Problems, Effects, and Methods of Monitoring and Sensing Oil Pollution in Water: A Review

Abstract

Oil pollution in water bodies is a substantial environmental concern that poses severe risks to human health, aquatic ecosystems, and economic activities. Rising energy consumption and industrial activity have resulted in more oil spills, damaging long-term ecology. The aim of the review is to discuss problems, effects, and methods of monitoring and sensing oil pollution in water. Oil can destroy the aquatic habitat. Once oil gets into aquatic habitats, it changes both physically and chemically, depending on temperature, wind, and wave currents. If not promptly addressed, these processes have severe repercussions on the spread, persistence, and toxicity of oil. Effective monitoring and early identification of oil pollution are vital to limit environmental harm and permit timely reaction and cleanup activities. Three main categories define the three main methodologies of oil spill detection. Remote sensing utilizes satellite imaging and airborne surveillance to monitor large-scale oil spills and trace their migration across aquatic bodies. Accurate real-time detection is made possible by optical sensing, which uses fluorescence and infrared methods to identify and measure oil contamination based on its particular optical characteristics. Using sensor networks and Internet of Things (IoT) technologies, wireless sensing improves early detection and response capacity by the continuous automated monitoring of oil pollution in aquatic settings. In addition, the effectiveness of advanced artificial intelligence (AI) techniques, such as deep learning (DL) and machine learning (ML), in enhancing detection accuracy, predicting leak patterns, and optimizing response strategies, is investigated. This review assesses the advantages and limits of these detection technologies and offers future research directions to advance oil spill monitoring. The results help create more sustainable and efficient plans for controlling oil pollution and safeguarding aquatic habitats.

1. Introduction

Water pollution is a global issue that threatens human health, wildlife, and ecosystem equilibrium. Contaminated water sources can lead to severe health conditions, including gastrointestinal disease, respiratory disease, and even cancer [1]. The presence of pollutants in water bodies destabilizes delicate ecosystems, reducing biodiversity and threatening marine life. Industrial waste is also one of the primary sources of water pollution, as factories and manufacturing plants tend to release harmful chemicals and pollutants into nearby water bodies, making them hazardous to human and animal consumption [2]. Agricultural activities are also a leading source of water contamination. Runoff of pesticides, fertilizers, and animal waste into lakes and rivers may cause algae blooms and oxygen loss, further deteriorating water quality. Agriculture is also the most water-intensive sector, which is a massive threat to water sustainability and availability [3].

Oil pollution in water bodies has become a critical environmental issue, driven by industrial activities and urbanization. The adverse impacts of oil spills extend beyond immediate ecological harm, affecting biodiversity, water quality, and local communities. Basterrechea et al. (2021) found that industrial oil discharge can severely damage irrigation ditches and nearby ecosystems [4], highlighting the need for efficient monitoring systems to ensure water safety. Understanding the sources and environmental consequences of oil pollution is essential for creating effective management strategies.

Oil spills have a great impact on the environment and diversity of organisms occupying spill sites, raising significant policy and regulatory questions on the transportation of oil, spill clean-up, and site rehabilitation in cases of spills. Increasing global demand for petroleum and petroleum derivatives raises the risks of their extraction, transport, and use. Accidental releases of oil, especially at elevated levels, are extreme environmental risks unless the oil is effectively diluted or degraded. The effects of oil spills are longerterm in the Arctic due to lower temperatures and the presence of ice, which inhibit natural processes of degradation compared to other more temperate habitats. In marine conditions, oil spreads across vast distances easily as opposed to land, where it remains localized in specific locations. In cold climates, oil pollution is regulated by biological and physical mechanisms such as dispersion, hardening, biodegradation, bioaccumulation, and dissolution. Although chemical processes such as photolysis affect oil pollution, they are limited in low-temperature regions [5]. The rapid expansion of oil production, transportation, and processing reflects the continuously expanding growth of the world economy. However, this expansion has led to a sudden increase in the number of crude oil pollution accidents, i.e., fuel oil spills, offshore platform leaks, and tanker discharges [5]. When spilled oil deteriorates, its chemical nature changes with the passage of time. Different parts of the oil evaporate, dissolve, and degrade at varying rates, and thus the relative concentration of the contaminants in the water varies with time and space. The toxicity of the fractions depends on their solubility, molecular structure, mode of action, metabolic pathways, and environmental conditions [6].

1.1. Relationship Between Oil Pollution and Water Pollution

Mass transportation and extraction of oil and gas always lead to oil leakage, which in turn pollutes the soil and groundwater. Alteration of the wettability of the soil due to contamination is one of the severe impacts of the pollution. Studies have proved that in clean soil, capillary water ascends considerably higher over the same time than in oil polluted soil. As the level of contamination increases, the ability of water to rise through the soil is reduced, further decreasing its ability to absorb and transmit water. Pollution by oil also makes it more challenging for water to penetrate the soil, thus adding to the problems of water movement and holding [7].

Oil pollution is a global environmental problem, with an estimated 2.4 million tonnes of oil entering marine waters each year. This pollution results from both natural and anthropogenic processes. Major sources include discharges from storage facilities and refineries, discharge of ballast water from ships, leakage of boreholes, pipeline breaks, carry-over discharges, urban and industrial effluents, and even atmospheric deposition. The ubiquity of oil pollution necessitates the imperative of effective monitoring and mitigation actions to safeguard soil, water resources, and marine ecosystems from its deleterious effects [8].

1.2. The Importance of Monitoring and Managing Oil Pollution in Water

Oil contamination of water bodies is a rapidly emerging worldwide concern with significant human health, aquatic life, and environmental implications. As industrial processes and energy demand continue to escalate, oil spillage has increased, with dire consequences for coastal economies and marine biodiversity. Pollution by oil not only disrupts aquatic life but also affects the human societies that rely upon unpolluted water sources for drinking, fishing, and other simple activities. The moment the oil is released into oceans, it undergoes a sequence of chemical and physical transformations, depending upon environmental conditions such as temperature, wind, wave currents, and salinity. These phenomena dictate the distribution of oil over the water surface and its influence upon marine organisms and eventually the beach. Newly released oil is composed of composite hydrocarbons of varied chemical composition and molecular weight. Components with low boiling points are more soluble and thinner and disperse quickly across the water surface, increasing the spread of pollution. Over time, oil thickens and persists, becoming harder to remove and causing more environmental harm [9].

Since there are severe environmental and economic impacts associated with oil pollution, effective and timely monitoring is required to minimize harm and direct successful response activities. Oil spill monitoring is required for a number of significant reasons: it enables prompt emergency measures, offers the legal framework for environmental surveys and damage claims, and assists in long-term ecological restoration activities. Implementation of efficient monitoring systems lies at the heart of improving safety in transportation and maritime industries, particularly where oil shipping and extraction activities are high [10]. Detection and control of oil spills at an early stage are crucial for minimizing their adverse effects since untreated oil has the potential to spread over a large area, hence rendering containment and clean-up processes laborious and expensive.

Technology advancements have significantly improved the detection and tracking of oil spills. Conventional techniques, including visual observations and satellite imagery, have been complemented by cutting-edge strategies that leverage artificial intelligence (AI) and machine learning (ML), which improve detection precision, accelerate the identification process, and ensure real-time monitoring. AI-driven systems are especially valuable in the management of massive oil spills, as they automatically detect oil slicks and forecast their spreading in relation to environmental conditions [11]. Furthermore, the utilization of deep learning (DL) models enables the investigation of extensive data sets, enhancing the precision of oil spill identification and minimizing false alarms [12]. Aside from making response more efficient, such technologies yield important information for examining environmental effects and establishing preventive measures.

In recent years, cost-effective monitoring methods have also developed as crucial instruments for oil spill management, particularly in developing nations where resources for complex systems may be restricted. For instance, a study in [13] presented a low-cost operational monitoring system that integrates open-source images, citizen science, and affordable multi-platform devices to identify oil spills in real-time. This technology has proven its usefulness during three major oil spills in June and July 2023, presenting a feasible solution for continuous monitoring and timely alarm issuance. Such technologies are especially helpful for countries highly reliant on offshore oil extraction, where continual monitoring is vital to balancing industrial activities with environmental protection.

Oil pollution significantly affects both ecosystems and human health. Demoner et al. (2023) revealed the rapid spread of oil plumes in freshwater systems under various hydrological conditions [14]. Effective response measures must be implemented swiftly, ideally within hours, to prevent severe and long-lasting damage (He et al., 2023) [15]. These findings emphasize the importance of real-time monitoring and sensing technologies to track oil pollution accurately and enable timely interventions.

To address these challenges, various monitoring and detection methods have been developed. For instance, low-cost optical sensors, as proposed by Basterrechea et al. (2021) [4], offer effective solutions for detecting oil in water. Additionally, numerical simulations and qualitative monitoring approaches developed by Demoner et al. (2023) [14,15] provide critical insights into oil dispersion dynamics and emergency response planning. This synthesis of recent research underscores the urgent need for advanced monitoring systems to mitigate oil pollution’s effects and ensure the sustainability of water resources.

This work seeks to give a complete assessment of the difficulties, consequences, and methodologies involved with oil pollution monitoring and sensing in aquatic bodies. It analyses the chemical behavior of spilled oil, the environmental and socio-economic repercussions of oil pollution, and the technical developments used in detection and monitoring. Special focus is given to modern sensing techniques, including remote sensing, optical technologies, and AI-based solutions, as well as the limitations of current methodologies. By identifying gaps in existing monitoring systems and researching novel solutions, this review intends to offer insights into enhancing oil spill response and supporting sustainable environmental management practices.

The review consists of five sections. Section 1 discusses the compound structure of oil. Section 2 describes the causes and effects of oil pollution. Section 3 explains the problems of oil pollution in oceans, rivers, and lakes. Section 4 discusses the effects of oil pollution towards the environment. Section 5 describes the methods used to monitor and sense oil pollution. Section 6 discusses the parameters used in the oil pollution sensing method. Section 7 concludes with the findings of this review. By exploring various technological applications and remote sensing, the review aims to enhance the understanding of oil pollution detection and monitoring techniques. The findings contribute valuable insights into the strengths and limitations of different methods, emphasizing innovative oil pollution sensing and monitoring approaches.

2. Compound Structure of Oil

Crude oil, also known as petroleum, is described as a blend of hydrocarbons that originally exist in the liquid state within natural underground reservoirs and retain their liquid form at atmospheric pressure following the passage through surface separating facilities [16]. Alkanes are often referred to as paraffins, cycloalkanes as naphthene, and compounds containing nitrogen, sulphur, and/or oxygen as NSOs [17,18]. These compounds can exist as single molecules or with alkyl side chains attached.

The interaction between oil and water involves intermolecular forces, primarily hydrophobic interaction and Van der Waals forces [19]. Hydrophobic interaction occurs due to the non-polar nature of oil molecules, causing them to repel water molecules because of differences in polarity. On the other hand, Van der Waals forces are attractive forces between molecules that contribute to the cohesion of oil molecules and their interactions with water molecules [20]. These forces play a crucial role in phenomena like oil—water separation by affecting the behavior and solubility of oil in water.

When oil is dropped into water, it resists dispersal due to its non-polar nature and the cohesive forces among its molecules. Oil, being hydrophobic, does not readily dissolve in water, resulting in phase separation or immiscibility. Thus, the mixture of both liquids can produce emulsions. Emulsions tend to divide into an oil and a water phase due to droplet formation, which makes them thermodynamically unstable. Emulsion kinetic stabilization can be achieved by delaying this formation among various molecules present at the oil—water interface [21]. The interaction between oil and water is characterized by limited solubility due to the disparate polarities of the two substances. While small oil droplets may disperse in water, true dissolution does not occur due to lack of molecular compatibility [22]. The separation between oil and water is a physical phenomenon driven by differences in polarity, leading to the formation of distinct layers or droplets. The time required for oil to disperse or separate in water depends on variables such as types of oil, the presence of emulsifying agents, and environmental conditions [23].

3. Oil Pollution Problems in Oceans, Rivers, and Lakes

Water pollution is a detrimental issue. Many studies have been done to monitor water pollution [24,25,26,27]. Oil spills are one of the causes of water pollution that normally occur in the ocean due to shipwreck [28]. Ocean water is denser than river water due to salt content. The effects of an oil spill in a river are different than an oil spill in the ocean because river water is less dense than certain types of oils. Oil also can interact with sediment carried by rivers, where the sediment can stick to the oil droplets when the droplets drift into the river. Then, the sediment—oil substance will precipitate to the bottom of the river after the water slows down [29]. River water is often utilized for irrigation to support crop growth in agriculture. Thus, contamination of river resources has resulted in the pollution of irrigation systems.

Anyanwu et al. explored the ecological consequences of extensive oil exploitation and global changes in the African Niger Delta. This research focuses on characterizing seasonal dynamics and pollution, particularly total-petroleum-hydrocarbons (TPHs), heavy metals (HMs), and nutrient loads, in correlation with climate-driven variables. There were increased of nutrient levels, turbidity, salinity, temperature, and sulphate (SO₄²⁻) which influenced TPHs/HMs variability, notably during the wet season [30]. These findings emphasize the immediate need for enhanced pollution control in the Niger Delta, considering the observed spatio-temporal variations and the exacerbating effects of climate change. The study calls for future assessments to include exposure effects and bioaccumulation in biota, incorporating potential climate change scenarios, especially for communities relying intensively on the system for drinking water, food supply, and livelihood.

4. Environmental Effects of Oil Pollution

Oil pollution has significant and strong impacts on ecosystems, human health, and other environmental elements. Aquatic life can be severely harmed by oil spills. Oil prevents light from reaching underwater life, which disturbs the plant’s growth and photosynthesis process. Birds and marine mammals that have oil on their fur and feathers face difficulties staying warm and float around. Oil also can cause direct or indirect harm to fish and other aquatic organisms [31].

The marine environment of the Gulf of Guine in Nigeria, is important for local communities that heavily rely on fish as a primary source of animal protein. Unfortunately, the contribution of fish to food security faces growing challenges due to unsustainable practices and the impacts of climate change. The study sheds light on the oil spillages in the Bonga Oil Field that harm the marine environment. The article states that pollution exacerbates food insecurity in the region by damaging vital ecosystems.

Another study on the effects of climate change on oil pollution concluded that increasing temperatures considerably influence the stability of oil and gas production systems and associated infrastructure. Permafrost melting reduces the amount of ice-based transportation, weakens foundations of buildings constructed on permafrost, and lowers the load-carrying capacity of these structures. In the Arctic (Beaufort Sea), an American oil firm was unable to build transport and extraction infrastructure due to a shortage of sea ice, which stopped its plan to open the first oil drilling facility in Arctic waters. Melting permafrost in Russia accounted for about 23% of technical failures in 2021, disrupting oil and gas production. Similarly, in Texas, USA, harsh cold conditions in 2021 led to a decline in oil refinery output by 2.4 million barrels per day [32].

Individuals exposed to oil spills or living in areas with chronic oil pollution may experience adverse health effects. Inhalation of oil vapors or fumes near affected areas can cause respiratory problems in humans. Hwang et al. [33] discussed the effect of a post-spill incident near Taean, South Korea, which affected people’s health over a long period. The study focused on adults who lived close to the accident site. The study shows that being near the oil spill or helping to clean it up can be linked to health issues. These issues include asthma, allergies, and skin problems. It shows that oil spills can affect the health of people for a long time.

In addition, oil spills can cause harmful effects on fishery and mariculture resources, causing physical contamination and toxic impacts on fish and other marine life [34]. Oil pollution can have severe economic consequences for industries such as fisheries. Contaminated water can lead to the decline of fish populations, affecting both commercial and subsistence fishing. Oil spills have the potential to significantly minimize business operations, particularly along the coast. The severity of the impact varies with factors such as the type of oil spilled, the cause of the spill, and the extent of its influence on fishing and agriculture in the sea locally. The research [34] focuses on understanding the consequences of ship-source oil pollution on fishing and mariculture. It offers guidance on response measures and management strategies to help reduce the severity of oil spill impacts. By exploring effective protective measures and cleanup efforts, the article provides insights that may prevent or minimize the damage caused by oil spills in aquatic environments, supporting the health of seafood production and the well-being of coastal communities.

Oil pollution causes serious consequences to the environment, ecosystems, and human well-being. In aquatic ecosystems, oil spills can harm marine life, affect fish, mammals, and birds, and contaminate water sources. Human health is directly affected through skin irritation and other health problems, while economic sectors like fisheries suffer due to declines in fish populations. The long-term environmental damage disrupts ecosystems, affecting food chains and habitats. While some natural processes can break down oil over time, the persistence of pollution and its far-reaching effects highlight the urgent need for preventive measures and effective environmental regulations to safeguard ecosystems and human health.

Ewim et al. [35] conducted a comprehensive analysis of the wastewater issues resulting from oil spills and pollution in Nigeria’s Niger Delta region. Utilizing a secondary data collection approach, the study drew on diverse sources to systematically review the existing literature, identifying challenges and proposing mitigation and remediation strategies. Moreover, the study reveals the insufficiency of existing measures for oil spill response, cleanup, compensation, and remediation, perpetuating adverse consequences for local communities. The article suggests innovative technologies, including advanced oil spill detection, bioremediation, and renewable energy to enhance mitigation.

5. Oil Pollution Sensing and Monitoring Methods

There are three common methods to detect oil pollution in oceans, rivers, or lakes, namely remote, optical, and wireless sensing.

Table 1. Various methods of oil pollution sensing.

Categories	Methods/Techniques	Description of the Methods	Areas for Future Improvement
Remote sensing	Camera in visible and infrared spectrum [36]	Some methods include passive observation, optical techniques, visible spectrum, infrared sensors, radar, satellite radar systems, laser fluorosensors, and slick thickness measurements using ultrasonics and chemical analysis	Currently, both visible and infrared cameras are installed on drones. To gain confidence with various oil kinds and conditions, the sensor needs to be used more frequently. The tactical and operational support platform of the future might be the automated aerial drone.
	Underwater acoustic method [37]	The study aims to assess the detection feasibility of Hazardous and Noxious Substances (HNS) using two methods. Firstly, a tank experiment with 200-kHz active sonar utilized castor oil as a chloroform alternative to examine underwater acoustic detection, and secondly, a computer simulation evaluated the potential of side scan sonar for detecting chloroform and tetrachloride randomly distributed on different seabed types.	The side scan sonar may be an effective instrument for monitoring the Hazardous and Noxious Substances (HNS) resting on the sediment interface, according to the results of simulating the sonar image using a sonar equation.
	Multi-temporal approach [38]	Change Vector Analysis (CVA) was employed to examine the present image of the region and compare it to a reference image dataset that had undergone statistical analysis to lessen sea spectral variability across time to develop an automatic detection algorithm.	Further validation and training needed by enriching the oil spill datasets.
Optical sensing	Light Emitting Diode (LED) and Light Dependent Resistor (LDR) [4]	The study proposes an optical sensor system using LEDs to monitor oil concentration in irrigation ditches. The sensor differentiates between the presence of diesel engine oil, identifies the gasoline engine oil source, and estimates the concentration of oil from 0 to 0.20 mL/cm2 range. The algorithm combines signals from white, blue, and red LEDs for comprehensive oil monitoring in the irrigation system.	Conduct experiments under actual conditions to assess the developed sensor’s performance in a dynamic scenario, providing insights into its quantification capacity for oil in irrigation systems.
	Light Detection and Ranging (LIDAR) sensor [39]	A 905nm laser LiDAR system, with 3 channels and 3 Hz scanning, distinguishes seawater from oil. It detects various oil thickness, presenting thick oil as brighter pink on the seawater surface. Key parameters include sensed area, spill thickness, detection Field-of-View (FoV), and time.	Conduct a field test to evaluate the LiDAR system’s performance in real-world conditions, considering the impact of waves and tidal currents.
	Laser-induced fluorescence (LIF) sensor & Unmanned Aerial Vehicle (UAV) [40]	The article discusses LIF spectral features for different states of oil products in seawater and thin slicks. It introduces a calibrated LIF method for identifying and measuring ocean pollution from bilge water disposal, along with a small-sized LIF sensor for unmanned aerial vehicles (UAVs) to monitor oil pollution at sea.	Utilize LIF spectroscopy to identify oil products in slicks, with a focus on determining the dependence of LIF spectra parameters on slick thickness.
	Oceanic polarization lidar (P-lidar) [41]	Expanding detection dimensions using P-lidar and considering parameters such as polarization degree, polarization angle, and ellipticity.	The research emphasizes the need for a customized P-lidar tailored for oceanic remote sensing by addressing issues related to spatial resolution and radiometric accuracy.
	Integration with FerryBox system and moored SmartBuoy [42]	To evaluate the feasibility of using fluorometric sensors in flow-through systems for real-time detection of oil spills	The impact of measurement depth on oil spill detection
	Optical reflectance [43]	The proposed method effectively estimated the thickness of oil samples in laboratory conditions and accurately determined the volume of thicker oil samples in outdoor settings using RGB images.	Accurately estimate the thickness of oil based on emulsions.
Wireless sensor	Wireless Sensor Networks (WSNs) [44]	The method involves incorporating intelligence at multiple aggregation levels, transforming sensor nodes into active, intelligent observers, thus enhancing the efficiency of the network and contributing to the improvement of oil spill detection in oceanographic research.	Upgrade the intelligence of sensor nodes to enable decision-making regarding oil spills.
	Internet-of-Things (IoT) [45]	The study describes the creation of an autonomous oil—water separator that is Internet of Things (IoT)-based and supports water ecosystems’ oil pollution monitoring system. The water-oil separation was carried out by applying semi-permeable membrane nanotechnology.	The potential for further improvement through device design modifications.
	Satellite Aperture Radar (SAR) [46]	This study introduces a novel method for detecting oil spills using satellite synthetic aperture radar (SAR) systems. The technique employs convolutional neural networks (CNNs) trained with an adversarial loss function for image-to-image translation, demonstrating promising results in discriminating between real oil spills and lookalikes in SAR data from Radarsat-2 and Sentinel-1 over the Mediterranean Sea, Atlantic Ocean, and the North Sea.	Further refining the semantic model solution for oil spill detection by exploring modifications to the generator in the Generative Adversarial Network (GAN) architecture, aiming to enhance visual results, accuracy, and Jaccard index scores beyond current state-of-the-art solutions.

summarizes the methods of sensing and monitoring oil pollution.

5.1. Remote Sensing

Remote sensing refers to the collection of information about an object without direct physical contact [47]. The most popular method of remote sensing for detecting oil spills is mapping the sea surface using passive observation. One of the remote sensing methods involves the usage of cameras in the visible and infrared spectrum [36]. The method is cost-effective to monitor oil spills, especially for operational countermeasures.

Ultraviolet and near infrared methods are less often applied, probably because of the light source and detector’s constraint [36]. Satellite and airborne sensors are employed to capture data related to water quality, temperature, and other environmental variables. For seawater, remote sensing helps in monitoring the sea surface temperature, chlorophyll concentration, and ocean color, which indicates phytoplankton abundance and overall water quality [48]. For lakes and rivers, remote sensing can provide information on water turbidity, sediment transport, and shoreline changes [49].

In addition, Babatunde et al. [50] studied acoustic methods, which involve the usage of sound waves to study underwater features and properties. For example, sonar systems can be used to map the bathymetry of the seafloor, assess sediment characteristics, and even detect underwater objects [37]. Doppler devices can measure water velocity and flow patterns [51]. Acoustic methods are particularly useful to study the physical characteristics of water bodies, providing insights into the structure and dynamics of underwater environments.

There are numerous sources of oil waste, and it takes decades to dispose of them. A multi-temporal approach has been used on the problem of oil leak detection. The current image of the area of interest and a dataset of reference images were compared using this implementation [38].

5.2. Optical Sensing

Basterrechea et al. has developed an optical sensor to monitor the discharge of industrial oil in irrigation ditches [4]. A low-cost system was created to detect and measure the presence of oil in irrigation ditches, which could help prevent the contamination of crops and water sources. Five different colors of LEDs were used as light sources, consisting of blue, yellow, red, green, white. Then, two photodetectors were positioned 0° and 180° from the LEDs to detect light that was absorbed by the oil. The amount of oil changed the percentages of light that were absorbed or reflected, leading to various LDR values. The light emitted by the LEDs reached the oil layer after passing through the water column. There was light reflected by the oil layer and returned to the LDR at a different point where it crossed to reach the LDR at a 180° angle [4].

The proposed sensor operated by setting threshold values, detecting oil presence using LEDs and an LDR, and employing a turbidity sensor to prevent false positives caused by water particles. When the oil presence was confirmed and turbidity was below the threshold, an alarm was triggered, and data about the oil’s concentration were stored. Otherwise, data were stored in an SD card of the node without triggering an alarm. The system had a limit of 3600 records stored, and once it was reached, it sent the stored data, erased the memory, and waited for a new measurement to start [4]

Jung Hwan Moon et al. [39] discussed the geometric properties of spilled oil on seawater using a LiDAR sensor, employing a near-infrared 905nm wavelength laser. The LiDAR gathered information from the target at each laser point in order to sense the surroundings.

The compact Laser-induced fluorescence (LIF) sensor is presently undergoing testing on Unmanned Aerial Vehicles (UAVs) to acquire on-site comparative data [40]. The utilization of UV radiation presents notable advantages, including the ability to monitor slicks and dissolved oil hydrocarbons during daylight hours, owing to the elevated quantum efficiency of LIF when subjected to UV radiation. This technology holds particular significance in the context of optical sensors designed for environmental monitoring on UAVs, providing valuable insights for research and contributing to the overall body of knowledge in the field.

Xudong Liu et al. [41] discussed the challenges encountered by oceanic polarization lidar (P-lidar) due to strong seawater attenuation, which limited the penetration of the 532 nm laser energy to a depth of 30 m. The research discussed fundamental principles of polarization and P-lidar. The methodology involved expanding detection dimensions using P-lidar and considering parameters such as polarization degree, polarization angle, and ellipticity. Results were presented from a 3D flash P-lidar based on a micro-polarizer camera, showcasing cross-polarization configurations at different visibility distances. The research emphasizes the need for a customized P-lidar tailored for oceanic remote sensing by addressing issues related to spatial resolution and radiometric accuracy.

Moreover, a large proportion of oil spills occur near busy marine fairways. Oil spill monitoring relies on satellite remote sensing, aerial vehicles, and ships. These methods have limitations in detecting and tracking oil spills effectively. Fluorometric sensors were tested for real-time oil detection in flow-through systems. The sensors detected diesel oil for 20 days under laboratory conditions. Detection was impacted by CDOM, turbidity, and algae-derived substances, particularly algae extract. The sensors were integrated into a FerryBox and a moored SmartBuoy for field testing. Field tests confirmed that interference compounds caused measurement variations. No oil spill peaks were observed during the two-month field study. Both autonomous systems provided reliable real-time data. Improved calibration and advanced sensors are necessary to address natural interferences [42].

Koirala et al. [43] evaluates visible, near-infrared, and shortwave infrared regions for oil spill monitoring. A physical model was developed for oil thickness and volume estimation using optical reflectance. The method accounts for variations in acquisition and illumination conditions. An artificial neural network algorithm was designed for detecting spilled oil. Training samples were generated using the proposed physical model. Experiments were conducted indoors and outdoors on four oil types for validation. Hyperspectral datasets were developed using Agrispec and Imec snapscan cameras under controlled conditions. Oil samples with thicknesses between 500 µm and 5000 µm were analyzed. An RGB drone camera demonstrated outdoor oil monitoring potential using visible wavelengths. The results highlight the method’s robustness and applicability in diverse scenarios.

In addition, fluorescence and spectroscopy techniques are employed to study the optical properties of water. Fluorometers measure the fluorescence emitted by substances in the water, such as chlorophyll-a, which can indicate the presence of phytoplankton and algal blooms [52]. Spectroscopy helps in analyzing the absorption and reflection of light at different wavelengths, providing information on water quality parameters like dissolved organic matter [53], turbidity and nutrient concentrations.

5.3. Wireless Sensing

One of the issues that cause oil spill in the ocean is breakage of oil pipes, leakage from ships, and industrial waste. To address the problem, a proposed solution was done by implementing intelligent Wireless Sensor Networks (WSNs) at multiple aggregation levels, creating an inter-network of intelligent nodes to enhance the efficiency of detecting and responding to oil spills without relying on light propagation [44]. To overcome the issue, an Internet of Things (IoT)-based oxygen reduction potential sensor, integrated into wireless sensor networks (WSNs) and underwater robotic submarines, was proposed [54]. Tabella et al. [55] used Wireless Sensor Networks (WSNs) for oil spill detection and localization, where a Fusion Center (FC) aggregated local decisions, enhancing the reliability of global binary decisions. The approach not only provides an estimated position of the leak source but specifically focuses on detecting the leakage source rather than just the presence of oil.

Sai et al. [56] discussed oil spill management system in the seawater by controlling oil pollution. A new water sprinkler system was developed to hasten cloud formation. The Internet of Things (IoT) and remote sensing were employed to expand the capability of the system to collect, store and display data. Various sensors, including barometers, temperature sensors, a 6-axis gyroscope and accelerometers, and ambient light sensors, have been used to ensure the successful operation of the systems. The main objective of the system was to remove or separate the oil spill from the water bodies. The sponges were used to separate crude oil from water by absorbing it. By reducing the amount of energy needed to evaporate a given amount of water from a surface, the sprinkler system assisted in cloud formation. The data obtained through IoT and remote sensing aid in the equipment’s correct operation.

The local system aboard the boat was isolated for data management. It was managed by an onboard software stack that was abstracted into multiple levels, from the application server at the top to the hardware firmware at the bottom. Sensory signal processing and storage layers were located between the storage levels of Structured Query Language (SQL) and the Linux kernel. The application servers periodically uploaded the data into the Cloud by using available connectivity options [56].

Meanwhile, Ronci et al. [46] combined the backscattering properties captured in SAR imagery with the capabilities of deep learning models. The process began with the collection of SAR images from satellites or airborne platforms. These images underwent preprocessing steps to enhance their quality and remove noise. A large, labeled dataset was then generated by manually annotating the SAR images, marking oil spill areas as the target class. Data were trained using the labeled dataset to learn relevant features and classify pixels or regions as oil spill or non-oil spill based on Convolutional Neural Network (CNN). The trained model was evaluated using a validation dataset and subsequently applied to new, unseen SAR images for oil spill detection. The method utilized SAR imagery, deep learning algorithms, a labeled dataset for training, and preprocessing techniques to achieve accurate and automated detection of oil spills, facilitating timely response and mitigation efforts. There are limitations in building the system where it is computationally expensive. Other than that, the method is not fully automated when it requires human intervention to select the SAR images to be used for training and testing.

The detection of oil spills in SAR images is essentially a segmentation problem. To address this, researchers have investigated an image-to-image translation method using CNN architectures that have been modified for the task [46]. Other than deep learning-based techniques, recent research has also focused on practical applications of SAR imaging in the detection and management of oil spills in various aquatic environments.

In recent years, oil pollution has emerged as a major concern for water ecosystems worldwide. Oil pollution also occurs in lake and rivers [30,35,57]. Al Bayaty et al. [57] focused on analyzing Synthetic Aperture Radar (SAR) images containing oil spills using a semi-automated processing approach. They found that a combination of visible image interpretation and image processing yielded highly accurate outcomes, allowing for effective monitoring and initial estimation of the disaster state during oil spill events. However, the study does not specifically address the incorporation of Internet of Things (IoT) systems, suggesting a potential research gap in exploring the synergy between oil pollution sensing methods and IoT technologies for enhanced environmental monitoring and response strategies.

In order to enable an oil pollution monitoring system in aquatic ecosystems, Budiman et al. developed an Internet of Things-based automatic oil-water separation device [45]. The constructed apparatus, which was made up of sensors, an actuator, and a microcontroller, could automatically distinguish between water and oil and identify oil pollution.

6. Important Parameters of Oil Pollution Sensing and Monitoring Methods

Oil pollution sensing and monitoring methods are investigated based on many parameters such as delay in time detection, oil compound, types of oil, types of oil and light, and spectral reflectance.

Table 2. Parameters studied in the oil pollution sensing method.

Methods	Parameters	Future Study
Fluorometric sensors [42]	Detection time, fluorescent compounds	Utilize advanced mathematical protocols or more sophisticated sensors to enhance the differentiation between actual oil pollution and optical interference.
An optical sensor system based on the absorption and dispersion of light [4]	Oil types, light sources, light receptor, water column heights	Test the sensor under dynamic conditions to simulate real-world scenarios where the oil stains may have movement on the water surface due to various factors such as vibration.
Ultrasonic and Image Processing Methods [58]	Oil slick thickness, area of thick oil slicks	Conduct more lab experiments to further validate the technical feasibility and accuracy of the proposed method in the oil spill response facility where real sea conditions can be simulated
Light refraction [59]	Estimation of oil thickness using refraction of light, laser beam wavelength, angle of incident, and angle of refraction in oil	The influence of environmental factors such as temperature, pressure, and humidity on the accuracy and consistency of oil thickness measurements
Spectral reflectance [60]	Reflectivity, wavelength, oil thickness	Impact of different types of oil on sensor performance

shows the parameters employed for the detection of oil spills in water.

6.1. Detection Time/Period

One of the methods to detect oil spills is by using fluorometric sensors that can detect diesel oil for at least 20 days in laboratory conditions. Part et al. [42] evaluates the feasibility of using fluorometric sensors in flow-through systems for real-time detection of oil spills using the integration of FerryBox system and moored SmartBuoy. According to the results, both autonomous systems provide real-time data. It was found that the measured data had systematic gaps, and a maintenance visit was made to reprogram the datalogger and manually clean the sensors.

In the second lab experiment, many detection techniques were compared to study the detection period of the sensors to identify oil molecules in seawater. There were multiple gaps in the Turner C3 data and one gap in the EnviroFlu-HC data due to lagging software. After 165 h, UviLux was moved because there were significant oscillations in the signal following the third addition of water accommodated fraction (WAF), indicating that it was not responding appropriately. Fluorescent compounds derived from diesel oil were found to present for at least 20 days from the beginning of the experiment, according to the responses from all sensors and the conventional HELCOM oil monitoring approach [42].

6.2. Types of Oil

Numerous studies have been conducted to explore and refine the parameters associated with oil spill detection methods utilizing remote sensing technologies. The sensor can distinguish the presence or absence of diesel engine oil regardless of the LED used [4]. The use of Light Dependent Resistors (LDRs) in conjunction with Light Emitting Diodes (LEDs) of various colors, enables the differentiation between the presence and absence of diesel engine oil and the quantification of gasoline engine oil concentrations, showcasing the effectiveness of the sensor in detecting oil contamination in water.

The author examines the variations in the output from various contaminants. The lowest and highest resistance values measured with two oils (diesel and gasoline engines) are shown in

Table 3. Maximum and minimum resistance measured for two oils [4].

Color	Gasoline		Diesel
	Minimum (kΩ)	Maximum (kΩ)	Minimum (kΩ)	Maximum (kΩ)
Yellow	138.68	160.91	150.01	178.81
Red	239.5	318.83	256.97	359.19
Blue	70.56	80.66	57.09	76.94
Green	50.88	65.57	51.44	63.85
White	10.35	19.29	11.98	19.29

. The difference between maximum and minimum resistance values of both oils is quite similar for green and white light. Meanwhile, larger resistance variations of the two tested oils can be observed for yellow, red, and blue lights [4]

6.3. Volume of Oil

Volume of oil is one of the parameters that have been studied in sensing and monitoring oil pollution. Du et al. discusses the method to assess the volume of oil slicks by measuring the oil thickness using ultrasonic sensor and image processing [58]. The method calculates oil slick thickness by analyzing ultrasonic traveling time through the cross-correlation method, while capturing images of the oil slick using an airborne drone equipped with an optical camera facilitates area calculation through image processing algorithms. The system is implemented on a remotely operated vehicle (ROV) with ultrasonic immersion transducers [58].

The measured thickness and area are directly multiplied to determine the amount of the oil slick. The oil slick volumes are seen to be periodically varying, with minimum and maximum of about 7 litres and 25 litres, respectively. This observation may be the result of the oil slick’s nonuniform thickness brought on by surface water flow [58].

The average oil slick volume was determined for each session to reduce the impact of thickness on measurement accuracy. The average amounts of the oil slick measured over each session varied between 9.0 L and 14.0 L and 14.7 L and 17.0 L, respectively, based on the addition of 10 L and 15 L of crude oil to the tests.

The volume of the oil slick was calculated by multiplying its thickness by its area. The volume correlated closely with the variation in thickness when the pump was activated or deactivated, indicating that thickness significantly influenced volume measurement more than area did. The measured volume varied by less than 10% from the additional oil, demonstrating great precision. The technology was successfully evaluated in the laboratory and may assist in monitoring oil spills, assessing their dimensions and viscosity, and improving response strategies to such incidents [58].

6.4. Signal to Noise Ratio

Identifying the system design of LiDAR is important toward the effectiveness of the research. Jing et al. discusses the signal-to-noise ratio (SNR) to obtain spectral information and two-dimensional images [61]. Signal-to-noise ratio (SNR) is an important index parameter used to evaluate the imaging performance of the LiDAR system. It reflects the system ability to detect radiation and helps in assessing the optical system parameters, imaging device selection, and subsequent electronic system design [61].

SNR estimation considers the radiological transmission and photoelectric conversion process of laser radiation passing through the atmosphere, target reflection, and reaching the imaging detector for quantification. The SNR of the visible light camera and hyperspectral detector in the system is calculated to ensure accurate and reliable detection of oil spills over a 24-h period. The proposed system design meets technical requirements for monitoring oil spill pollution effectively and can cover a working band of 400 nm–1100 nm [61].

LiDAR imaging involves the radiological transmission and photoelectric conversion of laser radiation through the atmosphere, reflecting off the target, reentering the atmosphere, and influencing the optical system until it reaches the imaging detector, where it is then detected, analyzed, and measured. LiDAR is impacted by the target’s spectral albedo during transmission, in addition to being impacted by other atmospheric components’ absorption and scattering of laser light energy.

6.5. Refraction of Light

Kumar et al. has studied parameters used to detect oil in water by employing Snell’s law to quantify the refraction of laser beams passing through the air, oil, or water layer [59]. The experimental setup involves recording the position of the refracted laser beam using a camera module controlled by a microcontroller, and the captured images are processed to calculate X and Y coordinates.

To estimate the unknown oil thickness t, triangle ABC of Figure 1 is taken into consideration. The refraction angles ${\theta }_1$, ${\theta }_2$, ${\theta }_3$ in the oil and water media are determined using Snell’s law in Equation (1):

$${\displaystyle \frac{{\eta }_1}{sin {\theta }_1 }}={\displaystyle \frac{{\eta }_2}{sin {\theta }_2 }}={\displaystyle \frac{{\eta }_3}{sin {\theta }_3 }}$$

(1)

where ${\eta }_1$, ${\eta }_2$, and ${\eta }_3$ are the refractive indices of air, oil, and water, respectively. The thickness of oil t can be calculated from the ratio between refracted beams and the geometry of triangle. Thickness equation t is formulated in Equations (2) and (3), while the total horizontal distance in Equation (4) is as follows:

$$X=t\times tan {\theta }_2$$

(2)

$$D=W\times {tan \theta }_3$$

(3)

$$E=X+D$$

(4)

Figure 1 depicts the path of a laser beam as it refracts while transitioning between different media specifically from air to oil and later to water. According to Snell’s Law, the refraction of a beam is dependent upon the refractive indices of the media traversed. The diagram illustrates the refraction of the laser beam at the boundary of each medium, resulting from variations in optical density, thus clarifying the fundamental principles of light transmission and refraction in layered media.

Kumar et al. [59] investigated the thickness of oil to identify the entire oil runoff using laser refraction, where the brightness of X and Y coordinates are measured. The findings of the study is shown in

Table 4. Varying oil thickness, the resulting Y coordinate, and the percentage error [60].

Oil Thickness	Measured Y Coordinates	Analytic Y Coordinate	Estimated Oil Thickness (mm)	Relative Error in Thickness
1	1340	1340.0729	1.00005440	5.44 × 105
2	1334	1332.8490	1.99827436	8.63 × 104
3	1324	1325.0283	3.00232998	7.77 × 104
4	1316	1316.6108	4.00185653	4.64 × 104
5	1308	1307.5965	4.99845756	3.08 × 104
6	1297	1297.9854	6.00455852	7.60 × 104
7	1290	1287.7775	6.98793992	1.72 × 103
8	1275	1276.9728	8.01237835	1.55 × 103
9	1265	1265.5713	9.00406458	4.52 × 104
10	1257	1253.573	9.97273667	2.73 × 103
11	1239	1240.9779	11.0175600	1.60 × 103
			Percentage error = 0.10246

.

Table 4 gives an overview of the measured data and the relative error in oil thickness. The values of the X and Y coordinates are obtained by image processing, and since oil thickness varies, there is a non-linear relationship between Y coordinates and oil thickness. The Y coordinates change with oil thickness and vice versa. The created quantitative remote sensing model can assess the oil thickness since the estimated values are almost similar.

6.6. Spectral Reflectance

Yin et al. examines the oil thickness detection at two interfaces, namely, water—oil and air—oil [60]. This study introduces a gold-film fiber-optic surface plasmon resonance (FOSPR) sensor for real-time oil slick thickness measurement, fabricated using polydopamine-accelerated wet chemical plating. Salinity and temperature have very little impact on the thickness of an oil slick. The manufactured fiber-optic surface plasmon resonance (FOSPR) sensor can both measure the oil thickness and identify its presence.

The thickness of the oil slick in the atmosphere is measured by the optical fiber sensor [60]. To detect thickness at the air—oil interface, oil was added until a thickness range of 0–5 mm was reached. The sensing region stays in the air while the silver end is submerged in water at the air—oil interface.

7. Conclusions

In conclusion, oil spills present significant challenges to environmental sustainability, particularly due to their adverse effects on aquatic ecosystems. Effective monitoring and detection of oil pollution are essential for mitigating the impacts and safeguarding marine environments, minimizing harm to wildlife and ecosystems. Through the review of current methods and exploration of various oil pollution sensing techniques, including remote, optical, and wireless sensing, it becomes evident that a diverse approach is necessary for comprehensive oil pollution monitoring. Understanding the compounds in oil, the issues caused by oil pollution, the various methods for detecting an oil spill, and the parameters involved in the sensing methods are crucial for developing and implementing effective strategies to address oil spills and protect our marine environments for future generations.

Oil pollution in watery environments is a significant threat for human health, ecological integrity, and the global economy. Efficient monitoring and detection are essential to mitigate the environmental consequences of oil spills and to prevent delays in response activities. Although current technologies, including remote sensing, optical sensing, wireless sensing, and artificial intelligence techniques such as machine learning and deep learning, have significantly enhanced the detection of oil spills and the response time for such incidents, numerous challenges persist. These encompass enhancing the precision and real-time detection capabilities of monitoring systems, identifying cost-effective strategies for large-scale application, and integrating these methods into diverse and dynamic environmental conditions.

Future research must investigate several critical avenues to enhance the efficacy of oil spill detection and response efforts. Initially, there is a necessity for ongoing advancement and refinement of AI-driven algorithms to improve the prediction and detection of oil spills. The incorporation of sophisticated machine learning and deep learning methodologies will significantly improve the real-time identification of oil slicks, facilitating faster localization of spill sites and more accurate estimations of their magnitude. Enhanced predictive models can assist decision-makers in formulating more precise and effective response strategies. In addition to AI technology, there is an urgent need for the creation of affordable, scalable monitoring systems, especially in areas with constrained technical or financial resources. Most locations, particularly in developing nations or those dependent on offshore oil extraction, are limited in their ability to adopt costly, advanced monitoring systems. Investigations into economical and scalable remote sensing platforms, wireless sensor networks, and IoT-based solutions can provide the requisite apparatus for continuous real-time monitoring in these areas. Economical solutions utilizing open-source photography and citizen science serve as sufficient and sustainable alternatives to conventional high-cost monitoring methods.

Moreover, a combination of wireless sensing, optical sensing, and remote sensing technologies can yield more precise and accurate oil spill detecting systems. A multi-sensor system employing the aforementioned technologies could enhance precision and accuracy for real-time detection and identification of oil contamination, especially in challenging or inaccessible situations. It would not only improve detection but also increase efficiency in response activities.

A crucial domain for future investigation is determining the long-term ecological consequences of oil pollution. Although considerable focus is placed on rapid spill response, further research is necessary to evaluate the long-term effects of oil pollution on aquatic ecosystems, biodiversity, and the potential for recovery in affected regions. Such studies would enhance cleanup procedures and improve long-term management and restoration practices for oil-contaminated ecosystems.

Future research should concentrate on enhancing bioremediation and biodegradation technologies, which provide an eco-friendly and sustainable approach to oil spill remediation. Through augmenting the natural degradation of oil in aquatic environments or creating novel bioremediation agents, more efficient and environmentally friendly alternatives could be devised to supplement existing mechanical and chemical remediation techniques. Significant advancements have been achieved in the detection and response to oil spills; yet, considerable potential for innovation and enhancement persist. Through following these guidelines for future research, the industry can advance towards more precise, efficient, and environmentally sustainable methods for mitigating oil pollution and safeguarding vulnerable aquatic ecosystems.