Does Granular Activated Carbon with Chlorination Produce Safer Drinking Water? From Disinfection Byproducts and Total Organic Halogen to Calculated Toxicity
Bibliographic record
Abstract
Granular activated carbon (GAC) adsorption is well-established for controlling regulated disinfection byproducts (DBPs), but its effectiveness for unregulated DBPs and DBP-associated toxicity is unclear. In this study, GAC treatment was evaluated at three full-scale chlorination drinking water treatment plants over different GAC service lives for controlling 61 unregulated DBPs, 9 regulated DBPs, and speciated total organic halogen (total organic chlorine, bromine, and iodine). The plants represented a range of impacts, including algal, agricultural, and industrial wastewater. This study represents the most extensive full-scale study of its kind and seeks to address the question of whether GAC can make drinking water safer from a DBP perspective. Overall, GAC was effective for removing DBP precursors and reducing DBP formation and total organic halogen, even after >22 000 bed volumes of treated water. GAC also effectively removed preformed DBPs at plants using prechlorination, including highly toxic iodoacetic acids and haloacetonitriles. However, 7 DBPs (mostly brominated and nitrogenous) increased in formation after GAC treatment. In one plant, an increase in tribromonitromethane had significant impacts on calculated cytotoxicity, which only had 7-17% reduction following GAC. While these DBPs are highly toxic, the total calculated cytotoxicity and genotoxicity for the GAC treated waters for the other two plants was reduced 32-83% (across young-middle-old GAC). Overall, calculated toxicity was reduced post-GAC, with preoxidation allowing further reductions.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".