Adapting direct filtration to increasing source water dissolved organic carbon using clarification and <scp>granular activated carbon</scp>
Bibliographic record
Abstract
Abstract Changing source water quality namely through increasing natural organic matter (NOM) concentration challenges surface water treatment, especially direct filtration. We conducted a pilot‐scale assessment of various adaptation strategies (e.g., clarification, granular activated carbon [GAC] filtration) for direct filtration facilities under the stress of rising NOM levels. Recognizing that changing source water can impact broader aspects of treatment, we considered the implications of Fe and Mn removal via KMnO4 pre‐oxidation. GAC media showed promise as an adaptation strategy, providing ~60% removal of dissolved organic carbon (DOC), and a significant reduction in disinfection by‐product formation potential (DBPfp). However, KMnO4 pretreatment showed limited Mn and Fe removal, and filters with GAC media released dissolved Mn at up to ~30% of prefilter levels. These data suggest that using GAC may come with the risk of poor Mn removal performance if Mn is not removed during pretreatment. This work highlights the complexities anticipated under emerging climate pressures and emphasizes the need for comprehensive treatment solutions that consider factors beyond NOM.
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 | 0.001 | 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".