Impact of recommissioning flushing on Legionella pneumophila in a large building during the COVID-19 pandemic
Bibliographic record
Abstract
COVID-19 shutdowns drastically increased the frequency and duration of water stagnation events in building plumbing systems, urging local authorities to issue guidance for the safe reopening of buildings mostly by recommissioning flushing. The objectives of this study were to document the dynamic changes of bacterial indicators [adenosine triphosphate (ATP), total and intact cell counts (TCC, ICC)] and the prevalence of Legionella pneumophila (Lp) in 20–21 showerheads in a large building before (16-week building closure) and then shortly (24 h) and monthly (4-week of distal water stagnation) after targeted recommissioning flushing. Following the 16-week shutdown, the highest mean of ATP (10 pg ATP/mL), TCC (1.7 × 106 count/mL) and ICC (5.2 × 105 count/mL) were measured in first draw samples. This bacterial amplification was mostly attributable to detachment from biofilm present in the distal devices and immediate connecting piping. Culture-based (mean of 4 487 MPN/L) and quantitative polymerase chain reaction (qPCR; mean of 63 822 gu/L) concentrations of Lp were respectively measured in 81 and 90% of first draw samples. Individual flushing of showerheads for 5 min resulted in 1.2–278-fold decreases in ATP, whereas TCC and ICC were lowered by 1.1- and 0.7-log on average. A one-log reduction in culture-based and qPCR Lp was only achieved in 63 and 29% of paired water samples, resulting in less than one-log reduction in mean risk values per exposure, thus demonstrating the limited effects of fixture-flushing for risk reduction. Clear short-term (24 h) benefits of device recommissioning flushing included lowered values of all bacterial indicators and Lp levels systematically under the common alert threshold of 1 000 MPN/L in first draws. However, after a period of 1 month without water use, these benefits were mostly lost with considerable rebounds of concentrations to similar levels than those measured following the 16-week building closure. Results highlight the temporary benefits of device recommissioning flushing for the control of Lp in shower systems, especially in buildings colonized by Legionella.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".