Coupled Vadose Zone and Atmospheric Surface‐Layer Transport of Carbon Dioxide from Geologic Carbon Sequestration Sites
Bibliographic record
Abstract
Geologic CO 2 sequestration is being considered as a way to offset fossil fuel–related CO 2 emissions to reduce the rate of increase of atmospheric CO 2 concentrations. The accumulation of vast quantities of injected CO 2 in geologic sequestration sites may entail health and environmental risks from potential leakage and seepage of CO 2 into the near‐surface environment. We are developing and applying a coupled subsurface and atmospheric surface‐layer modeling capability built within the framework of the integral finite difference reservoir simulator TOUGH2. The overall purpose of the modeling studies is to predict CO 2 concentration distributions under a variety of seepage scenarios and geologic, hydrologic, and atmospheric conditions. These concentration distributions will provide the basis for determining aboveground and near‐surface instrumentation needs for CO 2 sequestration monitoring and verification, as well as for assessing health, safety, and environmental risks. A key feature of CO 2 is its large density (ρ = 1.8 kg m −3 ) relative to air (ρ = 1.2 kg m −3 ), a property that may allow small leaks to cause concentrations in air above the occupational exposure limit of 4% in low‐lying and enclosed areas such as valleys and basements where dilution rates are low. The approach we take to coupled modeling involves development of T2CA, a TOUGH2 module for modeling the multicomponent transport of water, brine, CO 2 , gas tracer, and air in the subsurface. For the atmospheric surface‐layer advection and dispersion, we use a logarithmic vertical velocity profile to specify constant time‐averaged ambient winds, and atmospheric dispersion approaches to model mixing due to eddies and turbulence. Initial simulations with the coupled model suggest that atmospheric dispersion quickly dilutes diffuse CO 2 seepage fluxes to negligible concentrations, and that rainfall infiltration can cause CO 2 to return to the subsurface as a dissolved component in infiltrating rainwater.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".