Search Results (811)

The topic of environmental pollution by petroleum products is highly relevant due to rapid urbanisation, including industrial development, road infrastructure and fuel distribution. Potential threat areas include refineries, fuel stations, pipelines, warehouses and transshipment bases, as well as sites affected by accidents or fuel spills. This study aimed to determine whether organic and mineral materials could mitigate the effects of diesel oil pollution on the soil’s trace element content. The used materials were compost, bentonite and calcium oxide. Diesel oil pollution had the most pronounced effect on the levels of Cd, Ni, Fe and Co. The levels of the first three elements increased, while the level of Co decreased by 53%. Lower doses of diesel oil (2.5 and 5 cm³ per kg of soil) induced an increase in the levels of the other trace elements, while higher doses caused a reduction, especially in Cr. All materials applied to the soil (compost, bentonite and calcium oxide) reduced the content of Ni, Cr and Fe. Compost and calcium oxide also increased Co accumulation in the soil. Bentonite had the strongest reducing effect on the Ni and Cr contents of the soil, reducing them by 42% and 53%, respectively. Meanwhile, calcium oxide had the strongest reducing effect on Fe and Co accumulation, reducing it by 12% and 31%, respectively. Inverse relationships were recorded for Cd (mainly bentonite), Pb (especially compost), Cu (mainly compost), Mn (mainly bentonite) and Zn (only compost) content in the soil. At the most contaminated site, the application of bentonite reduced the accumulation of Pb, Zn and Mn in the soil, while the application of compost reduced the accumulation of Cd. Applying various materials, particularly bentonite and compost, limits the content of certain trace elements in the soil. This has a positive impact on reducing the effect of minor diesel oil pollution on soil properties and can promote the proper growth of plant biomass. Full article

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

In the wake of the continuous development of the international strategic petroleum reserve system, the tonnage and quantity of oil tankers have been increasing. This trend has driven the expansion of offshore oil exploration and transportation, resulting in frequent incidents of ship oil spills. Catastrophic impacts have been exerted on the marine environment by these accidents, posing a serious threat to economic development and ecological security. Therefore, there is an urgent need for efficient and reliable methods to detect oil spills in a timely manner and minimize potential losses as much as possible. In response to this challenge, a marine radar oil film segmentation method based on feature fusion and the artificial bee colony (ABC) algorithm is proposed in this study. Initially, the raw experimental data are preprocessed to obtain denoised radar images. Subsequently, grayscale adjustment and local contrast enhancement operations are carried out on the denoised images. Next, the gray level co-occurrence matrix (GLCM) features and Tamura features are extracted from the locally contrast-enhanced images. Then, the generalized least squares (GLS) method is employed to fuse the extracted texture features, yielding a new feature fusion map. Afterwards, the optimal processing threshold is determined to obtain effective wave regions by using the bimodal graph direct method. Finally, the ABC algorithm is utilized to segment the oil films. This method can provide data support for oil spill detection in marine radar images. Full article

Swarm robots often need to cover the designated area to complete specific tasks. While robots possess local perception and limited communication capabilities, they struggle to handle coverage issues in dynamic environments. This paper proposes a self-organizing algorithm for swarm robots based on Dynamic Virtual Force (DVF) to cover dynamic areas. Robots in the swarm can locally perceive their surrounding robots and dynamically select adjacent ones to generate virtual repulsion, thereby controlling their movement. The algorithm enables swarm robots to be rapidly and evenly deployed in unknown areas, adapt to dynamic area changes, and solve the problem of symmetrical robot distribution during coverage. It also allows for adaptive coverage of different density areas, divided as needed. Experimental validation across 20 benchmark scenarios (including obstacles, dynamic boundaries, and multi-density zones) demonstrates that the DVF method outperforms existing approaches in coverage rate, total robot movement distance, and coverage uniformity. The results validate its effectiveness and superiority in addressing area coverage problems. By addressing these challenges, the DVF algorithm can be widely applied to forest firefighting, oil spill cleanup in the ocean, and other swarm robot tasks. Full article

This study evaluates the risk assessment and hazard identification of hydrothermal liquefaction (HTL)-derived bio-oil from the MARINES project, which converts military organic waste into fuel. The high oxygen content (35–50 wt%), acidic pH (2–4), and viscosity (10–1000 cP) of bio-oils pose unique challenges, including oxidative polymerization, corrosion, and micro-explosions during combustion. Key hazards include storage instability, particulate emissions (20–30% higher than diesel), and aquatic toxicity (LC50 < 10 mg/L for phenolics). Mitigation strategies such as inert gas blanketing, preheating, and spill containment are proposed. While offering renewable fuel potential, HTL bio-oil demands rigorous safety protocols for military/industrial deployment, warranting further experimental validation. Full article

Semi-flexible pavement (SFP) is a durable and cost-effective alternative to conventional rigid and flexible pavement and is formed by permeating an open-graded asphalt (OGA) layer with high-fluidity cement grout. The degradation of SFP mattresses due to fuel oil spills can result in significant maintenance costs. Incorporating glass waste (GW) into the construction of SFPs offers an eco-friendly solution, helping to reduce repair costs and environmental impact by conserving natural resources and minimizing landfill waste. The main objective of this research is to investigate the mechanical performance and fuel oil resistance of SFP composites containing different levels of glass aggregate (GlaSFlex composites). Fine glass aggregate (FGA) was replaced with fine virgin aggregate at levels of 0%, 20%, 40%, 60%, 80%, and 100% by mass. The results indicated the feasibility of utilizing FGA as a total replacement (100%) for fine aggregate in the OGA structural layer of SFPs. At 100% FGA, the composite exhibited excellent mechanical performance and durability, including a compressive strength of 8.93 MPa, a Marshall stability exceeding 38 kN, and a stiffness modulus of 19,091 MPa. Furthermore, the composite demonstrated minimal permanent deformation (0.04 mm), a high residual stability of 94.7%, a residual compressive strength of 83.3%, and strong resistance to fuel spillage with a mass loss rate of less than 1%, indicating excellent durability. Full article

The increasing incidence of oil contamination in many aquatic ecosystems, particularly in oil-rich regions such as Qatar, poses significant threats to marine life and human activities. Our study addresses the critical need for effective and eco-friendly oil-water separation techniques, focusing on developing graphene and chitosan-based three-dimensional (3D) polymeric sponges. These materials have demonstrated potential due to their high porosity and surface area, which can be enhanced through surface treatment to improve hydrophobicity and oleophilicity. This study introduces a new technique dependent on the optimization of the graphene oxide (GO) concentration within the composite sponge to achieve a superior oil uptake capacity (51.4 g oil/g sponge at 3% GO), and the detailed characterization of the material’s performance in separating heavy oil-water emulsions. Our study seeks to answer key questions regarding the performance of these modified sponges and their scalability for industrial applications. This research directly aligns with Qatar’s environmental goals and develops sustainable oil-water separation technologies. It addresses the pressing challenges of oil spills, ultimately contributing to improved marine ecosystem protection and efficient resource recovery. 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

In this study, we tested a flexible polyurethane (PU) foam, synthesized from bio-based components, for the removal of petroleum-derived fuels from water samples. The PU was synthesized via the prepolymer method through the reaction of PEG 400 with L-lysine ethyl ester diisocyanate (L-LDI), followed by chain extension with 2,5-bis(hydroxymethyl)furan (BHMF), a renewable platform molecule derived from carbohydrates. Freshwater and seawater samples were artificially contaminated with commercial diesel, gasoline, and kerosene. Batch adsorption experiments revealed that the total sorption capacity (S, g/g) of the PU was slightly higher for diesel in both water types, with values of 67 g/g in freshwater and 70 g/g in seawater. Sorption kinetic analysis indicated that the process follows a pseudo-second-order kinetic model, suggesting strong chemical interactions. Equilibrium data were fitted using Langmuir and Freundlich isotherm models, with the best fit achieved by the Langmuir model, supporting a monolayer adsorption mechanism on homogeneous surfaces. The PU foam can be regenerated up to 50 times by centrifugation, maintaining excellent performance. This study demonstrates a promising application of this sustainable and bio-based polyurethane foam for environmental remediation. Full article

Oil spills on the water surface pose a significant environmental hazard, underscoring the critical need for developing Artificial Intelligence (AI) detection methods. Utilizing Unmanned Aerial Vehicles (UAVs) can significantly improve the efficiency of oil spill detection at early stages, reducing environmental damage; however, there is a lack of training datasets in the domain. In this paper, LADOS is introduced, an aeriaL imAgery Dataset for Oil Spill detection, classification, and localization by incorporating both liquid and solid classes of low-altitude images. LADOS comprises 3388 images annotated at the pixel level across six distinct classes, including the background. In addition to including a general oil class describing various oil spill appearances, LADOS provides a detailed categorization by including emulsions and sheens. Detailed examination of both instance and semantic segmentation approaches is illustrated to validate the dataset’s performance and significance to the domain. The results on the test set demonstrate an overall performance exceeding 66% mean Intersection over Union (mIoU), with specific classes such as oil and emulsion to surpass 74% of IoU part of the experiments. Full article

The oil and gas industry remains vital to the global economy, yet its operations contribute significantly to environmental degradation, one of the most urgent challenges of the 21st century. This study explores the lived experiences of those directly impacted by the negative externalities of oil and gas activities, with a focus on gas flaring, oil spills, and habitat loss. Corporate social responsibility (CSR) and environmental conservation in lower-income countries remain underexplored in the existing literature. This study addresses that gap by specifically examining Nigeria’s oil and gas industry context. It examines the extent to which CSR initiatives address or intensify these environmental issues, raising the central question: to what extent do CSR efforts contribute meaningfully to environmental conservation, and how are they perceived by affected communities? Using an exploratory qualitative approach, this study draws on in-depth, face-to-face interviews with key stakeholders, including oil company staff and host community members. Data were analysed thematically through inductive coding, leading to the construction of one overarching theme: “CSR as a strategic response.” This theme emerged from three central codes—afforestation, shore protection, and environmental conservation and remediation. Findings suggest that CSR must evolve from transactional interventionist gestures to long-term ecological stewardship. 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

In the present study, we synthesized fluorocarbon-coated cobalt ferrite (CoFe₂O₄) magnetic nanoparticles using alkoxysilanes such as trimethoxy(3,3,3-trifluoropropyl)silane (TFPTMS), trimethoxy(1H,1H,2H,2H-nonafluorohexyl)silane (NFHTMS), and triethoxy(1H,1H,2H,2H-perfluorodecyl)silane (PFDTES). The synthesized nanoparticles were characterized by various techniques, including X-ray diffractometry (XRD), transmission electron microscopy (TEM/HRTEM/EDXS), Fourier transform infrared spectroscopy (FTIR), specific surface area measurements (BET), and magnetometry (VSM). To understand their surface characteristics, contact angle (CA) measurements were carried out, providing valuable insights into their hydrophobic properties. Among the samples of CoFe₂O₄ coated with fluoroalkoxysilanes, those with PFDTES surface coating had the highest water contact angle of 159.2°, indicating their superhydrophobic character. The potential of the prepared fluoroalkoxysilane-coated CoFe₂O₄ nanoparticles for the removal of waste low-SAPS synthetic engine oil from a model aqueous solution was evaluated based on three key parameters: adsorption efficiency (%), adsorption capacity (mg/g), and desorption efficiency (%). All synthesized CoFe₂O₄ samples coated with fluoroalkoxysilane showed high oil adsorption efficiency, ranging from 87% to 98%. The average oil adsorption capacity for the samples was as follows: F₃-SiO₂@CoFe₂O₄ (3.1 g of oil/g of adsorbent) > F₉-SiO₂@CoFe₂O₄ (2.7 g of oil/g of adsorbent) > F₁₇-SiO₂@CoFe₂O₄ (1.5 g of oil/g of adsorbent) as a result of increasing oleophobicity with increasing fluorocarbon chain length. The desorption results, which showed 77–97% oil recovery, highlighted the possibility of reusing the adsorbents in multiple adsorption/desorption cycles. Full article

Most large-scale crude oil spills occur in marine environments. We screened easily propagable/maintainable halophiles to develop agents for the bioremediation of marine spills. A bacterial strain isolated from a polluted region of the Great Salt Lake was characterized and tested for its ability to degrade crude oil. The strain (Salinivibrio costicola) is motile, catalase- and lipase-positive, a facultative anaerobe, and an obligate halophile. Its growth optimum and tolerance ranges are: NaCl (5%, 1.25–10%), pH (8, 6–10), and temperature (22 °C, 4–45 °C). Its genome (3,166,267 bp) consists of two circular chromosomes and a plasmid, containing 3197 genes, including some genes potentially relevant to hydrocarbon metabolism. The strain forms a biofilm but is considered nonpathogenic and is sensitive to some common antibiotics. Lytic bacteriophages infecting the strain are rare in the water samples we tested. The strain survived on desiccated agar media at room temperature for a year, grew optimally in complex media containing 0.1–1% crude oil, but failed to reduce total recoverable petroleum hydrocarbons from crude oil. Thus, a recalcitrant halophile may endure crude oil without mineralizing. Due to some of their advantageous attributes, such strains can be considered for genetic manipulation to develop improved agents for bioremediation. Full article