Fundamentals of Ornamental Plants in Removing Benzene in Indoor Air
Why this work is in the frame
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Bibliographic record
Abstract
The concentration of benzene in indoor air has received appreciable attention due to its adverse health effects. Although phytoremediation has been considered as an eco-friendly method to remove benzene, it is unclear how to select plants with a high removal rate. In this study, we evaluated the benzene removal efficiency of four common ornamental plants, Epipremnum aureum, Chlorophytum comosum, Hedera helix and Echinopsis tubiflora, and we also explored the factors impacting benzene removal efficiency. The removal efficiency of all plants in this study averaged at 72 percent. The benzene absorption rates of Epipremnum aureum, Hedera helix and Chlorophytum comosum were 1.10, 0.85 and 0.27 µg·m−3·cm−2, respectively. This is due to the different transpiration rates and chlorophyll concentrations in the plants. The benzene removal efficiency of crassulacean acid metabolism plant (Echinopsis tubiflora) was 23% higher than C3 plant (Epipremnum aureum) under dark conditions. This can be attributed to the fact that the characteristic of Echinopsis tubiflora stomata is different from Epipremnum aureum stomata, which is still open under dark conditions. Therefore, Echinopsis tubiflora can take up more benzene than Epipremnum aureum. For different initial benzene concentrations, the benzene removal efficiency of Echinopsis tubiflora was always great (50–80%), owing to its high rate of transpiration and concentration of chlorophyll. Our findings indicate that transpiration rate and chlorophyll concentration can be used as reference parameters to facilitate ornamental plant screening for indoor air quality improvement.
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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 | 0.000 | 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) | 0.001 | 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 it