Mitigating Diesel Truck Impacts in Environmental Justice Communities
Bibliographic record
Abstract
This paper describes a series of sequentially implemented policies to mitigate local diesel truck impacts resulting from goods movement activity at two port facilities and simultaneously to improve traffic operations in the communities of Barrio Logan in San Diego, California, and Old Town in National City, California, both low-income communities of color. The paper provides the first comprehensive documentation of the unique process and solutions that emerged following the collaboration of all major stakeholders. Local impacts in Barrio Logan comprised air pollution, noise, and decreased pedestrian safety, while traffic operations in both communities were affected by congestion on the main freeway access, interchanges with insufficient capacity, and heavily mixed land uses both within and adjacent to the communities. These issues provided the impetus for the mitigation effort, the final implementation of which involved a permanent rerouting of all trucks weighing more than 5 tons to roads external to the community. Previous assessments of the project have described the extent to which mitigation strategies are expected to improve traffic operations or have assumed air quality improvements without carrying out an air quality analysis. A local-scale analysis of diesel particulate matter (DPM) emissions in Barrio Logan is given. The results show that while the mitigation did not result in improved regional air quality, it did significantly improve air quality in the primary affected corridor and resulted in a 99% reduction in DPM emissions and an 87% reduction in diesel truck vehicle miles traveled.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".