Investigating the use of portable air pollution sensors to capture the spatial variability of urban air pollution
Bibliographic record
Abstract
This thesis aims at developing air pollution exposure surfaces for nitrogen dioxide (NO2) and ozone (O3) in Montreal, Canada, using land use regression techniques, and thus capturing the effects of the built environment, traffic, and meteorology on the spatial variability of these two pollutants. Another objective of this work is to assess the potential of new handheld air quality sensors at capturing near-road air quality. To reach these objectives, we ran a data collection campaign on the island of Montreal, spanning 3 seasons (spring, summer and fall) in 2014. In total, 76 sites were identified and which represent the range of land-use and built environment characteristics in Montreal. Each site was visited at least 6 times throughout the campaign whereby measurements occurred for 30 minutes (per visit) with two O3 and two NO2 monitors. We also collected variables related to traffic and street characteristics on-site to assess their relationship with air pollution, and gathered a set of land-use and built environment characteristics at several buffer sizes around the sites (50, 100, 200, 300, 500, 750, 1000 m) using geographical information systems (GIS). The land use regression models we developed achieved R2 values of 0.86 for NO2 and 0.92 for O3, when corrected for regional meteorology. Based on the coefficients of the NO2 and O3 models, we developed an exposure surface for each pollutant by applying the model in areas where air pollution data were not available. Our NO2 surface is strongly correlated with older surfaces previously developed for Montreal, indicating that the spatial variability of air pollution has remained stable. Our O3 model is to our knowledge the first of its kind and for this reason cannot be compared with previous results. The relationship between NO2 and O3 in our models confirms our theoretical understanding of the behaviour of these pollutants. The exposure we generated in this study can be used for epidemiological studies in evaluating the associations between traffic related air pollution and health effects.
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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 | 0.002 | 0.001 |
| 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) | 0.000 | 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".