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Record W1984424652 · doi:10.1039/c2em30339f

Fingerprinting of petroleum hydrocarbons (PHC) and other biogenic organic compounds (BOC) in oil-contaminated and background soil samples

2012· article· en· W1984424652 on OpenAlex
Zhendi Wang, Chun Yang, Zeyu Yang, Bruce P. Hollebone, Carl E. Brown, Mike Landriault, Juan Sun, Stephen M. Mudge, F. Kelly-Hooper, D. George Dixon

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueJournal of Environmental Monitoring · 2012
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of WaterlooEnvironment and Climate Change Canada
Fundersnot available
KeywordsContaminationPetroleumEnvironmental remediationSoil waterEnvironmental chemistryEnvironmental scienceSoil testSoil contaminationPetroleum industryChemistryEnvironmental engineeringSoil scienceEcology

Abstract

fetched live from OpenAlex

Total petroleum hydrocarbons (TPH) or petroleum hydrocarbons (PHC) are one of the most widespread soil contaminants in Canada, the United States and many other countries worldwide. Clean-up of PHC-contaminated soils costs the Canadian economy hundreds of millions of dollars annually. In Canada, most PHC-contaminated site evaluations are based on the methods developed by the Canadian Council of the Ministers of the Environment (CCME). However, the CCME method does not differentiate PHC from BOC (the naturally occurring biogenic organic compounds), which are co-extracted with petroleum hydrocarbons in soil samples. Consequently, this could lead to overestimation of PHC levels in soil samples. In some cases, biogenic interferences can even exceed regulatory levels (300 μg g(-1) for coarse soils and 1300 μg g(-1) for fine soils for Fraction 3, C(16)-C(34) range, in the CCME Soil Quality Level). Resulting false exceedances can trigger unnecessary and costly cleanup or remediation measures. Therefore, it is critically important to develop new protocols to characterize and quantitatively differentiate PHC and BOC in contaminated soils. The ultimate objective of this PERD (Program of Energy Research and Development) project is to correct the misconception that all detectable hydrocarbons should be regulated as toxic petroleum hydrocarbons. During 2009-2010, soil and plant samples were collected from over forty oil-contaminated and paired background sites in various provinces. The silica gel column cleanup procedure was applied to effectively remove all target BOC from the oil-contaminated sample extracts. Furthermore, a reliable GC-MS method in combination with the derivatization technique, developed in this laboratory, was used for identification and characterization of various biogenic sterols and other major biogenic compounds in these oil-contaminated samples. Both PHC and BOC in these samples were quantitatively determined. This paper reports the characterization results of this set of 21 samples. In general, the presence of petroleum-characteristic alkylated PAH homologues and biomarkers can be used as unambiguous indicators of the contamination of oil and petroleum product hydrocarbons; while the absence of petroleum-characteristic alkylated PAH homologues and biomarkers and the presence of abundant BOC can be used as unambiguous indicators of the predominance of natural organic compounds in soil samples.

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.553

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 science0.0000.000
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 head0.015
GPT teacher head0.213
Teacher spread0.198 · 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