Oil Spill Area Research Articles

Bio-based herding and gelling agents from cholesterol powders and suspensions in organic liquids for effective oil spill clean-up

The negative impacts of an oil spill are severe and can last for decades after the incident. The development of techniques to overcome this threat is crucial to the ecosystem as well as human lives. The herder effectiveness of different cholesterol forms, including powders and suspensions, were measured and compared by using a MATLAB code to estimate the surface area of oil spill contracted/thickened. The powders were distinguished by their particle size distributions, crystal phases, and thermal properties. Formation of gel oil aggregates was achieved by all cholesterol powders. The highest herder effectiveness of 73.3 ± 2.0% was shown by CP/D, that is, the cholesterol powder that had the lowest particle sizes of 0.5–0.99 mm, anhydrous crystal phases, and lowest thermal degradation rate of 95.4%. Cholesterol suspensions were further prepared from the mixtures of CP/D and delivery liquids, namely gasoline, toluene, and biodiesel by varying CP/D-to-solvent ratio from 1:2 to 1:4. Herding of oil spill, instead of gel oil aggregation only, was achieved by cholesterol suspensions. Biodiesel was found to be the most efficient delivery liquid, resulting in the highest herder effectiveness of 79.1 ± 0.7%. CP/D-biodiesel suspension reduced the surface tension of water from 73.5 to 28 mN/m. Optimum herder effectiveness was estimated from full factorial experimental design and Response Surface Methodology at different conditions of temperature, water salinity and agent-to-oil ratio (AOR). Analysis of variance (ANOVA) confirmed that salinity and temperature had significant effects on the herder effectiveness of CP/D-biodiesel but the effect of AOR was insignificant. The optimum herder effectiveness was 79.5% and the accuracy of this prediction was obtained as 95%. This study demonstrated that CP/D-biodiesel suspension is a promising bio-based herding agent.

Growth and Reproduction of the Pacific Oyster Crassostrea gigas Cultured on Tidal Flat in Hebei Spirit Oil Spill Area on the West Coast of Korea Four Years After the Accident

The Hebei Spirit oil spill (HSOS) accident in December 2007 on the west coast of Korea devastated the intertidal oyster farms along the Taean coast, resulting in the shut-down of the farming for three years. In 2010, two years after the accident, the level of residual oil in the water, sediment, and oyster tissue in the spilled area became similar to the level before the accident, although the fitness of the oysters in the spilled area remained unknown. In an attempt to resume the oyster culture in the spilled area, we monitored the growth and reproduction of the Pacific oyster Crassostrea gigas in an area the most heavily impacted. The oyster spats used in this study were harvested from the oil-damaged area in September 2011 and hardened for 9 months before being transplanted into the grow-out facilities in May 2012. The transplanted oysters demonstrated a fast shell growth for the first 5 months, reaching 53.9 mm in shell length (SL) in October 2011. At the end of the survey in November 2013, the oyster became 65 mm, suggesting an additional year of cultivation to reach the market size. Histology indicated that the annual gametogenesis was synchronous, as the males and females initiated the gametogenesis in January when the water temperature was 5.2°C. The female oysters spawned from July to September, as the water temperature ranged from 21.2 to 24.8°C. An indirect enzyme-linked immunosorbent assay (ELISA) applied in the reproductive effort measurement indicated that the fully ripe female oysters produced 20-23% of their body weight as eggs prior to spawning, which was comparable to the reproductive effort of oysters in oil-spill-free areas. The data suggested that off-bottom rack cultured Pacific oysters at the Euhangri beach successfully recovered from HSOS stresses 4 years after the accident.

Oil Spill Environmental Risk Assessment and Mapping in Coastal China Using Automatic Identification System (AIS) Data

The rapid expansion in shipping traffic, oil tankers, and oil field exploration in coastal and marine areas has, inevitably, resulted in the occurrence of many oil spill accidents. Oil spill accidents, which cause serious socio-economic, health, and environmental risks in coastal and marine areas, are a global concern. An oil spill pollution risk distribution map, combining multiple spill sources, is an effective tool by which to identify high-risk areas, which may help decision-makers in adopting contingency response and integrated coastal management. However, the assessment of oil spill distribution and risk assessment has been restricted, due to their heavy dependence on laboratory experiments and model simulations lacking reliable shipping data, which often derive inaccurate mapping results. This study combines the automatic identification system (AIS) and other data to precisely quantify the spatial extent of accident risk in coastal China. Based on oil quantity, oil spill rate, and accident probability, the ship, oil storage tank, submarine pipeline, and oil platform accidents spill risk index is analyzed. Next, combined with the sensitive degree of a coastal area, considering environmental and social issues, the oil spill environmental risk index is calculated. The oil spill pollution risk level is classified into five categories based on the oil spill pollution risk index, namely the low-risk zone, relatively low-risk zone, moderate-risk zone, relatively high-risk zone, and high-risk zone. The relatively high oil spill environmental risk concentration zone is located in the Bohai Sea, inter-border area between the Yellow Sea and Bohai Sea, the Yangtze River estuary, south of the Taiwan Strait, and the Pearl River estuary. The high-risk zone in the Bohai Sea is 36,018 km2 in area, with an average risk value of 32.23, whereas the high-risk area in the Pearl River estuary is only 14,007 km2. The high-risk area proportions in Tianjin are 23.5%, while those in Fujian, Hainan, Jiangsu, and Guangxi are very low. The low-risk area proportion in Hainan Province is 62%, while the value in Tianjin is only 2.9%. This study will be helpful in assisting decision-makers in mapping the influence area of oil spills and adopting the important strategies and effective management and conservation countermeasures for ship accidents in the coastal areas of China.

Five-Year Monitoring of a Desert Burrow-Dwelling Spider Following an Environmental Disaster Indicates Long-Term Impacts.

Simple SummaryDeserts are characterized by unpredictable precipitation, extreme temperatures, and plants and animals that are specialized to live in these habitats. Consequently, desert organisms often recover slowly, if at all, from human-induced environmental disasters. We studied the effects of two nearby oil spills from a broken pipeline, one that occurred in 1975 and another recent one in 2014, on a burrow-dwelling spider in the extreme desert of the ‘Arava valley (Israel). We compared the abundance of spider burrows in plots contaminated by the oil with nearby unaffected plots over a 4-year period. The abundance was significantly lower in plots with oil-contaminated soil, both in the recent (2014) oil spill area and in the area affected by the 1975 spill. In the laboratory, we found that when offered oil-contaminated versus clean desert soil substrates, spiders chose the clean soil substrate. We conclude that the populations of this burrow-dwelling spider were affected negatively by the oil spills and, furthermore, showed long-lasting impacts from a 40-year-old spill. We propose that burrow-dwelling spiders can be used as effective bioindicators of persistent soil pollution in desert habitats.Deserts are characterized by unpredictable precipitation and extreme temperatures. Their fauna and flora are sensitive to anthropogenic environmental changes, and often recover slowly from environmental disasters. The effects of oil spills on the biota of desert regions, however, have scarcely been studied. We predicted that terrestrial invertebrates suffer long-term negative effects from an oil spill, due to their close association with the substrate. Thus, we investigated the effects of two oil spills that occurred in 1975 and 2014 in the hyper-arid ‘Arava desert (Israel), on a spider that constructs silk-lined nests in burrows in compact, sandy soil in this extreme desert habitat. The spider, Sahastata aravaensis sp. nov. (Filistatidae), is described herein. We assessed spider burrow abundance in plots located in oil-contaminated and nearby uncontaminated clean soil (control) areas over five consecutive years and measured habitat characteristics in these plots. In the laboratory, we determined the preference of individuals for clean vs. oil-contaminated soil as a resting substrate. Finally, as this species was previously undescribed, we added a new species description. The abundance of Sahastata was significantly lower in oil-contaminated plots, and this was the case in the 40-year-old oil spill (1975) as well as in the recent one (2014). In laboratory tests, spiders showed a significant preference for the clean soil substrate over the oil-contaminated substrate. In the field, soil crust hardness and vegetation density did not differ significantly between oil-contaminated and control plots, but these measures were highly variable. The burrows were significantly clustered, suggesting that the young disperse only short distances. In the laboratory adult spiders did not dig burrows, perhaps indicating that adults remain permanently in their natal burrows and that in the field they may use vacant burrows. We conclude that Sahastata populations were affected negatively by the oil spills and these effects were long-lasting. We propose that by monitoring their spatial distribution, burrow-dwelling spiders such as Sahastata can be used as effective bioindicators of soil pollution in desert habitats.

Spectral-Spatial Features Extraction of Hyperspectral Remote Sensing Oil Spill Imagery Based on Convolutional Neural Networks

Marine oil spills accidents has caused a large amount of crude oil to leak into the marine environment and threaten marine ecological environment. Hyperspectral remote sensing images (HRSI) technology can quickly and accurately identify oil film of different thickness on marine surface. In order to overcome the traditional spectrum analysis method and space extraction method of long time sampling, calculation, analysis and other shortcomings. On account of the advantages of the spectral and spatial information in the field of HRSI classification, a spectral-spatial features extraction (SSFE) method based convolutional neural networks (CNNs) was proposed to analyse oil spills. In this way, one and two dimensional models based on convolutional neural networks (1D-CNN,2D-CNN) have been introduced as the spectral and spatial features extractor. When extracting spatial features, double-two convolution layers are connected to increasing nonlinearity and reduce the number of parameters. Furthermore, in order to address overfitting and imbalance samples, L2 regularization, class_weight and drouput is added to classes data modeling. More importantly, principal component analysis (PCA) is applied to data dimension reduction, 1D-CNN and 2D-CNN is combined into a unified model further extract the joint spatial-spectral features. To evaluate the effectiveness of the proposed approach, three hyperspectral datasets were utilized, including: University of Pavia dataset, oil spill area 1, oil spill area 2. Experimental results reveal that the proposed method have a very satisfactory performance and better distinguish oil spills.

Retrieving Oil-Water Mixture Ratios of Marine Oil Spills From L-Band SAR Imagery

Retrieving the oil-water mixture ratio (M, the proportion of oil in an oil-water mixture) of the marine oil spill is of vital importance for the emergency treatment of oil pollution, which can be achieved by a copolarization ratio (PR) method. In the framework of the PR method, it is assumed that Bragg scattering dominates at moderate incidence angles, and the relative contribution of non-Bragg scattering to total scattering (RCNT) is typically neglected. However, in this work, it is found that the RCNT cannot be neglected for L-band, especially for HH polarization at higher wind speeds (the RCNT is approximately 0.6~0.8 for wind speeds of 10~12 m/s). The significant impact of non-Bragg scattering results in the PR method not being accurate enough for retrieving M. After separately investigating the influences of the damping effect and effective dielectric constant reduction on the RCNT, a novel approach is proposed for more accurately estimating the M of marine oil spills by eliminating the impact of non-Bragg scattering. The proposed method was applied to UAVSAR images collected during the Deepwater Horizon (DWH) oil spill accident. The inversion results of our approach show that the M of most oil spill areas ranges from approximately 0.3 to 0.6, which matches the reality better than that of a traditional PR method (ranging from approximately 0.6 to 0.8).

Effective and sustainable adsorbent materials for oil spill cleanup based on a multistage desalination process

Oil spills, which are often caused by crude oil transportation accidents, contaminate coastal waters and land and can harm aquatic life, seabirds, humans, and the entire ecosystem. Ocean currents and wind complicate oil spill cleanup and extend the oil spill area. This study proposes a new approach to control oil spills using solids recovered from the treatment of reject brine through a novel multistage desalination process. The aim is to produce applicable adsorbent for oil spill cleanup especially in the final cleaning stages. The multistage desalination process is based on the electrochemical treatment of high-salinity reject brine and Solvay and modified Solvay liquid effluents in a closed Plexiglas electrocoagulation cell. After the electrochemical treatment, the collected solids were dried and ground for utilization as adsorbents in oil spill cleanup. Results were promising for the adsorbent produced from the electrochemical treatment of the modified Solvay effluent. A maximum adsorption capacity of 2.8 g oil/g adsorbent was achieved, with an oil recovery of 98%. In addition, the regenerated solids after toluene extraction process were recycled and achieved an adsorption capacity of 2.1 g oil/g adsorbent in the second oil spill clean-up cycle. The structural and chemical characteristics of the adsorbents produced from the multistage desalination process were investigated using X-ray powder diffraction, Fourier transform infrared spectroscopy, and scanning electron microscopy. Results support the adoption of the collected solids as effective oil-adsorbent materials.

Monitoring of meadow biocenosis after oil spill rehabilitation measures in subtaiga subzone

This article presents results of long-term monitoring of meadow biocenosis restoration after an oil spill and subsequent rehabilitation procedures at southern subtaiga territories of the Tyumen Region, Russia. The data obtained show that the measures taken fostered development of vegetation with high abundance and diversity of species. There passed 25 years after rehabilitation, and the succession stage of phytocoenosis at the affected area is not any more determined by pollution, but by bogging, a process characteristic of the unaffected biocenoses of the area under study. Secondary succession is also evidenced by more than a two-fold decrease in diversity and abundance of decomposer fungi species in a forest biocenosis adjacent to the area of oil spill. Such a decrease is conditioned by a reduced amount and variety of forest litter, that had been formed after the accident and was later humified by the fungi. In 2020, the ratio of the fungal species used as indicators of forest stand health prompted that the impacted forest was nearing its original condition. The terrestrial vertebrates communities have almost recovered over the period of monitoring, although there have appeared synanthropes, and the proportion of anthropophilic species has gone up.

Feature Merged Network for Oil Spill Detection Using SAR Images

The frequency of marine oil spills has increased in recent years. The growing exploitation of marine oil and continuous increase in marine crude oil transportation has caused tremendous damage to the marine ecological environment. Using synthetic aperture radar (SAR) images to monitor marine oil spills can help control the spread of oil spill pollution over time and reduce the economic losses and environmental pollution caused by such spills. However, it is a significant challenge to distinguish between oil-spilled areas and oil-spill-like in SAR images. Semantic segmentation models based on deep learning have been used in this field to address this issue. In addition, this study is dedicated to improving the accuracy of the U-Shape Network (UNet) model in identifying oil spill areas and oil-spill-like areas and alleviating the overfitting problem of the model; a feature merge network (FMNet) is proposed for image segmentation. The global features of SAR image, which are high-frequency component in the frequency domain and represents the boundary between categories, are obtained by a threshold segmentation method. This can weaken the impact of spot noise in SAR image. Then high-dimensional features are extracted from the threshold segmentation results using convolution operation. These features are superimposed with to the down sampling and combined with the high-dimensional features of original image. The proposed model obtains more features, which allows the model to make more accurate decisions. The overall accuracy of the proposed method increased by 1.82% and reached 61.90% compared with the UNet. The recognition accuracy of oil spill areas and oil-spill-like areas increased by approximately 3% and reached 56.33%. The method proposed in this paper not only improves the recognition accuracy of the original model, but also alleviates the overfitting problem of the original model and provides a more effective monitoring method for marine oil spill monitoring. More importantly, the proposed method provides a design principle that opens up new development ideas for the optimization of other deep learning network models.

Terrestrial oil spill mapping using satellite earth observation and machine learning: A case study in South Sudan

Terrestrial oil spills are a major threat to environmental and human well-being. Rapid, accurate, and remote spatial assessment of oil contamination is critical to implementing countermeasures that prevent potentially lasting ecological damage and irreversible harm to local communities. Satellite remote sensing has been used to support such assessments in inaccessible regions, although mapping small terrestrial oil spills is challenging – partly due to the pixel size of remote sensing systems, but also due to the distinguishability of small oil spill areas from other land cover types. We assessed the usability of freely available Sentinel satellite images to map terrestrial oil spills with machine learning algorithms. Using two test sites in South Sudan, we demonstrated that information from the Sentinel-1 and -2 instruments can be used to map oil spills with more than 90 % classification accuracy. Classification accuracy was significantly increased (>95 %) with the addition of multi-temporal information and spatial predictor variables that quantify proximity to oil production infrastructure such as pipelines and oil pads. The mapping of terrestrial oil spills with freely available Sentinel satellite images may thus represent an accurate and efficient means for the regular monitoring of oil-impacted areas.

Potential impact of the extensive oil spill on primary productivity in the Red Sea waters

Red Sea is one of the world's major strategic shipping routes for crude oil transportation. Accidental spills of crude oil into the environmentally sensitive marine environments are known to impact the marine life. However, studies on direct assessment of oil spill on marine ecosystem, in particular on the primary productivity and bio-optical parameters in the Red Sea waters were poorly constrained owing to lack of sustained observations. Thus, in this study, an attempt has been made to use high spatial and temporal resolution of Moderate Resolution Imaging Spectroradiometer (MODIS) observations to monitor the oil spill explosion occurred on October 11, 2019, and its impact on the chlorophyll -a (Chl-a) concentrations. We noticed a significant increase in concentrations of Chl-a on the following day of oil spill. These changes are accounted for and are influenced by hydrocarbons along the track of oil spill. Our findings suggest that both wind speed and high Ekman pumping velocity support the upwelling of nutrient-rich water from subsurface, which facilitated the growth of phytoplankton and thus the high Chl-a in the oil spill areas.

Reconstruction Of Oil Spill Trajectory In The Java Sea, Indonesia Using Sar Imagery

Oil spill phenomena in the ocean possess a very serious threat to ocean health. On the ocean surface, oil slicks immediately start to spread and mostly end up in the ecosystem. Furthermore, it could threaten the organisms living in the ocean or impact nearby coastal area. The aim of this research was to investigate the trajectories of oil spill based on a real accident in the Java Sea. Tracking oil spills using satellite images is an efficient method that provides valuable information about trajectories, locations and the spread intensity. The objective of this study was to periodically track the trajectory of the oil spill from the Karawang incident using Sentinel-1 Synthetic Aperture Radar (SAR) images. Pre-processing of the images consisted of radiometric and geometric corrections. After the corrections, SAR images were mapped and plotted accordingly. To understand the oil spill trajectories in relation to the oceanic processes, the ocean current pattern map and surface wind roses were also analysed. The processed images from July to October 2019 show a trajectory dominated by the oil spill layers movement towards the west to northwest from the original location along with a decrease in the detected oil spill area over time. The identified trajectories of the oil spill followed the ocean current pattern and surface winds. Thus, these two parameters were considered to be the main factors responsible for the oil spill drift.

Functional metagenomic and enrichment metatranscriptomic analysis of marine microbial activities within a marine oil spill area.

Microorganisms can degrade petroleum hydrocarbons, providing the advantages of low cost and few side effects towards ecosystems. Here, we evaluated the mechanisms of microbial degradation of marine petroleum hydrocarbon using metagenomics and metatranscriptomics approaches in order to provide new insight into microbial degradation of petroleum hydrocarbon. Seawater samples were collected at a depth of ∼8m from an area near a drilling platform in the Bohai Bay and metagenomic sequencing was used to evaluate the functional potential of these marine microbial communities. Metatranscriptomic sequencing, fluorescence in-situ hybridization experiments, and flow cytometry were also performed on the microbial communities of samples subjected to 12 different culture conditions. The data were also subjected to Weighted Gene Co-expression Network Analysis (WGCNA) and co-transcription data visualization to evaluate co-transcription of gene functions. Metagenomic sequencing indicated the presence of numerous genes that were related to petroleum hydrocarbon metabolism. Further, the high co-transcription of genes in multiple pathways, indicated that groups of genes were synergistically transcribed to metabolize petroleum hydrocarbons. Metatranscriptomics also showed that microbial metabolism was highly active in the enrichments and that the transcription of a large number of prokaryotic replication and repair genes were significantly up-regulated including those encoding for the type VI secretion system (T6SS) protein, DNA polymerase I, thymidine phosphorylase, mevalonate kinase, and two-component systems. Concomitantly, the transcription of ribosomal genes involved in translation and photosynthetic genes involved in energy metabolism were down-regulated. Overall, oil and oxygen presence can increase the oil-degradation rates and related genes' transcription. Lot different metabolisms are co-regulated to exploit nutrients derived from the metabolism of petroleum hydrocarbons. Our analysis of metagenomic, metatranscriptomic and degradation data in this study show that a widespread gene spectrum involved in oil-degradation and the cooperation among genes is of great importance.

Numerical Simulation Study on the Effects of Oil Spill Diffusion in The Qitou Sea Area

A two-dimensional hydrodynamic mathematical model of the sea area near the Qitou sea area was established by using MIKE21 hydrodynamic module, and the tidal level, flow velocity and direction simulated by the hydrodynamic model were verified according to the measured data. Based on MIKE21 hydrodynamic model and particle tracking module, a mathematical model of oil spill diffusion centered on the sea area of Qitou was established, and the influence range of oil spill under six different working conditions and the time of oil spill reaching the main sensitive area near The sea area of Qitou were simulated. The analysis shows that tidal current and wind are the main factors affecting the diffusion process of oil spill in The Qitou sea. The calculation results show the influence area of oil spill 24 hours after the occurrence of the accident and the time when the oil spill reaches the main sensitive area in The sea area of Qitou, which has a certain guiding role in dealing with the offshore oil spill pollution and formulating the emergency treatment plan of oil spill accident in the sea area of Qitou.

Trajectory and weathering of oil spill in Daya bay, the South China sea

With the development of marine resources and marine transportation, oil spill accidents occur frequently which threaten the marine ecological environment and human life. In this paper, an oil spill model was established. The two-dimensional shallow water equation was discretized by the finite element weighted lumped mass method, and the time is discretized by the forward Euler scheme, then the planar two-dimensional hydrodynamic model was established. The model was verified by measure tidal level data. The oil particle drift model and oil spill weathering model were established in this paper, and it can be used to simulate the oil spill accidents on the sea area by inputting the terrain data, environmental conditions and oil spill information into the hydrodynamic model and oil spill model. The model is applied to Daya Bay, South China Sea, the oil spill behavior and destination under different residual currents were simulated and calculated, the pollution area of oil spill under clockwise residual flow is larger than that under anti-clockwise residual flow. The oil spill model is mainly used to simulate oil spill accidents on the sea surface such as ship oil spill accidents, and the simulation results can provide theoretical basis for taking effective emergency measures and risk assessment after oil spill.

Oil Spill Detection using Sentinel-1 Multitemporal Data in Offshore Karawang

Oil spill is a serious problem that could lead to economic and ecological losses, both in the short and long term. On July 12, 2019, there occurred an oil leakage around YYA-1 oil platform of Pertamina Hulu Energi Offshore North West Java (PHE ONWJ), located off the northern coast of Karawang, Java Sea. This incident has caused the death of fishes and marine animals, damage to coral reefs, mangroves, and seagrass beds, and several health problems of coastal communities. Therefore, it is necessary to map and monitor oil spills, so that actions can be taken to prevent the spread of oil spills. This study aims to map the distribution of oil spills in Karawang sea using multitemporal Sentinel-1 data from July to September 2019. The detection is carried out using the adaptive thresholding algorithm combined with manual interpretation. The result shows that the oil spills spread around Karawang sea from YYA-1 platform to Sedari Village and there are oil spills spreading from the Central Plant F/S platform. The oil spills tend to shift westward from July to September 2019. This shifting is supposed to be influenced by current and wave factors that were dominant moving westward at that time. Based on data processing, it was found that the oil spill area from July to September was respectively 24.79 km2, 20.05 km2, and 27.12 km2.

Variation of Microbial Diversity in Catastrophic Oil Spill Area in Marine Ecosystem and Hydrocarbon Degradation of UCMs (Unresolved Complex Mixtures) by Marine Indigenous Bacteria

The study targeted an assessment of microbial diversity during oil spill in the marine ecosystem (Kaohsiung port, Taiwan) and screened dominant indigenous bacteria for oil degradation, as well as UCM weathering. DO was detected lower and TDS/conductivity was observed higher in oil-spilled area, compared to the control, where a significant correlation (R2= 1; P< 0.0001) was noticed between DO and TDS. The relative abundance (RA) of microbial taxa and diversities (> 90% similarity by NGS) were found higher in the boundary region of spilled-oily-water (site B) compared to the control (site C) and center of the oil spill area (site A) (BRA/diversity> CRA/diversity> ARA/diversity). The isolated indigenous bacteria, such as Staphylococcus saprophyticus (CYCTW1), Staphylococcus saprophyticus (CYCTW2), and Bacillus megaterium (CYCTW3) degraded the C10-C30 including UCM of oil, where Bacillus sp. are exhibited more efficient, which are applicable for environmental cleanup of the oil spill area. Thus, the marine microbial diversity changes due to oil spill and the marine microbial community play an important role to biodegrade the oil, besides restoring the catastrophic disorders through changing their diversity by ecological selection and adaptation process.

A support vector regression model for the prediction of total polyaromatic hydrocarbons in soil: an artificial intelligent system for mapping environmental pollution

The significance of total polyaromatic hydrocarbons (TPAH) determination in assessing the carcinogenicity of environmental samples for measuring the level of environmental pollution cannot be overemphasized. Despite the environmental danger of TPAH, its laboratory quantification is laborious, which consumes appreciable time and other valuable resources. This research work develops a computational intelligence-based model for the first time, which directly estimates and quantifies the level of TPAH of any environmental solid samples using total petroleum hydrocarbons descriptor that can be easily determined experimentally. The hyperparameters of the developed support vector regression (SVR)-based model are optimized using manual search (MS) approach and genetic algorithm (GA) search approach with Gaussian and polynomial kernel functions. Experimental validation of the developed model was carried out using samples obtained from the marine sediments of Arabian Gulf Sea. The future generalization and predictive strength of the developed models were assessed using correlation coefficient (CC), root-mean-square error, mean absolute error and mean absolute percentage deviation (MAPD). GA-SVR-Gaussian performs better than MS-SVR and GA-SVR-poly with performance enhancement of 63.89% and 536.32%, respectively, on the basis of MAPD as a performance-measuring parameter, while MS-SVR model performs better than GA-SVR-poly with performance improvement of 288.25% using MAPD to evaluate the model performance. The estimation accuracy and generalization strength of the developed models indicate the potential of the models in measuring the level of environmental pollution of oil-spilled area without experimental stress, while experimental precision is preserved.

Oil Spill Detection in Quad-Polarimetric SAR Images Using an Advanced Convolutional Neural Network Based on SuperPixel Model

Oil spill detection plays an important role in marine environment protection. Quad-polarimetric Synthetic Aperture Radar (SAR) has been proved to have great potential for this task, and different SAR polarimetric features have the advantages to recognize oil spill areas from other look-alikes. In this paper we proposed an oil spill detection method based on convolutional neural network (CNN) and Simple Linear Iterative Clustering (SLIC) superpixel. Experiments were conducted on three Single Look Complex (SLC) quad-polarimetric SAR images obtained by Radarsat-2 and Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR). Several groups of polarized parameters, including H/A/Alpha decomposition, Single-Bounce Eigenvalue Relative Difference (SERD), correlation coefficients, conformity coefficients, Freeman 3-component decomposition, Yamaguchi 4-component decomposition were extracted as feature sets. Among all considered polarimetric features, Yamaguchi parameters achieved the highest performance with total Mean Intersection over Union (MIoU) of 90.5%. It is proved that the SLIC superpixel method significantly improved the oil spill classification accuracy on all the polarimetric feature sets. The classification accuracy of all kinds of targets types were improved, and the largest increase on mean MIoU of all features sets was on emulsions by 21.9%.

Algorithm to Estimate Oil Spill Area Using Digital Properties of Image

Algorithm to Estimate Oil Spill Area Using Digital Properties of Image