Oil Spill Identification Research Articles

Application of Bicadinane Biomarkers in Oil Spill Fingerprinting Studies Involving Southeast Asian Oils

Bicadinanes (C30H52) are a family of non-hopanoid, pentacyclic triterpane biomarkers whose utility in oil spill fingerprinting has, to our knowledge, not been previously explored. Owing to their derivation from Diptocarpaceae tree (dammar) resin, crude oils containing bicadinanes are geographically and temporally limited to Southeast Asian oils sourced from Oligocene or younger strata. Bicadinane-bearing oils produced from Southeast Asia, however, are shipped worldwide and thereby bicadinanes may be present in oils spilled both within and outside of Southeast Asia. Bicadinanes’ relative or absolute concentrations are readily measured using a standard oil spill identification analytical method (GC/MS-SIM) that routinely targets more conventional biomarkers (triterpanes, steranes, and aromatic steroids). In this study, the absolute concentrations of four bicadinane isomers (W, T, T1 and R) were measured in 42 tarballs collected from shorelines along the southern extent of the Straits of Singapore, an area with very high oil tanker and cargo vessel traffic. The tarballs’ specific origins are unknown but safely presumed to be derived from various unknown oil spills and/or oil seeps in the region. GC/FID was used to classify the tarball oils’ level of biodegradation (Levels 1, 4, and 6). The absolute concentration of total bicadinanes ranged from 7.5 to 208 µg/g and varied widely and independent of the level of biodegradation. The relative concentrations among the four bicadinane isomers (T > W>T1∼R; on average, 56% > 20% > 12% each) also showed no obvious or statistically significant trend with biodegradation for the samples studied. Thus, variations in both total and individual isomer concentrations are attributable to the tarballs’ multiple crude oil sources spilled or seeped in the study area. Diagnostic ratios (DRs) are critical components in oil spill identification protocols. A newly proposed DR of (W + T)/hopane varied widely (0.01 to 0.54) and also independently from the more conventionally used DR of 18α(H)-oleanane/hopane (oleanane/hopane; 0.04 to 0.42). Two newly proposed DRs among the four bicadinane isomers, W/T and T/(T1 + R), also varied among the 42 tarball oils studied (0.27 to 0.45 and 1.4 to 3.2, respectively). The variation in the (W + T)/hopane, oleanane/hopane, W/T and T/(T1 + R) ratios was unrelated to the level of biodegradation for the samples studied and exceeded the statistical criteria used in the CEN (2012) oil spill identification protocol (i.e., 95% repeatability limit) and thereby demonstrates these DRs’ utility in comparing and distinguishing among different crude oils containing bicadinanes – and, of course, between crude oils that do and do not contain bicadinanes. The additional specificity offered by bicadinanes argues they should be included as target analytes in oil spill identification studies throughout Southeast Asia or in other areas where Southeast Asian oils are believed to be present (shipped, handled, or used).

Research on marine oil spill identification based on laser-induced fluorescence LiDAR

The rise in oil extraction and transportation in marine environments has led to frequent oil spill incidents, posing a severe threat to marine ecosystems and becoming an urgent environmental issue. This paper presents a laser-induced fluorescence light detection and ranging (LiDAR) system specifically designed for monitoring marine oil spills. The system comprises a laser emission module, a receiving module, a data processing module, and a wireless transmission module. Through outdoor experiments, the system has demonstrated its effectiveness and reliability in detecting and identifying various oil types, including crude oil, diesel, heavy oil, gasoline, and lubricating oil. Additionally, a BP neural network model was employed to process the fluorescence spectral data collected by the LiDAR system. This model successfully predicted oil types with an accuracy of 96.58%. This research presents a new technological solution for marine oil spill monitoring, offering significant potential for practical applications and further research.

Rapid analysis of spilled petroleum oils by direct analysis in real time time-of-flight mass spectrometry with hydrophobic paper sample collection

BackgroundOil spills are widespread and can cause devastating environmental consequences. Rapid oil identification is critical to find the origin of the spill, monitor the environment, and lead to informed mitigation measures. The current standard methods in oil spill identification are precise and reliable, but require extensive sample preparation, long instrument runs, and time-consuming data processing. Direct analysis in real time time-of-flight mass spectrometry (DART-ToF MS) has been employed to screen for spilled petroleum oils, with results obtained in mere hours. The present study introduced an innovative, simple, and fast oil sampling method using hydrophobic filter paper and demonstrated its compatibility with DART-ToF MS analysis. Motor oils, jet fuels, marine diesels, crude oils, intermediate fuel oils, heavy fuel oils, and diluted bitumen were collected using the filter paper sampling method. Classification models were constructed from the spectral data by heat map inspection followed by principal component analysis (PCA) and discriminant analysis of principal components (DAPC). Oil slicks and weathered oil slicks were prepared from five oil types, and samples from each slick were collected using filter paper.ResultsThe filter paper technique allowed for effective oil sampling and data acquisition by DART-ToF MS for diluted source oils, oil slicks and weathered oil slicks. Classification via the constructed DAPC models indicated that the DART-ToF MS instrument in tandem with filter paper sampling and multivariate statistics can accurately identify common oil types, with significant improvement of sample collection and turnaround time.ConclusionsThe promising classification results, simple sample collection, and rapid data analysis illustrate the potential use of hydrophobic filter paper and DART-ToF MS as tools in managing large scale oil spill emergency situations.

Hyperspectral remote sensing identification of marine oil spills and emulsions using feature bands and double-branch dual-attention mechanism network

Hyperspectral remote sensing identification of marine oil spills and emulsions using feature bands and double-branch dual-attention mechanism network

Oil film identification via windspeed interference estimation using hyperspectral data

ABSTRACT Sea surface windspeed is one of the important factors that affects the reflection and radiation intensity of the sea surface oil film, posing challenges for the oil spill classification via marine optical remote sensing. A new method is proposed to extract significant windspeed features from the original hyperspectral remote sensing data and apply them to the qualitative analysis and quantitative estimation of local windspeed. First, a portable push-broom hyperspectral sensor was utilized to collect the hyperspectral data under different experimental windspeed conditions. After that the data was divided into spatial-spectral hyperspectral data along the spectrum slit direction and finally converted into spatial-spectral joint heat maps (SSHMs). Incorporating a transfer learning methodology, we utilized SSHMs as input to optimize the training process of windspeed estimation. This approach yielded dependable evaluation results. Moreover, we developed an effective deep learning framework that integrates spatial and spectral analyses for the categorization of sea surface oil films. This framework utilizes hyperspectral image datasets which have already removed the influence of windspeed variables. The proposed method demonstrated outstanding performance in estimating windspeed and classifying oil films using both experimental and AVIRIS datasets. This paper introduces a feasibility study for ocean surface windspeed estimation and precise identification of oil spills using optical remote sensing technology.

Multiphysical Interpretable Deep Learning Network for Oil Spill Identification Based on SAR Images

Multiphysical Interpretable Deep Learning Network for Oil Spill Identification Based on SAR Images

Research Progress On Identification Methods And Traceability Experimental Methods Of Oil Spills On The Sea

Oil spill composition is complex and difficult to identify. Traditional oil spill identification techniques include gas chromatography, infrared spectroscopy, liquid chromatography, fluorescence spectrometry, gas chromatography-mass spectrometry and nuclear magnetic resonance spectroscopy. Different methods of oil spill identification have their advantages and disadvantages. Oil spill identification is of guiding significance to the clean-up of oil spill at sea, liability disputes and the subsequent ecological restoration. In order to improve the persuasiveness and reliability of the identification results, it is necessary to combine different identification methods and develop a new oil spill identification technology. Based on the analysis of oil spill types, oil spill identification methods and relevant experimental facilities at home and abroad, it is necessary to carry out research on oil spill identification methods and traceability experiments, so as to provide a reliable basis for legal adjudication of oil spill accidents.

Remote Identification of Oil Films on Water via Laser-Induced Fluorescence LiDAR

Laser-induced fluorescence lidar (LIF-LiDAR) is an effective technology for the stand-off detection, identification, and quantification of oil spills on the water surface. In this work, by using a self-developed LIF-LiDAR system, the laser-induced spectra of different oils with different thicknesses on the water surface were remotely measured at a distance of 100 m. The traditional algorithms based on the similarity degree of matching spectra are adopted to identify the oil types, which results in the 65% (min = 33.68%) and 85% (max = 98.66%) average matching accuracy when the pure oil spectra (infinite thickness) and the average oil spectra (different thickness) are introduced as the database, respectively. Furthermore, to avoid the influence of the pretreatment process in the traditional matching method, the machine learning (ML) model is applied to classify the oil types and an identifying accuracy of nearly 100% is successfully achieved. Our results presented in this work not only demonstrate the good performance of the classification models of fine tree, linear discriminant analysis (LDA), support vector machine (SVM), <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> -nearest neighbor (KNN), and neural network (NN) in remote oil-type identification but also the capability of the combination of LIF-LiDAR and ML algorithms on the high-accuracy identification in the types of oil films on water.

An improved semantic segmentation model based on SVM for marine oil spill detection using SAR image

In the oil industry, oil spills occur due to offshore rig explosions, ship collisions, and other reasons. It is crucial to accurately and rapidly identify oil spills to protect marine ecosystems. Synthetic aperture radar (SAR) can all-weather and all-time work and provide a wealth of polarization information for identification of oil spills based on semantic segmentation model. However, the performance of classifiers in the semantic segmentation model has become a significant challenge to improving recognition ability. To solve this problem, an improved semantic segmentation model named DRSNet was proposed, which uses ResNet-50 as the backbone in DeepLabv3+ and support vector machines (SVM) as the classifier. The experiment was conducted using ten polarimetric features from SAR images and results demonstrate that the DRSNet performs best compared to other semantic segmentation models. Current work provides a valuable tool to enhance maritime emergency management capabilities.

Fingerprint characteristics of refined oils and their traceability in the groundwater environment

Chemical fingerprinting is essential for identifying the presence and responding to oil spills that frequently contaminate the groundwater environment of refineries. In this study, crude oil and oil products from the atmospheric and vacuum distillation units of a refinery were analyzed by gas chromatography-mass spectrometry (GC-MS) to evaluate their chemical variability before and after refinery. A series of experiments involving evaporation and soil column penetration were conducted to simulate refined oil spilling into groundwater and determine appropriate characteristic ratios (CRs) for principal component analysis (PCA) for oil source identification. The simulated study demonstrated that all products had bell-shaped n-alkane distributions, with dominant peaks that remained unchanged or shifted towards longer chain lengths compared to the source oil. Similarly, naphthalene and dibenzothiophene series remained the main PAH components like the source oil. Ten relatively stable CRs were selected for PCA to identify different oil products through the simulated experiments. The chosen CRs were then utilized to identify the sources for two groundwater oil spills recently occurred, one that occurred in an oil depot area, and another near a continuous catalytic reforming unit in a refinery. This study showed that the components with long-chain n-alkanes (n ≥ C18), pristane, phytane, and phenanthrene and dibenzothiophene series PAHs played an important role in the identification of refined oil products spilling into the groundwater environment. The selected CRs provide an effective tool for rapid and accurate identification of oil spills, especially for newly occurring spills in the groundwater environment, which can aid in developing appropriate response strategies.

Deep Learning Methods Used in Remote Sensing Images: A Review

Undeniably, Deep Learning (DL) has rapidly eroded traditional machine learning in Remote Sensing (RS) and geoscience domains with applications such as scene understanding, material identification, extreme weather detection, oil spill identification, among many others. Traditional machine learning algorithms are given less and less attention in the era of big data. Recently, a substantial amount of work aimed at developing image classification approaches based on the DL model's success in computer vision. The number of relevant articles has nearly doubled every year since 2015. Advances in remote sensing technology, as well as the rapidly expanding volume of publicly available satellite imagery on a worldwide scale, have opened up the possibilities for a wide range of modern applications. However, there are some challenges related to the availability of annotated data, the complex nature of data, and model parameterization, which strongly impact performance. In this article, a comprehensive review of the literature encompassing a broad spectrum of pioneer work in remote sensing image classification is presented including network architectures (vintage Convolutional Neural Network, CNN; Fully Convolutional Networks, FCN; encoder-decoder, recurrent networks; attention models, and generative adversarial models). The characteristics, capabilities, and limitations of current DL models were examined, and potential research directions were discussed.

Proof and Application of Discriminating Ocean Oil Spills and Seawater Based on Polarization Ratio Using Quad-Polarization Synthetic Aperture Radar

This paper focuses on the proof and application of discriminating between oil spills and seawater (including the “look-alikes”, named low wind areas) based on the polarization ratio. A new relative polarization ratio (PRr) method is proposed, which is based on the difference between the scattering mechanism and the dielectric constant for oil spills compared to that of seawater. The case study found that (1) PRr numerically amplifies the contrast between oil spills and seawater, reduces the difference between low wind areas and ordinary seawater, and exhibits better details of the image; (2) the threshold method based on Euclidean distance can obtain the highest classification overall accuracy within the allowable error range, and can be widely used in the study of different incidence angles and environmental conditions; and (3) the identification of oil spills and seawater by the proposed methods can largely avoid the misjudgment of low wind areas as oil spills. Considering visual interpretation as the reference ‘ground truth’, the overall classification accuracy of all cases is more than 95%; only the edge of the diffuse thin oil slick and oil–water mixture is difficult to identify. This method can serve as an effective supplement to existing oil spill detection methods.

Enhancement of oil forensic methodology through the addition of polycyclic aromatic nitrogen heterocycle biomarkers for diagnostic ratios

Current oil spill forensic identification of source oils relies upon hydrocarbon biomarkers resistant to weathering. This international technique was developed by the European Committee for Standardization (CEN), under EN 15522–2 Oil Spill Identification guidelines. The number of biomarkers have expanded at pace with technological advances, while distinguishing new biomarkers becomes more challenging due to interference of isobaric compounds, matrix effects, and high cost of weathering experiments. Application of high-resolution mass spectrometry enabled exploration of potential polycyclic aromatic nitrogen heterocycle (PANH) oil biomarkers. The instrumentation showed reduction in isobaric and matrix interferences, allowing for identification of low-level PANH and alkylated PANHs (APANHs). Weathered oil samples, obtained from a marine microcosm weathering experiment, enabled comparison with source oils to identify new, stable forensic biomarkers. This study highlighted eight new APANH diagnostic ratios that expanded the biomarker suite, increasing the confidence for identifying highly weathered oils back to their source oil.

Automated identification of spills oil with the help of a group of unmanned aircraft

The issues of automation of technological processes are of particular relevance because of the development of the information technology segment. One of the priority tasks for which digital tools are integrated is the monitoring of oil spills. The main purpose of the presented article is to analyze the issue of automated identification of oil spills by means of unmanned aerial vehicles. The paper uses theoretical and statistical research methods. To obtain more up-to-date and accurate information, the author uses the results of domestic and foreign scientific research. The article discusses the technical aspects of the use of unmanned aerial vehicles for the identification of oil spills.

Look-Alike Detection using Deep Convolutional Neural Networks for Ocean Oil Spills

The marine and coastal ecosystems are placed in grave danger whenever there is a spill of oil. For the sake of protecting future generations, the authorities need a dependable mechanism that allows them to respond quickly and effectively to oil spills. As a result of their consistency also effectiveness in a wide series of weather and light situations, synthetic aperture radar (SAR) sensors are frequently utilized for this determination. The dark spots that are typically associated with oil spills remain easy for SAR sensors to spot, but it may be difficult to differentiate them from other objects or phenomena. A great number of methods have been outlined in order to automatically differentiate and categories these dark spots. The results are frequently unable to be compared with one another because of the heterogeneous nature of the data collected. It is often difficult to fine-tune settings or extract meaningful information because SAR images remain often classified through a solo label that is applicable to the entire picture. Because of this, the process of dealing with the images is made more difficult. The Random Forest Classifier and deep convolutional neural networks (also known as DCNNs) are examples of approaches that have been suggested as potential workarounds for these constraints. In addition, we make available to the public a search-and-rescue photo library that might serve as a standard for the growth of upcoming oil spill recognition tools. On the dataset that was provided, a number of well-known DCNN segmentation models as well as the Random Forest technique are evaluated and compared with one another. When compared to other approaches, it was discovered that Random Forest had the best test correctness and the quickest implication time. Additionally, the dataset is used to explore and explain the challenges of the provided issue, which in this case is identifying between genuine and fabricated oil spills. These investigations and demonstrations are carried out with the help of the dataset. According to their training and evaluation on the dataset that was provided to them, DCNN segmentation models, when paired with a Random Forest classifier, did quite well when attempting to detect oil spills. The novel method is anticipated to be of significant use in subsequent research pertaining to the identification of oil spills and the dispensation of SAR images.

Analysis of sulfur compounds for crude oil fingerprinting using gas chromatography with sulfur chemiluminescence detector

Oil spills are a severe problem worldwide due to the resulting damage to marine and coastal ecosystems and to local economies. Identification of the source of spilled oils can be challenging, especially if the oils have undergone severe weathering. Due to their high durability, biomarker compounds (e.g. hopanes, steranes) are widely used for oil fingerprinting. Some sulfur-containing heterocyclic compounds e.g. alkylated dibenzothiophenes are also considered to be highly resistant. In this study, the use of Gas Chromatography with Sulfur Chemiluminescence Detection was investigated as a means of oil fingerprinting using the distribution the sulfur compounds in five different fresh and weathered crude oils. Chemometric analysis was also performed. The results indicate that the sulfur compounds distribution is unique for each crude oil. The distributions of the heavy sulfur compounds (i.e., C2DBTs and C3DBTs) are unchanged after weathering. Therefore, the GC-SCD technique can be considered to support the oil spill identification.

Oil Spill Identification based on Dual Attention UNet Model Using Synthetic Aperture Radar Images

Oil spills cause tremendous damage to marine, coastal environments, and ecosystems. Previous deep learning-based studies have addressed the task of detecting oil spills as a semantic segmentation problem. However, further improvement is still required to address the noisy nature of the Synthetic Aperture Radar (SAR) imagery problem, which limits segmentation performance. In this study, a new deep learning model based on the Dual Attention Model (DAM) is developed to automatically detect oil spills in a water body. We enhanced a conventional UNet segmentation network by integrating a dual attention model DAM to selectively highlight the relevant and discriminative global and local characteristics of oil spills in SAR imagery. DAM is composed of a Channel Attention Map and a Position Attention Map which are stacked in the decoder network of UNet. The proposed DAM-UNet is compared with four baselines, namely fully convolutional network, PSPNet, LinkNet, and traditional UNet. The proposed DAM-UNet outperforms the four baselines, as demonstrated empirically. Moreover, the EG-Oil Spill dataset includes a large set of SAR images with 3000 image pairs. The obtained overall accuracy of the proposed method increased by 3.2% and reaches 94.2% compared with that of the traditional UNet. The study opens new development ideas for integrating attention modules into other deep learning tasks, including machine translation, image-based analysis, action recognition, and speech recognition.

Dark Spot Detection from SAR Images Based on Superpixel Deeper Graph Convolutional Network

Synthetic Aperture Radar (SAR) is the primary equipment used to detect oil slicks on the ocean’s surface. On SAR images, oil spill regions, as well as other places impacted by atmospheric and oceanic phenomena such as rain cells, upwellings, and internal waves, appear as dark spots. Dark spot detection is typically the initial stage in the identification of oil spills. Because the identified dark spots are oil slick candidates, the quality of dark spot segmentation will eventually impact the accuracy of oil slick identification. Although certain sophisticated deep learning approaches employing pixels as primary processing units work well in remote sensing image semantic segmentation, finding some dark patches with weak boundaries and small regions from noisy SAR images remains a significant difficulty. In light of the foregoing, this paper proposes a dark spot detection method based on superpixels and deeper graph convolutional networks (SGDCNs), with superpixels serving as processing units. The contours of dark spots can be better detected after superpixel segmentation, and the noise in the SAR image can also be smoothed. Furthermore, features derived from superpixel regions are more robust than those derived from fixed pixel neighborhoods. Using the support vector machine recursive feature elimination (SVM-RFE) feature selection algorithm, we obtain an excellent subset of superpixel features for segmentation to reduce the learning task difficulty. After that, the SAR images are transformed into graphs with superpixels as nodes, which are fed into the deeper graph convolutional neural network for node classification. SGDCN leverages a differentiable aggregation function to aggregate the node and neighbor features to form more advanced features. To validate our method, we manually annotated six typical large-scale SAR images covering the Baltic Sea and constructed a dark spot detection dataset. The experimental results demonstrate that our proposed SGDCN is robust and effective compared with several competitive baselines. This dataset has been made publicly available along with this paper.

Chemometric techniques in oil spill identification: A case study in Dalian 7.16 oil spill accident of China

Chemometric methods have unique advantages regarding comprehensive consideration of multiple parameters and the classification of samples or variables. Classification of oil spill sources was carried out by using chemometric techniques, such as Repeatability Limit, hierarchical cluster analysis (HCA), Student's t-test and Principal component analysis (PCA) Biplot. In addition, this paper takes the fingerprint identification of a Dalian “7.16″ oil spill accident as an example to illustrate the effectiveness of chemometric techniques in oil identification. PCA scores plot (explaining 82.77% of variance accounted for three PCs) showed that samples belong to four clusters and result of HCA method further confirmed that. The residual oil in Jinshatan Beach and Haibei Square may be caused by the explosion of Dalian “7–16” oil pipeline accident. The use of chemometric techniques is significant in providing independent validation for classifying the types of spilled oil in the investigation of oil spill pollution. The results will be of great significance to improve the accuracy and efficiency of oil spill identification based on oil fingerprint.

Oil spill identification in X-band marine radar image using K-means and texture feature.

Marine oil pollution poses a serious threat to the marine ecological balance. It is of great significance to develop rapid and efficient oil spill detection methods for the mitigation of marine oil spill pollution and the restoration of the marine ecological environment. X-band marine radar is one of the important monitoring devices, in this article, we perform the digital X-band radar image by "Sperry Marine" radar system for an oil film extraction experiment. First, the de-noised image was obtained by preprocessing the original image in the Cartesian coordinate system. Second, it was cut into slices. Third, the texture features of the slices were calculated based on the gray-level co-occurrence matrix (GLCM) and K-means method to extract the rough oil spill regions. Finally, the oil spill regions were segmented using the Sauvola threshold algorithm. The experimental results indicate that this study provides a scientific method for the research of oil film extraction. Compared with other methods of oil spill extraction in X-band single-polarization marine radar images, the proposed technology is more intelligent, and it can provide technical support for marine oil spill emergency response in the future.