Spatial–Temporal Variations in Source-Specific PM2.5: Investigation of the Calgary Metropolitan Region, Alberta, Canada
Bibliographic record
Abstract
Abstract The Calgary Spatial and Temporal Exposure Modeling (CSTEM) Study collected 2-week long filter samples in two different ways to study the spatial–temporal distribution of source contributions to PM 2.5 across Calgary, Alberta, Canada and its peri-urban region in 2015–2016. The spatial study involved 125 sites in each of two periods: August 2015 and January–February 2016. Alternatively, the temporal study collected samples each month at a subset of 4 sites within the Calgary city limits. The samples were analyzed for their chemical compositions and the data subjected to positive matrix factorization (PMF) for source identification and quantification. Five sources (soil/road dust, traffic, road salt, secondary inorganic aerosol/coal, and refineries) were identified from the spatial data set and four sources (soil/road dust, road salt, secondary inorganic aerosol, traffic/wood burning) from the temporal data set. Results demonstrated that PMF can resolve meaningful source types from a spatial or temporal dataset even when they are limited by longer integrated sampling times. Results from the spatial dataset modelling showed higher spatial heterogeneity in PM 2.5 contributions within the urban area, particularly for the local source types (vehicles, road dust, and refineries), demonstrating a key limitation in applying central site estimates of PM 2.5 source contributions across a large urban and non-urban area. Analysis of the temporal data from the 4 urban sites generally showed similarity in their source contributions, reflecting similar local sources, particularly highways and residential areas. Graphical Abstract
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".