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"Oil pollution"
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Monitoring and modeling the Deepwater Horizon oil spill
Published by the American Geophysical Union as part of the Geophysical Monograph Series, Volume 195.Monitoring and Modeling the Deepwater Horizon Oil Spill: A Record-Breaking Enterprise presents an overview of some of the significant work that was conducted in immediate response to the oil spill in the Gulf of Mexico in 2010.
eBook
Problems, Effects, and Methods of Monitoring and Sensing Oil Pollution in Water: A Review
Oil pollution in water bodies is a substantial environmental concern that poses severe risks to human health, aquatic ecosystems, and economic activities. Rising energy consumption and industrial activity have resulted in more oil spills, damaging long-term ecology. The aim of the review is to discuss problems, effects, and methods of monitoring and sensing oil pollution in water. Oil can destroy the aquatic habitat. Once oil gets into aquatic habitats, it changes both physically and chemically, depending on temperature, wind, and wave currents. If not promptly addressed, these processes have severe repercussions on the spread, persistence, and toxicity of oil. Effective monitoring and early identification of oil pollution are vital to limit environmental harm and permit timely reaction and cleanup activities. Three main categories define the three main methodologies of oil spill detection. Remote sensing utilizes satellite imaging and airborne surveillance to monitor large-scale oil spills and trace their migration across aquatic bodies. Accurate real-time detection is made possible by optical sensing, which uses fluorescence and infrared methods to identify and measure oil contamination based on its particular optical characteristics. Using sensor networks and Internet of Things (IoT) technologies, wireless sensing improves early detection and response capacity by the continuous automated monitoring of oil pollution in aquatic settings. In addition, the effectiveness of advanced artificial intelligence (AI) techniques, such as deep learning (DL) and machine learning (ML), in enhancing detection accuracy, predicting leak patterns, and optimizing response strategies, is investigated. This review assesses the advantages and limits of these detection technologies and offers future research directions to advance oil spill monitoring. The results help create more sustainable and efficient plans for controlling oil pollution and safeguarding aquatic habitats.
Journal Article
Fluorometric Detection of Oil Traces in a Sea Water Column
This study focuses on broadening the knowledge of a fluorometric index to improve the detection of oil substances present in the marine environment. It is assumed that the value of this index will provide information about a possible oil discharge at some distance from the sensor. In this paper, the detection of oil present in seawater as a mixture of oils such as fuel, lubricate oil, or crude oil based on a fluorescence indicator-fluorometric index (FIo/w) is discussed. FIo/w was defined based on specific excitation and emission wavelengths coming from the obtained excitation–emission spectrum (EEM) of oil-free seawater and, in parallel, the same water but artificially polluted with oil. For this, measurements of a mixture of oils in seawater for an oil-to-water ratio in the range from 50 × 10−9 to 200 × 10−9 as well as oil-free seawater were performed. Laboratory measurements continued five times in months in the summer season with the coastal waters of the southern Baltic Sea (last spring, summer, and early autumn). The dependence of FIo/w on the presence of oil in seawater, the oil-in-water ratio, as well as months of the considered season has been demonstrated.
Journal Article
The basics of oil spill cleanup
\"An examination of pollution caused by crude oils and petroleum products derived from them, this book covers how oil spills are measured and detected and discusses the properties of the oil as well as its long-term fate in the environment. This third edition contains a new chapter devoted to pollution effects on wildlife. It focuses on the cleanup of oil spills that occur in water, since these spills spread most rapidly and cause the most visible environmental damage. It also includes coverage of the latest technologies as well as recent spills, including the Gulf of Mexico\"-- Provided by publisher.
Book
Characterization and identification of long-chain hydrocarbon-degrading bacterial communities in long-term chronically polluted soil in Ogoniland: an integrated approach using culture-dependent and independent methods
Escalating oil consumption has resulted in an increase in accidental spills of petroleum hydrocarbons, causing severe environmental degradation, notably in vulnerable regions like the Niger Delta. Complex mixture of these hydrocarbons particularly long-chain alkanes presents unique challenges in restoration of polluted environment due to their chemical properties. This study aimed to investigate the long-chain hydrocarbon-degrading bacterial communities within long-term chronically polluted soil in Ogoniland, by utilizing both traditional cultivation methods and modern culture-independent techniques. Results revealed that surface-polluted soil (SPS) and subsurface soil (SPSS) exhibit significantly higher total organic carbon (TOC) ranging from 5.64 to 5.06% and total petroleum hydrocarbons (TPH) levels ranging from 36,775 ppm to 14,087 ppm, compared to unpolluted soil (UPS) with 1.97% TOC and 479 ppm TPH, respectively. Analysis of carbon chain lengths reveals the prevalence of longer-chain alkanes (C20-28) in the surface soil. Culture-dependent methods, utilizing crude oil enrichment (COE) and paraffin wax enrichment (PWE), yield 47 bacterial isolates subjected to a long-chain alkane degradation assay. Twelve bacterial strains demonstrate significant degradation abilities across all enriched media. Three bacterial members, namely
Pseudomonas
sp. (
almA
),
Marinomonas
sp. (
almA
), and
Alteromonas
(
ladA
), exhibit genes responsible for long-chain alkane degradation, demonstrating efficiency between 50 and 80%. Culture-independent analysis reveals that surface SPS samples exhibit greater species richness and diversity compared to subsurface SPSS samples.
Proteobacteria
dominates as the phylum in both soil sample types, ranging from 22.23 to 82.61%, with
Firmicutes
(0.2–2.22%),
Actinobacteria
(0.4–3.02%), and
Acidobacteria
(0.1–3.53%) also prevalent. Bacterial profiles at genus level revealed that distinct variations among bacterial populations between SPS and SPSS samples comprising number of hydrocarbon degraders and the functional predictions also highlight the presence of potential catabolic genes (
nahAa
,
adh2
, and
cpnA
) in the polluted soil. However, culture-dependent analysis only captured a few of the dominant members found in culture-independent analysis, implying that more specialized media or environments are needed to isolate more bacterial members. The findings from this study contribute valuable information to ecological and biotechnological aspects, aiding in the development of more effective bioremediation applications for restoring oil-contaminated environments.
Journal Article
Metagenomic investigations on antibiotic resistance and microbial virulence in oil-polluted soils from China
Engine oil spills have been associated with a wide range of human health problems. However, little is known about the effects of petroleum hydrocarbon pollution on soil microbial communities. In this study, three samples were collected from oil-polluted soils (OPS), and one control soil (CS) from Taolin town, China, near the old engine’s scrapes was used. The aims of this study were to conduct metagenomic sequencing and subsequently perform resistome and virulome analysis. We also aimed to validate anti-microbial resistance and virulence genes and anti-bacterial sensitivity profiles among the isolates from oil-polluted soils. The OPS microbial community was dominated by bacterial species compared to the control samples which were dominated by metazoans and other organisms. Secondly, the resistosome and virulome analysis showed that ARGs and virulence factors were higher among OPS microbial communities. Antibiotic susceptibility assay and qPCR analysis for ARGs and virulence factors showed that the oil-polluted soil samples had remarkably enhanced expression of these ARGs and some virulence genes. Our study suggests that oil pollution contributes to shifting microbial communities to more resilient types that could survive the toxicity of oil pollution and subsequently become more resilient in terms of higher resistance and virulence potential.
Graphical abstract
Journal Article