Simulation and Assessment of Subsurface Contamination Caused by Spill and Leakage of Petroleum Products?A Multiphase, Multicomponent Modelling Approach
Notice bibliographique
Résumé
Abstract In this study, an integrated approach for environmental risk assessment of subsurface contamination is proposed. This integration is based on a Monte Carlo method for simulating pollutant transport in subsurface and the consideration of several scenarios for risk assessment guidelines. The method can reflect uncertainties associated with the simulation results and the environmental guidelines, as well as the resulting risks of human health injury. In detail, this research considers: (1) the fate and transport of the pollutant in heterogeneous porous media under uncertainty, (2) distribution of pollutant concentrations under natural attenuation, (3) relationships between drinking water standards and health risk guidelines, and (4) probabilistic quantification of health injury risks. This method is applied to a site contaminated by leaking underground storage tanks. The results indicate that reasonable outputs have been generated. They are useful for clarifying potential health effects when the groundwater is withdrawn for domestic uses, as well as providing support for the related risk-management and site-remediation decisions. Introduction Pollution problems associated with a number of processes in the petroleum industry have generated significant environmental concerns(1). Among them, leakage, spillage and failure of storage tanks and transport systems often lead to contamination in subsurface soil and groundwater. This will then cause impacts on public health through oral ingestion, dermal contact, inhalation, or food chain exposure pathways. Therefore, the related communities and industries are calling for systematic study on the environmental risks derived from these contamination problems. The general process of dealing with a petroleum-contaminated site involves the following steps: (1) identifying the pollution sources, (2) uncertainty analysis, (3) simulation of the flow, fate nd transport of the pollutants in subsurface, (4) assessment of the impacts and risks on environment, ecosystem, and public health, and (5) presentation of the entire process as well as its outputs(1). mong these steps, identification of the pollution sources needs to be done by site investigation on all aspects of hydrological and contamination conditions. Uncertainty analysis considers all kinds of uncertain information associated with sampling data, input parameters, and the risk assessment process. The simulation module considers numerical prediction of the physical, chemical, and biological behaviours of the pollutant subsurface. Previously, studies relating to subsurface modelling, uncertainty analysis, and risk assessment have been reported in a large body of literature. In the modelling aspect, Abriola and Pinder proposed a comprehensive approach to simulate simultaneous transport of a chemical contaminant in three physical forms: nonaqueous phase, solute component of a water phase, and mobile raction of a gas phase(3, 4). Kaluarachchi and Parker formulated a finite element model for simulating multi-phase flow of organic contaminants(5). Katyal et al. used a two-dimensional finite element program to simulate multi-phase and multi-component transport of contaminants in subsurface with an assumption of the first order decay(6). In general, most of the recent modelling efforts are based on multi-phase, multi-component analyses, which can effectively reflect complexities in subsurface systems. However, extensive applications of the developed models to practical problems were limited, due to the ineffectiveness
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».