Monitoring of polycyclic aromatic hydrocarbon contamination at four oil spill sites using fluorescence spectroscopy coupled with parallel factor-principal component analysis

The application of laser‑induced fluorescence in oil spill detection

Over the past two decades, oil spills have been one of the most serious ecological disasters, causing massive damage to the aquatic and terrestrial ecosystems as well as the socio-economy. In view of this situation, several methods have been developed and utilized to analyze oil samples. Among these methods, laser-induced fluorescence (LIF) technology has been widely used in oil spill detection due to its classification method, which is based on the fluorescence characteristics of chemical material in oil. This review systematically summarized the LIF technology from the perspective of excitation wavelength selection and the application of traditional and novel machine learning algorithms to fluorescence spectrum processing, both of which are critical for qualitative and quantitative analysis of oil spills. It can be seen that an appropriate excitation wavelength is indispensable for spectral discrimination due to different kinds of polycyclic aromatic hydrocarbons' (PAHs) compounds in petroleum products. By summarizing some articles related to LIF technology, we discuss the influence of the excitation wavelength on the accuracy of the oil spill detection model and proposed several suggestions on the selection of excitation wavelength. In addition, we introduced some traditional and novel machine learning (ML) algorithms and discussed the strengths and weaknesses of these algorithms and their applicable scenarios. With an appropriate excitation wavelength and data processing algorithm, it is believed that laser-induced fluorescence technology will become an efficient technique for real-time detection and analysis of oil spills.

Dispersant-enhanced photodissolution of macondo crude oil: A molecular perspective

Previous laboratory studies developed a conceptual model based on elevated non-volatile dissolved organic carbon (NVDOC) concentrations after photodegradation and subsequent dissolution of Macondo oil following the Deepwater Horizon blowout. However, those experiments did not account for the effects of ∼1 million gallons of dispersant applied to the surface oil. Here, laboratory results show photodissolution in the presence of dispersant results in > 2x increase in NVDOC concentrations after extensive photoprocessing relative to oil without dispersant. This result corresponds with an apparent increase in the percentage of surface oil photodissolution from approximately 4% in the absence of dispersant to 7% in the presence of dispersant. The oil and dissolved products were analyzed by excitation-emission matrix spectroscopy and ultrahigh resolution mass spectrometry. The compounds that persisted in the oil phase are relatively aromatic without dispersant, while those in the presence of dispersant are highly aliphatic, paraffinic, wax-like compounds. The composition of the dissolved compounds produced from both treatment types are nearly identical after 240 h of exposure to simulated sunlight. The NVDOC and chemical composition information indicate that the photodissolution of MC252 oil in the presence of dispersant is enhanced and accelerated, suggesting that the effects of dispersants should be included in mass transfer calculations from the oil to the aqueous phase.

A Comprehensive Review on the Excitation-Emission Matrix Fluorescence Spectroscopic Characterization of Petroleum-Containing Substances: Principles, Methods, and Applications

Petroleum-containing substance (PCS) is a general term used for petroleum and its derivatives. A comprehensive characterization of PCSs is crucial for resource exploitation, economic development and environmental protection. Fluorescence spectroscopy, especially excitation-emission matrix fluorescence (EEMF) spectroscopy, has been proved to be a powerful tool to characterize PCSs since its remarkable sensitivity, selectivity, simplicity and high efficiency. However, there is a lack of systematic review focusing on this field in the literature. This paper reviews the fundamental principles and measurements of EEMF for characterizing PCSs, and makes a systematic introduction to various information mining methods including basic peak information extraction, spectral parameterization and some commonly used chemometric methods. In addition, recent advances in applying EEMF to characterize PCSs during the whole life-cycle process of petroleum are also revisited. Furthermore, the current limitations of EEMF in the measurement and characterization of PCSs are discussed and corresponding solutions are provided. For promoting the future development of this field, the urgent need to build a relatively complete EEMF fingerprint library to trace PCSs, not only pollutants but also crude oil and petroleum products, is proposed. Finally, the extensions of EEMF to high-dimensional chemometrics and deep learning are prospected, with the expectation of solving more complex systems and problems.

Fingerprint characteristics of refined oils and their traceability in the groundwater environment

Chemical fingerprinting is essential for identifying the presence and responding to oil spills that frequently contaminate the groundwater environment of refineries. In this study, crude oil and oil products from the atmospheric and vacuum distillation units of a refinery were analyzed by gas chromatography-mass spectrometry (GC-MS) to evaluate their chemical variability before and after refinery. A series of experiments involving evaporation and soil column penetration were conducted to simulate refined oil spilling into groundwater and determine appropriate characteristic ratios (CRs) for principal component analysis (PCA) for oil source identification. The simulated study demonstrated that all products had bell-shaped n-alkane distributions, with dominant peaks that remained unchanged or shifted towards longer chain lengths compared to the source oil. Similarly, naphthalene and dibenzothiophene series remained the main PAH components like the source oil. Ten relatively stable CRs were selected for PCA to identify different oil products through the simulated experiments. The chosen CRs were then utilized to identify the sources for two groundwater oil spills recently occurred, one that occurred in an oil depot area, and another near a continuous catalytic reforming unit in a refinery. This study showed that the components with long-chain n-alkanes (n ≥ C₁₈), pristane, phytane, and phenanthrene and dibenzothiophene series PAHs played an important role in the identification of refined oil products spilling into the groundwater environment. The selected CRs provide an effective tool for rapid and accurate identification of oil spills, especially for newly occurring spills in the groundwater environment, which can aid in developing appropriate response strategies.

Molecular evidence for the production of labile, sulfur-bearing dissolved organic matter in the seep sediments of the South China Sea

Cold seeps with methane-rich fluids leaking out of the seafloor usually support massive biomass of chemosynthetic organisms and associated fauna. A substantial amount of methane is converted to dissolved inorganic carbon by microbial metabolism, and this process also releases dissolved organic matter (DOM) into pore water. Here, pore water samples from "Haima cold seeps" sediments and the non-seep reference sediments in the northern South China Sea were analyzed for optical properties and molecular compositions of pore water DOM. Our results showed that the relative abundance of protein-like DOM, H/C_wa and molecular lability boundary percentage (MLB_L%) in the seep sediments were significantly higher than those in the reference sediments, indicating that more labile DOM related to unsaturated aliphatic compounds is produced in the seep sediments. Spearman's correlation of the fluoresce and molecular data suggested that the humic-like components (C1 and C2) mainly constituted the refractory compounds (CRAM, highly unsaturated and aromatics compounds). In contrast, the protein-like component (C3) had high H/C ratios featuring high degree of DOM lability. The amount of S-containing formulas (CHOS and CHONS) was greatly elevated in the seep sediments, likely caused by abiotic and biotic sulfurization of DOM in the sulfidic environment. Although the abiotic sulfurization was proposed to have a stabilizing effect on organic matter, our results implied that the biotic sulfurization in the cold seep sediments would increase DOM lability. Overall, the labile DOM accumulated in the seep sediments is closely linked to methane oxidation, which not only support heterotrophic communities and but also likely have an impact on carbon and sulfur cycling in the sediments and the ocean.

Oil spill in northeastern Brazil: Application of fluorescence spectroscopy and PARAFAC in the analysis of oil-related compounds

Between November 2019 and February 2020, 53 water samples were collected along 430 km of coastline in northeastern Brazil, which was the location of an oil spill that occurred in August 2019. Synchronous fluorescence matrices (SFMs) were acquired to avoid regions affected by Raman Stokes scatterings and second harmonic signals, and then, the SFMs were converted into excitation-emission matrices (EEM) by shear transformation. The matrix coupled with parallel factor analysis (PARAFAC) was used in the study of fluorescent components present in the collected waters. A sample collected before the oil spill and another from Florianópolis-SC, 2000 km from the incident, were used as references for nonimpacted waters. In the postspill samples, 4 components were determined, with component 1 (λ_exc = 225 nm, λ_em = 475 nm) being associated with humic-like organic matter (terrestrial), component 2 (λ_exc = 230 nm, λ_em = 390 nm) being associated with humic-like organic matter (marine), component 3 (λ_exc = 225/295 nm, λ_em = 345 nm) being associated with dibenzothiophene-like components also observed in tests with crude oil samples, and component 4 (λ_exc = 220/280 nm, λ_em = 340 nm) being associated with a naphthalene-like substance. Principal component analysis (PCA) was performed on the PARAFAC scores. The distribution of samples along the 4 components was observed and compared with the reference samples.

PAS1-modified optical SIS sensor for highly sensitive and specific detection of toluene

We report on a novel solution immersed silicon (SIS) sensor modified with bio-receptor to detect toluene. To perform this approach, bio-receptor PAS1 which specifically interacts with toluene was chosen as a capture agent for SIS ellipsometric sensing. We constructed wild PAS1 and mutant PAS1 (F46A and F79Y) which are toluene binding-defective. Especially, we utilized an easily accessible capturing approach based on silica binding peptide (SBP) for direct immobilization of PAS1 on the SiO₂ surfaces. After the immobilization of SBP-tagged PAS1 to the sensing layers, PAS1-based SIS sensor was evaluated for its ability to recognize toluene. As a result, a significant up-shift in Psi (Ψ) was clearly observed with a low limit of detection (LOD) of 0.1 μM, when treated with toluene on wild PAS1-surface, but not on mutant PAS1-sensing layers, indicating the selective interactions between PAS1 and toluene molecule. The PAS1-SIS sensor showed no changes in Psi (Ψ), if any, negligible, when exposed to benzene, phenol, xylene and 4-nitrophenol as negative controls, thereby demonstrating the specificity of interaction between PAS1 and toluene. Taken together, our results strongly indicate that PAS1-modified ellipsometry sensor can provide a high fidelity system for the accurate and selective detection of toluene.