Comparison of sediment bioavailable methods to assess the potential risk of metal(loid)s for river ecosystems
Bibliographic record
Abstract
A heavily impacted river basin (Caudal River, NW Spain) by Hg and Cu mining activities, abandoned decades ago, was used to evaluate the environmental quality of their river sediments. The obtained results compared with reference values established by the US EPA and the Canadian Council of Ministers of the Environment for river sediments, have shown that the main elements of environmental concern are arsenic (As), mercury (Hg) and, to a lesser extent, copper (Cu), which reach concentrations up to 1080, 80 and 54 mg kg −1 , respectively. To understand the role that river sediments play in terms of risk to ecosystem health, a comparison has been made between the total content of metal(oid)s in the sediments and the bioavailable contents of the same elements in pore water, passive DGT (Diffusive Gradients in Thin films) samplers and the sediment extractant using acetic acid. A good correlation between the As and Cu contents in the DGTs and the pore water was found, resulting in a transfer from the pore water to the DGT of at least 47 % of the Cu and more than 75 % of the As when the concentrations were low, with a deployment time of 4 days. When As and Cu concentrations were higher, their transfer was not so high (above 23.6 % for As and 19.3 % for Cu). The transfer of Hg from the pore water to the DGT was practically nil and does not seem to depend on the content of this metal. The fraction extracted with acetic acid, conventionally accepted as bioavailable, was clearly lower than that captured by DGTs for As and Cu (≤5 % and ≤8.5 % of the total amount, respectively), while it was similar for Hg (0.2 %). • Three methods for determining the bioavailable fraction in sediments are compared. • Passive DGT (Diffusive Gradients in Thin films) samplers seem to be advantageous. • The methodology is applied in a river basin with anthropic impacts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".