Investigating the impact of emission reductions and traffic sector emissions on ambient PAH and nitrated PAH concentrations
Bibliographic record
Abstract
Polycyclic aromatic hydrocarbons (PAHs) pose a health risk as hazardous air pollutants, with nitrated PAHs (nitro-PAHs) being notably more toxic. However, limited research has explored how spatial PAH and nitro-PAH concentrations have evolve over time and the specific impact of traffic emissions on ambient PAH and nitro-PAH levels.This study investigates the effects of decreasing anthropogenic emissions and traffic-related emissions on both PAH and nitro-PAH concentrations using a high-resolution regional chemical transport model. The research introduces a new nitro-PAH species, 2-Nitrofluoranthene (2-NFLT), into the Global Environmental Multiscale model – Modelling Air quality and CHemistry (GEM-MACH), enhancing our understanding of regional air quality trends. By simulating 2-NFLT concentrations for varying emission levels over three decades, we aim to identify the changes in 2-NFLT concentrations compared to primary PAH concentrations. Additionally, we quantify the contribution from traffic sector to ambient PAH concentrations by conducting sensitivity simulations under a traffic-free scenario.This work underscores the significance of fine spatial resolution in nitro-PAH modeling and provides critical insights into the co-benefits of reducing primary PAH and NOX emissions over the past two decades. The findings have implications for informing policies aimed at improving air quality and safeguarding public health.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".