Microplastics Generate Less Mineral Protection of Soil Carbon and More CO<sub>2</sub> Emissions
Bibliographic record
Abstract
Abstract Microplastic pollution in terrestrial ecosystems threatens to destabilize large soil carbon stocks that help to mitigate climate change. Carbon‐based substrates can release from microplastics and contribute to terrestrial carbon pools, but how these emerging organic compounds influence carbon mineralization and sequestration remains unknown. Here, microcosm experiments are conducted to determine the bioavailability of microplastic‐derived dissolved organic matter (MP‐DOM) in soils and its contribution to mineral‐associated carbon pool. The underlying mechanisms are identified by estimating its spectroscopic and molecular signatures and comparing its sorption properties on model minerals with natural organic matter (NOM). The results show that MP‐DOM leads to 21–576% higher CO 2 emissions and 34–83% lower mineral‐associated organic carbon in soils than NOM, depending on the type of plastic polymer. DOM from biodegradable microplastics induces higher CO 2 emissions than conventional microplastics. It is found that MP‐DOM is 7.96 times more labile than NOM, making it more accessible for microbial utilization. The lower degree of humification, fewer polar functional groups, and higher H/C ratios in MP‐DOM also led to 3.96 times less sorption with mineral particles. The findings provide insights into the effects of microplastics on soil carbon storage and highlight their consequences for wider terrestrial carbon cycling and climate warming.
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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 | 0.000 | 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".