Location Of Oil Spill Research Articles

FRACTAL ANALYSIS FOR OIL SPILLS CLUSTERING IN AHOADA COMMUNITIES OF THE NIGER DELTA REGION OF NIGERIA

Abstract. This study uses fractal analysis to understand how oil spills are clustered at different scales in Ahoada communities of the Niger Delta region of Nigeria. The ArcGIS software was used in designing the hotspot map of the study area using Kernel density estimation. Results of spatial spill distribution from the Kernel density revealed that Bayelsa, Rivers, and Delta states are the hottest spots for oil spill occurrences and distribution and communities within the Rivers state including Ochebeke, Ukperede, Odieke and Akinima has been widely affected by the spills. The box counting method was employed for understanding clustering associated with oil spills in the area using ArcGIS interface. The method uses the conversion tool embedded in ArcGIS to convert oil spills points into raster. A basic analysis of this fractal plot suggest a dual fractal analysis for oil spill occurrences within the study location. This is evident by a low fractal dimension of 0.419 at distances of 1.1 km. At higher distances above 1.1 km, oil spill occurrences are characterised by a fractal dimension of 1.411. According to the fractal plot, regional and local fractal for oil spill occurrences is defined by a threshold distance of 1.2 km. At scales less than 1.2 km, oil spill occurrences exhibit a fractal dimension of 0.063 while at scales above this distance, the occurrences of oil spill are defined by a fractal of 1.249. A general analysis of fractal for both oil spill types suggests a small fractal dimension values and a more discrete scale compared to the regional scale. The relative low fractal dimension values at discrete scale suggest a high clustering of oil spill locations. This study gives a relative idea about the clustering tendency. This would enable proper remediation to be carried out in the area.

Environmental Risk Assessment of the Zhengrunzhou Water Source under the Influence of Oil Spill Accidents at the Wharf Group

To compensate for the research defects of strong subjectivity in determining oil spill amount, insufficient consideration of wharf distribution, and incomplete indexes for reflecting the influence degree of oil spill accidents on water sources, and to enhance the supervision efficiency of the supervision department, this paper constructs a risk assessment system of water sources under the influence of the wharf group. The system includes a wharf group division method considering the wharf distribution situation; the calculation method of oil spill amount at wharves considering the oil tank capacity of main ship types and the production supervision risk at the wharves; the calculation method of the oil spill amount at the wharf group considering the wharf number, distribution density, production supervision risk and wharf oil spillage; the determination method for the influence degree of oil spill at the wharf group on the water sources and judgment method of supervision level at the wharf group, which takes the arrival time of oil slicks, the duration of over-standard petroleum concentration and the maximum over-standard multiple of petroleum concentration at the water intake as indexes; the method of determining the risk of oil spill accidents at the water source considering the cumulative effect of oil spill at the wharf group on the risk of the water sources; and the environmental risk assessment method of water sources considering oil spill accident risk and the anti-risk ability. Applying this system to the environmental risk assessment of the Zhengrunzhou water source in Zhenjiang City, we discovered that the flow field, wind field, oil spill location and oil spill amount were correlated with the influence degree of oil spill accidents on water sources, for which the flow field demonstrated the strongest correlation, while the wind field presented the weakest. The supervision level of the wharf group is mainly sub-key or non-key levels, but the level of the wharf group SD07 is approximate to the key supervision level during rising tide. Due to the strong anti-risk ability of the Zhengrunzhou water source, the environmental risks of the Zhengrunzhou water source under different working conditions are scarcely different and belong to the medium-risk level.

Oil Spill Identification in Radar Images Using a Soft Attention Segmentation Model

Oil spills can cause damage to the marine environment. When an oil spill occurs in the sea, it is critical to rapidly detect and respond to it. Because of their convenience and low cost, navigational radar images are commonly employed in oil spill detection. However, they are currently only used to assess whether or not there are oil spills, and the area affected is calculated with less accuracy. The main reason for this is that there have been very few studies on how to retrieve oil spill locations. Given the above problems, this article introduces a model of image segmentation based on the soft attention mechanism. First, the semantic segmentation model was established to fully integrate multi-scale features. It takes the target detection model based on the feature pyramid network as the backbone model, including high-level semantic information and low-level location information. The channel attention method was then used for each of the feature layers of the model to calculate the weight relationship between channels to boost the model’s expressive ability for extracting oil spill features.Simultaneously, a multi-task loss function was used. Finally, the public dataset of oil spills on the sea surface was used for detection. The experimental results show that the proposed method improves the segmentation accuracy of the oil spill region. At the same time, compared with segmentation models, such as PSPNet, DeepLab V3+, and Attention U-net, the segmentation accuracy based on the pixel level improved to 95.77%, and the categorical pixel accuracy increased to 96.45%.

Automatic detection and classification of fish calls in the Northern Gulf of Mexico using energy detectors and a convolutional neural network

Long-term passive acoustic monitoring datasets can require substantial effort for manual analysis; therefore, automatic methods are a more effective way to conduct these analyses and extract calls of interest. In this study, acoustic recordings from the northern Gulf of Mexico, about 60 km north of the 2010 Deepwater Horizon oil spill location, collected between July 2010 and May 2017 were analyzed to determine if fish call abundance has changed after the oil spill. An energy detector was created to extract six likely fish calls found in these data and a neural network was subsequently applied to classify the detections into different call categories. Data augmentation and iterative training were used to optimize classification and compensate for the low number of training images for a couple call types. Daily, monthly, and yearly patterns in calls were investigated and a hypothesis was tested that there was an increase in call abundance over time, indicating recovery of fish populations since the oil spill. Such long-term studies demonstrate that PAM can be used as a population assessment tool for fisheries management.

Spatio-Temporal Analysis of Oil Spill Impact and Recovery Pattern of Coastal Vegetation and Wetland Using Multispectral Satellite Landsat 8-OLI Imagery and Machine Learning Models

Oil spills are a global phenomenon with impacts that cut across socio-economic, health, and environmental dimensions of the coastal ecosystem. However, comprehensive assessment of oil spill impacts and selection of appropriate remediation approaches have been restricted due to reliance on laboratory experiments which offer limited area coverage and classification accuracy. Thus, this study utilizes multispectral Landsat 8-OLI remote sensing imagery and machine learning models to assess the impacts of oil spills on coastal vegetation and wetland and monitor the recovery pattern of polluted vegetation and wetland in a coastal city. The spatial extent of polluted areas was also precisely quantified for effective management of the coastal ecosystem. Using Johor, a coastal city in Malaysia as a case study, a total of 49 oil spill (ground truth) locations, 54 non-oil-spill locations and Landsat 8-OLI data were utilized for the study. The ground truth points were divided into 70% training and 30% validation parts for the classification of polluted vegetation and wetland. Sixteen different indices that have been used to monitor vegetation and wetland stress in literature were adopted for impact and recovery analysis. To eliminate similarities in spectral appearance of oil-spill-affected vegetation, wetland and other elements like burnt and dead vegetation, Support Vector Machine (SVM) and Random Forest (RF) machine learning models were used for the classification of polluted and nonpolluted vegetation and wetlands. Model optimization was performed using a random search method to improve the models’ performance, and accuracy assessments confirmed the effectiveness of the two machine learning models to identify, classify and quantify the area extent of oil pollution on coastal vegetation and wetland. Considering the harmonic mean (F1), overall accuracy (OA), User’s accuracy (UA), and producers’ accuracy (PA), both models have high accuracies. However, the RF outperformed the SVM with F1, OA, PA and UA values of 95.32%, 96.80%, 98.82% and 95.11%, respectively, while the SVM recorded accuracy values of F1 (80.83%), OA (92.87%), PA (95.18%) and UA (93.81%), respectively, highlighting 1205.98 hectares of polluted vegetation and 1205.98 hectares of polluted wetland. Analysis of the vegetation indices revealed that spilled oil had a significant impact on the vegetation and wetland, although steady recovery was observed between 2015-2018. This study concludes that Chlorophyll Vegetation Index, Modified Difference Water Index, Normalized Difference Vegetation Index and Green Chlorophyll Index vegetation indices are more sensitive for impact and recovery assessment of both vegetation and wetland, in addition to Modified Normalized Difference Vegetation Index for wetlands. Thus, remote sensing and Machine Learning models are essential tools capable of providing accurate information for coastal oil spill impact assessment and recovery analysis for appropriate remediation initiatives.

Automatic detection and classification of fish calls in the Northern Gulf of Mexico using energy detectors and a convolutional neural network

Passive acoustic monitoring (PAM) is a relatively low-cost way to collect long-term datasets of animal occurrence, which is particularly effective when the animals are not constantly present. However, these long datasets can require substantial effort for manual analysis; therefore, automatic methods are a more effective way to conduct these analyses and extract calls of interest. In this study, acoustic recordings from the northern Gulf of Mexico, about 60 km north of the 2010 Deepwater Horizon oil spill location, collected between July 2010 and May 2017 were analyzed to determine if fish call abundance and diversity has changed after the oil spill. An energy detector was created to extract seven likely fish calls found in these data and a neural network was subsequently applied to classify the detections into different call categories. Daily, monthly, and yearly patterns in calls were investigated and a hypothesis was tested that there was an increase in call abundance and diversity over time, indicating recovery of fish populations since the oil spill. Such long-term studies demonstrate that PAM can be used as a population assessment tool for fisheries management.

An Improved Method to Estimate the Probability of Oil Spill Contact to Environmental Resources in the Gulf of Mexico

The oil spill risk analysis (OSRA) model is a tool used by the Bureau of Ocean Energy Management (BOEM) to evaluate oil spill risks to biological, physical, and socioeconomic resources that could be exposed to oil spill contact from oil and gas leasing, exploration, or development on the U.S. Outer Continental Shelf (OCS). Using long-term hindcast winds and ocean currents, the OSRA model generates hundreds of thousands of trajectories from hypothetical oil spill locations and derives the probability of contact to these environmental resources in the U.S. OCS. This study generates probability of oil spill contact maps by initiating trajectories from hypothetical oil spill points over the entire planning areas in the U.S. Gulf of Mexico (GOM) OCS and tabulating the contacts over the entire waters in the GOM. Therefore, a probability of oil spill contact database that stores information of the spill points and contacts can be created for a given set of wind and current data such that the probability of oil spill contact to any environmental resources from future leasing areas can be estimated without a rerun of the OSRA model. The method can be applied to other OCS regions and help improve BOEM’s decision-making process.

The Model of Optimal Allocation of Maritime Oil Spill Combat Ships

The paper describes a two-stage method for optimizing the location of marine oil spill combat forces and assessing the costs related to this action at the sea. An optimization problem relates to positioning the oil pollution combat ships in ports in such a way that they are able combat the anticipated number of oil spills in certain positions in the Polish coast of the Baltic Sea area in the shortest possible time. The problem was classified as NP-hard; therefore, in the first stage, an evolutionary algorithm was applied. The main output of the model based on the evolutionary algorithm is the time of arrival of combat ships from the port to the oil spill area, as well as the costs of such operation; this is defined as the aim function. The first stage of the model does not simulate combat pollution action; therefore, the results obtained were treated as approximate. In the second stage, the several best allocations of oil spill response resources obtained in the first stage were used as input to a detailed simulation model of combat action. For each chosen allocation, a number of oil combat action simulations were carried out. The model can be used to create and verify oil pollution contingency plans, and finally increase the sustainability of sea and coastal areas.

Long-term spatially distributed observations of deep diving marine mammals in the Northern Gulf of Mexico using passive acoustic monitoring

This paper will present the results of processing long-term passive acoustic monitoring (PAM) data collected July through October 2015 in the Northern Gulf of Mexico in the vicinity of the Deep Water Horizon oil spill site to aid in understanding factors driving the distribution of sperm and beaked whales in the Gulf of Mexico. The Littoral Acoustic Demonstration Center -Gulf Ecological Monitoring and Modeling Consortium (LADC-GEMM) deployed five bottom-anchored acoustic moorings (LADC EARS buoys) at 10, 25, and 50 nmi distance from the 2010 oil spill location. Autonomous surface vehicles and a glider were simultaneously operated in the area for the additional collection of PAM data. The daily and monthly activity of three species of beaked whales exhibits spatial and seasonal variability, which appear to be correlated with levels of anthropogenic noise at the monitoring sites. Acoustic detection data are used to estimate abundances at three sites and compare them to the estimates obtained from baseline data collected before (2007) and right after (2010) the oil spill. Long-term abundance trends for both beaked and sperm whales are discussed. [Research supported by GOMRI.]

Inferring spatial distribution of oil spill risks from proxies: Case study in the north of the Persian Gulf

Maps of oil spill probability are important tools in environmental risk assessment and decision making in coastal zone management. This paper describes the development of a spatial predictive model for the probability of oil spills in the northern part of the Persian Gulf. The model estimates the probability of oil spills at a pixel level as function of four proxies, i.e., ship routes, coastlines, oil facilities, oil wells. It uses a generalized linear model (GLM) with a polynomial function that is implemented in the R software environment. For training the model, we used reported oil spill events that represent the location of their occurrences. We trained and tested the model in 100 iterations, using a different subset of data for each. The model evaluations showed mean accuracy of 0.79% (range 0.68%–0.89%), expressed by the area under the curve (AUC). In the northern part of the Persian Gulf, the largest probability of oil spills was predicted in areas where actual oil facilities in combination with high intensity ship traffic are in evidence. The model can predict the probability of oil spills as raster map in a standard R data format. It can be used in environmental risk assessment as well as an input for more detailed oil spill simulation models. The advantages of the model include its’ high spatial resolution, accounting for uncertainty in oil spill locations, and the possibility of sharing as an open-source R script with other users.

Modeling of oil spill beaching along the coast of the Bohai Sea, China

On June 4 and 17, 2011, two separate oil spill accidents occurred at platforms B and C of the Penglai 19-3 oilfield located in the Bohai Sea, China. Based on the initial oil spill locations detected from the first available Synthetic Aperture Radar (SAR) image acquired on June 11, 2011, we performed a numerical experiment to simulate the potential oil spill beaching area with the General NOAA Operational Modeling Environment (GNOME) model. The model was driven by ocean surface currents from an operational ocean model (Navy Coastal Ocean Model) and surface winds from operational scatterometer measurements (the Advanced Scatterometer). Under the forcing of wind and ocean currents, some of the oil spills reached land along the coast of Qinhuangdao within 12 days. The results also demonstrate that the ocean currents are likely to carry the remaining oil spills along the Bohai coast towards the northeast. The predicted oil spill beaching area was verified by reported in-situ measurements and former studies based on MODIS observations.

Risk assessment of oil spill accidents

<div> <p>The objective of this study is to present an integrated stochastic approach for quantifying the risk of oil spill in marine waters and adjacent coasts. This was achieved via the effective cooperation between the National Technical University of Athens (NTUA) and the Bogazici University (BU) within the framework of a bilateral joint research project. The proposed methodology integrates four models: (1) a physics-based hydrodynamic model (HYM) which computes the spatial distribution of surface water currents as the main driving force for oil transport, (2) an expert-based accident assessment model (<st1:stockticker w:st="on">AAM</st1:stockticker>) to compute the frequency, location and characteristics of expected oil spills, (3) a physics-based oil spill model (<st1:stockticker w:st="on">OSM</st1:stockticker>) which computes the propagation and fate of the oil slick, and (4) an expert-based impact assessment model (IAM) to compute the distribution of coastal impact due to oil contamination. The model is applied to two pilot areas: the Saronicos Gulf, Greece and Izmir Bay, Turkey. The flow fields in these areas were determined by the HYM for a large number of wind scenarios, based on which the transport and weathering of an oil slick were computed by the <st1:stockticker w:st="on">OSM</st1:stockticker>. The most probable oil spill locations were identified by <st1:stockticker w:st="on">AAM</st1:stockticker> based on the bathymetry, the maritime traffic and the currents. Finally, the IAM was applied to draw Coastal Oil Impact Maps in the regions of interest. Emphasis was placed on the presentation of the risk of oil reaching the coastline. Environmental sensitivity and economic importance were taken into account by assigning index values to all coastal cells. </p> </div> <p> </p>

Assessing the performance and cost of oil spill remediation technologies

Abstract Oil spills are an especially challenging chemical contamination event to remediate. Predicting the fate and effects of spilled oil is a formidable task, complicated by its complex chemical composition and the potential for catastrophically large discharge volumes. The proper choice of cleanup technique is equally complex, and depends on a host of factors, including oil type, spill location, spill size, weather, and local regulations and standards. This paper aims to provide a broad review of the current technologies used to remediate oil spills, and the context in which they operate. The chemical characteristics of an oil spill are discussed, including implications for transport modeling, and impacts that arise from short-term and chronic toxicity. The most common remediation technologies (mechanical recovery, dispersants, and in-situ burning) are reviewed, as are emerging technologies (hydrophobic meshes). A comparative analysis is performed on these methods by calculating a maximum oil encounter rate for each device, which is a performance characteristic critical to planning a response effort. Finally, a review of cleanup cost estimation techniques is used to assess the cost-effectiveness of remediation methods. Analysis shows that waiving the legal penalty for recovered oil can result in significant cost savings for the liable party, and may drive improvements in recovery-focused technologies. The authors suggest continued research into improving oil spill recovery methods and understanding the fate of individual compounds in the spilled oil. This will both minimize potential environmental damages, and reduce the uncertainty of their impacts.

Satellite observations and modeling of oil spill trajectories in the Bohai Sea

On June 4 and 17, 2011, separate oil spill accidents occurred at two oil platforms in the Bohai Sea, China. The oil spills were subsequently observed on different types of satellite images including SAR (Synthetic Aperture Radar), Chinese HJ-1-B CCD and NASA MODIS. To illustrate the fate of the oil spills, we performed two numerical simulations to simulate the trajectories of the oil spills with the GNOME (General NOAA Operational Modeling Environment) model. For the first time, we drive the GNOME with currents obtained from an operational ocean model (NCOM, Navy Coastal Ocean Model) and surface winds from operational scatterometer measurements (ASCAT, the Advanced Scatterometer). Both data sets are freely and openly available. The initial oil spill location inputs to the model are based on the detected oil spill locations from the SAR images acquired on June 11 and 14. Three oil slicks are tracked simultaneously and our results show good agreement between model simulations and subsequent satellite observations in the semi-enclosed shallow sea. Moreover, GNOME simulation shows that the number of ‘splots’, which denotes the extent of spilled oil, is a vital factor for GNOME running stability when the number is less than 500. Therefore, oil spill area information obtained from satellite sensors, especially SAR, is an important factor for setting up the initial model conditions.

A GIS approach to mapping oil spills in a marine environment

A geographic information system (GIS) can contribute significantly to the correct interpretation of the slick signatures visible on synthetic aperture radar (SAR) images. Initially, GIS has proven to be an excellent management tool for resource assessment, oil spill response and planning, and damage assessment. The GIS approach to the problem of oil spill mapping includes integration of the geographical, remote sensing, oil and gas production/infrastructure data and slick signatures, detected by SAR, in GIS. Compiled from data from several sources including nautical maps, geodatabases, ground truth and remote sensing data, GIS allows the retrieval of key information, such as predicting oil spill locations, revealing offshore/onshore sources, and estimating the intensity of oil pollution. SAR and GIS technologies can improve the identification and classification of oil spills, leading to construction of the final product, an oil spill distribution map. This approach has been applied to oil spill mapping in the Sea of Okhotsk, the Caspian Sea, the Black Sea and the Gulf of Thailand. We conclude that the combination of GIS and SAR technologies provides an ideal solution for understanding the spatial/temporal distribution of oil spills in the marine environment and is considered as the core of the oil spill monitoring system.