Identifying the sources driving observed PM <sub>2.5</sub> temporal variability over Halifax, Nova Scotia, during BORTAS-B
Bibliographic record
Abstract
Abstract. The source attribution of observed variability of total PM2.5 concentrations over Halifax, Nova Scotia, was investigated between 11 July and 26 August 2011 using measurements of PM2.5 mass and PM2.5 chemical composition (black carbon, organic matter, anions, cations and 33 elements). This was part of the BORTAS-B (quantifying the impact of BOReal forest fires on Tropospheric oxidants using Aircraft and Satellites) experiment, which investigated the atmospheric chemistry and transport of seasonal boreal wildfire emissions over eastern Canada in 2011. The US EPA Positive Matrix Factorization (PMF) receptor model was used to determine the average mass (percentage) source contribution over the 45 days, which was estimated to be as follows: long-range transport (LRT) pollution: 1.75 μg m−3 (47%); LRT pollution marine mixture: 1.0 μg m−3 (27.9%); vehicles: 0.49 μg m−3 (13.2%); fugitive dust: 0.23 μg m−3 (6.3%); ship emissions: 0.13 μg m−3 (3.4%); and refinery: 0.081 μg m−3 (2.2%). The PMF model describes 87% of the observed variability in total PM2.5 mass (bias = 0.17 and RSME = 1.5 μg m−3). The factor identifications are based on chemical markers, and they are supported by air mass back trajectory analysis and local wind direction. Biomass burning plumes, found by other surface and aircraft measurements, were not significant enough to be identified in this analysis. This paper presents the results of the PMF receptor modelling, providing valuable insight into the local and upwind sources impacting surface PM2.5 in Halifax and a vital comparative data set for the other collocated ground-based observations of atmospheric composition made during BORTAS-B.
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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 | 0.000 | 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 | 0.001 | 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 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".