Sensitivity to input parameters of Mobile6.2-AERMOD simulated emissions and concentrations
Bibliographic record
Abstract
This study investigates the sensitivity of simulated emissions and airborne NO2 and benzene concentrations to model input parameters. Emission factors were estimated using Mobile6.2 and ambient concentrations were estimated using AERMOD. The simulation was performed for Huron Church Road in Windsor, Ontario, Canada, an arterial road 9 km in length with an average vehicle speed of 50 km/h. Eight scenarios were developed such that one input parameter was changed at a time in the first seven scenarios and compared with the base case. Results showed that emission factors were most sensitive to the choice of vehicle composition (Ontario versus default), followed by the choice of vehicle age distribution (Ontario versus default), and the average speed of vehicles. Simulated concentrations were sensitive to the hour-of-day variation in emission (mainly due to variation in vehicle counts) and when this was not considered, the annual mean concentrations were likely overestimated by up to 27% and maximum hourly concentrations were underestimated. The findings provide insights into determining the level of details of input parameters required for estimation of emissions and concentrations.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".