Polyethylene microplastic and biochar interactively affect the global warming potential of soil greenhouse gas emissions
Bibliographic record
Abstract
Emerging microplastic pollution and biochar application result in their coexistence in the soil. In this study, a polyethylene microplastic, a straw biochar, and a manure biochar were applied alone or in combination to an agricultural soil to explore their interactive effects on microbial biomass carbon and nitrogen, bacterial community composition, structure and function, and the resultant greenhouse gas emissions in a 45-day laboratory incubation. At the end of incubation, the co-application of microplastic and biochar suppressed the global warming potential of cumulative greenhouse gas emissions compared with the sum of their application alone. Specifically, coexisting with microplastics increased N 2 O emissions by 37.5% but decreased CH 4 emissions by 35.8% in the straw biochar added soil, and decreased N 2 O, CO 2 and CH 4 emissions by 24.8, 6.2, and 65.2%, respectively, in the manure biochar added soil. A correlation network analysis illustrated that the increased global warming potential was related to the changed bacterial function and microbial biomass carbon and nitrogen in the treatments with straw biochar and/or polyethylene microplastic added, and by the changed bacterial community structure and function in the treatments with manure biochar and/or polyethylene microplastic added. Bacterial functions associated with tricarboxylic acid cycle contributed to CO 2 emissions. Bacterial functions associated with the nitrogen cycle such as nosZ and AOBamoABC were negatively and positively correlated with N 2 O emissions, respectively. The interaction between different types of microplastics and soil amendments and the resultant effects on ecosystem function deserve further research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".