Application of Land Use Regression to Estimate Long-Term Concentrations of Traffic-Related Nitrogen Oxides and Fine Particulate Matter
Bibliographic record
Abstract
Land use regression (LUR) is a promising technique for predicting ambient air pollutant concentrations at high spatial resolution. We expand on previous work by modeling oxides of nitrogen and fine particulate matter in Vancouver, Canada, using two measures of traffic. Systematic review of historical data identified optimal sampling periods for NO and N02. Integrated 14-day mean concentrations were measured with passive samplers at 116 sites in the spring and fall of 2003. Study estimates for annual mean NO and NO2 ranged from 5.4-98.7 and 4.8-28.0 ppb, respectively. Regulatory measurements ranged from 4.8-29.7 and 9.0-24.1 ppb and exhibited less spatial variability. Measurements of particle mass concentration (PM2.5) and light absorbance (ABS) were made at a subset of 25 sites during another campaign. Fifty-five variables describing each sampling site were generated in a Geographic Information System (GIS) and linear regression models for NO, NO2, PM2.5, and ABS were built with the most predictive covariates. Adjusted R(2) values ranged from 0.39 to 0.62 and were similar across traffic metrics. Resulting maps show the distribution of NO to be more heterogeneous than that of NO2, supporting the usefulness of this approach for assessing spatial patterns of traffic-related 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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".