A CFD Model to Evaluate Near-Surface Oil Spill from a Broken Loading Pipe in Shallow Coastal Waters

Oil spills continue to generate various issues and concerns regarding their effect and behavior in the marine environment,owing to the related potential for detrimental environmental,economic and social implications.It is essential to have a solid understanding of the ways in which oil interacts with the water and the coastal ecosystems that are located nearby.This study proposes a simplified model for predicting the plume-like transport behavior of heavy Bunker C fuel oil discharging downward from an acutely-angled broken pipeline located on the water surface.The results show that the spill overall profile is articulated in three major flow areas.The first,is the source field,i.e.,a region near the origin of the initial jet,followed by the intermediate or transport field,namely,the region where the jet oil flow transitions into an underwater oil plume flow and starts to move horizontally,and finally,the far-field,where the oil re-surface and spreads onto the shore at a significant distance from the spill site.The behavior of the oil in the intermediate field is investigated using a simplified injection-type oil spill model capable of mimicking the undersea trapping and lateral migration of an oil plume originating from a negatively buoyant jet spill.A rectangular domain with proper boundary conditions is used to implement the model.The Projection approach is used to discretize a modified version of the Navier-Stokes equations in two dimensions.A benchmark fluid flow issue is used to verify the model and the results indicate a reasonable relationship between specific gravity and depth as well as agreement with the aerial data and a vertical temperature profile plot.

Oil Spill Risk Assessment of Offshore Pipeline in the Bohai Sea Under the Perspective of Ecological Protection

In recent decades,the exploration and development of marine oil and gas resources have increased significantly to meet the increasing energy demand of mankind.The Bohai Sea is a semi-closed continental sea that has a weak water exchange capacity and high ecological fragility.However,at present,more than 200 oil platforms have been built in the Bohai Sea,with more than 270 offshore oil pipelines having a length exceeding 1600 km.The oil spill pollution of offshore platforms has a great impact on the marine environment and ecosystems.Therefore,a comprehensive assessment of its risks is of great practical significance.This paper systematically constructs a comprehensive oil spill risk assessment model that combines the oil spill risk probability model and the ocean hydrodynamic model.This paper uses the Bohai Sea offshore pipeline as an example to assess its oil spill risk.The high-risk-value areas of the Bohai Sea offshore pipeline are mainly distributed at the bottom of Liaodong Bay,the bottom of Bohai Bay,near the Caofeidian area,and the northern part of the Yellow River Estuary.

Transport and behavior of marine oil spill containing polycyclic aromatic hydrocarbons in mesocosm experiments

Polycyclic aromatic hydrocarbons(PAHs)are one of the most important groups in oil,and re sponsible for major toxic and/or carcinogenic impact on humans and wildlife.It is important to understand the behavior of PAHs in marine environment after an oil-spill incident.However,interaction between petroleum PAHs and microbial communities in a marine environment remains unclear.Therefore,a series of mesocosm experiments were conducted.in which water-accommodated fraction(WAF)of oil was generated to simulate an oil-spill scenario and to analyze the transport and behavior of marine oil spill containing PAHs with and without dispersants.Results indicate that the application of dispersant could increase the concentration of total PAHs in water column due mainly to significant increase in the concentration of highmolecular weight(HMW)PAHs at a lower removal rate.At the end of the 7-day experiment,significant amount of HMW PAHs were accumulated in sediment.In general,the application of dispersant did not increase the sediment uptake of PAHs but increased the PAHs concentration in water column.

Parameterization Method of Wind Drift Factor Based on Deep Learning in the Oil Spill Model

Oil spill prediction is critical for reducing the detrimental impact of oil spills on marine ecosystems,and the wind strong-ly influences the performance of oil spill models.However,the wind drift factor is assumed to be constant or parameterized by linear regression and other methods in existing studies,which may limit the accuracy of the oil spill simulation.A parameterization method for wind drift factor(PMOWDF)based on deep learning,which can effectively extract the time-varying characteristics on a regional scale,is proposed in this paper.The method was adopted to forecast the oil spill in the East China Sea.The discrepancies between predicted positions and actual measurement locations of the drifters are obtained using seasonal statistical analysis.Results reveal that PMOWDF can improve the accuracy of oil spill simulation compared with the traditional method.Furthermore,the parameteriza-tion method is validated with satellite observations of the Sanchi oil spill in 2018.

Application of Vegetation Indices for Detection and Monitoring Oil Spills in Ahoada West Local Government Area of Rivers State,Nigeria

The study evaluated the environmental effects of an oil spill in Joinkrama 4 and Akimima Ahoada West LGA,Rivers State,Nigeria,using various vegetation indices.Location data for the spill were obtained from the Nigeria Oil Spill Detection and Response Agency,and Landsat imagery was acquired from the United States Geological Survey.Three soil samples were collected from the affected area,and their analysis included measuring total petroleum hydrocarbons(TPH),total hydrocarbons(THC),and polycyclic aromatic hydrocarbons(PAH).The obtained data were processed with ArcGIS software,utilizing different vegetation indices such as the Normalized Difference Vegetation Index(NDVI),Atmospheric Resistant Vegetation Index(ARVI),Soil Adjusted Vegetation Index(SAVI),Green Short Wave Infrared(GSWIR),and Green Near Infrared(GNIR).Statistical analysis was performed using SPSS and Microsoft Excel.The results consistently indicated a negative impact on the environment resulting from the oil spill.A comparison of spectral reflectance values between the oil spill site and the non-oil spill site showed lower values at the oil spill site across all vegetation indices(NDVI 0.0665-0.2622,ARVI-0.0495-0.1268,SAVI 0.0333-0.1311,GSWIR-0.183-0.0517,GNIR-0.0104--0.1980),indicating damage to vegetation.Additionally,the study examined the correlation between vegetation indices and environmental parameters associated with the oil spill,revealing significant relationships with TPH,THC,and PAH.A t-test with a significance level of p<0.05 indicated significantly higher vegetation index values at the non-oil spill site compared to the oil spill site,suggesting a potential disparity in vegetation health between the two areas.Hence,this study emphasizes the harmful effect of oil spills on vegetation and highlights the importance of utilizing vegetation indices and spectral reflectance analysis to detect and monitor the impact of oil spills on vegetation.

Research on the risk-based model for regional emergency resource allocation for ship-source oil spill

The key point for rational allocation of emergency resources is to match the oil spill response capacity with the risk of oil spill. This paper proposes an innovative risk-based model for quantitative regional emergency resource allocation, which comprehensively analyzes the factors such as oil spill probability, hazard consequences, oil properties, weathering process and operation efficiency, etc. The model calculates three major resources, i.e., mechanical recovery, dispersion and absorption, according to the results of risk assessment. In a field application in Xiaohu Port, Guangzhou, China, and the model achieved scientific and rational allocation of emergency resources by matching the assessed risk with the regional capacity, and allocating emergency resources according to capability target. The model is considered to be beneficial to enhancing the resource efficiency and may contribute to the planning of capacity-building programs in high-risk areas.

Experimental study of C-band microwave scattering characteristics during the emulsification process of oil spills

In this study,oil spill experiments were performed in a water tank to determine changes in the surface scattering characteristics during the emulsification of oil spills.A C-band fully-polarimetric microwave scatterometer and a vector network analyzer were used to observe films of the following oils:crude oil with an asphalt content below3%that is prone to emulsification(type A),fresh crude oil extracted from an oilfield(type B),and industrial crude oil that was dehydrated and purified(type C).The difference in the backscatter results between the emulsified oil film and the calm water surface under C-band microwaves and the influence of the emulsification of the oil film on the backscatter were analyzed in detail.The results demonstrate that under a low-wind and no-waves condition(the maximum wave height was below than 3 mm),the emulsification of crude oil could modulated the backscatter through changes in the surface roughness and the dielectric constant,where the surface roughness had the dominant effect.The surface backscatters of the type B oil were greater than that of the type C oil in both the emulsified and non-emulsified states.In the non-emulsified state,the average differences in the backscatter between the type B and C oils were 2.19 dB,2.63 dB,and 2.21 dB for the polarization modes of VV,HH,and HV/VH,respectively.Smaller corresponding average differences of 0.98 dB,1.49 dB,and 1.5 dB were found for the emulsified state with a 20%moisture constant for the oil film.The results demonstrated that the surface roughness of the different oil films could vary due to the differences in the oil compositions and the oil film properties,which in turn affect the backscatter of the oil film surface.

Oil Spill Dispersion Forecasting with the Aid of a 3D Simulation Model

The simulation of the transport and fate of an oil slick, accidentally introduced in the marine environment, is the focus of this research. An oil spill dispersion forecasting system （DIAVLOS forecasting system）, based on wind, wave and ocean circulation forecasting models is developed. The 3-D oil spill model, by the University of Thessaloniki, is based on a Lagrangian （tracer） model that accounts for the transport-diffusion-dispersion and physicochemical evolution of an oil slick. The high resolution meteorological, hydrodynamic and wave models are coupled with the operational systems ALERMO and SKIRON of the University of Athens. The modelling system was successfully assembled and tested under theoretical and realistic scenarios, in order to be applied in forecasting mode and be used by local authorities when an accident occurs. As a result, a 48-hours oil spill dispersion forecasting system was synthesized aiming primarily at the oil spill management at the Burgas-Alexandroupolis oil-pipe terminal, part of a greater busy coastal basin in North Aegean.

Characterization of water-in-crude oil emulsions in oil spill response

The formation of water in crude oil emulsions occurs when crude oils are spilled into sea. The water in crude oil emulsions significantly change the properties of the spilled crude oils and in turn influence the choices made relating to oil spill countermeasures. The water in crude oil emulsions were characterized using various techniques in this study. The environmental scanning electron microscopy observation of water droplets in the emulsions is also presented. It is a powerful tool in emulsion observations.

A review of numerical modeling research on the marine oil spill

Recently, as the oceanic activities are more and more frequently carried out, marine oil spill accidents bring to enormous harm to the economy and society in China, especially in the Offshore China. Marine oil spill is one kind of serious disasters which severely damages the marine environment. Aiming at the improvement of the emergency response system and response ability for the oil spill, the relative technologies on oil spill response are developed. This paper briefly introduces the developments and achievements of the oil spill numerical models, including the oil spill spreading model, the oil spill transport model, the oil particle model as well as the oil spill weathering model, which provide the theoretic criterions for the future work on the oil spill predicting and response.

Bioremediation of Oil Spills in Cold Environments: A Review

Oil spills have become a serious problem in cold environments with the ever-increasing resource exploitation, transportation, storage, and accidental leakage of oil. Several techniques, including physical, chemical, and biological methods, are used to recover spilled oil from the environment. Bioremediation is a promising option for remediation since it is effective and economic in removing oil with less undue environmental damages. However, it is a relatively slow process in cold regions and the degree of success depends on a number of factors, including the properties and fate of oil spilled in cold environments, and the major microbial and environmental limitations of bioremediation. The microbial factors include bioavailability of hydrocarbons, mass transfer through the cell membrane, and metabolic limitations. As for the environmental limitations in the cold regions, the emphasis is on soil temperatures, freeze-thaw processes, oxygen and nutrients availability, toxicity, and electron acceptors. There have been several cases of success in the polar regions, particularly in the Arctic and sub-Arctic regions. However, the challenges and constraints for bioremediation in cold environments remain large.

The Impact of Diferent Vertical Difusion Schemes in a Three-Dimensional Oil Spill Model in the Bohai Sea

Vertical transport is critical to the movement of oil spills in seawater. Breaking waves play an important role by developing a well-defined mixing layer in the upper part of the water column. A three-dimensional （3-D） Lagrangian random walk oil spill model was used here to study the influence of sea surface waves on the vertical turbulence movement of oil particles. Three vertical diffusion schemes were utilized in the model to compare their impact on oil dispersion and transportation. The first scheme calculated the vertical eddy viscosity semi-empirically. In the second scheme, the vertical diffusion coefficient was obtained directly from an Eulerian hydrodynamic model （Princeton Ocean Model, POM2k） while considering wave- caused turbulence. The third scheme was formulated by solving the Langevin equation. The trajectories, percentages of oil particles intruding into water, and the vertical distribution structures of oil particles were analyzed for a series of numerical experiments with different wind magnitudes. The results showed that the different vertical diffusion schemes could generate different horizontal trajectories and spatial distributions of oil spills on the sea surface. The vertical diffusion schemes caused different water-intruding and resurfacing oil particle behaviors, leading to different horizontal transport of oil particles at the surface and subsurface of the ocean. The vertical diffusion schemes were also applied to a realistic oil spill simulation, and these results were compared to satellite observations. All three schemes yielded acceptable results, and those of the third scheme most closely simulated the observed data.

A study of oil spill detection using ASAR images

The oil spilled worldwide causes ecological disasters that result in enormous damages to the quality of marine environment, and great expenses on clear-up operations are needed. Due to its wide coverage and day-night all-weather observation capability, Synthetic Aperture Radar （SAR） is an important tool for oil spill monitoring and detection. C-band SAR is well adapted to detect oil pollution because oil slicks dampen the Bragg waves and reduce radar backscattering coefficients. In order to detect the area of oil slicks, the algorithm consists of these steps： Preprocessing, Masking of land areas, Detection of dark spots, Spot feature extraction, Dark spot classification. In this paper, the authors examined two coastal regions around Hong Kong and Yantai, China. The obtained results performed on Envisat ASAR images have demonstrated that it is efficient to detect oil spill around the coastal regions. The methodology still needs to be refined with the collection of more SAR data in the near future.

The error source analysis of oil spill transport modeling: a case study

Numerical modeling is an important tool to study and predict the transport of oil spills. However, the accu- racy of numerical models is not always good enough to provide reliable information for oil spill transport. It is necessary to analyze and identify major error sources for the models. A case study was conducted to analyze error sources of a three-dimensional oil spill model that was used operationally for oil spill forecast- ing in the National Marine Environmental Forecasting Center （NMEFC）, the State Oceanic Administration, China. On June 4, 2011, oil from sea bed spilled into seawater in Penglai 19-3 region, the largest offshore oil field of China, and polluted an area of thousands of square kilometers in the Bohai Sea. Satellite remote sensing images were collected to locate oil slicks. By performing a series of model sensitivity experiments with different wind and current forcings and comparing the model results with the satellite images, it was identified that the major errors of the long-term simulation for oil spill transport were from the wind fields, and the wind-induced surface currents. An inverse model was developed to estimate the temporal variabil- ity of emission intensity at the oil spill source, which revealed the importance of the accuracy in oil spill source emission time function.

Field Experiments of Multi-Channel Oceanographic Fluorescence Lidar for Oil Spill and Chlorophyll-a Detection

A Multi-channel Oceanographic Fluorescence Lidar(MOFL), with a UV excitation at 355 nm and multiple receiving channels at typical wavelengths of fluorescence from oil spills and chlorophyll-a(Chl-a), has been developed using the Laser- induced Fluorescence(LIF) technique. The sketch of the MOFL system equipped with a compact multi-channel photomultiplier tube(MPMT) is introduced in the paper. The methods of differentiating the oil fluorescence from the background water fluorescence and evaluating the Chl-a concentration are described. Two field experiments were carried out to investigate the field performance of the system, i.e., an experiment in coastal areas for oil pollution detection and an experiment over the Yellow Sea for Chl-a monitoring. In the coastal experiment, several oil samples and other fluorescence substances were used to analyze the fluorescence spectral characteristics for oil identification, and to estimate the thickness of oil films at the water surface. The experiment shows that both the spectral shape of fluorescence induced from surface water and the intensity ratio of two channels(I495/I405) are essential to determine oil-spill occurrence. In the airborne experiment, MOFL was applied to measure relative Chl-a concentrations in the upper layer of the ocean. A comparison of relative Chl-a concentration measurements by MOFL and the Moderate Resolution Imaging Spectroradiometer(MODIS) indicates that the two datasets are in good agreement. The results show that the MOFL system is capable of monitoring oil spills and Chl-a in the upper layer of ocean water.

Studies on Marine Oil Spills and Their Ecological Damage

The sources of marine oil spills are mainly from accidents of marine oil tankers or freighters, marine oil-drilling platforms, marine oil pipelines, marine oilfields, terrestrial pollution, oil-bearing atmosphere, and offshore oil production equipment. It is concluded upon analysis that there are two main reasons for marine oil spills: (I) The motive for huge economic benefits of oil industry owners and oil shipping agents far surpasses their sense of ecological risks. (II) Marine ecological safety has not become the main concern of national security. Oil spills are disasters because humans spare no efforts to get economic benefits from oil. The present paper draws another conclusion that marine ecological damage caused by oil spills can be roughly divided into two categories: damage to marine resource value (direct value) and damage to marine ecosystem service value (indirect value). Marine oil spills cause damage to marine biological, fishery, seawater, tourism and mineral resources to various extents, which contributes to the lower quality and value of marine resources.

Hydrocarbon-Degrading Bacteria and Paraffin from Polluted Seashores 9 Years after the Nakhodka Oil Spill in the Sea of Japan

Pollution of petroleum hydrocarbons, in particular oil spills, has attracted much attention in the past and recent decades. Oil spills influence natural microbial community, and physical and chemical properties of the affected sites. The biodegradation of hydrocarbons by microorganisms is one of the primary ways by which oil spill is eliminated from contaminated sites. One such spill was that of the Russian tanker the Nakhodka that spilled heavy oil into the Sea of Japan on January 2, 1997. The impact of the Nakhodka oil spill resulted in a viscous sticky fluid fouling the shores and affected natural ecosystems. This paper describes the weathering of hydrocarbon-degrading bacteria （genus Pseudomonas） and crystallized organic compounds from the Nakhodka oil spill-polluted seashores after nine years. The Nakhodka oil has hardened and formed crust of crystalline paraffin wax as shown by XRD analysis （0.422, 0.377, and 0.250 nm d-spacing） in association with graphite and calcite after 9 years of bioremediation. Anaerobic reverse side of the oil crust contained numerous coccus typed bacteria associated with halite. The finding of hydrocarbon-degrading bacteria and paraffin wax in the oil crust may have a significant effect on the weathering processes of the Nakhodka oil spill during the 9- year bioremediation.

Oil spill detection by a support vector machine based on polarization decomposition characteristics

Marine oil spills have caused major threats to marine environment over the past few years.The early detection of the oil spill is of great significance for the prevention and control of marine disasters.At present,remote sensing is one of the major approaches for monitoring the oil spill.Full polarization synthetic aperture radarc SAR data are employed to extract polarization decomposition parameters including entropy（H） and reflection entropy（A）.The characteristic spectrum of the entropy and reflection entropy combination has analyzed and the polarization characteristic spectrum of the oil spill has developed to support remote sensing of the oil spill.The findings show that the information extracted from（1-A）×（1-H） and（1-H）×A parameters is relatively evident effects.The results of extraction of the oil spill information based on H×A parameter are relatively not good.The combination of the two has something to do with H and A values.In general,when H〉0.7,A value is relatively small.Here,the extraction of the oil spill information using（1-A）×（1-H） and（1-H）×A parameters obtains evident effects.Whichever combined parameter is adopted,oil well data would cause certain false alarm to the extraction of the oil spill information.In particular the false alarm of the extracted oil spill information based on（1-A）×（1-H） is relatively high,while the false alarm based on（1-A）×H and（1-H）×A parameters is relatively small,but an image noise is relatively big.The oil spill detection employing polarization characteristic spectrum support vector machine can effectively identify the oil spill information with more accuracy than that of the detection method based on single polarization feature.

Detection of oil spill based on CBF-CNN using HY-1C CZI multispectral images

Accurate detection of an oil spill is of great significance for rapid response to oil spill accidents.Multispectral images have the advantages of high spatial resolution,short revisit period,and wide imaging width,which is suitable for large-scale oil spill monitoring.However,in wide remote sensing images,the number of oil spill samples is generally far less than that of seawater samples.Moreover,the sea surface state tends to be heterogeneous over a large area,which makes the identification of oil spills more difficult because of various sea conditions and sunglint.To address this problem,we used the F-Score as a measure of the distance between forecast value and true value,proposed the Class-Balanced F loss function(CBF loss function)that comprehensively considers the precision and recall,and rebalances the loss according to the actual sample numbers of various classes.Using the CBF loss function,we constructed convolution neural networks(CBF-CNN)for oil spill detection.Based on the image acquired by the Coastal Zone Imager(CZI)of the Haiyang-1C(HY-1C)satellite in the Andaman Sea(study area 1),we carried out parameter adjustment experiments.In contrast to experiments of different loss functions,the F1-Score of the detection result of oil emulsions is 0.87,which is 0.03–0.07 higher than cross-entropy,hinge,and focal loss functions,and the F1-Score of the detection result of oil slicks is 0.94,which is 0.01–0.09 higher than those three loss functions.In comparison with the experiment of different methods,the F1-Score of CBF-CNN for the detection result of oil emulsions is 0.05–0.12 higher than that of the deep neural networks,supports vector machine and random forests models,and the F1-Score of the detection result of oil slicks is 0.15–0.22 higher than that of the three methods.To verify the applicability of the CBF-CNN model in different observation scenes,we used the image obtained by HY-1C CZI in the Karimata Strait to carry out experiments,which include two studies areas(study area 2 and study area 3).The experimental results show that the F1-Score of CBF-CNN for the detection result of oil emulsions is 0.88,which is 0.16–0.24 higher than that of other methods,and the F1-Score of the detection result of oil slicks is 0.96–0.97,which is 0.06–0.23 higher than that of other methods.Based on all the above experiments,we come to the conclusions that the CBF loss function can restrain the influence of oil spill and seawater sample imbalance on oil spill detection of CNN model thus improving the detection accuracy of oil spills,and our CBF-CNN model is suitable for the detection of oil spills in an area with weak sunglint and can be applied to different scenarios of CZI images.

Application of computational fluid dynamics simulation for submarine oil spill

Computational fluid dynamics （CFD） codes are being increasingly used in the simulation of submarine oil spills. This study focuses on the process of oil spills, from damaged submarine pipes, to the sea surface, using numerical models. The underwater oil spill model is developed, and a description of the governing equations is proposed, along with modifications required for the particalization of the control volume. Available experimental data were introduced to evaluate the validity of the CFD predictions, the results of which proved to be in good agreement with the experimental data. The effects of oil leak rate, leak diameter, current velocity, and oil density are investigated, by the validated CFD model, to estimate the undersea leakage time, the lateral migration distance, and surface diffusion range when the oil reaches the sea surface. Results indicate that the leakage time and lateral migration distance increase with decreasing leak rates and leak diameter, and increase with increasing current velocity and oil density. On the other hand, a large leak diameter, high density, high leak rate, or fast currents result in a greater surface diffusion range. The findings and analysis presented here will provide practical predictions of oil spills, and guidance for emergency rescues.