Comparison of ASTM and in-line microplastic sampling methods for drinking water
Bibliographic record
Abstract
Abstract Monitoring microplastics in source and treated drinking waters has become increasingly important due to existing and anticipated legislation. However, the absence of standardized protocols has led to a wide range of sampling methods being applied in previous studies, making it difficult to compare results. While ASTM International published in 2020 the only known standardized method for microplastic sampling in drinking water (ASTM D8332-20), concern exists regarding limitations associated with the use of open sieves when compared to enclosed “in-line” filtration methods. As such, direct comparison and evaluation is required in order to provide guidance regarding the monitoring of source and treated drinking water. This study compared the performance of both the ASTM sieve stack as well as in-line filtration methods in terms of recovery of environmentally and toxicologically relevant microplastic sizes (< 20 µm) as well as potential susceptibility to extraneous particles. The methods examined incorporated 20 and 5 µm pore size sieves (ASTM) or similarly sized membrane filters (in-line filtration), operated in-series. Spike and recovery trials were conducted by spiking fluorescent polyethylene terephthalate (PET) fragments of known size and concentration into the equipment while filtering source water on-site at three different drinking water treatment facilities. Particle recovery was analyzed using fluorescence microscopy, while microplastic particles in non-spiked blank samples were examined using Raman spectroscopy. The enclosed in-line filtration method achieved 82 ± 7.5% and 99 ± 6.9% recovery of microplastics in the 5–10 µm and 10–15 µm size ranges, respectively, compared to only 20 ± 5.3% and 66 ± 9.6%, respectively, for the sieve stack method; recovery of microplastics > 15 µm was comparable between the two methods. The sieve stack method resulted in 8.6 × more non-spiked particles than the in-line filtration method. The enclosed in-line method is therefore recommended to be used for microplastic sample collection. Future research should explore ways to improve particle recovery during sample extraction and digestion. These advancements will serve as significant steps towards standardization of microplastic sampling in drinking water.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".