Energy recovery ventilators to combat indoor airborne disease transmission: A sustainable approach
Bibliographic record
Abstract
Ventilation plays a crucial role in preventing indoor airborne disease transmission. Nevertheless, ventilation increases the energy consumption of HVAC systems. Therefore, energy efficiency measures or alternative methods must be adopted to reduce the energy demand of HVAC systems, which is necessary to achieve sustainability in the building sector. This study proposes a method of utilizing an energy recovery ventilator (ERV) to provide supplementary ventilation to reduce airborne disease transmission. The proposed method is tested for an office building with one source room (with an infected occupant) and two connected rooms (no infection source). The contributions of the present study are (i) the development and verification of a new supplement ventilation method using an ERV to reduce the probability of infection from airborne pathogens and (ii) providing the economic and environmental benefits of the proposed method to promote its adaption by the building managers/HVAC engineers. The results of the present study show that the proposed method can reduce the probability of infection by 10 to 40% and demonstrate that utilizing an ERV is a sustainable and economical method to improve ventilation to reduce indoor airborne disease transmission.
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.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".