Granular Activated Carbon Filtration as a Lead Control Strategy
Bibliographic record
Abstract
Climate change-related increases in organic carbon in surface waters may present challenges in meeting regulatory requirements with regard to drinking water quality. Treatment adaptation that alters natural organic matter (NOM), metal oxides, and water chemistry can have a downstream influence on lead release. We compared the effect of the coagulant and filter type on water quality and lead release in a galvanic lead solder-copper system. Aluminum sulfate (alum), polyaluminum chloride (PACl), anthracite/sand, and granular activated carbon (GAC) were tested in a pilot-scale system. Lead release was evaluated in a bench-scale dump and fill experiment with treated water dosed with 0-2 ppm zinc-orthophosphate. GAC contactors reduced organic carbon in both systems and had a strong protective effect on lead release, likely due to less NOM complexation and improved orthophosphate performance. At equivalent Al doses, organic carbon removal was comparable between PACl and alum, but PACl showed slower GAC exhaustion rates, improving the removal efficiency. PACl was linked with increased galvanic corrosion due to higher CSMR. Zinc-orthophosphate mitigated galvanic corrosion of lead solder. Treatment facilities can decrease lead release by removing NOM, but alternative coagulants that may be considered for enhanced NOM removal can increase the chloride concentration and have detrimental effects as well.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
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 teacher head, 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".