Low-density polyethylene microplastics alter chemical properties and microbial communities in agricultural soil
Bibliographic record
Abstract
Abstract Microplastic (MP) pollution in agricultural soils, resulting from the use of plastic mulch, compost, and sewage sludge, jeopardizes the soil microbial populations. However, the effects of MPs on soil chemical properties and microbial communities remain largely unknown. Here, we investigated the effects of different concentration levels (0, 0.1, 1, 3, 5, and 7%; w:w) of low-density polyethylene (LDPE) MPs on the chemical properties and bacterial communities of agricultural soil in an incubation study. The addition of LDPE MPs did not drastically change soil pH (ranging from 8.22 to 8.42). Electrical conductivity increased significantly when the LDPE MP concentrations were between 1 and 7%, whereas the total exchangeable cations (Na + , K + , Mg 2+ , and Ca 2+ ) decreased significantly at higher LDPE MP concentrations (3–7%). The highest available phosphorus content (2.13 mg kg −1 ) was observed in 0.1% LDPE MP. Bacterial richness (Chao1 and Ace indices) was the lowest at 0.1% LDPE MP, and diversity indices (Shannon and Invsimpson) were higher at 0 and 1% LDPE MP than at other concentrations. The effect of LDPE MP concentrations on bacterial phyla remained unchanged, but the bacterial abundance varied. The relative abundance of Proteobacteria (25.8–33.0%) was the highest in all treatments. The abundance of Acidobacteria (15.8–17.2%) was also high, particularly in the 0, 0.1, and 1% LDPE MPs. With the increase in LDPE MP concentration, the abundance of Actinobacteria gradually increased from 7.80 to 31.8%. Our findings suggest that different MP concentration levels considerably alter soil chemical properties and microbial composition, which may potentially change the ecological functions of soil ecosystems.
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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 | 0.001 | 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 | 0.001 |
| 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 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".