Global assessment of chemical quality of drinking water: The case of trihalomethanes
Bibliographic record
Abstract
BACKGROUND: Trihalomethanes (THM), a major class of disinfection by-products, are widespread and are associated with adverse health effects. We conducted a global evaluation of current THM regulations and concentrations in drinking water. METHODS: We included 120 countries (∼7000 million inhabitants in 2016), representing 94% of the world population. We searched for country regulations and THM routine monitoring data using a questionnaire addressed to referent contacts. Scientific and gray literature was reviewed where contacts were not identified or declined participation. We obtained or estimated annual average THM concentrations, weighted to the population served when possible. RESULTS: Drinking water regulations were ascertained for 116/120 (97%) countries, with 89/116 (77%) including THM regulations. Routine monitoring was implemented in 47/89 (53%) of countries with THM regulations. THM data with a varying population coverage was obtained for 69/120 (58%) countries consisting of ∼5600 million inhabitants (76% of world's population in 2016). Population coverage was ≥90% in 14 countries, mostly in the Global North, 50-89% in 19 countries, 11-49% among 21 countries, and ≤10% in 14 countries including India, China, Russian Federation and Nigeria (40% of world's population). DISCUSSION: An enormous gap exists in THM regulatory status, routine monitoring practice, reporting and data availability among countries, especially between high- vs. low- and middle-income countries (LMICs). More efforts are warranted to regulate and systematically assess chemical quality of drinking water, centralize, harmonize, and openly report data, particularly in LMICs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".