The Content of Heavy Metal in the Labu Riverbed Sediments: An Assessment of the Level of Pollution Applying Sediment Quality Guidelines and Geoaccumulation Index
Bibliographic record
Abstract
River bed sediments from thirteen (13) sampling stations, from Labu River were collected in June and December 2014. The aim is to identify the source and ascertain the current levels of heavy metal pollutions. The selected heavy metals (Cr, Cu, Fe, Ni, Zn, As, Cd and Pb) were analysed by using Inductively Couple Plasma Mass Spectrophotometry (ICPMS). Metals were statistically analysed and compared with Dutch/Malaysia Sediments Quality Guidelines (Target and intervention values) and the Canadian Sediment Quality Guidelines (ISQG and PEL). Geoaccumulation index (Igeo) was calculated and metals were classified. The results showed that the mean value for Cr is 19.74±10.36 mg kg-1, Cu (7.33±2.35 mg kg-1); Fe (7636.39±26.38 mg kg-1); As (11.67±3.59 mg kg-1); Cd (0.097±0.03 mg kg-1) and Pb (26.23±5.33 mg kg-1). The compared sediment guidelines revealed that Cr (51.55 mg kg-1) in SW9, Pb in SW1 (36.43 mg kg-1) and SW13 (37.42 mg kg-1) and As in all of the stations (SW1-SW13) did not meet the Canadian ISQG and were polluted. The geoaccumulation index (Igeo) showed that, Cr (-3.38±0.17), Cu (-3.22±0.12), Ni (-3.37±0.24), Zn (-2.14±0.23), Cd (-3.38±0.17) and Pb (-3.22±0.12) were practically uncontaminated. However, As (2.14±0.13) was classified as moderately contaminated. Based on mean concentrations of heavy metals with the compared Sediments Quality Guidelines (SQG) and Index, it is concluded that As is the heavy metal of concern in the Labu catchment. There is need for the authorities to pay more attentions to sediment pollution problem due to As and address riverbed sediment pollution problems in the different locations as indicated by ISQG due to anthropogenic influences from the KLIA, Dengkil sand mine and Agriculture developments projects in the study area.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".