Testing the robustness of land-use regression models for atmospheric nitrogen dioxide and ozone using data from fixed and mobile monitoring campaigns
Bibliographic record
Abstract
The objective of this thesis is to validate land-use regression models developed based on fixed and mobile measurements of ambient nitrogen dioxide (NO2) and ozone (O3) concentrations. For this purpose, a mobile monitoring campaign was conducted in the summer of 2015 in Montreal. The pollutant levels of 1,411 road segments were measured. Using data from repeated visits at each segment (N_vis), various land-use regression (LUR) models were developed based on segments with N_vis greater than or equal to 4, 8, 12, 16 and 20. Exposure surfaces for the island of Montreal based on these models were also developed. Previously, during the summer of 2014, a monitoring campaign had taken place at 76 fixed locations spread around Montreal using the same sensors to measure the same two pollutants. LUR models as well as associated exposure surfaces were developed, and compared to the results from summer 2015. We observed that the exposure surfaces resulting from both campaigns were highly dissimilar, and several possible explanations can be suggested. LUR models based on segments with a small number of repeated observations are associated with poor coefficients of determination (R²) and the exposure surfaces derived from them are poorly correlated with the summer 2014 exposure surface. On the other hand, restricting the LUR models to the road segments with N_vis greater than or equal to 16 leads to higher R² values at the expense of poor predictive capability outside of the sample mainly due to the fact that as N_vis increases, the variability in segment attributes within the sub-sample decreases as those segments become more concentrated in the downtown area. This study highlights the sensitivity of LUR models based on mobile monitoring campaigns to the number of visits per segment and to the location of the segments and stresses the importance of careful design of outdoor data collection campaigns.Keywords: Land-use regression; nitrogen dioxide; ozone; air pollution exposure; exposure surfaces; mobile monitoring, fixed stations; Montreal; micro-sensors
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".