A Co-Location Study of 87 Low-Cost Environmental Monitors: Assessing Outliers, Variability, and Uncertainty
Bibliographic record
Abstract
While field research in indoor air quality (IAQ) increasingly uses large numbers of low-cost sensors, detailed validation of each sensor is becoming infeasible, ignored, or undeclared. Questions remain about whether these sensors meet manufacturer or third-party specifications and whether their utility improves with some validation, even under imperfect conditions. This study details the outcomes of a co-location experiment with 87 low-cost IAQ monitors, conducted with the objective of verifying their performance in measuring total volatile organic compounds (tVOCs), particulate matter 2.5 (PM2.5), carbon dioxide (CO2), temperature, and relative humidity. The monitors were installed next to professional-grade reference IAQ instruments in an office with controlled ventilation. A mixed validation methodology was used, involving outlier detection, uncertainty quantification, and performance metric calculations. Results showed that CO2, temperature, and humidity sensors reliably met manufacturer specifications, while tVOC sensors had significant accuracy issues, deviating by up to 79% from reference measurements—substantially more than the stated uncertainty of ±20%. PM2.5 sensors were more consistent but had an error margin of ±27%, compared to the stated ±15%. A total of 5 of the 87 sensors (5.7%) were outliers for at least one IAQ indicator. Despite the need for further long-term validation, this study highlights the importance of performing an experimental evaluation of low-cost IAQ monitors before field deployment.
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 | 0.000 |
| 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".