DEVELOPING A LAND USE REGRESSION MODEL FOR ULTRAFINE PARTICLE CONCENTRATIONS IN VANCOUVER, CANADA
Bibliographic record
Abstract
Background and Aims: Epidemiologic studies have associated adverse health outcomes with exposure to traffic-related air pollutants, principally NO2, at levels below those showing effects in controlled exposure studies. (1) This suggests the importance of related contaminants in the traffic exhaust mixture such as ultrafine particles (UFP) (<0.1µm in diameter). Presently, no routine monitoring for UFP exists in North America and little information is available regarding UFP spatial distribution.We measured particle number concentrations (PNC) in Vancouver to develop a land use regression (LUR) model for use in epidemiologic studies and to identify important factors influencing concentrations. Methods: During a three-week sampling period in spring 2010, PNC were measured with portable condensation particle counters (CPC3007, TSI®, Shoreview, MN) for one hour at eighty locations previously used to characterize spatial variability in nitrogen oxides. PNC was measured continuously at four additional locations to assess temporal variation. LUR modeling was conducted using geographic predictors, including: road length, vehicle density, intersection and bus stop density, land use type, fast food restaurant density, population density and elevation. Results: The range of measured (one-hour median) PNC values was highly variable, 1500 -105000 particles/cm3, (mean [SD] = 18200 [15900] particles/cm3). Pearson correlations of PNC with two-week average NO, NO2 and NOx concentrations at the same sites were 0.59, 0.61 and 0.65. A preliminary LUR model (R2= 0.44) for temporally-adjusted PNC included ln-distance to nearest major road, area of industrial land within a 750m radius and density of bus stops within 100m. Conclusions: Measured PNC was highly correlated with measured nitrogen oxides. However, geographic predictors explained a smaller proportion of variability in PNC levels than found previously for nitrogen oxides, suggesting some common sources and additional unknown factors accounting for PNC spatial variability. (2) A subsequent UFP LUR model will incorporate wind speed and direction.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".