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Record W7106036674 · doi:10.7939/83410

Development and Optimization of Methods for Microplastic Analysis in Drinking Water: A Case Study of Glenmore Drinking Water Treatment Plant, Calgary

2025· dissertation· en· W7106036674 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2025
Typedissertation
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsnot available
Fundersnot available
KeywordsMicroplasticsRaw waterWater treatmentHuman healthSample preparationSample (material)Flushing

Abstract

fetched live from OpenAlex

In recent years, the presence of microplastics (MPs) in drinking water has garnered increasing attention due to their potential impact on human health. MPs are plastic particles ranging from 1 µm to 5 mm and can have varying size, morphology, and chemical composition. Although their full impact on human health remains unclear, characterizing the MPs and understanding their fate throughout the drinking water production process is crucial to managing potential risks. This thesis investigates the presence of MPs larger than 10 µm in raw and treated water from the Glenmore drinking water treatment plant (G-DWTP), one of the two facilities supplying drinking water to the City of Calgary. To facilitate the study, a novel sample collection device was developed to collect large (~1000 L) sample volumes for analysis. Experiments were conducted to improve particle recoveries by evaluating the application of high-pressure flushing (HPF) and ultrasonic treatment. Additionally, the optimal magnification for Raman microspectroscopic analysis of different-sized particles was assessed. Monthly samples from April–September 2024 were collected from both raw and treated drinking water. After pretreatment, samples were stained with Nile Red and suspected MPs were identified by fluorescence microscopy and analyzed using Raman microscopy for identification of their polymer composition. For sample pretreatment, the optimal procedure involved assembling the filter support and mesh together, followed by a one-minute ultrasonic treatment and HPF. This pretreatment method yielded differing recoveries across various size ranges: 76.7 ± 20.9% for 250-300 µm, 75.3 ± 7.8% for 106-125 µm, 64.6 ± 13.3% for 63-70 µm, and 26.3 ± 7.1% for 10-20 µm. The optimal magnification was 10x for particles larger than 90 µm and 50x for those smaller than 90 µm. The measured abundance of MPs in the raw water was 1.9 ± 2.8 MPs/m3, with a slight increase to 3.1 ± 3.1 MPs/m3 in the treated water. Among the detected MPs, particles in the size range of 1-80 µm accounted for 73%. Three morphological types of MPs were identified: fragments (89%), beads (8%), and fibers (3%). The predominant polymer types were polypropylene, polystyrene, polyethylene, and polyvinyl chloride. In terms of color, white and transparent MPs were the most common, comprising 54%, and 32% of the total, respectively. Overall, MP abundance at the G-DWTP was relatively low compared to other studies. This thesis represents the first assessment of MPs in the G-DWTP. While it provides baseline information, establishing a monitoring plan is important to adequately assess MP risks. Finally, acceptable particle recoveries achieved through the design of the sampling equipment and methods development offer valuable reference points for MPs monitoring activities at other DWTPs in Calgary.

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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.931
Threshold uncertainty score0.138

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.011
GPT teacher head0.221
Teacher spread0.211 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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