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Record W2963211967 · doi:10.1016/j.mex.2019.07.008

Evaluation of carcinogenic risks related to nitrate exposure in drinking water in Iran

2019· article· en· W2963211967 on OpenAlexaboutno aff
Mohammad Darvishmotevalli, Maryam Moradnia, Mohammad Noorisepehr, Ali Fatehizadeh, Saeid Fadaei, Hamed Mohammadi, Mehdi Salari, Hamzeh Ali Jamali, Seyede Shahrbanoo Daniali

Bibliographic record

VenueMethodsX · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsnot available
FundersAmerican Educational Research AssociationStudent Research Committee, Tabriz University of Medical SciencesU.S. Environmental Protection Agency
KeywordsNitrateCarcinogenEnvironmental scienceEnvironmental chemistryEnvironmental healthChemistryMedicineBiologyEcology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.076
GPT teacher head0.345
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations58
Published2019
Admission routes1
Has abstractyes

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