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Record W4416551019 · doi:10.1038/s41545-025-00531-w

Microplastic removal across ten drinking water treatment facilities and distribution systems

2025· article· en· W4416551019 on OpenAlex
Charles Balkenbusch, Judith Glienke, Yuhao Wu, Keenan Munno, Michael Jung, Husein Almuhtaram, Robert C. Andrews

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenpj Clean Water · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsHudbay Minerals (Canada)University of Toronto
FundersHealth Canada
KeywordsMicroplasticsWater treatmentFiltration (mathematics)Water sourceSewage treatmentWater pollutionDistribution (mathematics)

Abstract

fetched live from OpenAlex

The performance of conventional and advanced drinking water treatment processes for the removal of microplastics is poorly understood due to the use of a wide range of methods for sample collection, isolation, and analysis that make direct comparison among studies challenging. In this study, microplastic (>2 µm) removal across ten drinking water treatment facilities, as well as their presence in source waters and distribution systems, was characterized. Municipal drinking water treatment facilities achieved >97.5% removal, primarily due to chemically assisted granular media filtration or ultrafiltration. In untreated source waters, concentrations ranged from 1193 ± 64 to 7185 ± 64 particles/L, with polypropylene, polyethylene, polyamide, and plastic copolymers representing the most common polymer types identified. These findings provide insight regarding microplastic exposure via drinking water, as well as treatment process performance for their removal which may be used to inform the development and implementation of future regulations and/or guidelines.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.707
Threshold uncertainty score0.710

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.007
GPT teacher head0.209
Teacher spread0.201 · 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