Partitioning of inorganic contaminants between fluid fine tailings and cap water under end pit lake scenario: Biological, Chemical and Mineralogical processes
Notice bibliographique
Résumé
Fluid fine tailings (FFT) are generated during bitumen extraction from surface mined oil sands ore (in Alberta, Canada) and comprised of oil sands process-affected water (OSPW), fine particles, unrecovered bitumen and residual diluent. For reclaiming huge volumes of FFT, a viable remediation option is to place FFT in open pit covered by a mix layer of OSPW and fresh water to form an end pit lake (EPL). A potential concern is the flux of constituents of concern (COCs) from underlying FFT to overlying cap water that could affect the quality and sustainability of EPLs. In this research, chemical, mineralogical and microbiological approaches were used to investigate how biogeochemical processes in underlying FFT would affect COCs transport to cap water. For this study, 10 L columns were filled with FFT (7 L) and cap water (1.4L), sealed anaerobically and incubated at room temperature in the dark. Labile hydrocarbons (a mixture of short chain n-and iso-alkanes and monoaromatics compound representing extraction diluent) endogenous to FFT were added to FFT (amended columns) to accelerate methanogenesis for the enhancement of biogeochemical processes in FFT. Some hydrocarbon-amended columns also received nutrients such as nitrogen (N) and phosphorus (P) at C: N: P ratio of 100:10:1 for optimal microbial growth, whereas others that did not receive any amendment as served as control (unamended) columns. The results demonstrated that hydrocarbon addition increased methane (CH4) and carbon dioxide (CO2) production in the FFT and N addition exhibited incremental effect on methanogenesis and all other subsequent biogeochemical processes. Molecular analysis (16S rRNA gene) revealed that Syntrophaceae and Peptococcaceae (Bacteria) syntrophically worked with acetoclastic (Methanosaetaceae) and hydrogenotrophic (Methanoregulaceae) methanogens (Archaea) to metabolize hydrocarbons into CH4 and CO2 under methanogenic conditions.Methanogenesis in amended columns increased dewatering and consolidation of FFT by altering porewater chemistry and transforming iron (Fe) minerals. Biogenic CO2 productioniiidecreased pH that dissolved carbonate minerals in FFT and increased concentrations of Ca2+, Mg2+, HCO3- in the porewater. Some trace metals such as strontium (Sr) and barium (Ba) also increased significantly in the porewater of amended columns. These soluble ions/metals were transported to cap water via porewater expression and CH4 ebullition Iron fractionation in FFT revealed that methanogenesis also transformed crystalline FeIII minerals to amorphous FeII minerals decreasing the concentrations of certain metals such as arsenic (As), antimony (Sb), chromium (Cr), vanadium (V) and molybdenum (Mo) in porewater and cap water probably through reduction and precipitation with newly formed amorphous Fe minerals. Sequential metal extraction from FFT showed that carbonate, Fe, and manganese (Mn) oxide minerals in FFT were the major source of Sr and Ba in the porewater. Naphthenic acids (NAs) concentrations were also decreased in the porewater and capwater of amended columns. These results can help assess the water quality in EPL and understand the role of indigenous microbial communities in the sustenance of methanogenesis and partitioning of COCs from underlying FFT to overlying cap water.
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 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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».