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Record W7046228462

Controls on Microplastics Accumulation in Stormwater Ponds

2024· dissertation· en· W7046228462 on OpenAlexfundaboutno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2024
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanada Excellence Research Chairs, Government of Canada
KeywordsMicroplasticsStormwaterSedimentSurface runoffAquatic ecosystemTotal organic carbonUrban runoffHydrology (agriculture)
DOInot available

Abstract

fetched live from 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.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.020
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.013
GPT teacher head0.243
Teacher spread0.231 · 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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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