Influences of land use and depth profile on the characteristics of microplastics in agricultural soils
Bibliographic record
Abstract
Terrestrial soils are an environmental compartment in which microplastics are known to accumulate. Compared to the surface water of global oceans, soils contain more microplastics, however they are less well studied to date. In particular, the applications of wastewater and corresponding sludge as fertilizers are a major source of microplastics to agricultural soil, as they include washing machine effluent which is often concentrated in polyester fibres. Other relevant microplastic sources include plastic mulching, netting, greenhouses, plastic drainage pipes, and atmospheric deposition. The characteristics and transfer dynamics of microplastics between different environmental compartments including soil in the same agricultural watershed are not well understood. Additionally, very limited information is known on the stock of microplastics in soils. In this work, a long-term French research site, the Orgeval watershed (104 km2), was sampled for soil. This watershed, located slightly beyond the extremities of the Eastern Parisian suburbs, is composed largely of intensive cereal crops and minimal urban zones. Nine locations within the watershed were composite sampled at the soil surface including locations both upstream and at the watershed outlet. These soil samples were derived from various land use areas including agricultural zones such as tilled or undisturbed agricultural fields, greenhouses, and drainage canal riverbanks, plus soil in forested areas and an urban green space. Of these land use types, greenhouse soils demonstrated the highest concentrations of microplastics in surface soils up to 11,200 MPs/kg, where polyethylene and polypropylene made up the majority of the polymers identified. In comparison, forest soils contained far fewer microplastics up to a concentration of 880 MPs/kg. Soil cores were also collected from two of these sites down to a depth of 60 cm, the typical maximum tilling depth used in this watershed. The most noticeable concentration decrease was observed between soil samples collected at the soil surface versus a further 20 cm below it. This study helps better understand the sources of microplastics as well as their fate in agricultural soils.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".