Distribution of Nanoparticles near a Major U.S. and Canada Trade Bridge: Comparison of Simulations with Field Data
Bibliographic record
Abstract
Dispersion of ultrafine particles arising from traffic emissions on a major international bridge (the Peace Bridge) between U.S. and Canada was studied during the summer of 2004. A computational model for evaluating the transport and dispersion of vehicular emissions from the Peace Bridge Complex (PBC) into the downwind neighborhood was developed to improve the estimation of ultrafine particle number concentrations in this area of Buffalo, New York. An unstructured computational grid of the Peace Bridge and its vicinity was generated and the mean airflow was simulated using the standard k-ε turbulence model in the FLUENTTM code (ANSYS, Inc, Canonsburg, PA). A Discrete Random Walk (DRW) model was used to simulate the instantaneous turbulence fluctuating velocity. A Lagrangian particle-tracking model was used to simulate the transport and dispersion of particles from the motor vehicles on the bridge and in the Peace Bridge Plaza area. The particle transport model accounts for the drag and Brownian forces acting on the particle, as well as the gravitational sedimentation effects. These results were compared with a series of particle size distribution measurements made over the region of interest. For particulate emissions measured in the size range of 16 to 166 nm, the simulated size-fractionated particle concentrations show agreement with the field measurements with estimated errors of approximately 15%. These results suggest that CFD modeling could provide the basis for reasonable estimates of the exposure from specific major roads in the downwind area.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".