Methods Matter: Methods for Sampling Microplastic and Other Anthropogenic Particles and Their Implications for Monitoring and Ecological Risk Assessment
Bibliographic record
Abstract
Abstract To inform mitigation strategies and understand how microplastics affect wildlife, research is focused on understanding the sources, pathways, and occurrence of microplastics in the environment and in wildlife. Microplastics research entails counting and characterizing microplastics in nature, which is a labor-intensive process, particularly given the range of particle sizes and morphologies present within this diverse class of contaminants. Thus, it is crucial to determine appropriate sampling methods that best capture the types and quantities of microplastics relevant to inform the questions and objectives at hand. It is also critical to follow protocols with strict quality assurance and quality control (QA/QC) measures so that results reflect accurate estimates of microplastic contamination. Here, we assess different sampling procedures and QA/QC strategies to inform best practices for future environmental monitoring and assessments of exposure. We compare microplastic abundance and characteristics in surface-water samples collected using different methods (i.e., manta and bulk water) at the same sites, as well as duplicate samples for each method taken at the same site and approximate time. Samples were collected from 9 sampling sites within San Francisco Bay, California, USA, using 3 different sampling methods: 1) manta trawl (manta), 2) 1-L grab (grab), and 3) 10-L bulk water filtered in situ (pump). Bulk water sampling methods (both grab and pump) captured more microplastics within the smaller size range (<335 µm), most of which were fibers. Manta samples captured a greater diversity of morphologies but underestimated smaller-sized particles. Inspection of pump samples revealed high numbers of particles from procedural contamination, stressing the need for robust QA/QC, including sampling and analyzing laboratory blanks, field blanks, and duplicates. Choosing the appropriate sampling method, combined with rigorous, standardized QA/QC practices, is essential for the future of microplastics research in marine and freshwater ecosystems. Integr Environ Assess Manag 2021;17:282–291. © 2020 SETAC KEY POINTS It is critical to determine appropriate sampling methods that best capture the types and quantities of microplastics relevant to inform the questions and objectives at hand. Adhering to protocols with strict quality assurance and quality control (QA/QC) measures ensures that results reflect accurate estimates of microplastic contamination. Taking duplicate samples can reveal the variability between samples at a single site. Manta samples capture a greater diversity of morphologies than do grab samples, but they underestimate smaller-sized particles.
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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 | 0.016 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".