Evaluation of carcinogenic risks related to nitrate exposure in drinking water in Iran
Bibliographic record
Abstract
Nitrate is one of the most important contaminants that can release into the environment predominantly as a result of anthropogenic processes. Excessive intake of nitrates may increase the risk of certain types of cancer. The aim of this study was to investigate the concentration of nitrate in drinking water and its health to people in Iran. This cross-sectional study has performed in 2019. Nitrate concentrations in drinking water supplies were obtained from peer-reviewed publications. Monte Carlo stimulations and mathematical models were used to determine the excess cancer risk. Risk level for assessing the carcinogen risk was 10−5 (1 per 100,000 persons). Nitrate concentrations and cancer risk related to nitrate were classified by GIS software. According to the obtained results, the drinking water supplies of Tehran, Mashhad (Khorasan Razavi), Zahedan (Sistan and Baluchestan), Shiraz (Fars), Qom, Ardabil and Ahwaz (Khuzestan) have higher nitrate concentrations than the limit recommended by WHO and Institute of Standards and industrial Research of Iran (ISIRI). The estimated cancer risks for the provinces of Tehran, Mashhad (Khorasan Razavi), Zahedan (Sistan and Baluchestan), Shiraz (Fars), Qom, Ardabil and Ahwaz (Khuzestan) were in the no negligible range set by the Health Canada and WHO. The majority of Iran provinces that have impermissible level of nitrate in drinking water supplies had a significant association between cancer prevalence and nitrate exposure. The findings demonstrated that carcinogen risk values of nitrate exposure through drinking water was 0.001%. Results showed that Tehran, Mashhad (Khorasan Razavi), Zahedan (Sistan and Baluchestan), Shiraz (Fars), Qom, Ardabil and Ahwaz (Khuzestan) are more exposed to additional cancer risk related to nitrosamine. The results of this study is considered as the comprehensive report that indicate the association between gastrointestinal cancer and nitrate exposure through 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 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".