Environmental sensitivity index maps to manage oil spill risks: A review and perspectives

D'Affonseca, Fernando Mazo; Reis, Fábio Augusto Gomes Vieira; Corrêa, Claudia Vanessa dos Santos; Wieczorek, Arthur; Giordano, Lucília do Carmo; Marques, Maria Risoleta Freire; Rodrigues, Flávio Henrique; Costa, Daiana Marques; Kolya, André de Andrade; Veiga, Vinicius Mendes; Santos, Sarah Félix; Magalhães, Laila Milani; Gatto, Isadora Torres; Riedel, Paulina Setti

doi:10.1016/j.ocecoaman.2023.106590

Cited by 13 publications

(6 citation statements)

References 51 publications

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“…To identify coastal zones at risk from oil spills, the Environmental Sensitivity Index (ESI) serves as a fundamental method for evaluating and quantifying shoreline vulnerability to such incidents [32]. The concept of ESI was first formulated by Miles O. Hayes and his team at the Research Planning Institute in the 1970s, and has been utilized by the NOAA [33,34].…”

Section: Step 3: Shoreline Vulnerability Assessmentmentioning

confidence: 99%

Risk Assessment of Oil Spills along the Coastline of Jiaozhou Bay Using GIS Techniques and the MEDSLIK-II Model

Peng,

Wang,

et al. 2024

Water

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With the increasing global reliance on maritime oil transportation, oil spills pose significant environmental hazards to coastal ecosystems. This study presents a comprehensive quantitative framework for assessing oil spill risks along the Jiaozhou Bay coastline in China. The research begins with an analysis of historical spill data to construct representative oil spill simulation scenarios. The advanced MEDSLIK-II oil spill prediction model is then employed to simulate oil spill trajectories under these scenarios, focusing on key parameters such as oil thickness and mass to evaluate the hazard levels associated with each scenario. Subsequently, the Environmental Sensitivity Index (ESI) is utilized to assess the vulnerability of coastal zones, while Geographic Information System (GIS) techniques are employed for a spatial analysis and visualization of the results. The case study, covering a 26.87 km stretch of the Jiaozhou Bay coastline, reveals 10 high-risk zones with a total length of 8561.2 m. These areas are predominantly characterized by saltwater marshes, brackish water marshes, and inundated low-lying areas, with ESI rankings of 9 and 10, accounting for 24% of the 339 analyzed segments. The modeling results indicate that in the simulated scenarios, oil spills originating from the Huangdao Oil Port and Qianwan Port pose the greatest risks, with potential impacts extending up to 12 km and 15 km along the coastline, respectively. The study highlights the importance of considering multiple factors, including oil spill trajectories, coastal geomorphology, and ecological sensitivity, in comprehensive risk assessments. The proposed framework demonstrates potential for adaptation and application to other coastal regions facing similar oil spill risks, contributing to the advancement of coastal management practices worldwide.

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Section: Step 3: Shoreline Vulnerability Assessmentmentioning

confidence: 99%

Risk Assessment of Oil Spills along the Coastline of Jiaozhou Bay Using GIS Techniques and the MEDSLIK-II Model

Peng,

Wang,

et al. 2024

Water

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“…Furthermore, it can establish oil spill risk maps for marine environments, offering valuable reference information for emergency response plans, the design of rescue equipment, and accident mitigation. These maps also serve as useful references for risk assessment and management [51,52], which the present study aims to generate for both case studies.…”

Section: Gismentioning

confidence: 99%

Utilizing Numerical Models and GIS to Enhance Information Management for Oil Spill Emergency Response and Resource Allocation in the Taiwan Waters

Chiu,

Chuang,

Chuang

et al. 2023

JMSE

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This study aims to establish a comprehensive workflow for developing emergency response plans for both actual and scenario oil spill incidents in the Taiwan waters while addressing the resource allocation for oil spill containment as well. This workflow comprises two vital components. The first component involves the integration of numerical tools and observational data, which includes the incorporation of wind data from sources such as the National Centers for Environmental Prediction (NCEP) or meteorological stations. Additionally, it incorporates ocean current data simulated by the semi-implicit cross-scale hydroscience integrated system model (SCHISM) into the general NOAA operational modeling environment (GNOME) model, which is a new approach for this purpose. In order to assess the efficacy of this component, two distinct case studies were conducted. The first case study focused on an incident in a northern coastal area of Taiwan under open sea conditions, whereas the second case study examined an incident within a major commercial harbor in central Taiwan. The second component of this workflow involves creating oil risk maps by integrating the results from the first component with specific geographical factors into Google Earth. These oil risk maps serve multiple purposes. They offer real-time information to emergency response commanders regarding oil spill hazard prediction, and they also enable the effective development of emergency response strategies and disposal plans for potential oil spill incidents. This is achieved by generating risk maps for various scenarios using the approach outlined in the first component. Additionally, these maps assist in the assessment and planning of resource allocation for oil containment.

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“…Oil–water separation plays an important role in environmental protection, industrial production, , resource recovery, and other fields . Oil entering the natural water will cause great harm to the ecosystem, such as the Gulf of Mexico oil spill accident, which has seriously affected the survival and reproduction of marine life. − At the same time, as an important chemical raw material and energy source, the recycling of oil is conducive to reducing resource waste and aiding energy conservation and emission reduction. Meanwhile, in the process of oil production, efficient oil–water separation helps to improve the purity and quality of oil .…”

Section: Introductionmentioning

confidence: 99%

Intelligent Devices Harnessing Underwater Superoleophobic and Underoil Superhydrophobic Quartz Sands for the Separation of Diverse Stratified and Emulsified Water–Oil Mixtures

Lin,

Zhang,

Chen

et al. 2024

Langmuir

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To achieve the green, sustainable, and controllable recovery of oil−water resources and to address the limited functionality of single superwet materials in oil− water separation, this study reports a multifunctional oil−water separation strategy by compositing the underwater superoleophobic and underoil superhydrophobic materials (HS). The underwater superoleophobic quartz sands with an oil contact angle of 152.68°were prepared by adjusting the particle size. This material demonstrated a water flux of 4688 L m −2 h −1 and a low-density oil and water mixture separation efficiency of 98.6%, which remained above 97.9% over 50 cycles. It was effective in separating oil-in-water emulsions with a separation efficiency of >99%. For HS, quartz sands were modified with dodecyltrimethoxysilane. The optimized HS-4 exhibited superhydrophobic properties with a water contact angle of 157.06°. It achieved an oil flux of 5775 L m −2 h −1 and a water and dichloromethane mixture separation efficiency of 98.4%. Additionally, they exhibited significant potential in the separation of water-inoil emulsions. Furthermore, by placing the underwater superoleophobic and underoil superhydrophobic units at the bottom of the filter, we achieved cyclic separation of high-density oil and water mixtures, low-density oil and water mixtures, water-in-oil emulsions, and oil-in-water emulsions. The separation efficiency consistently exceeded 96.5% over 10 cycles. In addition, the oil−water separation mechanism of underwater oleophobic and underoil hydrophobic materials was demonstrated by the relative concentration distribution of water and oil with molecular dynamics simulations. This intelligent oil− water separation method marks a significant advancement in the sustainable separation of diverse oil−water mixtures.

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Environmental sensitivity index maps to manage oil spill risks: A review and perspectives

Cited by 13 publications

References 51 publications

Risk Assessment of Oil Spills along the Coastline of Jiaozhou Bay Using GIS Techniques and the MEDSLIK-II Model

Risk Assessment of Oil Spills along the Coastline of Jiaozhou Bay Using GIS Techniques and the MEDSLIK-II Model

Utilizing Numerical Models and GIS to Enhance Information Management for Oil Spill Emergency Response and Resource Allocation in the Taiwan Waters

Intelligent Devices Harnessing Underwater Superoleophobic and Underoil Superhydrophobic Quartz Sands for the Separation of Diverse Stratified and Emulsified Water–Oil Mixtures

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