Accuracy and precision of low-cost air quality sensors compared with Federal Equivalent Method monitors.
Bibliographic record
Abstract
Background: Low-cost sensors are an attractive tool to increase the geographical distribution of air quality monitoring. Previous studies have assessed the correlation of PM2.5 measures between low-cost sensors and Federal Equivalent Method (FEM) monitors; however, correlations may miss systemic biases. Aim: To compare the accuracy and precision of PM2.5 concentrations between PurpleAir (PA) sensors with FEM monitors. Methods: We placed 3 PA sensors within 2m of a FEM (Teledyne API A640) between Nov 22 – Dec 25, 2023 in Halifax, Canada. A Bland-Atman plot was used to compare accuracy and precision of PM2.5 between devices. Results: A total of 576 data points representing 30-minute intervals for 24 observation days were compared. During the observation period, PM2.5 ranged between 0.06 ug/m3 and 43.31 ug/m3, temperature between -7.2 oC and 16.0 oC, humidity between 32% and 84%, and pressure between 995.7 Pa and 1036.5 Pa. On average PM2.5 was similar between PA and FEM (mean difference -0.829 units); whereas the precision was poor (Figure). PM2.5 was up to 200% higher with PA, and biased by higher PM2.5, higher temperatures and greater humidity. erj;66/suppl_69/PA5857/F1 F1 F1 Figure 1. Bland-Altman plot comparing A) the difference in PM2.5 (μg/m3) and B) the percentage difference between PM2.5 (μg/m3). Conclusion: The systemic bias needs to be considered when interpreting PM2.5 measured by PA sensors.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".