Aerosol–computational fluid dynamics modeling of ultrafine and black carbon particle emission, dilution, and growth near roadways
Bibliographic record
Abstract
Abstract. Many studies have shown that on-road vehicle emissions are the dominant source of ultrafine particles (UFPs; diameter < 100 nm) in urban areas and near-roadway environments. In order to advance our knowledge on the complex interactions and competition among atmospheric dilution, dispersion, and dynamics of UFPs, an aerosol dynamics–computational fluid dynamics (CFD) coupled model is developed and validated against field measurements. A unique approach of applying periodic boundary conditions is proposed to model pollutant dispersion and dynamics in one unified domain from the tailpipe level to the ambient near-road environment. This approach significantly reduces the size of the computational domain, and therefore allows fast simulation of multiple scenarios. The model is validated against measured turbulent kinetic energy (TKE) and horizontal gradient of pollution concentrations perpendicular to a major highway. Through a model sensitivity analysis, the relative importance of individual aerosol dynamical processes on the total particle number concentration (N) and particle number–size distribution (PSD) near a highway is investigated. The results demonstrate that (1) coagulation has a negligible effect on N and particle growth, (2) binary homogeneous nucleation (BHN) of H2SO4–H2O is likely responsible for elevated N closest to the road, and (3) N and particle growth are very sensitive to the condensation of semi-volatile organics (SVOCs), particle dry deposition, and the interaction between these processes. The results also indicate that, without the proper treatment of the atmospheric boundary layer (i.e., its wind profile and turbulence quantities), the nucleation rate would be underestimated by a factor of 5 in the vehicle wake region due to overestimated dilution. Therefore, introducing atmospheric boundary layer (ABL) conditions to activity-based emission models may potentially improve their performance in estimating UFP traffic emissions.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".