Spatial Variability of Metals in Surface Water and Sediment in the Langat River and Geochemical Factors That Influence Their Water-Sediment Interactions
Bibliographic record
Abstract
This paper determines the controlling factors that influence the metals' behavior water-sediment interaction facies and distribution of elemental content ((75)As, (111)Cd, (59)Co, (52)Cr, (60)Ni, and (208)Pb) in water and sediment samples in order to assess the metal pollution status in the Langat River. A total of 90 water and sediment samples were collected simultaneously in triplicate at 30 sampling stations. Selected metals were analyzed using ICP-MS, and the metals' concentration varied among stations. Metal concentrations of water ranged between 0.08-24.71 μg/L for As, <0.01-0.53 μg/L for Cd, 0.06-6.22 μg/L for Co, 0.32-4.67 μg/L for Cr, 0.80-24.72 μg/L for Ni, and <0.005-6.99 μg/L for Pb. Meanwhile, for sediment, it ranged between 4.47-30.04 mg/kg for As, 0.02-0.18 mg/kg for Cd, 0.87-4.66 mg/kg for Co, 4.31-29.04 mg/kg for Cr, 2.33-8.25 mg/kg for Ni and 5.57-55.71 mg/kg for Pb. The average concentration of studied metals in the water was lower than the Malaysian National Standard for Drinking Water Quality proposed by the Ministry of Health. The average concentration for As in sediment was exceeding ISQG standards as proposed by the Canadian Sediment Quality Guidelines. Statistical analyses revealed that certain metals (As, Co, Ni, and Pb) were generally influenced by pH and conductivity. These results are important when making crucial decisions in determining potential hazardous levels of these metals toward humans.
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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 | 0.000 | 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) | 0.000 | 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".