Partitioning of inorganic contaminants between fluid fine tailings and cap water under end pit lake scenario: Biological, Chemical and Mineralogical processes
Bibliographic record
Abstract
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.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".