Incorporating Community Knowledge Into Analysis of Air Quality Monitoring Network Data
Bibliographic record
Abstract
Abstract We conducted a pilot study to explore methods of incorporating qualitative community knowledge into quantitative assessment of temporal and spatial air quality patterns in a neighborhood in Vancouver, British Columbia. We deployed a low‐cost sensor network measuring NO, NO 2 , and PM 2.5 . We used a variety of sources of community knowledge to complement a timeseries analysis and spatial model: a survey by the residents' association; odor reports from a citizen science project; and data from a community mapping event. Community knowledge highlighted, among other sources, areas where cars and heavy‐duty vehicles idle, locations of construction, and locations of wood stoves. When creating a “traditional” land use regression (LUR) using easily accessible, and publicly available data sources, and a “community” LUR that uses community‐reported air pollution sources, model fit was improved in the community LURs for NO 2 and NO x . This suggests that community knowledge can provide insight into sources that are not well captured in commonly used, publicly available data sets due to their transient and informal nature. Not all community‐reported short‐term events corresponded to peaks in monitor data, which could reflect that reports were more correlated with unmeasured pollutants. We suggest that future studies collecting community knowledge on short‐term pollution events through community mapping lower barriers to participation (i.e., through hosting a series of drop‐in events, providing childcare, or timing any event to coincide with neighborhood‐wide events). With these examples, we showcased ways to include community knowledge in quantitative air pollution studies and highlight opportunities to expand on these methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.031 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".