How quickly will the offshore ecosystem recover from the 2010 Deepwater Horizon oil spill? Lessons learned from the 1979 Ixtoc-1 oil well blowout

Rohal, Melissa; Barrera, Noe; Escobar-Briones, Elva; Brooks, Gregg R.; Hollander, David J.; Larson, Rebekka A.; Montagna, Paul A.; Pryor, Marissa; Romero, Isabel C.; Schwing, Patrick

doi:10.1016/j.ecolind.2020.106593

Cited by 13 publications

(3 citation statements)

References 44 publications

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“…Copepods can also be sensitive to variations in the quality of organic matter, particularly in the UMS zone. Therefore, any anthropogenic pressure that may change these variables, such as climate change (Danovaro et al, 2004), trawling disturbances (Schratzberger and Jennings, 2002), Predicting spatial patterns of marine meiofauna Gallucci et al mining (Miljutin et al, 2011), and oil and gas exploration (Netto et al, 2009;Montagna et al, 2013;Reuscher et al, 2017;Rohal et al, 2020) are expected to affect the meiofauna.…”

Section: Implications For the Ecosystem-based Management Of The Santo...mentioning

confidence: 99%

Predicting large-scale spatial patterns of marine meiofauna: implications for environmental monitoring

Gallucci,

Fonseca,

Vieira

et al. 2023

Ocean Coast. Res.

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This study aims model the distribution of meiofauna indicators in relation to environmental variables from the Santos Basin continental margin, SE Brazil, using machine learning techniques, to provide baseline information and foster future monitoring programs. A total of 100 sampling stations were distributed in eight transects and 11 isobaths (25 to 2,400 m) perpendicular to the coast. In each station, three replicates were sampled for meiofauna and 38 environmental parameters. A total of 28 meiofauna taxa were found, with a mean richness varying from 3 to 15 taxa per station. Meiofauna mean density varied between 55 and 2,001 ind. 10 cm -2 . Density of meiofauna and its most frequent taxa (Nematoda, Copepoda, Kinorhyncha, and Polychaeta), and taxa richness were used as descriptors for the models. Meiofauna and nematode density showed the highest training and testing accuracies, with R² values above 0.74. Based on the distribution of meiofauna descriptors and their responses to environmental conditions, we suggest a mosaic of six benthic zones. The La Plata Plume zone and the Cabo Frio Upwelling zone are two of the most diverse and productive zones in the continental shelf, wich are separated by the less productive Central Continental Shelf zone. A fourth zone, with very low meiofauna densities, corresponds to the carbonated sediments of the shelf-break. The Upper and Mid-Slope is a narrow zone along the entire basin, with intermediate densities and small amounts of high-quality organic carbon. The largest, impoverished zone, the Lower Slope and Plateau comprises the deepest areas and the São Paulo Plateau. The study showed that, although some zones can be recognized by most meiofauna descriptors, others are better characterized by specific ones, implying that meiofauna indicators should be monitored concomitantly. We recommend the optimization of sampling design based on our model to reduce costs and increase our understanding of the system.

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Section: Implications For the Ecosystem-based Management Of The Santo...mentioning

confidence: 99%

Predicting large-scale spatial patterns of marine meiofauna: implications for environmental monitoring

Gallucci,

Fonseca,

Vieira

et al. 2023

Ocean Coast. Res.

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“…Data on learner states (motivation, emotional states, for example) has also been taken into account, which can be deduced from physiological sensors (facial expression, seat posture and perspiration, for example). EDM uses methods and tools from the broader field of data mining [8,19], and a subdomain of computer science and AI that has been used for purposes as diverse as credit card fraud detection, genetic sequence analysis in bioinformatics, or analysis of customer buying behavior [20].…”

Section: Educational Data Mining (Edm)mentioning

confidence: 99%

Artificial Intelligent in Education

et al. 2022

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The application of Artificial Intelligence or AI in education has been the subject of academic research for more than 30 years. The field examines learning wherever it occurs, in traditional classrooms or at workplaces so to support formal education and lifelong learning. It combines interdisciplinary AI and learning sciences (such as education, psychology, neuroscience, linguistics, sociology and anthropology) in order to facilitate the development of effective adaptive learning environments and various flexible, inclusive tools. Nowadays, there are several new challenges in the field of education technology in the era of smart phones, tablets, cloud computing, Big Data, etc., whose current research questions focus on concepts such as ICT-enabled personalized learning, mobile learning, educational games, collaborative learning on social media, MOOCs, augmented reality application in education and so on. Therefore, to meet these new challenges in education, several fields of research using AI have emerged over time to improve teaching and learning using digital technologies. Moreover, each field of research is distinguished by its own vision and methodologies. In this article, to the authors present a state of the art finding in the fields of research of Artificial Intelligence in Education or AIED, Educational Data Mining or EDM and Learning Analytics or LA. We discuss their historical elements, definition attempts, objectives, adopted methodologies, application examples and challenges.

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“…For instance, Kuwait spilled approximately 20 million barrels of crude oil in 1991 as a result of the war, many recalcitrant contaminants (unresolved complex mixture) that require urgent degradation are still being found recently in the groundwater of the contaminated areas, and the degradation process will continue for a long time to come [8]. The Deepwater Horizon (DWH) accident occurred on 20 April 2010 in the Northern Gulf of Mexico, which spilled approximately 200 million gallons of crude oil and contaminated more than 20,000 square kilometers of sea area; the marsh soil strength is still not restored and the impact on the local biological community will last for at least 50 years [9,10]. The MV Wakashio grounding incident that occurred in the Mauritius wildlife sanctuary on 25 July 2020 resulted in the spill of 1000 metric tons of very low sulfur fuel oil into the Indian Ocean, causing varying degrees of harm to nearby coral reefs, the Île aux Aigrettes Nature Reserve, Mauritius dolphins, etc., and its ecological damage and impact on economic development will take a long time to repair [11].…”

Section: Introductionmentioning

confidence: 99%

Migration Movements of Accidentally Spilled Oil in Environmental Waters: A Review

Jiang,

Han,

Wang

et al. 2023

Water

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Accidentally spilled oil can cause great harm to the ecological balance of water once it enters the environmental waters. Clarifying its movement behavior and migration law in water has been the focus of environmental hydraulics research. This review starts from the mechanism of the oil spill migration process, and firstly reviews the kinematic characteristics of the smallest moving unit of the oil spill, the individual oil droplet, as well as focusing on several key aspects such as droplet shape, trajectory, terminal velocity and drag coefficient. Subsequently, considering the commonalities and differences between inland riverine and oceanic environments, different aspects of oil droplet collision, coalescence, breakage, particle size distribution, and vertical diffusion are discussed separately. Finally, the current status of research on the migration laws of accidental oil spills in environmental waters is summarized, and feasible future research directions are proposed to address the emerging research problems and research gaps.

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How quickly will the offshore ecosystem recover from the 2010 Deepwater Horizon oil spill? Lessons learned from the 1979 Ixtoc-1 oil well blowout

Cited by 13 publications

References 44 publications

Predicting large-scale spatial patterns of marine meiofauna: implications for environmental monitoring

Predicting large-scale spatial patterns of marine meiofauna: implications for environmental monitoring

Artificial Intelligent in Education

Migration Movements of Accidentally Spilled Oil in Environmental Waters: A Review

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