Health risk assessment of heavy metals and disinfection by-products in drinking water in megacities in China: A study based on age groups and Monte Carlo simulations
Bibliographic record
Abstract
Heavy metal(loid)s (HMs) and disinfection by-products (DBPs) in drinking water pose risks to human health and jeopardize drinking water. Water-related behaviors vary significantly among different age groups and regions. In this study, the carcinogenic and non-carcinogenic risks of HMs (As, Cd, Cr6+, Cu, Pb, and Zn) and DBPs (bromodichloromethane (BDCM), bromoform, chloroform, dibromochloromethane (DBCM), dichloroacetic acid (DCAA), and trichloroacetic acid (TCAA)) in drinking water in two Chinese megacities (Beijing in North China and Guangzhou in South China) via multiple exposure pathways were assessed. The results showed that children aged 9 months to 2 years had a total carcinogenic risk (TCR) and hazard index (HI) above acceptable levels, indicating that despite the drinking water quality in the selected megacities meeting the current Chinese national standards (GB 5749–2022), the health risks of exposure to HMs and DBPs in drinking water for local young children should not be neglected. Specifically, the carcinogenic risk (CR) of exposure to As in drinking water for children < 18-years-old, who were divided into different age groups, was 1.5–2.0- and 4.5–5.9-times higher than the TCR of exposure to DBPs in Beijing and Guangzhou, respectively. Regarding children aged 9 months to 2 years, the exposure to TCAA accounted for the largest proportion (35.6 %) of the TCR of exposure to DBPs in Beijing drinking water, 5.4-times higher than that in Guangzhou; whereas, the TCR of exposure to DBPs in Guangzhou drinking water was predominantly caused by exposure to chloroform, accounting for 40.6 % of the TCR and 1.5-times higher than that in Beijing. In addition, the CR of exposure to DCAA in drinking water in both megacities accounted for a large proportion of the TCR for children aged 9 months to 2 years. Monte Carlo simulations showed that 62.2 % and 42.6 % of the TCR of simultaneous exposure to As and DBPs in drinking water exceeded the acceptable level for sensitive populations, that is, children aged 1–2 years in Beijing (95th percentile = 4.2 × 10−4) and children aged 9–12 months in Guangzhou (95th percentile = 5.2 × 10−4), respectively. This elaborate health risk assessment sheds light on improving the water quality indices to guarantee drinking water safety in China.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".