Maritime Transport Research Articles

Distribution characteristics and ecological risk assessment of heavy metal pollution in seawater near the Yellow River Estuary of Laizhou Bay

To understand the characteristics and pollution status of heavy metals in the intersection areas of Laizhou Bay and the Yellow River Estuary in Shandong Province, the contents of various heavy metals in surface seawater samples collected from this area in August 2022 were detected. The spatial distributions of the heavy metals were analysed, which exhibits a general decreasing trend in the concentrations of Hg, As, Cr, and Pb from west to east, while Cd, Zn, and Cu show the opposite trend. The average single-factor index P values decrease in the following order Pb > Zn > Hg > Cu > As > Cd > Cr, among which Pb, Zn, Hg, and Cu caused pollution in this area. The potential ecological risk index values indicate that the main heavy metal pollutants in study area are Hg and Pb, with Hg having the highest value, accounting for 74.7%, and being the main potential ecological risk factor. Correlation analysis shows that the most significant environmental factor affecting heavy metal content is salinity. According to the interactive heatmap between cluster analysis and station classification, the heavy metals have originated from industrial, agricultural, and domestic sewage discharge, surface runoff, river inputs, seawater aquaculture, maritime transportation, and fuel exhaust emissions. This research hopes to provide some reference value for the protection of marine and estuarine ecosystems.

Optimization of operational parameters of marine methanol dual-fuel engine based on RSM-MOPSO

Methanol and natural gas, as green fuels for environmental protection, have shown great potential in the field of maritime transportation. This study explores how the methanol energy fraction (MEF), exhaust gas recirculation (EGR), excess air ratio (λ), and engine injection timing (EIT) influence combustion and emission traits in marine dual-fuel (DF) engine. Initially, a DF engine model was formulated through experiments using CONVERGE software, while the CHEMKIN program devised a chemical mechanism involving 100 compounds and 512 reactions. The response surface methodology (RSM) was subsequently utilized to design the experiment and develop the regression model. The improved multi-objective particle swarm optimization (MOPSO) algorithm was applied to optimize three critical variables: brake thermal efficiency (BTE), carbon monoxide (CO), and nitrogen oxide (NOx) emissions. Ultimately, feature-optimized scenarios from the Pareto front were chosen for comparative evaluation to derive the optimal configuration. The results show that when the decision variables MEF, EGR, λ and EIT are 22.33 %, 10 %, 1.81 and 11°CA BTDC respectively, there is a better trade-off between the three target variables. Compared with the original case, the change of ITE, NOx and CO emissions in the optimal case are +0.97 %, −39.53 % and −14.51 %, respectively. Overall, based on the intelligent optimization approach, the rational selection of DF engine parameters improved the ITE and most NOx and CO emissions were effectively suppressed.

The Impact of a New Container Port on the Greenhouse Gas Pollution

Abstract Background Large vessels that call at European ports will have to pay for their CO2 emissions from transporting cargo that enters or goes from a European port since January 2024. The costs will increase with increasing global trade. This results in a higher pollution level, including greenhouse gas (GHG) emissions like CO2. Methods/ Approach Based on the gravity model embedded in a global supply chain, we developed a model to evaluate maritime transport pollution in case a new, sufficiently large container port becomes operational. Additionally, we consider how lousy railway connections to European customers increase transportation costs and pollution. Results The approach to the well-connected sequences of gravity models in the intercontinental maritime chains evaluates the differences in quantities of cargo between ports when a new port is opened, and the waiting time does not change. We also highlight that poor rail connections can reduce this positive effect. Conclusions We showed how it is possible to estimate the optimal capacity of a new port with a multi-level gravity model and how this would affect the pollution around the port and on the routes from the port to the final consumers.

Assessing the Quality of Ship Crew Members in Sewage Pollution Prevention Practices on Marine Environmental Protection: A Case of Dar Es Salaam – Zanzibar Ropax Vessels

The maritime industry is crucial for global trade, but it also significantly impacts the marine environment, particularly through sewage pollution. This study assesses the quality of ship crew members in sewage pollution prevention practices on marine environmental protection, focusing on ROPAX vessels operating between Dar es Salaam and Zanzibar. The stud aims to assess the quality of ship’s crew members in training and education, experience and the number of crew in sewage pollution prevention practices. The study collected data from 80 respondents, whereby both questionnaires were used to collect the data. The data were analyzed by using T-test, Chi-square test, Descriptive statistics and inferential statistics. The study finding revealed that significant deficiencies in awareness and training among crew members regarding MARPOL Annex IV regulations, with many respondents rating their understanding as very low or low. This lack of knowledge, compounded by insufficient formal training programs, hampers the effective implementation of pollution prevention measures. The study recommends a multifaceted approach, including enhanced training, financial support for compliance, and stronger regulatory oversight to improve environmental protection practices. These findings provide crucial insights for policymakers and stakeholders aiming to reduce the environmental impact of the Tanzanian maritime industry and protect marine ecosystem.

Generalized transport costs in intermodal shipping: the context of the Northeast Passage

Intermodality is regarded as one way of achieving more sustainable transport solutions. To make intermodal transport the preferred solution among shippers, it must be attractive according to the concept of generalized transport costs. An extended model for generalized transport costs is developed which includes elements important for comparing maritime transport solutions in the Artic. This framework forms the basis for a principal discussion on the conditions that make one transport solution preferable to another within the context of maritime logistics. This model is then applied to the context of the Northeast Passage to discuss the necessary requirements for making an intermodal transport solution attractive relative to the current main route from Northeast Asia to Northwest Europe through the Suez Canal. Even though intermodality could be preferable in principle, current solutions cannot compete with either Arctic routes using unimodal solutions with high ice-class vessels or with the Suez route. Due to uncertainty and limited empirical evidence, a sensitivity analysis is conducted, focusing on the variables with the greatest impact on the result. Policymakers and stakeholders can consider the findings to improve transport competitiveness via the Northeast Passage.

Assessing Fatality Risks in Maritime Accidents: The Influence of Key Contributing Factors

This paper investigates the factors influencing the probability of fatality in various types of maritime accidents, including grounding, capsizing, sinking, man overboard incidents, and fatal falls, with a focus on several contributing factors—alcohol consumption, meteorological conditions, and visibility. Through comprehensive analysis, the alcohol consumption was examined in order to show how it impairs judgment and physical abilities, significantly increasing the risk of fatal outcomes in these accidents. The paper explores the interplay between alcohol consumption and other contributing factors, such as time of day (daytime/night) and weather conditions, providing a comprehensive understanding of how these variables collectively influence fatality rates in EU maritime transportation. The findings underscore the critical need for stringent alcohol regulations and enhanced safety protocols to mitigate the heightened risks associated with alcohol-impaired maritime operations.

A 500 kW hydrogen fuel cell-powered vessel: From concept to sailing

This paper presents the “Three Gorges Hydrogen Boat No. 1”, a novel green hydrogen-powered vessel that has been successfully delivered and is currently sailing. This vessel, integrated with a hydrogen production and bunkering station at its dedicated dock, achieves zero-carbon emissions. It stores 240 kg of 35 MPa gaseous hydrogen and has a fuel cell system rated at 500 kW. We analysed the engineering details of the marine hydrogen system, including hydrogen bunkering, storage, supply, fuel cell, and the hybrid power system with lithium-ion batteries. In the first bunkering trial, the vessel was safely refuelled with 200 kg of gaseous hydrogen in 156 min via a bunkering station 13 m above the water surface. The maximum hydrogen pressure and temperature recorded during bunkering were 35.05 MPa and 39.04 °C, respectively, demonstrating safe and reliable shore-to-ship bunkering. For the sea trial, the marine hydrogen system operated successfully during a 3-h voyage, achieving a maximum speed of 28.15 km/h (15.2 knots) at rated propulsion power. The vessel exhibited minimal noise and vibration, and its dynamic response met load change requirements. To prevent rapid load changes to the fuel cells, 68 s were used to reach 483 kW from startup, and 62 s from 480 kW to zero. The successful bunkering and operation of this hydrogen-powered vessel demonstrates the feasibility of zero-carbon emission maritime transport. However, four lessons were identified concerning bunkering speed, hydrogen cylinder leakage, hydrogen pressure regulator malfunctions, and fuel cell room space. The novelty of this work lies in the practical demonstration of a fully operational, hydrogen-powered maritime vessel achieving zero emissions, encompassing its design, building, operation, and lessons learned. These parameters and findings can be used as a baseline for further engineering research.

THE ROLE OF 4OCEAN IN CLEANING UP OF MARINE DEBRIS IN BALI IN 2022-2023

The garbage piled up in the ocean can be caused by household waste and increasing marine tourism and shipping activities. The increase of marine debris is an international case because it has impacted the global climate crisis resulting in the damage of marine ecosystems. Those large impacts caused by pollution in the ocean prompted many parties to make regulations regarding the marine debris crisis. In carrying out its project, 4ocean cleans up the garbage by transporting it from rivers, coasts, and the ocean, then brings it to the land and processes it into recycled products. The 4Ocean teams work using environmentally friendly equipment. 4Ocean cleans the ocean by removing around one million pounds of garbage every month. This project can help the government tackle the marine waste crisis. The method used in this research is a literature study where the author collects data through previous writings and searches for data from the internet. This research aims to look at the role of international organizations as one of the actors in international relations in dealing with the waste crisis.

Estimating Vertical Distribution of Total Suspended Matter in Coastal Waters Using Remote-Sensing Approaches

The vertical distribution of the marine total suspended matter (TSM) concentration significantly influences marine material transport, sedimentation processes, and biogeochemical cycles. Traditional field observations are constrained by limited spatial and temporal coverage, necessitating the use of remote-sensing technology to comprehensively understand TSM variations over extensive areas and periods. This study proposes a remote-sensing approach to estimate the vertical distribution of TSM concentrations using MODIS satellite data, with the Bohai Sea and Yellow Sea (BSYS) as a case study. Extensive field measurements across various hydrological conditions and seasons enabled accurate reconstruction of in situ TSM vertical distributions from bio-optical parameters, including the attenuation coefficient, particle backscattering coefficient, particle size, and number concentration, achieving a determination coefficient of 0.90 and a mean absolute percentage error of 26.5%. In situ measurements revealed two distinct TSM vertical profile types (vertically uniform and increasing) and significant variation in TSM profiles in the BSYS. Using surface TSM concentrations, wind speed, and water depth, we developed and validated a remote-sensing approach to classify TSM vertical profile types, achieving an accuracy of 84.3%. Combining this classification with a layer-to-layer regression model, we successfully estimated TSM vertical profiles from MODIS observation. Long-term MODIS product analysis revealed significant spatiotemporal variations in TSM vertical distributions and column-integrated TSM concentrations, particularly in nearshore regions. These findings provide valuable insights for studying marine sedimentation and biological processes and offer a reference for the remote-sensing estimation of the TSM vertical distribution in other marine regions.

A Bibliometric Analysis of Green Shipping: Research Progress and Challenges for Sustainable Maritime Transport

This paper aims to systematically review and analyze the literature on green shipping, focusing on research trends, key areas, and future directions. A bibliometric analysis is conducted on 1339 articles published between 2000 and 2023, using data from the SCI-EXPANDED and SSCI core collections on the Web of Science platform. Co-occurrence analyses are performed for authors, countries, institutions, and keywords. The analysis reveals exponential growth in green shipping research over the past decade, with a significant increase since 2019. China leads in publication output and international collaboration. Key research areas identified include sustainable development, green energy, green ports, and green supply chain management. Green shipping research is rapidly expanding, emphasizing sustainable practices and technological advancements. Future research is expected to integrate artificial intelligence, big data analytics, and other advanced technologies to further promote sustainability. Strengthening international cooperation and interdisciplinary integration will be crucial for advancing the global green shipping agenda.

Letting losses be lessons: Human-machine cooperation in maritime transport

Navigation safety has been a critical guarantee of global shipping, and it becomes more challenging given the increasing employment of advanced technologies and novel ship design in the era of Maritime Autonomous Surface Ships (MASS). The human-centred risk analysis of human-machine cooperation is scarce in general and emerging in maritime transport in specific. This paper aims to develop a new approach enabling the analysis of significant risk influencing factors (RIFs) in human-machine cooperation through an in-depth investigation of the occurred mistakes and violations in the cooperative operations of seafarers and machines in maritime transport. Its novelties consist of (1) a novel approach to analysing and quantifying the connectivity between humans and machines in safety-critical operations, (2) new integration of the frequency and impact of RIFs in the human-machine cooperation model, and (3) ultilisation of graph theory to generate a network to analyse critical human-machine RIFs and their interactions with the system. The connectivity analysis of RIFs is conducted through a weighted undirected network, showing the features of RIF connectivity accommodating the closed-loop system. The proposed novel approach, which combines the frequency and impact features to identify critical RIFs and analyses graphical features, will aid to realise the human-centred risk analysis for MASS. The findings make contributions for ship designers to rationalise the clustering design of function-based automation and training organisations to improve seafarer skills by rationally considering the identified risk-based human-machine cooperation features, and providing new competence schemes that can fit the demands of MASS in future.

Boater’s Preference for Coral Reef Restoration and Sustainable Resource Management in Florida

The Florida Reef is under intense pressure from excessive human disturbance and climate change. A group of Florida residents including boaters and fishers was surveyed about their concerns and willingness to pay (WTP) for restorative services. A discrete choice valuation experiment was conducted, involving nearly 1,300 respondents, to estimate WTP for coral reef restoration, wide beaches, and clean water. The boaters demonstrated a somewhat higher WTP for a more extensive beach size and coral restoration. The analysis allowed quantification of maritime transportation passengers’ preferences for sustainable resource management. The findings provide useful insights for maritime transportation and coastal management agencies wishing to finance conservation initiatives. Furthermore, the study offers valuable methodological contributions and policy relevant information to incorporate ecosystem service valuations into the decision-making process beyond the study areas.

Improved YOLOv8n for Lightweight Ship Detection

Automatic ship detection is a crucial task within the domain of maritime transportation management. With the progressive success of convolutional neural networks (CNNs), a number of advanced CNN models have been presented in order to detect ships. Although these detection models have achieved marked performance, several undesired results may occur under complex maritime conditions, such as missed detections, false positives, and low detection accuracy. Moreover, the existing detection models endure large number of parameters and heavy computation cost. To deal with these problems, we suggest a lightweight ship model of detection called DSSM–LightNet based upon the improved YOLOv8n. First, we introduce a lightweight Dual Convolutional (DualConv) into the model to lower both the number of parameters and the computational complexity. The principle is that DualConv combines two types of convolution kernels, 3x3 and 1x1, and utilizes group convolution techniques to effectively reduce computational costs while processing the same input feature map channels. Second, we propose a Slim-neck structure in the neck network, which introduces GSConv and VoVGSCSP modules to construct an efficient feature-fusion layer. This fusion strategy helps the model better capture the features of targets of different sizes. Meanwhile, a spatially enhanced attention module (SEAM) is leveraged to integrate with a Feature Pyramid Network (FPN) and the Slim-neck to achieve simple yet effective feature extraction, minimizing information loss during feature fusion. CIoU may not accurately reflect the relative positional relationship between bounding boxes in some complex scenarios. In contrast, MPDIoU can provide more accurate positional information in bounding-box regression by directly minimizing point distance and considering comprehensive loss. Therefore, we utilize the minimum point distance IoU (MPDIoU) rather than the Complete Intersection over Union (CIoU) Loss to further enhance the detection precision of the suggested model. Comprehensive tests carried out on the publicly accessible SeaShips dataset have demonstrated that our model greatly exceeds other algorithms in relation to their detection accuracy and efficiency, while reserving its lightweight nature.

The impact of offshore energy hub and hydrogen integration on the Faroe Island’s energy system

This study explores the integration of offshore wind energy and hydrogen production into the Faroe Islands’ energy system to support decarbonisation efforts, particularly focusing on the maritime sector. The EnergyPLAN model is used to simulate the impact of incorporating green hydrogen, produced via electrolysis, within a closed energy system. The study evaluates different configurations of hydrogen production and their feasibility focusing on electrolyser technologies and placement options (in-turbine, platform-based, and shoreline). The hydrogen produced is intended for ammonia production, replacing 11% of the fossil fuels used in maritime transport by 2030. Results indicate that integrating hydrogen with offshore wind energy can reduce fossil fuel reliance and carbon dioxide emissions. The in-turbine electrolyser setup offers the cost-effective placement option, while the platform setup is the most expensive. Among the three electrolyser technologies evaluated (alkaline, solid oxide and proton exchange membrane), the alkaline electrolyser results in the lowest overall system cost. The findings provide insights into the potential for renewable energy systems in a small island context and contribute to a broader understanding of green hydrogen’s role in energy transitions.

New framework of port logistics in the post-COVID-19 period with 6th-generation ports (6GP) model

ABSTRACT Since the outbreak of COVID-19, its impacts on the maritime transportation and logistics field have been multi-dimensional. In addition to the green shipping corridor proposed by the Clydebank Declaration in the United Kingdom in 2021, port digitalisation and decarbonisation of the maritime industry have become focal issues in the field. The industry needs a new framework to offset the negative impacts of the pandemic and to accommodate integrated technologies comprising of artificial intelligence (AI), blockchain, cloud systems, internet of things (IoT) and others, which have been applied to the industry. Having considered these circumstances, this paper aims to propose the 6th-generation ports model with smart port (6GP) as a new framework for the port logistics industry in the post-COVID-19 period. The proposed 6GP contributes to providing business development strategy and port development policy for stakeholders in the industry in the post-pandemic era reflecting focal challenges such as digitalisation, decarbonisation, sustainability and smart transformation. It also contributes to expanding port devolution theory from the fifth-generation ports (5GP) to 6GP.

A knowledge graph-based inspection items recommendation method for port state control inspection of LNG carriers

The safe navigation and operation of Liquified natural gas (LNG) carriers is crucial in maritime transportation. The Port State Control (PSC) inspection is the primary method for identifying deficiencies in vessels. Different from other vessels, the PSC inspection of LNG carriers requires knowledge of specialized transport equipment. Therefore, effective management of inspection knowledge and precise targeting of inspection items are particularly crucial. Knowledge graph is an efficient way to manage knowledge in the context of artificial intelligence. In this study, an LNG carrier PSC inspection knowledge graph is constructed for fusing multisource knowledge data from PSC inspection. Additionally, a knowledge graph-based recommendation model, namely the improved knowledge graph convolutional network combined with pretrained translation embedding (PT-KGCN), is developed to recommend inspection items and knowledge. The PT-KGCN model first carries out knowledge graph embedding. It then predicts possible deficiencies on the basis of historical PSC inspection data. Finally, it recommends inspection items by correlating the predicted deficiencies with knowledge from the knowledge graph. The results show that more than 87% of defective items in historical data are correctly predicted. The research findings can provide ideas for the practical application of knowledge data in the inspection fields.

Water Quality Modeling of Wastewater Discharges for Prediction of Sediment Transport and Deposition in Surface Water

Sediment deposition in surface water bodies can have a range of significant impacts, affecting everything from the aquatic ecosystem to water quality and infrastructure. It can affect the availability of food sources for aquatic organisms; and change the physical structure of habitats, such as riverbeds and lake bottoms. It can equally smother aquatic plants and animals, leading to a decline in biodiversity. A model of the governing equations of particle motion and fluid flow was developed with COMSOL Multiphysics 5.3a. With a coefficient of determination of 0.99, the model result demonstrated good agreement after result validation using experimental data. According to study findings, sediments in the study area, Ele River move more slowly than sediments in the midstream which causes a high deposition of sediments at the river banks. This sediment deposition affects the irrigation system for crop production considering the ability of the sediments to block sprinkler nozzles which limits it from supplying sufficient water for the production of crops.

O PAPEL DA ORGANIZAÇÃO MARÍTIMA INTERNACIONAL NA PROTEÇÃO DO MEIO AMBIENTE MARINHO

Tankers transport liquid cargoes in bulk across the globe, representing a crucial and highly lucrative mode of maritime transport. Given this context, the secure transport of crude oil, derivatives, petrochemicals, and liquefied gases, as well as cargo transfers between inshore, onshore, and offshore terminals, necessitates standardization and effective regulations. In response to a series of oil tanker accidents in 1976-1977, the International Convention for the Prevention of Pollution from Ships (MARPOL) was established, which is the primary international framework for preventing marine pollution. This convention includes regulations aimed at preventing and mitigating pollution from ships, addressing both accidental discharges and routine operational emissions, and is currently supported by six technical annexes. Consequently, ensuring safe operations and environmental protection is a global priority for shipping companies. Researching and promoting this topic serves as a means of disseminating information to users.

A hybrid failure analysis model design for marine engineering systems: A case study on alternative propulsion system

Marine engineering systems have a fundamental role in ensuring the efficiency, safety, and sustainability of maritime transportation. The performance and reliability of these systems, particularly propulsion systems, are of paramount importance. This paper introduces a pioneering hybrid failure analysis model that integrate the Variable Weighted Synthesis Analytic Hierarchy Process (VWS-AHP) with the conventional Failure Mode and Effect Analysis (FMEA) methodology. The primary objective is to enhance the comprehension of failure modes within marine engineering systems, particularly focusing on alternative propulsion systems. While conventional FMEA is a widely employed methodology for analysis of failure modes, its capacity and effectiveness can be further augmented by integrating complementary techniques. The proposed hybrid model combines the robustness of FMEA in identifying failure modes with the VWS-AHP method’s ability to handle complex decision-making scenarios involving multiple criteria and varying levels of importance. This integration affords a comprehensive framework to assess failure risks, prioritize critical failure pathways, and evaluate the potential impacts of failures in marine engineering systems. To demonstrate the applicability of the proposed model, we conduct a case study on an alternative propulsion system. The outcomes of the case study showcase the efficacy of the hybrid model in enhancing FMEA.

Merging the Application of Artificial Intelligence Technology in Maritime Industry: A Systematic Literature Review

This article presents a thorough literature review on the use of technology in artificial intelligence technology within the maritime industry. The aim is to analyse existing research and identify crucial themes, theoretical frameworks, methodologies, and areas of research gaps in this field. Specific keywords related to artificial intelligence and the maritime industry were used to search the Web of Science (WoS) and Scopus databases. A list of the 34 most frequently cited articles was compiled and analysed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a systematic and rigorous approach to selecting relevant manuscripts. The analysis revealed three key themes: (1) Role of Artificial Intelligence technology in the maritime industry, (2) challenges in adapting Artificial Intelligence technology in the maritime industry and (3) digitalisation and smart shipyards. Additionally, navigational safety and environmental impact were identified as important considerations in this field. Integrating artificial intelligence into maritime organisations able to develop immersive and impactful syllabus that simulate advanced technology. This allows maritime practitioners to develop related skills and better understand advanced technology. Furthermore, this integration fosters growth and success in the maritime industry, preparing for future development.