Investigation of Air Quality Problems in an Indoor Swimming Pool: A Case Study
Bibliographic record
Abstract
INTRODUCTION: Trichloramine (NCl3) is the contaminant suspected the most to cause irritative respiratory symptoms among swimmers and swimming pool workers. Following complaints by employees working in an indoor swimming pool, this study set out to identify the determinants of NCl3 air concentrations in that particular swimming pool. METHODS: To document NCl3 air levels, air samples (n = 26) were collected once or twice a day for 3 h, at least 3 days per week, between October and December 2011. Water samples were taken three times during air sampling to verify free chlorine, chloramines, alkalinity, conductivity, pH, water temperature, and turbidity. Water changes were also recorded, along with the number of bathers. Ventilation (outdoor air flow) was modified to verify the influence of this important variable. Data were evaluated by analysis of variance. RESULTS: Mean NCl3 air concentration was 0.38 mg m(-3). The best model explaining variations of NCl3 air levels (r2 = 0.83) included sampling period (P = 0.002, NCl3 was higher in the evening versus the morning), water changes (P = 0.02, NCl3 was lower with water changes between 60 and 90 min day(-1) versus <60 min day(-1)), and ventilation (P = 0.0002, NCl3 was lower with ≥2 air changes per hour (ACH) versus <1 ACH). DISCUSSION AND CONCLUSION: Although based on only 26 air samples, our results indicate that ventilation is an important determinant of NCl3 air concentration in swimming pool air. There is limited information available on the air quality of indoor swimming pools and the relationship with ventilation. Efforts are needed to document the situation and to develop state-of-the-art facilities for ventilation of indoor swimming pools.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 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".