An Assessment of the Spatial and Temporal Distribution of Microplastics in Surface and Subsurface Sediment of Lake Huron, North America
Bibliographic record
Abstract
The awareness of and the data on the prevalence of microplastic (plastic particles <5mm) pollution in freshwater environments is rapidly increasing, as low-degrading polymers are being detected in various environmental matrices of the Laurentian Great Lakes. However, the accumulation, distribution and deposition of microplastics in offshore depositional environments of the Great Lakes, and particularly Lake Huron, is relatively unknown. In this study, benthic sediment from various Lake Huron waterbodies (main basin, Georgian Bay, the North Channel, and Saginaw Bay) was quantified for microplastic particles (fibres, fragments, films, and beads). The North Channel contained the greatest microplastic abundances, averaging 47,398 particles per kg-1 dry weight sediment (p kg-1 dw), followed by Georgian Bay (21,390 p kg-1 dw), the main basin (15,910 p kg-1 dw) and Saginaw Bay (1,592 p kg-1 dw). The results suggest that microplastic abundances in offshore settings are positively correlated with increasing water depth (p=0.004) and are controlled by lake bottom geomorphology. Hydrodynamic processes are a prevailing force driving microplastic dispersion and deposition into the offshore, in contrast to source-based drivers closer to the shoreline. Sediment cores were examined from Lake Huron and Lake Ontario, for which historical microplastic accumulation rates were determined using dated sediment profiles. The 210Pb dating method was used to establish a chronology for the Lake Huron sediment core (LH43), and approximate chronologies were constructed for the Lake Ontario cores (403A and 209C) using previously documented sedimentation rates using the polonium distillation method. The results suggest that microplastics have been accumulating in offshore benthic sediment of Lake Huron and Lake Ontario for ~75 years and ~80 years, respectively. Concentrations at depth (0-15 cm) reveal regional and temporal trends, consistent with increased plastic production since the 1950s. Significant increases in abundance were observed from the early 1950s to the late 1970s, and from the late 1980s to 2014. This implies that surface and subsurface sediment is an effective indicator of past and present microplastic contamination in the Great Lakes. Results from this work provide a benchmark for future microplastic assessments in the Lake Huron basin, and the wider Great Lakes system.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".