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Record W3170848586

Evaluation of Potential Health Risks from Microplastics in Drinking Water

2021· dissertation· en· W3170848586 on OpenAlexaboutno aff
Omar S. Chowdhury

Bibliographic record

VenueUWSpace (University of Waterloo) · 2021
Typedissertation
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsnot available
Fundersnot available
KeywordsMicroplasticsEnvironmental scienceEnvironmental healthEnvironmental chemistryHealth riskEnvironmental engineeringEnvironmental planningChemistryMedicine
DOInot available

Abstract

fetched live from OpenAlex

Microplastics have been detected, often abundantly, in freshwater environments over the past decade. While understanding of the ecological health implications of microplastics in aquatic environments has advanced considerably, the health risks of microplastics in drinking water are not well understood. Direct health impacts are attributed to the ingestion of microplastics materials themselves. In contrast, indirect health impacts are attributed to the chemical contaminants that sorb on and in microplastics in the aquatic environment and are concurrently ingested. While it is desirable to evaluate both types of health risks, there are currently no available and conclusive toxicological investigations of the health implications of microplastics ingestion by humans; current understanding is limited to microplastics impacts on small organisms or cell cultures. In contrast, considerable information regarding the health effects of some contaminants that sorb on or in microplastics is available. Although this information has not been integrated to inform health risks associated with microplastics ingestion via contaminated drinking water, this integration is pressingly needed to guide risk management. \nHere, the potential health risks attributable to chemical contaminants retained on or in microplastics in the aquatic environment and ingested via contaminated drinking water were assessed using a new concept developed in this research: the Threshold Microplastics Concentration (TMC). The TMC indicates the total number of microplastics particles per liter of water that, if ingested, constitutes exposure to potentially harmful concentrations of chemical contaminants retained on or in microplastics via sorption mechanisms. A TMC of 0.024 microplastics particles per liter was identified given currently available contaminant sorption data; this value increased to 2.550 microplastics particles per L in absence of antimony. Thus, these respective values indicate that source water concentrations of 24 or 2,550 microplastics particles per L or less should not pose health concerns attributable to sorbed chemical contaminants for well-operated conventional treatment systems in which a 3-log (i.e., 99.9%) reduction in microplastics concentration can be reasonably expected by physico-chemical filtration. Critically, a source water microplastics concentration that exceeds the TMC is not necessarily indicative of health risks from microplastics in drinking water; rather, it indicates that more detailed analysis may be warranted. For example, system specifics such as types of treatment implemented, sorbed contaminants present in the source water, size distribution of the microplastics, etc. affect the TMC. Notably, antimony was identified as a potential sentinel indicator of potential health risk from microplastics because it is especially toxic. Similarly, PVC was identified as a key microplastics type because of its contaminant sorption propensity. Only 11 contaminants and seven common microplastics materials were included in this analysis because of limited sorption and toxicity data for known chemical contaminants of human health concern; however, the “Microplastics Calculator” developed herein to calculate TMCs can be easily updated as chemical, plastics, and treatment data become available. \nMicroplastics are particles—in many ways they are not different than other particles removed during drinking water treatment. Their removal can therefore be explained by the physico-chemical processes that are involved in particle removal during filtration. Here, a synthesis of the current knowledge regarding the treatment of particulate contaminants including microplastics and a limited series of surface charge assessments and bench-scale coagulation and filtration experiments were conducted to confirm microplastics removal expectations during drinking water treatment. These experiments demonstrated the size dependency that would be expected by classical filtration theory: the order of particle removal efficiency by filtration was 45 μm > 10 μm > 1 μm. The surface charge of several common microplastics (polyethylene, polystyrene, acrylic, and polyetheretherketone) varied considerably and was impacted by the quality of the matrix in which they were suspended, as would be expected. Notably, however, coagulant addition at doses sufficient for achieving optimal particle destabilization in absence of the microplastics was also sufficient for destabilizing microplastics suspended at environmentally relevant concentrations in all matrices investigated (i.e., distilled deionized MilliQTM water; 100 mM KCl electrolyte solution; low turbidity, low dissolved organic carbon (DOC) Lake Ontario water; and moderate DOC, higher turbidity Grand River water). Overall, this analysis confirmed that the removal of microplastics particles by engineered physico-chemical filtration processes should be consistent with that which would be expected of other particles and particulate contaminants.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.017
GPT teacher head0.226
Teacher spread0.208 · 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 designObservational
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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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