New Solutions of Laser-Induced Fluorescence for Oil Pollution Monitoring at Sea

Букин, О. А.; Proschenko, D. Yu.; Chekhlenok, A. A.; Korovetskiy, D. A.; Bukin, Ilya; Yurchik, Viktoria; Соколова, И. В.; Nadezhkin, Andrey

doi:10.3390/photonics7020036

Cited by 18 publications

(8 citation statements)

References 24 publications

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“…References [30,31] described the development of the LIF spectroscopy method for monitoring oil products dissolved in seawater. According to the results of these studies, a small-sized LIF spectrometer was developed, the 3D model and assembled version of which is shown in Figure 3a,b.…”

Section: Hardware and Software Sub-framework For Active Lif Spectrosc...mentioning

confidence: 99%

Development of the Artificial Intelligence and Optical Sensing Methods for Oil Pollution Monitoring of the Sea by Drones

et al. 2021

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The oil pollution of seas is increasing, especially in local areas, such as ports, roadsteads of the vessels, and bunkering zones. Today, methods of monitoring seawater are costly and applicable only in the case of big ecology disasters. The development of an operative and reasonable project for monitoring the sea surface for oil slick detection is described in this article using drones equipped with optical sensing and artificial intelligence. The monitoring system is implemented in the form of separate hard and soft frameworks (HSFWs) that combine monitoring methods, hardware, and software. Three frameworks are combined to fulfill the entire monitoring mission. HSFW1 performs the function of autonomous monitoring of thin oil slicks on the sea surface, using computer vision with AI elements for detection, segmentation, and classification of thin slicks. HSFW2 is based on the use of laser-induced fluorescence (LIF) to identify types of oil products that form a slick or that are in a dissolved state, as well as measure their concentration in solution. HSFW3 is designed for autonomous navigation and drone movement control. This article describes AI elements and hardware complexes of the three separate frameworks designed to solve the problems with monitoring slicks of oil products on the sea surface and oil products dissolved in seawater. The results of testing the HSFWs for the detection of pollution caused by marine fuel slicks are described.

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Section: Hardware and Software Sub-framework For Active Lif Spectrosc...mentioning

confidence: 99%

Development of the Artificial Intelligence and Optical Sensing Methods for Oil Pollution Monitoring of the Sea by Drones

et al. 2021

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“…However, the phenomenon of dark areas in SAR images is difficult to clarify as they can also be caused by natural surface films produced by plankton or fish, grease, floating algae, and internal waves. Laser fluorescence sensors also have all-weather characteristics and distinguish oil from other substances by its strong fluorescence characteristics (Bukin et al, 2020). Thermal sensors can detect oil by thermal comparisons generated by the different emissivity of water and oil on the sea surface, which can also indicate its thickness.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Thickness Measurement of Marine Oil Spill by Fiber-Optic Surface Plasmon Resonance Sensors

et al. 2022

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Rapid detection of marine oil spills is becoming increasingly critical in the face of frequent marine oil spills. Oil slick thickness measurement is critical in the hazard assessment of such oil leaks. As surface plasmon resonance (SPR) sensors are sensitive to slight changes in refractive index, they can monitor offshore oil spills arising from significant differences in the refractive index between oil and water. This study presents a gold-film fiber-optic surface plasmon resonance (FOSPR) sensor prepared by polydopamine accelerated wet chemical plating for rapid and real-time measurement of oil slick thickness. We examined oil thickness detection at two interfaces, namely, water-oil and air-oil. Detection sensitivity of −1.373%/mm is obtained at the water-oil interface in the thickness range of 0–5 mm; detection sensitivity of −2.742%/mm is obtained at the air-oil interface in the thickness range of 0–10 mm. Temperature and salinity present negligible effects on the oil slick thickness measurement. The fabricated FOSPR sensor has the ability to detect the presence of oil as well as quantify the oil thickness. It has favorable repeatability and reusability, demonstrating the significant potential for use in the estimation of marine oil slick thickness.

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“…Laser-induced fluorescence was used to detect and classify the fresh and emulsified forms of hydrocarbons [32][33][34]. Fluorescence-based methods were also successful in underwater in situ detection of dissolved hydrocarbons [35,36].…”

Section: Introductionmentioning

confidence: 99%

Influence of Dispersed Oil on the Remote Sensing Reflectance—Field Experiment in the Baltic Sea

Haule

Toczek

Borzycka

et al. 2021

Sensors

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Remote sensing techniques currently used to detect oil spills have not yet demonstrated their applicability to dispersed forms of oil. However, oil droplets dispersed in seawater are known to modify the local optical properties and, consequently, the upwelling light flux. Theoretically possible, passive remote detection of oil droplets was never tested in the offshore conditions. This study presents a field experiment which demonstrates the capability of commercially available sensors to detect significant changes in the remote sensing reflectance Rrs of seawater polluted by six types of dispersed oils (two crude oils, cylinder lubricant, biodiesel, and two marine gear lubricants). The experiment was based on the comparison of the upwelling radiance Lu measured in a transparent tank floating in full immersion in seawater in the Southern Baltic Sea. The tank was first filled with natural seawater and then polluted by dispersed oils in five consecutive concentrations of 1–15 ppm. After addition of dispersed oils, spectra of Rrs noticeably increased and the maximal increase varied from 40% to over three-fold at the highest oil droplet concentration. Moreover, the most affected Rrs band ratios and band differences were analyzed and are discussed in the context of future construction of algorithms for dispersed oil detection.

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New Solutions of Laser-Induced Fluorescence for Oil Pollution Monitoring at Sea

Cited by 18 publications

References 24 publications

Development of the Artificial Intelligence and Optical Sensing Methods for Oil Pollution Monitoring of the Sea by Drones

Development of the Artificial Intelligence and Optical Sensing Methods for Oil Pollution Monitoring of the Sea by Drones

Real-Time Thickness Measurement of Marine Oil Spill by Fiber-Optic Surface Plasmon Resonance Sensors

Influence of Dispersed Oil on the Remote Sensing Reflectance—Field Experiment in the Baltic Sea

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