Quantification and Characterization of Microplastics in Selected Water Bodies in and around Dhaka City
Bibliographic record
Abstract
Plastics pose serious threats to aquatic environments because of their persistence and non-biodegradability. Microplastics, defined as microscopic, manufactured particles (primary sources) or fragments derived from larger plastic debris (secondary sources), can persist in waterbodies for prolonged periods. Extensive research has been conducted to assess microplastic contamination in marine systems; however, freshwater ecosystems remain underexplored, particularly in Bangladesh. To address this gap, the present study identified, quantified, and characterized microplastics across key waterbodies in and around Dhaka City—Dhanmondi Lake, Ramna Lake, Hatirjheel, Buriganga River, and Turag River—during the winter (dry) and summer (wet) seasons. Collected samples were processed through sieving, wet peroxide oxidation (H₂O₂), and density separation (NaCl solution) to isolate, quantify, and characterize microplastics according to their size, shape, color, and texture. Results revealed significant seasonal and spatial variations. Microplastic content ranged from 0.44% (Dhanmondi Lake) to 9.34% (Turag River) in winter, increasing to 1.08% (Hatirjheel)–22.6% (Turag River) in summer. Peripheral rivers (Buriganga and Turag) consistently showed higher concentrations than inland lakes. By weight, larger particles (1.18–4.75 mm) dominated, while smaller particles prevailed by count. Most microplastics were irregular with rough surfaces, indicating prolonged exposure; however, sharp-edged larger particles in Dhanmondi Lake and Buriganga River suggested recent inputs. This study provides the first comprehensive assessment of microplastic pollution in Dhaka's freshwater systems, highlighting the seasonal dynamics and spatial variability of contamination. Advanced techniques such as Raman and Fourier Transform Infrared Spectroscopy can improve microplastic identification and quantification. Developing more sensitive detection methods, coupled with public awareness of their environmental and health impacts, is essential for effective management. Moreover, enforcing stricter regulations on single-use plastics is critical to mitigate microplastic pollution.
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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) | 0.000 | 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 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".