Coastal and Marine Pollution in Bangladesh: Pathways, Hotspots and Adaptation Strategies
Bibliographic record
Abstract
Marine and coastal pollution is a global issue for human health and biodiversity. We have investigated pollution sources, flow patterns, hotspots, challenges, and adaptation policies in Bangladesh. Industries, ship breaking yards, sewage, tourism, and transboundary depositions are the main sources of pollutions. The Ganges, Padma, Jamuna, Brahmaputra and Meghna carry wastes to the Bay of Bengal. Pollution hotspots are Dhaka, Gazipur, Narshingdi, Narayanganj, Chittagong, Khulna, Mongla port and Sylhet city. Textile and dyeing industries discharge 12.7–13.5 million m3 waste waters annually and pollute 20% of fresh water. Ship breaking yards dump about 22.5 tons polychlorinated biphenyls in a year. More than 50% of the marine oil pollution comes from urban activities. Plastic wastes at 3000 t day-1 and tourism are also contributing to the coastal pollution. Effluent releasing standards are not maintained, and thus higher concentrations of heavy metals are found with marine fishes. Use of heavy metal tolerant crops (rice: BRRI dhan47, potato: Cardinal, mustard: Brassica napus, flower: Marigold, vegetables: Cucumber, fibre: Kenaf, and so on), trap cropping, deep placement of fertilizers, integrated rice-fish-duck culture, etc can be adopted in polluted areas. There are laws for environmental issues, but coordination and financial capabilities does not warrant its effectiveness. Necessary steps are to be taken to improve infrastructure to ensure sanitation and benign discharge of industrial effluents. Systematic study on sources, fate and extent of current effluents dumping in water ways need to be assessed for wellbeing of aquatic life and human health.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".