Search Results (318)

Seafood contamination by heavy metals is a growing public health concern, particularly in regions like Tunisia where seafood is a major dietary component. This study assessed concentrations of cadmium (Cd), copper (Cu), lead (Pb), and zinc (Zn) in the muscle tissue of the red shrimp Parapenaeus longirostris, collected in 2023 from four coastal regions: Bizerte, Monastir, Kerkennah, and Gabes. Metal analysis was conducted using flame atomic absorption spectroscopy. This species was chosen due to its ecological and economic importance. The study sites were chosen based on their differing levels of industrial, urban, and agricultural influence, providing a representative overview of regional contamination patterns. Mean concentrations were 1.04 µg/g for Zn, 0.59 µg/g for Cu, 1.56 µg/g for Pb, and 0.21 µg/g for Cd (dry weight). Pb was the most prevalent metal across sites. Statistically significant variation was observed only for Cu (p = 0.0334). All metal concentrations were below international safety limits set by FAO/WHO and the European Union. Compared to similar studies, the levels reported were similar or slightly lower. Human health risk was evaluated using target hazard quotient (THQ), hazard index (HI), and cancer risk (CR) values. For adults, THQ ranged from 5.44 × 10⁻⁶ to 8.43 × 10⁻⁴, while for children it ranged from 2.40 × 10⁻⁵ to 3.72 × 10⁻³. HI values were also well below 1, indicating negligible non-carcinogenic risk. CR values for Cd and Pb in both adults and children fell within the acceptable risk range (10⁻⁶ to <10⁻⁴), suggesting no significant carcinogenic concern. This study provides the first field-based dataset on metal contamination in P. longirostris from Tunisia, contributing valuable insights for seafood safety monitoring and public health protection. Full article

Flooding due to dam failures is a critical issue with significant impacts on human safety, infrastructure, and the environment. This study assessed the potential flood hazard that could be generated from breaching of the Alacranes dam in Villa Clara, Cuba. Thirteen reservoir breaching scenarios were simulated under several criteria for modeling the flood wave through the 2D Saint Venant equations using the Hydrologic Engineering Center’s River Analysis System (HEC-RAS). A sensitivity analysis was performed on Manning’s roughness coefficient, demonstrating a low variability of the model outputs for these events. The results show that, for all modeled scenarios, the terrain topography of the coastal plain expands the flood wave, reaching a maximum width of up to 105,057 km. The most critical scenario included a 350 m breach in just 0.67 h. Flood, velocity, and hazard maps were generated, identifying populated areas potentially affected by the flooding events. The reported depths, velocities, and maximum flows could pose extreme danger to infrastructure and populated areas downstream. These types of studies are crucial for both risk assessment and emergency planning in the event of a potential dam breach. Full article

Currently, most studies evaluating storm surges are conducted at the provincial level, and there is a lack of detailed research focusing on cities. This paper focuses on the urban scale, using some fine-scale data of coastal areas obtained through remote sensing images. This research is based on the Hazard–Exposure–Vulnerability (H-E-V) framework and PPRR (Prevention, Preparedness, Response, and Recovery) crisis management theory. It focuses on 52 Chinese coastal cities as the research subject. The evaluation system for the disaster response capabilities of Chinese coastal cities was constructed based on three aspects: the stability of the disaster-incubating environment (S), the risk of disaster-causing factors (R), and the vulnerability of disaster-bearing bodies (V). The significance of this study is that the storm surge capability of China’s coastal cities can be analyzed based on the results of the evaluation, and the evaluation model can be used to identify its deficiencies. In this paper, these storm surge disaster response capabilities of coastal cities were scored using the entropy weighted TOPSIS method and the weight rank sum ratio (WRSR), and the results were also analyzed. The results indicate that Wenzhou has the best comprehensive disaster response capability, while Yancheng has the worst. Moreover, Tianjin, Ningde, and Shenzhen performed well in the three aspects of vulnerability of disaster-bearing bodies, risk of disaster-causing factors, and stability of disaster-incubating environment separately. On the contrary, Dandong (tied with Qinzhou), Jiaxing, and Chaozhou performed poorly in the above three areas. Full article

Losses from floods and the wide range of impacts have been at the forefront of hazard-triggered disasters in China. Affected by large-scale human activities and the environmental evolution, China’s defense flood situation is undergoing significant changes. This paper constructs a comprehensive flood disaster risk assessment model through systematic analysis of four key factors—hazard (H), exposure (E), susceptibility/sensitivity (S), and disaster prevention capabilities (C)—and establishes an evaluation index system. Using the Analytic Hierarchy Process (AHP), we determined indicator weights and quantified flood risk via the following formula R = H × E × V × C. After we applied this model to 16 towns in coastal Zhejiang Province, the results reveal three distinct risk tiers: low (R < 0.04), medium (0.04 ≤ R ≤ 0.1), and high (R > 0.1). High-risk areas (e.g., Longxi and Shitang towns) are primarily constrained by natural hazards and socioeconomic vulnerability, while low-risk towns benefit from a robust disaster mitigation capacity. Risk typology analysis further classifies towns into natural, social–structural, capacity-driven, or mixed profiles, providing granular insights for targeted flood management. The spatial risk distribution offers a scientific basis for optimizing flood control planning and resource allocation in the district. Full article

The preservation and protection of historic urban centers in climate-sensitive coastal areas contributes to the promotion of culture as a driver and enabler of achieving temporal and spatial sustainability, as it is recognized that urban heritage is an integral part of the urban landscape, culture, and economy. The aim of this study was to enhance the resilience and protection of cultural heritage and historic urban centers (HUCs) in the coastal area of Kaštela, Croatia, by providing recommendations and action guidelines in response to climate change impacts, including rising temperatures, sea levels, storms, droughts, and flooding. Preserving HUCs is essential to maintain their cultural values, original structures, and appearance. Many ancient coastal Roman HUCs lie partially or entirely below mean sea level, while low-lying medieval castles, urban areas, and modern developments are increasingly at risk. Based on vulnerability assessments, targeted mitigation and adaptation measures were proposed to address HUC vulnerability sources. The Historical Urban Landscape Approach tool was used to transition and manage HUCs, linking past, present, and future hazard contexts to enable rational, comprehensive, and sustainable solutions. The effective protection of HUCs requires a deeper understanding of the evolution of urban development, climate dynamics, and the natural environments, including both tangible and intangible urban heritage elements. The “hazard-specific” vulnerability assessment framework, which incorporates hazard-relevant indicators of sensitivity and adaptive capacity, was a practical tool for risk reduction. This method relies on analyzing the historical performance and physical characteristics of the system, without necessitating additional simulations of transformation processes. Full article

Urban expansion in coastal areas involves infrastructure development, industrial growth, and mining activities. These coastal environments face various environmental and geological hazards that require geo-engineers to devise solutions. An integrated geophysical approach aims to address such complex challenges as sea level rise, sea water intrusion, shoreline erosion, landslides and previous anthropogenic activity in coastal settings. In this study, the proposed methodology involves the systematic application of geophysical methods (FDEM, 3D GPR, 3D ERT, seismic), starting with a broad-scale survey and then proceeding to a localized exploration, in order to identify lithostratigraphy, bedrock depth, sea water intrusion and detect anthropogenic buried features. The critical aspect is to leverage the unique strengths and limitations of each method within the coastal environment, so as to derive valuable insights for survey design (extension and orientation of measurements) and data interpretation. The coastal zone of Throrikos valley, Attica, Greece, serves as the test site of our geophysical investigation methodology. The planning of the geophysical survey included three phases: The application of frequency-domain electromagnetic (FDEM) and 3D ground penetrating radar (GPR) methods followed by a 3D electrical resistivity tomography (ERT) survey and finally, using the seismic refraction tomography (SRT) and multichannel analysis of surface waves (MASW). The FDEM method confirmed the geomorphological study findings by revealing the paleo-coastline, superficial layers of coarse material deposits and sea water preferential flow due to the presence of anthropogenic buried features. Subsequently, the 3D GPR survey was able to offer greater detail in detecting the remains of an old marble pier inland and top layer relief of coarse material deposits. The 3D ERT measurements, deployed in a U-shaped grid, successfully identified the anthropogenic feature, mapped sea water intrusion, and revealed possible impermeable formation connected to the bedrock. ERT results cannot clearly discriminate between limestone or deposits, as sea water intrusion lowers resistivity values in both formations. Finally, SRT, in combination with MASW, clearly resolves this dilemma identifying the lithostratigraphy and bedrock top relief. The findings provide critical input for engineering decisions related to foundation planning, construction feasibility, and preservation of coastal infrastructure. The methodology supports risk-informed design and sustainable development in areas with both natural and cultural heritage sensitivity. The applied approach aims to provide a complete information package to the modern engineer when faced with specific challenges in coastal settings. Full article

As climate change intensifies, assessing vulnerability at territorial levels such as cities, countries, and regions is essential for effective adaptation planning. This study evaluates the applicability of the United Nations Environment Programme and South Pacific Applied Geoscience Commission’s Environmental Vulnerability Index (EVI) for coastal regions in South Korea. By adapting and localizing 50 international indicators and a Geographic Information System framework, this research developed a Korean Coastal Vulnerability Index and used spatial regression analysis to compare results to historical water-related disaster data from 2010 to 2019. The findings reveal that contrary to South Korea’s global classification of “extremely vulnerable”, most coastal counties appear relatively resilient when viewed through the localized model. Sub-index analyses indicate that ecological and anthropogenic damage factors show the strongest correlation with past disasters among the hazard, resistance, and damage categories. While the model’s explanatory power was modest (R² = 0.017), the regression nonetheless provides meaningful insight into how global indices can reflect local vulnerability patterns. The regression results confirm that based on historical hazard records, the international model effectively predicts Korean coastal vulnerability. It demonstrates the potential of scaling down global models to fit national contexts, offering a replicable approach for countries lacking localized vulnerability frameworks. It advances climate adaptation research through methodological innovation, policy-relevant spatial analysis, and theoretical insights into the multidimensional nature of vulnerability. The results support more precise, data-driven resilience planning and promote international collaboration in climate risk management. Full article

Accurate predictions of beach user numbers are important for coastal management, resource allocation, and minimising safety risks, especially when considering surf-zone hazards. The present work applies an XGBoost model to predict beach attendance from automatically video-derived data, incorporating input variables such as weather, waves, tide, and time (e.g., day hour, weekday). This approach is applied to data collected from Biscarrosse Beach during the summer of 2023, where beach attendance varied significantly (from 0 to 2031 individuals). Results indicate that the optimal XGBoost model achieved high predictive accuracy, with a coefficient of determination ($R^2$) of 0.97 and an RMSE of 70.4 users, using daily mean weather data, tide and time as input variables, i.e., disregarding wave data. The model skilfully captures both day-to-day and hourly variability in attendance, with time of day (hour) and daily mean air temperature being the most influential variables. An XGBoost model using only daily mean temperature and hour of the day even shows good predictive accuracy ($R^2$ = 0.90). The study emphasises the importance of daily mean weather data over instantaneous measurements, as beach users tend to plan visits based on forecasts. This model offers reliable, computationally inexpensive, and high-frequency (e.g., every 10 min) beach user predictions which, combined with existing surf-zone hazard forecast models, can be used to anticipate life risk at the beach. Full article

Plastic and especially microplastic (MP) pollution has posed a serious threat to the marine environment for decades. Studies on MPs have started to gain momentum especially in the Sea of Marmara (SoM), which is an international waterway, under the pressure of intense maritime traffic and exposure to domestic and industrial discharges. The aim of this study was to evaluate the MPs found in surface sediments collected from the coastal area of the SoM according to the locations and to reveal the extent of the existing pollution. This is the first study to examine MPs in both the surface sediments of the entire shorelines of the SoM, which have not been previously reported, and in the surface sediments of Çanakkale Strait. Accordingly, the highest MP abundance was detected at Yenice station (St 15) with 1286 items/kg, and the lowest MP abundance was detected at Turan Village station (St 14) with 199 items/kg. The most dominant shapes across all sampling stations and months were fiber (37%) and fragment (26%), while the most dominant color was blue (35%). According to the polymer characterization results, PE (polyethylene) was found to be the most dominant polymer type. Additionally, most stations were found to have “Moderate” and “High” pollution levels in terms of the contamination factor (CF), and regions were classified as “Moderate” and ‘High’ in terms of the pollution load index (PLI), with the St 15 station specifically exhibiting “Very High” pollution levels. Furthermore, hazard index (HI) and pollution risk index (PRI) values were also calculated regionally, revealing that regions have pollution levels classified as “High”, “Very High”, and even “Dangerous”. This study concluded that there are no areas with low pollution levels in SoM, and that the threat posed by MP pollution in this sea is increasing. Furthermore, this study found that stations with high MP pollution levels are located near river discharges and that rivers significantly contribute to MP pollution in the seas. The findings are of great importance in terms of the need to implement sustainable plans and measures to prevent pollution in the SoM and to take concrete steps to protect and ensure the sustainability of coastal ecosystems, particularly those under serious pollution threats. Full article

Climate change poses substantial threats to natural ecosystems and human livelihoods, particularly in coastal regions, by intensifying coastal erosion. This process leads to land loss, infrastructure damage, and habitat destruction while amplifying challenges such as sea-level rise, flooding, desertification, and salinization. In Vietnam’s Red River Delta (RRD), the dynamic interplay between erosion and accretion presents a highly complex challenge, necessitating effective risk assessment and management to safeguard communities and resources. Using the principles of natural disaster risk assessment and comprehensive analysis, this study develops a coastal erosion risk assessment framework incorporating hazard, exposure, and vulnerability dimensions. The framework integrates 17 indicators, including human activities, socioeconomic factors, shoreline type, and vegetation cover, with indicator weights determined through expert evaluation and the analytic hierarchy process. The application of this framework reveals that coastal erosion risk in the RRD is relatively high, with greater risk concentrated in the central and northern segments of the coastline compared to the flanking areas. This framework offers valuable insights for coastal erosion prevention, mitigation strategies, and the optimization of coastal spatial planning. The application of coastal erosion risk assessment methods provides a relatively complete foundation for developing comprehensive prevention and adaptation solutions in the future. Through the system of parameters and corresponding weights, it provides an overview of potential responses to future impacts while identifying current high-risk zones specifically and accurately, thereby assessing the importance of each parameter on that impact. Based on specific analysis of assessment results, a reasonable resource use and management policy can be developed to minimize related natural disasters. Therefore, two main groups of solutions proposed under the “Protection—Adaptation” strategy are proposed to prevent natural disasters, minimize risks and sustainably develop the coastal area of the RRD. Full article

Rip currents at featureless beaches (i.e., beaches lacking sandbars or channels) are often hydrodynamically controlled, exhibiting intermittent and unpredictable behaviors that pose significant risks to recreational beach users. This study assessed occurrences of rip currents under a range of idealized morphology configurations and hydrodynamic wave forcing parameters using a wave-resolving Boussinesq-type model. Numerical experiments revealed that rip currents with durations on the time scale of 10 min are generated in the forms of vortex pairs, intensified eddies, mega-rips, and eddies shedding from longshore currents. In general, the key conditions that promote rip current formation at featureless beaches include shoreline curvature, headlands, moderately mild beach slopes (e.g., 0.02–0.03), normal or near-normal wave incidence, and large wave heights. Most importantly, this study highlights inherent uncertainties in rip current occurrences, particularly under conditions usually perceived as low risk: low wave heights, short wave periods, oblique wave incidence, and straight shorelines. These conditions can lead to transient rip currents and pose an unexpected hazard that coastal communities should be aware of. Full article

Assessing the size and situation of the population exposed to natural hazards is a fundamental step in addressing natural hazard management and emergency planning. Although much progress has been made in recent years in population geolocation by competent public bodies, gathering historical data beyond the present century to learn about the sequential evolution the affected population has experienced remains a difficult task. The recent publication of a historical population grid with adequate resolution allows progress to be made in resolving this problem. This paper is based on these data together with a map of landslide susceptibility in the study area and on the abundant resources provided by the Spanish Cadastre on dates of construction, surface area, and location of built plots. The size of the residential area built in the risk zone and its affected population was calculated since the early 1900s and with a decennial sequence. The risk to the population has been found to be stable or decreasing slightly over the entire historical series in the study area. However, the intensive tourism in some coastal municipalities in the north of Alicante has led to the construction of holiday homes in unsuitable locations in mountainous areas and with it an appreciable increase in risk. Full article

Zhejiang Province in China is one of the most typhoon-prone regions globally, making typhoon and storm surge hazard analysis critically important for disaster mitigation. This study integrates the Tropical Cyclone Risk Model (TCRM) with a machine learning-based storm surge forecasting model to analyze typhoon hazards and storm surge risks at four representative coastal sites in Zhejiang Province: Haimen, Ruian, Wenzhou, and Zhapu. Firstly, the input database of the TCRM has been updated and subsequently used to generate a 1000-year synthetic typhoon event catalog for the Northwest Pacific region. Secondly, four machine learning models—Long Short-Term Memory (LSTM), Back Propagation (BP), Support Vector Regression (SVR), and Random Forest (RF)—were developed to forecast storm surge component at the four sites, with sensitivity analysis conducted on the input parameters. Among the four models, RF consistently outperformed the others across all four sites. Thirdly, by integrating the storm surge forecasting model with the Yan Meng (YM) typhoon wind field model, extreme wind speed sequences and extreme surge component sequences were derived for the four coastal sites. Finally, four extreme value distribution models—empirical distribution, Weibull, Gumbel, and Generalized Pareto Distribution (GPD)—were applied to fit the extreme wind and surge sequences. Goodness-of-fit tests indicated that the GPD best captured extreme wind speeds at all four sites and extreme surge levels at Haimen, Ruian, and Wenzhou. Using the optimal distributions, return periods (10-, 50-, 100-, and 200-year) for extreme wind speeds and surge components were calculated, providing actionable references for disaster risk management authorities. Full article

Coastal zones represent the most active interfaces where natural processes and human activities converge, making them crucial for biodiversity and socioeconomic development. These zones are characterized by their fragility and susceptibility to frequent natural disasters, such as floods and erosion, which are exacerbated by high-intensity human activities and urban expansion. The ongoing challenges posed by rising sea levels and climate change necessitate robust scientific assessments of coastal vulnerability to ensure effective disaster prevention and environmental protection. This paper introduces a comprehensive evaluation system for assessing coastal zone vulnerability, utilizing multi-source data to address ecological vulnerabilities stemming from sea-level rise and climate change impacts. This system is applied to examine the specific case of Qatar, where rapid urban development and a high population density in coastal areas heighten the risk of flooding and inundation. Employing remote sensing data and Geographic Information Systems (GISs), this research leverages spatial interpolation techniques and high-resolution digital elevation models (DEMs) to identify and evaluate high-risk zones susceptible to sea-level rise. In this study, the hydrological connectivity model, bathtub technique, and CVI are interconnected tools that complement each other to assess future flooding risks under various climate change projections, highlighting the increased probability of coastal hazards. The findings underscore the urgent need for adaptive planning and regulatory frameworks to mitigate these risks, providing technical support for the sustainable development of coastal communities globally and in Qatar. This approach not only informs policy makers, but also aids in the strategic planning required to foster resilient coastal infrastructure capable of withstanding both current and future environmental challenges. Full article

Currently, regional flood research often lacks a synergistic assessment of both flood occurrence risk and flood duration, limiting the comprehensive understanding needed for sustainable disaster risk reduction. To address this gap, this study applies advanced machine learning approaches to assess flood hazards in the Yangtze River Delta, one of China’s most economically and environmentally significant regions. Specifically, XGBoost is employed to evaluate flood occurrence risk, while LSTM is used to predict flood duration. A novel flood risk index (FRI) is proposed to quantify the integrated risk by combining these two dimensions, supporting more sustainable and effective flood risk management strategies. Furthermore, SHAP analysis is conducted to identify the most critical factors contributing to flooding. The results demonstrate that XGBoost delivers strong predictive performance, with average precision, recall, F1-score, accuracy, and AUC values of 0.823398, 0.831667, 0.827090, 0.826435, and 0.871062, respectively. Areas with high flood risk, long duration, and elevated FRI values are mainly concentrated in major river basins and coastal zones. The range of flood risk spans from 0.000073 to 0.998483 (mean: 0.237031), flood duration from 0.223598 to 2.077040 (mean: 0.940050), and FRI from 0 to 0.934256 (mean: 0.091711). Cities with over 40% of their areas falling in medium to high FRI zones include Suzhou (48.99%), Jiaxing (48.07%), Yangzhou (46.87%), Suqian (44.19%), Changzhou (43.43%), Wuxi (43.20%), Lianyungang (42.21%), Yancheng (40.88%), Huai’an (40.73%), and Bengbu (40.06%). SHAP analysis reveals that elevation and rainfall are the most critical factors influencing flood occurrence, underscoring the importance of integrating environmental variables into sustainable flood risk governance. Full article