Refining Air Pollution Exposure Estimates: A Comparison of Citywide and Neighborhood Land Use Regression Models in Toronto
Bibliographic record
Abstract
Land use regression (LUR) models assess air pollution exposure but often struggle with transferability (predicting concentrations in areas without measurements) and generalizability (capturing spatial patterns across neighborhoods). This study evaluated transferability and generalizability of Toronto City LUR models for black carbon (BC) and ultrafine particles (UFP) using mobile monitoring data. Models were developed using multiple linear regression (MLR) and XGBoost under three spatial configurations: Toronto City (TC), Toronto City minus a neighborhood (TCM-NB), and neighborhood-specific (NB). Transferability of TCM-NB models and generalizability of TC models were tested using neighborhood-specific data and compared to NB models. XGBoost outperformed MLR, achieving R 2 of 0.77 for UFP and 0.54 for BC in TC models compared to 0.32 and 0.27 for MLR. TC models exhibited poor generalizability, with R 2 dropping to 0.1 in certain neighborhoods. Similarly, TCM-NB models exhibited limited transferability, with MLR slightly outperforming XGBoost ( R 2 of 0.3 vs 0.2). Hyperparameter tuning with spatial cross-validation improved XGBoost transferability and generalizability, with R 2 increases of up to 0.2 for both UFP and BC depending on the neighborhood. These findings highlight the importance of monitoring campaigns covering diverse urban environments and adopting tailored modeling approaches to capture neighborhood-specific pollution sources to advance air pollution exposure assessment.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".