Search Results (956)

The coastal sandy land in northeast Hainan Province is typical for this land type, also exhibiting strong sand activity. This study is based on wind speed, wind direction, and sediment transport data obtained at a field meteorological station using an omnidirectional sand accumulation instrument from 2020 to 2024, studying the coastal aeolian environment and sediment transport distribution characteristics in the region. Its findings provide a theoretical basis for comprehensively analyzing the evolution of coastal aeolian landforms and the evaluation and control of coastal aeolian hazards. The research results show the following: (1) The annual average threshold wind velocity for sand movement in the study area is 6.84 m/s, and the wind speed frequency (frequency of occurrence) is 51.54%, dominated by easterly (NE, ENE) and southerly (S, SSE) winds. (2) The drift potential (DP) refers to the potential amount of sediment transported within a certain time and spatial range, and the annual drift potential (DP) and resultant drift potential (RDP) of Mulan Bay from 2020 to 2024 were 550.82 VU and 326.88 VU, respectively, indicating a high-energy wind environment. The yearly directional wind variability index (RDP/DP) was 0.59, classified as a medium ratio and indicating blunt bimodal wind conditions. The yearly resultant drift direction (RDD) was 249.45°, corresponding to a WSW direction, indicating that the sand in Mulan Bay is generally transported in the southwest direction. (3) When the measured data extracted from the sand accumulation instrument in the study area from 2020 to 2024 were used for statistical analysis, the results showed that the total sediment transport rate (the annual sediment transport of the observation section) in the study area was 110.87 kg/m·a, with the maximum sediment transport rate in the NE direction being 29.26 kg/m·a. These results suggest that when sand fixation systems are constructed for relevant infrastructure in the region, the construction direction of protective forests and other engineering measures should be perpendicular to the net direction of sand transport. Full article

Due to increased precipitation intensity and sea-level rise, low-lying coastal roads are increasingly vulnerable to subbase saturation. Widely applied lumped hydrological approaches cannot accurately represent time and space-varying groundwater levels in some highly conductive coastal soils, calling for more sophisticated tools. This study assesses the suitability of the Gridded Surface Subsurface Hydrologic Analysis model (GSSHA) for representing hydrological processes and groundwater dynamics in a unique coastal roadway setting in Alabama. A high-resolution model was developed to assess a 2 km road segment and was calibrated for hydraulic conductivity and aquifer bottom levels using observed groundwater level (GWL) data. The model configuration included a fixed groundwater tidal boundary representing Mobile Bay, a refined land cover classification, and an extreme precipitation event simulation representing Hurricane Sally. Results indicated good agreement between modeled and observed groundwater levels, particularly during short-duration high-intensity events, with NSE values reaching up to 0.83. However, the absence of dynamic tidal forcing limited its ability to replicate certain fine-scale groundwater fluctuations. During the Hurricane Sally simulation, over two-thirds of the segment remained saturated for over 6 h, and some locations exceeded 48 h of pavement saturation. The findings underscore the importance of incorporating shallow groundwater processes in hydrologic modeling for coastal roads. This replicable modeling framework may assist DOTs in identifying critical roadway segments to improve drainage infrastructure in order to increase resiliency. Full article

Pharmaceuticals are increasingly being detected in coastal ecosystems globally, but contamination and bioaccumulation levels are understudied in temporarily closed estuaries. In these systems, limited freshwater inputs and periodic closure may predispose them to pharmaceutical accumulation. We quantified in situ water column pharmaceutical levels at five sites in a temporarily closed model urban estuary (Zandvlei Estuary) in Cape Town, South Africa, that has been heavily anthropogenically modified. The results indicate an almost 100-fold greater concentration of pharmaceuticals in the estuary relative to False Bay, into which the estuary discharges, with acetaminophen (max: 2.531 µg/L) and sulfamethoxazole (max: 0.138 µg/L) being the primary pollutants. Acetaminophen was potentially bioaccumulative, while nevirapine, carbamazepine and sulfamethoxazole were bioaccumulated (BAF > 5000 L/kg) by sandprawns (Kraussillichirus kraussi), which are key coastal endobenthic ecosystem engineers in southern Africa. The assimilative capacity of temporarily closed estuarine environments may be adversely impacted by wastewater discharges that contain diverse pharmaceuticals, based upon the high bioaccumulation detected in key benthic engineers. Full article

Posidonia oceanica seagrass, endemic to the Mediterranean Sea, provides ecological goods and ecosystem services of paramount importance. In shallow and sheltered bays, P. oceanica meadows can reach the sea surface, with leaf tips slightly emerging, forming fringing and barrier reefs. During the 20th century, P. oceanica declined conspicuously in the vicinity of large ports and urbanized areas, particularly in the north-western Mediterranean. The main causes of decline are land reclamation, anchoring, bottom trawling, turbidity and pollution. Artificial sand nourishment of beaches has also been called into question, with sand flowing into the sea, burying and destroying neighbouring meadows. A fringing reef of P. oceanica, located at Saint-Mandrier-sur-Mer, near the port of Toulon (Provence, France), is severely degraded. Analysis of aerial photos shows that, since the beginning of the 2000s, it has remained stable in some parts or continued to decline in others. This contrasts with the trend towards recovery, observed in France, thanks to e.g., the legally protected status of P. oceanica, and the reduction of pollution and coastal developments. The sand nourishment of the study beach, renewed every year, with the sand being washed or blown very quickly (within a few months) from the beach into the sea, burying the P. oceanica meadow, seems the most likely explanation. Other factors, such as pollution, trampling by beachgoers and overgrazing, may also play a role in the decline. Full article

Heavy metal pollution from human activities is an increasing environmental concern. This study investigates the concentrations of Cu, Pb, Zn, Cd, Hg, and As in the coastal seawater offshore of Beihai, Guangxi, in April 2021, and explores their relationships with dissolved inorganic nitrogen, phosphate, and salinity. Our results reveal higher heavy metal concentrations in the northern nearshore waters and lower levels in southern offshore areas, with surface waters generally exhibiting greater enrichment than bottom waters. Surface concentrations show a decreasing trend from the northeast to the southwest, likely influenced by prevailing northeast monsoon winds. While bottom water concentrations decline from the northwest to the southeast, which indicates the influence of riverine runoff, particularly from the Qinzhou Bay estuary. Heavy metal levels in southern Beihai waters are comparable to those in the Beibu Gulf, except for Hg and Zn, which are significantly higher in the water of the Beibu Gulf. Notably, heavy metal concentrations in both Beihai and Beibu Gulf remain considerably lower than those observed in the coastal waters of Guangdong. Overall, Beihai’s coastal seawater meets China’s Class I quality standards. Nonetheless, continued monitoring is essential, especially of the potential ecological impacts of Hg and Zn on marine life. Full article

Dark skies, a vital natural and cultural resource, have been increasingly threatened by light pollution due to rapid urbanization, leading to ecological degradation and biodiversity loss. As a key strategy for sustainable regional development, dark sky parks (DSPs) not only preserve nocturnal environments but also enhance livability by balancing urban expansion and ecological conservation. This study develops a novel framework for evaluating DSP suitability, integrating ecological and socio-economic dimensions, including the resource base (e.g., nighttime light levels, meteorological conditions, and air quality) and development conditions (e.g., population density, transportation accessibility, and tourism infrastructure). Using the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) as a case study, we employ Delphi expert consultation, GIS spatial analysis, and multi-criteria decision-making to identify optimal DSP locations and prioritize conservation zones. Our key findings reveal the following: (1) spatial heterogeneity in suitability, with high-potential zones being concentrated in the GBA’s northeastern, central–western, and southern regions; (2) ecosystem advantages of forests, wetlands, and high-elevation areas for minimizing light pollution; (3) coastal and island regions as ideal DSP sites due to the low light interference and high ecotourism potential. By bridging environmental assessments and spatial planning, this study provides a replicable model for DSP site selection, offering policymakers actionable insights to integrate dark sky preservation into sustainable urban–regional development strategies. Our results underscore the importance of DSPs in fostering ecological resilience, nighttime tourism, and regional livability, contributing to the broader discourse on sustainable landscape planning in high-urbanization contexts. Full article

This study investigates the implications of sewage contamination in the coastal and riverine environments of Hout Bay, Cape Town, South Africa. Chemical analyses were applied to quantify the presence of pollutants such as pharmaceutical and personal care products (PPCPs) in sentinel marine organisms such as mussels, as well as microbial indicators of faecal contamination in river water and seawater, for estimating the extent of impact zones in the coastal environment of Hout Bay. This research investigated the persistent pharmaceuticals found in marine outfall wastewater effluent samples in Hout Bay, examining whether these substances were also detectable in marine biota, specifically focusing on Mytilus galloprovincialis mussels. The findings reveal significant levels of sewage-related pollutants in the sampled environments, with concentrations ranging from 32.74 to 43.02 ng/g dry weight (dw) for acetaminophen, up to 384.96 ng/g for bezafibrate, and as high as 338.56 ng/g for triclosan. These results highlight persistent PPCP contamination in marine organisms, with increasing concentrations observed over time, suggesting a rise in population and pharmaceutical use. Additionally, microbial analysis revealed high levels of E. coli in the Hout Bay River, particularly near stormwater from the Imizamo Yethu settlement, with counts exceeding 8.3 million cfu/100 mL. These findings underscore the significant impact of untreated sewage on the environment. This study concludes that current sewage treatment is insufficient to mitigate pollution, urging the implementation of more effective wastewater management practices and long-term monitoring of pharmaceutical levels in marine biota to protect both the environment and public health. Full article

Laizhou Bay, a semi-enclosed bay, is prone to storm surges from cold waves due to its geographic and environmental characteristics. This study uses satellite data, in situ measurements, and the MIKE numerical model to analyze storm surges along Laizhou Bay’s coast under no-dike conditions. It examines the surges caused by cold waves with different intensities and directions. This study provides the storm surge disaster risk levels along Laizhou Bay’s coast. The results show that the maximum sustained wind speed during cold waves is distributed between the NW and NE. The NE wind direction causes the most severe storm surge along Laizhou Bay. Under NE-directed cold waves with level 12 wind, the maximum risk areas for Level III and IV are approximately 1341 km² and 1294 km², respectively. Dongying, Shouguang, and Hanting exhibit large Level I and II risk zones. The maximum seawater intrusion distance along the Kenli coast is about 41 km. The coastal segment from Kenli to Changyi is most severely affected by storm surges. It is recommended to effectively maintain and heighten seawalls along this segment to mitigate storm surge disasters caused by strong NE winds. Full article

The paper presents the results of a study on littoral algae communities along the Murmansk coast from 2021–2024. The emphasis is on fucus algae and green algae communities as the most abundant ones. For the first time, an annual monitoring of littoral algae distribution in the bays of the Barents Sea was performed using a set of methods, allowing a better understanding of the dynamics of their biomass. Unlike most classical studies, which only focus on biomass and population structure, this work shows the results of using UAV-based remote sensing in combination with traditional coastal sampling techniques. The features and limitations of this approach in Arctic latitudes are discussed. According to the monitoring results, an increase in fucus algae biomass is observed in the study area, which may be associated with an increase in summer temperatures and water salinity. Fucus serratus and Pelvetia canaliculata populations remain stable. Ulvophycean algae show seasonal peaks of development with abnormally high biomass in areas of anthropogenic impact, which may indicate local eutrophication. The map of algae spatial distribution is presented. The results are important for understanding the structure and functioning of the Arctic ecosystem and for assessing the environmental impact in the region. Full article

A critical gap exists between coastal communities’ need for accessible flood risk assessment tools and the availability of sophisticated modeling, which remains limited by technical barriers and computational demands. This study introduces three key innovations through Coastal Defense Pro: (1) the first operational web-based AI ensemble for coastal flood risk assessment integrating real-time multi-agency data, (2) an automated regional calibration system that corrects systematic model biases through machine learning, and (3) browser-accessible implementation of research-grade modeling previously requiring specialized computational resources. The system combines Bayesian neural networks with optional LSTM and attention-based models, implementing automatic regional calibration and multi-source elevation consensus through a modular Python architecture. Real-time API integration achieves >99% system uptime with sub-3-second response times via intelligent caching. Validation against Hurricane Isabel (2003) demonstrates correction from 197% overprediction (6.92 m predicted vs. 2.33 m observed) to accurate prediction through automated identification of a Chesapeake Bay-specific reduction factor of 0.337. Comprehensive validation against 15 major storms (1992–2024) shows substantial improvement over standard methods (RMSE = 0.436 m vs. 2.267 m; R² = 0.934 vs. −0.786). Economic assessment using NACCS fragility curves demonstrates 12.7-year payback periods for flood protection investments. The open-source Streamlit implementation democratizes access to research-grade risk assessment, transforming months-long specialist analyses into immediate browser-based tools without compromising scientific rigor. Full article

The development in coastal engineering and maritime transport demands accurate wave height prediction. In this study, hybrid deep learning models, including CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-BiGRU, are employed to develop regional multivariate wave prediction models that incorporate multiple features, such as wave height, wind stress, water depth, pressure, and sea surface temperature (SST), for the entire Bay of Bengal area. Sensitivity analysis is performed to evaluate the accuracy using statistical metrics, such as the correlation coefficient, RMSE, and MAE. The findings demonstrate that regional multivariate models offer satisfactory results for the entire Bay of Bengal region. The multivariate model performs better compared to the univariate model as the forecast horizon increases. Performance assessment of each environmental factor, employing the integrated gradient method, reveals that sea surface temperature has the most significant influence, while wind stress is the least dominant factor in the wave prediction model. Among the tested models, the CNN-BiGRU has superior performance with a correlation of 0.9872, an RMSE of 0.1547, and an MAE of 0.1005 for the 3 h prediction and is proposed as the optimal model. This study contributes to assessing the contribution of each environmental feature and improving the accuracy of regional wave prediction. Full article

Accurately hindcasting waves in semi-enclosed, fetch-limited basins remains challenging for reanalysis models, which tend to underestimate storm peaks near the coast. We developed interpretable ML models for Rijeka Bay (northern Adriatic) using only wind observations from two land-based wind stations to predict buoy H_m0 measurements spanning 2009–2011 (testing) and 2019–2021 (training and validation). The tested tree-based models included Random Forest, XGBoost, and Explainable Boosting Machine. This study introduces a novel approach in the literature by employing weighted schemes and feature engineering to enhance the predictive performance of interpretable, low-complexity machine learning models in hindcasting waves. Representing wind direction as sine–cosine components generally reduced RMSE and BIAS relative to traditional speed–direction inputs, while an exponential sample weight scheme that emphasized storm waves halved extreme H_m0 underprediction without inflating overall RMSE. The best-performing model, a Random Forest model, achieved an RMSE of 0.096 m and a correlation of 0.855 on the unseen test set—30% lower overall RMSE and 50% lower extreme wave RMSE than the MEDSEA and COEXMED hindcasts. Additionally, the underprediction was reduced by 90% compared to these reanalysis models. The method offers a computationally lightweight, transferable supplement to numerical wave guidance for coastal engineering and harbor operations. Full article

South Korea is one of the world’s major centers for marine shellfish aquaculture. Since the industry’s rapid expansion began in the 1980s, concerns have grown regarding its environmental impacts on coastal marine ecosystems. Evaluating the benthic ecological quality status (EcoQs) of shellfish farms using benthic indices provides a scientific foundation for the sustainable management of aquaculture areas. In our study, five benthic indices (AZTI’s marine biotic index, BENTIX, benthic opportunistic polychaeta amphipoda index, benthic pollution index, and multivariate AMBI) and one composite index were selected to assess EcoQs of shellfish farms in Gangjin Bay, South Korea. Our results revealed significant differences in macrobenthic community structure and EcoQs between November and December in Gangjin Bay. Spearman correlation analysis and principal coordinates analysis (PCoA) demonstrated that the multivariate AMBI (M-AMBI) exhibited the best overall performance among indices. However, considering the ecological complexity, variability in farming practices, and site-specific conditions typical of shellfish aquaculture environments, the use of five benthic indices and a composite index is recommended to ensure a more comprehensive and robust evaluation of EcoQs in Korean shellfish farms. Full article

This study proposed a sea cliff stability model that accounted for the coupled effects of sea erosion and rainfall, offering an improved quantitative assessment of the toppling risk. The approach integrated the notch morphology (height and depth) and rainfall infiltration to quantify stability, validated by field data from six toppling sites near Da’ao Bay, where the maximum erosion distance error between model predictions and measurements ranged from 0.81% to 48.8% (with <20% error for Sites S2, S3, and S4). The results indicated that the notch morphology and rainfall exerted significant impacts on the sea cliff stability. Site S4 (the highest site) corresponded to a 17.5% decrease in K per 0.1 m notch depth increment. The rainfall infiltration reduced the maximum stable notch depth, decreasing by 8.86–21.92% during prolonged rainfall. This model can predict sea cliff stability and calculate the critical notch depth (e.g., 0.56–1.22 m for the study sites), providing a quantitative framework for coastal engineering applications and disaster mitigation strategies under climate change scenarios. Full article

The focus of this study was on young populations of Kandelia obovata within a constructed coastal wetland in Haicang Bay, Xiamen, Southeast China. The objective was to systematically examine their allometric growth characteristics and carbon sequestration potential over an 8-year monitoring period (2016–2024). Allometric equations were developed to estimate biomass, and the spatiotemporal variation in both plant and soil carbon stocks was estimated. There was a significant increase in total biomass per tree, from 120 ± 17 g at initial planting to 4.37 ± 0.59 kg after 8 years (p < 0.001), with aboveground biomass accounting for the largest part (72.2% ± 7.3%). The power law equation with D²H as an independent variable yielded the highest predictive accuracy for total biomass (R² = 0.957). Vegetation carbon storage exhibited an annual growth rate of 4.2 ± 0.8 Mg C·ha⁻¹·yr⁻¹. In contrast, sediment carbon stocks did not show a significant increase throughout the experimental period, although long-term accumulation was observed. The restoration of mangroves in urban coastal constructed wetlands is an effective measure to sequester carbon, achieving a carbon accumulation rate of 21.8 Mg CO₂eq·ha⁻¹·yr⁻¹. This rate surpasses that of traditional restoration methods, underscoring the pivotal role of interventions in augmenting blue carbon sinks. This study provides essential parameters for allometric modeling and carbon accounting in urban mangrove afforestation strategies, facilitating optimized restoration management and low-carbon strategies. Full article