By-Products Associated with Structural Rehabilitation for Water Distribution Systems
Bibliographic record
Abstract
ISEE-0802 Background and Objectives: The rehabilitation of water distribution systems can be effected using a membrane impregnated with a thermosetting polymer. The objective of this study was to assess whether this process releases by-products in drinking water, especially bisphenol A diglycidyl ether (BADGE) and triethylenetetramine (TETA). Methods: Water samples were collected at two sites on the network. At each site, 2 samples were obtained before the rehabilitation work, and 8 additional samples were collected over a period of 48 hours after water circulation was restored. Samples were collected from the tap in glass bottles after a purge of 5 minutes. The samples were stored at −20° C and analyzed using liquid chromatography/time-of-flight mass spectrometry. Results: Concentrations of BADGE and TETA in samples collected before rehabilitation were all below the limit of detection (LOD: 0.05 and 2 μg/L respectively). Samples collected after water circulation was restored contained concentrations of BADGE ranging from 0.36 to 1.3 μg/L. Additional samples collected 1 year later still contained detectable concentrations (0.29 and 0.32 μg/L). Concentrations of TETA were below the LOD in all samples. Conclusion: The rehabilitation of the drinking water network with polymers may lead to the release of by-products in trace concentrations over a long period of time. Further analyses are needed to better understand the factors affecting their presence in drinking water.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".