An Integrated Subsurface Modeling and Risk Assessment Approach for Managing the Petroleum-Contaminated Sites
Bibliographic record
Abstract
Soil and groundwater contamination can lead to a variety of impacts and risks to the communities. Identifications of management schemes with sound environmental and socio-economic efficiencies is desired. In fact, before any decisions regarding site remediation actions can be made, three major questions may have to be answered. They include "What happened underground, and what will happen in the future under the given remediation scenarios?," "Are there specific risks on the surrounding community?" and "What remediation alternatives are suitable for the site?" In this study, an integrated subsurface modeling and risk assessment method for petroleum-contaminated site management is proposed. It incorporates multi-phase flow multi-component transport modeling and ELCR-based human health risk assessment into a general framework. The proposed method is applied to a case study within a western Canada context for identifying effective management schemes with improved environmental and socio-economic efficiencies. Given conditions at the study site, six remediation alternatives based on combinations of several technologies are recommended, with the provision of analyses for equipment/manpower requirements, system designs, operations, efficiencies, and costs. These alternatives can be categorized into two groups: hybrid ex situ and in situ remediation approaches, and integrated in situ remediation approaches. This study is a new attempt that integrates issues of subsurface-contamination simulation, risk assessment, and site remediation for a real-world problem within a general research framework. The research outputs are directly useful for the industry to gain insight of the site and to make decisions of the relevant remediation actions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".