Corridor-Level Air Quality Analysis of Freight Movement
Bibliographic record
Abstract
The main objective of this research is to investigate the issues and opportunities for improving climate change and air quality implications of freight movement along a North American corridor. The paper presents a case study to determine the impact of truck and rail freight movement on air quality along the corridor from Mexico City, Mexico, to Montreal, Quebec, Canada. Network and freight activity data were assembled for the corridor for a base case (corresponding to the year 2010) and a future case (corresponding to the year 2035). Emission rates for the case study were obtained from the U.S. Environmental Protection Agency's MOBILE6.2 emission model. Parameters such as vehicle age distribution from vehicle registration data were used to refine the emission rates. Rail emissions calculations are based on U.S. average emission and fuel consumption rates. These rates were revised to reflect the ongoing improvements in locomotive engine standards. The results show that freight movement will continue to cause substantial amounts of carbon dioxide emissions. Current levels of rail emissions are not significant compared with those of trucks; however, the share of rail emissions for some pollutants will increase over time. Because of the vast differences between truck and rail operations in terms of routing and operational practices, it is recommended that rail and truck analyses be performed separately to gauge environmental and air quality impacts. It was also determined that the emerging sources of data such as Global Positioning Systems and engine loggers can lead to improved monitoring accuracy; however, making use of these potential resources requires cooperation between the freight industry and transportation and environmental agencies.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".