Seasonal lead release to drinking water and the effect of aluminum
Bibliographic record
Abstract
Lead is a neurotoxin and an environmental contaminant. Many jurisdictions require that it be monitored in drinking water, especially where lead plumbing remains in use. But seasonal variation in lead concentrations can bias monitoring programs if it is not understood and accounted for. Here, we describe an unexpected pattern in lead release to drinking water, identified through point-of-use sampling. The median lead concentration representing paired first-draw water samples—collected in multiple years—was 46% lower in October compared to February. Seasonal variation in orthophosphate, pH, and alkalinity accounted for at least some of this pattern and predicted lead solubility in October was 76% of that in February. But seasonally varying aluminum may also have been a factor; as a supplement to the field study, we evaluated the effects of aluminum residual, temperature, and orthophosphate concentration on lead release from lead coupons. Increasing the orthophosphate concentration from 0–1 mg/L decreased lead release by 34%, and increasing water temperature from 4–21˚C increased lead release by 120%. Increasing the aluminum concentration from 20–500 µg/L increased lead release by 41% and modified the effect of orthophosphate, rendering it less effective in controlling lead release. We attributed this to a decrease in the concentration of soluble (<0.45 µm) phosphorus with increasing aluminum and an accompanying increase in particulate lead and phosphorus (>0.45 µm). These data suggest that residual aluminum from coagulation may interfere with orthophosphate corrosion control, especially when the treatment process does not allow the pH of minimum aluminum hydroxide solubility to be targeted.
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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".