The Fresh Air Wristband: A wearable air pollutant monitor
Bibliographic record
Abstract
S14: Use of Exposomic Methods Incorporating Sensors in Environmental Epidemiology, Room 217, Floor 2, August 27, 2019, 1:30 PM - 3:00 PM Background: Evaluation of cumulative exposure to air pollutant mixtures has been challenged by traditional measurement techniques. Wearable passive air pollutant monitors have emerged as a tool for assessing personal exposure to environmental chemicals. These monitors concentrate airborne pollutants onto a substrate which can subsequently be analysed off-line for a broad range of compounds using mass spectrometry (MS). Longitudinal exposure assessment in vulnerable populations is facilitated by the lightweight, wearable form factor of these monitors. The low cost of this sampling technique further enables deployment across large populations, increasing the quantity of environmental data available for evaluating environmental risk factors for disease. Methods: We have applied a polydimethylsiloxane (PDMS) sorptive extraction technique to passively concentrate non-polar compounds from the air. Gas rods are coated with a thin PDMS film and then mounted into a silicone band (the Fresh Air Wristband). The wristband is worn by an individual for period of several hours to days depending on ambient levels. Sample analysis is performed using a gas chromatograph Orbitrap MS equipped with the thermal desorption unit. Using this technique, we can quantify time-integrated exposure to a range of semi-volatile organic compounds. We deployed the Fresh Air wristband in several large epidemiologic studies based in the U.S., Canada, South Africa and China. Results: The PDMS sorbent in the Fresh Air wristband exhibited linear uptake of chemicals across a range of exposure concentrations that are representative of ambient air levels (R^2>0.99). Low variation was observed across replicate samples exposure to airborne pollutants in field tests (%CV<10). Deployment of the wristband with children, pregnant women the elderly has enabled characterisation of unique exposure profiles. Conclusions: Capturing the cumulative exposure of an individual to mixtures of air pollutants using wearable passive samplers is a novel advancement towards identifying disease risk factors.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.018 |
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; both teacher heads agree on what is shown here.
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".