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Record W4413400709 · doi:10.5539/ep.v14n2p9

Quantification and Characterization of Microplastics in Selected Water Bodies in and around Dhaka City

2025· article· en· W4413400709 on OpenAlexvenueno aff
Faika T. Ayshi, Nur Shadia, Muhammad A. Ali

Bibliographic record

VenueEnvironment and Pollution · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsnot available
Fundersnot available
KeywordsMicroplasticsEnvironmental scienceCharacterization (materials science)FisheryOceanographyGeographyBiologyGeologyMaterials scienceNanotechnology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.006
GPT teacher head0.185
Teacher spread0.179 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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