Search Results (19)

Emerging and persistent contaminants (EPCs) were detected at high concentrations in Chiang Mai’s Mae Kha Canal, identifying urban waterways as important sources of pollution in the Ping River system in northern Thailand. Maximum levels of metformin (20,000 ng/L), fexofenadine (15,900 ng/L), gabapentin (12,300 ng/L), sucralose (38,000 ng/L), and acesulfame (23,000 ng/L) point to inadequately treated wastewater as a plausible contributor. Downstream enrichment patterns relative to upstream sites highlight the cumulative impact of urban runoff. Five compounds—acesulfame, gemfibrozil, fexofenadine, TBEP, and caffeine—consistently emerged as reliable tracers of urban wastewater, forming a distinct chemical fingerprint of the riverine exposome. Median EPC concentrations were highest in Mae Kha, lower in other urban canals, and declined with distance from the city, reflecting spatial gradients in urban density and pollution intensity. Although most detected concentrations fell below predicted no-effect thresholds, ibuprofen frequently approached or exceeded ecotoxicological benchmarks and may represent a compound of ecological concern. Non-targeted analysis revealed a broader “chemical cocktail” of unregulated substances—illustrating a witches’ brew of pollution that likely escapes standard monitoring efforts. These findings demonstrate the utility of wide-scope surveillance for identifying key compounds, contamination hotspots, and spatial gradients in mixed-use watersheds. They also highlight the need for integrated, long-term monitoring strategies that address diffuse, compound mixtures to safeguard freshwater ecosystems in rapidly urbanizing regions. Full article

The Pinglu Canal is a strategic inland restricted waterway under construction in southwest China. Its ship wave superposition characteristics under conditions of high-density shipping and large ships may threaten navigation safety, but little related research has been performed. Based on the Pinglu Canal project, this study uses the XBeach numerical model, which is validated by field observations on the Chengzi River waterway, to analyze the ship wave characteristics under single-ship navigation (SN) and two-ship navigation in opposite directions (2NOD). The results show the influences of ship type and water depth. For SN, secondary waves of the navigation administration boat (NAB) dominate, with wave height decreasing as water depth increases. Larger cargo ships (CSs) present significant primary wave effects and a complex relationship between the secondary wave’s height and water depth. For 2NOD, the NAB wave effect is stronger due to superposition. As for larger CSs, the primary wave effect is significantly enhanced and occupies the dominant position, with secondary wave height tending to increase with the increase in water depth. The study reveals the characteristics of single-ship and two-ship waves in the Pinglu Canal, providing a theoretical basis and technical support for ship wave risk assessment and ecological revetment design. Full article

Efficient ship scheduling in inland waterways is critical for maritime transportation safety and economic viability. However, traditional scheduling methods, primarily based on First Come First Served (FCFS) principles, often produce suboptimal results due to their inability to account for complex spatial–temporal dependencies, directional asymmetries, and varying ship characteristics. This paper introduces SSRL (Ship Scheduling through Reinforcement Learning), a novel framework that addresses these limitations by integrating three complementary components: (1) a Q-learning framework that discovers optimal scheduling policies through environmental interaction rather than predefined rules; (2) a clustering mechanism that reduces the high-dimensional state space by grouping similar ship states; and (3) a sliding window approach that decomposes the scheduling problem into manageable subproblems, enabling real-time decision-making. We evaluated SSRL through extensive experiments using both simulated scenarios and real-world data from the Xiaziliang Restricted Waterway in China. Results demonstrate that SSRL reduces total ship waiting time by 90.6% compared with TSRS, 48.4% compared with FAHP-ES, and 32.6% compared with OSS-SW, with an average reduction of 57.2% across these baseline methods. SSRL maintains superior performance across varying traffic densities and uncertainty conditions, with the optimal information window length of 13–14 ships providing the best balance between solution quality and computational efficiency. Beyond performance improvements, SSRL offers significant practical advantages: it requires minimal computation for online implementation, adapts to dynamic maritime environments without manual reconfiguration, and can potentially be extended to other complex transportation scheduling domains. Full article

The development of China’s digital waterways has led to the extensive deployment of cameras along inland waterways. However, the limited processing and utilization of digital resources hinder the ability to provide waterway services. To address this issue, this paper introduces a novel waterway perception approach based on an intelligent navigation marker system. By integrating multiple sensors into navigation markers, the fusion of camera video data and automatic identification system (AIS) data is achieved. The proposed method of an enhanced one-stage object detection algorithm improves detection accuracy for small vessels in complex inland waterway environments, while an object-tracking algorithm ensures the stable monitoring of vessel trajectories. To mitigate AIS data latency, a trajectory prediction algorithm is employed through region-based matching methods for the precise alignment of AIS data with pixel coordinates detected in video feeds. Furthermore, an augmented reality (AR)-based traffic situational awareness framework is developed to dynamically visualize key information. Experimental results demonstrate that the proposed model significantly outperforms mainstream algorithms. It achieves exceptional robustness in detecting small targets and managing complex backgrounds, with data fusion accuracy ranging from 84.29% to 94.32% across multiple tests, thereby substantially enhancing the spatiotemporal alignment between AIS and video data. Full article

Identifying the main sources of risk for different types of waterways helps to develop targeted risk control strategies for different river segments. To improve the level of risk management in inland waterways for sustainable development, a two-stage risk evaluation model is proposed in this study by integrating a fuzzy rule base and Bayesian networks. The model evaluates risk sources from the following four dimensions: probability of occurrence, visibility, probability of causing accidents, and consequences. Typical river sections in the upper, middle, and lower reaches of the Yangtze River were selected as cases, and 19 risk sources were identified and comparatively analyzed from the perspectives of humans, ships, the environment, and management. The fuzzy rule base is employed to compare expert opinions, yielding three key risk sources for each section based on their risk values. The findings reveal certain commonalities in the principal risk sources across sections. For example, natural disasters (landslides, earthquakes, and extreme hydrological conditions) are present in both the middle and lower reaches, and an insufficient channel width is common in the upper and middle reaches. However, the key risk sources differ among the sections. The upper reaches are primarily threatened by the improper management of affiliated vessels and adverse weather, while the middle reaches suffer from insufficient channel width surplus, and the lower reaches are mainly threatened by high vessel traffic density and low-quality crews. The results of the study show that the key risk sources in each section of the Yangtze River have obvious differences and need to be assessed according to the characteristics of different sections. This study can provide a reference for decision-making in inland waterway risk management by maritime safety authorities. Full article

The number of alien species introduced into South Korea continues to increase over the years. In particular, several plants have been introduced as ornamentals. Ludwigia peploides, which is native to the Americas and Australia, is believed to have been planted as an ornamental aquatic plant called “water primrose” and “primrose”. It spread to natural ecosystems through rivers, and its distribution is gradually expanding in Suwon, Hwaseong, Busan, and Jeju. However, there has been no specific study on the ecological risk of L. peploides introduced into South Korea. This study, therefore, investigates the distribution status and ecological risks of L. peploides in South Korea through field surveys and allelopathic material analysis, as well as assessing abiotic risk factors. The distribution was confirmed at a total of 19 sites, with high-density mats of a single species forming along the water’s edge and on the water surface. The maximum distribution area was 13,922 m² in Chilgok Reservoir in Anseong. Stems and plant fragments transported along waterways were continuously forming colonies through vegetative propagation. When evaluating the overall risk, it is determined that L. peploides has a high potential to cause significant damage to the ecosystem if not managed promptly. Therefore, continuous monitoring is necessary to effectively manage and prevent the habitat expansion of L. peploides. The results of this study are expected to aid in the identification of the current distribution and potential ecological risks of L. peploides in South Korea, providing essential data for ecological risk assessment and the development of effective management strategies. Full article

Population growth exacerbates the pressure on land carrying capacity, affecting the sustainability of agricultural production, and also impacts non-agricultural industries. This paper utilizes grain price data from southern China during the Qing Dynasty (1776–1910) to examine the impact of population and land pressure on the development of the commodity economy under the “involution” of smallholder agriculture. This study finds that under conditions of stagnant technological advancement and limited natural resources, population growth during the Qing Dynasty created significant “Malthusian” population pressure. This pressure on land first resulted in the over-concentration of agricultural labor and saturation of the farming population. Surplus labor, unable to be absorbed by agriculture, shifted to non-agricultural sectors, engaging in the transportation and trade of grain. The pressure on land carrying capacity facilitated the cultivation and processing of cash crops, and product trade was supported by efficient waterway transportation. These activities generated commercial profits that alleviated survival pressures and promoted the prosperity of the commodity economy. However, this prosperity did not accompany significant productivity improvements; instead, it was a product of “involution” agriculture under high population density pressures. Full article

Intelligent navigation is a crucial component of intelligent ships. This study focuses on the situational awareness of intelligent navigation in inland waterways with high vessel traffic densities and increased collision risks, which demand enhanced vessel situational awareness. To address perception data association issues in situational awareness, particularly in scenarios with winding waterways and multiple vessel encounters, a method based on trajectory characteristics is proposed to determine associations between Automatic Identification System (AIS) and radar objects, facilitating the fusion of heterogeneous data. Firstly, trajectory characteristics like speed, direction, turning rate, acceleration, and trajectory similarity were extracted from ship radar and AIS data to construct labeled trajectory datasets. Subsequently, by employing the Support Vector Machine (SVM) model, we accomplished the discernment of associations among the trajectories of vessels collected through AIS and radar, thereby achieving the association of heterogeneous data. Finally, through a series of experiments, including overtaking, encounters, and multi-target scenarios, this research substantiated the method, achieving an F1 score greater than 0.95. Consequently, this study can furnish robust support for the perception of intelligent vessel navigation in inland waterways and the elevation of maritime safety. Full article

In inland waterways, there is a high density of various objects, with a predominance of small objects, which can easily affect navigation safety. To improve the navigation safety of inland ships, this paper proposes a new lightweight water-surface object-detection model named LWS-YOLOv7, which is based on the baseline model YOLOv7. Firstly, the localization loss function is improved and the w-CIoU function is introduced to reduce the model’s sensitivity to position deviations of small objects and to improve the allocation accuracy of positive and negative sample labels. Secondly, a new receptive field amplification module named GSPPCSPC is proposed to reduce the model’s parameters and enhance its receptive field. Thirdly, a small-object feature-fusion layer, P2, is added to improve the recall rate of small objects. Finally, based on the LAMP model pruning method, the weights with lower importance are pruned to simplify the parameters and computational complexity of the model, facilitating the deployment of the model on shipborne devices. The experimental results demonstrate that, compared to the original YOLOv7 model, the map of LWS-YOLOv7 increased by 3.1%, the parameters decreased by 38.8%, and the GFLOPS decreased by 28.8%. Moreover, the model not only has better performance and higher speed for input images of different sizes, but it can also be applied to different meteorological conditions. Full article

Over the past few decades, there has been a notable surge in interest in green stormwater infrastructure (GSI). This trend is a result of the need to effectively address issues related to runoff, pollution, and the adverse effects of urbanization and impervious surfaces on waterways. Concurrently, umanned aerial vehicles (UAVs) have gained prominence across applications, including photogrammetry, military applications, precision farming, agricultural land, forestry, environmental surveillance, remote-sensing, and infrastructure maintenance. Despite the widespread use of GSI and UAV technologies, there remains a glaring gap in research focused on the evaluation and maintenance of the GSIs using UAV-based imagery. This study aimed to develop an integrated framework to evaluate plant density and health within GSIs using UAV-based imagery. This integrated framework incorporated the UAV (commonly known as a drone), WebOpenDroneMap (WebDOM), ArcMap, PyCharm, and the Canopeo application. The UAV-based images of GSI components, encompassing trees, grass, soil, and unhealthy trees, as well as entire GSIs (e.g., bioretention and green roofs) within the Morgan State University (MSU) campus were collected, processed, and analyzed using this integrated framework. Results indicated that the framework yielded highly accurate predictions of plant density with a high R² value of 95.8% and lower estimation errors of between 3.9% and 9.7%. Plant density was observed to vary between 63.63% and 75.30% in the GSIs at the MSU campus, potentially attributable to the different types of GSI, varying facility ages, and inadequate maintenance. Normalized difference vegetation index (NDVI) maps and scales of two GSIs were also generated to evaluate plant health. The NDVI and plant density results can be used to suggest where new plants can be added and to provide proper maintenance to achieve proper functions within the GSIs. This study provides a framework for evaluating plant performance within the GSIs using the collected UAV-based imagery. Full article

Annex VI of the International Convention for the Prevention of Pollution from Ships (MARPOL Convention), adopted in October 2008, was dedicated to addressing environmental issues caused by ships, especially in ports, inland waterways, and some sea areas with concentrated routes and high navigational density. This study utilizes a regional-level ship dataset to assess the influences of emission-control areas (ECAs) on the ecological shipbuilding industry by fitting the policy utility through the synthetic control method and testing robustness via the difference-in-differences method. The outcomes of this study show that the cumulative new orders for eco-designed ships in China, The Netherlands, Republic of Korea, the UK, and the USA increased by 3401, 81, 234, 549, and −1435, respectively, after the implementation of ECAs. Compared to the implementation of ECAs, the increases were about 32%, 20%, 41%, 66%, and −83%, respectively. Full article

Urban flooding disasters have become increasingly frequent in rural-urban fringes due to rapid urbanization, posing a serious threat to the aquatic environment, life security, and social economy. To address this issue, this study proposes a flood disaster risk assessment framework that integrates a Weighted Naive Bayesian (WNB) classifier and a Complex Network Model (CNM). The WNB is employed to predict risk distribution according to the risk factors and flooding events data, while the CNM is used to analyze the composition and correlation of the risk attributes according to its network topology. The rural-urban fringe in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) is used as a case study. The results indicate that approximately half of the rural-urban fringe is at medium flooding risk, while 25.7% of the investigated areas are at high flooding risk. Through driving-factor analysis, the rural-urban fringe of GBA is divided into 12 clusters driven by multiple factors and 3 clusters driven by a single factor. Two types of cluster influenced by multiple factors were identified: one caused by artificial factors such as road density, fractional vegetation cover, and impervious surface percentage, and the other driven by topographic factors, such as elevation, slope, and distance to waterways. Single factor clusters were mainly based on slope and road density. The proposed flood disaster risk assessment framework integrating WNB and CNM provides a valuable tool to identify high-risk areas and driving factors, facilitating better decision-making and planning for disaster prevention and mitigation in rural-urban fringes. Full article

Advanced air mobility (AAM) is a sustainable aviation initiative to deliver cargo and passengers in urban and regional locations by electrified drones. The widespread expectation is that AAM adoption worldwide will help to reduce pollution, reduce transport costs, increase accessibility, and enable a more reliable and resilient supply chain. However, most countries lack regulations that legalize AAM. A fragmented regulatory approach hampers the progress of business prospectors and international organizations concerned with human welfare. Therefore, amidst high uncertainty, knowledge of indicators that can predict the propensity for AAM adoption will help nations and organizations plan for drone use. This research finds predictive indicators by assembling a unique dataset of 36 economic, social, environmental, governance, land use, technology, and transportation indicators for 204 nations. Subsequently, the best of 12 different machine learning models ranks the predictive importance of the indicators. The gross domestic product (GDP) and the regulatory quality index (RQI) developed by the Worldwide Governance Indicators (WGI) project were the two top predictors. Just as importantly, the poor predictors were as follows: the social progress index developed by the Social Progress Imperative, the WGI rule-of-law index, land use characteristics such as rural and urban proportions, borders on open waterways, population density, technology accessibility such as electricity and cell phones, carbon dioxide emission level, aviation traffic, port traffic, tourist arrivals, and roadway fatalities. Full article

Comparable port efficiency among ports of the European northern range leads to a competitive shift toward hinterland connectivity. North Rhine-Westphalia (NRW), having a high population and industry density and an extensive road, rail and waterway network, is prone to such inter port competition due to its proximity. Using a simulation model, the potential hinterland scope by each port and mode in NRW is depicted and a sensitivity analysis with increasing carbon tax rates is conducted. With an increasing tax rate, the scope for central areas of NRW, prone to a shift to rail transport, expands and become heavily contested among multiple ports. A major profiteer of an increase is projected to be the Port of Rotterdam due to its good connectivity at the cost of Antwerp. The market share of German ports is likely to stay the same with a mode shift occurring. Policy measures like a carbon tax not only have an effect on environmentally friendly mode shift but can severely impact the competitive situation of infrastructure components. While achieving the primary goal of transport sustainability, national interests might mandate the economical existence of a functioning maritime port, which leads to the consideration of additional measures when increasing carbon tax rates. Full article

As a new type of clean and renewable energy, tidal current energy has attracted more and more attention from scholars. The Zhoushan Guishan Channel area (GCA) is an important part of the East China Sea port area, with strong currents due to its special terrain. In order to more comprehensively evaluate the characteristics of tidal energy development near the GCA, this paper uses the MIKE21 FM hydrodynamic model to simulate the tidal hydrodynamic process in the Zhoushan sea area and verifies the reliability of the model through the measured data. Based on the results of numerical simulations, the energy flow density, frequency of flow rate occurrence, flow asymmetry, flow rotation, and effective flow time that can be exploited are considered as the key factors affecting the development of tidal current energy. The distribution characteristics of each influencing factor in the region and the different influences on tidal current energy development are analyzed. Numerical simulations show that the average high-tide velocity in the GCA is lower than the ebb-tide velocity, and the duration of the high tide is also shorter than that of the ebb tide, which has a higher flow velocity than the surrounding area. The annual average energy flow density in the GCA is the highest at 4520 W/m², and the spatial distribution is uneven. The resource level in the central part is much higher than that at both ends of the waterway. Three sections, i.e., A-A′, B-B′, and C-C′, with different key influence factors are selected for specific analysis, and it is concluded that the tidal energy development conditions are relatively superior near the B-B’ section in the middle of the GCA, and the exploitable power calculated using the Flux method is about 24.19 MW. The discussion of the results provides a certain reference for the development of local tidal current energy. These key factors affecting tidal current energy development can also be applied to assess the suitability of tidal current energy resource development in other regions. Full article