Retrieval of bulk hygroscopicity from PurpleAir PM<sub>2.5</sub> sensor measurements
Bibliographic record
Abstract
PurpleAir sensors offer a unique opportunity for a large-scale and densely populated array of sensors to study surface air quality. While PurpleAir sensors are inexpensive and abundant, they must be corrected to better agree with validated coincident measurements from more sophisticated instrumentation. Traditionally, this correction is performed using statistical methods. We propose a method to both correct PurpleAir PM 2.5 measurements and allow for an estimate of the hygroscopic growth of aerosols, using a novel correction approach based on the optimal estimation method (OEM). The hygroscopic growth of aerosols can be retrieved using the sensitivity of the correction to water activity, which influences the measured size distribution of the aerosols. By employing the physically based correction using calibrated measurements from the nearby Ontario Ministry of the Environment, Conservation and Parks air quality site, the average daily mean absolute error (MAE) of the PurpleAir PM 2.5 measurements is decreased from 5.58 to 1.68 µg m −3 , and the average daily bias decreases from 4.75 to −0.23 µg m −3 . This improvement in the correction is comparable to that seen using conventional statistical methodologies. Our OEM retrieval also 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 for atmospheric particulate matter (0.1 to 0.9) determined in previous studies using calibrated air quality measurement instruments, which suggests that our method allows a new aerosol product to be determined from a large sensor network.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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 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".