Effects of polycyclic aromatic hydrocarbons on the UV-induced fluorescence spectra of crude oil films on the sea surface

Hou, Yongchao; Liu, Ying; Liu, Yu; Li, Guannan; Zhang, Zhenduo

doi:10.1016/j.marpolbul.2019.07.058

Cited by 34 publications

(12 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In contrast, great deviations were observed for the 380# and 180# fuel oil samples, while the small peaks in the composite fluorescence spectra, especially those of 380# fuel oil at 350-400 nm, could not be detected in the experimental fluorescence spectra (Figure 4(c)). ese results were similar to the fluorescence spectral patterns reported in previous studies on oil-contaminated seawater samples [41][42][43]. ese significant differences could be explained considering the effect of heavy petroleum hydrocarbons (high API gravity and viscosity) or the possible interaction between AHs and other aromatic ring systems in the oil samples.…”

Section: Composite and Experimental Fluorescence Spectrasupporting

confidence: 89%

Species Identification and Effects of Aromatic Hydrocarbons on the Fluorescence Spectra of Different Oil Samples in Seawater

Hou

et al. 2021

Journal of Spectroscopy

Self Cite

View full text Add to dashboard Cite

The identification of oil species and their characteristics in oil spills is particularly important for their efficient disposal. Since aromatic hydrocarbons (AHs) with various characteristics are the major fluorescent components of oils in seawater, they can be used to detect different oil species in seawater. Here, we developed a composition method using the fluorescence spectra of eight AH categories analyzed by gas chromatography-mass spectrometry (GC-MS) to evaluate the contribution of AHs to the total fluorescence of six different oil species. Qualitative and quantitative difference analysis of the experimental and composite fluorescence spectra was performed based on the redshift of the main peak position, the fluorescence intensity distance, and the generalized included angle cosine, while correlation analysis was used to establish the relationship between the different fluorescence spectral parameters and the American Petroleum Institute gravity and viscosity of the oil species. The fluorescence spectra recorded for heavy oil samples indicated a reduction in the fluorescence signal of fluorene series and an increase in the contribution of acenaphthene and pyrene series, indicating that the currently developed composition method by fluorescence spectroscopy combined with GC-MS can be used to distinguish and identify oil species in seawater samples.

show abstract

Section: Composite and Experimental Fluorescence Spectrasupporting

confidence: 89%

Species Identification and Effects of Aromatic Hydrocarbons on the Fluorescence Spectra of Different Oil Samples in Seawater

Hou

et al. 2021

Journal of Spectroscopy

Self Cite

View full text Add to dashboard Cite

show abstract

“…The algorithm treats all pairs equally, no matter their relative intensity, and seeks the model with the smallest errors, as can be seen by the metrics presented. Pairs, especially the ones between Ex 260 to 400 and Em 300 to 500, could be correlated to the fluorescence of benzothiophenes and dibenzothiophenes observed in other works [64][65][66][67]. However, those same works state that the differences in solvents and radicals attached to the compounds can shift the fluorescence peak to other regions.…”

Section: Diesel S10mentioning

confidence: 53%

“…After HDS, most of the oil's sulfur is contained in stable polycyclic aromatic molecules as benzothiophenes and dibenzothiophenes, being the latter the hardest sulfur to remove in diesel oil [62,63]. These compounds and their derivatives present luminous proprieties, being natural fluorophores [64][65][66][67].…”

Section: Phase Threeglobal Solutionmentioning

confidence: 99%

Prediction of sulfur content in diesel fuel using fluorescence spectroscopy and a hybrid ant colony - Tabu Search algorithm with polynomial bases expansion

Ranzan

Trierweiler

2020

Chemometrics and Intelligent Laboratory Systems

View full text Add to dashboard Cite

It is widely accepted that feature selection is an essential step in predictive modeling. There are several approaches to feature selection, from filter techniques to meta-heuristics wrapper methods. In this paper, we propose a compilation of tools to optimize the fitting of black-box linear models. The proposed AnTSbe algorithm combines Ant Colony Optimization and Tabu Search memory list for the selection of features and uses l1 and l2 regularization norms to fit the linear models. In addition, a polynomial combination of input features was introduced to further explore the information contained in the original data. As a case study, excitation-emission matrix fluorescence data were used as the primary measurements to predict total sulfur concentration in diesel fuel samples. The sample dataset was divided into S10 (less than 10 ppm of total sulfur), and S100 (mean sulfur content of 100 ppm) groups and local linear models were fit with AnTSbe. For the Diesel S100 local models, using only 5 out of the original 1467 fluorescence pairs, combined with bases expansion, we were able to satisfactorily predict total sulfur content in samples with MAPE of less than 4% and RMSE of 4.68 ppm, for the test subset. For the Diesel S10 local models, the use of 4 Ex/Em pairs was sufficient to predict sulfur content with MAPE 0.24%, and RMSE of 0.015 ppm, for the test subset. Our experimental results demonstrate that the proposed methodology was able to satisfactorily optimize the fitting of linear models to predict sulfur content in diesel fuel samples without need of chemical of physical pre-treatment, and was superior to classic PLS regression methods and also to our previous results with ant colony optimization studies in the same dataset. The proposed AnTSbe can be directly applied to data from other sources without need for adaptations.

show abstract

“…Almost all types of petroleum hydrocarbons (PHCs) have unique uorometric characteristics, which can be used for PHC identi cation on soil, sea water, ice, and other complicated background (Fingas and Brown, 2013b;Araújo et al, 2021). Therefore, laser-induced uorescence (LIF) is an effective, all-weather oil spill identi cation method that has been widely applied for oil spill monitoring (Hou et al, 2017(Hou et al, , 2019.…”

Section: Introductionmentioning

confidence: 99%

Experimental analysis on the optimal excitation wavelength for fine-grained identification of refined oil pollutants on water surface based on laser-induced fluorescence

Xie

Jia

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Laser-induced fluorescence (LIF) is an effective, all-weather oil spill identification method that has been widely applied for oil spill monitoring. However, the distinguishability on oil types is seldom considered while selecting excitation wavelength. This study is intended to find the optimal excitation wavelength for fine-grained classification of refined oil pollutants using LIF by comparing the distinguishability of fluorometric spectra under various excitation wavelengths on some typical types of refined-oil samples. The results show that the fluorometric spectra of oil samples significantly vary under different excitation wavelengths, and the four types of oil applied in this study are most likely to be distinguished under the excitation wavelengths of 395 nm and 420 nm. This study is expected to improve the ability of oil types identification using LIF method without increasing time or other cost, and also provides theoretical basis for the development of portable LIF devices for oil spill identification.

show abstract

Effects of polycyclic aromatic hydrocarbons on the UV-induced fluorescence spectra of crude oil films on the sea surface

Cited by 34 publications

References 36 publications

Species Identification and Effects of Aromatic Hydrocarbons on the Fluorescence Spectra of Different Oil Samples in Seawater

Species Identification and Effects of Aromatic Hydrocarbons on the Fluorescence Spectra of Different Oil Samples in Seawater

Prediction of sulfur content in diesel fuel using fluorescence spectroscopy and a hybrid ant colony - Tabu Search algorithm with polynomial bases expansion

Experimental analysis on the optimal excitation wavelength for fine-grained identification of refined oil pollutants on water surface based on laser-induced fluorescence

Contact Info

Product

Resources

About