ANALISIS CEMARAN LIMBAH INDUSTRI DAN DOMESTIK TERHADAP BIOTA LAUT DI PERAIRAN KOTA TANJUNGPINANG, PROVIPNSI KEPULAUAN RIAU
Bibliographic record
Abstract
Industrial sector is the second priority in development of Tanjungpinang city. The mining industry, processing industry, transport and food are thriving . People has the opinion that a small industry is an industry that does not threaten the environment, so that the small-scale industrial waste are sometimes were forgotten due to it is not significant, and not too dangerous, whereas the B3 waste contained in domestic waste can cause disturbance of marine life and the ecosystem this will have potential to destroy the ecosystem. This study aims to explain the impact of B3 and domestic waste pollution to the environment, especially marine waters to marine life, and feedback to the provincial government for the formulation strategy of the management of the Tanjungpinang waters environment. For the analysis, 10 water samples and 15 aquatic biota was taken at different locations. While the quantitative analysis of pollutants carried by observing a population of the elements of hazardous substances from sediment samples, water and biota network. XRF techniques (X-Ray Fluorescence) and AAS (Atomic Absorbance Spectroscopy) used for the analysis content of the samples. The pollution index determined by compare metal concentration ratio the polluted areas with the standard metal concentration areas that were not polluted. The results show that the coastal water of tanjungpinang have been contaminated by heavy metals (As, Cd, Cu , Pb, Zn, and Ni) with pollution index 2.91 - 5.96. The pollutant Metals were came from the human activities in the shipbuilding industry usually Pb and Zn which is the main component of the paint. While heavy metals such as arsenic (As), Cadmium (Cd), copper (Cu) probably derived from bauxite mining activity, the high levels of nitrate is a sign of agricultural activities that use fertilizers. Unfortunately the rest of it discharged into the coastal waters of Tanjungpinang city, and there is also pollution of E-coli from human waste. Biota that live in the waters of Tanjungpinang have been contaminated by heavy metals (Hg, Zn, and Ar) by bioaccumulation. The related activity of the pollutant was the bauxite processing industry in the past. Heavy metal pollution is highest in Kijing (Pilsbryoconcha exillis) which includes : Hg, Cr, As, Cu, Zn, Ni, and the dimersal fish that have limited movement. Feedback given is that provincial governments do mangrove reforestation along the coast and estuaries, and create marine conservation areas determination of areas (KKLD) in the strait Dompak water. Key Words : heavy metals, marine life, coastal water of tanjungpinang, mangrove
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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 | 0.001 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".