Campus–Community Partnership to Characterize Air Pollution in a Neighborhood Impacted by Major Transportation Infrastructure
Bibliographic record
Abstract
This study investigates air quality in a Toronto community located between an airport and an expressway. A community science approach was adopted for data collection and interpretation, and a partnership was formed between a local neighborhood association, university researchers, the municipal government, and the local airport authority. Community scientists placed low-cost sensors on outdoor balconies and inside homes for 28 weeks between 2020 and 2022, measuring particle number (PN) concentrations of particulate matter (PM) with diameters between 0.5 and 2.5 μm. Indoors, the PN concentrations increased during cooking and other activities. During periods with minimal indoor activities, indoor levels closely followed the outdoor signal. Median indoor/outdoor (IO) ratios varied between 0.4 and 0.87 across sampling months. Median outdoor PN concentrations varied from 1 to 4 #/cm 3 and were influenced by local and regional sources. Outdoor PN concentrations were significantly correlated to PM 2.5 and nitrogen dioxide at a downtown reference station; the latter suggests that traffic emissions from the nearby expressway contribute to PN concentrations in the neighborhood. An analysis of outdoor ultrafine particle (UFP) data collected at a single location suggests that the airport is a source of UFP in the neighborhood. Community engagement was enabled through involvement in study design, execution, and knowledge mobilization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".