Shape matters: microplastic fibers disrupt soil carbon cycling via shape-driven physical disturbance
Bibliographic record
Abstract
Soil microplastic (MP) pollution is an emerging concern with uncertain carbon (C) cycling impacts. Among MPs, fiber-shaped particles may exert distinct effects due to their high surface area and dissimilarity from natural soil structures. This study aimed to assess the physical and chemical mechanisms by which MP addition alters soil C dynamics, especially through their impacts on priming effect. We assessed two common MP polymers and shapes-low-density polyethylene beads and acrylic fibers-at two addition rates (0.1% and 1% w/w), paired with inert glass particle controls mimicking the same MP shapes and a negative control with soil only. Natural abundance δ1³C isotopic analysis distinguished MP-derived and soil-derived CO2 emissions over a 63-day incubation period. Fiber-shaped additions of either glass or MPs reduced total CO2 emissions by 25%, whereas bead-shaped additions had no measurable effect. Plastic-derived CO2 constituted a minor but detectable portion of total emissions, with similar decomposition rates across polymer types. Surprisingly, higher MP concentrations decomposed up to 10 times slower than lower concentrations, suggesting that MPs may clump together, reducing the surface area available for microbial colonization, thereby slowing down breakdown. Our results show that fiber-shaped MPs significantly disrupt soil C cycling, mainly through physical disturbance, while chemical effects (ie, presence/absence of plastics) are secondary. Finally, soil priming effect increased with the amount of MPs added, raising additional concerns about the consequences of rising soil plastic contaminations worldwide.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".