Modelling PAH Degradation in Contaminated Soils in Canada using a Modified Process‐Based Model (DNDC)
Bibliographic record
Abstract
Core Ideas A novel process‐based model for degradation of soil PAHs is proposed. Model is based on dynamic interactions between water, soil, vegetation and climate. Model effectively simulates biogeochemical processes of PAH degradation in soils. Simulated results agree well with data from remediation sites in Alberta. Polycyclic aromatic hydrocarbons (PAHs) are persistent pollutants of concern. A process‐based model of the PAH degradation can improve our understanding of ecological drivers and processes. In this paper, a process‐based biogeochemistry model, DeNitrification‐DeComposition (DNDC) is modified to simulate the dynamics of PAHs degradation in soils at abandoned oil and gas well sites. This new version of DNDC‐Organic Pollutants, called DNDC‐OP, coupled the rates of PAH degradation with dynamics of soil, vegetation and climate, such as soil moisture and temperature. The model was parameterized and validated against datasets of four soil PAHs: pyrene, fluorene, chrysene and anthracene, at three different abandoned oil and gas well site locations in Alberta, Canada. The sensitivity of the parameters was analyzed and tested. The simulated results were in good agreement with the measured data with a coefficient of determination ( R 2 ) of 70 to 97%, and the root mean square error (RMSE) of 4.5 to 9.1 at all three sites. We also evaluated the influence of environmental factors, such as soil temperature and moisture, on the degradation of PAHs. An increased degradation of all four PAHs occurred with increasing soil moisture content. An increase of soil temperature from 10 to 20°C and subsequently to 25°C resulted in a decreased appearance of all four PAHs from the three well sites. The result shows that this model can be used as a tool for evaluating PAH degradation for effective reclamation strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".