A land use regression model for ultrafine particles 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. This suggests the importance of related outdoor air contaminants, such as ultrafine particles (UFP) (<0.1µm in diameter). Presently, no UFP monitoring exists in North America and little information is available regarding UFP spatial distributions. 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 sources of UFP. Methods: During a two-week sampling period in spring 2010, PNC were measured with portable condensation particle counters (CPC) for 60-minutes at eighty locations used previously to characterize spatial variability in nitrogen oxides. Continuous PNC measuring occurred at four additional locations to assess temporal variation. LUR modeling was conducted using 135 geographic predictors, including: road length, vehicle density, intersection and bus stop density, land use type, fast food restaurant density, population density and others, following previously developed methods. A novel buffer approach incorporated meteorologic data through wedge-shaped wind roses from measurements made during PNC sampling, in addition to circular buffers. Results: The range of measured (60-minute median) PNC across locations varied 70-fold (range: 1500 – 105 000 particles/cm³, mean [SD] = 18 200 [15 900] particles/cm³). Correlations of PNC with concurrently measured two-week average NO₂, NO and NOX concentrations at the same sites were 0.64, 0.65 and 0.70. A model (R² = 0.48, leave-one-out cross validation R² = 0.32) predicting PNC included length of truck routes within 50m, density of fast food locations within 200m and ln-distance to the nearest port. LUR models created with wind rose shaped buffers had lower predictive power than models with circular buffers (R² = 0.29 – 0.34). Conclusions: Measured PNC was highly variable across the Metro Vancouver region and correlated with nitrogen oxides. Geographic predictors explained a smaller proportion of variability in PNC than found previously for nitrogen oxides, suggesting some common sources and additional unknown factors influencing PNC spatial variability. This represents the first LUR model for UFP in North America.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".