PREDICTING SPATIAL VARIABILITY OF AMBIENT NITROGEN DIOXIDE IN MONTRÉAL, CANADA, WITH A LAND USE REGRESSION MODEL
Bibliographic record
Abstract
ISEE-508 Abstract: The purpose of this study is to develop a land use regression model for predicting ambient concentrations of nitrogen dioxide in Montreal, Canada. Estimates of NO2 concentrations will be used to assess exposure in subsequent epidemiologic studies on the health effects of traffic-related air pollution. In May 2003, we deployed Ogawa™ passive diffusion samplers for 14 consecutive days at 100 sites across Montreal, Canada. Concentrations ranged from 5.6 to 26.1 ppb (median 12.1 ppb). Linear regression analysis was used to assess the association between logarithmic NO2 concentrations and land use variables derived using the ESRI Arc 8 geographic information system. In simple regressions, NO2 was associated with the area of open space, the distance from nearest highway, traffic count on nearest highway, the length of highways within any radius from 100 to 750 meters, the length of major roads within 750 m, and population density within 2000 m. Industrial land use and the length of local and residential roads showed no association with NO2. In multiple regression analysis, the best-fitting regression model (shown below), based on 93 observations, had a determination coefficient (R2) of 0.39.TableAlthough the land use regression model develop in Montréal was similar to that developed previously in Toronto, Canada, the R2 found in Montréal was smaller. This may be explained in part by the lower variability of NO2 levels measured in Montréal. Future work will include repeated NO2 measurements in the same locations in different seasons, as well as measurements of other pollutants such as volatile organic compounds (VOCs).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".