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Enregistrement W2409907720

Design and optimization of surfactant based enhanced remediation of bunker C fuel oil contaminated soil

2015· dissertation· en· W2409907720 sur OpenAlexfundaboutno aff

Notice bibliographique

RevueMemorial University Research Repository (Memorial University) · 2015
Typedissertation
Langueen
DomaineEnvironmental Science
ThématiqueMicrobial bioremediation and biosurfactants
Établissements canadiensnon disponible
Organismes subventionnairesMitacs
Mots-clésEnvironmental remediationEnvironmental scienceSoil contaminationWaste managementContaminationRemedial actionUnderground storage tankHazardous wastePetroleumPollutionFuel oilBrownfieldEnvironmental engineeringEngineeringSoil waterStorage tankCivil engineeringChemistry
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Soil pollution, particularly by petroleum compounds is a very important global issue. It is typically a result of improper storage and disposal, historic careless spills, accidental spills, and leaks from tanks. This environmental damage can result in wide spread groundwater and surface water contamination, and limit soil use for agricultural purposes. There are a large number of brownfield sites across Canada. These are sites that have been previously used for industrial and commercial activities and have been contaminated with hazardous wastes. Newfoundland and Labrador (NL) in particular, has numerous sites due to a legacy of fuel oil use for power generation at abandoned, old and emerging mining fields. Data from the federal contaminated site inventory indicate that on federal owned sites in NL, there are over 3000 m³ of oil-contaminated soil. It has therefore become necessary to develop remediation technologies that are economically and technically feasible, environmentally friendly, fast, and applicable in a wide range of physical settings. A wide range of technologies have been developed over the past few decades for the remediation of contaminated sites. Some of these processes have been found to be successful in removing only a specific group of contaminants from the soil. As a result, combinations of different approaches are usually adopted for more effective remediation thereby leading to more expensive remedial operations. Surfactant-enhanced remediation technology, otherwise known as soil washing has proven to be an effective method to remove a mixture of contaminants such as heavy metals, petroleum compounds, volatile organic compounds, pesticides and herbicides as well as other hazardous and non-degradable substances from contaminated soils. Surfactant remediation relies on the ability of surfactant formulations to remove both organic and inorganic contaminants from soil and sediments by desorbing them from the solid to liquid phase. The technology removes contaminants via two mechanisms; one occurs below the Critical micelle concentration (CMC) of the surfactants known as mobilization mechanism, and the other above the CMC known as solubilization mechanism. However, this technology has not been widely reported for the remediation of Bunker C fuel oil, a complex and recalcitrant petroleum compound that is persistent in the environment and extensively reported to be resistant to chemical and biological degradation, and therefore difficult to remediate. This research presents an assessment of the performance of soil washing technology for the remediation of Bunker C contaminated oil from weathered contaminated soil using patented surfactant formulations. Weathered soil are known to be more difficult to remediate than freshly contaminated soil hence, the study used weathered soil in order to be certain of the effects of soil washing on Bunker C removal. Specific research objectives include: (1) determining the micellar properties (Surface Tension and CMC) of the patented surfactants, (2) development of a reliable analytical method for the analysis of (heavy) petroleum hydrocarbons in soil, (3) evaluation of the parameters that govern the implementation of soil washing and an assessment of their effects on the washing of weathered Bunker C oil from contaminated soil at surfactants concentrations below the CMC (mobilization mechanism) using the multivariate experimental and statistical approach, (4) determination of the optimum conditions (optimization) for the removal of Bunker C oil with the tested surfactants. The research, in the course of analysing the Total Petroleum Hydrocarbon in the soil using the Canada-Wide Standard (CWS) for Petroleum Hydrocarbon in soil is used for the analysis of petroleum hydrocarbon compounds found considerable variability and inconsistencies in the results. This could be due, in part, to the different gas chromatography (GC) conditions, soil properties, as well as and other steps involved in the method. These differences impacts the determination of the effectiveness of remediation at hydrocarbon-contaminated sites. The thesis therefore, developed validated the analytical method for the quantitative analysis of TPH in (contaminated) soil. The newly developed method was successfully applied for a faster and more reliable analysis of TPH of Bunker C oil in contaminated soil. It is expected that this research will lay a road map for further studies on the application, optimization and development of a treatment system protocol for the use of soil washing for Bunker C remediation. It should be noted that although Bunker C is the contaminant, the hydrocarbon composition of Bunker C makes it a good surrogate for other heavy oils.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,009
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,021
Tête enseignante GPT0,238
Écart entre enseignants0,217 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2015
Routes d'admission2
Résumé présentoui

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