Modeling long-term uptake and re-volatilization of semi-volatile organic compounds (SVOCs) across the soil–atmosphere interface
Bibliographic record
Abstract
Soil-atmosphere exchange is important for the environmental fate and atmospheric transport of many semi-volatile organic compounds (SVOCs). This study focuses on modeling the vapor phase exchange of semi-volatile hydrophobic organic pollutants between soil and the atmosphere using the multicomponent reactive transport code MIN3P. MIN3P is typically applied to simulate aqueous and vapor phase transport and reaction processes in the subsurface. We extended the code to also include an atmospheric boundary layer where eddy diffusion takes place. The relevant processes and parameters affecting soil-atmosphere exchange were investigated in several 1-D model scenarios and at various time scales (from years to centuries). Phenanthrene was chosen as a model compound, but results apply for other hydrophobic organic compounds as well. Gaseous phenanthrene was assumed to be constantly supplied to the system during a pollution period and a subsequent regulation period (with a 50% decline in the emission rate). Our results indicate that long-term soil-atmosphere exchange of phenanthrene is controlled by the soil compartment - re-volatilization thus depends on soil properties. A sensitivity analysis showed that accumulation and transport in soils in the short term is dominated by diffusion, whereas in the long term groundwater recharge and biodegradation become relevant. As expected, sorption causes retardation and slows down transport and biodegradation. If atmospheric concentration is reduced (e.g. after environmental regulations), re-volatilization from soil to the atmosphere occurs only for a relatively short time period. Therefore, the model results demonstrate that soils generally are sinks for atmospheric pollutants. The atmospheric boundary layer is only relevant for time scales of less than one month. The extended MIN3P code can also be applied to simulate fluctuating concentrations in the atmosphere, for instance due to temperature changes in the topsoil.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".