Accuracy and precision of low-cost air quality sensors compared with Federal Equivalent Method monitors.
Bibliographic record
Abstract
<bold>Background:</bold> Low-cost sensors are an attractive tool to increase the geographical distribution of air quality monitoring. Previous studies have assessed the correlation of PM<sub>2.5</sub> measures between low-cost sensors and Federal Equivalent Method (FEM) monitors; however, correlations may miss systemic biases. <bold>Aim:</bold> To compare the accuracy and precision of PM<sub>2.5</sub> concentrations between PurpleAir (PA) sensors with FEM monitors. <bold>Methods:</bold> 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 PM<sub>2.5</sub> between devices. <bold>Results:</bold> A total of 576 data points representing 30-minute intervals for 24 observation days were compared. During the observation period, PM<sub>2.5</sub> 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 PM<sub>2.5</sub> was similar between PA and FEM (mean difference -0.829 units); whereas the precision was poor (Figure). PM<sub>2.5</sub> was up to 200% higher with PA, and biased by higher PM<sub>2.5</sub>, higher temperatures and greater humidity. <fig><object-id>erj;66/suppl_69/PA5857/F1</object-id><object-id>F1</object-id><object-id>F1</object-id><graphic></graphic></fig> Figure 1. Bland-Altman plot comparing A) the difference in PM<sub>2.5</sub> (μg/m<sup>3</sup>) and B) the percentage difference between PM<sub>2.5</sub> (μg/m<sup>3</sup>). <bold>Conclusion:</bold> The systemic bias needs to be considered when interpreting PM<sub>2.5</sub> measured by PA sensors.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".