Evaluating Microplastics Removal Efficiency of Textile Industry Conventional Wastewater Treatment Plant of Thailand
Bibliographic record
Abstract
Global plastic pollution is a serious problem. From manufacture to disposal, microplastics appear at every point in the textile life cycle. Numerous case studies demonstrated that wastewater treatment facilities cannot remove the microplastics they produce. The purpose of this study was to evaluate the amount of microplastics that leaks into the canal and adjacent water bodies from a wastewater treatment facility serving the textile industry in Thailand, as well as to discover the differences between the samples taken upstream and downstream. NOAA protected laboratory investigation based findings indicated that 590–601 microplastics particles per cubic meter (particles/m3) flowed into the canal; however, the upstream sample (344–349) had more particles/m3 than the downstream sample (246–252). The industry leaked microplastics on average 172 particles/m3 upstream and 123 particles/m3 downstream. Our research revealed that the wastewater treatment plant's ability to capture microplastics particles was insufficient. A reliable mechanism to remove microplastics particles from wastewater treatment is required to protect environment, aquatic life, and water quality without interfering with industrial operations. This research emphasizes the Sustainable Development Goals, Responsible Production and Consumption (Goal 12), and Life below Water (Goal 14).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".