Health Risk-Oriented Source Apportionment of Hazardous Volatile Organic Compounds in Eight Canadian Cities and Implications for Prioritizing Mitigation Strategies
Bibliographic record
Abstract
Traditionally, environmental authorities make regulatory policies for controlling volatile organic compound (VOC) pollution based on the mitigation of dominant VOC sources. However, the emission from each VOC source has a unique combination of VOC species of different toxicities. Without quantitatively assessing the health risk associated with each source, the effectiveness of the mitigation policy could be undermined. To address this shortcoming, we developed a new health risk-oriented source apportionment method that can provide quantitative health risk assessment and source-specific mitigation strategies for hazardous VOCs. We estimated that the integrated inhalation cancer risk (ICR) of hazardous VOCs was 7.7 × 10 –5 in Western Canada, indicating a 100% likelihood of exceeding Health Canada’s acceptable risk level (1.0 × 10 –5 ). Anthropogenic sources were responsible for 56.3–73.8% of cancer risks across eight Canadian cities except for the regional background island, where natural sources contributed over 77% to the integrated ICR. Thus, substantial environmental and health cobenefits could be achieved via reducing the ambient levels of benzene and 1,3-butadiene by 39.3–75.7 and 14–69.3%, respectively, and mitigating emissions from fuel combustion (by 31.3–54.1%), traffic source (3.0–36.8%), and other anthropogenic sources (5.3–20.1%) in Western Canada. Our study has significant implications for prioritizing air pollution mitigation policies, especially for quantitative reduction of hazardous air pollutants.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".