Bibliographic record
Abstract
Portable air cleaners (PACs) are appliances that use filtration to reduce indoor concentrations of particulate matter (PM), a harmful air pollutant associated with an array of adverse health outcomes. PACs may be particularly beneficial in homes, where people receive a substantial portion of their exposure to PM. However, any benefit from using a PAC is contingent on indoor concentrations consistently being lowered by the device. This work examines how PAC performance is affected by environmental and behavioural factors, and how these factors can be addressed to improve PAC efficacy. A review of 41 randomized interventions identified that device size and operation, the background loss rate (i.e., other particle removal mechanisms), and the strength of PM sources affected measured concentration reductions. Following this, two field studies were performed to further explore these specific factors. A method for processing continuous PM measurements was used to estimate variation in the background loss rate over one year in 16 apartments in a multifamily building. Background loss rate varied widely and was significantly increased by opening windows and exterior doors. A randomized crossover trial was performed in 60 apartments in three multifamily buildings, evaluating PAC performance when operating constantly and with device automation. Median weekly concentrations were reduced in all 60 apartments with constant air cleaning, while automation was similarly effective in homes where concentrations were relatively high. Weekly concentrations were significantly increased by PM-generating activities and significantly reduced when the exterior door was frequently opened, affecting measurements of PAC performance within and between homes. Noise was consistently identified as a factor that negatively impacted satisfaction. This was followed by an evaluation of two control strategies for PAC automation, which may help to address concerns about noise by reducing how often the device operates. There was no meaningful difference between a threshold-based strategy and one that achieves optimal performance based on balancing concentration reduction and runtime (model predictive control). This simpler threshold-based strategy can effectively automate PACs, so long as an appropriate threshold is selected. Together, this thesis provides a basis for improving guidelines for PAC selection and operation as well as for evaluating PAC performance.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".