VOC emission rates from an indoor surface using a flux chamber and PTR-MS
Bibliographic record
Abstract
Arising from the Chemical Assessment of Surfaces and Air (CASA) 2022 study at the National Institute of Standards and Technology (NIST) Net-Zero Energy Residential Test Facility (NZERTF), this paper presents the first evaluation of indoor surface emissions to a house measured with a surface flux chamber coupled to an online, non-targeted volatile organic compound (VOC) mass spectrometric detection. These surface emissions are compared to those assessed using ambient, whole house indoor VOC measurements and the outdoor air change rate. Chamber emission rates varied by almost four orders of magnitude across 35 quantified VOCs. The whole house emissions measured by campaign-long ambient measurements and the flux chamber emissions (when scaled to the painted surface area of the house) are similar, with an average ratio between the two of 1.3 ± 1.0. The general agreement between these two approaches indicates that the flux chamber was not solely measuring primary emissions from building materials located below the chamber. Rather, the results suggest that over the 12-year house lifetime, VOCs have been widely distributed around the house, migrating from their primary sources to secondary surface reservoirs. With the house in a quasi-steady state, the thermodynamic activities (i.e., the vapor pressures) of the VOCs within the different reservoirs become similar. Emissions of aromatics and monoterpenes have declined since the house was built, whereas aldehyde emissions have remained relatively constant. • First deployment of a flux chamber in a house with online gas measurements. • Quantification of the emissions of 35 compounds to the indoor air. • Close agreement between flux chamber emissions and whole house emissions. • VOCs have been widely distributed into the house surface reservoirs. • Emissions for some chemicals have decreased since the house was built.
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 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) | 0.009 | 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; both teacher heads agree on what is shown here.
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".