The transferability of NO and NO² land use regression models between cities and pollutants
Bibliographic record
Abstract
Land use regression (LUR) models are commonly used for exposure assessment in epidemiologic studies of traffic-related air pollution. Models in different cities often contain similar predictors, suggesting that models may be transferable between cities with similar characteristics. LUR models of NO or NO2 may also be useful for estimating exposure to other pollutants in the traffic pollution mixture. We evaluated the transferability of NO and NO2 LUR models between Winnipeg, Manitoba and Edmonton, Alberta, and the ability of the Winnipeg NO and NO2 LUR models to predict concentrations of benzene and toluene. In both cities, LUR models were developed based on measurements with Ogawa passive samplers at 50 locations during two 14-day sampling campaigns, while benzene and toluene concentrations in Winnipeg were measured at 46 independent locations using 3M #3500 passive badges during a single 14-day period. Locally calibrated LUR models explained more variability in NO2 (R2: 0.81-0.84) than NO(R2: 0.55-0.56). Although models transferred to the opposite city did not perform as well as the locally calibrated models for NO2 (R2: 0.37-0.52) or NO (R2: 0.24-0.41), the transferred models explained more variability than simple binary or continuous road proximity metrics (R2 <=0.19), which are commonly used in epidemiologic studies. In Winnipeg, the NO2 LUR model explained 34% and 19% of the variation in benzene and toluene, respectively, while road proximity metrics explained <=19% of the variation in both pollutants. In conclusion, epidemiologic studies will achieve better exposure assessments by developing LUR models locally and for the pollutant of interest, though transferred LUR models may provide a cost-effective improvement over road proximity metrics for assessing exposure to trafficrelated air pollution.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".