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Record W7020903806

Micro- and Nanoplastics Removal through Drinking Water Treatment Processes: Insights from Published Investigations and Development of a Possible Regulatory Approach

2023· dissertation· en· W7020903806 on OpenAlexafffund

Bibliographic record

VenueUWSpace (University of Waterloo) · 2023
Typedissertation
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsBlackberry (Canada)
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
KeywordsWater treatmentPollutantHuman healthFiltration (mathematics)Waterborne diseasesWater quality
DOInot available

Abstract

fetched live from OpenAlex

Micro- and nanoplastics (MNPs) have emerged as significant environmental pollutants with potential implications for human health. The presence of MNPs in freshwater bodies, such as lakes, rivers, and sometimes groundwater, is primarily attributed to the extensive use and degradation of plastic products. This contamination raises concerns for human health, given the critical role of freshwater as the primary drinking water source, through which MNPs can be ingested. However, there is limited information available regarding human exposure and the associated toxicity of MNPs in freshwater, and the area of nanoplastics (NPs) in freshwater environments remains relatively unexplored. This thesis addresses the limited knowledge surrounding MNPs in freshwater sources, particularly in relation to their abundance, removal efficiencies during treatment, potential toxicity, and regulatory considerations. The thesis is focused on evaluating data from full-scale drinking water treatment plant (DWTP) surveys, assessing bench-scale studies on MNPs removal, understanding toxicological impacts, summarizing legislative standards, and developing a regulatory framework. \nThe research approach involves analyzing data from full-scale DWTP surveys and bench-scale studies to assess MNPs abundance and removal efficiencies of different categories of MNPs, considering their size, shape, and polymer type. Special attention is given to coagulation-flocculation-sedimentation (CFS) and filtration processes. The findings from full-scale DWTP surveys reveal significant variability in MNPs abundance and removal efficiencies. Advanced DWTPs generally exhibit higher removal rates compared to conventional plants. However, the variability in results highlights the differences in MNPs properties, analysis techniques, and treatment procedures, making it challenging to establish definitive conclusions. Bench-scale studies indicate the effectiveness of CFS and filtration processes in MNPs removal, although findings differ due to variations in experimental conditions and methodological inconsistencies. This thesis highlights the need for consistency in sampling, quantification techniques, and reporting standards to establish a uniform dataset. Further research is necessary to better understand MNPs removal efficiencies, especially for NPs, and to address the lack of standardized methods. Additionally, the thesis examines current legislative standards, if any, related to MNPs in drinking water sources. The potential toxicological impacts of MNPs and their distinct characteristics compared to other micropollutants are also evaluated. Based on the findings, a regulatory framework is proposed to address the presence of MNPs in drinking water and reduce potential health risks. The proposed regulatory framework draws inspiration from existing approaches, but acknowledges the unique challenges posed by MNPs properties based on their classification and prevalence in the source water. Challenges in implementation include financial barriers and monitoring difficulties, along with the need for continuous assessment and further research. \nThis thesis contributes to the understanding of MNPs in freshwater sources and provides insights into their removal efficiencies. The proposed regulatory framework lays the groundwork for developing guidelines to mitigate human health risks associated with MNPs in drinking water. Further research and collaboration are essential to address the current knowledge gaps and establish effective strategies for managing MNPs in drinking water.

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)0.003
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: Systematic review · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.181
Teacher spread0.167 · 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 designSystematic review
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
Published2023
Admission routes2
Has abstractyes

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