Explore of High Arsenic Levels in Agricultural Soils: Observations from Hashtroud County, Northwest of Iran
Bibliographic record
Abstract
Global concern about the accumulation of heavy metal(loid)s (HMs) in agricultural soils and its subsequent effects on human health has increased. In particular, arsenic is a metalloid with known toxic and carcinogenic properties. This study was conducted with the aim of investigating arsenic contamination in agricultural soils of Hashtroud County, while assessing the levels of some heavy metals and the associated ecological and health risks due to possible exposure to these contaminants. The average concentrations of zinc (Zn), lead (Pb), nickel (Ni), copper (Cu), chromium (Cr), arsenic (As), cobalt (Co), and cadmium (Cd) (respectively, 72, 42, 41, 40, 24, 16, 13, and 0.6 (mg/kg)) did not exceed the national guideline value except for As. Arsenic was the element most enriched in the study area (enrichment factor (EF): 3.3). In general, the soil samples showed moderate ecological risk (RI = 174.17). The non-carcinogenic risks of HMs were acceptable, while the carcinogenic risk value (LCR) for As exceeded the threshold of 1 × 10−4 at 10% of the samples. Source attribution of HMs using the positive matrix factorization (PMF) model showed that Zn and Pb were mainly due to vehicle emissions. Agricultural activities and atmospheric deposition were the predominant sources of Cu and Zn; As and Cd were from natural sources; and Ni, Cr, and Co were from mixed sources of pedogenic factors and agricultural activities. To conclude, in study area, addressing arsenic as a main concern is crucial due to its ecological and health risks. The agro-geogenic source with a contribution rate of 64.87%, was identified as the primary origin of the elements. Further research is needed to investigate the bioaccumulation and bioavailability of HMs in the soil-crop system.
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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 | 0.000 | 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 | 0.001 |
| 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 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".