Search Results (129)

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

Marine oil spills have caused significant environmental problems. Among the array of clean-up methods, the utilization of sorbents emerges as promising for removing and recovering oil from spills. Developing cost-effective, reliable, and eco-friendly material that efficiently and sustainably removes oil from water is increasingly seen as crucial and pressing. In the present study, we report the development of coco-polyurethane (PU) foam (CCF) through the conventional foaming process using varying amounts of coconut-oil-derived polyol (CODP) in a PU matrix. Characterization of the foams showed an increased ester band with the incorporation of COPD into the polyurethane networks and no direct influence of the cell size distribution on the surface morphology. Furthermore, this study highlighted the increasing CODP in every CCF formulation, showing high oil sorption and low water uptake due to its porous structure. The experimental results revealed that CCF is a potential candidate sorbent for the recovery of spilled oil. This signifies a significant leap towards reducing the dependency on petroleum in developing sorbent materials and advancing sustainable responses to oil spills in marine environments. 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

Efficient superhydrophobic oil–water separation materials are essential for environmental remediation and industrial wastewater treatment. In this study, by optimizing printing parameters, such as printing speed, extrusion multiplier, and layer height, we achieved high-precision 3D porous architectures with uniform pore sizes. The pore size could reach 677.3 µm, with a maximum deviation of less than 36.1 µm. Additionally, we successfully printed pores as small as 186.7 µm, representing the smallest FFF-printed pore size reported in the literature. The printed structures were modified using a spray-coating method, achieving a superhydrophobic surface with a water contact angle of 158.2°. The material was tested in a continuous oil–water separation system, maintaining stable oil removal performance for 24 h. The highest separation efficiency reached 88.6%, demonstrating strong durability and long-term applicability. This study establishes a scalable, low-cost approach for fabricating highly efficient 3D superhydrophobic porous materials, offering new opportunities for continuous oil spill cleanup and industrial wastewater treatment. Full article

This study presents an innovative, eco-friendly approach for converting waste zeolite dust into efficient petroleum sorbents through an integrated agglomeration–deagglomeration process using high-pressure grinding rolls (HPGRs). This method generates secondary porosity without calcination, enhancing sorption while reducing greenhouse gas emissions and supporting sustainable development by valorizing industrial by-products for environmental remediation. The study aimed to assess the influence of binder and water content on petroleum sorption performance, textural properties, and mechanical strength of the produced sorbents, and to identify correlations between these parameters. Sorbents were characterized using mercury porosimetry (MIP), sorption measurements, mechanical resistance tests, scanning electron microscopy (SEM), and digital microscopy. Produced zeolite sorbents (0.5–1 mm) exceeded the 50 wt.% sorption threshold required for oil spill cleanup in Poland, outperforming diatomite sorbents by 15–50% for diesel and 40% for used engine oil. The most effective sample, 3/w/22.5, reached capacities of 0.4 g/g for petrol, 0.8 g/g for diesel, and 0.3 g/g for used oil. The sorption mechanism was governed by physical processes, mainly diffusion of nonpolar molecules into meso- and macropores via van der Waals forces. Sorbents with dominant pores (~4.8 µm) showed ~15% higher efficiency than those with smaller pores (~0.035 µm). The sorbents demonstrated amphiphilic behavior, enabling simultaneous uptake of polar (water) and nonpolar (petrochemical) substances. Full article

Oil pollution in water bodies is a substantial environmental concern that poses severe risks to human health, aquatic ecosystems, and economic activities. Rising energy consumption and industrial activity have resulted in more oil spills, damaging long-term ecology. The aim of the review is to discuss problems, effects, and methods of monitoring and sensing oil pollution in water. Oil can destroy the aquatic habitat. Once oil gets into aquatic habitats, it changes both physically and chemically, depending on temperature, wind, and wave currents. If not promptly addressed, these processes have severe repercussions on the spread, persistence, and toxicity of oil. Effective monitoring and early identification of oil pollution are vital to limit environmental harm and permit timely reaction and cleanup activities. Three main categories define the three main methodologies of oil spill detection. Remote sensing utilizes satellite imaging and airborne surveillance to monitor large-scale oil spills and trace their migration across aquatic bodies. Accurate real-time detection is made possible by optical sensing, which uses fluorescence and infrared methods to identify and measure oil contamination based on its particular optical characteristics. Using sensor networks and Internet of Things (IoT) technologies, wireless sensing improves early detection and response capacity by the continuous automated monitoring of oil pollution in aquatic settings. In addition, the effectiveness of advanced artificial intelligence (AI) techniques, such as deep learning (DL) and machine learning (ML), in enhancing detection accuracy, predicting leak patterns, and optimizing response strategies, is investigated. This review assesses the advantages and limits of these detection technologies and offers future research directions to advance oil spill monitoring. The results help create more sustainable and efficient plans for controlling oil pollution and safeguarding aquatic habitats. Full article

The increasing contamination of water bodies by fats, oils, and grease (FOG) poses significant environmental and operational challenges, necessitating the development of advanced remediation technologies. Aerogels, with their ultra-lightweight structure, high porosity, and tunable surface chemistry, have emerged as promising sorbents for efficient FOG removal. This comprehensive review explores recent advancements in aerogel materials, highlighting novel formulations, functional modifications, and nanotechnology integrations that enhance sorption capacity and reusability. It delves into the mechanistic aspects of FOG sorption, providing insights into how surface interactions and structural properties influence performance. The sustainability of aerogels is emphasized, particularly the use of bio-based and eco-friendly materials that align with green remediation strategies. A comparative analysis with conventional sorbents underscores the advantages of aerogels in terms of efficiency, environmental impact, and cost-effectiveness. Furthermore, real-world applications, including oil spill cleanup and wastewater treatment, are discussed alongside challenges, regulatory considerations, and future research directions. By offering a holistic perspective on the potential of aerogels in water remediation, this review serves as a valuable resource for researchers and industry professionals seeking innovative and sustainable solutions for FOG management. Full article

This study presents a method for the simultaneous determination of six acidic herbicides and their metabolites in various matrices, including fruits, vegetables, grains, and edible oils. The method employs acidified acetonitrile extraction combined with dispersive solid-phase extraction cleanup (dSPE) using MgSO₄, Florisil, and Graphitized carbon black (GCB). The analysis was performed by ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS) with electrospray ionization (ESI) in both positive and negative modes using multiple reaction monitoring (MRM). The mass concentrations of six herbicide pesticides and their metabolites were predominantly within the range of 0.0005~0.050 mg/L and exhibited strong linear relationships with the corresponding peak area, with the coefficient of determination (R²) exceeding 0.993. The limits of detection (LOD) for the method ranged from 0.0001 to 0.008 mg/kg. The recovery rates of adding recovery experiments to cabbage, chives, pear, wheat flour, and soybean oil were 69.8~120%, and the relative standard deviation (RSD) was 0.6~19.5%. The results indicate that this method is efficient and fast, and can be used for the detection of compounds in various actual matrices. Full article

Moniliformin (MON) is a highly polar, emerging Fusarium mycotoxin with a low molecular weight. It is known to exhibit potentially harmful effects on public and animal health. This study aimed to comprehensively assess the natural occurrence of MON in various foods marketed in South Korea and to perform a risk assessment. An analytical method for MON quantification using strong anion exchange clean-up combined with ultra-high-performance liquid chromatography–tandem mass spectrometry was validated across four different food matrices (white rice, sorghum, corn oil, and baby food), exhibiting excellent accuracy, precision, and sensitivity. A total of six food categories, 33 food commodities, and 253 food samples were included in this study. Maize, sorghum, Job’s tears, and perilla seeds were identified as the major contributors to MON contamination. Estimated daily intake (EDI) was calculated for both mean and 95th percentile extreme dietary scenarios using upper and lower bound approaches. The highest EDI was observed in the 0–2-year and 3–6-year age groups, primarily for cereal grains. The margin of exposure (MOE) values for maize consumption ranged from 2544 to 7482. These results highlight the potential health concerns associated with MON, necessitating targeted risk management strategies. Full article

The widespread oil extraction in the Niger Delta and the impacts on different types of vegetation are poorly understood due to lack of ground access. This study aims to determine the impact of oil spills on vegetation in the Niger Delta using a Landsat satellite-derived normalised difference vegetation index (NDVI). The impact of oil spill volume and time after an oil spill on the health of different types of vegetation were evaluated, and the time series of the changes in NDVI were analysed to determine the medium- and long-term responses of vegetation to oil spill exposure, using a combination of linear regression and paired t-tests. With regards to the relationship between spill volume and NDVI, a moderate correlation (R² = 0.5018) was observed for spill volumes in the range of 401–1000 barrels for sparse vegetation, for volumes greater than 1000 barrels for dense vegetation (R² = 0.4356), whilst no correlation was found for mangrove vegetation at any range of spill volume. Similarly, the results of the paired t-test confirmed a significant difference (p-value < 0.05) between the change in NDVI values for spill sites and non-spill sites for all vegetation types, with the sparse vegetation being the most affected of the three types. However, the impact of the oil spill on vegetation over a period is not statistically significant. The outcomes of this study provide insights into how different types of vegetation in the Niger Delta respond to oil spills, which could ultimately help in designing an oil spill clean-up program to reduce the impact on the environment. Full article

This study monitored the contamination of 32 plasticizers in olive oil throughout the production and storage process. Samples were collected at different stages of production from three olive oil production lines in distinct regions of Portugal and analyzed for 23 phthalates and 9 phthalates substitutes to identify contamination sources. The developed analytical method employed liquid–liquid extraction with hexane/methanol (1:4, v/v), followed by centrifugation, extract removal, and freezing as a clean-up step. Analysis was conducted using gas chromatography tandem mass spectrometry (GC-MS/MS), with detection limits ranging from 0.001 to 0.103 mg/kg. The results revealed that plasticizer concentrations progressively increased at each stage of the production process, although unprocessed olives also contained contaminants. Di-isononyl phthalate (DINP) was the most prevalent compound, but all phthalates regulated by the European Union for food contact materials were detected, as well as some unregulated plasticizers. In a few packaged olive oils, DINP concentrations exceeded the specific migration limits established by European regulations. Samples stored in glass and plastic bottles showed no significant differences in plasticizer concentrations after six months of storage. However, higher concentrations were observed in plastic-packaged samples after 18 months of storage. Our findings indicate that the primary source of plasticizer contamination in olive oil originates from the production process itself, except for prolonged storage in plastic bottles, which should be avoided. Full article

One of the effective and safe methods of soil cleanup from oil pollution is bioremediation by introducing microorganisms or their metabolites. In this study, the effect of biosurfactants produced by Nocardiopsis sp. 3mo on the rate of bioremediation of oil-contaminated soils was assessed. Biosurfactants were introduced into soils contaminated with 2% oil at a concentration of 0.05 and 0.1%, and the degree of hydrocarbon degradation was estimated within 63 days. It was found that the studied biosurfactant belonged to the glycopeptide type. The aeration and irrigation of oil-contaminated soil (PSA) resulted in a 5% decrease in the number of hydrocarbons. The introduction of biosurfactants into oil-contaminated soil at a concentration of 0.5 (BS(0.5)) and 1 g kg⁻¹ (BS(1)) resulted in a 29 and 35% decrease in the content of hydrocarbons, respectively. The state of the soil microbiome was assessed by its metabolic activity. Thus, the respiratory activity of microorganisms on the first day after contamination increases by 5–7 times, and urease activity by 3–4 times. The introduction of oil into the soil during the first day reduces the activity of dehydrogenase by 2.3–1.6 times. In the process of bioremediation, the indicators of microbial activity returned to values close to the original. Thus, it was established that the use of biosurfactants produced by Nocardiopsis sp. 3mo increases the ability of the native soil community to degrade hydrocarbons. Full article

Onshore oil spills are directly related to soil contamination and significantly impact groundwater, vegetation, and human life. Immediate cleanup work is carried out when an oil spill occurs, but the currently used preventive measures are insufficient. Therefore, this study independently developed a smart liner that allows general groundwater flow but blocks groundwater in the event of a spill to prevent further spread, and aims to verify the excellence of the product through verification. Because the verification of the smart liner performance in real-life conditions is difficult for various reasons, large-scale experiments were simulated using a container. The Roll Spreading and Inserting Method (RSIM) and Panel Injecting Method (PIM) were used as installation methods due to the properties of the material employed. Through rainfall simulations, the discharge amount and groundwater levels before and after an oil spill were measured, and a reaction diagram was created following the smart liner’s demolition. From the results, it was found that both installation methods successfully blocked more than 99% of the drainage, and soil contaminants were not detected outside the installation area. These results confirm the effectiveness of the smart liner. Additionally, the reaction diagram indicated that the RSIM and PIM installation reaction areas were identical, validating the suitability of both methods. By conducting this study, the performance of the smart liner was verified, demonstrating its potential as an effective preventive measure against the spread of oil contamination in soil. Full article

An oil spill at sea represents a catastrophic environmental event resulting from the release of oil into marine ecosystems. These incidents pose substantial risks to marine biodiversity, wildlife habitats, and coastal populations, often engendering enduring and widespread repercussions. Cleaning up oil spills is costly due to logistical challenges. The accurate measurement of spill characteristics like the volume, thickness, and area of the spill is crucial before deploying cleanup crews to optimize resource allocation and reduce expenses. The main objective of this research is to use computer vision to detect oil spills and estimate its thickness, helping in decision-making processes to clean up the spill area. The system architecture proposed in this study integrates a drone equipped with a camera module to inspect sea areas and capture images. These images are processed using a deployed computer vision segmentation model to detect oil spills and estimate oil thickness. Predicted results help in decision making via a dedicated application by applying predefined criteria to determine the thickness of the spill, which further help in taking actions for the removal of oil spills. The computer vision model developed in this research could detect and estimate oil thickness with an mAP of 91%. The proposed system in this study uses instance segmentation to detect and segment oil spills in drone footage. This computer vision-based approach accurately identifies and outlines oil spill areas, aiding in the selection of efficient cleanup strategies. Real-time monitoring and assessment capabilities enable quick decision making and effective response measures. Full article

In this study, we demonstrate a new all bio-based adsorbent material by treating Enteromorpho prolifera (EP) fibers with tannic acid-ferric chloride complex and then grafting hydrophobic group octadecylamine. All raw materials are easily available, low-cost, and safe. The modified EP fibers have approximately 63.4 g ${\mathrm{g}}^{-1}$ of oil absorption and 1.4 g ${\mathrm{g}}^{-1}$ of water absorption, which is an 62.8% increase in oil absorption and an 82% increase in hydrophobicity over that of untreated EP fibers, respectively, exhibiting high hydrophobicity and oleophilicity. The affinity discrimination to water and oil enables hydrophobic algae candidate materials to separate oils and water efficiently, both in an oil–water mixture and a water-in-oil emulsion. In summary, the as-synthesized modified EP demonstrates a broad application prospect in the treatment of oil spill accidents and oily wastewater. Full article