Oil Spill Risk Assessment Research Articles

Mapping the shoreline risk assessment of oil spill in the eastern region of Qeshm channel

The northern shores of the Strait of Hormuz constitute one of the most diverse shorelines in the Persian Gulf, characterized by both utility and environmental richness. Situated in the Qeshm channel, which hosts the largest mangrove habitat, major industries, and commercial port, these shores are subject to the occurrence of oil spills, posing potential substantial harm. This study employs General NOAA Operational Modeling Environment (GNOME) and numerical modeling to assess the potential risks to shorelines from hypothetical oil spills, considering the distinctive features of the shores and their environmental sensitivity. The results indicate that high-risk levels are not excessively prevalent overall and are confined to approximately 24 km. The shape of the channel and hydrodynamic conditions highlight the eastern sector of the Bandar Abbas urban area as particularly susceptible to oil spill entrapment. The findings indicate that high-risk areas are predominantly located away from industrial-oil shores and primarily consist of muddy shores. Therefore, internal strategies of Oil Spill Contingency Plan (OSCP) of companies are deemed insufficient and necessitate comprehensive planning initiatives.

Temperate Versus Arctic: Unraveling the Effects of Temperature on Oil Toxicity in Gammarids.

Shipping activities are increasing with sea ice receding in the Arctic, leading to higher risks of accidents and oil spills. Because Arctic toxicity data are limited, oil spill risk assessments for the Arctic are challenging to conduct. In the present study, we tested if acute oil toxicity metrics obtained at temperate conditions reflect those at Arctic conditions. The effects of temperature (4 °C, 12 °C, and 20 °C) on the median lethal concentration (LC50) and the critical body residue (CBR) of the temperate invertebrate Gammarus locusta exposed to water accommodated fractions of a fuel oil were determined. Both toxicity metrics decreased with increasing temperature. In addition, data for the temperate G. locusta were compared to data obtained for Arctic Gammarus species at 4 °C. The LC50 for the Arctic Gammarus sp. was a factor of 3 higher than that for the temperate G. locusta at 4 °C, but its CBR was similar, although both the exposure time and concentration were extended to reach lethality. Probably, this was a result of the larger size and higher weight and total lipid content of Arctic gammarids compared to the temperate gammarids. Taken together, the present data support the use of temperate acute oil toxicity data as a basis for assessing risks in the Arctic region, provided that the effects of temperature on oil fate and functional traits (e.g., body size and lipid content) of test species are considered. As such, using the CBR as a toxicity metric is beneficial because it is independent of functional traits, despite its temperature dependency. To the best of our knowledge, the present study is the first to report CBRs for oil. Environ Toxicol Chem 2024;43:1627-1637. © 2024 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

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

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

Quantitative Inversion Ability Analysis of Oil Film Thickness Using Bright Temperature Difference Based on Thermal Infrared Remote Sensing: A Ground-Based Simulation Experiment of Marine Oil Spill

Oil spills on the sea surface have caused serious harm to the marine ecological environment and coastal environment. Oil film thickness (OFT) is an important parameter for estimating oil spills amount, and accurate quantification of OFT is of great significance for rapid response and risk assessment of oil spills. In recent years, thermal infrared remote sensing has been gradually applied to quantify the OFT. In this paper, the outdoor oil spill simulation experiments were designed, and the bright temperature (BT) data of different OFTs were obtained for 24 consecutive hours in summer and autumn. On the basis of the correlation analysis of OFT and bright temperature difference (BTD) between oil and water, the traditional regression fitting model, classical machine learning model, ensemble learning model, and deep learning model were applied to the inversion of OFT. At the same time, inversion results of the four models were compared and analyzed. In addition, the best OFT inversion time using thermal infrared was studied based on 24-h thermal infrared data. Additionally, the inversion results were compared with the measured results; the optimal OFT range detectable using thermal infrared was explored. The experimental results show that: (1) Compared with ensemble learning model, traditional regression fitting model, and classical machine learning model, Convolutional Neural Network (CNN) has the advantages of high stability while maintaining high-precision inversion, and can be used as the preferred model for oil film thickness inversion; (2) The optimal time for OFT detection is around 10:00 to 13:00 of the day, and is not affected by seasonal changes; (3) During the day, thermal infrared has good detection ability for OFT greater than 0.4 mm, and weak detection ability for thinner oil films; (4) At night, thermal infrared has certain detection ability for relatively thick oil film, but the accuracy is lower than that in the daytime.

Geographic information system-based comprehensive oil spill risk assessment in China’s Bohai Sea

The Chinese Bohai Sea is a semi-enclosed, highly vulnerable continental sea that is exposed to the risk of oil spills due to offshore oil and gas activities. In this study, a geographic information system (GIS)-based “tanks–pipelines–platforms–ships–channels” (TPPSC) comprehensive framework, including a storage tank indicator, a pipeline indicator, a platform indicator, a ship indicator, and a channel indicator, was built to analyze the comprehensive marine oil spill risk in the Bohai Sea and to show the spatial differentiation of risk using GIS and the entropy weight method (EWM). The proposed method will overcome the shortcomings of the traditional statistical and scenario analysis theories. The results show that extremely high-risk areas are mostly located in Liaodong Bay and Bohai Bay; high-risk areas are located in the Bohai Strait and on the north side of the Shandong Peninsula; moderate-risk areas are mainly located on both sides of the Bohai Strait; low-risk areas are located on the south side of Liaodong Bay, in the middle of the Bohai Strait, and in Laizhou Bay; and extremely low-risk areas are located on the west side of Bohai Bay, the south side of Laizhou Bay, and the middle of the Bohai Sea. This framework demonstrated the applicability of the new risk assessment model, and the evaluation tests provided useful information for future research on marine oil spill risk management.

Quantitative ecological risk assessment of oil spills: The case of the Fernando de Noronha Archipelago

The upward trend in maritime oil transport increases the risk of oil spills, which have the potential to cause considerable damage to the marine environment. Therefore, a formal approach to quantify such risks is required. In mid-2010, a conservative Quantitative Ecological Risk Assessment based on population modeling, was performed in the Fernando de Noronha Archipelago. In this research, we enhance a previous assessment using the following models: (i) a Lagrangian approach to perform oil spill simulations, and (ii) the estimated frequency of accidents aggregating databases and expert opinions through a Bayesian-based method. Then, we quantify ecological risks as probabilities of half loss (i.e., 50 % population size decline) of a representative species of the archipelago's ecosystem. The results are summarized into risk categories to be straightforwardly communicated to the general public and provide reliable information that can aid decision-makers in coping with these events.

Ecological Risk Assessment of Oil Spill in Seribu Islands Coastal Area (SICA)

The Study of Ecological Risk Assessment (ERA) of oil spill in Seribu Islands Coastal Area (SICA) was done in Oil Company near Pertamina’s authorization mining territory. From the simulation of chance release model using fault tree analysis, the rate of chance release at 4.95 tons of particles of oil is acquired in an amount of 105.009 x 10 −7 . Supported by data on the oceanographic dynamics existing in the SICA which consist of climate, physical and chemistry characteristics are processed using Gnome Analysis was predicted. The final position of oil particle from oil company near Pertamina. From the simulation using Gnome for the locations of SICA at North of Jakarta, the endpoint of the spill for the simulation period is two months after the oil spill event in both. The results of the simulation of oil spills in part of SICA (East Karawang waters) where the location of the Pertamina Oil Refinery was leaked off the East Karawang coast showed the distribution of crude oil headed to the northwest and touched Bekasi Beach and Happy be achin the SICA in two months of observation. There are oceanographic dynamics and characteristics of oil spilled on 29 July 2019 in Karawang Waters provided information to coastal communities in both locations to give a negative perception of the impact of ecological risks. Nearly 85% of the Karawang coastal communities to the Thousand Islands border provide negative perspectives to the government, especially polluters, in this case Pertamina YYA-1 East Karawang.

Calibration of an acute toxicity model for the marine crustacean, Artemia franciscana, nauplii to support oil spill effect assessments

Oil spill risk and impact assessments rely on time-dependent toxicity models to predict the hazard of the constituents that comprise crude oils and petroleum substances. Dissolved aromatic compounds (ACs) are recognized as a primary driver of aquatic toxicity in surface spill exposure scenarios. However, limited time-dependent toxicity data are available for different classes of ACs to calibrate such models. This study examined the acute toxicity of 14 ACs and 3 binary AC mixtures on Artemia franciscana nauplii at 25 °C. Toxicity tests for 3 ACs were also conducted at 15 °C to evaluate the role of temperature on toxicity. The ACs investigated represented parent and alkylated homocyclic and nitrogen-, sulfur- and oxygen-containing heterocyclic structures with octanol-water partition coefficients (log Kow) ranging from 3.2 to 6.6. Passive dosing was used to expose and maintain concentrations in toxicity tests which were confirmed using fluorometry, and independently validated for 6 ACs using GC–MS analysis. Mortality was assessed at 6, 24, and 48 h to characterize the time course of toxicity. No mortality was observed for the most hydrophobic AC tested, 7,12-dimethylbenz[a]anthracene, due to apparent water solubility constraints. Empirical log LC50 s for the remaining ACs were fit to a linear regression with log Kow to derive a critical target lipid body burden (CTLBB) based on the target lipid model. The calculated 48 h CTLBB of 47.1 ± 8.1 μmol/g octanol indicates that Artemia nauplii exhibited comparable sensitivity to other crustaceans. A steep concentration-response was found across all compounds as evidenced by a narrow range (1.0–3.1) in the observed LC50 /LC10 ratio. Differences in toxicokinetics were noted, and no impacts of temperature-dependence of AC toxicity were found. Toxicity data obtained for individual ACs yielded acceptable predictions of observed binary AC mixture toxicity. Results from this study advance toxicity models used in oil spill assessments.

Bayesian inference modeling to rank response technologies in arctic marine oil spills.

Marine oil spills have a detrimental effect on aquatic systems. Yet, it is challenging to select appropriate technologies in the Arctic because of limited logistics support, inclement weather conditions, and remoteness, and limited research has been conducted in this direction. This article suggests a method to rank the oil response technologies, including mechanical recovery, chemical dispersant, and in-situ burning, for use in Arctic oil spill risk assessment and preparedness planning. The proposed Preference Learning based Bayesian Inference Modeling offers data-driven ranking of systems by learning a label function and considers factors such as ice covered sea areas, cold weather, and spill volume. A data generation system is developed to produce numerous oil spill scenarios, using a state-of-the-art engineering tool. Results demonstrate that the model, while simple, can efficiently and accurately select the best available technique, making it suitable primarily for marine pollution preparedness and response planning in strategic risk assessments.

Risk assessment of marine oil spills using dynamic Bayesian network analyses

Oil spills are serious threats to the marine ecosystem. Especially when an oil spill is faced with extreme weather, the consequences might be more severe. Until now, no such researches focus on the risk of these extreme scenarios. This paper proposes a novel dynamic assessment method to quantify the risk of oil spills in extreme winds based on dynamic Bayesian networks (DBNs). The physical models of advection, spreading, evaporation, and dispersion are transformed into DBNs, and the vulnerability model is established according to coastline types and socio-economic resources. By integrating all the sub-models, the overall DBN to quantify the dynamic risk of oil spills occurring in extreme winds is obtained. The proposed method is demonstrated by the Laizhou Bay. The developed model is validated by a three-axiom-based approach. Temporal and spatial dynamics of risk caused by oil spills in potential locations could be calculated. Based on the developed DBN, the risk of the Laizhou Bay coast caused by oil spills in annual extreme wind speeds corresponding to different mean recurrence intervals is studied. In addition, the effects of the occurrence time of annual extreme winds are also researched.

Risk assessment of offshore oil spill in the northern South China Sea: A stochastic scenario modeling approach

The coastal area in the northern South China Sea (nSCS) is one of China’s most economically active regions. The fast-growing industry of marine sector and the shipping has increased the probability of oil spill accidents. An oil spill is a usually unexpected incident that is harmful to the ecological environment and marine organisms of the coastal area. As a result, utilizing a numerical model to simulate the fate and transport trajectory of spilled oil and to assess the potential risk of an oil spill accident to the coastal marine environment is of great necessity. The present study aimed to examine the impact of the risk of oil spills on the coastal environment in the nSCS. We built a hydrodynamic model and an oil spill dispersion model based on MIKE21 FM to study the tidal dynamics and oil spill dispersion in the coastal area in the nSCS. Moreover, the stochastic approach was used to simulate and predict the fastest arrival time and the probability distribution of the pollution of the oil film to the surrounding environment in the coastal regions of the nSCS. We examined the mechanisms for the influences of tides and wind on the fastest arrival time of oil film and the spatial distribution of the pollution probability of oil spill incidents at different locations in the nSCS based on the model. The results showed that the wind direction has a dominating effect on the direction of the oil dispersion. Due to the differences in the tidal dynamics in different regions, the migration and dispersion of the oil are also affected by the tidal current. This research provides guidance on the risk assessment of oil spill accidents for marine environmental management.

Numerical simulation of multiphase oil behaviors in ice-covered nearshore water

There has been an increase in marine transportation in cold regions, which in turn has led to an increasing risk of oil spills in these areas. To better support risk assessment and pollution control of oil spills, it is important to have a good understanding of oil transport in the environment. This information is essential to manage response priorities and help prepare contingency and mitigating measures. This study aims to simulate 3D wave propagation in shallow water with different broken-ice aerial coverage percentages to assess the fate and transport of oil spill in a nearshore area under different conditions. Based on the Reynolds-averaged Navier-Stokes momentum equations for an incompressible viscous fluid and the Volume of Fluid (VOF) method that is coupled with Six Degree of Freedom (6-DOF) model, a 3D numerical model of three-phase transient flow was developed. It was found that the presence of ice makes the spreading of spilled oil slower in the horizontal direction since the ice can build natural barriers to oil movement. The higher the ice concentration, the slower spilled oil migrates in all directions. The maximum oil volume fraction varies with increasing ice coverage on the water surface area. The wave frequency, the averaged flow velocity, and oil properties affect the oil spread extent and the oil volume fraction. The dumping effect of the wave due to the presence of ice makes the impact of this factor less critical than those in open water.

Strategic environmental sensitivity mapping for oil spill contingency planning in the Peruvian marine-coastal zone

Major oil spills can cause significant impacts on marine-coastal zones, particularly on areas with a high oil spill risk, which combine a high oil spill hazard—high likelihood of oil stranding at high concentrations, and a high environmental sensitivity—high concentration of highly sensitive ecological and socioeconomic resources. In this context, a straightforward multicriteria methodology is proposed to determine the second factor of the oil spill risk, namely the strategic environmental sensitivity (SES), in 68 sectors covering the entire Peruvian marine-coastal zone. The methodology comprised the weighted integration of physical, biological, and socioeconomic sensitivity indicators based on their relevance in surface marine oil spills and the Peruvian ecological and socioeconomic context. As a result, relative SES levels from very low to very high were assigned to the sectors. To demonstrate the SES applicability, an oil spill risk assessment at a screening level was performed in a selected sector with current oil production activities. The oil beaching likelihood of worst-case discharge scenarios modelled for January 2021 was used to determine an overall screening oil spill hazard level in the selected sector, while a matrix relating the SES and hazard determined the screening oil spill risk. The results can be used as a decision-support tool to enhance the oil spill contingency planning in Peru or be used in other relevant processes such as the integrated coastal zone management, the marine spatial planning, or the contingency planning of other liquid contaminants. In addition, the proposed methodologies can be adapted to different local and international contexts and scales.

The bibliometric analysis and visualization mapping of net environmental benefit analysis (NEBA)

This paper aims to determine the worldwide research trends on searching queries of “oil spill* and risk assessment*” and “net environmental benefit analysis” and its most productive authors and journals. A bibliometric approach was performed to analyze publications including highly cited papers and only papers that were published in the Dimensions database from 2000 to 2022, April was selected. The necessary data were extracted from the Dimensions database and processed using visualization and mapping software such as VOSviewer 1.6.17 and Tableau Public 2021.1. The findings identified significant study fields, co-cited authors, country contributions, productive journals, as well as the most cited authors' articles. This study contributes significantly to the relevant studies as one of the few that utilizes bibliometric analysis as a network visualization and mapping technique for the analysis of one of the primary oil spill response decision-making tools and risk assessment sciences. The findings of this study can assist the researcher perform their research more effectively by providing insight into journal selection, contributing authors, research trends, countries, and keywords. Further research is recommended in light of longer period data contained in oil spill response strategies, oil spill modeling, or oil spill risk subjects.

Oil Spill Environmental Risk Assessment and Mapping in Coastal China Using Automatic Identification System (AIS) Data

The rapid expansion in shipping traffic, oil tankers, and oil field exploration in coastal and marine areas has, inevitably, resulted in the occurrence of many oil spill accidents. Oil spill accidents, which cause serious socio-economic, health, and environmental risks in coastal and marine areas, are a global concern. An oil spill pollution risk distribution map, combining multiple spill sources, is an effective tool by which to identify high-risk areas, which may help decision-makers in adopting contingency response and integrated coastal management. However, the assessment of oil spill distribution and risk assessment has been restricted, due to their heavy dependence on laboratory experiments and model simulations lacking reliable shipping data, which often derive inaccurate mapping results. This study combines the automatic identification system (AIS) and other data to precisely quantify the spatial extent of accident risk in coastal China. Based on oil quantity, oil spill rate, and accident probability, the ship, oil storage tank, submarine pipeline, and oil platform accidents spill risk index is analyzed. Next, combined with the sensitive degree of a coastal area, considering environmental and social issues, the oil spill environmental risk index is calculated. The oil spill pollution risk level is classified into five categories based on the oil spill pollution risk index, namely the low-risk zone, relatively low-risk zone, moderate-risk zone, relatively high-risk zone, and high-risk zone. The relatively high oil spill environmental risk concentration zone is located in the Bohai Sea, inter-border area between the Yellow Sea and Bohai Sea, the Yangtze River estuary, south of the Taiwan Strait, and the Pearl River estuary. The high-risk zone in the Bohai Sea is 36,018 km2 in area, with an average risk value of 32.23, whereas the high-risk area in the Pearl River estuary is only 14,007 km2. The high-risk area proportions in Tianjin are 23.5%, while those in Fujian, Hainan, Jiangsu, and Guangxi are very low. The low-risk area proportion in Hainan Province is 62%, while the value in Tianjin is only 2.9%. This study will be helpful in assisting decision-makers in mapping the influence area of oil spills and adopting the important strategies and effective management and conservation countermeasures for ship accidents in the coastal areas of China.

Multi-Risk Source Oil Spill Risk Assessment Based on a Fuzzy Inference System

Oil is one of the most important sources of energy, about 25 percent of which comes from offshore sources. As a result, the transportation of oil tankers, and the construction of offshore oil platforms and subsea pipelines have increased, to facilitate offshore oil exploitation. However, most oil spill risk assessments analyze the impact of one risk source, and rarely consider multiple risk sources in the study area. This paper focuses on three risk sources that may cause oil spills in a certain area, and establishes an oil spill risk assessment model through a fuzzy inference system. Oil spill probabilities for different risk sources are calculated through the model. According to the definition of oil spill risk, the risk probability of multiple risk sources in the study area is obtained, which can provide technical support for regional oil spill emergency capacity and emergency resource allocation.

Retraction Note: Oil spill risk assessment of submarine oil pipeline based on fuzzy comprehensive evaluation and accounting

Retraction Note: Oil spill risk assessment of submarine oil pipeline based on fuzzy comprehensive evaluation and accounting

Enhancing science-policy interface in marine environmental governance: Oil spill response models as boundary objects in the Gulf of Finland, Baltic Sea

Scientific models provide important input to the governance of complex socio-ecological risks. However, scientific knowledge rarely translates to policy in a simple, direct manner. By focusing on the use of scientific models for oil spill risk assessment and management in the Gulf of Finland (GoF), Baltic Sea, this paper aims to enhance the understanding of the capacity of scientific models to connect science, operational decision-making, and policy. In this study, we conceptualize scientific models as boundary objects, i.e. tools that facilitate interactions between different actors, types of knowledge, and perspectives across system boundaries. The study focuses on the different affordances associated with the models regarding their ability to represent, share, and convey knowledge between science and policy and to link the involved knowledge to action (i.e. changes in practice and in policy). We explore 1) how do the different oil spill models work as boundary objects in the science-policy interface, 2) how do different science-policy contexts affect the model affordances, and vice versa. We also provide recommendations for future research. The study is based on interviews of modelers/researchers, response operators, and policymakers. The results suggest that the existing models lack several of the important affordances that are required to successfully integrate different types of knowledge and transform new knowledge to action. As such, we suggest that currently models remain as instrumental, calculative, tools that support pre-determined policies rather than as means for exploring alternative framings of risks and the possible solutions. Finally, we argue that the co-production of knowledge best supports the plurality of model affordances needed to enable transformative change in policy and practice.

RETRACTED ARTICLE: Oil spill risk assessment of submarine oil pipeline based on fuzzy comprehensive evaluation and accounting

RETRACTED ARTICLE: Oil spill risk assessment of submarine oil pipeline based on fuzzy comprehensive evaluation and accounting

Key performance indicators for environmental contamination caused by offshore oil spills

Oil spills during offshore operations are likely to cause severe contamination of the sea. The identification of the environmental effects of accidental releases from offshore oil and gas facilities plays a crucial role in the prevention and mitigation of marine pollution. Key Performance Indicators (KPIs) are largely recognized as an effective tool to address and communicate multifaceted issues related to accidental events in the framework of risk management. In the context of Oil Spill Risk Assessment (OSRA) studies, this study proposes a set of KPIs addressing the potential environmental contamination caused by on-surface oil spills from offshore oil and gas installations. A layered approach was defined, based on three different levels of KPIs having an increasing complexity and providing an increasing amount of information. The environmental KPIs defined allow a preliminary quantitative assessment of the environmental contamination due to the oil spill scenarios defined in the ENVironmental hazards IDentification (ENVID) studies carried out for oil and gas installations, providing a preliminary ranking of the expected environmental effects of the spills and supporting their prioritization in order to select those which should undergo a more accurate Environmental Risk Assessment study (ERA).