MétaCan
Menu
← Retour à la cohorte
Enregistrement W7046228462

Controls on Microplastics Accumulation in Stormwater Ponds

2024· dissertation· en· W7046228462 sur OpenAlexfundaboutno aff

Notice bibliographique

RevueUWSpace (University of Waterloo) · 2024
Typedissertation
Langueen
DomainePhysics and Astronomy
ThématiqueMagnetic confinement fusion research
Établissements canadiensnon disponible
Organismes subventionnairesNatural Sciences and Engineering Research Council of CanadaCanada Excellence Research Chairs, Government of Canada
Mots-clésMicroplasticsStormwaterSedimentSurface runoffAquatic ecosystemTotal organic carbonUrban runoffHydrology (agriculture)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Microplastics (MPs), or plastic particles that are less than 5 mm in diameter, are an emerging threat to aquatic and terrestrial ecosystems because of their potential toxicity and their resistance to degradation. In urban watersheds, stormwater runoff is a major carrier of MPs to downstream water bodies, which often drains into green infrastructure such as stormwater ponds (SWPs). The existing evidence indicates that SWPs may be effective at reducing the export loads of MPs from urban areas. However, the effectiveness of SWPs in retaining MPs and the controls on their accumulation in SWP remain understudied. Hence, it is significant to investigate MP occurrence and factors controlling MP distribution in SWPs. \nIn this thesis, I aimed to (1) assess the variability in MP types, sizes, and abundances within SWP sediment and water samples, (2) determine the influence of sediment properties (e.g., organic carbon concentration, particle size) on the types, shapes and sizes of MPs accumulating in sediments versus within and between SWPs, and (3) investigate the impact of SWP characteristics on MP accumulation, including land use and land cover (LULC). I addressed these research objectives by collecting sediment and water samples from five SWPs with different LULC (industrial, residential, and commercial) in the City of Kitchener, Ontario, Canada, extracting MPs from environmental samples, and characterizing MP particles using laser direct infrared (LDIR) imaging spectroscopy. \nIn Chapter 2, I extracted MPs from sediment cores in triplicates and determined MP counts and morphologies using the LDIR. I also analyzed sediment for grain size, mineralogy, and organic carbon (OC) content. The results revealed that the MPs accumulated in the sediments were predominantly fragments, with concentrations approximately 50 times higher than fibers, implying an important role of particle shape in controlling the accumulation of MP particles in SWP sediments. The highest fragment concentrations (2.3×108 particles kg dw-1) were found in the commercial SWP, while the highest fiber concentrations (4.5×106 particles kg dw-1) were found in the industrial SWP. Surface area-normalized MP accumulation rates in the forebays were generally 2-5 times higher than in the main basins. Sediment grain size and catchment impervious cover were significantly correlated with MP accumulation rates, with increasing MP concentrations observed with finer sediment grain size and higher catchment imperviousness. Polyamide and polyethylene were the two most abundant polymers found in the pond sediment, along with an overwhelming dominance of MP particles less than 50 µm. MP polymer composition and size distribution thus reflected the contribution of urban activities to MP pollution in a watershed. These findings indicate the important role of catchments’ land cover in the build-up and wash-off of sediments and MPs to downstream areas such as SWPs. \nIn Chapter 3, I quantified and characterized MP shapes, types, and sizes in stormwater samples collected bi-monthly from 5 SWPs. In a one-year water sample collections, MPs appeared to fluctuate with significant seasonal variation throughout the year with the highest concentration recorded in a residential pond (up to 20,166 fragment L-1 and 559 fiber L-1). Polyamide and polyethylene accounted for approximately 80% of total MP in the pond water, while small-sized MPs make up 85% of the particles, highlighting the impact of catchment land use on MP occurrence in SWP. Precipitation, wind speed, and pond hydraulic loading were found to wash away surface MPs and dilute MP concentration in the water column. These findings demonstrate the diversity in MP profiles associated with climate factors, implying a need for long-term monitoring to address those spatial and temporal variability. \nThe results from Chapters 2 and 3 overall provided an insight into MPs' preferential partitioning between two different environmental matrices, which can be applied in future research to assess the sources, transport, and fate of MPs in the freshwater ecosystem. Data from this research can be applied to generate MP accumulation rates across the Grand River watershed and eventually the Great Lakes. The outcomes from this study, moreover, actors influencing MP accumulation in urban catchments, and therefore, can support further studies on characterizing MP mass balance and budget for urban watersheds. Since SWPs are an effective indicator of local sources of MP pollution, understanding the MP from urban catchment can inform policymakers in a larger aquatic ecosystem to tailor management strategies accordingly. The research study presented in this thesis therefore contributes to the development of local policies and regulations, which not only address specific sources of MP pollution but also serve as models for larger-scale regulations aimed at protecting the freshwater environment.

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)0,000
score de la tête « metaresearch » (Gemma)0,000
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 enseignants0,020
Score d'incertitude au seuil0,041

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
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 Opus0,013
Tête enseignante GPT0,243
Écart entre enseignants0,231 · 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
Publié2024
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueUWSpace (University of Waterloo)→Même sujetMagnetic confinement fusion research→Travaux en français237 207→