Effect of temperature on the dissolution of the lead (II) carbonate hydrocerussite for varying pH and dissolved inorganic carbon conditions
Bibliographic record
Abstract
The effect of temperature on the solubility of lead-bearing solid phases in water distribution systems for different water chemistry conditions remains unclear although lead concentrations are known to vary seasonally. The study objective is to explore the effect of temperature on the solubility of the lead(II) carbonate hydrocerussite under varying pH and DIC conditions. This is achieved through batch dissolution experiments conducted at multiple pHs (6–10) and DIC concentrations (20–200 mg CL−1) at temperatures ranging from 5 to 40 °C. A thermodynamic model was also applied to evaluate the model’s ability to predict temperature effects on lead(II) carbonate solubility including solid phase transformations. In general, increasing temperature increased total dissolved lead at high pHs and the effect of temperature was greater for high DIC conditions, particularly for pH > 8. Temperature also influenced the pH at which the dominant lead(II) solid phase switched from hydrocerussite to cerussite (occurred between pH 7.25 to 10). Finally, the model was able to capture the overall trends observed despite thermodynamic data limitations. While this study focuses on a simple lead solid-aqueous system, findings provide important insights regarding the way in which temperature and water chemistry interact to affect lead 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".