Assessment of potentially toxic elements in vegetables and soil samples irrigated with treated sewage and human health risk assessment
Bibliographic record
Abstract
In this work, a new microextraction approach termed as vortex-assisted liquid phase microextraction based on deep eutectic solvent (VALPME-DES) combined with graphite furnace atomic absorption spectrometry (GFAAS) has been developed for the extraction, preconcentration and determination of potentially toxic elements (PTEs) in vegetables and soil samples irrigated with treated sewage from two different regions of Iran. The new DES was prepared by mixing a 1:1 molar ratio of choline chloride and citric acid monohydrate. Some effective parameters on extraction were studied and optimised. Under the optimum conditions, the repeatability and reproducibility of the VALPME-DES coupled with ETAAS for 5.0 µg L−1 of As(III) and 0.50 µg L−1 of Pb and Cd were determined to be 2.7–4.3 and 3.8–6.2%, respectively. The correlation coefficient (r2) of the calibration curves was in the range of 0.995–0.998. The limit of detections was in the range of 0.03 and 0.1 µg kg−1 for different metal ions. Linear range of 0.3 − 100 µg kg−1 for As(III) and, 0.03–200 µg kg−1 for Cd and Pb were obtained. The results showed among the target metals, the highest impact on the total value of non-carcinogenic risk was related to arsenic. Furthermore, the non-carcinogenic risk value for all vegetable types was lower than the permitted level. We also found that the risk of arsenic carcinogenicity was higher than the acceptable levels in all four types of vegetables. According to the findings, interventions to reduce arsenic should be used, especially in cultivated soils.
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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 | 0.001 | 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) | 0.002 | 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".