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Record W2028866975 · doi:10.3390/ijerph9082562

Occurrence and Spatial and Temporal Variations of Disinfection By-Products in the Water and Air of Two Indoor Swimming Pools

2012· article· en· W2028866975 on OpenAlexaff
Cyril Catto, Sabrina Simard, Ginette Charest‐Tardif, Rodriguez Manuel

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsUniversité LavalUniversité de Montréal
Fundersnot available
KeywordsHaloacetic acidsEnvironmental scienceContaminationSampling (signal processing)Exposure assessmentChloramineAir contaminationEnvironmental engineeringEnvironmental chemistryIndoor airHydrology (agriculture)Water treatmentWaste managementChemistryEnvironmental healthEcologyChlorine

Abstract

fetched live from OpenAlex

In order to improve disinfection by-product (DBP) exposure assessment, this study was designed to document both water and air levels of these chemical contaminants in two indoor swimming pools and to analyze their within-day and day-to-day variations in both of them. Intensive sampling was carried out during two one-week campaigns to measure trihalomethanes (THMs) and chloramines (CAMs) in water and air, and haloacetic acids (HAAs) in water several times daily. Water samples were systematically collected at three locations in each pool and air samples were collected at various heights around the pool and in other rooms (e.g., changing room) in the buildings. In addition, the ability of various models to predict air concentrations from water was tested using this database. No clear trends, but actual variations of contamination levels, appeared for both water and air according to the sampling locations and times. Likewise, the available models resulted in realistic but imprecise estimates of air contamination levels from water. This study supports the recommendation that suitable minimal air and water sampling should be carried out in swimming pools to assess exposure to DBPs.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.248

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.047
GPT teacher head0.343
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations65
Published2012
Admission routes1
Has abstractyes

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