Search Results (261)

Oil spill simulation models are essential for predicting the oil spill behavior and movement in marine environments. In this study, we comprehensively reviewed a large and diverse body of peer-reviewed literature obtained from Scopus and Web of Science. Our initial analysis phase focused on examining trends in scientific publications, utilizing the complete dataset derived after systematic screening and database integration. In the second phase, we applied elements of a systematic review to identify and evaluate the most influential contributions in the scientific field of oil spill simulations. Our analysis revealed a steady and accelerating growth of research activity over the past five decades, with a particularly notable expansion in the last two. The field has also experienced a marked increase in collaborative practices, including a rise in international co-authorship and multi-authored contributions, reflecting a more global and interdisciplinary research landscape. We cataloged the key modeling frameworks that have shaped the field from established systems such as OSCAR, OIL-MAP/SIMAP, and GNOME to emerging hybrid and Lagrangian approaches. Hydrodynamic models were consistently central, often integrated with biogeochemical, wave, atmospheric, and oil-spill-specific modules. Environmental variables such as wind, ocean currents, and temperature were frequently used to drive model behavior. Geographically, research has concentrated on ecologically and economically sensitive coastal and marine regions. We conclude that future progress will rely on the real-time integration of high-resolution environmental data streams, the development of machine-learning-based surrogate models to accelerate computations, and the incorporation of advanced biodegradation and weathering mechanisms supported by experimental data. These advancements are expected to enhance the accuracy, responsiveness, and operational value of oil spill modeling tools, supporting environmental monitoring and emergency response. Full article

The current work is established with the object of modifying the source of Fenton system and substituting iron source as a catalyst with magnetite/potato peels composite material (POT400-M) to be an innovative solar photocatalyst. The structural and morphological characteristics of the material are assessed through X-ray diffraction (XRD) and scanning electron microscopy (SEM). The technique is applied to treat oil spills that pollute seawater. The effectiveness of the operating parameters is studied, and numerical optimization is applied to optimize the most influential parameters on the system, including POT400-M catalyst (47 mg/L) and hydrogen peroxide reagent (372 mg/L) at pH 5.0, to maximize oil removal, reaching 93%. Also, the aqueous solution and wastewater temperature on the oxidation reaction is evaluated and the reaction exhibited an exothermic nature. Kinetic modeling is evaluated, and the reaction is found to follow the second-order kinetic model. Thermodynamic examination of the data exhibits negative enthalpy (ΔH′) values, confirming that the reaction is exothermic, and the system is verified to be able to perform at the minimal activation energy barrier (−51.34 kJ/mol). Full article

This study investigates the spatiotemporal evolution of oil released during the Agia Zoni II shipwreck in the Saronic Gulf in 2017, employing the OpenOil module of the OpenDrift framework. The simulation integrates oceanographic and meteorological data to model the transport, weathering, and fate of spilled oil over a six-day period. Oil behavior is examined across key transformation processes, including dispersion, emulsification, evaporation, and biodegradation, using particle-based modeling and a comprehensive set of environmental inputs. The modeled results are validated against in situ observations and visual inspection data, focusing on four critical dates. The study demonstrates OpenOil’s potential for accurately simulating oil dispersion dynamics in semi-enclosed marine environments and highlights the significance of environmental forcing, vertical mixing, and shoreline interactions in determining oil fate. It concludes with recommendations for improving real-time response strategies in similar spill scenarios. Full article

Deep learning models have obvious advantages in detecting oil spills, but the training of deep learning models heavily depends on a large number of samples of high quality. However, due to the accidental nature, unpredictability, and urgency of oil spill incidents, it is difficult to obtain a large number of labeled samples in real oil spill monitoring scenarios. Surprisingly, few-shot learning can achieve excellent classification performance with only a small number of labeled samples. In this context, a new cross-domain few-shot SAR oil spill detection network is proposed in this paper. Significantly, the network is embedded with a hybrid attention feature extraction block, which consists of a coordinate attention module to perceive the channel information and spatial location information, as well as a global self-attention transformer module capturing the global dependencies and a multi-scale self-attention module depicting the local detailed features, thereby achieving deep mining and accurate characterization of image features. In addition, to address the problem that it is difficult to distinguish between the suspected oil film in seawater and real oil film using few-shot due to the small difference in features, this paper proposes a double loss function category determination block, which consists of two parts: a well-designed category-perception loss function and a traditional cross-entropy loss function. The category-perception loss function optimizes the spatial distribution of sample features by shortening the distance between similar samples while expanding the distance between different samples. By combining the category-perception loss function with the cross-entropy loss function, the network’s performance in discriminating between real and suspected oil films is thus maximized. The experimental results effectively demonstrate that this study provides an effective solution for high-precision oil spill detection under few-shot conditions, which is conducive to the rapid identification of oil spill accidents. Full article

A new integrated multi-user monitoring system for Brazilian Jurisdictional Waters (BJW), developed by Instituto Nacional de Pesquisas Espaciais (INPE) with participation from leading universities and research centers in Brazil, proposes a national approach to address oil spills in the South Atlantic. The system incorporates a range of technologies, such as satellite data, AI algorithms, autonomous sensors, and high-resolution modeling, to detect and respond to oil spills and maritime threats. This initiative not only aims to strengthen Brazil’s readiness to address the oil spills but also contribute to the protection of BJW resources and ecosystems. This opinion paper presents third-party viewpoints on SisMOM, analyzing both the positive and negative aspects of the project. It also explores some expectations for SisMOM, including some main and alternative methodologies. This article only reflects the authors’ perspectives, interpretations, points of view, opinions, and discussions about SisMOM’s propositions. This paper does NOT represent an official communication of the program, nor its methodologies and developments. Full article

Oil spills represent a serious threat to marine ecosystems. Remote sensing monitoring, especially based on synthetic aperture radar (SAR), have been extensively employed in marine environments due to its unique advantages. However, SAR images of marine oil spills exhibit challenges of weak boundaries, confusion with look-alike phenomena, and the difficulty of detecting small-scale targets. To address these issues, we propose LRA-UNet, a Lightweight Residual Attention UNet for semantic segmentation in SAR images. Our model integrates depthwise separable convolutions to reduce feature redundancy and computational cost, while adopting a residual encoder enhanced with the Simple Attention Module (SimAM) to improve the precise extraction of target features. Additionally, we design a joint loss function that incorporates Sobel-based edge information, emphasizing boundary features during training to enhance edge sharpness. Experimental results show that LRA-UNet achieves superior segmentation results, with a mIoU of 67.36%, surpassing the original UNet by 4.41%, and a 5.17% improvement in IoU for the oil spill category. These results confirm the effectiveness of our approach in accurately extracting oil spill regions from complex SAR imagery. Full article

Unmanned aerial vehicle (UAV) remote sensing has become an important tool for modern remote sensing technology with its low cost and high flexibility. Sun glitter (SG) remote sensing based on satellite platforms shows great potential in the fields of marine dynamic environment and marine oil spill, but the analysis and application of SG images based on UAV need to be further studied. In this study, we conduct a multi-angle water surface SG remote sensing experiment using multi-UAV and collect images under different observation parameters. Then, we analyze and discuss the SG signal in the multi-angle images, especially the distribution and intensity of SG. In addition, a model for extracting SG signals from images based on region-based dark pixel retrieval is proposed in this study. Since the current Cox-Munk model is only applicable to statistical SG, the extracted SG images are reduced in resolution by mean filtering. Based on the multi-angle SG remote sensing model, the water surface roughness and equivalent refractive index are estimated. The estimated results are compared with measured and literature data. Additionally, the influence of different observation angle combinations on the inversion results is also discussed. The results of the study show that multi-angle SG remote sensing of water surface based on UAVs provides a new idea for the analysis and application of image signals, which has an important role to play. Full article

Coastal habitats are crucial in mitigating the impact of coastal hazards on society. However, the shortage of information about the role of these habitats in reducing floods in Rivers State, Nigeria, is limited. This study aims to assess the contribution of mangrove habitats in protecting coastal communities from flooding using the InVEST coastal vulnerability model (version 3.10.2). The model analyzes various data inputs and assigns relative numbers, ranging from 1 to 5, indicating different levels of exposure. Data on population, bathymetry, shoreline type, land use land cover, and continental shelf were obtained from relevant websites and the InVEST model package. The findings indicate that the mangrove habitats in Rivers State offer minimal protection against coastal flooding due to their degraded state caused by oil spills and over-exploitation. Additionally, sandy beaches provide little to no protection, and the socio-economic conditions in the communities contribute to increased vulnerability to flooding. The study recommends awareness programs to educate the public about the importance of mangroves for coastal protection in addition to their conservation and restoration. Full article

Marine oil spills significantly adversely affect the socio-economic environment and marine ecosystems. Establishing an efficient emergency cooperation mechanism that enables swift and coordinated responses from all stakeholders is crucial to mitigate the harmful consequences of such spills and protect regional security. This study uses stochastic evolutionary game theory to develop an emergency cooperation model, focusing on the strategic interactions and dynamic evolution between three main parties: the local government, port enterprises, and specialized oil spill cleanup units. The findings indicate the following: (1) The strategy choice of the local government plays a dominant role in the three-party game and has a significant guiding effect on the behavioral decisions of port enterprises and specialized oil spill cleanup units. (2) The strength of the government’s reward and punishment mechanism directly affects the cooperation tendency of the port enterprises and specialized oil spill cleanup units. (3) When the emergency response is more efficient and the cooperation effect is significant, the cleanup units may choose negative cooperation based on payoff maximization in order to prolong the cleaning time. (4) In the process of system evolution, the strategies of local governments and port enterprises are more stable and less affected by random perturbations, while the strategy fluctuations of cleanup units are more sensitive. The findings enrich the theoretical framework for handling marine oil spill emergencies and provide valuable insights for developing efficient collaborative mechanisms and formulating well-grounded regulatory incentive policies. Full article

This study utilized the FVCOM model to establish a hydrodynamic model for the waters from Haikou to Danzhou. Based on this framework, a numerical model for oil spill drift and diffusion was developed using the Lagrangian particle method, incorporating processes such as advection, diffusion, spreading, emulsification, dissolution, volatilization, and shoreline adsorption. Sea experiments involving drifters and dye were conducted to validate the oil spill model. The model was subsequently applied to analyze the impacts of tidal phases and wind fields on oil spill trajectories, predict affected areas, and assess risks to environmentally sensitive zones. The results demonstrate that the hydrodynamic model accurately reproduces the tidal current characteristics of the study area. Validation using drifter and dye experiments confirmed that the model’s predictive error remains within 20%, meeting operational forecasting standards. Potential sources of error include uncertainties in wind–wave–current interactions and discrepancies in windage coefficients between oil spills and drifters. Tidal currents and wind fields were identified as the dominant drivers of oil spill drift and diffusion. Under southerly wind conditions, the oil spill exhibited the largest spatial extent, covering 995.25 km² with a trajectory length of 226.92 km. A sensitivity analysis highlighted the Lingao Silverlip Pearl Oyster Marine Protected Area and Shatu Bay Beach as high-risk regions. The developed model provides critical technical support for oil spill emergency response under diverse environmental conditions, enabling proactive pathway forecasting and preventive measures to mitigate ecological damage. Full article

Oil spills represent a significant environmental hazard, particularly in marine ecosystems, where their impacts extend to coastal infrastructure, biodiversity, and economic activities. This study utilizes GNOME v.47.2 (General NOAA Operational Modeling Environment) and ADIOS2 v.2.10.2 (Automated Data Inquiry for Oil Spills) to simulate and analyze oil spill dynamics in the Romanian sector of the Black Sea, focusing on trajectory prediction, hydrocarbon weathering, and shoreline contamination risk assessment. The research explores multiple spill scenarios involving different hydrocarbon types (light vs. heavy oils), vessel dynamics, and real-time environmental variables (wind, currents, temperature). The findings reveal that lighter hydrocarbons (e.g., gasoline, aviation fuel) tend to evaporate quickly, while heavier fractions (e.g., crude oil, fuel oil #6) persist in the marine environment and pose a higher risk of coastal pollution. In the first case study, a spill of 10,000 metric tons of medium oil (Arabian Medium EXXON) was simulated using GNOME v.47.2, showing that after 22 h, the slick reached the shoreline. Under forecasted hydro-meteorological conditions, 27% evaporated, 1% dispersed, and 72% remained for mechanical or chemical intervention. In the second simulation, 10,000 metric tons of gasoline were released, and within 6 h, 98% evaporated, with only minor residues reaching the shore. A real-world validation case was also conducted using the December 2024 Kerch Strait oil spill incident, where the model accurately predicted the early arrival of light fractions and delayed coastal contamination by fuel oil carried by subsurface currents. These results emphasize the need for future research focused on the vertical dispersion dynamics of heavier hydrocarbon fractions. Full article

Oil spills represent a critical environmental hazard with far-reaching ecological and economic consequences, necessitating the development of sophisticated modelling approaches to predict, monitor, and mitigate their impacts. This study presents a computationally efficient and physically grounded modelling framework for simulating oil spill drift in marine environments, developed using Python coding. The proposed model integrates core physical processes—advection, diffusion, and degradation—within a simplified partial differential equation system, employing an integrator for numerical simulation. Building on recent advances in marine pollution modelling, the study incorporates real-time oceanographic data, satellite-based remote sensing, and subsurface dispersion dynamics into an enriched version of the simulation. The research is structured in two phases: (1) the development of a minimalist Python model to validate fundamental oil transport behaviours, and (2) the implementation of a comprehensive, multi-layered simulation that includes NOAA ocean currents, 3D vertical mixing, and support for inland and chemical spill modelling. The results confirm the model’s ability to reproduce realistic oil spill trajectories, diffusion patterns, and biodegradation effects under variable environmental conditions. The proposed framework demonstrates strong potential for real-time decision support in oil spill response, coastal protection, and environmental policy-making. This paperwork contributes to the field by bridging theoretical modelling with practical response needs, offering a scalable and adaptable tool for marine pollution forecasting. Future extensions may incorporate deep learning algorithms and high-resolution sensor data to further enhance predictive accuracy and operational readiness. Full article

A rapid identification of oil would facilitate a prompt response and efficient removal in the event of an oil spill. Traditional chemical methods in oil fingerprinting have limitations in terms of both time and cost. This study considers machine learning models that can be applied immediately upon measurement of oil density and viscosity. The main objective was to compare models generated from various combinations of features and data. Under five different algorithms, the resulting models were evaluated in terms of their feasibility, advantages, and limitations (FAL). The extra tree (ET) and histogram-based gradient boosting (HGB) models, which incorporated physical features, their rates of change, and environmental features, were found to be the most accurate, achieving 88.55% and 88.41% accuracy, respectively. The accuracy of the models was further enhanced by adjusting the features. In particular, incorporating the rate of change in oil properties led to an enhancement in the accuracy of ET to 92.83%. However, further inclusion of secondary features led to a reduction in accuracy. The effect of input imprecision was analyzed. A 10% of inherent error reduced the accuracy of the HGB model to 60%. Comparing these FAL, machine learning can be a simple, rapid, and cost-effective auxiliary for forensic analysis in diverse spill environments. Full article

The rapid expansion of the petrochemical industry has led to significant environmental issues, including groundwater and soil contamination from hydrocarbon spills. This study investigates the movement and dispersion of hydrocarbon contaminants in the Rey industrial area in Tehran (Iran) using a two-dimensional finite element model. The results indicate that the oil plume exhibits slow migration, primarily due to low soil permeability and high hydrocarbon viscosity, leading to localized contamination. High-density pollution zones, such as TORC and REY7, are characterized by persistent hydrocarbon accumulation with minimal lateral migration. The findings emphasize the limited effectiveness of natural attenuation alone, highlighting the need for targeted remediation measures in high-density zones to accelerate contamination reduction. This study provides insights into the dynamics of hydrocarbon pollution and supports the development of effective remediation strategies. Full article

Oil spills and marine litter pose significant threats to marine ecosystems, necessitating innovative real-time monitoring solutions. This research presents a novel AI-driven multisensor system that integrates RGB, thermal infrared, and hyperspectral radiometers to detect and classify pollutants in dynamic offshore environments. The system features a dual-unit design: an overview unit for wide-area detection and a directional unit equipped with an autonomous pan-tilt mechanism for focused high-resolution analysis. By leveraging multi-hyperspectral data fusion, this system overcomes challenges such as variable lighting, water surface reflections, and environmental interferences, significantly enhancing pollutant classification accuracy. The YOLOv5 deep learning model was validated using extensive synthetic and real-world marine datasets, achieving an F1-score of 0.89 and an mAP of 0.90. These results demonstrate the robustness and scalability of the proposed system, enabling real-time pollution monitoring, improving marine conservation strategies, and supporting regulatory enforcement for environmental sustainability. Full article