Elucidating real-world vehicle emission factors from mobile measurements over a large metropolitan region: a focus on isocyanic acid, hydrogen cyanide, and black carbon
Bibliographic record
Abstract
A mobile laboratory equipped with state-of-the-art gaseous and particulate instrumentation was deployed across the Greater Toronto Area (GTA) during two seasons. A high-resolution time-of-flight chemical ionization mass spectrometer (HR-TOF-CIMS) measured isocyanic acid (HNCO) and hydrogen cyanide (HCN), and a high-sensitivity laser-induced incandescence (HS-LII) instrument measured black carbon (BC). Results indicate that on-road vehicles are a clear source of HNCO and HCN and that their impact is more pronounced in the winter, when influences from biomass burning (BB) and secondary photochemistry are weakest. Plume-based and time-based algorithms were developed to calculate fleet-average vehicle emission factors (EFs); the algorithms were found to yield comparable results, depending on the pollutant identity. With respect to literature EFs for benzene, toluene, C2 benzene (sum of m- , p- , and o -xylenes and ethylbenzene), nitrogen oxides, particle number concentration (PN), and black carbon, the calculated EFs were characteristic of a relatively clean vehicle fleet dominated by light-duty vehicles (LDV). Our fleet-average EF for BC (median: 25 mg kgfuel-1; interquartile range, IQR: 10–76 mg kgfuel-1) suggests that overall vehicular emissions of BC have decreased over time. However, the distribution of EFs indicates that a small proportion of high-emitters continue to contribute disproportionately to total BC emissions. We report the first fleet-average EF for HNCO (median: 2.3 mg kgfuel-1, IQR: 1.4–4.2 mg kgfuel-1) and HCN (median: 0.52 mg kgfuel-1, IQR: 0.32–0.88 mg kgfuel-1). The distribution of the estimated EFs provides insight into the real-world variability of HNCO and HCN emissions and constrains the wide range of literature EFs obtained from prior dynamometer studies. The impact of vehicle emissions on urban HNCO levels can be expected to be further enhanced if secondary HNCO formation from vehicle exhaust is considered.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".