Ocean & Coastal Management最新文献

Plastic pollution in the marine ecosystem and their sources have been widely investigated, however some parts of the world remain under studied. Herein, we report on plastic pollution in the Namibian marine ecosystem, based on data collected by fisheries observers between 2003 and 2020 during commercial fishing activities. A total of 79 plastic pollution incidents were reported between 2003 and 2020, which consisted of unspecified non-biodegradable objects (55.7%), unspecified plastic items (25.3%), fishing gear (8.9%), plastic bottles (5.0%), plastic gloves (1.3%) and single-use plastic bags (3.8%). We found no significant Pearson correlation between the number of disposal incidents and the fisheries observer coverage (r = 0.3254, df = 14, p = 0.2187). The spatial analysis in the disposal of non-biodegradable objects show a fair distribution along the Namibian coast with more concentrations around latitude 18⁰S, 22⁰S, and 26⁰S. We conclude that fishing vessels are important marine-based sources of plastic pollution, highlighting a continuous need to raise awareness over the fishing industry, in particular seafarers, on plastic use and management. The study expands the plastic pollution knowledge by quantifying incidents of plastic pollution, plastic litter composition, and identifying spatial distribution in the Namibian waters, thus, to the best of our knowledge, represents a baseline for studies on marine plastic pollution in this oceanic region. Identifying sources of marine plastic litter and providing a spatial picture is a step closer to develop and implement specific regulatory tools to combat marine pollution.

To assess the global security risks of the submerged floating tunnel (SFT) in marine environments during operation and provide a basis for risk control, a security risk assessment method using a multistate fuzzy Bayesian network (MFBN) considering complex disaster-inducing factors is proposed. A fault tree model of SFT security risk is established to analyze the causal relationships between global risk and influence factors such as structural components and environmental loads. For root nodes, fuzzy probabilities for each state are obtained through expert knowledge. An improved similarity aggregation method is proposed to integrate expert opinions, mitigating the impact of significant option discrepancies. For non-root nodes, the Leaky Noisy-Max model is used to calculate complex conditional probabilities within the SFT. The probabilities of various security risk states and key risk factors could be determined through reasoning by MFBN. Additionally, a risk prediction method that incorporates domain expert opinions and leverages the BN's ability of updating node probabilities with new information was developed to forecast the security risks of the SFT under wave and current loads.

Maritime carbon emissions, contributing to approximately 2.89% of global anthropogenic CO₂ emissions, have prompted governments to actively promote a low-carbon shift in the freight forwarding sector. The effect of carbon tax implementation on guiding freight forwarders towards offering more eco-friendly freight forwarding services and facilitating a low-carbon transition remains unclear. This study categorizes freight forwarding services models based on delivery speed, ranging from fast to slow, and environmental friendliness, from low to high. It examines scenarios involving a singular carbon tax and consumer rebates, establishing a freight forwarder model without carbon tax constraints (N-T model), a singular carbon tax model for freight forwarders (C-T model), and a combined carbon tax-consumer rebate model (C-C model). The findings suggest that carbon tax pressures from regulatory bodies ultimately affect consumers; thus, under tax cost pressures, freight forwarders must adapt their shipping strategies. A shift towards more eco-friendly freight forwarding services occurs if the rate of demand growth is less than the increase in fixed delivery costs, and an optimal tax rate can drive this shift. Conversely, when the perceived rebate difference between any two services surpasses a certain threshold, eco-friendly freight forwarding services are consistently favored. In the absence of such conditions, a well-designed carbon tax policy is essential to steer freight forwarders toward reducing carbon emissions.

Effective monitoring and early detection of invasive alien plant species (IAPs) are crucial for mitigating their spread and safeguarding native habitats. Unmanned Aerial Vehicles (UAVs) offer a cost-efficient solution, providing high resolution images. In this study, we aimed to develop a semi-automated methodology using a machine learning algorithm, spatial metrics, and clustering techniques on UAV images to monitor, map, and suggest management measures to counteract Yucca gloriosa, an invasive plant colonizing coastal fixed dunes in central Italy.

UAV flights were conducted using two drones: one for the visible spectrum and the other for multispectral bands (Blue, Green, Red, Red Edge, and Near Infrared) along with a Digital Surface Model (DSM). Derived vegetation indices were also utilized. For mapping Y. gloriosa distribution, a Geographic Object Based Image Analysis (GEOBIA) approach was applied to the orthophoto segmentation, followed by a Random Forest algorithm in a training phase, considering three variable combinations (DSM + vegetation indices, DSM + spectral bands, DSM + mixed variables). The most accurate Y. gloriosa map was used to suggest management measures combining the spatial pattern of invaded patches (size, height, isolation level, and aggregation degree) and a mixed clustering approach (hierarchical and partitioning).

The results highlighted that the most accurate prediction map was based on the DSM + mixed variables dataset, showing the important role of using a combination of spectral bands and vegetation indices. In all three cases, the DSM emerged as the pivotal variable for discriminating Y. gloriosa from the surrounding environment. Additionally, our results demonstrate the advantages of incorporating vegetation indices in discerning the target invasive alien plant (IAP) from the broader environment, particularly considering its distinctive photosynthesis process and biomass production. From a managerial standpoint, our pilot study indicates that the UAV-based mapping methodology represents an optimal balance between field efforts and costs. This approach allows for the precise identification of containment and removal areas of Y. gloriosa, without compromising the accuracy of the method. The generated prediction maps also hold potential significance for the conservation of coastal dune ecosystems, providing a promising tool for the effective management of invasive species and biodiversity conservation by suggesting management measures for Y. gloriosa.

Beach nourishment is the leading coastal protection technique in the United States to combat erosion, enhance resilience to storm surge, and maintain recreational value. Despite these benefits, anecdotal reports suggest that beach nourishments elevate the surf zone hazard to beach patrons by steepening the beach face and altering the shoreface morphology such that conditions are more favorable for rip current formation. This study analyzes lifeguard rescue reports collected on the United States Atlantic Coast before and after a 2019 beach nourishment in Virginia Beach, Virginia, to assess whether the nourishment was correlated with an increased hazard to beach patrons. The data indicate that regardless of nourishment status, rescues were most probable during periods of high rip current probability (moderate to large wave heights and low-obliquity wave angles), along with low water level. To formally quantify pre-versus post-nourishment hazards, the proportion of rescues observed in nourished versus unnourished beach zones was compared with bootstrapped distributions of the pre-nourishment rescue proportions. Although the proportion of rescues in the nourished section of the beach exceeds the pre-nourishment average, it is not outside the overall range of pre-nourishment values obtained by random resampling. Consequently, there is insufficient evidence to conclude that the existing coastal management beach nourishment strategy increased the hazard to beach patrons at Virginia Beach.

Fishing exclusion zones aim to reduce cetacean bycatch, but often neglect social and ecological side effects, exacerbating social injustices and reshaping fishing practices. We present a case study of an endangered population of Lahille's bottlenose dolphins (Tursiops truncatus gephyreus) in a Brazilian coastal lagoon, known for its unique cooperation with net-casting fishers. However, the local small-scale fisheries (SSF) also employ other fishing gears that contribute to dolphin bycatch, prompting the enforcement of a fishing ban in the area. We investigated fishers' socioeconomic conditions, evidence of social disparities, perceptions on dolphin bycatch, and projected changes on fisheries activities to understand the repercussions of the ban. We categorized 128 surveyed fishers into five groups based on socioeconomic factors and their reliance on dolphin-cooperative and gillnet fisheries. Our findings revealed significant disparities in social capital among fishers and their different attitudes towards dolphin bycatch, highlighting unforeseen consequences. To investigate how fishers would likely shift fishing practices, we considered two management scenarios: 1) should dolphin-fishing cease due to unsuccessful bycatch mitigation, fishing efforts would shift towards shrimp trawling, seine nets, and gillnet effort; 2) if gillnet-fishing is successfully banned as a bycatch mitigation measure, a shift to shrimp trawling and cast net effort. Both scenarios would intensify pressure on shrimp stocks, which are already heavily exploited in this socioecological system. Our research underscores the importance of contemplating alternatives to achieve sustainable outcomes when designing bycatch mitigation measure. Furthermore, our results point to the importance of a participatory governance approach, grounded in the socioeconomic context, to safeguard the livelihoods of small-scale fishers and foster broader conservation efforts beyond species-specific concerns.

The distribution and composition of zooplankton are influenced by a combination of physico-chemical and biological processes, which in turn have effects on the entire ecosystem. In this study, the influence of environmental factors on zooplankton diversity in mangrove-estuarine coastal waters of Parangipettai, southeast coast of India, was investigated. Water samples were collected for a period of four years from January 2014 to December 2017. Totally, 145 species of zooplankton were recorded with highest belonging to the Arthropoda (63.21%) followed by Protozoa (10.53%), Cnidaria (7.95%), Urochordata (6.03%), Chordata (2.92%), Chaetognatha (2.62%), Mollusca (2.42%), Echinodermata (1.14%), Annelida (1.05%), Brachiopoda (1.01%), Ctenophora (0.57%) and Phoronida (0.55%). The findings of cluster, MDS, Factor analysis and CCA showed that variations in nutrient concentration may significantly alter the biotic community of zooplankton species. Furthermore, site score confirmed the effect of environmental conditions on zooplankton distribution. The result of TRIX showed the dry seasons are classified as scarcely eutrophied whereas wet seasons as moderately eutrophied. The findings of this study offer enhanced insight into how physico-chemical factors interact and vary over space and time, which is crucial for evaluating the effects of climate change on ecosystem services mediated by zooplankton.

A method of compartmentalization has been developed to address the safety concerns of urban areas from coastal flooding, especially in situations where reinforcing the first line of defence (dunes, levees, etc.) is challenging due to, for instance, environmental limitations. Various compartmentalization options can be considered and evaluated in order to govern the flooding. Each option should be modelled with a suited numerical tool to better define the alternatives, determine the minimum height of the elements that bound the compartments and verify their effectiveness for the preliminary design, e.g. for the environmental screening stage. The methodology is demonstrated for a real case study, the flooding of Lido di Volano that occurred on November 22, 2022, using a simplified shallow water equations model designed for GPU computing to simulate large-scale flooding scenarios. Two compartmentalization schemes have been identified, both of which involve raising the existing road level. The maximum water levels in different zones of the study area are assessed as a function of crest height. These schemes have proven to be effective in protecting the urban settlement from flooding. Overall, the proposed method of compartmentalization, supported by a real case study analysis, offers a systematic approach to enhance the safety and the resilience of urban coastal areas. This type of solution capitalises on the experience of the local managers and technicians and may represent a shared solution among the coastal governance actors.

Efficient berth allocation profoundly influences container terminal operations, affecting vessel waiting and turnaround times, and overall performance. This study presents a mixed-integer linear programming (MILP) model addressing the Berth Allocation Problem (BAP) in a Malaysian container port. By incorporating the Pyomo optimization library and CBC (Coin-or Branch and Cut) solver in Google Colab, optimal berth allocations are determined, minimizing vessel turnaround times. Visualized in a Space-Time diagram, the results highlight efficient allocation strategies. Despite limitations, the study optimally resolved three instances, achieving a remarkable 38.54% reduction in overall vessel turnaround time compared to FCFS (First-Come-First-Serve) allocation. By prioritizing port turnaround time, the optimization substantially reduced berthing and departure delays, aligning with UNCTAD's call for enhanced port efficiency and accelerated decarbonization efforts.

Maritime transport is a crucial mode of transportation, with automated container ports playing a significant role in enhancing maritime efficiency and representing a key trend in maritime logistics. Challenges exist in researching and applying methods to ensure efficient and stable operation of automated container ports, such as rational task assignment for Automated Guided Vehicles (AGVs) and rapid conflict-free path planning. This paper studies a combinatorial optimization scheme for AGVs by designing a new task assignment method and proposing an improved AGV automatic obstacle avoidance A-star algorithm (IAOA-A algorithm) to address these problems in dynamic obstacle avoidance situations. Firstly, to enhance the effectiveness and timeliness of task allocation, this paper redefines the calculation index of the container urgency level (CUL) for use in the path optimization algorithm’s weight calculation. Secondly, to achieve collision-free path planning for AGVs, a two-dimensional spatio-temporal obstacle model is designed to describe the dynamic location information of AGVs, and the search process is accelerated by optimizing the search space and improving the evaluation function strategy. Finally, these methods are integrated into a rolling scheduling model to achieve global conflict-free path planning for AGVs. Experimental comparisons on AGV transport tasks show that the allocation rule based on CUL effectively reduces the average relative percentage difference (ARPD), resulting in more timely task completion. Compared to the traditional A-star algorithm, the proposed IAOA-A algorithm improves time efficiency by 24.45%, with visualization results indicating a significant reduction in the number of search nodes. In tests with varying numbers of AGVs and task scales, the results demonstrate that the rolling scheduling model proposed in this paper can efficiently and quickly perform global scheduling of AGVs.