Global assessment of trihalomethanes in drinking water
Bibliographic record
Abstract
BACKGROUND. Chlorination of drinking water is a major public health intervention to avoid water-borne infections. Disinfection generates undesired by-products such as trihalomethanes (THMs), some of which are carcinogenic. Global information on population exposure to disinfection by-products (DBPs) in water is lacking. We developed global country-wide estimates of the concentrations of THMs in drinking water as a marker of DBP exposure. METHODS. In this global study we collected information about the regulatory status of DBPs and concentrations of total and specific THMs (chloroform, bromoform, dibromochloromethane, bromodichloromethane) in drinking water from the latest year available. Global THM data were collected using a structured questionnaire and database from key national contacts and experts (national agencies, universities, water utilities). We conducted on-line searches of published reports, research studies, and grey literature. We calculated population-weighted average THM levels in each country. Data quality analysis considered the percentage of population covered, the number of water samples, and the source of information used. RESULTS. From the 121 countries included, 90 (74%) regulate THMs in drinking water. In countries with THM regulations, 42 (47%) conduct routine monitoring. Data collection is ongoing. Average THM levels (in μg/L) varied e.g., from 0.02 in Denmark, 0.2 in Netherlands, 24 in UK, 27 in Canada, 34 in USA to 60 in South Africa, and 72 in Australia. Very high levels above 600 were observed in certain areas in India. There were major gaps in global coverage, primarily in Africa, but also in Asia. DISCUSSION. This is the first global assessment of THM levels in drinking water. National data were available for most high and several middle income countries. Results will become open access, and are expected to promote research and policy developments, including better estimates of global burden of disease, comparative risk assessment, and will facilitate control of DBPs in drinking water.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".