Bibliographic record
Abstract
Abstract. PurpleAir sensors offer a unique opportunity for a large-scale and densely populated array of sensors to study surface air quality. While the PurpleAir sensors are inexpensive and abundant, they require calibration against a validated coincident measurement to ensure the quality of the measurement. Traditionally, this calibration is performed using statistical (empirical) methods. We propose a method to extend the aerosol properties determined by PurpleAir sensors to include estimates of the hygroscopic growth of aerosols using a novel calibration method based on the optimal estimation method (OEM). The hygroscopic growth can be estimated during calibration due to the calibration’s sensitivity to relative humidity, which influences the measured size distribution of the aerosols. Our OEM-based retrieval for calibration includes an estimation of the bulk hygroscopicity. By employing the physically-based calibration with the London’s Ministry of the Environment, Conservation and Parks site calibrated measurements, the average daily Mean Absolute Error (MAE) of the PurpleAir PM2.5 measurements decreased from 5.58 μg/m3 to 1.68 μg/m3, and the average daily bias from 4.75 μg/m3 to -0.23 μg/m3. This improvement is comparable to the improvement seen using conventional statistical methodologies. In addition to calibration, using our OEM retrieved allowed us to estimate seasonal bulk hygroscopicity values ranging from 0.33 to 0.40. These values are consistent with the accepted ranges of bulk hygroscopicity values determined in previous studies using sophisticated air quality measurement instruments.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.035 | 0.030 |
| Insufficient payload (model declined to judge) | 0.142 | 0.119 |
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".