Why nanoplastics do not enhance the transport of contaminants in the critical zone
Bibliographic record
Abstract
The impact of nanoplastics on the co-transport of emerging contaminants is a subject of ongoing debate. Agricultural soils face potential contamination from micro- and nanoplastics through diverse agricultural practices. Various authors argue that the substantial surface area of small particles and their high sorption potential may considerably augment the mobility of numerous contaminants within the critical zone. Concerns have been expressed regarding the role of micro- and nanoplastics as carriers for organic contaminants into deeper soil layers, posing a potential threat to groundwater resources, particularly in agricultural soils where sewage sludge and plant protection products are frequently applied.In this study, we investigated the correlation between transport and desorption timescales by employing two diffusion models for micro- and nanoplastics ranging from 100 nm to 1 mm. To assess the transport of contaminants bound to these plastics, we examined the diffusion and partitioning coefficients of prominent agrochemicals and additives, along with commonly used polymers like polyethylene and tire material. Our modeling analysis reveals that the desorption rate of most organic contaminants is too rapid for micro- and nanoplastics to serve as effective transport facilitators in soil. Notably, the transport of contaminants facilitated by microplastics was observed to be significant only for highly hydrophobic contaminants under preferential fast-flow conditions.While micro- and nanoplastics could potentially introduce harmful contaminants into agricultural soils, our study suggests they do not significantly enhance contaminant mobility. Importantly, we found that nanoplastics, in particular, do not promote contaminant relocation under conditions relevant to almost all contaminants of concern.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".