Immiscible Rayleigh-Taylor turbulence: Implications for bacterial degradation in oil spills.

An unstable density stratification between two fluids mixes spontaneously under the effect of gravity, a phenomenon known as Rayleigh-Taylor (RT) turbulence. If the two fluids are immiscible, for example, oil and water, surface tension prevents intermixing at the molecular level. However, turbulence fragments one fluid into the other, generating an emulsion in which the typical droplet size decreases over time as a result of the competition between the rising kinetic energy and the surface energy density. Even though the first phenomenological theory describing this emulsification process was derived many years ago, it has remained elusive to experimental verification, hampering our ability to predict the fate of oil in applications such as deep-water spills. Here, we provide the first experimental and numerical verification of the immiscible RT turbulence theory, unveiling a unique turbulent state that originates at the oil-water interface due to the interaction of multiple capillary waves. We show that a single, non-dimensional, and time-independent parameter controls the range of validity of the theory. Our findings have wide-ranging implications for the understanding of the mixing of immiscible fluids. This includes in particular oil spills, where our work enables the prediction of the oil-water interface dynamics that ultimately determine the rate of oil biodegradation by marine bacteria.

Bayesian phylodynamics reveals the transmission dynamics of avian influenza A(H7N9) virus at the human-live bird market interface in China.

In 2013 to 2017, avian influenza A(H7N9) virus has caused five severe epidemic waves of human infections in China. The role of live bird markets (LBMs) in the transmission dynamics of H7N9 remains unclear. Using a Bayesian phylodynamic approach, we shed light on past H7N9 transmission events at the human-LBM interface that were not directly observed using case surveillance data-based approaches. Our results reveal concurrent circulation of H7N9 lineages in Yangtze and Pearl River Delta regions, with evidence of local transmission during each wave. Our results indicate that H7N9 circulated in humans and LBMs for weeks to months before being first detected. Our findings support the seasonality of H7N9 transmission and suggest a high number of underreported infections, particularly in LBMs. We provide evidence for differences in virus transmissibility between low and highly pathogenic H7N9. We demonstrate a regional spatial structure for the spread of H7N9 among LBMs, highlighting the importance of further investigating the role of local live poultry trade in virus transmission. Our results provide estimates of avian influenza virus (AIV) transmission at the LBM level, providing a unique opportunity to better prepare surveillance plans at LBMs for response to future AIV epidemics.

Ultrafast Laser-Induced Dynamics of Non-Equilibrium Electron Spill-Out in Nanoplasmonic Bilayers.

Contemporary quantum plasmonics capture subtle corrections to the properties of plasmonic nano-objects in equilibrium. Here, we demonstrate non-equilibrium spill-out redistribution of the electronic density at the ultrafast time scale. As revealed by time-resolved 2D spectroscopy of nanoplasmonic Fe/Au bilayers, an injection of the laser-excited non-thermal electrons induces transient electron spill-out thus changing the plasma frequency. The response of the local electronic density switches the electronic density behavior from spill-in to strong (an order of magnitude larger) spill-out at the femtosecond time scale. The superdiffusive transport of hot electrons and the lack of a direct laser heating indicate significantly non-thermal origin of the underlying physics. Our results demonstrate an ultrafast and non-thermal way to control surface plasmon dispersion through transient variations of the spatial electron distribution at the nanoscale. These findings expand quantum plasmonics into previously unexplored directions by introducing ultrashort time scales in the non-equilibrium electronic systems.

Metatranscriptomic response of deep ocean microbial populations to infusions of oil and/or synthetic chemical dispersant.

Oil spills are a frequent perturbation to the marine environment that has rapid and significant impacts on the local microbiome. Previous studies have shown that exposure to synthetic dispersant alone did not enhance heterotrophic microbial activity or oxidation rates of specific hydrocarbon components but increased the abundance of some taxa (e.g., Colwellia). In contrast, exposure to oil, but not dispersants, increased the abundance of other taxa (e.g., Marinobacter) and stimulated hydrocarbon oxidation rates. Here, we advance these findings by interpreting metatranscriptomic data from this experiment to explore how and why specific components of the microbial community responded to distinct organic carbon exposure regimes. Dispersant alone was selected for a unique community and for dominant organisms that reflected treatment- and time-dependent responses. Dispersant amendment also led to diverging functional profiles among the different treatments. Similarly, oil alone was selected for a community that was distinct from treatments amended with dispersants. The presence of oil and dispersants with added nutrients led to substantial differences in microbial responses, likely suggesting increased fitness driven by the presence of additional inorganic nutrients. The oil-only additions led to a marked increase in the expression of phages, prophages, transposable elements, and plasmids (PPTEPs), suggesting that aspects of microbial community response to oil are driven by the "mobilome," potentially through viral-associated regulation of metabolic pathways in ciliates and flagellates that would otherwise throttle the microbial community through grazing.IMPORTANCEMicrocosm experiments simulated the April 2010 Deepwater Horizon oil spill by applying oil and synthetic dispersants (Corexit EC9500A and EC9527A) to deep ocean water samples. The exposure regime revealed severe negative alterations in the treatments' heterotrophic microbial activity and hydrocarbon oxidation rates. We expanded these findings by exploring metatranscriptomic signatures of the microbial communities during the chemical amendments in the microcosm experiments. Here we report how dominant organisms were uniquely associated with treatment- and time-dependent trajectories during the exposure regimes; nutrient availability was a significant factor in driving changes in metatranscriptomic responses. Remarkable signals associated with PPTEPs showed the potential role of mobilome and viral-associated survival responses. These insights underscore the time-dependent environmental perturbations of fragile marine environments under oil and anthropogenic stress.

Solvent Geometry Regulated J- and H-Type Aggregates of Photoswitchable Organogelator: Phase-Selective Thixotropic Gelation and Oil Spill Recovery.

We demonstrate supramolecular polymerization and formation of 1D nanofiber of azobenzene based organogelator (AZO-4) in cyclic hydrocarbon solvents (toluene and methylcyclohexane). The AZO-4 exhibits J- and H-type aggregates in toluene: MCH (9 : 1) and MCH: toluene (9 : 1) respectively. The type of aggregate was governed by the geometry of the solvents used in the self-assembly process. The J-type aggregates with high thermal stability in toluene is due to the enhanced interaction of AZO-4 π- surface with the toluene π-surface, whereas H-aggregate with moderate thermal stability in MCH was due to the interruption of the cyclic hydrocarbon in van der Waals interactions of peripheral chains of AZO-4 molecule. The light induced reversible photoisomerization is observed for both J- and H-aggregates. The macroscopic property revealed spontaneous and strong gelation in toluene preferably due to the strong interactions of the AZO-4 nanofibers with the toluene solvent molecules compared to the MCH. The rheological measurements revealed thixotropic nature of the gels by step-strain experiments at room temperature. The thermodynamic parameter (ΔHm) of gel-to-sol transition was determined for all the gels to get more insight into the gelation property. Furthermore, the phase selective gelation property was extended to the oil spill recovery application using diesel/water and petrol/water mixture.

Performance evaluation of rhamnolipid biosurfactant produced by Pseudomonas aeruginosa and its effect on marine oil-spill remediation.

This study aimed to reveal that the effect of biosurfactant on the dispersion and degradation of crude oil. Whole genome analysis showed that Pseudomonas aeruginosa GB-3 contained abundant genes involved in biosurfactant synthesis and metabolic processes and had the potential to degrade oil. The biosurfactant produced by strain GB-3 was screened by various methods. The results showed that the surface tension reduction activity was 28.6 mN·m-1 and emulsification stability was exhibited at different pH, salinity and temperature. The biosurfactant was identified as rhamnolipid by LC-MS and FTIR. The fermentation conditions of strain GB-3 were optimized by response surface methodology, finally the optimal system (carbon source: glucose, nitrogen source: ammonium sulfate, C/N ratio:16:1, pH: 7, temperature: 30-35 °C) was determined. Compared with the initial fermentation, the yield of biosurfactant increased by 4.4 times after optimization. In addition, rhamnolipid biosurfactant as a dispersant could make the dispersion of crude oil reach 38% within seven days, which enhanced the bioavailability of crude oil. As a biostimulant, it could also improve the activity of indigenous microorganism and increase the degradation rate of crude oil by 10-15%. This study suggested that rhamnolipid biosurfactant had application prospect in bioremediation of marine oil-spill.

Genome-wide DNA methylation changes in Oryzias melastigma embryos exposed to the water accommodated fraction of crude oil.

The water accommodated fraction (WAF) of crude oil exerts considerable impacts on marine fish during embryonic stage. Clarifying changes in epigenetic modifications is helpful for understanding the molecular mechanism underlying the toxicity of embryonic WAF exposure. The aim of this study was to explore genome-wide DNA methylation changes in Oryzias melastigma embryos after exposure to the nominal total petroleum hydrocarbon concentration of 500 µg/L in WAF for 7 days. Whole-genome bisulfite sequencing revealed that 8.47 % and 8.46 % of all the genomic C sites were methylated in the control and WAF-exposed groups, respectively. Among the three sequence contexts, methylated CG site had the largest number in both the two groups. The sequence preferences of nearby methylated cytosines were consistent between the two groups. A total of 4798 differentially methylated regions (DMRs) were identified in the promoter region. Furthermore, Gene Ontology analysis revealed that DMR-related genes were enriched mainly for functions related to development and nervous system. Additionally, the Kyoto Encyclopedia of Genes and Genomes pathways enriched in DMR-related genes were related to nervous system and endocrine system. These novel findings provide comprehensive insights into the genome-wide DNA methylation landscape of O. melastigma following embryonic WAF exposure, shedding light on the epigenetic regulatory mechanisms underlying WAF-induced toxicity.

Chemical exposure from the Hebei spirit oil spill accident and its long-term effects on mental health.

While evidence indicates that exposure to oil spill incidents can affect mental health, it is unclear whether the mental health effects result from the incident itself or from exposure to associated chemicals. Oil contains chemicals that can impact mental health and these chemicals may have long-term effects due to their persistence in the environment. To address the gap in current knowledge, we conducted cross-sectional and prospective analyses of data from adults who participated in the Health Effects of the Hebei Spirit Oil Spill study. To assess chemical exposure from oil spills, we used indirect exposure indicators such as distance from the contaminated oil band to residences and duration of clean-up work, along with direct exposure indicators such as urine metabolite concentrations of volatile organic compounds and polycyclic aromatic hydrocarbons. Mental health assessments covered posttraumatic stress disorder (PTSD), depression, state anxiety, and trait anxiety. In the cross-sectional analyses, all four mental health issues were found to be associated with proximity to the oil band (p-value<0.05) and showed a positive association with clean-up work duration (p-value<0.05). Cox regression analysis revealed that higher urinary t, t-muconic acid levels were associated with an increased risk of depression (Hazard Ratio [HR] = 1.55, 95 % Confidence Interval [CI] = 1.05-2.28), and elevated 1-hydroxypyrene levels increased the risk of PTSD (HR = 1.60, 95 % CI = 1.03-2.48). Additionally, higher urinary 2-naphthol levels were associated with increased state anxiety (HR = 1.39, 95 % CI = 1.00-1.93) and trait anxiety (HR = 1.64, 95 % CI = 1.15-2.32). These associations persisted even after controlling for distance and duration variables related to psychosocial exposure. Our findings suggest that environmental disaster response plans should prioritize minimizing chemical exposure while also considering the duration and nature of the mental health impacts.

Salmonid smolt caudal fin and liver transcriptome responses to low sulfur marine diesel and high sulfur fuel oil water accommodated fractions for assessing oil spill effects in marine environments.

The environmental impact of oil spills is a critical concern, particularly pertaining to low sulfur marine diesel (LSMD) and high sulfur fuel oil (HSFO) that are commonly involved in coastal spills. Although transcriptomic biomonitoring of sentinel animals can be a powerful tool for assessing biological effects, conventional methods utilize lethal sampling to examine the liver. As a non-lethal alternative, we have previously shown salmonid caudal fin cyp1a1 is significantly responsive to LSMD-derived toxicants. The present study further investigated the transcriptomic biomonitoring potential of coho salmon smolt caudal fin in comparison to liver tissue in the context of LSMD and HSFO seawater accommodated fraction (seaWAF) exposure in cold-water marine environments. Assessing the toxicity of these seaWAFs involved quantifying polycyclic aromatic hydrocarbon (tPAH50) concentrations and generating gene expression profiles. Initial qPCR analyses revealed significant cyp1a1 response in both liver and caudal fin tissues of both genetic sexes to all seaWAF exposures. RNA-Seq analysis, focusing on the highest LSMD and HSFO seaWAF concentrations (28.4±1.8 and 645.08±146.3 µg/L tPAH50, respectively), revealed distinct tissue-specific and genetic sex-independent transcriptomic responses with an overall enrichment of oxidative stress, cell adhesion, and morphogenesis-related pathways. Remarkably, the caudal fin tissue exhibited transcriptomic response patterns comparable to liver tissue, particularly consistent differential expression of 33 gene transcripts in the liver (independent of sex and oil type) and 44 in the caudal fin. The present work underscores the viability of using the caudal fin as a non-lethal alternative to liver sampling for assessing and tracking oil spill exposure in marine environments.

Mercury concentrations in Seaside Sparrows and Marsh Rice Rats differ across the Mississippi River Estuary.

Mercury (Hg) concentrations and their associated toxicological effects in terrestrial ecosystems of the Gulf of Mexico are largely unknown. Compounding this uncertainty, a large input of organic matter from the 2010 Deepwater Horizon oil spill may have altered Hg cycling and bioaccumulation dynamics. To test this idea, we quantified blood concentrations of total mercury (THg) in Seaside Sparrows (Ammospiza maritima) and Marsh Rice Rats (Oryzomys palustris) in marshes west and east of the Mississippi River in 2015 and 2016. We also tested for a difference in THg concentrations between oiled and non-oiled sites. To address the potential confounding effect of diet variation on Hg transfer, we used stable nitrogen (δ15N) and carbon (δ13C) isotope values as proxies of trophic position and the source of primary production, respectively. Our results revealed that five to six years after the spill, THg concentrations were not higher in sites oiled by the spill compared to non-oiled sites. In both species, THg was higher at sites east of the Mississippi River compared to control and oiled sites, located west. In Seaside Sparrows but not in Marsh Rice Rats, THg increased with δ15N values, suggesting Hg trophic biomagnification. Overall, even in sites with the most elevated THg, concentrations were generally low. In Seaside Sparrows, THg concentrations were also lower than previously reported in this and other closely related passerines, with only 7% of tested birds exceeding the lowest observed effect concentration associated with toxic effects across bird species (0.2 µg/g ww). The factors associated with geographic heterogeneity in Hg exposure remain uncertain. Clarification could inform risk assessment and future restoration and management actions in a region facing vast anthropogenic changes.

Offshore Oil Spill Detection Based on CNN, DBSCAN, and Hyperspectral Imaging.

Offshore oil spills have the potential to inflict substantial ecological damage, underscoring the critical importance of timely offshore oil spill detection and remediation. At present, offshore oil spill detection typically combines hyperspectral imaging with deep learning techniques. While these methodologies have made significant advancements, they prove inadequate in scenarios requiring real-time detection due to limited model detection speeds. To address this challenge, a method for detecting oil spill areas is introduced, combining convolutional neural networks (CNNs) with the DBSCAN clustering algorithm. This method aims to enhance the efficiency of oil spill area detection in real-time scenarios, providing a potential solution to the limitations posed by the intricate structures of existing models. The proposed method includes a pre-feature selection process applied to the spectral data, followed by pixel classification using a convolutional neural network (CNN) model. Subsequently, the DBSCAN algorithm is employed to segment oil spill areas from the classification results. To validate our proposed method, we simulate an offshore oil spill environment in the laboratory, utilizing a hyperspectral sensing device to collect data and create a dataset. We then compare our method with three other models-DRSNet, CNN-Visual Transformer, and GCN-conducting a comprehensive analysis to evaluate the advantages and limitations of each model.

OS-BREEZE: Oil Spills Boundary Red Emission Zone Estimation Using Unmanned Surface Vehicles.

Maritime transport, responsible for delivering over eighty percent of the world's goods, is the backbone of the global delivery industry. However, it also presents considerable environmental risks, particularly regarding aquatic contamination. Nearly ninety percent of marine oil spills near shores are attributed to human activities, highlighting the urgent need for continuous and effective surveillance. To address this pressing issue, this paper introduces a novel technique named OS-BREEZE. This method employs an Unmanned Surface Vehicle (USV) for assessing the extent of oil pollution on the sea surface. The OS-BREEZE algorithm directs the USV along the spill edge, facilitating rapid and accurate assessment of the contaminated area. The key contribution of this paper is the development of this novel approach for monitoring and managing marine pollution, which significantly reduces the path length required for mapping and estimating the size of the contaminated area. Furthermore, this paper presents a scale model experiment executed at the Coastal and Marine Engineering Research Institute (CAMERI). This experiment demonstrated the method's enhanced speed and efficiency compared to traditional monitoring techniques. The experiment was methodically conducted across four distinct scenarios: the initial and advanced stages of an oil spill at the outer anchoring, as well as scenarios at the inner docking on both the stern and port sides.

Detection of Oil Spill in SAR Image Using an Improved DeepLabV3.

Oil spill SAR images are characterized by high noise, low contrast, and irregular boundaries, which lead to the problems of overfitting and insufficient capturing of detailed features of the oil spill region in the current method when processing oil spill SAR images. An improved DeepLabV3+ model is proposed to address the above problems. First, the original backbone network Xception is replaced by the lightweight MobileNetV2, which significantly improves the generalization ability of the model while drastically reducing the number of model parameters and effectively addresses the overfitting problem. Further, the spatial and channel Squeeze and Excitation module (scSE) is introduced and the joint loss function of Bce + Dice is adopted to enhance the sensitivity of the model to the detailed parts of the oil spill area, which effectively solves the problem of insufficient capture of the detailed features of the oil spill area. The experimental results show that the mIOU and F1-score of the improved model in an oil spill region in the Gulf of Mexico reach 80.26% and 88.66%, respectively. In an oil spill region in the Persian Gulf, the mIOU and F1-score reach 81.34% and 89.62%, respectively, which are better than the metrics of the control model.

Characterization of oil exposure parameters affecting impact, recovery, and reestablishment of mangrove forests: Lessons learned.

The recovery and reestablishment times of mangrove forests exposed to oil are highly dependent on the oil type and oil-related contaminant levels in water and sediments, spill magnitude, oil exposure duration and frequency, duration of the oil contamination remaining in water and sediments, and post-spill events and clean up actions. Estimating the adverse effects of oil exposure and recovery times of mangrove forests is often done by referencing other spills in different geographical regions or locations with different coastal and environmental characteristics. As a result, these projections may not provide reliable estimates for recovery times and the success of the recovery efforts. A systematic methodology is needed to categorize the prespill conditions and oil exposure vulnerability of mangrove forests. The objectives of this review are to: 1) emphasize the need for a systematic process to document the pre- and post-spill characteristics of mangrove forests, 2) identify key factors that influence the vulnerability of mangrove forests to oil exposure, 3) highlight the differences in mangrove forest characteristics and types of spilled oil to develop effective recovery strategies, 4) emphasize the importance of developing a recovery estimation tool to project the adverse effects of oil spills and recovery times, considering geographical features, mangrove characteristics, and the nature of the spilled oil, and 5) stress the necessity for using appropriate monitoring tools for systematically tracking the recovery of mangrove forests after exposure to oil.

Managing deepsea oil spills through a systematic modeling approach.

Offshore oil exploration and production in deepwater are associated with environmental risks to marine ecosystems. This research introduces DWOSM (Deep Water Oil Spill Model), a three-dimensional Lagrangian model, which is developed to simulate the transport and fate of oil spills resulting from subsea blowouts. DWOSM comprises three interconnected modules: DWOSM-DSD, which predicts the oil droplet size distribution from a blowout release; DWOSM-NearField, simulating plume dynamics and tracking oil droplets within the plume region; and DWOSM-FarField, modeling the evolution of dispersed oil beyond the near-field. Compared to other oil spill models, this integrated approach improves the transition between near and far fields using a near-field particle tracking algorithm. It also employs the thermodynamic models to enable the prediction of oil properties under varying deep water pressure and temperature. To gauge the reliability and efficacy of DWOSM, a hypothetical case situated within a North American context is employed for model testing. The DWOSM and its each module are juxtaposed with other established oil spill models. The outcomes indicate that DWOSM yields comparable results to these models by providing reasonable predictions of a deepwater blowout. The model's verification through case scenario testing and comparison underscores its potential as a decision tool for assessing and managing the potential environmental impacts of offshore petroleum activities.

Artificial intelligence modeling and experimental studies of oily pollutants uptake from water using ZIF-8/carbon fiber nanostructure.

In this study, the experimental and modeling of oily pollutants (crude oil, asphaltene, and maltene) uptake by ZIF-8/carbon fiber nanostructure was investigated. The influence of pollutant type, concentration, ionic strength, and sorption time on uptake was systematically examined using a batch absorption system. Then, the experimental data of uptake was modeled using cascade forward (CFNN), multi-layer perceptron (MLP), radial basis function (RBF), and generalized regression (GRNN) neural networks. ZIF-8/carbon fiber nanostructure distinguished by its high hydrophobicity (WCA of 150°) and a complex meso-micro pore structure, demonstrated remarkable efficiency in oil pollutant uptake. Furthermore, the modeling results unveiled that the CFNN-LM model yielded superior predictions, achieving an impressive accuracy rate, as approximately 98% of the uptake data demonstrated an average absolute percent relative error (AAPRE,%) below 3%. Moreover, sensitivity analysis showed that the concentration of pollutants had the most notable impact on the pollutant uptake. Furthermore, the uptake values exhibited an upward trend with elevated concentrations of the pollutant and extended process time, while showing a decline with an increase in ionic strength. These results affirm the reliability of the proposed CFNN-LM model in accurately estimating uptake amounts during the separation process. In summary, the ZIF-8/carbon fiber nanostructure stands out as a highly promising remedy for eliminating oil pollutants from oil/water mixtures, with the added benefit of accurate uptake predictions facilitated by the CFNN-LM model.

Modeling study on oil spill transport in the Great Lakes: The unignorable impact of ice cover.

The rise in oil trade and transportation has led to a continuous increase in the risk of oil spills, posing a serious worldwide concern. However, there is a lack of numerical models for predicting oil spill transport in freshwater, especially under icy conditions. To tackle this challenge, we developed a prediction system for oil with ice modeling by coupling the General NOAA Operational Modeling Environment (GNOME) model with the Great Lakes Operational Forecast System (GLOFS) model. Taking Lake Erie as a pilot study, we used observed drifter data to evaluate the performance of the coupled model. Additionally, we developed six hypothetical oil spill cases in Lake Erie, considering both with and without ice conditions during the freezing, stable, and melting seasons spanning from 2018 to 2022, to investigate the impacts of ice cover on oil spill processes. The results showed the effective performance of the coupled model system in capturing the movements of a deployed drifter. Through ensemble simulations, it was observed that the stable season with high-concentration ice had the most significant impact on limiting oil transport compared to the freezing and melting seasons, resulting in an oil-affected open water area of 49 km2 on day 5 with ice cover, while without ice cover it reached 183 km2. The stable season with high-concentration ice showed a notable reduction in the probability of oil presence in the risk map, whereas this reduction effect was less prominent during the freezing and melting seasons. Moreover, negative correlations between initial ice concentration and oil-affected open water area were consistent, especially on day 1 with a linear regression R-squared value of 0.94, potentially enabling rapid prediction. Overall, the coupled model system serves as a useful tool for simulating oil spills in the world's largest freshwater system, particularly under icy conditions, thus enhancing the formulation of effective emergency response strategies.

Use of TLM derived models to estimate toxicity of weathered MC252 oil based on conventional chemical data and the potential impact of unresolved polar components.

Target lipid model (TLM) and toxic unit (TU) approaches were applied to ecotoxicity and chemistry data from low-energy WAFs (LE-WAFs) of source and weathered crude oils originating from the Deepwater Horizon oil spill. The weathered oils included artificially weathered oils and naturally weathered samples collected in the Gulf of Mexico after the spill. Oil weathering greatly reduced the concentrations of identified LE-WAF components, however, the mass of uncharacterized polar material (UPC) in the LE-WAFs remained largely unchanged during the weathering process. While the TLM-derived calculations displayed a significant decrease in toxicity (TUs) for the heavily weathered oils, copepod toxicity, expressed as LC10-based TUs, were comparable between LE-WAFs of fresh and weathered oils. The discrepancy between observed and predicted toxicity for the LE-WAFs of artificially weathered oils may be related to limitations by the chemical analyses or increased toxicity due to generation of new unknown compounds during the weathering process.

An affordable operational oil spill monitoring system in action: A diachronic multiplatform analysis of recent incidents in the southern Gulf of Mexico.

The coexistence of marine sensitive areas with the oil industry requires robust preparedness and rapid response capabilities for monitoring and mitigating oil spill events. Scientifically proven satellite-based methods for the visual detection of oil spills are widely recognized as effective, low-cost, transferable, scalable, and operational solutions, particularly in developing economies. Following meticulous design and implementation, we adopted and executed a relatively low-cost operational monitoring and alert system for oil spill detection over the ocean surface and alert issuance. We analyzed over 1500 satellite images, issuing over 70 warning reports on oil slicks and spills in the southern Gulf of Mexico. To assess the system's efficiency and performance, we leveraged data from three major oil spill incidents in the study region during June and July of 2023 in the study region, covering a maximum area of 669 km2 and tracked for 12 to 24 days. We documented the evolution of these oil spills by integrating satellite sensing data with on-site Lagrangian drifting buoys, a network of high-frequency radars, and citizen reports to validate the outcomes of this system. We generated timely technical information on the spill's evolution, informing decision-makers and local community leaders to strengthen their mitigation response capabilities. Additionally, we developed a robust database with spectral and spatiotemporal features of satellite-detected oil, thereby contributing to advancing the scientific understanding of sea surface dynamics related to natural and anthropogenic oil sources. This study also highlights immediate-, medium-, and long-term research agendas and establishes a reference for a sustained, transferable, and operational oil spill monitoring system.

Multiple-Integrated Biomarker Indexes to Assess the Responses of the Flatfish Achirus lineatus during Exposure to Light Crude Oil Water Accommodated Fraction.

In the present study, we evaluated the biological response of Achirus lineatus to water accommodated fraction (WAF) of light crude oil (American Petroleum Institute gravity 35°) during a sub-chronic bioassay (14 and 28 days) at two different concentrations: 5% v/v (1.20 µg∙L- 1 expressed as total polycyclic aromatic hydrocarbons [∑25 PAH]) and 10% v/v (6.61 µg∙L- 1 [∑25 PAH]). The responses were evaluated through the biomarker response index (BRI), the integrated biomarker response (IBRv2) and the bioconcentration factor (BCF). The results showed an increase in biological response in relation to WAF concentration and exposure time, which resulted in a slight and moderate disturbance in the basal condition and bioconcentration level of metals (Pb > Ni > V > Cd) in fish tissue. Results in the present study denote that flatfish such as A. lineatus may be negatively influenced by spilled light crude oil.