Search Results (156)

Oil spills in inland rivers pose a significant threat to the surrounding environment, and the emergency response differs greatly from that in ocean or coastal areas. In this study, we focused on several commonly used emergency water treatment strategies in China’s inland oil spills, as well as the spilled washing oil in a serious accident case. We investigated the changes in oil-related chemical components before and after water treatment using GCxGC-TOF MS (Comprehensive Two-dimensional Gas Chromatography Time of Flight Mass Spectrometer). We tracked the shifts of microbial communities in the microcosms incubated with clean river water, simulated oil-contaminated water, and the treatment effluent. The results revealed that typical components, especially nitrogen-containing heterocyclic compounds, had different removal efficiencies among treatments. The diversity, composition, and potential functions of microbial communities responded differently to the treatments, and could be related to various substances, including PAHs (polycyclic aromatic hydrocarbons) and heterocyclic compounds. A few genera, such as SC-I-84, exhibited a high correlation with washing oil-related components and could serve as an indicator in such an oil spill emergency response. Our findings indicated that simply using petroleum oil or PAHs to evaluate oil spills was likely to underestimate the ecological impact, especially when the spilled substances were coal chemical products widely used in China. This will provide an important scientific basis for decision-making and strategy evaluation in emergency responses to inland oil spills. Full article

In this study, we introduce a novel Electrowetting-on-Dielectric (EWOD) sensor designed to quantify marine dispersants at the spill point. The sensor quantifies changes in the surface tension of liquid droplets at varying dispersant concentrations through the deformation response of the droplet under applied voltage. Analyzed responses include droplet length and contact angle (CA) on the device surface upon sensor activation. This sensor offers significant advantages over existing chemical methods, which are costly and complex. Moreover, compared to conventional methods based on the same principle, it demonstrates enhanced sensitivity at low concentrations. Additionally, the sensor’s portability enables instantaneous and in situ measurements of marine dispersant concentrations, thus providing a crucial tool for effective oil spill response by facilitating on-site decision-making and offering higher temporal resolution for studies on the marine dispersant’s environmental impact. The device’s potential extends beyond marine dispersants to detecting various contaminants affecting surface tension. Its adaptability underscores the EWOD device’s role as a versatile tool for environmental monitoring and on-site analysis, addressing the urgent need for efficient and sustainable solutions in environmental management. Full article

The kraft pulp process generates liquors with different physicochemical characteristics at each treatment stage. These liquors can accidentally spill into the biological treatment, hindering it and harming ecosystems where the effluents are discharged. Due to the lack of studies on the effects these liquors can have on the aerobic biomass of activated sludges and ecosystems, this investigation aims to assess the toxicity of each liquor spill to the aerobic biomass of an activated sludge, using Selenastrum capricornutum as a bioindicator of water quality. This evaluation used a laboratory-scale activated sludge, which was fed with an effluent with pH 6.62–6.67 and chemical organic demand (COD) of 611–638.5 mg/L. The liquors used had the following parameters: pH = 13 and COD = 1911 mg/L (white); pH = 13 and COD = 141,350 mg/L (black); pH = 13 and 2755 mg/L (green); and pH = 7.5 and COD = 358 mg/L (condensate). White liquor produced the greatest toxicity (EC₂₀ of 17.8 mgCOD/L) and lowest oxygen uptake rate (8.42 mgO₂/L·h with 287.7 mgCOD/L) in the aerobic biomass compared to the other liquors. White liquor presented the greatest inhibition of Selenastrum capricornutum, with 81.7% (48 h) and 98.0% (96 h). Meanwhile, black liquor presented an inhibition of 94.7% (48 h), but a 13% increase in microalga growth at 96 h of culture. The information from this study makes it possible to calculate how much liquor can be fed to an activated sludge system, keeping it optimized to eliminate liquor discharges generated within the kraft mill’s processing units. 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

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

Spills involving fuels and lubricating oils in industrial environments caused by the fueling of machines, inadequate storage and the washing of equipment are significant sources of environmental pollution, impacting soil and water bodies. Such incidents alter the microbiological, chemical and physical properties of affected environments. The use of biosurfactants is an effective option for the cleaning of storage tanks and the remediation of contaminated soils and effluents. The scope of this work was to assess the production and application of a Starmerella bombicola ATCC 22214 biosurfactant to remediate marine and terrestrial environment polluted by oil. The production of the biosurfactant was optimized in terms of carbon/nitrogen sources and culture conditions using flasks. The performance of the biosurfactant was tested in clayey soil, silty soil, and standard sand, as well as smooth surfaces and industrial effluents contaminated with oils (fuel oils B1 for thermal power generation, diesel, and motor oil). The ideal culture medium for the production of the biosurfactant contained 2% glucose and 5% glycerol, with agitation at 200 rpm, fermentation for 180 h and a 5% inoculum, resulting in a yield of 1.5 g/L. The biosurfactant had high emulsification indices (86.6% for motor oil and 51.7% for diesel) and exhibited good stability under different pH values, temperatures and concentrations of NaCl. The critical micelle concentration was 0.4 g/L, with a surface tension of 26.85 mN/m. In remediation tests, the biosurfactant enabled the removal of no less than 99% of motor oil from different types of soil. The results showed that the biosurfactant produced by Starmerella bombicola is a promising agent for the remediation of environments contaminated by oil derivatives, especially in industrial environments and for the treatment of oily effluents. Full article

With the continuous and rapid development of global industries, issues such as offshore oil spills, leakage of organic chemicals, and the direct discharge of industrial oily sewage have caused serious damage to the ecological environment and water resources. Efficient oil–water separation is widely recognized as the solution. However, there is an urgent need to address the difficulties in treating thin oil films on the water surface and the low separation efficiency of existing oil–water separation materials. In view of this, this study aims to investigate high-efficiency oil–water separation materials for thin oil films. Four types of oil–water separation materials with different materials are designed to treat thin oil films on the water surface. The effects of factors such as oil film thickness, pressure, and temperature on the oil–water separation performance of these materials are studied. The viscosities of kerosene and diesel oil are tested, and the adsorption and separation effects of the oil–water separation materials on different oil products and oily organic solvents are examined. In addition, the long-term stability of the movable and portable oil–water separation components is verified. The results show that the oil-absorbing sponge-based oil–water separation membrane has an excellent microporous structure and surface roughness, endowing the membrane surface with excellent hydrophobicity and lipophilicity, and exhibiting good oil–water separation performance. The filtration flux of oil increases with the increase in pressure and temperature. It has good adsorption and separation performance for different oil products and oily organic solvents. Moreover, it maintains stable operation performance during the 12-month long-term oil–water separation process for kerosene and diesel oil. Full article

At present, the oil extraction and chemical industry and other industries produce a large amount of oily wastewater and organic sewage, and the world is suffering from oil spills and organic wastewater pollution. As a porous material, aerogels are promising in the field of oil adsorption. In this work, the nanocellulose/aminated multi-walled carbon nanotube (NC-MWCNT-NH₂) nanocomposite aerogel with a high porosity of up to 97.80% is prepared by varying the weight percentage of MWCNTs-NH₂, among which the nanocomposite aerogel with 0.1% weight percentage of MWCNTs-NH₂ exhibits the best adsorption performance with the adsorption capacity to cyclohexane, ethyl acetate, anhydrous ethanol, methylene dichloride, acetone, kerosene, pump oil, and used pump oil of 39.77 ± 0.82, 44.54 ± 1.67, 43.03 ± 1.06, 62.13 ± 0.36, 39.92 ± 1.09, 39.37 ± 0.27, 43.48 ± 0.06, and 38.45 ± 0.84 g·g⁻¹, respectively. Compared with pure nanocellulose aerogel, the adsorption capacity of the NC-MWCNT-NH₂ aerogel to pump oil is improved by up to 93.9%, which exhibits excellent adsorption properties and could be utilized in the field of oil adsorption. Full article

Hazard detection in industrial and public environments is critical for ensuring safety and regulatory compliance. This paper presents an energy-efficient, cost-effective IoT-based hazard detection system utilizing an ESP32-CAM microcontroller integrated with temperature (DHT22) and motion (PIR) sensors. A custom-built convolutional neural network (CNN) deployed on a Flask server enabled real-time classification of hazard signs, including “high voltage”, “radioactive”, “corrosive”, “flammable”, “no hazard”, “no smoking”, and “wear gloves”. The CNN model, optimized for embedded applications, achieves high classification accuracy with an F1 score of 85.9%, ensuring reliable detection in diverse environmental conditions. A key feature of the system is its adaptive monitoring mechanism, which dynamically adjusts image capture frequency based on detected activity, leading to 31–37% energy savings compared to continuous monitoring approaches. This mechanism ensures efficient power usage by minimizing redundant image captures while maintaining real-time responsiveness in high-activity scenarios. Unlike traditional surveillance systems, which rely on high-cost infrastructure, centralized monitoring, and subscription-based alerting mechanisms, the proposed system operates at a total cost of SGD 38.60 (~USD 28.50) per unit and leverages free Telegram notifications for real-time alerts. The system was validated through experimental testing, demonstrating high classification accuracy, energy efficiency, and cost-effectiveness. In this study, a hazard refers to any environmental condition or object that poses a potential safety risk, including electrical hazards, chemical spills, fire outbreaks, and industrial dangers. The proposed system provides a scalable and adaptable solution for hazard detection in resource-constrained environments, such as construction sites, industrial facilities, and remote locations. The proposed approach effectively balances accuracy, real-time responsiveness, and low-power operation, making it suitable for large-scale deployment. Full article

Incidents involving hazardous materials (HAZMAT incidents) can impact human health and the environment. For the development of risk mitigation strategies, it is essential to understand the circumstances of such incidents. A retrospective study (2016–2023) of acute occupational HAZMAT incidents involving multiple patients (>1, including workers, emergency responders and bystanders) reported to the Dutch Poisons Information Center was conducted. We only included incidents that occurred during the performance of work or as a result of a disruption of a work-related process. Patient characteristics, exposure circumstances (such as the substances involved, chemical phase, and type of release (e.g., spill/release or fire/explosion)) and business classes were analyzed to identify risk factors. From 2016 to 2023, the DPIC was consulted about 516 HAZMAT incidents. Inhalation was the most common route of exposure (89%). Patients were often exposed to chemical asphyxiants (n = 156) and acids (n = 151). Most incidents occurred in fixed facilities (n = 447), while 49 incidents occurred during transport. The primary cause was a spill/release (n = 414), followed by a fire/explosion (n = 65). Most patients were exposed to a gas/vapor (n = 421), followed by a liquid (n = 59) or solid (n = 28). Incidents frequently occurred in industry (20%). The majority of patients reported mild to moderate health effects. Surveillance data on HAZMAT incidents are essential for incident preparedness. Poison Center data can help identify risk factors, which can be used to develop risk mitigation strategies to prevent future incidents. Full article

Tank vehicles are highly prone to rollover accidents, especially on curved–slope combination sections, which can cause hazardous chemical spills, endangering the environment, public safety, and human health. Therefore, it is crucial to conduct research aimed at reducing the risk of such incidents. Method: The rollover risk of tank vehicles under various loading conditions while traveling on curved–slope combination sections was investigated using driver–vehicle–road dynamics simulation. A multiple linear regression model was then developed to further quantify the impact of key factors on the rollover risk. Results: The results revealed that the road curve radius, vehicle operating speed, and liquid cargo fill level have the greatest impact on a tank vehicle’s rollover risk, and higher fill levels, higher speeds, and steeper downhill slopes all amplify the impact of curve radius on the rollover risk. In some cases, adhering to the road’s speed limit alone was insufficient to ensure the safe passage of the tank vehicle through curves. Conclusions: This study introduced, for the first time, a rollover risk assessment model for tank vehicles operating on curved–slope combination sections. The findings reveal effective methods to improve the transportation safety of tank vehicles. Practical Applications: The findings of this study can assist transportation agencies in selecting routes with lower rollover risks for tank vehicles with different configurations, as well as guide the development of loading standards and curve speed limits. This will effectively reduce rollover accidents of tank vehicles and support sustainable, safer transportation practices. Full article

The search for efficient oil spill remediation techniques leads to the use of physical, chemical, and biological methods, featuring both natural and modified lipophilic materials. Kapok fibers (Ceiba pentandra (L.) Gaerth) have been studied as a result of their unusual natural oil sorption capacity, which can be improved even further through physical or chemical treatments that augment their rugosity and alter their functional properties. Furthermore, the exact role of fiber morphology is not completely clear regarding the sorption process. Hence, this study investigated the efficacy of kapok fibers using different treatments, based on chemical and physical approaches, and characterized using advanced techniques (FTIR, SEM, AFM), aiming to improve the understanding of application possibilities in oil contamination scenarios. The results indicate that treatments using a low thermal intensity and low concentration do not lead to variation in sorption properties nor in surface structural features. Fiber rugosity varied from 4.40 to 12.35 nm, whereas an excessive increase in roughness was observed when the material was subject to more extreme conditions, such as a temperature of 120 °C and high concentrations (2.0 M) of both acid or alkali, accompanied by a loss in functionality and affecting the material sorption capacity. Thus, the study provides conditions to suggest that these treatments are not necessary for this type of material when inserted into sorption processes. Full article

The conformal fractional derivative (CFD) has become a hot research topic since it has a physical interpretation and is easier to grasp and apply to problems compared with other fractional derivatives. Its application to heat transfer, diffusion, diffusion-advection, and wave propagation problems can be found in the literature. Fractional diffusion equations have received great attention recently due to their applicability in physical, chemical, and biological processes and engineering. The diffusion of the pollutants within the ground, which is an important environmental problem, can be modeled with a diffusion equation. Diffusion in some porous materials or soil can be modeled more accurately with fractional derivatives or the conformal fractional derivative. In this study, the diffusion problem of a spilled pollutant leaking into the ground modeled with the conformal fractional time derivative in spherical coordinates has been solved analytically using the Fourier series for a constant mass flow rate and complete symmetry under the assumptions of homogeneous and isotropic soil, constant soil temperature, and constant permeability. The solutions have been simulated spatially and in time. A parametric analysis of the problem has been performed for several values of the CFD order. The simulation results are interpreted. It has also been suggested how to find the parameters of the soil to see whether the CFD can be used to model the soil or not. The approach described here can also be used for modeling pollution problems involving different boundary conditions. Full article