Release and mineral formation of lead corrosion products with orthophosphate-polyphosphates
Bibliographic record
Abstract
Phosphate addition is a popular strategy to minimize lead release. Despite continuous research on orthophosphate for lead control, studies exploring the complexity of the interaction between lead and orthophosphate-polyphosphate blends in drinking water are scarce. Three model polyphosphates—tripoly-, trimeta- and hexametaphosphate— were used to examine the structural impacts of polyphosphate on lead release. We used a continuously-stirred tank reactor with a lead (II) carbonate solid to evaluate the impact of orthophosphate-polyphosphate blends compared to orthophosphate on lead solubility, speciation, and mineralogy under conditions relevant to drinking water. Tripolyphosphate was a stronger complexing agent for lead than trimetaphosphate (1 ± 0.01 vs 0.07 ± 0.01 molPb/molPolyphosphate). Hexametaphosphate’s increased chain length was associated with greater lead solubility (1.6-2.1 ± 0.1 molPb/molPolyphosphate). At equivalent orthophosphate to polyphosphate concentrations, orthophosphate-trimetaphosphate had minimal impact on lead release, while orthophosphate-tripolyphosphate increased dissolved by 554 ± 29 and 213 ± 22 μg Pb0.2μm m-2 at 30-min and 24-hr reaction times, respectively. Meanwhile, orthophosphate-hexametaphosphate increased dissolved lead only at the 24-hr reaction time (by 256 ± 13 μg Pb0.2μm m-2). Both orthophosphate-tripolyphosphate and orthophosphate-hexametaphosphate increased small colloidal lead concentrations over a 24-hr stagnation. Except with orthophosphate-trimetaphosphate, having more polyphosphate than orthophosphate intensified dissolved lead release. All three polyphosphates inhibited the formation of hydroxypyromorphite and reduced the phosphorous content of the resulting lead solids. The impacts of orthophosphate-polyphosphates are due to a combination of complexation, adsorption, colloidal dispersion, polyphosphate hydrolysis, and lead mineral precipitation.
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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 | 0.000 |
| 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 | 0.000 |
| 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".