Source and distribution of heavy metals in sediment samples from select creeks of the highly urbanized Metro Vancouver Watersheds, Canada
Bibliographic record
Abstract
Forty-seven samples collected from 23 creeks in Metro Vancouver, BC, Canada, were analyzed using X-ray Fluorescence Spectroscopy (XRF) to assess how different land use activities affect physicochemical properties in surface water and metal concentrations in sediments. The concentrations of the heavy metals (Ag, As, Ba, Cr, Cd, Cu, Fe, Mn, Ni, Pb, V, Zn) ranged from 2.53–4.11, 7.51–16.27, 98.06–674.21, 22.83–136.68, 2.72–9.10, 6.70–146, 14,516.76–62,755.68, 385.16–5126.12, 11.89–102.12, 5.02–161.83, 50.93–151.63, and 21.40–660.87 mg/kg, respectively, in the study area. High concentrations of some metals were recorded to be above the Canadian Council of Ministers of the Environment Sediment Quality Guidelines, along with elevated contamination indices in some sampling sites. The pH, total dissolved solids (TDS), and electrical conductivity (EC) in surface water samples ranged from 5.50 to 8.27, 14.0 to 410.0 mg/L, and 19.0 to 903.0 µS/cm, respectively, with no discernible relationship between the surrounding land use. Principal component analyses revealed anthropogenic and natural processes enrich the sediments with metals. The geo-accumulation index ( I geo ) showed extremely contaminated sediment and a >64-fold increase due to high levels of Ba, Cr, Cu, Fe, Mn, Ni, Pb, V, and Zn. The contamination factors fall in the following sequences: Cd > Ag > As > Pb > Zn > Mn > Cu > Ba > Cr > Ni > V > Fe . Creeks closer to highways with heavy vehicular activities reported significantly higher concentrations of metals. It is therefore important to adhere to the Riparian Areas Protection Regulation Model of the Government of British Columbia, which is vital for maintaining water and sediment quality, stream health, and productivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".