Fast dentification of overlapping fluorescence spectra of oil species based on LDA and two-dimensional convolutional neural network.

Although most petroleum oil species can be identified by their fluorescence spectra, overlapping fluorescence spectra make identification difficult. This study aims to address the issue that fluorescence spectroscopy is ineffective in identifying overlapping oil species. In this study, an equivalent model of overlapping oil species with fluorescence spectra was established. The linear discriminant analysis (LDA)-assisted machine learning (ML) algorithms K nearest neighbor (KNN), decision tree (DT), and random forest (RF) improved the identification of fluorescent spectrally overlapping oil species for diesel-lubricant oils. The identification accuracies of two-dimensional convolutional neural network (2DCNN), LDA combined with the ML algorithms effectively all 100 %. Furthermore, Partial Least Squares Regression (PLSR) algorithm, Support Vector Regression (SVR) algorithm, DT regression algorithm, and RF regression algorithm were also used to identify the lubricant concentration in diesel-lubricant oils. The coefficient of determination of the DT was 1, and the root-mean-square error was 0, which identified the concentration of lubricant oils in them accurately and without error.

Synthesis of dicholesteryl organogelator as a green sorbent nanomaterial for oil spill remediation.

In the field of global water purification, the issue of marine oil spills represents a significant challenge. The use of phase-selective organogelators (PSOGs) as sorbent materials in oil spill remediation is a promising solution due to their environmental adaptability and high absorption capacity. However, there are limited reports on PSOGs that can be used in powder form for rapid phase-selective gelation of crude oils. In this context, the development of innovative dicholesteryl derivatives as low-molecular-weight organogelators (LMOGs) offers a promising solution in powder form. These gelators are synthesized through a one-pot multi-component reaction as green synthesis method, which ensures high purity and eliminates the need for harsh conditions. The incorporation of cholesterol into the gelator structure demonstrate environmental adaptability. The exceptional sorption capacity was attributed to the structured 2D/3D networks observed through scanning electron microscopy (SEM). The hydrophobic properties of these gelators, as evidenced by a water contact angle of 118 degrees, enable them to efficiently gel various organic solvents at low concentrations (1% w/v) at ambient temperatures, without the need for heating-cooling cycles or co-solvents. The eco-friendly nature and efficient oil-water separation capabilities of these gelators in powder form represent a significant advancement in global water purification efforts.

Detection of Oil Spill in SAR Image Using an Improved DeepLabV3.

Oil spill SAR images are characterized by high noise, low contrast, and irregular boundaries, which lead to the problems of overfitting and insufficient capturing of detailed features of the oil spill region in the current method when processing oil spill SAR images. An improved DeepLabV3+ model is proposed to address the above problems. First, the original backbone network Xception is replaced by the lightweight MobileNetV2, which significantly improves the generalization ability of the model while drastically reducing the number of model parameters and effectively addresses the overfitting problem. Further, the spatial and channel Squeeze and Excitation module (scSE) is introduced and the joint loss function of Bce + Dice is adopted to enhance the sensitivity of the model to the detailed parts of the oil spill area, which effectively solves the problem of insufficient capture of the detailed features of the oil spill area. The experimental results show that the mIOU and F1-score of the improved model in an oil spill region in the Gulf of Mexico reach 80.26% and 88.66%, respectively. In an oil spill region in the Persian Gulf, the mIOU and F1-score reach 81.34% and 89.62%, respectively, which are better than the metrics of the control model.

Oil spill impact on Brazilian coral reefs based on seawater polycyclic aromatic hydrocarbon contamination, biliary fluorescence and enzymatic biomarkers in damselfish Stegastes fuscus (Teleostei, Pomacentridae).

The crude oil contamination along the Brazilian Northeast coast significantly impacted reef ecosystems. This study assessed polycyclic aromatic hydrocarbons (PAHs) in seawater, fluorescence of bile PAHs, and biochemical biomarkers in damselfish Stegastes fuscus across four coral reef areas pre- and post-oil contamination. Serrambi (SE) and Japaratinga (JP1) were identified as suitable reference areas. PAH concentrations significantly increased in water post-contamination, predominantly 2 to 3 ring parent and alkylated PAHs. Biliary PAHs naphthalene, phenanthrene, chrysene, pyrene and benzo(a)pyrene increased on Paiva post-spill versus pre-spill to 173 %, 449 %, 334 %, 331 % and 131 %, respectively. Significant increases in ethoxy-resorufin-O-deethylase (EROD) (852 %), catalase (CAT) (139 %) and decrease in lipid peroxidation (LPO) (40 %) and acetylcholinesterase (AChE) (75 %) were verified in Paiva samples. Biliary PAHs and biochemical biomarkers were altered in S. fuscus after exposure to PAHs dissolved from the oil. Stegastes fuscus emerges as a promising sentinel organism for coastal reef oil pollution monitoring.

Environmental hazard and preliminary risk assessment of herding agents used in next generation oil spill response.

Herding agents offer a significant advance in oil spill response by overcoming past barriers limiting effectiveness of in-situ burning. This paper reviews the use, environmental fate and effects of two commercial herders, Siltech OP-40 and ThickSlick 6535. A conceptual model is proposed to describe herder fate followed by a screening exposure analysis. Hazard concentrations intended to protect aquatic life are derived using empirical toxicity data, interspecies correlation estimation and group target site models. Using exposure and hazard evaluations, a preliminary risk assessment is performed demonstrating acceptable risk to aquatic life. Hazards posed to wildlife are also reviewed. Potential harm to wildlife can be avoided or minimized by adopting best management application practices. This synthesis is intended to provide a valuable resource describing the rationale for herder use, evaluating environmental risk trade-offs and informing future oil spill response planning and decision-making. Priorities for further research are identified.

Sunken oil mats: A biomarker-based investigation.

Sunken oil mats (SOMs) pose a significant threat to marine ecosystems, yet their long-term fate and weathering processes remain poorly understood. This study investigates the degradation of biomarkers in a SOM sample collected from the Brazilian coast following the long-lasting 2019 oil spill. The SOM clearly distinguished from spilt oil and were attributed to tanker-washing residues. Analysis of its sections revealed a consistent depletion pattern, associated with losses of volatile and photooxidation-sensitive compounds, even in the most preserved inner fractions, this suggests weathering of the oil prior SOM formation. While biodegradation was minor, photooxidation revealed a significant weathering factor, especially for aromatic compounds as indicated by preferential degradation of highly-alkylated tricyclic aromatic sulfur heterocycles, and specific methylpyrenes and benzofluorenes. This study underscore the complex interplay of weathering processes shaping the fate of SOMs and the importance of biomarker-based diagnosis in assessing the origin and ecological impacts of long-lasting oil spills.

Polycyclic aromatic hydrocarbons in coral reefs from Southwestern Atlantic: A seascape approach using tissue and skeleton of the coral Montastraea cavernosa (Cnidaria; Scleractinia).

Coastal marine ecosystems, such as coral reefs, are severely threatened by climate changes, overexploitation, and marine pollution. Particularly, environmental pollution caused by petroleum-derived substances is poorly studied in coral reefs in tropical developing countries, with a total absence of data about these contaminants in some regions. In this work, we determined the levels of conventional and unconventional PAHs in the tissue and skeleton of the coral Montastraea cavernosa in a seascape scale of the Southwest Atlantic. We sampled in 12 coral reefs adjacent to the coast along approximately 200 km. We found 14 PAHs, 2 Oxy-PAHs, and 15 Nitro-PAHs in the samples, and among them, benzo[a]pyrene, chrysene, benzo[a]anthracene, benzo[k]fluoranthene, indeno[1,2,3-c,d]pyrene and dibenz[a,h]anthracene, which are mutagenic, teratogenic and carcinogenic substances. Skeletons presented predominantly lower quantities of ∑PAHs than the respective tissue, except for the skeletons from one reef severely impacted by oil spills. The ∑PAHs levels were lower in a bay near an urbanized region than in open sea reefs. Diagnostic ratios indicate mixogenic sources, with the predominance of petrogenic origin. Our study provides the first occurrence of PAHs, Nitro-PAHs, and Oxy-PAHs distribution in corals from the Southwest Atlantic Ocean, and we expect that these data will help to evaluate any future impacts and management of this ecosystem.

Behavior and toxicological impact of spilled diluted bitumen and conventional heavy crude oil in the unsaturated zone.

Demand for unconventional crude oils continues to drive the production of diluted bitumen (dilbit) within Western Canada, promoting increased transport volumes across the extensive 700,000 km pipeline system of Canada and the USA. Despite this vast extent of terrestrial transport, the current understanding of the behavior and fate of spilled dilbit within shallow groundwater systems is limited. To this end, oil spill experiments with a dilbit (Cold Lake Blend) and a physicochemically similar conventional heavy crude oil (Conventional Heavy Blend) were conducted for 104 days in large soil columns (1 m height × 0.6 m diameter) engineered to model contaminant transport in the unsaturated (vadose) zone. Around two-fold greater concentrations and 6-41 % faster rates of vadose zone transport of benzene, toluene, ethylbenzene and xylenes (BTEX) and polycyclic aromatic compounds (PACs) were observed in the dilbit- compared to conventional heavy crude-contaminated columns. As determined by Orbitrap mass spectrometry, the OxSx species abundances in the acid extractable organics (AEOs) fraction of column leachate from both oil types increased over time, ostensibly due to microbial degradation of petroleum. Bioaccumulation of petroleum constituents in fathead minnow (Pimephales promelas) larvae exposed to contaminated leachate was confirmed through the induction of developmental malformations lasting up to 34 days and increased abundance of cyp1a mRNA observed throughout the experiment. Toxicity was comparable between the two oils but could not be fully attributed to metals, BTEX, PACs or AEOs, implying the presence of uncharacterized teratogens capable of being transported within the vadose zone following terrestrial dilbit and conventional heavy crude oil surface spills.

Exploring the glycoprotein washing fluid-assisted cleanup for the restoration of oil-contaminated shorelines with environmental integrity.

Spilled oil in ocean can spread to the shoreline and cause long-term impacts on the shoreline's ecological environment. Therefore, removing oil accumulated on shorelines is crucial. This study proposed an innovative ovalbumin (OVA) fluid-assisted method for the cleanup of oiled shoreline substrates. The oil removal efficiency of OVA fluids was systematically investigated. Higher concentrations of OVA fluids effectively enveloped and immobilized the oil, aiding in its separation from the sand surface. The increased temperature reduced the viscosity of emulsions, facilitating improved flow and oil removal. High salinity promoted the creation of oil particle aggregates molecules and facilitated the release of oil from the sand surface. The factorial analysis demonstrated that a high salt environment significantly enhances the combined impact of temperature and pH on oil removal performance. Different methods for the responsive separation of washing effluents were studied, and the most effective separation method was adjusting the pH of effluents to 4.54 (the isoelectric point of OVA). Separated precipitates exhibited good decomposition efficiency through thermal decomposition and biodegradation. OVA fluids boast advantages, such as low cost, easy recyclability, and non-toxicity, while ensuring high oil removal efficiency and making them a promising eco-friendly technique for the cleanup of oiled shorelines.

A marine oil spill detection framework considering special disturbances using Sentinel-1 data in the Suez Canal.

The Suez Canal is a crucial international waterway due to its strategic location. The significant traffic flow not only stimulates economic development along the coast but also leads to a high frequency of oil spill accidents, which negatively impact the ecosystem and natural resources. Synthetic aperture radar (SAR) is an important remote sensing technology for monitoring oil spills, offering all-day and all-weather capabilities. However, special disturbances (SD) caused by imaging conditions, sensor parameters, and other factors can affect image quality, reducing the accuracy and efficiency of oil spill detection. To mitigate the negative impact of SD, an original oil spill detection framework was developed, based on the analysis of these disturbances, to detect oil spills at the northern entrance of the Suez Canal from 2015 to 2019. The framework included an advantageous featureset with SD adaptability and designs a classifier, Boosting Random Support Vector Machine (BRSVM), which combines a boosting strategy with Support Vector Machine (SVM). The study found that the superiority of the featureset was pivotal in oil spill detection. The classification accuracy and F-1 score achieved by BRSVM were 94.72 % and 95.33 %, respectively, outperforming other algorithms in functionality. These results indicate that the proposed framework holds significant potential for applications requiring large-scale, automated oil spill detection.

SGDBNet: A scene-class guided dual branch network for port UAV images oil spill detection.

The unmanned aerial vehicle (UAV) is usually flexible and frequently low-altitude flying without the influence of clouds and severe weather, and it is widely used for port oil spill detection (OSD). However, the background of the port is usually complex, the oil spills in UAV images are usually small and irregular, as well as the oil boundary is fuzzy, which has led to the failure of existing methods in accurately detecting the port oil spill. Here, we propose a scene-class guided dual branch network for port OSD based on UAV images, which can locate the oil spill areas of different sizes and suppress the influence caused by complex backgrounds. Specifically, the dual-branch network consists of semantic segmentation and image classification branches. The image classification branch utilizes the scene-class as the label and further can extract the feature attention, which can guide the semantic segmentation branch to learn the key area features. Second, we propose a multi-scale arbitrary shape convolution module, which can address the challenges caused by fuzzy oil boundaries and irregular small objects. Finally, due to the imbalance between oil spill pixels and other pixels, we design a joint loss to optimize the network. We evaluate our proposed method on a public UAV OSD dataset. The results show that our method is superior to the state-of-the-art method, achieving mIoU of 90.22 %, A of 96.03 %, P of 91.99 %, R of 92.56 %, and F1 of 92.28 %, which represents the feasibility of our method in port OSD and its potential to save a lot of manpower and material resources. The ablation experiment further demonstrates the effectiveness of each designed part.

Study on the variation patterns of sea surface oil spill characteristics based on GNSS-R under different wind speeds.

Marine oil spills severely pollute marine environments, making rapid and accurate detection crucial. However, current Global Navigation Satellite System-Reflectometry (GNSS-R) oil spill detection studies overlook the impact of sea wind, leading to false or missed detections. This study simulates the mean square slope (MSS), scattering coefficient, coherent power, and delay-Doppler map (DDM), and explores the impact of sea wind on GNSS-R oil spill detection parameters. Results indicate that at wind speeds ≤3 m/s, the differences in MSS and scattering coefficient between oil and water are minimal, but become significant at speeds >3 m/s. Oil-covered sea surfaces exhibit unusually high coherent power and DDM in spill zones compared to clean surfaces, with both metrics decreasing as wind speed rises. Finally, using Cyclone Global Navigation Satellite System (CYGNSS) data, this study validated the reliability of GNSS-R oil spill parameter variation patterns derived from simulation data, providing references for oil spill detection research.

Acute gastrointestinal symptoms associated with oil spill exposures among U.S. coast guard responders to the Deepwater Horizon oil spill.

PURPOSE: Research investigating gastrointestinal (GI) symptoms from oil spill-related exposures is sparse. We evaluated prevalent GI symptoms among U.S. Coast Guard responders deployed to the Deepwater Horizon oil spill cleanup. METHODS: Crude oil (via skin contact, inhalation, or ingestion routes), combined crude oil/oil dispersant exposures, other deployment exposures, deployment characteristics, demographics, and acute GI symptoms during deployment (i.e., nausea/vomiting, diarrhea, stomach pain, and constipation) were ascertained cross-sectionally via a post-deployment survey (median time between deployment end and survey completion 185 days) (N = 4885). Log-binomial regression analyses were employed to calculate prevalence ratios (PRs) and 95 % confidence intervals (CI). Effect modification was evaluated. RESULTS: In adjusted models, responders in the highest (versus lowest) tertile of self-reported degree of skin contact to crude oil were more than twice as likely to report nausea/vomiting (PR=2.45; 95 %CI, 1.85-3.23), diarrhea (PR=2.40; 95 %CI, 2.00-2.88), stomach pain (PR=2.51; 95 %CI, 2.01-3.12), and constipation (PR=2.21; 95 %CI, 1.70-2.89). Tests for trend were statistically significant (p < 0.05). Results were similar for crude oil exposure via inhalation and ingestion. Higher PRs for all symptoms were found with combined crude oil/dispersant exposure than with crude oil exposure alone. CONCLUSIONS: These results indicate positive associations between self-reported crude oil and combined crude oil/oil dispersant exposures and acute GI symptoms.

Polyurethane Foam and Algae-Based Activated Carbon Biocomposites for Oil Spill Remediation.

This study investigates the incorporation of algae-based activated carbon into polyurethane foam to improve a biocomposite for gasoil sorption. The biocomposites were thoroughly analyzed using various techniques to examine the properties of both the blank foam and the algae activated carbon foam with a carbon content of 4.41 mass% and particle diameter of 500 µm. These techniques included Scanning Electron Microscopy (SEM), thermogravimetric analysis (TGA), and density analysis. The TGA analysis revealed that the biocomposites had an impact on the onset temperature (Tonset) of the foams. Higher concentrations of the biocomposites resulted in a decrease in Tonset from approximately 310 °C in the blank foam (PUF0) to 300 °C in the composite (PUF3B). The final residue percentage also decreased from around 20% in PUF0 to 10% in PUF3B. Density analysis showed that the apparent density of the foam increased from 0.016 g/cm3 in the blank foam to 0.020 g/cm3 in the biocomposite (PUF3B), while the real density slightly decreased from 0.092 g/cm3 to 0.076 g/cm3, indicating a reduction in overall porosity from 82.5% to 74.4%. All foams that were modified showed an increase in their ability to absorb gasoil in a PUF/gasoil/water system. The optimized biocomposite (PUF1B), with 1.14 mass% of 500 µm algae carbon, displayed the highest sorption capacity, starting at approximately 50 g/g at 1.5 h and increasing to 53 g/g over 72 h. The analysis of adsorption kinetics revealed that by utilizing adsorption isotherms, particularly the Langmuir isotherm, a more accurate fit to the data was achieved. This allowed for the prediction of the maximum gasoil adsorption capacity. This study aims to further develop, analyze, and utilize biocomposites made from algae-based activated carbon and polyurethane. These materials offer a sustainable and environmentally friendly approach to cleaning up oil spills.

An overview of cellulose aerogels and foams for oil sorption: Preparation, modification, and potential of 3D printing.

Sorption is one of the most efficient methods to remediate the increasing oil spill incidents, but the currently available absorbents are inadequate to tackle such a global threat. Recently, numerous researchers have attempted to develop sustainable oil sorbents. Cellulose aerogels and foams, a type of lightweight porous material with excellent sorption performance, are one of the most promising candidates. Significant progress has been made in the past decade towards the development of cellulose porous materials as effective oil sorbents, with improvements in their oil sorption capacity, reusability, and enhanced multifunctionality, indicating their potential for oil spill remediation. This article reviews recent reports and provides a comprehensive overview of the preparation and modification strategies for cellulose porous materials, with a specific emphasis on their oil sorption performance and structure control. We also focus on the burgeoning 3D printing technology within this field, summarizing the latest advances with a discussion of the potential for using 3D printing to customize and optimize the structure of cellulose porous materials. Lastly, this review addresses current limitations and outlines future directions for development.

Toxicity of the oil spilled on the Brazilian coast at different degrees of natural weathering to early life stages of the zebrafish Danio rerio.

Toxicity of water accommodated fractions (WAF) from the oil spilled on the Brazilian coast at different stages of weathering were investigated using Danio rerio. Weathering stages included emulsified oil that reached the coast (OM) and oil collected 50 days later deposited on beach sand (OS) or adhered to shore rocks (OR). Parent and alkylated naphthalenes decreased whereas phenanthrenes increased from less weathered WAF-OM to more weathered WAF-OS and WAF-OR. More weathered WAF-OS and WAF-OR were more potent inducers of zebrafish developmental delay, suggesting that parent and alkylated phenanthrenes are involved. However, less weathered WAF-OM was a more potent inducer of failure in swim-bladder inflation than more weathered WAF-OS and WAF-OR, suggesting that parent and alkylated naphthalenes are involved. Decreases in heart rates and increased heart and skeletal deformities were observed in exposed larvae. Lowest observed effect concentrations for different developmental toxicity endpoints are within environmentally relevant polycyclic aromatic hydrocarbon concentrations.

Hydrocarbon composition of oils spilt on the Brazilian coast in 2019 and 2022.

The chemical composition of spilt oils from events that took place on the north-eastern coast of Brazil in 2019 and 2022 was investigated to better understand their sources, and post-spill processes. Oils from both events originated from different sources, based on their fingerprints, hydrocarbons composition and specific biomarkers, such as the C23 tricyclic terpane and oleanane. Despite the differences, the source rocks share similarities in paleoenvironments and depositional conditions and both oils suffered little weathering, mainly due to evaporation and dissolution. Our findings for 2019 spilt oil reinforce that it is a mixed product, enriched both in lighter n-alkanes and 25-norhopanes. Differently, the 2022 samples exhibited characteristics of a non-processed crude oil that originated from a paraffinic deposit in storage tanks. The molecular composition and diagnostic ratios reported for samples from these spill events help to establish baselines for ongoing monitoring of oil spills in marine ecosystems.

A new ship tracing technology from oil spills based on multi-source data.

Oil spill from ship can cause serious pollution to the Marine environment, but it is very difficult to find and confirm the troublemaker. In order to determine the oil spill ship, this paper proposes a new method to trace the source of ship oil spills and find the suspected ship that spills oil based on SAR imagery, AIS data and related marine environment data. First, we filter AIS data based on position of oil spill areas on remote sensing imagery and convert oil spill areas into trajectory points. Secondly, based on the Lagrangian particle motion model, a bidirectional drift model is proposed to calculate the average similarity between the forward and backward drift results. Finally, the most likely oil spill ship is determined according to the average similarity results. The results of the case study show that the method is effective and practical.

Efficient oil spill cleanup from water: Investigating the effectiveness of a sustainable anti-swelling hydrogel for rapid water repellency and oil absorption.

Considering the significant harm caused to aquatic ecosystems and marine life by oil spills and the discharge of oily wastewater, there is a pressing need to address this issue to protect our environment and prevent the wastage of valuable resources. We introduced a two-step approach to create an anti-swelling, water-repellent sorbent using a green polysaccharide called gum gellan, functionalized with Octadecyl trichlorosilane (OTS) through dip coating method. Natural gums like gellan have high absorption capability due to their large surface area. However, they are hydrophilic, which means they can only absorb water. This property makes them unsuitable for oil spill applications. To make gum gellan suitable for oil spill applications, we have modified it in this study. We have introduced a material called octadecyltrichlorosilane, which has low surface energy and hierarchical roughness. This modification changes the wettability of gellan from hydrophilic to hydrophobic/oleophilic, allowing it to absorb oil and repel water. The sorbent is analyzed using several techniques, such as FTIR, XRD, TGA, FE-SEM, BET, Raman, EDX, and H1-NMR. The hydrophobic sorbent obtained demonstrates low density, high surface area, and high porosity. These characteristics give it excellent floatability and immediate and exceptional selectivity for absorbing oil from water. Additionally, it does not absorb any detectable amount of water. The sorbent exhibited a water contact angle (WCA) of 140 ± 3 ° and an oil contact angle (OCA) of 0° for various oils and organic solvents. It has rapid oil absorption capacity of 3.72 g/g for diesel, and can be easily recovered after use. The BET analysis revealed that after the modification with OTS, the sorbent's total surface area increased from 0.579 m2/g to 4.713 m2/g. This indicates that the OTS modification greatly enhances the surface area and pore volume of the, thus improving its ability to absorb oil. This sorbent efficiently separates oil-in-water emulsions, both surfactant-stabilized and surfactant-free, achieving over 90% separation through gravity alone. Moreover, the sorbent can sustain its wettability even under harsh environmental conditions, including exposure to acids, alkalis, and salts. The absorption data predominantly aligned with the pseudo-2nd-order model. Thus, this sorbent provides a cost-effective alternative for efficiently absorbing and separating oil-water emulsions in households and industries.

Strategic location model for oil spill response vessels (OSRVs) considering oil transportation and weather uncertainties.

In the event of oil spills in offshore oil and gas projects, containment and dispersion equipment must be sent to the affected areas within a critical time by vessels known as oil spill response vessels (OSRVs). Here, we developed an optimization tool, integrated with an oil spill trajectory simulation model, both in deterministic and stochastic alternatives, to support decision-making during the strategic planning of OSRV operations. The tool was constructed in Python using GNOME for oil spill simulations and the GUROBI to solve the optimization model. The tool was applied to a case study in Brazil and afforded relevant recommendations. In terms of research contributions, we proved the viability of the integration between oil spill simulation and mathematical modeling for OSRV strategic operation planning, we explored the stochasticity of the problem with an innovative strategy and we demonstrated flexibility and easy applicability of the framework on real operations.