Effects Of Oil Spill Research Articles

Dose conversion coefficients to marine biota due to natural radionuclides in an oil spill accident using Monte Carlo simulation

The crude oil that reached the Brazilian coast in 2019 was the most significant environmental disaster ever recorded in Brazilian marine waters, with severe ecological and economic repercussions not fully dimensioned and understood. One consequence of this kind of oil spill is the absorbed dose delivered to marine organisms. The biota exposure to radiation can introduce consequences that range from fertility decrease to death of exposed population. Therefore, the current study aims to use the ICRP reference organisms (flatfish, crab, and brown seaweed) to assess marine biota exposure due to uranium and thorium series radiation. The oil spill scenario, as well as the reference biota, were simulated in MCNP. Thorium series stood out for presenting a significant contribution to the dose. Furthermore, it was noticed that a remedial action capable of removing Tl-208 would significantly decrease the effects of radiation on marine biota. Finally, dose conversion coefficients for both uranium and thorium series were obtained as oil activity concentration functions. The results obtained here can be used in an oil spill event along with other worldwide recognized models. In addition, the dose coefficients can be used strategically to assess the maximum exposure time for emergency oil control, removal, and mitigation.

Offshore Petroleum Leaking Source Detection Method From Remote Sensing Data via Deep Reinforcement Learning With Knowledge Transfer

A marine oil spill is an environmental pollution incident that generally has the attributes of a high speed, widespread, and long duration. It seriously threatens the marine ecological environment and related industries. It is vital to determine the source of the oil leakage so that it may be stopped and related hazards can be reduced. Oil spill accidents in the sea are generally located in offshore and navigation channels. With the rapid development of remote-sensing techniques, oil leak extraction using remote-sensing data has played an essential role in oil spill research. This paper proposes a Monte Carlo-based Deep Q-Transfer-learning Network (DQTN) offshore oil leak detection method that uses remote-sensing data. Remote-sensing data are utilized to continuously monitor a marine oil spill on the surface. The Estuarine and Coastal Ocean Model (ECOM) is utilized to simulate a marine oil spill event. The Deep Q Network (DQN) method with offline transferred knowledge is then utilized to determine the marine oil spill source location. In an experiment, based on the Bohai oil spill incident on June 2, 2011, the effectiveness of the remote-sensing-based DQTN marine oil spill search algorithm is verified. The accuracy of the targeted oil spill point is up to <inline-formula><tex-math notation="LaTeX">$98.97\%$</tex-math></inline-formula>.

Salmonid Smolt Caudal Fin and Liver Transcriptome Responses to Low Sulfur Marine Diesel and High Sulfur Fuel Oil Seawater Accommodated Fractions for Assessing and Tracking Oil Spill Effects in Marine Environments

Salmonid Smolt Caudal Fin and Liver Transcriptome Responses to Low Sulfur Marine Diesel and High Sulfur Fuel Oil Seawater Accommodated Fractions for Assessing and Tracking Oil Spill Effects in Marine Environments

Review of Potential Effects of Oil Spills on Coastal and Marine Resources on Western Libyan Coastal

An oil spill can significantly affect coastal resources and the economy through economic activities that depend on clean seawater, coastal areas, and tourist sites, which may cause substantial economic losses after oil spills. However, this effect usually depends on the volume and type of oil spill and how sensitive it is to coastal and marine resources. Libya has one of the important Mediterranean marine biodiversity hotspots, which is the western Libyan coast that extends from western Tripoli city to the Gulf of Gabes with Tunisia. This work considers some of the effects of oil spills on a variety of coastal industries and social activities. In addition, this work provides a systematic literature review of biological, economic, and cultural resources and their sensitivity to oil spill scenarios on this coast. The findings of this work help provide a basis for the definition of priorities for protection to develop the capacity to respond to oil spills on the Libyan coast.

Remote Sensing Detection of Algal Blooms in a Lake Impacted by Petroleum Hydrocarbons

The purpose of this study was to combine all available information on the state of Lake Pertusillo (Basilicata, Italy), both in the field and published, which included Sentinel-2A satellite data, to understand algal blooms in a lacustrine environment impacted by petroleum hydrocarbons. Sentinel-2A data was retrospectively used to monitor the state of the lake, which is located near the largest land-based oil extraction plant in Europe, with particular attention to chlorophyll a during algal blooms and petroleum hydrocarbons. In winter 2017, a massive dinoflagellate bloom (10.4 × 106 cell/L) of Peridinium umbonatum and a simultaneous presence of hydrocarbons were observed at the lake surface. Furthermore, a recent study using metagenomic analyses carried out three months later identified a hydrocarbonoclastic microbial community specialized in the degradation aromatic and nitroaromatic hydrocarbons. In this study, Sentinel-2A imagery was able to detect the presence of chlorophyll a in the waters, while successfully distinguishing the signal from that of hydrocarbons. Remotely sensed results confirmed surface reference measurements of lacustrine phytoplankton, chlorophyll a, and the presence of hydrocarbons during algal blooms, thereby explaining the presence of the hydrocarbonoclastic microbial community found in the lake three months after the oil spill event. The combination of emerging methodologies such as satellite systems and metagenomics represent an important support methodology for describing complex contaminations in diverse ecosystems.

Assessing the effect of oil spills on the dynamics of the microbial plankton community using a NPZD model

A mesocosms approach was used to assess the seasonal variability and the effect of oil spills on the structure and dynamics of marine microbial plankton communities in a coastal upwelling system. To this aim, four experiments were conducted during the main characteristic periods of the seasonal cycle in the Ría de Vigo (NW of Spain): spring phytoplankton bloom, summer stratification, autumn upwelling and winter mixing. In each of these experiments, enclosed communities in control and oiled bags were monitored and key variables and rates were measured during 9 days. Temporal changes in community structure and function were interpreted applying a simple NPZD (Nutrients-Phytoplankton-Zooplankton-Detritus) model in which the strategy was to maintain invariant as many model parameters as possible. The analysis of those parameters that were modified to accurately simulate the time evolution of the microbial communities in the mesocosms, provided important information about the main processes influencing the plankton community structure and dynamics in the bags as well as about the effect of oil. This modelling approach allowed assessing the dynamics of the four communities just varying phytoplankton maximum growth rates, bacterial growth efficiencies, microzooplankton grazing rates and phytoplankton sinking rates. Comparison of parameters in control and oiled bags in each period allowed to infer the negligible effect of oil spills on the structure and dynamics of the microbial plankton communities in this coastal upwelling system.

DSS-OSM: An Integrated Decision Support System for Offshore Oil Spill Management

The marine ecosystem, human health and social economy are always severely impacted once an offshore oil spill event has occurred. Thus, the management of oil spills is of importance but is difficult due to constraints from a number of dynamic and interactive processes under uncertain conditions. An integrated decision support system is significantly helpful for offshore oil spill management, but it is yet to be developed. Therefore, this study aims at developing an integrated decision support system for supporting offshore oil spill management (DSS-OSM). The DSS-OSM was developed with the integration of a Monte Carlo simulation, artificial neural network and simulation-optimization coupling approach to provide timely and effective decision support to offshore oil spill vulnerability analysis, response technology screening and response devices/equipment allocation. In addition, the uncertainties and their interactions were also analyzed throughout the modeling of the DSS-OSM. Finally, an offshore oil spill management case study was conducted on the south coast of Newfoundland, Canada, demonstrating the feasibility of the developed DSS-OSM.

Heavy Metals, Proximate Analysis and Brine Shrimp Lethality of Vernonia amygdalina and Ocimum gratissimum Growing in Crude Oil-Rich Delta State, Nigeria.

Vernonia amygdalina (VA) and Ocimum gratissimum (OG) are among the most frequently consumed vegetables in Kokori and Abraka communities of Delta State, Nigeria. However, the continuous crude oil exploration and spillages in Kokori may threaten their safety for use as food and medicine. Twelve samples of VA and OG obtained from crude oil-rich and crude oil-free communities were comparatively analysed for proximate composition, heavy metals, and cytotoxicity. Data obtained were subjected to various multivariate statistical techniques, including principal component analysis (PCA), biplot, and analysis of variance (ANOVA), to investigate the correlations between the vegetables from the different communities and the effect of crude oil exploration and spill on plant biomass. Results obtained indicate a significant difference (p < 0.05) in the proximate composition of VA and OG and higher heavy metal content for VA from the crude oil-spill Kokori. Two VA collections from Kokori were exceptionally toxic to cellular crustaceans.

Acceleration the bacterial biodegradation of crude oil pollution using Fe2O3 and ZnO nanoparticles

Oil spill effects on environments and habitats can be catastrophic, where they can kill plants and animals, disturb salinity/pH levels, and pollute air/water. The aim of this work was to accelerate the biodegradation of crude oil by combination with nanoparticles (NPs). Two hundred and sixty-five bacterial isolates were selected from polluted soil and water samples. The most promising isolates were identified by 16S rRNA genes sequencing. Iron oxide nanoparticles (Fe2O3 NPs) and zinc oxide nanoparticles (ZnO NPs) were synthesized and characterized. The SEM and TEM images of Fe2O3 and ZnO NPs revealed that nanoparticles were mostly spherical in shape forming chain-like aggregates with average sizes 48 and 25 nm, respectively. The combination of Fe2O3 NPs and biodegradation bacterial species Bacillus thuringiensis S1, Pseudomonas aeruginosa S2, and Psychrobacter faecalis S3 achieved 97.62, 97.23, and 97.89%, while with ZnO NPs were 96.27, 95.81, and 96.35%, respectively after 3 days only. This study proven that the biodegradation process improved and reaches 98 and 96% by a combination of selected optimum bacterial isolates with Fe2O3 and ZnO NPs, respectively. The novel combination method between bacterial species and nanoparticles accelerated the biodegradation reaching up to 98% of crude oil within three days and can be used to enhance in situ bioremediation of petroleum contamination.

Hydrodynamic model of an oil spill on the Earth’s surface and the use of expert technologies in its implementation to assess pollution methods

A volumetric model of accidental oil spills on the land surface was developed, based on numerical methods for solving hydrodynamic equations, and taking into account the processes of oil spreading over the surface, its filtration into the soil and evaporation into the atmosphere. Based on the results of calculations using the hydrodynamic model for the most probable scenarios of oil spills, it is possible to obtain an estimate of the spatial-temporal scale of the spill, which, together with data on the terrain and the presence of water bodies, is the input data for the expert model. Based on the joint use of the hydrodynamic model of the oil spill and expert technologies, the territory of the Nenets Autonomous Okrug and the South Khylchuyu-Varandey oil pipeline were zoned in more detail. It makes it possible to predict the scale and areas most susceptible to negative impact in the event of an accidental oil spill, and to make the necessary decisions for the location of the spill response facilities, as well as the facilities themselves, already at the stage of selection and design of the pipeline route.

Characteristics and oil sorption effectiveness of areca nut husk

Oil spill accident occur due to human carelessness and error during oil transportation, storage, and production of the hydrocarbon. Effect of oil spill can be either short or long term. It is not only affecting the environment but also marine life, economy, and social. Thus, effective recovery and control method must be carefully implemented to minimise the effect. There are various studies and technologies to mitigate the oil spill incident but mostly the techniques are very expensive, difficult and use of non-environmental friendly material. Hence, this study is conducted to study areca nut husk as a potential oil adsorbent for oil spill recovery. The characteristics of areca nut husk include morphological structures and chemical composition. The areca nut husk oil sorption effectiveness and capacity also been investigated. Areca nut husk were tested for both heavy crude oil and diesel adsorption. The result shows that the efficiency of oil sorption of areca nut husk toward heavy crude oil is 14.8% and it can be an alternative sorbent material to the current modern material. Meanwhile, the areca nut husk is not recommended to be used for diesel absorption activity for its low effectiveness (i.e., 2.04%) showed in this study.

Effect of spilled diluted bitumen on chemical air-water exchange in boreal lake limnocorrals

Following spills into water, petroleum oils can spread widely and produce surface slicks. Resulting slicks may impede volatilization and possibly increase chemical persistence in water. While the influence of oil films on chemical air-water exchange has been examined through theoretical and laboratory studies, field studies have not been conducted to assess the relevance of these effects following actual oil spill events. Here we evaluated the effect of diluted bitumen (dilbit) experimentally spilled in limnocorrals installed in a boreal lake on the volatilization of sulfur hexafluoride (SF6), a non-reactive volatile tracer gas. Dilbit spills were monitored over 70 days and SF6 was introduced twice (after 7 and 48 days) to evaluate the influence of spilled dilbit on the loss of SF6 from water. Volatilization rate constants of SF6 (kVOL) significantly decreased by up to 80% with increasing total dilbit spill cover. Using a theoretical equation, decreases in kVOL were largely explained by a reduction in open water area where chemical exchange across the air-water interface occurs. Apparent effects of the slick on SF6 mass transfer were estimated to be smaller by comparison (20%).To account for this reduction in volatilization, oil spill fate models should include a correction to consider the impact of spill cover on the air-water exchange of organic chemicals.

From the Worldwide Impact of a Global Strategy for Asthma (GINA) to the Tragic Effects of Sea Oil Spill

From the Worldwide Impact of a Global Strategy for Asthma (GINA) to the Tragic Effects of Sea Oil Spill

PETROLEUM OIL SPILL MANAGEMENT IN OFFSHORE

Oil spills are disasters occurring because of human activities. Due to harmful effects of oil spill to environment similarly as marine life, it becomes essential to search out suitable solution both for oil spill prevention and oil spill response. However, preventing oil spill is certainly better option. Nevertheless, when a spill occurs, the simplest approach for controlling the spill is to require action quickly and in a very wellorganized and efficient manner. This paper discusses about oil spill and related factors. It will also present potential solutions for prevention of oil spill.

Effect of WAF crude oil exposure to larvae development of the black-scar oyster Crasosstrea iredalei

Oil spills potentially effect exposed organisms at various stage of life. This work aimed to access health risk of crude oil to larva development of a sessile organism the Black scar oyster Crassostrea iredalei by using water accommodate fraction (WAF) of crude oil. Male and female gametes was collect and fertilized to obtained larvae at cleavage stage. The larvae were then incubate in various concentrations of WAF (0, 6.25, 12.5, 25, 50, and 100 %). After 24 hour of exposure, normal D-shaped veliger larva (D-larva) was observe. The result showed that WAF crude oil affected the development and the successful of D-larva development. Severity of WAF effect was increasing with dosages of exposure. The abnormal larva developments were increasing in the high concentrations. The information obtaining from current work is important for health risk assessment of crude oil contamination incident in marine ecosystem. This study will also contribute valuable knowledge needed for aquaculture to know effect of crude oil spill to oyster farming area.

Antibacterial and anatomical defenses in an oil contaminated, vulnerable seaduck.

Oil spills have killed thousands of birds during the last 100 years, but nonlethal effects of oil spills on birds remain poorly studied. We measured phenotype characters in 819 eiders Somateria mollissima (279 whole birds and 540 wings) of which 13.6% were oiled. We tested the hypotheses that (a) the morphology of eiders does not change due to oil contamination; (b) the anatomy of organs reflects the physiological reaction to contamination, for example, increase in metabolic demand, increase in food intake, and counteracting toxic effects of oil; (c) large locomotion apparatus that facilitates locomotion increases the risk of getting oiled; and (d) individual eiders with a higher production of secretions from the uropygial grand were more likely to have oil on their plumage. We tested whether 19 characters differed between oiled and nonoiled individuals, showing a consistent pattern. The final model retained seven predictor variables showing relationships between eiders contaminated with oil and food consumption, flight, and diving abilities. We tested whether these effects were due to differences in body condition, liver mass, empty gizzard mass, or other characters that could have been affected by impaired flight and diving ability. There was no evidence of such negative impact of oiling on eiders. We found that significant exposure to oil was associated with increased diversity of antibacterial defense. Oiled eiders did not constitute a random sample, and superior diving ability as reflected by large foot area was at a selective disadvantage during oil spills. Thus, specific characteristics predispose eiders to oiling, with an adaptation to swimming, diving, and flying being traded against the costs of oiling. In contrast, individuals with a high degree of physiological plasticity may experience an advantage because their uropygial secretions counteract the effects of oil contamination.

An optimized metamodel for predicting damage and oil outflow in tanker collision accidents

Society is concerned about maritime accidents since pollution, such as oil spills from ship accidents, adversely affects the marine environment. Operational and strategic pollution preparedness and response risk management are essential activities to mitigate such adverse impacts. Quantitative risk models and decision support systems (DSS) have been proposed to support these risk management activities. However, there currently is a lack of computationally fast and accurate models to estimate oil spill consequences. While resource-intensive simulation models are available to make accurate predictions, these are slow and cannot easily be integrated into quantitative risk models or DSS. Hence, there is a need to develop solutions to accelerate the computational process. A fast and accurate metamodel is developed in this work to predict damage and oil outflow in tanker collision accidents. To achieve this, multiobjective optimization is applied to three metamodeling approaches: Deep Neural Network, Polynomial Regression, and Gradient Boosting Regression Tree. The data used in these learning algorithms are generated using state-of-the-art engineering models for accidental damage and oil outflow dynamics. The multiobjective optimization approach leads to a computationally efficient and accurate model chosen from a set of optimized models. The results demonstrate the metamodel’s robust capacity to provide accurate and computationally efficient estimates of damage extents and volume of oil outflow. This model can be used in maritime risk analysis contexts, particularly in strategic pollution preparedness and response management. The models can also be linked to operational response DSS when fast, and reasonably accurate estimates of spill sizes are critical.

Oil Spill Detection Using Satellite Imagery

&lt;span&gt;Since oil exploration began, oil spills have become a serious problem. When drilling for oil, there is always a risk of an oil spill. With the new development of technology over the years, oil spill detection has become much easier making the clean-up of a spill to happen much faster reducing the risk of a large spread. In this study, remote sensing techniques were used to detect the Deep-water Horizon oil spill through a change detection method. The change detection method allows the viewer to determine the difference of an area before and after an oil spill as well as detect the irregular difference on a surface. To confirm the effectiveness of change detection method, two approaches were used each showing the differences in the images before and after the spill allowing the size and shape to be identified. The swipe tool in the ArcGIS software was used to visually show the changes. The difference tool was also used to both visually and statistically to investigate the difference before and after the Deep-water Horizon oil spill event.&lt;/span&gt;

Performance Comparison of Oil Spill and Ship Classification from X-Band Dual- and Single-Polarized SAR Image Using Support Vector Machine, Random Forest, and Deep Neural Network

It is well known that the polarization characteristics in X-band synthetic aperture radar (SAR) image analysis can provide us with additional information for marine target classification and detection. Normally, dual-and single-polarized SAR images are acquired by SAR satellites, and then we must determine how accurate the marine mapping performance from dual-polarized (pol) images is versus the marine mapping performance from the single-pol images in a given machine learning model. The purpose of this study is to compare the performance of single- and dual-pol SAR image classification achieved by the support vector machine (SVM), random forest (RF), and deep neural network (DNN) models. The test image is a TerraSAR-X dual-pol image acquired from the 2007 Kerch Strait oil spill event. For this, 824,026 pixels and 1,648,051 pixels were extracted from the image for the training and test, respectively, and sea, ship, oil, and land objects were classified from the image by using the three machine learning methods. The mean f1-scores of the SVM, RF, and DNN models resulting from the single-pol image were approximately 0.822, 0.882, and 0.889, respectively, and those from the dual-pol image were about 0.852, 0.908, and 0.898, respectively. The performance improvement achieved by dual-pol was about 3.6%, 2.9%, and 1% in SVM, RF, and DNN, respectively. The DNN model had the best performance (0.889) in the single-pol test while the RF model was best (0.908) in the dual-pol test. The performance improvement was approximately 2.1% and not noticeable. If the condition that dual-pol images have two-times lower spatial resolution versus single-pol images in the azimuth direction is considered, a small improvement may not be valuable. Therefore, the results show that the performance improvement by X-band dual-pol image may be not remarkable when classifying the sea, ships, oil spills, and sea and land surfaces.

Cellular accumulation of crude oil compounds reduces the competitive fitness of the coral symbiont Symbiodinium glynnii

Oil spill events in the marine environment can have a deleterious impact on the affected ecosystems, such as coral reefs, with direct consequences for their socioeconomic value. The mutualistic relationship between tropical corals and their dinoflagellate symbionts (Symbiodiniaceae) provide structural and nutritional basis for a high local biodiversity in oligotrophic waters. Here, we investigated effects of crude oil water-accommodated fraction on the competitive fitness of the model zooxanthellae species Symbiodinium glynnii. Results of laboratory essays demonstrate that crude oil carbon is incorporated into the cellular biomass with a concomitant change of δ13C isotopic value. Carcinogenic/mutagenic polycyclic aromatic hydrocarbons were identified in the culture media and were responsible for a linear reduction in population growth of S. glynnii, presumably related to energy relocation for DNA repair. Additionally, the experiments revealed that physiological effects induced by crude oil compounds are genetically inherited by the following generations under non-contaminated growth conditions, and induce a reduction in the competitive fitness to cope with other environmental parameters, such as low salinity. We suggest that the effects of crude oil contamination represent an imparing factor for S. glynnii coping with anthropogenic drivers (e.g. warming and acidification) and interfere with the delicate symbiont-host relationship of tropical corals. This is especially relevant in the coastal areas of northeastern Brazil where an oil spill event deposited crude oil on shallow water sediments with the potential to be resuspended to the water column by physical and/or biological activity, enhancing the risk of future coral bleaching events.