Effects of Mining on the Geochemistry of Fine Sediments in Streams; a Study in the Quesnel River Catchment
Bibliographic record
Abstract
This study investigated the influence of mining on the geochemistry of fine sediments in creeks and rivers. The data for this study was collected by conducting fieldwork in the Quesnel River catchment, BC, Canada. \nThe study area includes the drainage area of an active open pit gold- and copper mine and the drainage area of a historic hydraulic gold- and copper mine. In several creeks in the study area, five sampling sites were selected of which one drains a pristine forested area and is functioning as a control site (C1, Edney Creek). Hazeltine Creek drains an active mine and represents two sampling sites (H1 and H2). In the delta that has formed in Quesnel Lake by Hazeltine Creek, another sampling site was selected (D1, delta). In the creek draining the inactive mine, the fifth sampling site was selected (P1, Pine Creek). \nData were collected by sampling bed sediment, suspended sediment and vertical profiles, and by collecting depth samples at each sampling site. Two cores were collected: one in the delta that has formed in Quesnel Lake by Hazeltine Creek and one in a pond formed upstream of a weir in Hazeltine Creek at sampling site H2. \nTo assess the extent of the increase in heavy metal concentrations in the stream sediments and to indicate the relation to background concentrations and the adsorption properties of the sediment, the enrichment ratio was calculated. The enrichment ratio is a measure for the actual difference between background concentrations and elevated concentrations. The enrichment ratio is calculated by dividing the actual metal concentration by the regression prediction of the background concentrations. \nThe heavy metal concentrations that were used to estimate background concentrations include the deeper samples of the core collected in the delta (n=11), the deeper samples of the vertical profiles at sampling sites H2, C1 and P1 (n=10) and the bed sediment samples taken from sampling site C1, the control site (n=6). \nFor sampling sites H1 and H2 and the suspended sediments, heavy metal concentrations were enriched for Se, Cu, Cd, Hg, Mn and Zn. The sampling site in the delta formed in Quesnel Lake by Hazeltine Creek shows enrichment for Se, Hg and Mn. \nSampling site P1 which is draining the inactive mine shows enrichment for Pb and Ni in all stream sediments. \nThe age of the sediment in the two cores was determined in two separate ways. The first method employed the amount of 210Pb and 214Pb (Bq/Kg) in order to apply the constant rate of supply model. The second method employed the amount of 137Cs. In this method the year 1963 can be traced back. \nThe data collected from the cores gathered in the delta formed in Quesnel Lake by Hazeltine Creek and the core gathered at sampling site H2 show different results. No sediment deposition occurred over the last 30 years in the core taken in the delta and the periods of active mining are untraceable. The core collected from sampling site H2 shows enrichment of Se during the two periods of active mining (1997-2001 and 2005-present). Further, the core shows the two active mining periods by an increase in heavy metal concentrations.\nThis study concludes that mining activities do influence the geochemistry of fine sediments in creeks and rivers, but the influence can be minor and it does not directly indicate that the mines that have been investigated are contaminating the research areas. However, this study only concentrated on the input of heavy metal concentrations of stream sediments. \nIn the future close monitoring of the Quesnel Lake research area is considered advisable in order to detect possible elevated heavy metal concentrations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".