Ventilation and Indoor Air Quality in Natatoriums: A CiteSpace‐Based Bibliometric Review
Bibliographic record
Abstract
This study presents a comprehensive bibliometric review of research on ventilation and air quality in swimming pool facilities from 2001 to 2025. Based on literature retrieved from Web of Science, Scopus, and PubMed, this study employs the CiteSpace visualization tool to analyze and shows that research has evolved from basic assessments of air exchange to multidimensional concerns including disinfection by‐products (DBPs), pollutant exposure, thermal comfort, and energy efficiency. Despite these advances, significant gaps remain: most studies prioritize energy performance over detailed pollutant control, long‐term high‐resolution monitoring data are scarce, and ventilation standards often lack explicit criteria for swimmer health. Geographically, Canada, China, and the United States have led contributions in this field. The findings demonstrate a thematic transition from experience‐based to precision control, supported by tools such as CFD, TRNSYS, and model predictive control, and signal growing potential for artificial intelligence and data‐driven facility management. This review identifies core themes and technical trajectories, offering theoretical insight and decision‐making support for the design and operation of low‐carbon, health‐oriented swimming pool environments.
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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.130 | 0.148 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".