Identification of microplastics extracted from field soils amended with municipal biosolids
Bibliographic record
Abstract
Microplastic particles in arable soil are expected to impact the environment and potentially human health. The application of municipal biosolids (MBs) to agricultural land presents a further dilemma in that biosolids act as a fertilizer for crop growth, and a disposal pathway for wastewater treatment plants. They are also a direct path for emerging contaminants, such as microplastics to enter the terrestrial environment. Reliable methods are needed to identify and quantify microplastics, found in agricultural soils to determine how microplastics are being cycled in the terrestrial environment. In this study, we developed a method for extracting microplastics from soil, and characterized their composition and identity for particles sized 5 μm to 2 mm. Method development was initially completed using natural soils spiked with microplastics and MBs, followed by the analyses of soil sampled from an agricultural field where MBs were recently applied at a rate of 13 tons dw/ha. The procedures that used the spiked samples showed that microplastics can be reliably extracted from soil in a laboratory setting, and identified and semi-quantified by thermogravimetric analysis combined with Fourier-transform infrared spectroscopy (TGA-FTIR). However, when the same methods were applied to the soil samples collected from the agricultural field, reproducibility became a challenge, as the number and type of microplastics changed even within the same soils (i.e., collected the same day from the same exact location). The variation in reproducibility observed between laboratory and field samples underscores the significant heterogeneity present in the environment. This heterogeneity, in turn, affects the identification and quantity of microplastics detected, a phenomenon observed even when comparing different fields within a single treatment regimen.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".