Mobile Monitoring Capability for Citizen Science Approaches to Smoke Exposure Mapping
Bibliographic record
Abstract
Background: Residential woodsmoke is the dominant source of ambient air pollution in many smaller communities throughout the heating season, which raises concerns about individual and public health. Given the localized nature of this source and the spatial variability in ambient concentrations, more spatially resolved data can help communities to adequately characterize woodsmoke impacts and to implement interventions at the local scale. We made mobile monitoring equipment and online mapping tools available to citizen action groups to collect and visualize their own residential woodsmoke data.Methods: First, we conducted fixed and mobile monitoring in three woodsmoke-impacted communities in British Columbia (BC), Canada to establish the general relationship between levoglucosan within fine particulate matter (PM2.5) and Delta-C (880 – 370nm) measurements from a dual wavelength aethalometer. Second, we used this relationship to build an online PM2.5 mapping tool using the Shiny package for R. Finally, we loaned the aethalometer and its integrated global positioning system (GPS) to citizen scientists in other woodsmoke-impacted communities to assess whether they could feasibly collect and visualize their own data with the assistance of a comprehensive user guide.Results: The relationship between daily levoglucosan PM2.5 and Delta-C varied across the first three communities, but the slope of the pooled data was 1:1 with an R2 value of 0.90. Citizen groups were able to collect mobile monitoring data. Following multiple modifications to simplify for lay users, the online Shiny application was successful in providing citizen groups with useful woodsmoke maps.Conclusions: Citizen groups can effectively collect and visualize mobile woodsmoke monitoring data if given adequate support from academic and government partners, especially in the early part of the study period.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".