Detection Of Oil Pollution Research Articles

Ten years of oil pollution detection in the Eastern Mediterranean shipping lanes opposite the Egyptian coast using remote sensing techniques

The Eastern Mediterranean region, a vital conduit for global maritime trade, faces significant environmental challenges due to marine pollution, particularly from oil spills. This is the first study covering the long period of comprehensive monitoring of oil pollution using the full mission of Sentinel-1 Synthetic Aperture Radar (SAR) data in the Mediterranean Sea, so this research aims to detect and analyze comprehensively the occurrence of oil spills in the Eastern Mediterranean over a decade (2014–2023). This study focuses on identifying geographical distribution patterns, proximity to shorelines, frequency across maritime zones, and potential sources of these spills, especially around major ports and maritime routes. This study utilizes SAR data from the Sentinel-1 satellite. The methodology included automated detection algorithms within the Sentinel application platform (SNAP) and integration with GIS mapping to study oil spill patterns and characteristics. Over 1000 Sentinel-1 scenes were investigated in the northern Mediterranean waters off the coast of Egypt, to detect and analyze 355 oil spill events with a total impacted area of more than 6000 km2. The analysis of temporal spill distribution reveals significant fluctuations from year to year. Within the entire timeline of the study, 2017 had the largest spatial areas covering one thousand square kilometers. In contrast, the single largest spill recorded during the study period occurred in 2020, covering 198.73 square kilometers. The results identified a non-uniform distribution of oil spills and primarily exhibiting elongated patterns aligned with the navigation routes. The distinct increase of oil spill incidents was within the Exclusive Economic Zone (EEZ), obviously drifted to the coastline and around major ports. The study emphasizes the critical role of remote sensing technologies in addressing environmental challenges caused by the maritime transport sector, advocating for enhanced monitoring and regulatory enforcement to protect marine ecosystems and support sustainable naval activities. The findings highlight the urgent need for targeted continuous monitoring and rapid response strategies in high-traffic maritime areas, particularly around the EEZ and major ports.

A Hydrogel-coated Wood Membrane with Intelligent Oil Pollution Detection for Emulsion Separation.

Considering the potential threats posed by oily wastewater to the ecosystem, it is urgently in demand to develop efficient, eco-friendly, and intelligent oil/water separation materials to enhance the safety of the water environment. Herein, an intelligent hydrogel-coated wood (PPT/PPy@DW) membrane with self-healing, self-cleaning, and oil pollution detection performances is fabricated for the controllable separation of oil-in-water (O/W) emulsions and water-in-oil (W/O) emulsions. The PPT/PPy@DW is prepared by loading polypyrrole (PPy) particles on the delignified wood (DW) membranes, further modifying the hydrogel layer as an oil-repellent barrier. The layered porous structure and selective wettability endow PPT/PPy@DW with great separation performance for various O/W emulsions (≥98.69% for separation efficiency and ≈1000Lm-2h-1bar-1 for permeance). Notably, the oil pollution degree of PPT/PPy@DW can be monitored in real-time based on the changed voltage generated during O/W emulsion separation, and the oil-polluted PPT/PPy@DW can be self-cleaned by soaking in water to recover its separation performance. The high affinity of PPT/PPy@DW for water makes it effective in trapping water from the mixed surfactant-stabilized W/O emulsions. The prepared eco-friendly and low-cost multifunctional hydrogel wood membrane shows promising potential in on-demand oil/water separation and provides new ideas for the functional improvement of new biomass oil/water separation membrane materials.

A high sensitivity cross-capacitive sensor for water droplets and bubble detection in hydraulic oil.

The presence of moisture and air in hydraulic oil will seriously affect the reliability of machines. This paper proposes a new cross-capacitive oil pollution detection sensor, which is based on the Thompson and Lampard theorem. The sensing unit consists of four identical copper electrodes with infinitesimally small gaps. The sensor can effectively distinguish water droplets and air bubble pollutants mixed in the oil through the pulse direction of the signal. Compared with traditional capacitive sensors, the sensor has a significant improvement in detection accuracy and detection throughput. In this paper, the relationship between the cross-capacitance value with the dielectric constant and the frequency in an alternating electric field was deduced, and the best excitation frequency was chosen as 1.9MHz. Experiments show that the sensor can effectively detect water droplets of 140-160 µm and bubbles of 170-190 µm and has good linearity for detecting water droplets and air bubbles of different sizes. The sensor provides a new method for machine condition monitoring of hydraulic systems.

Effective Detection of Potential Oil Pollution in the Egyptian Maritime Routes of the Mediterranean Sea Using SAR Data

Effective Detection of Potential Oil Pollution in the Egyptian Maritime Routes of the Mediterranean Sea Using SAR Data

Strengthening oil pollution monitoring system in aquatic environment through development of IoT-based Oil-Water Separator Device

Oil spills are a serious pollution problem in water ecosystems, causing significant damage to living things and the environment. The demand for devices to handle oil remediation is increasing due to the frequent occurrence of oil spills. To date, numerous works have been reported to address this pollution problem, but most of them do not utilize Internet of Things (IoT) technology. Having an IoT system of this kind would improve operational efficiency by providing real-time data, alerts, and notifications of oil pollution through monitoring systems. This study presents the development of an IoT-based automatic oil-water separator device to support an oil pollution monitoring system in water ecosystems. The built device consists of sensors, a microcontroller, and an actuator, with the capability to automatically detect oil pollution and perform water-oil separation. The device incorporates a monitoring system. The water-oil separation was carried out by applying semi-permeable membrane nanotechnology. Moreover, the device has undergone a series of oily-water separation experiments to simulate oil pollution, achieving an oil separation efficiency of 62.8% (on average) with good repeatability. Additionally, the automatic device is developed with IoT pairing with a website application for an online monitoring system. Users can monitor the process using a personal computer or smartphone via the web/mobile application at any time and from any place. It is of interest to explore this further since it offers an effective and implementable method to solve oil pollution problems in real-world applications.

Development of a laser spectroscopy method and a hard-software framework for ecological monitoring of underwater areas by remote operated vehicles

The results of the experimental development of the method and the hard-software framework for a remote-operated underwater vehicle are presented in this work. The method of laser induced fluorescence (LIF) and a small-sized laser spectrometer are designed to monitor of the dissolved oil products pollution of underwater areas and to assess the impact of pollution on the phytoplankton community state. The excitation technique of the sea water LIF was experimentally tested to simultaneously measure the concentration of chlorophyll A, the specific reproduction of dissolved organic matter by phytoplankton cells, and the concentration of oil products dissolved in sea water. It has been experimentally shown that to implement this technique, it is necessary to carry out the excitation of the LIF by two wavelength radiation. To measure the concentration of chlorophyll A and the specific reproduction of dissolved organic matter (DOM), it is necessary to use excitation by radiation in the green region of the spectrum (532 nm in this work). To measure the concentration of dissolved petroleum products in sea water, it is necessary to use UV radiation (278 nm in this work). The results of tests of the spectrometer under laboratory conditions on sea water samples containing phytoplankton cells and solutions of petroleum products are described. The results of the work create a methodological and software-hardware basis for carrying out the mission of ecological monitoring of underwater areas with the simultaneous solution of the problem of detecting oil pollution and assessing its impact on the phytoplankton community.

Conditions for effective detection of the boundary between the oil-covered and clean sea surface in the IR range

This study is devoted to evaluating the most effective conditions that allow detecting oil pollution on the water surface using an on-board lidar operating in the infrared range. The results of the CO2 – lidar simulation show that even in the presence of a highly agitated sea surface, difficult weather conditions and low optical visibility, it is possible to search for the boundary between the water surface and oil from a flight altitude of 250-300 meters.

Detection and Quantification of Daily Marine Oil Pollution Using Remote Sensing

Detection and Quantification of Daily Marine Oil Pollution Using Remote Sensing

Analysis of Remotely Piloted Aircraft Payload for Oil Spill Detection

Abstract Operational monitoring of large sea aquatorium areas with the aim of detecting and controlling oil pollution is now carried out using various technological systems, such as satellite remote sensing, sea-going vessels, various aircraft and remotely piloted aircraft (RPA). Currently, the use of RPA for the fulfilment of monitoring tasks in the aquatorium is being intensively developed and can eliminate problems of remote sensing performed by satellites and piloted aircraft, such as short presence in the monitoring area, very long delay of information (up to 48 hours) and low quality of imagery. This paper presents mathematical modelling of RPA multi-sensor pay-loads for oil spill detection, monitoring and control. Information obtained from payload sensors is critical for increasing effectiveness of detection and monitoring of oil spills. Nowadays, many types of sensors are used for oil spill detection and monitoring. The most common sensors for detection of oil pollution are optical, multispectral, hyperspectral, thermal and laser fluorometers. Some oil pollution detection sensors have limitations, such as false alarm, only daytime operation, weather restrictions. Airborne remote sensors cannot provide all information required for detection of and response to oil spills, and water quality monitoring in the spill area. A model for selecting sensors for multi sensor payload that will make it possible to optimize the application of RPA for oil spill detection was developed. The RPA payload can be increased/reduced to the greatest possible extent with the help of different types of equipment at various parameters. The mathematical model of the integrated payload considers detection capability of sensors, weather conditions, sensor characteristics, and false alarm rate. The optimal multi-sensor payload will optimize the application of RPA for oil spill detection and monitoring.

A deep learning based oil spill detector using Sentinel-1 SAR imagery

ABSTRACT The Eastern Mediterranean Sea has been known as an oil pollution hotspot due to its heavy marine traffic and an increasing number of oil and gas exploration activities. To provide automatic detection of oil pollution from not only maritime accidents but also deliberate discharges in this region, a deep learning-based object detector was developed utilizing freely available Sentinel-1 Synthetic Aperture Radar (SAR) imagery. A total of 9768 oil objects were collected from 5930 Sentinel-1 scenes from 2015 to 2018 and used for training and validating the object detector and evaluating its performance. The trained object detector has an average precision (AP) of 69.10% and 68.69% on the validation and test sets, respectively, and it could be applied for building an early-stage oil contamination surveillance system.

Remote laser fluorescence method for oil leak detection at an excitation wavelength of 266 nm

Subject of study. The possibility of implementing a remote laser fluorescence method to detect oil leaks on the Earth’s surface at a fluorescence excitation wavelength of 266 nm was analyzed and substantiated. Method. Laser-induced fluorescence spectra of clean oil, oil spilled on different types of soil at different time intervals after leakage, and natural and anthropogenic terrain elements were investigated. The experimental setup for remote measurements of laser-induced fluorescence spectra of oil pollution on the Earth’s surface using a laser source with a wavelength of 266 nm, pulse energy of 0.3 mJ, and a single spectral channel of a detector with a central wavelength of 510 nm and a bandwidth of 84 nm is described. The detection possibility of oil pollution against a background of natural vegetation (shrubs and grass) and soil without oil pollution were investigated remotely at a distance of 75 m using emission with a fluorescence excitation wavelength of 266 nm. Main results. Laser-induced fluorescence spectra of commercial oil from the Ryazan oil refinery and oil from Almetyevsk oil field were obtained in addition to the fluorescence spectra of vegetation, soil, and asphalt. Analysis of the data in laser-induced fluorescence spectra indicates that the maximum of the oil spectra is in the range 450–550 nm. Laser-induced fluorescence from vegetation is the largest interference signal in the detection of oil leaks on the Earth’s surface. Remote detection of oil pollution against the background of vegetation and other landscape elements was experimentally demonstrated both immediately after pollution and two days later. Practical significance. The possibility of remote monitoring of oil pollution on the Earth’s surface was experimentally demonstrated at a fluorescence excitation wavelength of 266 nm at distances close to safe aircraft flying altitudes.

ОБНАРУЖЕНИЕ И КАРТИРОВАНИЕ НЕФТЯНЫХ СЛИКОВ В МОРЕ КОМБИНАЦИЕЙ РАЗЛИЧНЫХ ИСТОЧНИКОВ ДАННЫХ ДИСТАНЦИОННОГО ЗОНДИРОВАНИЯ ЗЕМЛИ

Earth remote sensing methods, used to detect and map oil spills, are considered. The research is aimed at solving problems in the field of aerospace monitoring of oil development sites and oil spills. The object of the research is images or a sequence of images of the natural environment. The subject of the research is presented by mathematical modeling and software for image processing and analysis, recognition and classification of images, to assess the environmental condition of offshore oilfields. The aim of the research is to detect oil pollution in the shelf area by means of semi-automated image analysis using super-accurate neural network algorithms (ResNetIO) with long- and short-term memory (LSTM) when processing materials from several information sources, which require spatial matching between the images. To recognize objects on the ground, visual data have been analyzed based on the formation of stable features using deep neural networks. Multidimensional heterogeneous (multiple images) remote sensing data has been proposed as the basis. The research tasks, which help to realize the aim are: to form a set of image sets, containing scenes in a changing environment (weather conditions, illumination, seasonality, viewing angle); to use own base, allowing to allocate stable image features; to develop methods and algorithms for processing and analysis of images for formation of stable features forming scenes from neural networks; to conduct computer experiments for comparative analysis and evaluation results of classification and categorization with the use of neural networks. For this purpose the author has made automatic registration of geometrical deformations (displacement, rotation, scale change), using bilinear interpolation and testing for possible variation of statistical model inside the inhomogeneous sliding window based on the semi-automatic approach for the shelf area of the Oil Rocks (Caspian Sea). Standard single-layer 2D LSTM networks [18] are used to solve the texture segmentation problem by classifying texture perpixels. The network accurately estimates texture regions and automatically adapts the different scale, orientation and shape of texture regions in the image. A simple way of using LSTM networks for texture segmentation is shown, and the efficiency (accuracy) is compared, using research-based measures of classification quality using a new similarity measure based on a statistical model (three versions of the nearest-neighbor rule and the maximum likelihood method) [6]. The results of the studies have generally confirmed the effectiveness of the proposed model. The second front of the research (object recognition analysis) has shown that context information must be taken into account when applying object recognition systems. It has become possible to find evidence that the natural formation of clusters indicates that contexts emerged, which appeared to be fundamental to performance results. However, it is important to emphasize that these experiments are empirical in nature and are conducted on a particular image base that is well known in the academic scientific community

Abundance and diversity of n-alkane and PAH-degrading bacteria and their functional genes – Potential for use in detection of marine oil pollution

Monitoring environmental status through molecular investigation of microorganisms in the marine environment is suggested as a potentially very effective method for biomonitoring, with great potential for automation. There are several hurdles to that approach with regards to primer design, variability across geographical locations, seasons, and type of environmental pollution. Here, qPCR analysis of genes involved in the initial activation of aliphatic and aromatic hydrocarbons were used in a laboratory setup mimicking realistic oil leakage at sea. Seawater incubation experiments were carried out under two different seasons with two different oil types. Degenerate primers targeting initial oxygenases (alkane 1-monooxygenase; alkB and aromatic-ring hydroxylating dioxygenase; ARHD) were employed in qPCR assays to quantify the abundance of genes essential for oil degradation. Shotgun metagenomics was used to map the overall community dynamics and the diversity of alkB and ARHD genes represented in the microbial community. The amplicons generated through the qPCR assays were sequenced to reveal the diversity of oil-degradation related genes captured by the degenerate primers. We identified a major mismatch between the taxonomic diversity of alkB and ARHD genes amplified by the degenerate primers and those identified through shotgun metagenomics. More specifically, the designed primers did not amplify the alkB genes of the two most abundant alkane degraders that bloomed in the experiments, Oceanobacter and Oleispira. The relative abundance of alkB sequences from shotgun metagenomics and 16S rRNA-based Oleispira-specific qPCR assay were better signals for oil in water than the tested qPCR alkB assay. The ARHD assay showed a good agreement with PAHs degradation despite covering only 25% of the top 100 ARHD genes and missing several abundant Cycloclasticus sequences that were present in the metagenome. We conclude that further improvement of the degenerate primer approach is needed to rely on the use of oxygenase-related qPCR assays for oil leakage detection.

OIL EFFLUENT EXPOSURE INDUCED BIOCHEMICAL MODULATIONS IN SUBCELLULAR / TISSUES (CYTOSOL,MICROSOMES) OF FOOT, GILL AND DIGESTIVE GLANDS OF LAMELLIDENS MARGINALIS.

Sub-cellular fractions (cytosolic and microsomal) were prepared from the tissues (foot, gill and digestive gland) of freshwater mussel Lamellidens marginalis and were scrutinized to investigate the modulation of biochemical components (protein, carbohydrate and lipids) after exposure of sub-lethal concentrations of 1/4th (11.88 ppt) and 1/10th (8.55 ppt) of 96-hr LC 50 of oil effluent, and recovery period (depuration-without oil effluent) after seven days interval i.e., 1st, 8th, 15th 22nd and 30th days. At the end of 30th day, biochemical components in both exposure and depuration periods, were analysed from the tissues of mussels. The accumulation of oil effluent in the tissues gradually increased, when exposed to both sub-lethal concentrations of oil effluent and significantly and gradually decreased levels of protein, carbohydrate and lipid contents in the sub-cellular tissues of mussels were observed. During recovery period (without adding oil effluent), all depleted biochemical contents were recovered, increased, gradually, significantly from 30th day to Ist day (P<0.05) and one-way ANOVA showed progressive positive regression coefficient values (P<0.05) in all days. The enhanced and recovered levels of biochemical components in oil-exposed mussels demonstrate a well-established defense mechanism in mollusc, and this response offers the possibility of use as a biomarker for the early detection of oil pollution.

Detection of Oil Pollution Using SAR and Optical Remote Sensing Imagery: A Case Study of the Persian Gulf

Detection of Oil Pollution Using SAR and Optical Remote Sensing Imagery: A Case Study of the Persian Gulf

Detection of Oil Pollution in the Form of Emulsion and Individual Films on the Water Surface of the Bering Sea Using Hyperspectral Visible Radiometry in August 2013

Detection of Oil Pollution in the Form of Emulsion and Individual Films on the Water Surface of the Bering Sea Using Hyperspectral Visible Radiometry in August 2013

An Algorithm for Processing Data Received from a Distributed Monitoring Network Consisting of Several Sensors

The paper presents an algorithm for detecting an object during the coherent reception of signals coming from an environmental monitoring network consisting of several sensors. The sensors of the monitoring network can change their location in space, for example, as it happens in a freely drifting system of sensors for detecting oil pollution on the water surface. The proposed algorithm uses statistics that take into account the most stable features of the distribution of the source data. It provides a constant probability of false alarm at any noise level. This algorithm can be implemented in software in an automated decision support system for the presence or absence of environmental pollution. At the same time, decisions on the detection of a monitoring object made by an automated system will be more reliable.

Detection of oil pollution in the form of emulsion and individual films on the water surface of the Bering Sea using hyperspectral visible radiometry in August 2013

Detection of oil pollution in the form of emulsion and individual films on the water surface of the Bering Sea using hyperspectral visible radiometry in August 2013

Neural Network Classifier of Oil Pollution on the Water Surface when Processing Radar Images

The paper proposes a solution to the problem of detecting oil pollution on a monochrome radar image. The detection of oil pollution in the image includes the solution of three tasks: detecting a dark object on the image, highlighting the main characteristics of a dark object, classifying a dark object as oil pollution or natural slick. Various characteristics of a dark object are proposed based on the contrast between the object and the background. It is proposed to use a neural network as a classifier. The input parameters of the neural network classifier of the dark image object are proposed. A technique for determining the structure of a neural classifier is presented. An algorithm for testing the selected structure of the neural network for the suitability of classifying the dark area on the image of the water surface as oil pollution or wind slick is proposed. The results of the work of the neural network classifier program for detecting abnormal objects in radar images are demonstrated.

Авария танкера Sanchi в Восточно-Китайском море: обнаружение и мониторинг нефтяного загрязнения по радиолокационным и оптическим спутниковым изображениям

Авария танкера Sanchi в Восточно-Китайском море: обнаружение и мониторинг нефтяного загрязнения по радиолокационным и оптическим спутниковым изображениям