Investigating the Use Of Portable Air Pollution Sensors to Capture the Spatial Variability Of Traffic-Related Air Pollution
Bibliographic record
Abstract
Advances in microsensor technologies for air pollution monitoring encourage a growing use of portable sensors. This study aims at testing their performance in the development of exposure surfaces for nitrogen dioxide (NO2) and ozone (O3). In Montreal, Canada, a data-collection campaign was conducted across three seasons in 2014 for 76 sites spanning the range of land uses and built environments of the city; each site was visited from 6 to 12 times, for 20 min, using NO2 and O3 sensors manufactured by Aeroqual. Land-use regression models were developed, achieving R(2) values of 0.86 for NO2 and 0.92 for O3 when adjusted for regional meteorology to control for the fact that all of the locations were not monitored at the same time. A total of two exposure surfaces were then developed for NO2 and O3 as averages over spring, summer, and fall. Validation against the fixed-station data and previous campaigns suggests that Aeroqual sensors tend to overestimate the highest NO2 and O3 concentrations, thus increasing the range of values across the city. However, the sensors suggest a good performance with respect to capturing the spatial variability in NO2 and O3 and are very convenient to use, having great potential for capturing temporal variability.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".