Toxic volatile organic air pollutants across Canada: multi-year concentration trends, regional air quality modelling and source apportionment
Bibliographic record
Abstract
A Unified Regional Air-quality Modelling System, AURAMS, was expanded to predict six toxic volatile organic compounds (VOCs) within a continental domain and two nested domains covering eastern and western Canada. The model predictions were evaluated against Environment Canada’s National Air Pollution Surveillance (NAPS) data set to assess the predictive capability of the model at daily and seasonal time scales. The predictions were also evaluated with satellite-derived column total maps for formaldehyde, carbon monoxide, and nitrogen dioxide. In general, the model showed fair to good predictive skill in terms of both correlation (R) and normalized mean bias (NMB) for benzene (R = 0.53 NMB = 26 %), formaldehyde (R = 0.73, NMB = −15 %) and acetaldehyde (R = 0.55, NMB = 29 %). For the other toxics VOCs, the model showed less predictive skill in the order 1,2,4-trimethylbenzene (R = 0.50, NMB = −41 %), 1,3-butadiene (R = 0.26, NMB = 40 %) and acrolein (R = 0.052, NMB = −51 %). The goal of this study was to apply an air quality model to assess the contribution of mobile sources to ambient levels of toxic VOCs at urban locations across Canada. The mobile source contribution varied in a complex manner for each species for different regions. For benzene and 1,2,4-trimethylbenzene, the mobile source contribution was in the range 40–65 % for major Canadian cities. The model predicted considerably lower mobile source contributions for rural locations in the Canadian Prairies, where other area sources dominate, such as the petrochemical industry. Measured concentration trends in toxics are also presented from 2004 to 2010. The primary emitted toxics declined gradually (13–16 % over 6 yr) whereas the toxic aldehydes showed no trend.
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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 | 0.000 | 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".