High throughput application of ASTM D8332: Detailed prototype design and operating conditions for microplastic sampling of riverine systems
Bibliographic record
Abstract
Microplastic sampling strategies for aquatic systems commonly employ small mesh nets to collect suspended microparticles. These methods work well for marine sampling campaigns; however, complex water systems such as freshwater rivers, effluent discharges, and stormwater ponds characterized by high total suspended solids and fast-moving water can cause the nets to clog, rip, or tear. Published in 2020, ASTM D8332 is an alternative approach to sampling complex water systems for microplastics involving pumping large volumes of water across a cascading stack of sieves to collect suspended particles. Here we show that ASTM D8332 can be applied to sample freshwater rivers for microplastic collection. A high throughput sampling prototype developed in this work is capable of pumping 1500 L of river water in 45 min to collect particles as small as 45 µm. The system is lightweight, modular, and easily transportable. It has a discrete power supply, allowing for the collection of microplastics anywhere along the river, including municipal discharges. The design minimizes the amount of plastic in the flow path and provides a practical way to measure field contamination. Finally, we outline lessons learned through extensive field trials and testing using this system sampling the North Saskatchewan River in Edmonton, Alberta. •Existing small mesh nets face limitations in freshwater rivers, encountering clogging and tearing issues from high suspended solids and fast moving water.•Using a standardized method, ASTM D8332 - a pumping-based approach is efficient for microplastic collection in freshwater rivers.•Lightweight, modular, plastic free prototype system pumps 1500 L of river water in 45 min, collecting particles as small as 45 µm. Successfully tested in the North Saskatchewan 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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".