Leveraging regulatory monitoring data for quantitative microbial risk assessment of <i>Legionella pneumophila</i> in cooling towers
Bibliographic record
Abstract
Abstract Cooling towers are critical engineered water systems for air conditioning and refrigeration but can create favorable conditions for Legionella pneumophila growth and aerosolization. Human exposure to L. pneumophila -contaminated aerosols can cause Legionnaire’s disease. Routine monitoring of L. pneumophila in cooling towers offers possibilities to develop quantitative microbial risk assessment (QMRA) to guide system design, operation, control, and maintenance. Here, we used the regulatory monitoring database from Quebec, Canada, to develop statistical models for predicting L. pneumophila concentration variability in cooling towers and integrate these models into a screening-level QMRA to predict human health risks. Analysis of 105,463 monthly L. pneumophila test results revealed that the exceedance rate of the 10 4 colony forming unit (CFU) per liter threshold was constant at 10% from 2016 to 2020, emphasizing the need to better validate the efficacy of corrective measures following the threshold exceedances. Among 2,852 cooling towers, 51.2% reported no detections, 38.5% had up to nine positives, and 10.2% over ten. The gamma or the lognormal distributions adequately described site-specific variations in L. pneumophila concentrations, but parametric uncertainty was very high for the lognormal distribution. We showed that rigorous model comparison is essential to predict peak concentrations accurately. Using QMRA, we found that, to meet a health-based target of 10 -6 DALY/pers.-year for clinical severity infections, an average L. pneumophila concentration below 1.4 × 10 4 CFU L -1 should be maintained in cooling towers. We identified 137 cooling towers at risk of exceeding this limit, primarily due to the observation or prediction of rare peak concentrations above 10 5 CFU L -1 . Effective mitigation of those peaks is critical to controlling public health risks associated with L. pneumophila .
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".