many Southeast European settings, comparable subnational data remain scarce. This study assesses perceived community disaster resilience across Serbia by combining BRIC–DROP dimensions into a single index and analyzing differences across hazard types and sociodemographic factors. A cross-sectional household survey was conducted using multistage random sampling and the “next birthday” method for respondent selection. The final sample included 1200 adults from 22 local government units across four regions: Belgrade, Vojvodina, Šumadija & Western Serbia, and Southern & Eastern Serbia. Participants evaluated preventive measures and societal resilience for ten hazard types and considered five social dimensions: social structure, social capital, social mechanisms, social equity/diversity, and social beliefs. Descriptive statistics, bivariate analyses (including Pearson correlations, t-tests, and ANOVA), and multiple linear regression identified key predictors of preventive behavior and perceived resilience. Composite scores highlighted spatial resilience differences. Overall perceptions were generally low, mostly falling below the midpoint of the scale. Furthermore, the highest ratings for implemented preventive measures were recorded for pandemics/epidemics, storms/hail, and floods, whereas the lowest were observed for environmental pollution and droughts. Perceived resilience was highest for snowstorms, storms/hail, and pandemics/epidemics, and lowest for environmental pollution and droughts. Also, respondents reported relatively strong family ties and favorable perceptions of communication and access to basic supplies, but weak institutional capacity, particularly in budget allocation, early warning and public notification, rapid decision-making, and evacuation and shelter readiness. Regression results were statistically significant but explained only a small portion of the variance. Age and public-sector employment positively predicted perceived resilience; fear, income, and, to a lesser extent, education were negatively associated. These findings highlight the structural and psychosocial factors that shape perceptions of resilience. The BRIC–DROP composite indicates generally low perceived preparedness and resilience, especially in risk communication, evacuation and shelter readiness, and financing—the key bottlenecks in strengthening local resilience. The results recommend combining institutional reform with targeted risk communication to reduce fear and build trust, especially focusing on hazard areas with the lowest confidence, such as environmental pollution and drought. Full article

Extreme rainfall poses an increasing threat to mountainous transportation systems by frequently triggering landslides along road corridors. Most existing studies focus on long-term landslide susceptibility, whereas event-scale assessments remain limited, particularly in road environments. This study develops an event-scale framework for assessing landslide risk along mountain roads under extreme rainfall conditions, using the July 2023 “23·7” rainfall event in Mentougou District, Beijing, as a case study. A Random Forest model was constructed by integrating multi-source geospatial data with an event-specific inventory of 8930 landslides. The model achieved high predictive performance, with ROC–AUC values of 0.9187 and 0.9166 for the validation and test datasets, respectively. Feature importance analysis further indicates that landslide occurrence is controlled by the combined effects of rainfall, terrain conditions, vegetation cover, and anthropogenic disturbance, with rainfall acting as the primary trigger. High-risk road segments are mainly concentrated in the southeastern part of the study area, showing clear spatial clustering. These results highlight the value of event-scale analysis and demonstrate the effectiveness of the road-oriented framework for identifying hazardous segments under extreme rainfall conditions. The proposed approach provides practical support for landslide monitoring, risk mitigation, and resilient management of mountainous transportation infrastructure. Full article

Sustainable groundwater management represents a main goal for the future in the context of climate change and increasing anthropogenic pressure. In recent decades, intrinsic vulnerability assessment and risk mapping have been established as some of the most important tools for groundwater preservation, but they have also shown limitations due to their static nature and their failure to account for the inherent uncertainty of hydrogeological parameters. This study proposes an innovative hybrid framework that integrates traditional overlay-index methodology (SINTACS Release 5) with stochastic numerical modeling to assess groundwater contamination risk and evolve it into a dynamic time-dependent tool. This methodology was applied to a case study of the Lapisina Valley phreatic aquifer (Northeastern Italy), a strategic area for drinking water supply. Numerical simulations were implemented to reproduce groundwater flow using the MODFLOW-NWT code. To address parametric uncertainty, 237 stochastic realizations of the modeling domain were generated using the Latin Hypercube Sampling method, randomizing hydraulic conductivity values. Advective transport was simulated through forward particle tracking using the MODPATH code, starting from the identified and classified hazard sources within the study area. Assuming the absence of attenuation during transport allowed for a conservative worst-case scenario. The result was the definition of a probabilistic contaminant propagation factor, a time-dependent indicator that quantifies the probability of pollution arrival to a specific discrete portion of the domain. This probabilistic factor was combined with three indexes commonly utilized for risk assessment (the intrinsic vulnerability index, hazard index, and value of the resource) to generate four contamination risk maps representing different timestep scenarios (5, 10, 20, and 50 years) after the arrival of a hypothetical contaminant in the saturated zone. This approach transforms risk mapping from being a useful but static snapshot to a predictive dynamic framework. Full article

Landslides are a critical environmental hazard in mountainous regions like eastern Uganda, posing serious threats to lives, infrastructure, and ecosystems. While recent advances in geospatial technology have improved hazard assessment, existing research often lacks high-resolution, cloud-based analysis for dynamic landscapes such as the Mount Elgon region. This study addresses that gap by developing a landslide susceptibility map (LSM) using Google Earth Engine (GEE), which integrates remote sensing and geospatial data for scalable analysis. The main objective is to identify landslide-prone zones by analyzing eight conditioning factors, namely slope, elevation, vegetation cover, rainfall, land use land cover, soil type, soil moisture, and groundwater levels using the weighted overlay method (WOM). The methodology produced a classified LSM with zones of high (37.7%), moderate (58%), low (2%), and very low (2.3%) susceptibility, with validation via historical landslide data and ROC analysis yielding an AUC of 0.76, confirming strong predictive performance. The study underscores the value of GEE in hazard modeling and provides actionable insights for targeted risk mitigation, sustainable land use planning, and early warning system development in landslide-prone areas. Full article

In order to assess their influence on the marine hazard offshore the Cilento Promontory, the gassy sediments of the Cilento offshore have been thoroughly examined using the geological interpretation of a closely spaced grid of Sub-bottom Chirp profiles. Based on the general stratigraphic framework three areas have been previously identified, highlighting the different acoustic features occurring in the Cilento area. The acoustic anomalies include acoustic blanking, shallow gas pockets, and seismic units impregnated of gas, showing distinct acoustic responses. Understanding these anomalies and the related seismo-stratigraphic units in the offshore Cilento Promontory provides a valuable foundation for evaluating marine geohazards and may assist in developing strategies to mitigate geohazards in the Cilento area. Full article