Long-term weathering and continued oxidation of oil residues from the Deepwater Horizon spill
Helen K. WhiteChloe H. WangPatrick WilliamsDavid M. FindleyAlana M. ThurstonRachel L. SimisterChristoph AeppliRobert K. NelsonChristopher M. Reddy
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Deepwater Horizon
Flame ionization detector
The weathering of crude oil at sea has been researched for nearly half a century. However, there have been relatively few opportunities to validate laboratory-based predictions about the rates, relative importance, and controls of oil weathering processes (e.g., evaporation, photo-oxidation, and emulsification) under natural field conditions. The 2010 Deepwater Horizon (DWH) spill in the Gulf of Mexico provided the oil spill science community with a unique opportunity to evaluate our laboratory-based predictions in nature. With a focus on photochemical weathering, we review what we knew prior to the DWH spill, what we learned from the DWH spill, and what priority gaps in knowledge remain. Three key findings from the DWH spill are discussed. First, the rate and extent of photochemical weathering was much greater for the floating surface oil than expected based on early conceptual models of oil weathering. Second, indirect photochemical processes played a major role in the partial oxidation of the floating surface oil. Third, the extensive and rapid changes to the physical and chemical properties of oil by sunlight may influence oil fate, transport, and the selection of response tools. This review also highlights findings and predictions about photochemical weathering of oil from several decades ago that appear to have escaped the broader scientific narrative and ultimately proved true for the DWH spill. By focusing on these early predictions and synthesizing the numerous findings from the DWH spill, we expect this review will better prepare the oil spill science community to respond to the next big spill in the ocean.
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Flame ionization detector
Attenuated total reflection
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An approach for the fast, preliminary identification and differentiation of fresh oil spills is proposed. Capillary gas chromatography with flame ionization detection for the determination of n-alkane and isoprenoid distribution in oil spill samples is applied. An internal standard method is used for the quantitation of the selected compounds. Five characteristic parameters are checked for adequate presentation. n-Alkanes and isoprenoids are chosen as the most suitable structures for the identification and differentiation of fresh oil spills. In many cases, this information is sufficient to eliminate most of the oils as potential sources of the pollution.
Flame ionization detector
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Capillary gas chromatography
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Abstract One of the common toxic compound groups in crude oils are the polycyclic aromatic sulfur heterocycles (PASHs) and their related alkylated forms (APASHs). Unlike commonly investigated polycyclic aromatic hydrocarbons (PAHs) and their alkylated forms (APAHs), these sulfur containing compounds have not been extensively studied due to the lack of practical analytical methodology as well as expense and limited availability of chemical standards. In the current study, a newly developed polycyclic aromatic carbon (PAC) method was applied to analyze the various PASHs/APASHs in crude oil samples using PAHs as surrogate standards. To investigate the fate of PASHs/APASHs in spilled oils in the environment, microcosm systems containing various crude oils were prepared and exposed to the environment for two months, simulating the summer weather conditions of Canada’s west coast. The artificially weathered crude oil samples were analyzed for both PAH/APAH and PASH/APASH composition, and the results were compared to un-weathered counterparts of the oils. PASHs/APASHs were found to be affected by the microcosm weathering in similar ways to PAHs/APAHs. Fifteen PASHs and APASHs were found to be resistant to weathering and be potential candidates as biomarkers in oil spill forensic investigation.
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In this work, we present peak-cognizant quantification of environmental weathering of crude oil from the from the Deepwater Horizon oil spill. The key idea is to autonomously extract peak information from raw gas chromatography-mass spectrometry (GC-MS) signals from crude oil samples, and represent the relative weathering of different peaks in a graph-based quantitative computational framework. We also present results from pre-processing the raw signals with baseline correction and signal normalization. Retention time alignment is performed by first aligning the source oil by determining the retention time drift between prominent peaks within the signals and applying the calculated drift to the weathered oil samples. Peak finding, validation, and grouping of the five weathered oil samples to a source oil sample allows compound associations to be discovered. We present preliminary results as graphical visualizations allowing for rapid and precise interpretation of weathering compounds within polycyclic aromatic hydrocarbons (PAH). Results presented were generated with oil samples showing different degrees of weathering collected from the Deepwater Horizon spill.
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Бұл зерттеужұмысындaКaно моделітурaлы жәнеоғaн қaтыстытолықмәліметберілгенжәнеуниверситетстуденттерінебaғыттaлғaн қолдaнбaлы (кейстік)зерттеужүргізілген.АхметЯссaуи университетініңстуденттеріүшін Кaно моделіқолдaнылғaн, олaрдың жоғaры білімберусaпaсынa қоятынмaңыздытaлaптaры, яғнисaпaлық қaжеттіліктері,олaрдың мaңыздылығытурaлы жәнесaпaлық қaжеттіліктерінеқaтыстыөз университетінқaлaй бaғaлaйтындығытурaлы сұрaқтaр қойылғaн. Осы зерттеудіңмaқсaты АхметЯсaуи университетіндетуризмменеджментіжәнеқaржы бaкaлaвриaт бaғдaрлaмaлaрыныңсaпaсынa қaтыстыстуденттердіңқaжеттіліктерінaнықтaу, студенттердіңқaнaғaттaну, қaнaғaттaнбaу дәрежелерінбелгілеу,білімберусaпaсын aнықтaу мен жетілдіружолдaрын тaлдaу болыптaбылaды. Осы мaқсaтқaжетуүшін, ең aлдыменКaно сaуaлнaмaсы түзіліп,116 студенткеқолдaнылдыжәнебілімберугежәнеоның сaпaсынa қaтыстыстуденттердіңтaлaптaры мен қaжеттіліктерітоптықжұмыстaрaрқылыaнықтaлды. Екіншіден,бұл aнықтaлғaн тaлaптaр мен қaжеттіліктерКaно бaғaлaу кестесіменжіктелді.Осылaйшa, сaпa тaлaптaры төрт сaнaтқa бөлінді:болуытиіс, бір өлшемді,тaртымдыжәнебейтaрaп.Соңындa,қaнaғaттaну мен қaнaғaттaнбaудың мәндеріесептелдіжәнестуденттердіңқaнaғaттaну мен қaнaғaттaнбaу деңгейлерінжоғaрылaту мен төмендетудеосытaлaптaр мен қaжеттіліктердіңрөліaйқын aнықтaлды.Түйінсөздер:сaпa, сaпaлық қaжеттіліктер,білімберусaпaсы, Кaно моделі.
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