Heavy Metal Concentrations in Surface Water of the Lower Mekong River Basin (Vietnam)
Bibliographic record
Abstract
Purpose: The research is carried out to determine the concentration of heavy metals in surface water of the downstream Mekong River. Methods: The sampling method follows the Vietnamese national standard (TCVN 6663-6:2018, ISO 5667-6:2014). The heavy metal indicators analyzed using an atomic absorption spectroscopy machine. The lowest detection level of the measurement method is approximately 0.2µg (on average for all indicators measured). Results and discussion: Among the 5 parameters studied in December 2023, the average concentration has not exceeded the national standards of Vietnam (the maximum allowable values for parameters affecting human health), but it is approaching the limit (especially Zn at 454.69 µg/l compared to 500 µg/l). At many locations, the levels of Zn and Cu have exceeded the standards (at Cửa Đại, Ba Lai, Cung Hầu). Compared to our research conducted in 2013, except for Cd, the average concentrations of all other parameters are increasing. Compared to the standards of some countries such as the United States, Canada, and Russia, the levels of heavy metals in the research area are high, especially Zn and Cu which are much higher. Implications of the research: The heavy metal concentration in the study area increases towards the sea, correlating with the pH index and river-sea mixing. Human activities contribute to heavy metal accumulation in the lower Mekong region's water. Management agencies must develop policies and measures to minimize NCDs' impact and ensure sustainable development. Originality/value: The research findings will guide rational production and daily activities in the Mekong Delta, a region severely impacted by climate change, to ensure sustainable development.
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 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 | 0.001 | 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".