Predicting the Environmental Impact of CO<sub>2</sub> Leakage on Groundwater Quality in Onshore Regions: Integrating Geochemical Modeling with Machine Learning Approaches
Bibliographic record
Abstract
A critical factor in gaining public and regulatory acceptance of carbon sequestration is the assurance that groundwater resources will be protected. Concern have been raised about the potential for CO2 to leak from abandoned oil wells and migrate into groundwater zones, posing risks to water quality such as freshwater acidification and the potential mobilization of heavy metals and other trace element through mineral dissolution. While extensive research on hydrocarbon pollution in Ogoni land abandoned oil well in Nigeria, has been conducted over decades. Studies simulating pH variation and carbonate equilibrium under CO2 influence remain rare. Here, the PHREEQC geochemical modeling software was used to study carbonate equilibrium dynamics in the groundwater of the abandoned oil well sites in Ogoni land, Nigeria. Initial groundwater chemistry was simulated using baseline data from the literature, including pH, alkalinity, and major ion concentrations. The study modeled varying pH levels (5.0 to 8.5) and CO2 partial pressures (10-1 to 10-3 atm) to evaluate changes in mineral stability, ion mobilization, and pH buffering capacity. This unbiased study analysis explored the dissolution and precipitation processes of carbonate minerals and their implications for groundwater quality in contaminated regions. Findings indicate that CO2 leakage significantly lowers groundwater pH, enhances bicarbonate production, and mobilizes calcium and magnesium ions, potentially degrading water quality.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".