Contamination level and human non-carcinogenic risk assessment of diazinon pesticide residue in drinking water resources – a case study, IRAN
Bibliographic record
Abstract
Today, crop pesticides are increasingly used to protect crops against pests. These pesticides can infiltrate to water resources via agricultural runoff and accordingly impose serious health risks to human health. This study was designed to ascertain the contamination levels of water resources in Sistan plain (south-east of Iran) in terms of Diazinon and its health risk assessment for two groups of ages: adults and children. To this end, a total of 70 water samples from different water sources (of which 35 samples were taken in spring and 35 ones in summer) were analytically analysed using high-performance liquid chromatography (HPLC). Based on the results presented here, the mean concentration levels of Diazinon in all water resources were found to be lower than the maximum permissible level recommended by the guidelines for Canadian drinking water quality (GCDWQ). In addition, the probable non-carcinogenic risk attributed to Diazinon through the water consumption for both children and adults groups was lower than the recommended value (HQ = 1). Therefore, the quality of water in Sistan plain is safe for drinking. However, control measurements and continuous monitoring are suggested for sources with high levels of Diazinon concentration in order to promote the health levels of residents live in Sistan plain.
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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 | 0.000 | 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 | 0.001 | 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) | 0.000 | 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".