Applying Decision Analysis to Site Remediation
Bibliographic record
Abstract
Abstract Monitored natural attenuation (MNA) is rapidly being incorporated into contaminated site management plans. The underlying premise behind MNA is that contaminant mass or concentrations are reduced by naturally occurring processes, and the contaminant behaviour is sufficiently well understood to be predictable with an accepted level of confidence. An MNA strategy requires characterizing the contaminant mass, identifying potential environmental receptors, and defining acceptable impacts. In contrast, active remediation strategies focus on removing, controlling or containing the contaminant mass through engineered intervention. Both approaches have inherent uncertainties that can only be reduced or bounded by extra effort, that is, increased cost. In this paper, we use a decision analysis process to compare the application of monitored natural attenuation against other site remediation strategies for a common upstream oil and gas site contamination scenario. Introduction Natural attenuation refers to the reduction of a contaminant mass or concentration by a series of naturally occurring physical, chemical, and biological processes. For petroleum hydrocarbons, biodegradation is the only process that destroys contaminant mass. The other processes represent either spreading the mass out over a larger area, fixation, or some form of phase change. Monitored Natural Attenuation (MNA) refers a strategy whereby site data are collected over regular intervals to demonstrate that natural attenuation processes reducing the level of contamination in an acceptable time frame. MNA represents an alternative approach to site remediation that could be used either as stand-alone strategy, or in combination with conventional engineered remediation techniques. MNA is recommended only after having an appropriate and detailed understanding of site conditions including the contaminant(s) and their distribution, transport behaviour, and attenuation characteristics. Multiple lines of evidence are needed to assess natural attenuation (refer to USEPA, 1999, ASTM, 1998 for a more detailed description). A research consortium, CORONA (Consortium for Research on Natural Attenuation), was formed at the University of Alberta under Principal Investigator, Dr. Kevin Biggar. Thisprogram involves a variety of office-, laboratory- and fieldbased investigations to examine natural attenuation of hydrocarbon contamination associated with upstream oil and gas facilities. Three research sites were selected for CORONA. This program used Site 1 as an example case to illustrate the power of the decision analysis process, and insight gained regarding selection of possible remediation approaches. Site Condition Summary The site is an actively producing facility located in west central Alberta. Subsurface contamination is related to hydrocarbon migration from a former flare pit, which had been excavated before the CORONA program started. Excavation was limited to the original flare pit area and subsurface contamination remains. The site is remote, with no nearby active groundwater users, water bodies, or human receptors in the immediate area. Wildlife presence is readily observed; however, there is no evidence of surface impacts related to contaminated groundwater. The soil generally comprises sand, silt and clay layers, with sand layers concentrated near the former pit. Topography slopes southward from the former flare pit area, and is reflected in the shallow groundwater flow pattern.
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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 | 0.016 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".