Smog and socioeconomics: an evaluation of equity in traffic-related air pollution generation and exposure
Bibliographic record
Abstract
How traffic-related air pollution generation and exposure is distributed among different population groups is an important environmental justice concern. From a social equity perspective, many questions arise at the metropolitan scale. Do socially disadvantaged communities have higher exposure levels to traffic-related air pollution? Do discrepancies exist wherein neighborhoods are not exposed to levels of pollution similar to those they themselves generate? And, is there a relationship between this discrepancy and social disadvantage? These questions are examined for the Montreal Metropolitan Region through the development of an integrated transport and emissions model. Two measures of traffic-related air pollution are estimated at the traffic analysis zone level: (1) generation (average emissions per household), and (2) exposure (average residential zone concentration). A social disadvantage index is also calculated that incorporates elements of social and material deprivation. Three levels of inequity exist regarding emissions, exposure, and socioeconomics. Social disadvantage was found to have a positive relationship with exposure, meaning that the most socially disadvantaged communities tend to experience the highest levels of traffic-related air pollution. Spatial discrepancies in emission generation versus emission exposure are also present for most of the metropolitan region. Furthermore, the communities that face a double burden of greater disadvantage and higher exposure also tend to create the lowest quantities of pollution.
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 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 | 0.003 | 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".