Validating the performance of low-cost IAQ sensors through co-location
Bibliographic record
Abstract
Low-cost indoor air quality (IAQ) sensors offer new opportunities for real-time monitoring in the built environment by occupants and researchers. However, their performance can vary substantially depending on the environmental conditions. This study presents a comprehensive evaluation of carbon dioxide (CO 2 ) and fine particulate matter (PM 2.5 ) measurements from two consumer-grade low-cost sensors (the Airthings View Plus for CO 2 only and Air Gradient Pro for CO 2 and PM 2.5 ) through co-location tests with two reference instruments, Graywolf DSII-8 for CO 2 and Lighthouse Handheld 3016 for PM 2.5 . Using time-series analysis, linear regression, Pearson correlation, Root-Mean Squared Error (RMSE), Bland-Altman test, and paired t -tests, we assess the precision and accuracy of these sensors. At a 5-minute sampling interval, the Air Gradient sensor had a higher coefficient of determination ( R 2 ), stronger Pearson correlation, and narrower range of limits of agreement (LoAs), but higher bias (i.e. the mean difference) and RMSE, suggesting higher precision but lower accuracy when compared to Airthings. As a result, it can perform well for tracking the relative changes in CO 2 , though less ideal for absolute concentrations without calibration. For PM 2.5 , the Air Gradient also had relatively high R 2 (0.79), moderately strong Pearson correlation (ρ = 0.69, p < 0.05), and a narrow range of LOAs (30.1 μg/m 3 ) and low RMSE (5.8 μg/m 3 ). Averaging the 5-minute measurements over 30-minute intervals generally improved the accuracy and precision of both sensors. However, statistically significant differences from the reference instruments remained for both sensors. Overall, this study offers a multi-metric assessment of consumer-grade sensors and highlights the need for in-situ calibration prior to long-term deployment.
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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".