Source identification of sea surface oil with geochemical data in Cantarell, Mexico

Texeira, Caroline C.; Siqueira, Celeste Yara dos Santos; Neto, Francisco Radler de Aquino; Miranda, Fernando; Cerqueira, José Roberto; Vasconcelos, A.; Landau, L.; Herrera, Miguel; Bannermaman, Karen

doi:10.1016/j.microc.2014.06.025

Cited by 22 publications

(6 citation statements)

References 16 publications

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“…An important task to perform during oil spills is to specify the oil type to predict the environmental damage to maritime life. The oil type classification could be qualitatively similar to what is proposed by [60,[79][80][81]. Another way for classification is by analyzing the physical characteristics of the oil material, namely the relative dielectric constant, as we proposed in [76].…”

Section: Classificationmentioning

confidence: 57%

Recent Advances in Oil-Spill Monitoring Using Drone-Based Radar Remote Sensing

Hammoud¹,

Wehn²

2023

Environmental Sciences

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Oil spills are regrettably common and have socioeconomic implications on communities and disastrous consequences on the marine ecosystem and maritime life. The European Space Agency (ESA) has stated that worldwide spillage exceeds 4.5 million tons of oil annually, where 45% of the amount is due to operative discharges from ships. To alleviate the severity of oil spills and promptly react to such incidents, it is crucial to have oil-spill monitoring systems, which enable an effective contingency plan to dictate the best actions for dealing with oil spills. A quick and efficient intervention requires the (1) detection of oil slicks, (2) thickness estimation, and (3) oil classification. The European Maritime Safety Agency (EMSA) highlighted in 2016 the need to use drones as complementary systems supporting satellite maritime surveillance. While multiple sensors could be used, active radars appear to be prominent for oil spill monitoring. In this chapter, we present recent advances in drone-based radar remote sensing as an effective oil spill monitoring system. It shows from the system-level perspective the capability of radar systems on drones, using high spectral resolution and parallel scanning, to perform the above-required functionalities (1, 2, and 3) and provide valuable information to contain the damage.

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Section: Classificationmentioning

confidence: 57%

Recent Advances in Oil-Spill Monitoring Using Drone-Based Radar Remote Sensing

Hammoud¹,

Wehn²

2023

Environmental Sciences

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“…Whether Pr or Ph is formed from degradation of phytol depends on the toxicity of the environment (Didyk et al 1978;Rontani et al 1990;Elias et al 2000). The studied samples have Pr/Ph in the range 0.05-1.09, indicating that the organic matter was subjected to an anoxic, highly reductive conditions (Peters et al 2005;Texeira et al 2014). Elias et al (2000) also reported in their study in the Amazon that Pri/Phy ratio values close to 1 were found in samples close to the source of rivers and could be the result of a mixture of allochthonous and autochthonous contributions.…”

Section: Aliphatic Hydrocarbonsmentioning

confidence: 83%

Evaluation of the organic matter sources and δ13C composition of individual alkanes for the baseline assessment of hydrocarbons in a pristine area from Amazon region, Brazil

et al. 2016

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This study focuses on the surroundings of the Urucu oil and gas province and the adjoining Solimões terminal in the Amazon region of Brazil, which is of great global importance due to its biodiversity richness and freshwater resources. There are few results related to aliphatic hydrocarbons and polycyclic aromatic hydrocarbons (PAHs), and there are no results for individual stable isotopic compositions (d 13 C) in the studied region. Results showed a distribution of n-alkanes that is characteristic of a major input from higher plants. Furthermore, d 13 C values of individual n-alkanes varied between -35.0 and -23.1 %, suggesting a dominance of 12 C-enriched n-alkanes indicative of an origin from lacustrine algae. The levels of some PAHs (4.64-31.70 ng g -1 ) which are classified as priority pollutants by the United States Environmental Protection Agency (US-EPA) and perylene (biogenic PAH) were measured in surface sediments to identify pollution sources and determine baseline data looking for future management strategies. The majority of the compounds present in the samples from Coari Lake and the Solimões River are of biogenic origin. However, a minor anthropogenic contribution of aromatic hydrocarbons derived from biomass burning (anthracene, phenanthrene, fluoranthene, pyrene, chrysene, benzo[b]fluoranthene and benzo[k]fluoranthene) was detected. No petrogenic contribution was found in the samples.

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“…Despite sufficient studies in the area of petroleum hydrocarbons (PHCs) spill monitoring [3], the studies that identify or classify the oil spill types is rather limited. Chemical methods, such as chromatographic or mass spectroscopic analysis on oil samples, can provide accurate inferences on oil spill types [4], [5]. Some studies also suggested that oil types This research was funded in part by China National Key R&D Program grant number 2020YFE0201500, and in part by China Postdoctoral Science Foundation, grant number 2020M670730.…”

Section: Introductionmentioning

confidence: 99%

Identifying Oil Spill Types Based on Remotely Sensed Reflectance Spectra and Multiple Machine Learning Algorithms

Liu¹,

Yu²,

Xie³

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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An accurate identification of oil spill types is the basis of determining the source of leakage, evaluating the potential damage, and deciding a plan of responses for an oil spill event. Despite sufficient studies that interpreted and analyzed hyperspectral data of oil spills, these studies that identify or classify oil spill types is rather limited. Aiming at identifying different types of oil spills, this study analyses the reflectance spectra obtained from high-resolution hyperspectral sensors using multiple machine learning methods. Four types of machine learning models are applied in this study: random forest (RF), support vector machine (SVM), and deep neural network (DNN), and DNN with differential pooling (DP-DNN). The training and testing data are collected by field experiments under different environmental condition in order to verify the robustness of the machine learning models. The characteristics of reflectance is briefly described, and the results conform with results from previous studies. The performances of the machine learning models are evaluated and compared in terms of both accuracy of prediction and computational complexity. The results indicate that the two DNN models are able to achieve the most accurate prediction among the four machine learning models at the cost of more computation. The SVM model, or the proposed DP-DNN model may be a favorable choice when training time is limited.

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Source identification of sea surface oil with geochemical data in Cantarell, Mexico

Cited by 22 publications

References 16 publications

Recent Advances in Oil-Spill Monitoring Using Drone-Based Radar Remote Sensing

Recent Advances in Oil-Spill Monitoring Using Drone-Based Radar Remote Sensing

Evaluation of the organic matter sources and δ13C composition of individual alkanes for the baseline assessment of hydrocarbons in a pristine area from Amazon region, Brazil

Identifying Oil Spill Types Based on Remotely Sensed Reflectance Spectra and Multiple Machine Learning Algorithms

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