INITIAL SURVEY OF HEAVY METAL CONCENTRATIONS IN PADDY SOIL AND RICE PLANTS (Oryza sativa L.) NEAR AND FAR FROM OPEN LANDFILL IN SOUTHERN VIET NAM
Bibliographic record
Abstract
The concentrations of heavy metals in soil and rice plants around the landfill area in Dong Thang commune, Co Do District, Can Tho City, Viet Nam needed to be assessed for environmental pollution. Soil samples were collected from four sites (three sites S1, S2, S3 near and one site S4 far away from the landfill area) at soil depths of 0 to 25 and 25 to 50 cm. The rice and soil samples were simultaneously collected at the same locations for analysis of heavy metals. The heavy metals Mn, Zn, Cu, Cr, Ni, Pb and Cd were analyzed using atomic absorption spectroscopy. Six heavy metals including Mn, Zn, Cu, Cr, Ni, and Pb were detected and ranged from 12.3 to 291.0 mg/L for the top soil and 11.2 to 370.0 mg/L for 25 to 50 cm soil layer. However, concentrations of Ni, Cu, and Pb in soil tended to decrease while Mn, Zn and Cr tended to increase with an increase of soil depth near the landfill. A similar tendency of heavy metal concentration with depth was found at S4 except for Cu. The decreasing order of the selected heavy metals concentrations in the two soil layers at near the landfill was Mn>Zn>Ni>Cr>Cu>Pb and these concentrations of heavy metals were within the limits of QCVN 03-MT: 2015/BTNMT and Canadian Council of Ministers of the Environment (CCME, 2007). The result of the bioaccumulation factor (BAF) in rice plants showed that the selected heavy metals were accumulated more in the root rather than the stem-leaf and grain. Mn was accumulated dominantly in both root and stemleaf, while Zn, Cu, and Pb only accumulated in the root. Thus, result of this study suggests that is essential to collect and treat the heavy metals in the leachate properly to minimize the distribution of heavy metals to the paddy soil environment.
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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 | 0.000 | 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) | 0.001 | 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".