Presence of High-Density Polyethylene Nanoplastics (HDPE-NPs) in Soil Can Influence the Growth Parameters of Tomato Plants (Solanum lycopersicum L.) at Various Stages of Development
Bibliographic record
Abstract
Contamination of aquatic and terrestrial ecosystems with microplastics (MPs) and nanoplastics (NPs) has raised significant global concerns. While most studies have focused on aquatic contamination, knowledge concerning the effect of MPs and NPs in biosolids on agricultural field crops remains limited, as is the range of polymer types tested. In this study, polyethylene nanoplastics (HDPE-NPs, <500 nm diameter) were produced in the lab, and their effect on tomato plants (Solanum lycopersicum L.) was studied at different growth stages. Physical and chemical characterizations of the HDPE-NPs were performed. Compared to the control group, the presence of 2.8 mg/kg HDPE-NPs in soil increased tomato leaf greenness (p < 0.05), while the presence of 0.5 mg/kg HDPE-NPs in the soil lowered water use efficiency (WUE, p < 0.05) of the plants in the early vegetative stage. Soil CO2 emissions were significantly lower under both the 0.5 mg/kg (p < 0.05) and 2.8 mg/kg HDPE-NPs treatments (p < 0.05). At the early germination stage, HDPE-NPs in the soil resulted in stunted seedlings (p < 0.001). Moreover, the average fruit weight and number of fruits borne by mature plants were adversely affected, possibly because of potential alterations in soil nitrogen content and associated plant uptake pathways. A pattern of hormetic dose response was observed for some measured parameters, including leaf greenness, plant WUE, and soil CO2 emissions, although the underlying mechanisms remain unclear. Overall, the range between 1 and 5 mg/kg concentration of HDPE-NPs in soil was found to have the greatest impact on tomato plants, while other factors may contribute to the observed effects.
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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".