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Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Characterization of Athabasca Oil Sand Process-Affected Waters Incubated in the Presence of Wetland Plants

2017· article· en· W2569122134 on OpenAlexafffund
Chukwuemeka Ajaero, Dena W. McMartin, Kerry M. Peru, Jon Bailey, Monique Haakensen, Vanessa Friesen, Rachel Martz, Sarah Hughes, Christine Brown, Huan Chen, Amy M. McKenna, Yuri Corilo, John V. Headley

Bibliographic record

VenueEnergy & Fuels · 2017
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsShell (Canada)University of ReginaContango Strategies (Canada)Environment and Climate Change Canada
FundersNatural Resources CanadaFlorida State UniversityNational Science Foundation
KeywordsOil sandsExtraction (chemistry)WetlandNaphthenic acidEnvironmental scienceBiodegradationTypha angustifoliaEnvironmental chemistryWaxLeachateChemistryAquatic plantPulp and paper industryChromatographyAsphaltMacrophyteEcologyMaterials scienceBiology

Abstract

fetched live from OpenAlex

Naphthenic acid fraction compounds (NAFCs) are naturally present in the oil sand. These compounds become integrated into the oil sands process-affected water (OSPW) during the bitumen extraction process. NAFCs have been identified as causing toxicity in the OSPW to aquatic organisms. Water treatment technologies that are largely passive, such as constructed treatment wetlands, are a sought-after technology for the degradation of NAFCs in aquatic environments, partly because of their low energy intensity. However, it can be challenging to accurately assess the performance regarding decreased NAFC concentration and biodegradation characteristics in water samples that have been exposed to such systems. This is due to interferences of biological products such as fatty acids and humic-like materials, which may give false-positive information on NAFCs estimation with conventional analytical sample cleanup methods such as liquid–liquid extraction (LLE). It is recognized that this same issue exists when attempting to characterize NAFCs in natural wetlands for environmental monitoring purposes and, therefore, an analytical method that can remove background interferences in water samples is desirable on several fronts. Studies were thus conducted to develop and compare methods for NAFC isolation in an experimental wetland setting. A controlled greenhouse experiment was conducted with sedge ( Carex aquatilis ), bulrush ( Schoenoplectus acutus ), and cattail ( Typha latifolia ) grown in OSPW. Two methods—the Isolute Biotage ENV+ SPE method and a new weak anion exchange (WAX SPE)—were assessed for their ability to isolate, clean up, and concentrate NAFCs in OSPW and municipal tap water (control) that were exposed to samples of plants and associated microbes. Negative-ion-electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI-FT-ICR-MS) data revealed that WAX SPE method has better relative enhancement (5%–50%) of O 2 classes in OSPW exposed to wetland plants, compared to ENV+ SPE method. The WAX SPE method is a good candidate for the isolation of organic compounds in complex environmental matrices and supports the development of analytical protocols for isolation and characterization of NAFCs. Compound classes from negative-ion ESI-FT-ICR-MS data were further probed using principal component analysis (PCA) to evaluate the NAFCs that are potential indicators of efficiency of engineered wetlands for monitoring in future wetland studies. Given the PCA results, future wetland NAFC degradation investigations should target O 2 classes for detailed evaluation of the performance of treatment systems, or measurement of the fate and distributions of NAFCs in natural wetlands exposed to OSPW.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.496

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.228
Teacher spread0.220 · 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 designBench or experimental
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

Citations31
Published2017
Admission routes2
Has abstractyes

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