Spatial and temporal variability in trihalomethane concentrations in the bromine-rich public waters of Perth, Australia
Bibliographic record
Abstract
High concentrations of trihalomethanes (THMs) in public water supplies potentially pose a health hazard, but exposure assessment remains a complex task. To interpret research findings and monitoring data for THMs, it is important to evaluate spatial and temporal variations in both total THM and the individual constituent compounds (including brominated species). We therefore aimed to determine the concentrations, and spatial and temporal variability of concentrations, of THMs public water supplies in Perth, Western Australia, which is known historically to have high brominated THM concentrations. We analysed water samples from 21 water distribution zones around Perth (including Busselton and Bunbury) across different seasons over a period of two years. A total of 250 samples provided a median total THM of 72 µg/L (range of 0–157 µg/L), which falls well within Australia’s National Health and Medical Research Council guidelines. The concentration of all species, including brominated forms, also fell the World Health Organization’s guidelines. Total THM concentrations were typically higher in spring and summer. A high degree of spatial variability was detected and appears to relate to the source water. Both the temporal and spatial variability in THM concentrations have implications for epidemiological studies, and monitoring.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".