Walk the Less Trodden Path, or rather, the Less Driven: Mapping Pedestrian Paths and Local Air Pollution for Selective Walking in Cleaner Air
Bibliographic record
Abstract
When we walk outdoors, we breathe ambient air. Walking in an urban environment exposes us to varying levels of air pollution. Yet, air quality is only emerging as a dimension of walkability. Disregarding air quality when assessing walkability is just the tip of the iceberg: we tend to be unaware of the variability of air pollution over space, even though we are aware of the meteorological variability within our city. Yet, variations in pollution levels within urban environments can be large. Just think about Calgary: a metropolitan area over 800 km2, with a 300 m elevation range, strong winds, and localized pollution sources. Truly, where we walk can make a difference in the quality of the air we breathe. Further, several traffic-related pollutants exhibit a spatial pattern with pollutant concentration declining rapidly as distance from roads increases; therefore, walking within a few hundred meters of a major road leads to higher pollution exposure than if we were to walk further away from the road. Indeed, air pollution is measured regularly and frequently over time, but irregularly and sparsely over space, failing to capture its spatial variation. Over the last several years, in collaboration with Health Canada, our group has conducted spatially detailed air monitoring campaigns, deploying up to 100 monitors within the urban area of Calgary. We subsequently developed land use regression models, which yield reliable spatially detailed estimates of air pollution, e.g., at the postal code level. We further integrated our spatial estimates with the temporal measurements of the regulatory network, which yielded air pollution estimates at the postal code level on a monthly base; therefore, we can provide integrated estimates of spatial and seasonal variation in air pollution levels. We are integrating our estimates with walkability assessments, using Calgary as a pilot study. We further analyzed pollution levels over the city’s existing extensive pathway network. The outcome of this study is a map reporting pedestrian (and bicycle) paths, with associated levels of pollution in each season. The map shall help guiding ‘where to walk’ choices, promoting walking (and cycling) on pathways where pollution levels are lower.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".