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Record W4392357017 · doi:10.26434/chemrxiv-2024-jrjkk

Microplastics and nanoplastics in water: Improving removal in wastewater treatment plants with alternative coagulants

2024· preprint· en· W4392357017 on OpenAlexafffund
Sinan Abi Farraj, Mathieu Lapointe, Rafael S. Kurusu, Nathalie Tufenkji

Bibliographic record

VenueChemRxiv · 2024
Typepreprint
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsÉcole de Technologie SupérieureMcGill University
FundersFonds de recherche du Québec – Nature et technologiesMcGill UniversityKillam TrustsNatural Sciences and Engineering Research Council of CanadaMitacsCanada Research Chairs
KeywordsMicroplasticsWastewaterEnvironmental scienceWaste managementSewage treatmentEnvironmental engineeringChemistryEnvironmental chemistryEngineering

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.011
GPT teacher head0.209
Teacher spread0.197 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations3
Published2024
Admission routes2
Has abstractyes

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