Microplastic-Derived Dissolved Organic Matter Regulates Soil Carbon Respiration via Microbial Ecophysiological Controls
Bibliographic record
Abstract
Microbial regulation of soil carbon sequestration is vulnerable to anthropogenic stressors, notably microplastic pollution. Microplastics can release carbon-based compounds that serve as potential substrates for soil heterotrophic microbes. The impact of this novel microplastic-derived dissolved organic matter (MP-DOM) on soil carbon cycling and the underlying mechanisms remain largely unexplored. Here, short-term soil microcosm experiments were conducted with MP-DOM sourced from pristine and aged microplastics to compare their effects on soil carbon respiration against natural dissolved organic matter (NOM). The underlying mechanisms are investigated by estimating the spectroscopic and molecular signatures of MP-DOM and NOM, and comparing their effects on soil microbial physiological properties. Our findings reveal that MP-DOM leads to 36.9–42.3% higher CO 2 emissions from soils than NOM, attributed to its greater bioavailability. MP-DOM simultaneously stimulates greater microbial quantity, phenotypic activities, and carbon utilization efficiency because this carbon is more biochemically accessible than NOM. Network analysis indicates stronger interconnections among labile molecules and bacterial taxa in MP-DOM treatments compared to NOM-treated soils, suggesting enhanced microbial capacity to utilize the more readily available MP-DOM. This study demonstrates that MP-DOM accelerates soil microbial respiration by mediating their physiological traits, with potential implications for climate change.
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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) | 0.000 | 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".