Microplastics and nanoplastics in water: Improving removal in wastewater treatment plants with alternative coagulants
Bibliographic record
Abstract
Due to growing concerns of plastic pollution release into water bodies, it is imperative to understand and assess the removal of nanoplastics and microplastics during wastewater treatment processes. Although earlier studies have shown high removal of plastic contaminants during coagulation, flocculation and settling, a limited number of experiments have examined the removal of nanoplastics in relevant water chemistries including higher pH values and in the presence of organics. In this study, the removal of nanoplastics and microfibers is assessed using conventional (alum) and alternative coagulants including aluminum chlorohydrate (ACH) and cationic polyamines (pDADMAC) in a synthetic wastewater matrix for pH conditions between 7 and 8.6. Our results show that plastic removal is considerably reduced at higher pH values when alum is used as a coagulant. At the shorter settling time of 30 s, removal of pristine and aged nanoplastics declined from 64 ± 3% and 76 ± 3% respectively (pH 7) to less than 20% at pH values higher than 7.8. Similarly, polyester microfiber removal was observed to decline from 97 ± 1% at pH 7 to 85 ± 3% at pH 8.6 for samples collected after 3 min of settling. Replacing alum with polynuclear aluminum coagulants resulted in greater plastic contaminant removal at pH values above 8 with a maximum observed microfiber removal of 95 ± 1% (ACH; pH 8.6; 3 min settling time) and a maximum observed nanoplastic removal of 71 ± 5% (ACH+pDADMAC; pH 8.2; 30 s settling time). Quartz crystal microbalance with dissipation monitoring (QCM-D) measurements revealed that ACH coagulant species results in a thicker deposition layer on negatively charged surfaces compared to alum. The addition of pDADMAC to ACH led to a more favorable interaction with a model negatively charged surface, indicated by a faster and more rigid deposition of the alternative coagulant. Taken together, these results show that environmental conditions, including water pH and interactions with wastewater colloids, affect plastic contaminant removal during primary wastewater treatment. Alternative coagulants, particularly ones that contain stable cationic species, offer municipalities improved removal of plastic contaminants under these challenging treatment conditions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".