The M/V Cosco Busan spill: Source identification and short-term fate

Lemkau, Karin L.; Peacock, Emily E.; Nelson, Robert K.; Ventura, G. Todd; Kovecses, Jennifer L.; Reddy, Christopher M.

doi:10.1016/j.marpolbul.2010.09.001

Cited by 52 publications

(70 citation statements)

References 37 publications

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“…This confirms our observation that increased weathering results in fewer shared peaks with the parent HFO (Table S2) and is consistent with prior GC-FID analyses [30] and current understanding of oil weathering. respectively.…”

Section: Multivariate Statisticssupporting

confidence: 91%

“…Compound ratios indicative of evaporation/water washing, biodegradation, and photodegradation suggested that these processes were responsible for observed changes. [30] More recent work by Incardona et al, (2011) found unexpectedly high toxicity of this spill and suggested that this was the result of products formed through photochemical alteration of the non-GC amenable portion of oil. [31] The present work expands on our previous work by investigating weathering of the non-GC amenable fraction of this oil.…”

Section: Introductionmentioning

confidence: 88%

“…[30] The samples analyzed in the present study were collected from Shorebird Park (Berkeley, CA; Figure S1) where significant and variable degrees of biodegradation and evaporation/water washing, and no significant photodegradation trends were observed. [30] Oil was scraped with a stainless steel spatula from coastal rocks and stored in combusted Al-foil envelopes. Samples were transported to Woods Hole, MA, frozen (-20˚ C) and subsequently shipped to the National High Magnetic Field Laboratory in Tallahassee, FL.…”

Section: Sample Collection Oil Residues Were Scraped From a Rocky Shmentioning

confidence: 99%

“…[30] Compound class evolution was used to estimate biodegradation indices as done by Peters and Moldowen. [33,34] Biomarker ratios were also used for source identification (Table S1).…”

Section: Biodegradation Indices Biomarkers Were Measured By Gcgc-fimentioning

confidence: 99%

“…Much of this increase is likely due to the loss of lower-molecular-weight saturate and aromatic compounds; [30] however, polar compounds may be formed from LMW saturate and aromatic fractions, too. Previous work proposed conversion of aromatic compounds 172 to more polar species through photo-and biodegradation.…”

Section: Multivariate Statisticsmentioning

confidence: 99%

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Comprehensive study of a heavy fuel oil spill : modeling and analytical approaches to understanding environmental weathering

Lemkau¹

2012

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Driven by increasingly heavy oil reserves and more efficient refining technologies, use of heavy fuel oils for power generation is rising. Unlike other refined products and crude oils, a large portion of these heavy oils is undetectable using the traditional gas chromatography-based techniques on which oil spill science has been based. In the current study, samples collected after the 2007 M/V Cosco Busan heavy fuel oil spill (San Francisco, CA) were analyzed using gas chromatography (GC)-based techniques, numerical modeling and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) to examine natural weathering of the oil over a one and a half year period. Traditional GC techniques detected variable evidence of evaporation/ dissolution, biodegradation and photodegradation. Petroleum hydrocarbon compounds smaller than ~n-C 16 were rapidly lost due to evaporation and dissolution. Significant biodegradation was not detected until one month post spill while photodegradation was only observed at one field site. To further examine the processes of evaporation and dissolution, samples were analyzed with comprehensive two-dimensional GC (GC×GC) and a physiochemical model developed to approximate quantitative apportionment of compounds lost to the atmosphere and water. Model results suggest temperature is the primary control of evaporation. Finally, to examine the prominent non-GC amenable component of the oil, samples were analyzed with FT-ICR MS. Results showed expected clustering of samples, with those samples collected sooner after the spill having the most compositional similarity to the unweathered oil. Analysis of dominant heteroatom classes within the oil showed losses of high molecular weight species and the formation of stable core structures with time. These results highlight the susceptibility to weathering of these higher molecular weight components, previously believed to be recalcitrant in the environment. Research findings indicate that environmental weathering results in removal or alteration of larger alkylated compounds as well as loss of lower molecular weight species through evaporation/dissolution, biodegradation and photodegradation, with a resultant fraction of stable compounds likely to remain in the environment years after the spill. This research demonstrates the advantages of combining multiple analytical and modeling approaches for a fuller understanding of oil spill chemistry. AcknowledgementsThere are so many people that have helped me -personally and scientifically-along this journey. First I would like to thank my advisor Chris Reddy. You have been an amazing mentor, pushing me, allowing me to grow as a scientist and never wavering in your encouragement and support. I appreciate your willingness to explore the world outside of science through your public outreach. Although you may be a scientific workaholic (a topic of debate), you have always been tolerant of those of us who are less so, and I thank you for that. I appreciate the many people and experiences you...

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Section: Multivariate Statisticssupporting

confidence: 91%

Section: Introductionmentioning

confidence: 88%

Section: Sample Collection Oil Residues Were Scraped From a Rocky Shmentioning

confidence: 99%

“…[30] Compound class evolution was used to estimate biodegradation indices as done by Peters and Moldowen. [33,34] Biomarker ratios were also used for source identification (Table S1).…”

Section: Biodegradation Indices Biomarkers Were Measured By Gcgc-fimentioning

confidence: 99%

Section: Multivariate Statisticsmentioning

confidence: 99%

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