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Enregistrement W3170848586

Evaluation of Potential Health Risks from Microplastics in Drinking Water

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

Notice bibliographique

RevueUWSpace (University of Waterloo) · 2021
Typedissertation
Langueen
DomaineEnvironmental Science
ThématiqueMicroplastics and Plastic Pollution
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMicroplasticsEnvironmental scienceEnvironmental healthEnvironmental chemistryHealth riskEnvironmental engineeringEnvironmental planningChemistryMedicine
DOInon disponible

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,003
Score d'incertitude au seuil0,009

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0000,001
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,017
Tête enseignante GPT0,226
Écart entre enseignants0,208 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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