At present, environmental impacts from offshore oil and gas activities are partly determined by measuring changes in macrofauna diversity. Morphological identification of macrofauna is time-consuming, expensive and dependent on taxonomic expertise. In this study, we evaluated the applicability of using foraminiferal-specific metabarcoding for routine monitoring. Sediment samples were collected along distance gradients from two oil platforms off Taranaki (New Zealand) and their physico-chemical properties, foraminiferal environmental DNA/RNA, and macrofaunal composition analyzed. Macrofaunal and foraminiferal assemblages showed similar shifts along impact gradients, but responded differently to environmental perturbations. Macrofauna were affected by hypoxia, whereas sediment grain size appeared to drive shifts in foraminifera. We identified eight foraminiferal molecular operational taxonomic units that have potential to be used as bioindicator taxa. Our results show that metabarcoding represents an effective tool for assessing foraminiferal communities near offshore oil and gas platforms, and that it can be used to complement current monitoring techniques.
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http://dx.doi.org/10.1016/j.marenvres.2016.08.009 | DOI Listing |
Heliyon
January 2025
Centre for Nature Positive Solutions, School of Science, STEM College, RMIT University, Melbourne, Australia.
Seaweed aquaculture is growing 8.9 % annually to a forecast US$ 22.13 billion in 2024 and has several environmental, economic and social co-benefits.
View Article and Find Full Text PDFMar Environ Res
January 2025
South China Sea Ecological Center of Ministry of Natural Resources (MNR), Nansha Islands Coral Reef Ecosystem National Observation and Research Station, & Key Laboratory of Marine Environmental Survey Technology and Application of MNR, Guangzhou, 510300, China.
Microplastic pollution in marine environments has become a global concern due to its potential ecological risks. However, long-term data on microplastic distribution are scare, hindering the assessment of the ecological threats. This study monitored microplastics pollution in the surface water of the northern South China Sea from 2019 to 2023.
View Article and Find Full Text PDFSci Rep
January 2025
Geophysics Department, Faculty of Science, Cairo University, Giza, 12613, Egypt.
The Red Sea remains a largely under-explored basin, with the Northern Egyptian Red Sea requiring further investigation due to limited borehole data, sparse case studies, and poor seismic quality. A petroleum system, regional structural cross-section, and geological block diagrams integrating onshore fieldwork from Gebel Duwi and offshore subsurface geology were utilized to assess the hydrocarbon potential of the Northern Egyptian Red Sea (NERS). The findings highlight that pre- and syn-rift organic-rich source units in the NERS could generate oil and gas, similar to the capped reservoirs of the Southern Gulf of Suez.
View Article and Find Full Text PDFSci Rep
January 2025
School of Information Engineering, Hunan University of Science and Engineering, Yonzhou, 425199, Hunan, China.
As the global energy landscape shifts and sustainability becomes crucial, the offshore oil and gas sector confronts significant challenges and opportunities. This paper addresses the issues of energy efficiency and environmental impact of optimizing offshore micro-energy systems (OMIES) by proposing a multi-objective optimization model that integrates chaotic local search and particle swarm optimization (PSO). The model aims to achieve optimal scheduling of the energy system by comprehensively considering operational costs, carbon emissions, energy utilization efficiency, and energy fluctuation risks.
View Article and Find Full Text PDFPLoS One
January 2025
Geosciences Department, King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, KSA.
Estimating seismic anisotropy parameters, such as Thomson's parameters, is crucial for investigating fractured and finely layered geological media. However, many inversion methods rely on complex physical models with initial assumptions, leading to non-reproducible estimates and subjective fracture interpretation. To address these limitations, this study utilizes machine learning methods: support vector regression, extreme gradient boost, multi-layer perceptron, and a convolutional neural network.
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