Search Results (65)

This study investigates staged surcharge preloading at a coastal test section by integrating field monitoring (pore-water pressure, settlement/settlement rate, and layer-by-layer deformation) with laboratory consolidation tests and field vane shear measurements. Responses at the surcharge center and slope-toe margin are compared to quantify spatial non-uniformity and pore-pressure–deformation coupling. Pronounced heterogeneity is observed (this field response represents three-dimensional deformation behavior that cannot be reproduced by 1D consolidation tests), with an empirical transition depth of ~24 m for this Wenzhou coastal soft soil site: above this depth, strains concentrate near the margin, whereas below it, compression at the center becomes dominant. The pore-pressure–settlement relationship is stage-dependent: during loading, pore pressure fluctuates markedly and settlement lags; during maintained consolidation, pore pressure dissipates, effective stress develops, and settlement is governed mainly by consolidation compression. After surcharging, water content decreases, and soil sensitivity reduces from 4.0 to 3.0 and stabilizes, indicating post-disturbance structural re-stabilization. These findings inform surcharge scheme design, monitoring layouts, and subsequent model calibration. Full article

Coastal urban environments are increasingly exposed to natural hazards, including storm surges, tsunamis, coastal erosion, and flooding, which threaten lives, livelihoods, and infrastructure. Despite their widespread use, conventional hard and soft engineering measures have often proved insufficient to address the escalating risks posed by climate change and rapid urbanisation. This study explores the potential of Nature-Inspired Solutions (NiS) as a complementary pathway to advance resilience in architecture, urban design, and planning. Unlike Nature-Based Solutions that utilise existing ecosystems directly, NiS draw design principles from both biotic and abiotic natural systems, offering innovative models for resilient settlements, coastal infrastructure, and adaptive urban planning. Using a mixed-methods approach that includes systematic and narrative reviews, semi-structured expert interviews, analysis of urban development plans, a panel discussion, and expert brainstorming, this research examines how natural coastal systems inform design interventions. Sri Lanka was selected as the primary case study context due to its exceptional coastal vulnerability, significant climate adaptation policy gaps, and status as a small island developing state representative of the coastal challenges faced by similar contexts globally. Furthermore, Sri Lanka was selected as the case study in accordance with the original research proposal submitted to the University of Huddersfield, which identified the country as a suitable context due to its significant vulnerability to coastal hazards, as outlined above. Field investigations in the Lunawa coastal area documented community-based adaptive practices emerging from multi-generational environmental observation. Analysis reveals how dune morphologies, root structures, living shorelines, and rock pool formations translate into architectural and engineering applications. Findings identify critical implementation challenges, including context-specific requirements, technical knowledge gaps, insufficient policy frameworks, limited practitioner awareness, and uncertainties about economic feasibility, as well as key enablers such as demonstrated ecological effectiveness and the potential of multifunctional infrastructure. The study demonstrates that embedding NiS into risk-informed planning and resilient urban design contributes to climate change adaptation, ecological sustainability, and inclusive governance, while highlighting persistent barriers that require strategic intervention. By bridging ecological wisdom and architectural innovation, NiS offers transformative opportunities to reimagine resilient coastal cities and communities facing escalating climate-induced hazards. Full article

Solid waste management (SWM) is a major global challenge for environmental sustainability and public health. This study analyzed SWM perceptions and practices before and during the COVID-19 pandemic in Playa Boca Chica, Tecpan de Galeana, Guerrero, Mexico, using a descriptive and quantitative approach. Data was collected from 60 households between September and October 2022 and analyzed using SPSS statistical software version 26; reliability was confirmed with Cronbach’s Alpha; and the generation-associated greenhouse gas (GHG) emissions were estimated using the SWM-GHG climate calculator. This study explored socio-environmental dynamics in informal coastal settlements through a case study in the Global South. The results showed that waste generation remained stable during the pandemic (3.25 kg/day; p = 0.116), suggesting a pattern of behavioral rigidity in which entrenched waste management practices persisted despite the global health crisis, likely due to the absence of structural environmental interventions and policy-driven behavioral incentives. The climate calculator estimated GHG emissions of 92 and 99 tons of CO₂-eq/year before and during the pandemic, respectively. Residents highlighted the need for improved infrastructure, recycling, and composting, while 97% emphasized environmental education and waste separation. The absence of a local waste management policy contributes to persistent emissions, underscoring the need for integrated and sustainable SWM strategies. Full article

This study presents the first fine-scale Coastal Vulnerability Index (CVI) assessment for Togo, evaluating coastal vulnerability and risk along the country’s 50 km barrier coastline in the context of accelerating erosion, rising sea level, and growing human exposure. Using remote sensing, GIS, and a CVI framework, shoreline trend rates, beach width, land use, and the role of existing coastal defences were analysed to support risk-informed decision-making. The coastline was segmented into 99 coastal units of 500 m, and shoreline trend rates were computed using the End Point Rate (EPR) method based on multi-temporal satellite-derived shorelines spanning from 1988 to 2024. Results show strong spatial contrasts in vulnerability, with the eastern sector of the Port of Lomé, particularly a 24.5 km stretch, exhibiting high vulnerability due to persistent shoreline retreat and narrow beach widths. In contrast, the western coastline displays lower vulnerability levels. Several erosion hotspots were identified, including Baguida and Dévinkemé, where recent shoreline retreat reaches up to −12.8 m/year. Existing coastal defences locally mitigate erosion impacts, reducing the extent of highly vulnerable shoreline from 23.5 km to 15 km. The integrated risk assessment identifies 6.5 km of coastline, primarily in the eastern port area, as being at high risk due to the combined effects of erosion and dense human settlement. These results provide spatially explicit information to support integrated coastal zone management, land-use planning, and adaptation strategies in Togo. Full article

Coastal erosion poses a growing threat to natural systems and critical infrastructures, particularly in touristic coastal areas where beaches represent both ecological assets and economic resources. Beyond shoreline retreat, erosion processes progressively reduce emerged beach surfaces and increase the exposure and vulnerability of coastal roads, railways, and urban settlements, with cascading socio-economic consequences. This study presents an integrated geomorphological and economic assessment of coastal erosion impacts. The Coastal Degradation Calculator (CoDeC) is applied along the Tyrrhenian coast of southern Italy, focusing on the municipality of San Lucido. Shoreline variations are quantified to reconstruct long-term changes in the Surface of the Emerged Beach (SEB) before and after major coastal defense interventions using multi-temporal remote sensing data (1954–2018). Simple, science-based box models are implemented to estimate sediment deficits, restoration needs, and associated economic damages, expressed in both €/m² and €/year. Results highlight a reduction in SEB area exceeding 60%, significant downdrift erosion linked to hard defenses and additional losses caused by coastal urbanization. The methodology proved effective in supporting damage quantification and informed the resolution of a long-standing legal dispute between public authorities. Owing to its transparency and reproducibility, the proposed framework offers a transferable tool for coastal risk assessment and management under increasing climate-driven pressures. Full article

Floods are among the extreme events associated with climate variability in the Lacramarca River basin, located in the department of Ancash, Peru. Meteorological phenomena such as El Niño during the periods 1982–1983 and 1997–1998, as well as the Coastal El Niño in 2017, constitute key reference events that motivated the development of the present study, based on a case study conducted in the area between the rural settlements of Santa Clemencia and Pampadura. This research is based on maximum precipitation data derived from historical climate records and from the climate scenarios ACCESS 1-3, HadGEM2-ES, and MPI-ESM-MR, as well as the median projected scenario for 2050, obtained from the National Meteorology and Hydrology Service of Peru (SENAMHI) data platform. This information was analyzed considering the spatial location of the basin and its position relative to the area of interest, using Intensity–Duration–Frequency (IDF) curves. To demonstrate the changes in the river hydrological behavior before and after the 2017 Coastal El Niño event, a Random Forest modeling approach was applied using Sentinel-2 satellite imagery. Design peak discharges for return periods of 50, 100, and 140 years were estimated using the HEC-HMS software. Hydraulic simulation of the Lacramarca River basin, carried out using HEC-RAS version 6.7 beta 3 and IBER version 3.3.1 software, made it possible to identify flood-prone areas affecting agricultural land and areas adjacent to population centers, covering 149,000 m² and 172,000 m² for return periods of 100 and 140 years, respectively, based on information from the historical scenario. In contrast, using data from the 2050 projection scenario, affected areas of 242,000 m² and 323,000 m² were estimated for the same return periods. Full article

The growing occurrence of emerging contaminants, particularly pharmaceutical residues, in aquatic environments represents a major environmental concern worldwide. While pharmaceutical contamination has been increasingly studied in marine systems, port environments remain largely understudied despite their complex anthropogenic pressures. This study investigates the occurrence, spatial distribution, and potential environmental risk of pharmaceutical residues in surface waters of the port of Cartagena, a multifunctional port on the Spanish Mediterranean coast. Fifteen pharmaceuticals were analysed across nine sampling sites, of which six were not detected. Diclofenac and several antibiotics (erythromycin, azithromycin, clindamycin, and trimethoprim) were the most frequently detected compounds, reaching maximum concentrations of up to 12,294.1 ng/L. Elevated concentrations were observed at sites associated with intense human activity, while the detection of multiple pharmaceuticals at a designated Special Area of Conservation suggests additional diffuse pollution sources, likely linked to insufficient wastewater management in nearby informal settlements. Most detected concentrations exceeded established environmental-quality or risk-threshold values, indicating a potential threat to marine ecosystems. These findings highlight the vulnerability of port environments to pharmaceutical pollution and underscore the need for continuous monitoring programs to support effective environmental management and biodiversity protection in coastal port areas. Full article

Monitoring construction-phase settlement of estuary-crossing tunnels founded on coastal soft soils is critical for risk management, yet dense in situ measurements are often unavailable along linear corridors. This study uses Sentinel-1A ascending SAR imagery (65 scenes, September 2022–August 2025) to retrieve time-series deformation along the Sanya Estuary Channel tunnel (China) using Permanent Scatterer InSAR (PS-InSAR) and Small Baseline Subset InSAR (SBAS-InSAR). The two approaches reveal a consistent subsidence hotspot at Tunnel Section D (DK0+000–DK0+330), while most of the corridor remains within ±5 mm/a. The line-of-sight deformation rates range from −24 to 17.7 mm/year (PS-InSAR) and −29.9 to 18.7 mm/a (SBAS-InSAR). Time-series analysis at representative points in Section D indicates a maximum cumulative settlement of −75.7 mm and a clear acceleration after May 2023. By integrating the deformation results with geological reports, construction logs and rainfall records, we infer that compressible marine clays and interbedded sand/aquifer zones control the hotspot, whereas excavation/dewatering and rainfall-related groundwater fluctuations further promote consolidation. The results provide a practical basis for subsidence risk screening and monitoring prioritization for estuary-crossing infrastructure in coastal soft-soil settings. From a sustainability perspective, the proposed joint PS–SBAS InSAR framework provides a scalable and cost-effective tool for continuous deformation surveillance, supporting preventive maintenance and risk-informed management of urban underground infrastructure. Full article

Ground settlement is a multifaceted geological phenomenon driven by natural and man-made forces, posing a significant impediment to sustainable urban development. Thus, ground settlement susceptibility (GSS) mapping has emerged as a critical tool for understanding and mitigating cascading hazards in seismically active and anthropogenically modified sedimentary basins. Here, we develop an integrated framework for assessing GSS in the Pohang region, South Korea, by integrating Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR)-derived vertical land motion (VLM) data with seismological, geotechnical, and topographic parameters (i.e., peak ground acceleration (PGA), effective shear-wave velocity (V_s³⁰), site period (T_s), general amplification factor (A_F), seismic vulnerability index (K_g), soil depth, topographic slope, and landform classes) through ensemble machine learning models such as Random Forest (RF), XGBoost, and Decision Tree (DT). Analysis of 56 Sentinel-1 SLC images (2017–2023) revealed persistent subsidence concentrated in Quaternary alluvium, reclaimed coastal plains, and basin-fill deposits. Among the tested models, RF achieved the best performance and strongly agreed with field evidence of sand boils, liquefaction, and structural damage from the 2017 Pohang earthquake. The very-high-susceptibility zones exhibited mean subsidence rates of −3.21 mm/year, primarily within soft sediments (V_s³⁰ < 360 m/s) and areas of thick alluvium deposits. Integration of the optimal RF-based GSS index with regional building inventories revealed that nearly 65% of existing buildings fell within high- to very-high-susceptibility zones. The proposed framework demonstrates that integrating PSInSAR and ensemble learning provides a robust and transferable approach for quantifying ground settlement hazards and supporting risk-informed urban planning in seismically active and complex geological coastal environments. Full article

Natural hazards pose an increasing threat to infrastructures, lives, and livelihoods in alpine regions due to climate change and the growing demand for settlement space. While grey protective structures are commonly deployed to provide immediate safety, their sustainability, and thus protective function, is limited by cost-intensive maintenance. Nature-based solutions (NbS) can alleviate these shortcomings by offering cost-effective, adaptive protection that strengthens over time, making their deployment a key factor in building resilience to climate-induced hazards. This paper introduces a systematic methodology for the strategic deployment of NbS to enhance climate resilience. It integrates a three-level hazard classification system with an expert-led assessment rating 74 NbS against 29 hazards. A subsequent Principal Component Analysis (PCA) synthesises these into six functional groupings based on their shared mitigation characteristics. The core of this framework introduces two key innovations: a novel Mitigation Score and a Hazard Mitigation Profile. Together, they evaluate NbS effectiveness dynamically through the different phases of natural hazards, surpassing traditional static ratings by evaluating NbS performance across the hazard management cycle—from predisposition to post-event recovery. Significant variation in mitigation scoring was observed for individual hazard classes and types. Erosion processes (e.g., sheet, rill, and gully erosion) achieved the highest mitigation scores (1.90), as they can be addressed by many highly effective NbS (21–33 types). Conversely, flood-related hazards, such as fluvial and pluvial floods, showed moderate scores (1.64–1.66) with a balanced mix of mitigative and supportive NbS, while options for mitigating impact floods and coastal floods were far more limited (1.00–1.42). The resulting methodology provides a crucial, practical link between specific climate-related threats and viable, nature-based responses, serving as a robust framework to guide the decisions of planners, engineers, and policymakers. By enabling a more strategic and temporally aware deployment of NbS, our findings inform the development of adaptive management strategies to ensure their long-term effectiveness. Full article

The Yellow River Delta region is not only a concentrated area of human activities in coastal zones, but also a zone strongly influenced by regional environmental changes, where land cover changes are significantly affected by natural factors. Current historical LUCC datasets overlook the importance of partitioning to obtain accurate information on the potential maximum distribution range, which may lead to uncertainties in climate and environmental predictions. This study aims to reconstruct historical cropland changes in the Yellow River Delta via a region-adapted allocation model, supporting improved LUCC data accuracy and related research. Based on historical river course, settlement, and cropland survey data, this study identifies natural factors using historical settlement density through correlation analysis. Subsequently, a reclamation suitability model conforming to regional characteristics was constructed, and it obtains the cropland changes in the Yellow River Delta Basin at a spatial resolution of 0.5′ × 0.5′ over five time periods since 1855. The research indicates the following: (1) Through the method of analyzing the correlation between historical settlement density and natural factors, it is found that elevation (−), soil pH (+), soil organic carbon density (−), and NDVI (+) are the primary natural factors influencing the distribution of farmland in the Yellow River Delta. (2) The amount of farmland in the Yellow River Delta increased initially and then decreased after 1885; the average reclamation rate increased from 5.65%, peaked at 23.46% in the early 20th century, and then fell back to 7.68%. Spatially, the reclamation area expanded from scattered local areas along the Yellow River towards the sea, with a distinct coastal distribution. (3) Evaluation through absolute difference analysis shows that, compared with the HYDE 3.2 data, our reconstruction reflects the impacts of coastal changes, river distribution, and regional policy history on the allocation results. Based on the findings of this study, relevant issues can be improved from two aspects: first, by correlating settlement density with natural factors to identify key regional natural factors, which can then be applied to the update of LUCC data in small spatial units and similar regions to enhance data accuracy; second, by referring to the historical laws of cropland reclamation and suitability conditions, to optimize the current land planning of the Yellow River Delta and balance cropland utilization with ecological protection. Full article

In this study, we sampled malaria mosquito larvae in natural and artificial breeding places to study the geographical distribution, ecological preferences, and chromosomal variability of different species of the genus Anopheles in the territory of the Crimean Peninsula and the Black Sea coast of the Caucasus. Species were diagnosed using a combination of morphological, cytogenetic, and molecular markers. The ecological conditions of the larval habitats, such as dissolved oxygen content in the water, acidity, salinity, and temperature, were measured. Seven species of malaria mosquitoes were identified in the pool of 2229 individual mosquitoes collected at 56 breeding sites, including An. atroparvus, An. claviger, An. daciae (formerly identified as An. messeae s. l.), An. hyrcanus, An. maculipennis s. s., An. plumbeus, and An. melanoon. The previously recorded species of An. algeriensis, An. messeae s. s., An. sacharovi, and An. superpictus were not found in this study. Anopheles maculipennis was dominant in typical anophylogenic water bodies. Anopheles plumbeus, which used to breed mainly in tree holes in coastal forests, has spread to urban settlements along the Black Sea coast and breeds in artificial containers. Chromosomal polymorphism was studied and found in An. atroparvus and An. daciae populations. Differences in the chromosomal composition of An. daciae populations in Crimea and on the Black Sea coast of the Caucasus were revealed. The Crimean population had a low level of polymorphism in autosomal inversions. The data obtained in this study can be used to inform a better control of potential malaria vectors in the Black Sea coastal region. Full article

Defensiveness is a pivotal characteristic of traditional military settlements in ancient China, influenced by various factors linked to settlement construction and the occurrence of battles. Previous studies have typically focused on specific periods or moments, overlooking the dynamic interrelationships that evolve over time. The study proposes the concept of ‘defensive efficiency’ as a specific indicator for measuring the level of military defense system construction. Meanwhile, the analytic hierarchy process (AHP) was introduced to decompose the complex defense efficiency indicators into a quantitative system consisting of four levels of evaluation factors, analyse and statistically analyse the individual evaluation factors, and finally integrate and visualise the comparison of the data over multiple periods of time with the help of a geographic information system (GIS). The results of the study show that the changes in defensive efficiency in the four periods reflected the four stages of coastal defense construction in Ningbo during the Ming Dynasty: the preliminary construction period, the full construction period after repeated Wokou infestations, the basic completion period, and the period when Wokou encroachments were reduced and the point of attack was relocated to the south. While the spatial distribution of battles drove the continuous construction and improvement of coastal defense settlements, the improvement of the coastal defense system had an obvious effect on resisting Wokou intrusion. The study enriches the understanding of the cultural significance of traditional Chinese military settlement systems, emphasises the importance of tailor-made strategies appropriate to different historical periods and regional contexts, and provides a basis for the sustainable conservation of extant remains. Full article

This Study delves on changes in the extent of mangroves over a 42 years span in the Douala-Edea NP, Cameroon. Mangroves are valuable ecosystems that provide significant biological, environmental, ecological, and cultural functions. To inform the development of management plans for the ecosystem’s sustainability, it is crucial to evaluate how their land cover, levels of degradation, and phases of restitution have changed. GIS and remote sensing techniques were used to classify and analyze Landsat images from 1980 to 2022 categorized into nine classes: bare ground, Nypa palms, settlements, coastal sedimentation, river sedimentation, regeneration, matured mangroves, dense forest, and water body. Using the Markovian chain approach, the changes noted during the period were utilized to forecast future trends up to 2052. Findings demonstrated that the mature mangrove area decreased throughout the study. The surface area covered by mature mangroves was 80,628.78 hectares in 1980, which decreased by 7.31%, 1.51%, 3.70%, and by 17% for the overall period of 42 years. Additionally, a gain of 6.84% from 1980 to 2022 was observed, probably from artificial mangrove regeneration. Settlements, invasive Nypa palms, bare ground (resulting from over-exploitation), and the sedimentation of rivers and coast primarily replaced mangroves. The prediction derived indicated the continuous decline in mangroves if not fully protected by law. The gazettement to National Park and recent promulgation of two laws are steps in providing the needed protection. These results provide vital information to direct future mangrove conservation actions in the recently gazetted Douala-Edea National Terrestrial and Marine Park and other mangrove blocks along the Gulf of Guinea. Full article

Wildfire susceptibility maps play a crucial role in preemptively identifying regions at risk of future fires and informing decisions related to wildfire management, thereby aiding in mitigating the risks and potential damage posed by wildfires. This study employs eXplainable Artificial Intelligence (XAI) techniques, particularly SHapley Additive exPlanations (SHAP), to map wildfire susceptibility in Izmir Province, Türkiye. Incorporating fifteen conditioning factors spanning topography, climate, anthropogenic influences, and vegetation characteristics, machine learning (ML) models (Random Forest, XGBoost, LightGBM) were used to predict wildfire-prone areas using freely available active fire pixel data (MODIS Active Fire Collection 6 MCD14ML product). The evaluation of the trained ML models showed that the Random Forest (RF) model outperformed XGBoost and LightGBM, achieving the highest test accuracy (95.6%). All of the classifiers demonstrated a strong predictive performance, but RF excelled in sensitivity, specificity, precision, and F-1 score, making it the preferred model for generating a wildfire susceptibility map and conducting a SHAP analysis. Unlike prevailing approaches focusing solely on global feature importance, this study fills a critical gap by employing a SHAP summary and dependence plots to comprehensively assess each factor’s contribution, enhancing the explainability and reliability of the results. The analysis reveals clear associations between factors such as wind speed, temperature, NDVI, slope, and distance to villages with increased fire susceptibility, while rainfall and distance to streams exhibit nuanced effects. The spatial distribution of the wildfire susceptibility classes highlights critical areas, particularly in flat and coastal regions near settlements and agricultural lands, emphasizing the need for enhanced awareness and preventive measures. These insights inform targeted fire management strategies, highlighting the importance of tailored interventions like firebreaks and vegetation management. However, challenges remain, including ensuring the selected factors’ adequacy across diverse regions, addressing potential biases from resampling spatially varied data, and refining the model for broader applicability. Full article