Investigating\nthe Use Of Portable Air Pollution Sensors\nto Capture the Spatial Variability Of Traffic-Related Air Pollution
Bibliographic record
Abstract
Advances\nin microsensor technologies for air pollution monitoring\nencourage a growing use of portable sensors. This study aims at testing\ntheir performance in the development of exposure surfaces for nitrogen\ndioxide (NO<sub>2</sub>) and ozone (O<sub>3</sub>). In Montreal, Canada,\na data-collection campaign was conducted across three seasons in 2014\nfor 76 sites spanning the range of land uses and built environments\nof the city; each site was visited from 6 to 12 times, for 20 min,\nusing NO<sub>2</sub> and O<sub>3</sub> sensors manufactured by Aeroqual.\nLand-use regression models were developed, achieving <i>R</i><sup>2</sup> values of 0.86 for NO<sub>2</sub> and 0.92 for O<sub>3</sub> when adjusted for regional meteorology to control for the\nfact that all of the locations were not monitored at the same time.\nA total of two exposure surfaces were then developed for NO<sub>2</sub> and O<sub>3</sub> as averages over spring, summer, and fall. Validation\nagainst the fixed-station data and previous campaigns suggests that\nAeroqual sensors tend to overestimate the highest NO<sub>2</sub> and\nO<sub>3</sub> concentrations, thus increasing the range of values\nacross the city. However, the sensors suggest a good performance with\nrespect to capturing the spatial variability in NO<sub>2</sub> and\nO<sub>3</sub> and are very convenient to use, having great potential\nfor capturing temporal variability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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 teacher head, 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".