Field, Laboratory and Modeling Evidence for Strong Attenuation of a Cr(VI) Plume in a Mudstone Aquifer Due to Matrix Diffusion and Reaction Processes
Bibliographic record
Abstract
This study uses a combination of conventional and high resolution field and laboratory methods to investigate processes causing attenuation of a hexavalent chromium (Cr(VI)) plume in sedimentary bedrock at a former industrial facility. Groundwater plume Cr(VI) concentrations decline by more than three orders of magnitude over a 900 m distance down gradient from the site. Internal plume concentrations generally exhibit stable to declining trends due to diffusive and reactive transport in the low permeability matrix as fluxes from the contamination source dissipate due to natural depletion processes and active remediation efforts. The strong attenuation is attributed to diffusion from mobile groundwater in fractures to immobile porewater in the rock matrix, and reactions causing transformation of aqueous Cr(VI) to low-solubility Cr(III) precipitates, confirmed by high spatial resolution rock matrix contaminant concentrations and comparisons with groundwater concentrations from multi-level sampling within the plume. Field characterization data for the fracture network and matrix properties were used to inform 2-D discrete-fracture matrix (DFM) numerical model simulations that quantify attenuation due to diffusion and reaction processes, which show consistency with field datasets, and provide insights regarding future plume conditions. The combination of field, laboratory and modeling evidence demonstrates effects of matrix diffusion and reaction processes causing strong attenuation of a Cr(VI) plume in a sedimentary bedrock aquifer. This approach has important implications for characterization of sites with Cr(VI) contamination for improved site conceptual models and remediation decision-making.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".