A practical method and its applications to prioritize volatile organic compounds emitted from building materials based on ventilation rate requirements and ozone-initiated reactions
Bibliographic record
Abstract
Volatile organic compounds emissions from building materials can be a major pollution source in low-occupant-density spaces. Composite-style indoor air quality references, which reflect the combined effects of multiple volatile organic compounds, can be used to determine ventilation rate requirements based on building material emissions. The lowest concentration of interest concept was adopted to implement the idea. Twenty-eight building materials selected from the National Research Council of Canada database were subjected to emission modelling, resulting in 101 volatile organic compounds as a starting volatile organic compound pool. A method was proposed to generate a volatile organic compound priority list that determines ventilation rate requirements while considering ozone-initiated reactions. Three priority lists were obtained based on three lowest concentration of interest schemes, i.e., AFSSET, AgBB and EU-LCI, with each consisting of 17–21 volatile organic compounds that were most likely to attribute to large ventilation rate requirements. Also, analyses of selected volatile organic compounds showed that the changes in the composition of the priority lists due to ozone-initiated reactions could be ignored at a typical indoor ozone concentration level. The application of priority lists was discussed for source control and air cleaning device testing. This paper provides a method to prioritize the chemicals based on ventilation rate requirements with a goal of developing volatile organic compound control strategies at building design stage.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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 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".