Impacts of Household Characteristics, Activities and Building Characteristics on Indoor Concentrations of Semi-volatile Organic Compounds
Bibliographic record
Abstract
When compared to outdoors, the indoor environment often has higher concentrations of some SVOCs, such as brominated flame retardants (BFRs), organophosphate esters (OPEs) and phthalates that are used as additive flame retardants and plasticizers from consumer products and building materials. Some outdoor SVOCs, such as polycyclic aromatic hydrocarbons (PAHs), can be transported into indoor environments, increasing indoor exposures to these compounds. With the increasing concerns about the implications of exposure for human health, attention has turned towards concentrations of SVOCs in indoor environments, particularly in residential dwellings where North Americans spend more than 60% of their time. This thesis documented concentrations of these SVOCs in residential buildings, focussed on the exposure disparities in SVOCs according to socio-economic status (SES), and developed SVOC sampling methods in low-SES homes. Quantitative filter forensics (QFF) was extended to portable air cleaners with portable filters to quantitatively estimate indoor particle-bound SVOCs, overcoming the limitations of traditional QFF requiring forced-air heating, ventilation and air conditioning (HVAC) systems, while also improving indoor air quality by removing particular matter (PM). This thesis also advanced knowledge on the impacts of household characteristics, activities and building characteristics on indoor SVOC concentrations. The research found SVOC exposure disparities according to SES in Canada, reinforcing the importance of better understanding of the intersection between contaminant exposure, housing quality, household characteristics, and activities that can elevate SVOC levels, and SES, with the aim of reducing exposures from residences in an equitable manner.
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".