Understanding hydrocarbon fate and transport in peat soils using column experiments
Bibliographic record
Abstract
Increasing hydrocarbon resource developments in and around peatlands impose risks of petroleum hydrocarbon spills on these important wetland landscapes. Despite the potential severity of consequences, there is a big gap of knowledge on parameter values controlling liquid hydrocarbons’ redistributions in peat soil after a spill. Complete excavation of contaminated peat soil is a common practice in contaminated sites, but destroys wetland function, and contributes nothing to the understanding of the problem. To partially fill this knowledge gap and to examine potential remediation strategies that are less destructive, we examined the fate, transport, and degradation of petroleum hydrocarbon non-aqueous phase liquids (NAPLs) in peat soils using a series of column tests on intact peat monoliths. Three-phase flow experiments with numerical simulations provided values of multiphase flow parameters that control NAPL redistribution in a variety of peat soils. We observed that water table fluctuations reduced residual NAPL saturation from 8.1-11.3% to 7.7-9.5%; increased headspace concentrations of n-C8 and n-C12 an average 163.7% and 13.4%, due to volatilization. Results also illustrated that water table dynamics promoted growth (from 104 CFU/gram to 106 CFU/gram peat) of specialized microbial communities in NAPL polluted peat columns. These results suggest that water table fluctuation can be a suitable tool for physical and microbial NAPL removal in peat soils, and for the first time provide evidence for it. We also observed a high ratio of Proteobacteria to Acidobacteria in the NAPL contaminated zone, which can be linked to the restoration success for a NAPL polluted peatland. The results could help environmental scientists in forecasting the behavior of spilled non-aqueous phase liquids (NAPLs) in peatland.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".