Heavy Metals Contamination and Water Quality Parameter Conditions in Jatiluhur Reservoir, West Java, Indonesia
Bibliographic record
Abstract
Waste pollution into the Citarum River (main water source of Jatiluhur Reservoir) was dominated by the manufacturing industry sector (textile, chemical, metal, pharmaceutical). Industrial was the most common contributor to heavy metal waste. Heavy metal contamination into waters will cause any problems, one of which was the emergence of various diseases both short and long term. Based on the issues, the study of heavy metal contamination and also water quality parameter conditions in the Jatiluhur Reservoir was necessary. The heavy metals (Cu, Zn, Hg, Pb, Cd) contents were determined using the X-Ray Fluorescence (XRF) Spectrometry method (for sediment) and Atomic Absorption Spectrometry (AAS) method (for water). The other water quality parameters were analyzed using the methods from the Indonesian National Standard (SNI). Furthermore, the data were compared to the Canadian Sediment Quality Guidelines (for heavy metal in sediment) and water quality standards from Government Regulation of the Republic of Indonesia Number 82 of 2001 (Class 3) (for water quality parameters). Concerning the discussion, Jatiluhur Reservoir was divided into three zones i.e. the inlet area, main inundation area, and outlet area. Conditions in the sedimentary layer, mercury (Hg) have accumulated throughout the Jatiluhur Reservoir area with conditions exceeding the maximum limit, while Cu metal tends to accumulate in the inlet area with conditions exceeding the minimum limit. For other heavy metals, exceed the minimum limit at some locations, but more results were below that. Although all heavy metals have not been detected in water, this was a warning that the presence of heavy metals in sediments can potentially dissolve into the water, the most extreme thing that can happen was upwelling. If this happens, the heavy metals can be contained excessively in water, harmful to and possibly consumed by aquatic biota and human. Considering these conditions, the biota that was most likely to be exposed was benthic organisms. In general, the water quality parameters in Jatiluhur Reservoir meet the quality standards. Only ammonia that does not meet the quality standards for sensitive fish life, regarding the massively of aquaculture activity in this reservoir.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".