Galvanic Corrosion of Lead by Iron (Oxyhydr)Oxides: Potential Impacts on Drinking Water Quality
Bibliographic record
Abstract
Abstract Lead exposure via drinking water remains a significant public health risk; this study explored the potential effects of upstream iron corrosion on lead mobility in water distribution systems. Specifically, galvanic corrosion of lead by iron (oxyhydr)oxides was investigated. Coupling an iron mineral cathode with metallic lead in a galvanic cell increased lead release by 531 μg L–1 on average─a 9-fold increase over uniform corrosion in the absence of iron. Cathodes were composed of spark plasma sintered Fe3O4 or α-Fe2O3 or field-extracted Fe3O4 and α-FeOOH. Orthophosphate immobilized oxidized lead as insoluble hydroxypyromorphite, while humic acid enhanced lead mobility. Addition of a humic isolate increased lead release due to uniform corrosion by 81 μg L–1 and─upon coupling lead to a mineral cathode─release due to galvanic corrosion by 990 μg L–1. Elevated lead in the presence of humic acid appeared to be driven by complexation, with 208Pb and UV254 size-exclusion chromatograms exhibiting strong correlation under these conditions (R2average = 0.87). A significant iron corrosion effect was consistent with field data: lead levels after lead service line replacement were greater by factors of 2.3–4.7 at sites supplied by unlined cast iron distribution mains compared with the alternative, lined ductile iron.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".