Ultraviolet-induced fluorescence of oil spill recognition using a semi-supervised algorithm based on thickness and mixing proportion–emission matrices

Gong, Bowen; Zhang, Hongji; Wang, Xiaodong; Lian, Ke; Li, Xinkai; Chen, Bo; Wang, Hanlin; Niu, Xiaoqian

doi:10.1039/d2ay01776h

Cited by 4 publications

(2 citation statements)

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“…Emilia Baszanowsk measured the fluorescence of dissolved oil in seawater by measuring the excitation emission matrix (EEM) with a fluorescence spectrometer. 22 Rather than measuring the fluorescence spectra after sampling using a fluorescence spectrophotometer, 23 a simulated field test of emulsified oil on the sea surface was employed to measure the fluorescence. The fluorescence of the oil emulsion was measured using a shorter excitation light source wavelength of 222 nm.…”

Section: Introductionmentioning

confidence: 99%

Mineral oil emulsion species and concentration prediction using multi-output neural network based on fluorescence spectra in the solar-blind UV band

Gong,

Mao,

et al. 2024

Anal. Methods

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Section: Introductionmentioning

confidence: 99%

Mineral oil emulsion species and concentration prediction using multi-output neural network based on fluorescence spectra in the solar-blind UV band

Gong,

Mao,

et al. 2024

Anal. Methods

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“…In April 2021, the Panamanian general cargo ship "Sea Justice" collided with the Liberian tanker "A Symphony" in the waters off Qingdao, Shandong Province. This collision resulted in a spill of approximately 9400 tons of cargo oil into the sea [2], causing extensive oil spill pollution along hundreds of kilometers of coastline. Furthermore, leaks from offshore drilling platforms significantly contribute to the persistence of residual oil pollution in water bodies.…”

Section: Introductionmentioning

confidence: 99%

Rapid Investigation of Oil Pollution in Water-Combined Induced Fluorescence and Random Sample Consensus Algorithm

Wu,

Wang,

Zhao

2024

Sustainability

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The global issue of oil spreading in water poses a significant environmental challenge, emphasizing the critical need for the accurate determination and monitoring of oil content in aquatic environments to ensure sustainable development of the environment. However, the complexity arises from challenges such as oil dispersion, clustering, and non-uniform distribution, making it difficult to obtain real-time oil concentration data. This paper introduces a sophisticated system for acquiring induced fluorescence spectra specifically designed for the quantitative analysis of oil pollutants. The paper involved measuring the fluorescence spectra across 20 concentration gradients (ranging from 0 to 1000 mg/L) for four distinct oil samples: 92# Gasoline, Mobil Motor Oil 20w-40, Shell 10w-40 engine oil, and Soybean Oil. The research focused on establishing a relationship model between relative fluorescence intensity and concentration, determined at the optimal excitation wavelength, utilizing the segmented Random Sample Consensus (RANSAC) algorithm. Evaluation metrics, including standard addition recovery, average recovery, relative error, and average relative error, were employed to assess the accuracy of the proposed model. The experimental findings suggest that the average recovery rates for the four samples ranged between 99.61% and 101.15%, with the average relative errors falling within the range of 2.04% to 3.14%. These results underscore the accuracy and efficacy of the detection methodology presented in this paper. Importantly, this accuracy extends to scenarios involving heavier oil pollution. This paper exhibits exceptional sensitivity, enabling precise detection of diverse oil spills within the concentration range of 0~1000 mg/L in water bodies, offering valuable insights for water quality monitoring and sustainable development of the environment.

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Experimental and theoretical research on the leakage mechanisms of the flexible oil bladder breakage for a novel underwater flexible oil storage method

Zhang,

Wu,

Hou

et al. 2024

Ocean Engineering

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Ultraviolet-induced fluorescence of oil spill recognition using a semi-supervised algorithm based on thickness and mixing proportion–emission matrices

Abstract: The fluorescence of oil films with different thicknesses and mixing proportions was measured and classified using an improved co-training algorithm, which improves the classification accuracy and provides prospects for recognition of similar oil species.

Cited by 4 publications

References 40 publications

Mineral oil emulsion species and concentration prediction using multi-output neural network based on fluorescence spectra in the solar-blind UV band

Mineral oil emulsion species and concentration prediction using multi-output neural network based on fluorescence spectra in the solar-blind UV band

Rapid Investigation of Oil Pollution in Water-Combined Induced Fluorescence and Random Sample Consensus Algorithm

Experimental and theoretical research on the leakage mechanisms of the flexible oil bladder breakage for a novel underwater flexible oil storage method

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