Evaluation of Total Petroleum Hydrocarbons (TPH) Measurement Methods for Assessing Oil Contamination in Soil
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Bibliographic record
Abstract
Abstract The most commonly used total petroleum hydrocarbons (TPH) analysis method measures petroleum hydrocarbon concentrations in soil by carbon range that can be detected by gas chromatography/flame ionization detection (GC/FID). Different cleanup procedures have been performed for removing some naturally occurring organics from petrogenic hydrocarbons prior to GC/FID analysis. To evaluate the different pre-treatment methods, more than 60 samples (including background soil and plant samples, as well as oil contaminated soil samples) were sampled from 2008 to 2010 in Canada. TPH values without cleanup (TPH-T), with column cleanup (TPH-F 3) and with in-situ cleanup (TPH-F) were compared to evaluate the effects of different pre-treatment methods on the TPH analysis values. Different total solvent extractable materials (TSEM) loading amounts were applied for in-situ silica gel cleanup method to evaluate the effect of the TSEM loading amount on the measured TPH-F values. The column cleanup method was evaluated by comparing the representative polar biogenic organic compounds (BOCs) and TPH in polar fraction (designated as TPH-F 4). Qualitatively, cleanup procedures removed most of the BOCs for background samples with high content of BOCs, but the GC/FID chromatograms did not show significant alteration for samples with heavy oil contamination. The quantified TPH-T, TPH-F 3 and TPH-F showed good agreement for oil contaminated samples, even though the loading dosage exceeded the maximum TSEM limits of silica gel (16.7 mg TSEM on per gram silica gel). For background samples, the measured TPH values were ranked as: TPH-T > TPH-F > TPH-F 3 when the TSEM loading amount exceeded 16.7 mg/g of silica gel, but no obvious difference was observed when the TSEM loading amount was less than 16.7 mg/g. Therefore, the TSEM loading capacity played an important role for the cleanup of background samples. The comparison of the measured TPH-T and TPH-F 3 obtained from background soil and plant samples did not show an obvious relationship, thus TPH values of soil samples can not be replaced by those from plants grown in the soil sampled area. The evaluation of column cleanup method showed that this method can effectively remove most of the BOCs, but the removal of the hydrocarbons which contribute to the measured TPH-F 3 can be negligible. Keywords: total petroleum hydrocarbons, column cleanup, in-situ cleanupwithout cleanupmeasurement method[Supplementary materials are available for this article. Go to publisher's online edition of Environmental Forensics to view the free supplementary files.] Acknowledgements This work was funded and supported by the Program of Energy Research and Development (PERD), the Scientific Fund from State Ethnic Affairs Commission of China (12ZNZ003), the Natural Science Foundation of South-Central University for Nationalities (YCZZ11006), the Fundamental Research Funds for the Central Universities, South-Central University for Nationalities (CZZ11006), and the Ministry of Science and Technology of China (No. 2012ZX07503-003-002). Thanks to the ALS laboratories (Waterloo, Canada) for the sample extraction support.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it