A state-of-the-art model for spatial and stochastic oil spill risk assessment: A case study of oil spill from a shipwreck

Decolonising Environmental Risk Assessments of Potentially Polluting Wrecks: a Case Study of the Wreck of the USS Mississinewa in Ulithi Lagoon, Federated States of Micronesia

Millions of tonnes of oil lie entombed within wrecks from two world wars which, when released, can cause environmental devastation. Wrecks are predominantly risk assessed by the Global North Nations responsible, resulting in an epistemology that separates human from nature. This research aimed to decolonise risk assessments to capture the spatially heterogeneous nature of human vulnerability to oil pollution. Triangulation analysis of interviews and official reports relating to the USS Mississinewa oil spill identified three Global South issues a Eurocentric risk assessment failed to capture: region-specific meteorological conditions causing the leak, remoteness making external resources slow to arrive, and the impact of the fishery closure on traditional subsistence lifestyles. A vulnerability assessment is proposed to prioritise wrecks in susceptible locations. Recommendations are made for a collaborative approach to wreck management by including local voices, resisting the Global North assumption of generality, and recognising the priorities of those living with wrecks.

Azolla Pinnata: Sustainable Floating Oil Cleaner of Water Bodies

Various plant-based materials effectively absorb oil contaminants at the water/air interface. These materials showcase unparalleled efficiency in purging oil contaminants, encompassing rivers, lakes, and boundless oceans, positioning them as integral components of environmental restoration endeavors. In addition, they are biodegradable, readily available, and eco-friendly, thus making them a preferable choice over traditional oil cleaning materials. This study explores the phenomenal properties of the floating Azolla fern (Azolla pinnata), focusing on its unique hierarchical leaf surface design at both the microscale and nanoscale levels. These intricate structures endow the fern with exceptional characteristics, including superhydrophobicity, high water adhesion, and remarkable oil or organic solvent absorption capabilities. Azolla's leaf surface exhibits a rare combination of dual wettability, where hydrophilic spots on a superhydrophobic base enable the pinning of water droplets, even when positioned upside-down. This extraordinary property, known as the parahydrophobic state, is rare in floating plants, akin to the renowned Salvinia molesta, setting Azolla apart as a natural wonder. Submerged in water, Azolla leaves excel at absorbing light oils at the air-water interface, demonstrating a notable ability to extract high-density organic solvents. Moreover, Azolla's rapid growth, doubling in the area every 4-5 days, especially in flowing waters, positions it as a sustainable alternative to traditional synthetic oil-cleaning materials with long-term environmental repercussions. This scientific lead could pave the way for more environmentally friendly approaches to mitigate the negative impacts of oil spills and promote a cleaner water ecosystem.

A quantified risk analysis for oil spill during crude oil loading operation on tanker ship under improved Z-number based Bayesian Network approach

Crude oil cargo operation poses significant oil spill risk although utmost care is exercised by ship and shore crew. This paper focuses on quantitative risk analysis for oil spill incidents in crude oil tanker ships to enhance safety at the operational level and prevent potential pollution. To achieve this purpose, the Bayesian network (BN) is used under the improved Z-numbers theory. While BN provides a powerful tool based on cause and effect network between the variables, the improved Z-numbers are capable of handling uncertainty and improving the reliability of qualitative expert judgments. The findings show that the occurrence probability of oil spill risk in crude oil tanker ships is found 2.90E-02 during the cargo loading operation. The findings of the research are expected to contribute ship crew, safety inspectors, ship owners, HSEQ managers, and terminal managers in risk management decision-making, improving operational safety, taking control actions, and minimizing oil spills.

An ocean-wave-trajectory forecasting system for the eastern Baltic Sea: Validation against drifting buoys and implementation for oil spill modeling

We present the implementation and validation of OpenDrift, an open-source Lagrangian particle trajectory modeling framework for oil spill modeling in the coastal waters of Estonia in the Baltic Sea. The framework was coupled with ECMWF winds, NEMO-EST05 hydrodynamical model, and SWAN-EST wave model, and validated using six drift experiments from 2022. The sensitivity analysis revealed the importance of incorporating additional forcing factors, such as Stokes drift and currents, which generally improved the accuracy of the trajectory model compared to using wind alone. Nevertheless, the benefits of providing OpenDrift with, for example, the Stokes drift seemed to depend on whether currents are also included or not. The wind drift factors of the utilized drifters align closely with those commonly employed in oil spill modeling. Furthermore, the modeling results for hypothetical oil spills in severe weather conditions and high-risk regions emphasize the critical need for preparedness and rapid response strategies.

Modeling the Agia Zoni II tanker oil spill in Saronic Gulf, Greece

We employed GNOME to simulate the oil spill due to the sinking of the tanker "Agia Zoni ΙΙ" in September 2017 in Saronic Gulf. We performed simulations using various combinations of wind and current input, and values of the GNOME parameters, and compared the simulated oil spill trajectories with coastal pollution and satellite data. The best scenario, i.e., the combination that showed the most satisfactory agreement with field data, uses wind data from one of the closest meteorological stations, calculated currents by a hydrodynamic model and default values of the parameters, except for the windage and the refloat half-life whose proposed values are 3-4 % and 6 h, respectively. Neglecting the effect of the wind in the best scenario worsened the agreement. Mass balance results depicted that approximately 47 % of the total 500 tons of the oil spill ended up on the coastline of Attica peninsula and Salamina Island.

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.

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.

Assessing the exposure risk of large pelagic fish to oil spills scenarios in the deep waters of the Gulf of Mexico

Exposure risk is assessed based on modeling suitable habitat of large pelagic fish and oil spill scenarios originating at three wells located in the western GM's deep waters. Since the fate of the oil depends on the oceanographic conditions present during the accident, as well as the magnitude and duration of the spill, which are not known a priori, the scenarios used are a statistical representation of the area in which oil spilled from the well could be found, given all possible outcomes. The ecological vulnerability assessment identified a subset of bony fish with low-medium vulnerability and elasmobranchs with medium-high vulnerability. The oiling probability and exposure risk of both bony fish and elasmobranchs hotspots vary by well analyzed. Thus, these results provide essential information for a risk management plan for the assessed species and others with economic or conservation importance distributed in the GM and worldwide.

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.

Environmental vulnerability to oil spills in Itapuã State Park, Rio Grande do Sul, Brazil: An approach using two-dimensional numerical simulation

The growing use of coastal areas for different economic purposes is responsible for increasing pollution by hydrocarbons in marine environments. As a consequence of these activities, accidents during fuel extraction, transport, and storage can occur, causing intense environmental degradation. Numerical modeling of the trajectory of oil stains becomes an important tool with low operational costs, providing powerful support to the government agencies in charge of risk management associated with possible oil accidents, by helping to generate scenarios and strategies for containment and cleaning of affected environments. In this sense, the aim of this study is to estimate environmental vulnerability to oil at beaches located in the Itapuã State Park (PEI), a Protection Conservation Unit. This work focused on describing a methodology to estimate the vulnerability of coastal areas, with emphasis on the fact that the study was carried out in a closed environment. For that, an approach was used based on the integration of: (1) an intrinsic variable to the environment; (2) a dynamic variable determined through diesel oil surface dispersion scenarios. Four hypothetical accident scenarios with 20 m³ of diesel oil were simulated in 2018, during five days of simulations with instant dumping in the navigation channel of the local waterway near the PEI. The results suggest the forcing of the field of intensity and direction of the local winds as preponderant for the dynamics of movement and structure of the spots, with the zonal and meridional components of the fields of superficial currents acting in this process as a secondary factor. The study showed that all beaches in the park are susceptible to contact with oil throughout the simulated year, with Pombas Beach, Pedreira Beach, and Onça Beach being affected in all simulated scenarios, which classifies them as very high vulnerability and defines them as priority protection areas.

Fate of MV Wakashio oil spill off Mauritius coast through modelling and remote sensing observations

This study aims at assessing the fate of MV Wakashio oil spill, and the driving forces responsible for possible environmental consequences of polluted coastal region. GNOME simulations were performed, considering various meteo-oceanographic forcings such as (i) winds and currents, (ii) only winds, and (iii) only winds with different diffusion coefficients, and validated with the satellite images. The results revealed that the simulations performed with 'only winds' reasonably match with the satellite observations, indicating that winds are the primary driving forces. The conducive stokes drift is an added contribution to the predominant northwestward drift of the spill. The oil budget analysis suggests that beaching and evaporation together accounted for a significant portion of the spilled oil (1000 tons), in which ~60% of the oil was accounted only for beaching. Our results depict that the diffusion coefficient of 100,000 cm²/s and 3% windages are optimal for oil-spill simulations off the southeastern Mauritius coast.

Toward monitoring and estimating the size of the HFO-contaminated seabed around a shipwreck using MBES backscatter data

Despite a progressive reduction of oil spills caused by the activity of maritime transportation, the latent sources of pollution still exist. Although the harmful impact of heavy fuel oil (HFO) on the marine environment is widely known, many shipwrecks cause contamination of the surrounding areas. In this paper, an approach to monitor the area of the HFO spill around a shipwreck is made using a bottom backscattering strength (BBS) obtained by a multibeam echosounder (MBES). As a case study, the s/s Stuttgart wreck located in the Gulf of Gdansk (Poland) is verified. Two different measurement campaigns have been carried out in shallow waters using low (190 kHz) and high (420 kHz) MBES frequency. The results indicate that the polluted area around s/s Stuttgart was estimated at 49.1 ha, which is around 18.3% more in comparison to the geological surveys made four years earlier.

Decision support tools for oil spill response (OSR-DSTs): Approaches, challenges, and future research perspectives

Marine oil spills pose a significant threat to ocean and coastal ecosystems. In addition to costs incurred by response activities, an economic burden could be experienced by stakeholders dependent on coastal resources. Decision support tools for oil spill response (OSR-DSTs) have been playing an important role during oil spill response operations. This paper aims to provide an insight into the status of research on OSR-DSTs and identify future directions. Specifically, a systematic review is conducted including an examination of the advantages and limitations of currently applied and emerging decision support techniques for oil spill response. In response to elevated environmental concerns for protecting the polar ecosystem, the review includes a discussion on the use of OSR-DSTs in cold regions. Based on the analysis of information acquired, recommendations for future work on the development of OSR-DSTs to support the selection and implementation of spill response options are presented.

Monitoring oil spill in Norilsk, Russia using satellite data

This paper studies the oil spill, which occurred in the Norilsk and Taimyr region of Russia due to the collapse of the fuel tank at the power station on May 29, 2020. We monitored the snow, ice, water, vegetation and wetland of the region using data from the Multi-Spectral Instruments (MSI) of Sentinel-2 satellite. We analyzed the spectral band absorptions of Sentinel-2 data acquired before, during and after the incident, developed true and false-color composites (FCC), decorrelated spectral bands and used the indices, i.e. Snow Water Index (SWI), Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI). The results of decorrelated spectral bands 3, 8, and 11 of Sentinel-2 well confirmed the results of SWI, NDWI, NDVI, and FCC images showing the intensive snow and ice melt between May 21 and 31, 2020. We used Sentinel-2 results, field photographs, analysis of the 1980-2020 daily air temperature and precipitation data, permafrost observations and modeling to explore the hypothesis that either the long-term dynamics of the frozen ground, changing climate and environmental factors, or abnormal weather conditions may have caused or contributed to the collapse of the oil tank.

Integrated environmental vulnerability to oil spills in sensitive areas

As the typical range of influence of oil spills surrounds urbanised and economically active areas, it is likely that fragile regions may not be part of the most vulnerable zones. This premise is remediated in this paper with the adoption of a vulnerability approach based on the integration of static and dynamic information, such as oil pollution susceptibility. Susceptibility is a poorly consolidated term and is often used as synonym for environmental sensitivity; it is considered here to be the distribution areas of oil slicks. To test the proposed approach, an integrated estimation of environmental vulnerability is carried out for an environmentally sensitive area in the south of Brazil by merging static data inherent to the medium with information of a dynamic nature related to trajectory, behaviour and the fate of oil at sea. Moreover, the oil pollution intensity and environmental sensitivity data in susceptible areas are addressed. Subsequently, the environmental vulnerability is estimated by integrating hazard maps, concentrations and losses of the mass of the oil slick, oil beaching time and the littoral sensitivity index hierarchy. Results will prove to be useful to highlight critical areas for which the highest levels of severity are expected, which can lead to improvements in decision-making processes to support oil-spill prevention, as well as improve response readiness, especially in developing countries that have historically under-protected their sensitive regions.

Advances in Remote Sensing Technology, Machine Learning and Deep Learning for Marine Oil Spill Detection, Prediction and Vulnerability Assessment

Although advancements in remote sensing technology have facilitated quick capture and identification of the source and location of oil spills in water bodies, the presence of other biogenic elements (lookalikes) with similar visual attributes hinder rapid detection and prompt decision making for emergency response. To date, different methods have been applied to distinguish oil spills from lookalikes with limited success. In addition, accurately modeling the trajectory of oil spills remains a challenge. Thus, we aim to provide further insights on the multi-faceted problem by undertaking a holistic review of past and current approaches to marine oil spill disaster reduction as well as explore the potentials of emerging digital trends in minimizing oil spill hazards. The scope of previous reviews is extended by covering the inter-related dimensions of detection, discrimination, and trajectory prediction of oil spills for vulnerability assessment. Findings show that both optical and microwave airborne and satellite remote sensors are used for oil spill monitoring with microwave sensors being more widely used due to their ability to operate under any weather condition. However, the accuracy of both sensors is affected by the presence of biogenic elements, leading to false positive depiction of oil spills. Statistical image segmentation has been widely used to discriminate lookalikes from oil spills with varying levels of accuracy but the emergence of digitalization technologies in the fourth industrial revolution (IR 4.0) is enabling the use of Machine learning (ML) and deep learning (DL) models, which are more promising than the statistical methods. The Support Vector Machine (SVM) and Artificial Neural Network (ANN) are the most used machine learning algorithms for oil spill detection, although the restriction of ML models to feed forward image classification without support for the end-to-end trainable framework limits its accuracy. On the other hand, deep learning models’ strong feature extraction and autonomous learning capability enhance their detection accuracy. Also, mathematical models based on lagrangian method have improved oil spill trajectory prediction with higher real time accuracy than the conventional worst case, average and survey-based approaches. However, these newer models are unable to quantify oil droplets and uncertainty in vulnerability prediction. Considering that there is yet no single best remote sensing technique for unambiguous detection and discrimination of oil spills and lookalikes, it is imperative to advance research in the field in order to improve existing technology and develop specialized sensors for accurate oil spill detection and enhanced classification, leveraging emerging geospatial computer vision initiatives.

Estimating offshore exposure to oil spill impacts based on a statistical forecast model

A statistical oil spill risk forecast model in support of emergency response and environmental risk assessment is presented by combing the deterministic model, probabilistic strategy and frequency estimation. When applied to evaluate various potential spill sources (oil port, fairway, anchorage and pipeline) in the Zhoushan offshore area, the model provides the probability of slick spatial position, oil slick thickness, and exposure duration of floating slick. An oil spill risk map is generated after integrating multiple spill sources, which is a powerful tool for identifying high-risk areas and developing contingency plan. Impact scope and damage degree vary among different sources because of special local topographical, hydrological, and meteorological conditions, where generally exists high pollution intensity of point-source and wide range of line-source. Huge Changjiang River runoff prevents coastal sea in the north from being contaminated by spilled oil from the southern Zhoushan offshore area.

A probabilistic model of decision making regarding the use of chemical dispersants to combat oil spills in the German Bight

Oil spills are one of the major threats to the marine environment in the German Bight (North Sea). In case of an accident, application of chemical dispersants would be one response option among others. Dispersion breaks oil slicks into small droplets which get then mixed into the water column. Removal of the oil from the water surface may reduce contamination of the coast. However, the window of opportunity for effective dispersant application is short and there are concerns about potential effects to the marine life. We propose a Bayesian network (BN) as an interactive and intuitive tool for responders to justify decisions on using chemical dispersants and possibly the provision of appropriate assets. The BN combines detailed sub-BNs for different criteria that govern the decision process. Expected drift trajectories are estimated based on comprehensive numerical ensemble simulations of hypothetical oil spills. Ecological impacts are represented prototypically, focusing on vulnerability of seabird concentrations to pollution in coastal areas. Dispersant effectiveness is estimated considering oil properties and weather conditions. Decision making is supposed to be based on expected satisfaction. The definition of what is considered satisfactory is of central importance for the whole analysis.

A new stochastic oil spill risk assessment model for Persian Gulf: Development, application and evaluation

Persian Gulf is a semi-enclosed highly saline reverse estuary that is exposed to the risk of oil spills in offshore oil and gas activities. In the early 2000s, a specific version of NOAA's Trajectory Analysis Planner (TAP II) was developed for Persian Gulf to assist regional organizations in preparing oil spill contingency plans. In this research, a new stochastic model is developed to cover the limitations of TAP II. The new model is based on an advanced trajectory model, which is now linked with high resolution spatiotemporal data of the wind and sea current. In a case study, the developed model is compared with TAP II, and evaluated by multiple tests designed for analysis of uncertainty, sensitivity, reliability and variability. The case study proved the applicability of the new model, and the evaluation tests provided useful information for the future development of the model.