Assessing drinking water quality based on water quality indices, human health risk, and burden of disease attributable to heavy metals in rural communities of Yazd County, Iran, 2015–2021
Bibliographic record
Abstract
The water quality indices, health risk, and burden of disease attributable to heavy metals in rural communities of Yazd County, Iran during 2015–2021 were studied. The drinking water quality index (DWQI) based on 27 parameters (including heavy metals) and heavy metal pollution index (HPI) were used for assessing drinking water quality. The health risk and burden of disease from heavy metals in drinking water were estimated in terms of hazard quotient (HQ), hazard index (HI), incremental lifetime cancer risk (ILCR), and disability-adjusted life year (DALY). Based on the DWQI scores by community, the drinking water quality in rural communities of Yazd County was characterized as good for 61 %, fair for 25 %, marginal for 2 %, and poor for 12 %. The distribution of the rural communities into the HPI categories was as follows: 43 % for excellent, 36 % for good, 14 % for poor, and 7 % for unsuitable. In about 20 % of the rural communities, the average HI level of heavy metals was higher than the boundary limit of one. The highest average HQ level at the county level was related to arsenic (As) to be 0.44. In all the communities, the total ILCR values of the heavy metals were in the category of significant increased cancer risk (10 −6 to 10 −4 ). At the county level, As and cadmium (Cd) exhibited the two highest cancer risk levels to be 1.96 × 10 −4 and 1.87 × 10 −4 for ILCR, respectively. The DALY rate (per 100,000 people) induced by exposure to the heavy metals via drinking water was 13.9, which was considered relatively high as compared to that of other drinking water pollutants obtained in the previous studies. The drinking water quality improvement through decreasing Cd and As levels below the standard values can drastically reduce the attributable burden of disease and promote the public health in the rural communities.
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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 | 0.005 | 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) | 0.001 | 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 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".