Can free chlorine residuals entering building plumbing systems really be maintained to prevent microbial growth?
Bibliographic record
Abstract
Secondary disinfection aims to prevent microbial regrowth during distribution by maintaining disinfectant residuals in water systems. However, multi-factorial interactions contribute to free chlorine decay in distribution systems, and even more so in building plumbing. Assembling 1737 samples from nine large institutional buildings, a meta-analysis was conducted to determine whether building managers can actively rely on incoming free chlorine residuals to prevent in-building microbial amplification. Findings showed that free chlorine concentrations in first draws met the 0.2 mg/L common guide level in respectively 26 %, 6 % and 2 % of cold, tepid and hot water samples, whereas flushing for 2-60 min only significantly increased this ratio in cold water (83 %), without reaching background levels found in service lines. Free chlorine was significantly but weakly (R≤ 0.2) correlated to adenosine triphosphate, heterotrophic plate count and total and intact cell counts, thus evidencing that residuals contributed to decreased culturable and viable biomass. Detection of culturable Legionella pneumophila spanning over a 4-log distribution solely occurred when free chlorine levels were below 0.2 mg/L, but no such trend could be distinguished clearly for culturable Pseudomonas aeruginosa. Water temperatures below 20 °C and >60 °C also completely prevented L. pneumophila detection. Overall, the majority of elevated microbial counts were measured in distal sites and in tepid and hot water, where free chlorine is less likely to be present due to stagnation and increased temperature. Therefore, building managers cannot solely rely on this chemical barrier to mitigate bacterial growth in bulk water.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".