Microplastic pollution: Phytotoxicity, environmental risks, and phytoremediation strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Microplastics (MPs) are emerging contaminants that adversely affect environmental health. In this review, we discuss the uptake of MPs by plants via endocytosis and crack-entry pathways in the roots and stomata of leaves; the translocation of MPs via xylem and phloem; and the toxicity of MPs to diverse plant species through oxidative stress, inhibition of photosynthesis, cytotoxicity, and genotoxicity. It’s difficult to assess the health risks of MPs because they directly cause toxicity and also change soil properties and the bioavailability of coexisting pollutants, such as plastic additives, in the plant rhizosphere, and bioaccumulate along the food chain. Moreover, compared to the uptake behavior and phytotoxicity effects of MPs in laboratory and hydroponic studies, MPs of various shapes, sizes, and types are likely to cause different effects on plants in complex natural environments. This review proposes potential phytoremediation strategies, including phytoextraction, immobilization, and rhizoremediation, for MP pollutants and provides guidelines for the bioremediation of MP-contaminated environments to enhance environmental sustainability. In the phytoremediation of MP pollution, the selection and disposal of plants used for phytoremediation and the optimization of functional microbes in the rhizosphere remain challenging. Future studies should address knowledge gaps in (i) methods for determining environmentally-relevant concentrations of MPs, (ii) the assessment of the ecological and human health risks of MPs in the natural environment, and (iii) the development of effective strategies for the phytoremediation of MP pollution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it