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Record W7011315812

Modeling of Microplastic Emission, Transport, and Retention in Urban Stormwater Ponds

2024· other· en· W7011315812 on OpenAlexaffabout

Bibliographic record

VenueUWSpace (University of Waterloo) · 2024
Typeother
Languageen
FieldBusiness, Management and Accounting
TopicDigital Innovation in Industries
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsStormwaterMicroplasticsSettlingSedimentHydrology (agriculture)TurbidityUrban runoffDrainage basin
DOInot available

Abstract

fetched live from OpenAlex

Urban areas are the major sources of microplastics to the environment, given their high concentration of people, road traffic, and mismanaged waste. Stormwater runoff, one of the major carriers of urban-derived MPs to downstream environments, is often treated by stormwater management systems, including stormwater ponds (SWPs). SWPs are known to accumulate MPs in their sediments; however, their effectiveness in reducing urban MP loads and the magnitude of urban MP emissions remain understudied. We established MP particle balances for 5 SWPs draining catchments with different land uses in Kitchener, Ontario, Canada to quantify catchment MP emission factors and SWP MP retention efficiencies using measurements of MP sediment accumulation rates and water column concentrations combined with catchment hydrological modeling. MPs were separated from the sediment and water samples using a density-based separation method and then analyzed by laser direct infrared spectroscopy for particle counts, size distributions and polymer compositions. The results show that fragment-shaped MP particles preferentially accumulate in sediments whereas fibers are relatively higher in the water columns and therefore not as well retained by SWPs. Polyamide and polyethylene were the dominant polymer types in the SWP sediments, despite their relatively low densities, suggesting an important role of mechanisms other than just density-based settling for the accumulation of MPs in SWPs. Total MP retention efficiencies ranged from 24 to 87% across the 5 SWPs. The calculated MP emission factors were negatively correlated with catchment road coverage yet positively correlated with mixed imperviousness, suggesting that while road traffic may produce MPs, they are not accumulators of MPs for wash off within the catchment, and the produced MPs are instead likely emitted outside of the catchment boundaries via atmospheric deposition. Overall, these results provide insight into the processes responsible for MP emissions in catchments as well as for MP accumulation in SWPs and into the roles of urban catchments as emitters of MPs and SWPs as accumulators of MPs in urban environments.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.720
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.179
Teacher spread0.163 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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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