Sourcing of microplastics entering Lake Winnipeg, Manitoba, Canada
Bibliographic record
Abstract
The accumulation of plastic pollution is a pressing issue, especially the presence of microplastics in the environment. Microplastics are ubiquitous in the environment, however a research gap exists in understanding the sources of microplastics in freshwater environments. The lack of standardized methodologies for the collection, quantification, and identification of microplastics hinders our understanding of the sources and amounts of microplastics in the environment. This thesis evaluates the potential of carbon and hydrogen stable isotope (δ13C and δ2H) and elemental (%C and %H) analysis to identify the polymer type of microplastics. The polymer type of microplastics provides important information on their source. Commercial plastic products (n = 53) from six different polymer types were used to create a database of characteristic δ13C, δ2H, %C, and %H values for each polymer type. Environmental microplastics (n = 18) of unknown polymer types were also analyzed and their values were compared to the characteristic values for each polymer type in the database. Fourier transform infrared spectroscopy was used to confirm the identification of the polymer type of the unknown microplastics. The combination of the δ2H and %H values was best able to distinguish the different polymer types and was most useful for identifying the unknown microplastics. The identification of the unknown microplastics using their δ2H and %H values correlated strongly with identifications using FTIR, confirming that this represents a novel method that can be used to identify the polymer type of microplastics. Because it is a quantitative analysis, it is more attractive than other qualitative methods of identifying the polymer type of microplastics. Additionally, this thesis investigates spatial and temporal trends in microplastic concentrations in the Red River in Manitoba. Surface water samples were collected from nine sites in the spring, summer, and fall of 2022 to evaluate spatial and temporal trends in microplastic concentrations. Microplastics were found in all samples, with concentrations ranging from 70.0 to 268.7 particles m-3. Increased flow rates and stormwater runoff in the spring likely influenced seasonal variations observed in microplastic concentrations. Stormwater runoff, combined sewage overflows, and agricultural runoff are potential sources of microplastics to the Red River.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".