Using low-cost particle sensors in HVAC ducts
Bibliographic record
Abstract
Low-cost sensors (LCSs) are increasingly used to measure particulate matter (PM). The possibility of using this kind of sensor within the HVAC system opens opportunities for improved control and management of indoor air quality and predictive maintenance of air filtration systems. This experimental study evaluates the effectiveness of LCSs in detecting particles within air ducts, employing three distinct types of LCSs under different testing aerosols, particle concentrations, and airstream velocities with particular emphasis non-statutory concentrations of PM2.5 and PM10. Data analysis includes statistical assessments to determine correlations and agreements to a reference laboratory-grade optical spectrometer (TSI OPS 3330). The findings underscore the suitability of LCSs for relative measurements, especially for PM2.5 concentrations, with varying degrees of accuracy for PM10 concentrations. In particular, one LCS did not perform well for PM10 for either a standardized test aerosol or for measurements of outdoor aerosol, potentially owing to the lack of a sampling fan in the sensor. The other two sensors generally had a linear response with the reference instrument for PM2.5 under test conditions with both the standardized aerosol and outdoor aerosol and at different velocity conditions. However, the agreement was generally worse at higher velocity conditions, especially for PM10, suggesting challenges associated with accurate PM10 assessments with the tested LCSs. This investigation highlights opportunities and constraints for using LCSs in HVAC systems.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".