Carcinogenic and non-carcinogenic health risk assessment of heavy metals in drinking water of Khorramabad, Iran
Bibliographic record
Abstract
The continuous urbanization and industrialization in many parts of the world and Iran has led to high levels of heavy metal contamination in the soil and then on the surface and groundwater. In this study, the concentrations of 8 heavy metals were determined in forty water samples along distribution drinking water of Khorramabad, Iran. The ranges of heavy metals in this study were lower than EPA and WHO drinking water recommendations and guidelines and so were acceptable. The mean values of CDI total of heavy metals concentrations in adults were found in the order of Zn > Ba > Pb > Ni > Cr > Cu > Cd > Mo. The health-risk estimation indicated that total hazard quotient (HQ ing + HQ derm ) and hazard index values were below the acceptable limit, representing no non-carcinogenic risk to the residents via oral intake and dermal adsorption of water. Moreover, the results of total risk via ingestion and dermal contact showed that the ingestion was the predominant pathway. This study also presents that the carcinogenic risk for Pb, Cr, Cd and Ni were observed higher than the acceptable limit (1 × 10 −6 ). The present study will be quite helpful for both inhabitants in taking protective measures and government officials in reducing heavy metals contamination of urban drinking water. • The data analyzed in this study show a clear situation regarding the quality of drinking water in Khorramabad. • The results of this study can be used to improve and develop the quality of drinking water that directly affects the health of consumers. • The present study will be quite helpful for both inhabitants in taking protective measures and government officials in reducing heavy metals contamination of urban 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".