Disinfection by-products in indoor swimming pools: A critical review to prioritize commonly occurring species and influencing factors
Bibliographic record
Abstract
Despite the reported occurrence of several disinfection by-products (DBPs) in swimming pools, it is challenging to identify important DBP species due to strewn and conflicting information about their occurrence and importance in the literature. There is a need to provide clear direction and decisive information to support regulators' and pool facilities' efforts to control DBPs. In this review, alongside providing detailed occurrence data, we have developed a novel prioritization approach to classify DBPs as Tier-1 (critical priority), Tier-2 (medium priority), and Tier-3 (low-priority) DBPs in chlorinated indoor swimming pools (ISPs). After compiling an exhaustive database of published literature on chlorinated ISPs, DBP species were evaluated on a defined scoring system based on their occurrence (concentration levels and reported frequency) and toxicity. The normalized aggregated scores from these criteria were used to prioritize the DBPs. The DBPs identified as Tier-1 species with the highest occurrence and potential toxicity include Trichloromethane (TCM), Trichloroacetic acid (TCAA), and Dichloroacetic acid (DCAA). Respectively, ten DBP species were identified in Tier-2 and fifteen in Tier-3. Implications of the prioritization results for regulatory agencies, pool facilities, and researchers have been provided. Furthermore, a comparative analysis of the available studies, that described the correlation of water quality and pool operational factors with Tier-1 DBPs, was carried out. The identified critical factors include the number of swimmers, free residual chlorine, disinfection methods, total organic carbon (TOC), and temperature. These factors can be used to control the formation of DBPs and reduce the associated risk, especially for Tier-1 species.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".