Spatio-temporal analysis of water chemistry and ecotoxicological risk characterisation for a constructed pilot-scale pit lake in the Athabasca oil sands region, Canada
Bibliographic record
Abstract
Substantial quantities of fine tailings and oil sands process affected water (OSPW) require reclamation in the Athabasca oil sands (AOS) region, Canada. Towards this end, Lake Miwasin was created as a pilot-scale pit lake containing treated fluid tailings (bottom sediment) capped with a blend of OSPW and surface water. This is a recent approach to waste reclamation and long-term monitoring is ongoing to determine the trajectory of water quality in this test lake. The current study characterized spatial and temporal changes in surface water chemistry using a wireless sensor network (WSN), particularly to identify contaminant release from the consolidating tailings and potential periods of sediment resuspension. The WSN technology was deployed during the open water season from September 2020 to October 2022 to remotely measure water quality parameters at different depths of the water column. Field measurements and manual water sampling were conducted periodically to validate sensor measurements and to analyse additional variables requiring more complex analysis. During the study, increased electrical conductivity (EC) near the sediment-water interface during water column stratification indicated expression of pore water with elevated salt content, as the bottom tailings progressively consolidated. A decreasing trend in EC towards the end of the monitoring season suggested water input from the surrounding catchment and possibly a decline in porewater expression. A Tier 1 screening-level risk assessment using hazard quotients (HQs) was conducted for individual metals using Canadian water quality benchmarks and published toxicity data. Total osmolarity was used to identify risks associated with major ions. Cumulative HQs for trace elements were >1 but decreased over time. The risk due to major ions was <1 for all years and seasons except 2019 and winter 2020. Modelling results indicated that the predicted 90 th percentile HQs for Se and As were 1.6 and 0.44, respectively, suggesting that Se represents a potential ecotoxicological risk and should be further investigated. Overall, water quality monitoring and modelling insights gained from this study have the potential to inform AOS pit lake design and prediction of reclamation trajectories. • A wireless sensor network (WSN) captured complex spatio-temporal variability in water quality at the sediment-water interface. • Surface water quality improved over the four years of study from 2019 to 2022. • As of 2022, most metal and ion concentrations appear to pose a negligible risk to aquatic organisms, except for selenium. • The WSN enabled the estimation of metal distribution and the assessment of associated ecotoxicological risk to aquatic life.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".