Odor, air quality, and well-being: understanding the urban smellscape using crowd-sourced science
Bibliographic record
Abstract
Abstract Odors are a topic of emerging environmental health interest given their potential links to air quality, health, well-being, and quality of life. However, odors have traditionally been challenging to study given variability in individual sensitivity and perception, atmospheric physico-chemical processes, and emissions of mixtures of odorous contaminants. Here, we explore the potential utility of crowd-sourced odor report data in improving understanding of spatiotemporal patterns of odor experiences and their impacts. We conduct quantitative and qualitative analyses of a 12-month data set from a web application collecting crowd-sourced odor reports, including spatiotemporal information, odor and self-reported impacts description (OSAC: odors, symptoms, actions in response, and suspected causes), and demographics, in Vancouver, Canada. Users report diverse OSAC with strong seasonality and spatial variability. Reported symptoms, ranging from neurological to emotion- and mood-related, highlight the complexity of odor-related health and well-being impacts. Odors can trigger maladaptive actions, where individuals are exposed to other environmental stressors (e.g. heat stress) or curtail healthy behaviors (e.g. exercising outside) to cope with odor impacts. Clustering analysis of OSAC suggests that odor exposures may be linked to health, well-being, and quality of life impacts through complex mechanisms, related not only to the odor experienced but also perceived causes. Spatiotemporal patterns in reports highlight the potential influence of persistent sources (e.g. waste management) and transient events (e.g. accidents). Exploratory multiple linear regression models suggest that monitoring of air quality and meteorology may be insufficient to capture odor issues. Overall, these results suggest that crowd-sourced science incorporating self-reported health and well-being effects and behavioral responses can enrich understanding of the impacts of odorous emissions at large spatiotemporal scales and complement traditional air pollution monitoring.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".