Health Risk Assessment of Metal Elements in Drinking Water in 10 Cities,Guangdong Province
Bibliographic record
Abstract
Objective To evaluate the human health risk of metal elements in drinking water in the urban area of 10 cities,Guangdong province.Methods The concentration of 9 kinds of metals(As,Cr~(6+),Cd,Pb,Hg,Se,Mn,Cu,Zn) in drinking water sampled from centralized water supply systems were determined in 2nd quarter to 4th quarter of 2011.The health risks of exposure to 9 kinds of metal elements through oral route were assessed,according to the models recommended by the US EPA.Results The average qualified rate of Hg in drinking water was 98.8%(169/171),and the concentration of other 8 kinds of metal elements were in compliance with the requirements of the standard for drinking water quality.The levels of carcinogenic risk caused by three kinds gene toxic substances ranked as Cr~(6+)(3.71×l0~(-5)/a)As(1.04×l0~(-5)/a)Cd(0.16×l0~(-5)/a). The total carcinogenic risk was 4.91×l0~(-5)/a.The levels of hazard indices caused by non-gene toxic substances ranked as Cu (11.82×l0~(-10)/a)Pb(6.41×10~(-10)/a)Hg(3.06×10~(-10)/a)Se(1.04×10~(-10)/a)Mn(0.58×10~(-10)/a)Zn(0.24×10~(-10)/a).Conclusion The health risk of 9 kinds of metal elements in drinking water is respectively below the maximum tolerable value recommended by ICRP(5.0×l0~(-5)/a),in 10 cities of Guangdong province.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".