Source apportionment of indoor and outdoor volatile organic compounds at homes in Edmonton, Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The objective of this analysis was to get a better understanding of emission sources of volatile organic compounds (VOCs) and their contributions to indoor and outdoor concentrations in residences of Edmonton, Alberta. Seven consecutive 24-h indoor and outdoor air samples were collected using Summa canisters in 50 non-smoking homes in both winter and summer of 2010, with 26 homes participating in both seasons. In addition, data were also collected on housing characteristics and occupants’ daily activities. A total of 193 polar and non-polar VOC species were analyzed by gas chromatograph – mass spectrometry (GC–MS). In general, indoor VOC species were found to be substantially higher than outdoor levels during both seasons. A source receptor model positive matrix factorization (PMF) was applied to identify VOC emission sources and apportion airborne concentrations into 13 indoor factors and 10 outdoor factors. More than 70% of total indoor VOCs were attributed to different indoor sources within the residences, where household products were the major contributor (44%, 648 μg/m3), followed by combustion processes and environmental tobacco smoke (ETS) (10.5%, 153 μg/m3), deodorizers (8.4%, 122 μg/m3) and off-gassing of building materials (5.9%, 86 μg/m3). Major outdoor VOC sources included oil and gas industry, traffic emissions, background and biogenic emissions. The findings provide key information about the impact of indoor and outdoor sources on VOC levels in Edmonton homes, which can be useful for developing appropriate risk management measures to improve indoor air quality.
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.
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) | 0.000 | 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