Lead solubility in seawater: an experimental study
Bibliographic record
Abstract
Environmental context Many trace metals including lead are only sparingly soluble in seawater and may exist in both dissolved and particulate forms (e.g. as precipitates). Aquatic organisms may experience different toxic effects from exposure to dissolved and particulate trace metals. This study reports the limits to lead solubility in seawater that influence the exposure to these forms of lead in the field and the laboratory. Abstract A combination of laboratory investigations and thermodynamic modelling were conducted in order to gain an understanding of the factors controlling lead solubility in seawater. In experiments where increasing amounts of lead were added to seawater (in order to avoid supersaturation) and equilibrated for up to 28 days, the maximum solubility was ~2 mg L–1 (pH 8.15, 22 °C). However, at higher added lead concentrations, which caused the rapid formation of lead precipitates, the solution chemistry became dynamic and the observed solubility was markedly lower, varying with both reaction time and precipitate concentration. For instance, when seawater solutions were spiked with 10 mg L–1 of total lead, precipitation occurred immediately and only 1.6 mg L–1 of dissolved lead was measured after 1 h, with this concentration decreasing to 1.3 mg L–1 after 28 days. The solubility of lead in artificial seawater (0.68 mg L–1) was much lower than in natural seawater. This difference was attributed to the significant role played by natural organic matter in complexing dissolved lead. X-Ray diffraction and elemental analysis data suggest that the phase controlling lead solubility is a previously unidentified lead chlorocarbonate, which rapidly transforms to hydrocerussite on washing with deionised water. These observations are of particular relevance to toxicity tests where organisms are exposed to wide ranges of metal concentrations in order to obtain dose–response curves.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".