Improving Indoor Air Quality with Green Walls: An Experimental Study
Bibliographic record
Abstract
Poor indoor air quality (IAQ), particularly elevated CO2 concentrations, is known to impair cognitive function and comfort.In sealed or energy-efficient buildings, natural air exchange is limited, increasing the need for passive pollutant removal strategies.This study evaluates the CO2 removal capacity of a living wall system installed in a full-scale climatic chamber (38 m³ ).Controlled CO2 decay tests were performed under two conditions: with LED illumination (photosynthesis active) and without lighting.The temporal evolution of CO2 concentration was modeled using a first-order exponential decay function, and equivalent air change rates (ACHₑ) were calculated.Under illumination, the half-life of CO₂ decreased from 90 to 63 minutes (-30%), with an additional removal of approximately 6 g of CO₂ over 6 hours.The ACHₑ increased from 0.46 h⁻¹ (LED OFF) to 0.66 h⁻¹ (LED ON), representing a 43% improvement in effective air renewal attributable to photosynthesis.The findings confirm the synergistic role of lighting and vegetation in enhancing passive indoor air purification.Properly illuminated living walls (LWs) can achieve substantial CO2 removal rates, offering a sustainable strategy for improving IAQ in energy-efficient buildings.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".