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Record W4281849594 · doi:10.3389/fenvs.2022.908984

Modeling Emulsification Influence on Oil Properties and Fate to Inform Effective Spill Response

2022· article· en· W4281849594 on OpenAlexfundno aff
Deborah French-McCay, Matthew A. Frediani, Melissa D. Gloekler

Bibliographic record

VenueFrontiers in Environmental Science · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsnot available
FundersFisheries and Oceans CanadaNanjing Institute of TechnologyChevron
KeywordsEmulsionViscosityOil dropletDissolutionEvaporationChemical engineeringChemistryPetroleum engineeringEnvironmental scienceMaterials scienceGeologyOrganic chemistryComposite materialThermodynamics

Abstract

fetched live from OpenAlex

Water-in-oil emulsification affects spilled oil fate and exposure, as well as the effectiveness of oil spill response options, via changes in oil viscosity. While oil weathering processes such as evaporation, dissolution, photo-oxidation, and biodegradation increase oil viscosity about 10-fold, incorporation of water droplets into floating oil can increase viscosity by another order of magnitude. The objective of this study was to evaluate how changes in viscosity by oil type, with weathering, and with emulsification affect oil fate. Oil spill modeling analyses demonstrated that the increase in viscosity from emulsification prolonged floating oil exposure by preventing the oil from dispersing into the water column. Persistent emulsified oils are more likely to come ashore than low viscosity oils that readily disperse. Through a rapid increase in viscosity, emulsification restricted entrainment and slowed evaporation. Water column exposure to dissolved concentrations increased with lower viscosity oils. Thus, the ability to emulsify, and at what weathered state, are important predictors of oil fate. Oil viscosity is an important consideration for choosing response alternatives as it controls effectiveness of mechanical removal, in-situ -burning and surface-active chemicals. Therefore, understanding and quantification of oil emulsification are research priorities. The most influential model input determining emulsification and the emulsion’s viscosity is its maximum water content, as it controls the ultimate viscosity of the emulsion. Viscosities were also influenced by the volatile content and initial viscosity of the oil. Algorithms quantifying emulsion stability under field conditions have not been developed, so emulsions were assumed stable over the 30-day simulations. Changes in emulsion stability over time would affect oil properties and so floating oil and shoreline exposures, as well as response effectiveness. However, water column exposures to dissolved concentrations are determined within a few days of oil release, and as such would not be affected by differences in long-term stabilities of the emulsions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score0.600

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.198
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations10
Published2022
Admission routes1
Has abstractyes

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