Applying Decision Analysis to Site Remediation
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,016 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».